Journal of Personalized Medicine

21 pages, 472 KiB

Open AccessEditor’s ChoiceReview

Non-Pharmacological Nursing Interventions to Prevent Delirium in ICU Patients—An Umbrella Review with Implications for Evidence-Based Practice

by Sandra Lange, Wioletta Mędrzycka-Dąbrowska, Adriano Friganovic, Ber Oomen and Sabina Krupa

J. Pers. Med. 2022, 12(5), 760; https://doi.org/10.3390/jpm12050760 - 7 May 2022

Cited by 14 | Viewed by 11067

Abstract

Delirium in ICU patients is a complication associated with many adverse consequences. Given the high prevalence of this complication in critically ill patients, it is essential to develop and implement an effective management protocol to prevent delirium. Given that the cause of delirium [...] Read more.

Delirium in ICU patients is a complication associated with many adverse consequences. Given the high prevalence of this complication in critically ill patients, it is essential to develop and implement an effective management protocol to prevent delirium. Given that the cause of delirium is multifactorial, non-pharmacological multicomponent interventions are promising strategies for delirium prevention. (1) Background: To identify and evaluate published systematic review on non-pharmacological nursing interventions to prevent delirium in intensive care unit patients. (2) Methods: An umbrella review guided by the Joanna Briggs Institute was utilized. Data were obtained from PubMed, Scopus, EBSCO, Web of Science, Cochrane Library, and Google Scholar. The last search was conducted on 1 May 2022. (3) Results: Fourteen reviews met the inclusion criteria. Multicomponent interventions are the most promising methods in the fight against delirium. The patient’s family is an important part of the process and should be included in the delirium prevention scheme. Light therapy can improve the patient’s circadian rhythm and thus contribute to reducing the incidence of delirium. (4) Conclusions: Non-pharmacological nursing interventions may be effective in preventing and reducing the duration of delirium in ICU patients. Full article

(This article belongs to the Special Issue Advances in Personalized Nursing Care)

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16 pages, 901 KiB

Open AccessEditor’s ChoiceArticle

Important Risk Factors in Patients with Nonvalvular Atrial Fibrillation Taking Dabigatran Using Integrated Machine Learning Scheme—A Post Hoc Analysis

by Yung-Chuan Huang, Yu-Chen Cheng, Mao-Jhen Jhou, Mingchih Chen and Chi-Jie Lu

J. Pers. Med. 2022, 12(5), 756; https://doi.org/10.3390/jpm12050756 - 6 May 2022

Cited by 12 | Viewed by 3328

Abstract

Our study aims to develop an effective integrated machine learning (ML) scheme to predict vascular events and bleeding in patients with nonvalvular atrial fibrillation taking dabigatran and identify important risk factors. This study is a post-hoc analysis from the Randomized Evaluation of Long-Term [...] Read more.

Our study aims to develop an effective integrated machine learning (ML) scheme to predict vascular events and bleeding in patients with nonvalvular atrial fibrillation taking dabigatran and identify important risk factors. This study is a post-hoc analysis from the Randomized Evaluation of Long-Term Anticoagulant Therapy trial database. One traditional prediction method, logistic regression (LGR), and four ML techniques—naive Bayes, random forest (RF), classification and regression tree, and extreme gradient boosting (XGBoost)—were combined to construct our scheme. Area under the receiver operating characteristic curve (AUC) of RF (0.780) and XGBoost (0.717) was higher than that of LGR (0.674) in predicting vascular events. In predicting bleeding, AUC of RF (0.684) and XGBoost (0.618) showed higher values than those generated by LGR (0.605). Our integrated ML feature selection scheme based on the two convincing prediction techniques identified age, history of congestive heart failure and myocardial infarction, smoking, kidney function, and body mass index as major variables of vascular events; age, kidney function, smoking, bleeding history, concomitant use of specific drugs, and dabigatran dosage as major variables of bleeding. ML is an effective data analysis algorithm for solving complex medical data. Our results may provide preliminary direction for precision medicine. Full article

(This article belongs to the Special Issue Big Data Analysis in Personalized Medicine)

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18 pages, 4822 KiB

Open AccessEditor’s ChoiceReview

FFPE-Based NGS Approaches into Clinical Practice: The Limits of Glory from a Pathologist Viewpoint

by Filippo Cappello, Valentina Angerilli, Giada Munari, Carlotta Ceccon, Marianna Sabbadin, Fabio Pagni, Nicola Fusco, Umberto Malapelle and Matteo Fassan

J. Pers. Med. 2022, 12(5), 750; https://doi.org/10.3390/jpm12050750 - 5 May 2022

Cited by 15 | Viewed by 5205

Abstract

The introduction of next-generation sequencing (NGS) in the molecular diagnostic armamentarium is deeply changing pathology practice and laboratory frameworks. NGS allows for the comprehensive molecular characterization of neoplasms, in order to provide the best treatment to oncologic patients. On the other hand, NGS [...] Read more.

The introduction of next-generation sequencing (NGS) in the molecular diagnostic armamentarium is deeply changing pathology practice and laboratory frameworks. NGS allows for the comprehensive molecular characterization of neoplasms, in order to provide the best treatment to oncologic patients. On the other hand, NGS raises technical issues and poses several challenges in terms of education, infrastructures and costs. The aim of this review is to give an overview of the main NGS sequencing platforms that can be used in current molecular diagnostics and gain insights into the clinical applications of NGS in precision oncology. Hence, we also focus on the preanalytical, analytical and interpretative issues raised by the incorporation of NGS in routine pathology diagnostics. Full article

(This article belongs to the Section Methodology, Drug and Device Discovery)

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15 pages, 635 KiB

Open AccessEditor’s ChoiceReview

Asthma-COPD Overlap Syndrome: Recent Insights and Unanswered Questions

by Evangelia Fouka, Andriana I. Papaioannou, Georgios Hillas and Paschalis Steiropoulos

J. Pers. Med. 2022, 12(5), 708; https://doi.org/10.3390/jpm12050708 - 28 Apr 2022

Cited by 15 | Viewed by 4212

Abstract

The term asthma-COPD overlap (ACO) has been used to identify a heterogeneous condition in which patients present with airflow limitation that is not completely reversible and clinical and inflammatory features of both asthma and chronic obstructive pulmonary disease (COPD). ACO diagnosis may be [...] Read more.

The term asthma-COPD overlap (ACO) has been used to identify a heterogeneous condition in which patients present with airflow limitation that is not completely reversible and clinical and inflammatory features of both asthma and chronic obstructive pulmonary disease (COPD). ACO diagnosis may be difficult in clinical practice, while controversy still exists regarding its definition, pathophysiology, and impact. Patients with ACO experience a greater disease burden compared to patients with asthma or COPD alone, but in contrast they show better response to inhaled corticosteroid treatment than other COPD phenotypes. Current management recommendations focus on defining specific and measurable treatable clinical traits, according to disease phenotypes and underlying biological mechanisms for every single patient. In this publication, we review the current knowledge on definition, pathophysiology, clinical characteristics, and management options of ACO. Full article

(This article belongs to the Special Issue Asthma: From Phenotypes to Personalized Medicine)

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14 pages, 536 KiB

Open AccessEditor’s ChoiceArticle

Human Papillomavirus Infection and the Risk of Erectile Dysfunction: A Nationwide Population-Based Matched Cohort Study

by Sin-Ei Juang, Kevin Sheng-Kai Ma, Pei-En Kao, James Cheng-Chung Wei, Hei-Tung Yip, Mei-Chia Chou, Yao-Min Hung and Ning-Chien Chin

J. Pers. Med. 2022, 12(5), 699; https://doi.org/10.3390/jpm12050699 - 27 Apr 2022

Cited by 13 | Viewed by 3334

Abstract

Background: Male patients with genital warts are known for higher rates of sexual dysfunction. This study was conducted to investigate whether human papillomaviruses (HPV) infection is associated with an increased risk of erectile dysfunction (ED). Methods: Patients aged over 18 with HPV infection [...] Read more.

Background: Male patients with genital warts are known for higher rates of sexual dysfunction. This study was conducted to investigate whether human papillomaviruses (HPV) infection is associated with an increased risk of erectile dysfunction (ED). Methods: Patients aged over 18 with HPV infection (n = 13,296) and propensity score-matched controls (n = 53,184) were recruited from the Longitudinal Health Insurance Database (LHID). The primary endpoint was the diagnosis of ED. Chi-square tests were used to analyze the distribution of demographic characteristics. The Cox proportional hazards regression was used to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs) for the development of ED in both groups, after adjusting for sex, age, relevant comorbidities, co-medication, and surgery. Results: ED developed in 181 patients of the study group. The incidence density of ED was 2.53 per 1000 person-years for the HPV group and 1.51 per 1000 person-years for the non-HPV group, with an adjusted HR (95% CI) of 1.63 (1.37–1.94). In stratification analysis, adjusted HR of diabetes-, chronic obstructive pulmonary disease (COPD-), and stroke-subgroup were 2.39, 2.51, and 4.82, with significant p values for interaction, respectively. Sensitivity analysis yields consistent findings. Conclusions: The patients with HPV infection had a higher risk of subsequent ED in comparison to the non-HPV controls. The mechanism behind such association and its possible role in ED prevention deserves further study in the future. Full article

(This article belongs to the Topic Big Data in Healthcare, Bioinformatics and Precision Medicine)

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19 pages, 2807 KiB

Open AccessEditor’s ChoiceReview

Endotypes of Prematurity and Phenotypes of Bronchopulmonary Dysplasia: Toward Personalized Neonatology

by Maria Pierro, Karen Van Mechelen, Elke van Westering-Kroon, Eduardo Villamor-Martínez and Eduardo Villamor

J. Pers. Med. 2022, 12(5), 687; https://doi.org/10.3390/jpm12050687 - 26 Apr 2022

Cited by 27 | Viewed by 7747

Abstract

Bronchopulmonary dysplasia (BPD), the chronic lung disease of prematurity, is increasingly recognized as the consequence of a pathological reparative response of the developing lung to both antenatal and postnatal injury. According to this view, the pathogenesis of BPD is multifactorial and heterogeneous with [...] Read more.

Bronchopulmonary dysplasia (BPD), the chronic lung disease of prematurity, is increasingly recognized as the consequence of a pathological reparative response of the developing lung to both antenatal and postnatal injury. According to this view, the pathogenesis of BPD is multifactorial and heterogeneous with different patterns of antenatal stress (endotypes) that combine with varying postnatal insults and might distinctively damage the development of airways, lung parenchyma, interstitium, lymphatic system, and pulmonary vasculature. This results in different clinical phenotypes of BPD. There is no clear consensus on which are the endotypes of prematurity but the combination of clinical information with placental and bacteriological data enables the identification of two main pathways leading to birth before 32 weeks of gestation: (1) infection/inflammation and (2) dysfunctional placentation. Regarding BPD phenotypes, the following have been proposed: parenchymal, peripheral airway, central airway, interstitial, congestive, vascular, and mixed phenotype. In line with the approach of personalized medicine, endotyping prematurity and phenotyping BPD will facilitate the design of more targeted therapeutic and prognostic approaches. Full article

(This article belongs to the Special Issue Personalized Diagnosis and Treatment of Pulmonary Diseases)

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15 pages, 1553 KiB

Open AccessEditor’s ChoiceArticle

Masticatory Muscle Thickness and Activity Correlates to Eyeball Length, Intraocular Pressure, Retinal and Choroidal Thickness in Healthy Women versus Women with Myopia

by Grzegorz Zieliński, Marcin Wójcicki, Maria Rapa, Anna Matysik-Woźniak, Michał Baszczowski, Michał Ginszt, Monika Litko-Rola, Jacek Szkutnik, Ingrid Różyło-Kalinowska, Robert Rejdak and Piotr Gawda

J. Pers. Med. 2022, 12(4), 626; https://doi.org/10.3390/jpm12040626 - 13 Apr 2022

Cited by 13 | Viewed by 3295

Abstract

This study aims to examine the correlations between masticatory and neck muscle thickness and activity versus eyeball length, retinal thickness, choroidal thickness, and intraocular pressure in healthy women versus women with myopia. The study group consisted of 21 women aged 24 years and [...] Read more.

This study aims to examine the correlations between masticatory and neck muscle thickness and activity versus eyeball length, retinal thickness, choroidal thickness, and intraocular pressure in healthy women versus women with myopia. The study group consisted of 21 women aged 24 years and a control group of 19 women (mean age 23 years). For bioelectrical activity analysis within the temporalis anterior, the superficial part of the masseter muscle, the middle part of the sternocleidomastoid muscle, and the anterior belly of the digastric muscle, an eight-channel BioEMG III electromyograph were used. An M-Turbo ultrasound machine was used to analyze masticatory and neck muscle thickness. The eyeball length was examined by IOL Master 500; choroidal and retinal thickness by Optovue Angiovue; and intraocular pressure by Tono-Pen XL. Refractive errors are related to differences in muscle thickness and electromyographic activity. Bioelectrical activity within the temporalis anterior seems to be associated with ocular length, retinal thickness, and choroidal thickness in women with myopia. Full article

(This article belongs to the Special Issue The Challenges and Therapeutic Prospects in Eye Disease)

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17 pages, 2244 KiB

Open AccessEditor’s ChoiceArticle

Explainable Artificial Intelligence for Prediction of Complete Surgical Cytoreduction in Advanced-Stage Epithelial Ovarian Cancer

by Alexandros Laios, Evangelos Kalampokis, Racheal Johnson, Amudha Thangavelu, Constantine Tarabanis, David Nugent and Diederick De Jong

J. Pers. Med. 2022, 12(4), 607; https://doi.org/10.3390/jpm12040607 - 10 Apr 2022

Cited by 20 | Viewed by 3431

Abstract

Complete surgical cytoreduction (R0 resection) is the single most important prognosticator in epithelial ovarian cancer (EOC). Explainable Artificial Intelligence (XAI) could clarify the influence of static and real-time features in the R0 resection prediction. We aimed to develop an AI-based predictive model for [...] Read more.

Complete surgical cytoreduction (R0 resection) is the single most important prognosticator in epithelial ovarian cancer (EOC). Explainable Artificial Intelligence (XAI) could clarify the influence of static and real-time features in the R0 resection prediction. We aimed to develop an AI-based predictive model for the R0 resection outcome, apply a methodology to explain the prediction, and evaluate the interpretability by analysing feature interactions. The retrospective cohort finally assessed 571 consecutive advanced-stage EOC patients who underwent cytoreductive surgery. An eXtreme Gradient Boosting (XGBoost) algorithm was employed to develop the predictive model including mostly patient- and surgery-specific variables. The Shapley Additive explanations (SHAP) framework was used to provide global and local explainability for the predictive model. The XGBoost accurately predicted R0 resection (area under curve [AUC] = 0.866; 95% confidence interval [CI] = 0.8–0.93). We identified “turning points” that increased the probability of complete cytoreduction including Intraoperative Mapping of Ovarian Cancer Score and Peritoneal Carcinomatosis Index < 4 and <5, respectively, followed by Surgical Complexity Score > 4, patient’s age < 60 years, and largest tumour bulk < 5 cm in a surgical environment of optimized infrastructural support. We demonstrated high model accuracy for the R0 resection prediction in EOC patients and provided novel global and local feature explainability that can be used for quality control and internal audit. Full article

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15 pages, 1957 KiB

Open AccessEditor’s ChoiceArticle

Machine-Learning-Based Late Fusion on Multi-Omics and Multi-Scale Data for Non-Small-Cell Lung Cancer Diagnosis

by Francisco Carrillo-Perez, Juan Carlos Morales, Daniel Castillo-Secilla, Olivier Gevaert, Ignacio Rojas and Luis Javier Herrera

J. Pers. Med. 2022, 12(4), 601; https://doi.org/10.3390/jpm12040601 - 8 Apr 2022

Cited by 18 | Viewed by 4993

Abstract

Differentiation between the various non-small-cell lung cancer subtypes is crucial for providing an effective treatment to the patient. For this purpose, machine learning techniques have been used in recent years over the available biological data from patients. However, in most cases this problem [...] Read more.

Differentiation between the various non-small-cell lung cancer subtypes is crucial for providing an effective treatment to the patient. For this purpose, machine learning techniques have been used in recent years over the available biological data from patients. However, in most cases this problem has been treated using a single-modality approach, not exploring the potential of the multi-scale and multi-omic nature of cancer data for the classification. In this work, we study the fusion of five multi-scale and multi-omic modalities (RNA-Seq, miRNA-Seq, whole-slide imaging, copy number variation, and DNA methylation) by using a late fusion strategy and machine learning techniques. We train an independent machine learning model for each modality and we explore the interactions and gains that can be obtained by fusing their outputs in an increasing manner, by using a novel optimization approach to compute the parameters of the late fusion. The final classification model, using all modalities, obtains an F1 score of

96.81 \pm 1.07

, an AUC of

0.993 \pm 0.004

, and an AUPRC of

0.980 \pm 0.016

, improving those results that each independent model obtains and those presented in the literature for this problem. These obtained results show that leveraging the multi-scale and multi-omic nature of cancer data can enhance the performance of single-modality clinical decision support systems in personalized medicine, consequently improving the diagnosis of the patient. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Personalized Medicine)

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14 pages, 2645 KiB

Open AccessEditor’s ChoiceReview

Should Neurosurgeons Try to Preserve Non-Traditional Brain Networks? A Systematic Review of the Neuroscientific Evidence

by Nicholas B. Dadario and Michael E. Sughrue

J. Pers. Med. 2022, 12(4), 587; https://doi.org/10.3390/jpm12040587 - 6 Apr 2022

Cited by 21 | Viewed by 4181

Abstract

The importance of large-scale brain networks in higher-order human functioning is well established in neuroscience, but has yet to deeply penetrate neurosurgical thinking due to concerns of clinical relevance. Here, we conducted the first systematic review examining the clinical importance of non-traditional, large-scale [...] Read more.

The importance of large-scale brain networks in higher-order human functioning is well established in neuroscience, but has yet to deeply penetrate neurosurgical thinking due to concerns of clinical relevance. Here, we conducted the first systematic review examining the clinical importance of non-traditional, large-scale brain networks, including the default mode (DMN), central executive (CEN), salience (SN), dorsal attention (DAN), and ventral attention (VAN) networks. Studies which reported evidence of neurologic, cognitive, or emotional deficits in relation to damage or dysfunction in these networks were included. We screened 22,697 articles on PubMed, and 551 full-text articles were included and examined. Cognitive deficits were the most common symptom of network disturbances in varying amounts (36–56%), most frequently related to disruption of the DMN (n = 213) or some combination of DMN, CEN, and SN networks (n = 182). An increased proportion of motor symptoms was seen with CEN disruption (12%), and emotional (35%) or language/speech deficits (24%) with SN disruption. Disruption of the attention networks (VAN/DAN) with each other or the other networks mostly led to cognitive deficits (56%). A large body of evidence is available demonstrating the clinical importance of non-traditional, large-scale brain networks and suggests the need to preserve these networks is relevant for neurosurgical patients. Full article

(This article belongs to the Special Issue Personalized Medicine in Neurological and Neurosurgical Diseases)

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12 pages, 1959 KiB

Open AccessEditor’s ChoiceArticle

Personalized Self-Monitoring of Energy Balance through Integration in a Web-Application of Dietary, Anthropometric, and Physical Activity Data

by Giada Bianchetti, Alessio Abeltino, Cassandra Serantoni, Federico Ardito, Daniele Malta, Marco De Spirito and Giuseppe Maulucci

J. Pers. Med. 2022, 12(4), 568; https://doi.org/10.3390/jpm12040568 - 2 Apr 2022

Cited by 13 | Viewed by 2718

Abstract

Self-monitoring of weight, diet and physical activity is a valuable component of behavioral weight loss treatment. The validation and user-friendliness of this approach is not optimal since users are selected from homogeneous pools and rely on different applications, increasing the burden and achieving [...] Read more.

Self-monitoring of weight, diet and physical activity is a valuable component of behavioral weight loss treatment. The validation and user-friendliness of this approach is not optimal since users are selected from homogeneous pools and rely on different applications, increasing the burden and achieving partial, generic and/or unrelated information about their metabolic state. Moreover, studies establishing type, time, duration, and adherence criteria for self-monitoring are lacking. In this study, we developed a digital web-based application (ArmOnIA), which integrates dietary, anthropometric, and physical activity data and provides a personalized estimation of energy balance. Moreover, we determined type, time, duration, and adherence criteria for self-monitoring to achieve significant weight loss in a highly heterogeneous group. A single-arm, uncontrolled prospective study on self-monitored voluntary adults for 7 months was performed. Hierarchical clustering of adherence parameters yielded three behavioral approaches: high (HA), low (LA), and medium (MA) adherence. Average BMI decrease is statistically significant between LA and HA. Moreover, we defined thresholds for the minimum frequencies and duration of dietary and weight self-monitoring. This approach can provide the correct clues to empower citizens with scientific knowledge, augmenting their self-awareness with the aim of achieving long-lasting results when pursuing a healthy lifestyle. Full article

(This article belongs to the Special Issue From Basic to Clinical Research: Toward Precision Medicine for Metabolic Diseases)

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17 pages, 708 KiB

Open AccessEditor’s ChoiceReview

Emerging Biomarkers for Early Detection of Chronic Kidney Disease

by Maja Mizdrak, Marko Kumrić, Tina Tičinović Kurir and Joško Božić

J. Pers. Med. 2022, 12(4), 548; https://doi.org/10.3390/jpm12040548 - 31 Mar 2022

Cited by 40 | Viewed by 8894

Abstract

Chronic kidney disease (CKD) is a major and serious global health problem that leads to kidney damage as well as multiple systemic diseases. Early diagnosis and treatment are two major measures to prevent further deterioration of kidney function and to delay adverse outcomes. [...] Read more.

Chronic kidney disease (CKD) is a major and serious global health problem that leads to kidney damage as well as multiple systemic diseases. Early diagnosis and treatment are two major measures to prevent further deterioration of kidney function and to delay adverse outcomes. However, the paucity of early, predictive and noninvasive biomarkers has undermined our ability to promptly detect and treat this common clinical condition which affects more than 10% of the population worldwide. Despite all limitations, kidney function is still measured by serum creatinine, cystatin C, and albuminuria, as well as estimating glomerular filtration rate using different equations. This review aims to provide comprehensive insight into diagnostic methods available for early detection of CKD. In the review, we discuss the following topics: (i) markers of glomerular injury; (ii) markers of tubulointerstitial injury; (iii) the role of omics; (iv) the role of microbiota; (v) and finally, the role of microRNA in the early detection of CKD. Despite all novel findings, none of these biomarkers have met the criteria of an ideal early marker. Since the central role in CKD progression is the proximal tubule (PT), most data from the literature have analyzed biomarkers of PT injury, such as KIM-1 (kidney injury molecule-1), NGAL (neutrophil gelatinase-associated lipocalin), and L-FABP (liver fatty acid-binding protein). Full article

(This article belongs to the Special Issue Chronic Kidney Disease: Early Detection, Mechanisms, and Therapeutic Implications)

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12 pages, 1056 KiB

Open AccessEditor’s ChoiceSystematic Review

Prevalence of New-Onset Atrial Fibrillation and Associated Outcomes in Patients with Sepsis: A Systematic Review and Meta-Analysis

by Bernadette Corica, Giulio Francesco Romiti, Stefania Basili and Marco Proietti

J. Pers. Med. 2022, 12(4), 547; https://doi.org/10.3390/jpm12040547 - 30 Mar 2022

Cited by 14 | Viewed by 3166

Abstract

Background: New-onset atrial fibrillation (NOAF) is a common complication in patients with sepsis, although its prevalence and impact on outcomes are still unclear. We aim to provide a systematic review and meta-analysis on the prevalence of NOAF in patients with sepsis, and its [...] Read more.

Background: New-onset atrial fibrillation (NOAF) is a common complication in patients with sepsis, although its prevalence and impact on outcomes are still unclear. We aim to provide a systematic review and meta-analysis on the prevalence of NOAF in patients with sepsis, and its impact on in-hospital mortality and intensive care unit (ICU) mortality. Methods: PubMed and EMBASE were systematically searched on 26 December 2021. Studies reporting on the prevalence of NOAF and/or its impact on in-hospital mortality or ICU mortality in patients with sepsis or septic shock were included. The pooled prevalence and 95% confidence intervals (CI) were calculated, as well as the risk ratios (RR), 95%CI and 95% prediction intervals (PI) for outcomes. Subgroup analyses and meta-regressions were performed to account for heterogeneity. Results: Among 4988 records retrieved from the literature search, 22 articles were included. Across 207,847 patients with sepsis, NOAF was found in 13.5% (95%CI: 8.9–20.1%), with high heterogeneity between studies; significant subgroup differences were observed, according to the geographical location, study design and sample size of the included studies. A multivariable meta-regression model showed that sample size and geographical location account for most of the heterogeneity. NOAF patients showed an increased risk of both in-hospital mortality (RR: 1.69, 95%CI: 1.47–1.96, 95%PI: 1.15–2.50) and ICU mortality (RR: 2.12, 95%CI: 1.86–2.43, 95%PI: 1.71–2.63), with moderate to no heterogeneity between the included studies. Conclusions: NOAF is a common complication during sepsis, being present in one out of seven individuals. Patients with NOAF are at a higher risk of adverse events during sepsis, and may need specific therapeutical interventions. Full article

(This article belongs to the Section Epidemiology)

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41 pages, 18497 KiB

Open AccessEditor’s ChoiceSystematic Review

Endoscopic Combined Intrarenal Surgery Versus Percutaneous Nephrolithotomy for Complex Renal Stones: A Systematic Review and Meta-Analysis

by Yung-Hao Liu, Hong-Jie Jhou, Meng-Han Chou, Sheng-Tang Wu, Tai-Lung Cha, Dah-Shyong Yu, Guang-Huan Sun, Po-Huang Chen and En Meng

J. Pers. Med. 2022, 12(4), 532; https://doi.org/10.3390/jpm12040532 - 28 Mar 2022

Cited by 14 | Viewed by 3513

Abstract

Background: Endoscopic combined intrarenal surgery (ECIRS) adds ureteroscopic vision to percutaneous nephrolithotomy (PCNL), which can be helpful when dealing with complex renal stones. Yet, there is still no consensus on the superiority of ECIRS. We aimed to critically analyze the available evidence of [...] Read more.

Background: Endoscopic combined intrarenal surgery (ECIRS) adds ureteroscopic vision to percutaneous nephrolithotomy (PCNL), which can be helpful when dealing with complex renal stones. Yet, there is still no consensus on the superiority of ECIRS. We aimed to critically analyze the available evidence of studies comparing efficacy, safety, bleeding risk, and efficiency of ECIRS and PCNL. Methods: We searched for studies comparing efficacy (initial and final stone-free rate), safety (postoperative fever, overall and severe complications), efficiency (operative time and hospital stay) and bleeding risk between ECIRS and PCNL. Meta-analysis was performed. Results: Seven studies (919 patients) were identified. ECIRS provided a significantly higher initial stone-free rate, higher final stone-free rate, lower overall complications, lower severe complications, and lower rate of requiring blood transfusion. There was no difference between the two groups in terms of postoperative fever, hemoglobin drop, operative time, and hospital stay. In the subgroup analysis, both minimally invasive and conventional ECIRS were associated with a higher stone-free rate and lower complication outcomes. Conclusions: When treating complex renal stones, ECIRS has a better stone-free rate, fewer complications, and requires fewer blood transfusions compared with PCNL. Subgroups either with minimally invasive or conventional intervention showed a consistent trend. Full article

(This article belongs to the Special Issue Postoperative Complications and Personalized Medicine)

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21 pages, 1771 KiB

Open AccessEditor’s ChoiceArticle

Impact of the Genotype and Phenotype of CYP3A and P-gp on the Apixaban and Rivaroxaban Exposure in a Real-World Setting

by Camille Lenoir, Jean Terrier, Yvonne Gloor, Pauline Gosselin, Youssef Daali, Christophe Combescure, Jules Alexandre Desmeules, Caroline Flora Samer, Jean-Luc Reny and Victoria Rollason

J. Pers. Med. 2022, 12(4), 526; https://doi.org/10.3390/jpm12040526 - 24 Mar 2022

Cited by 13 | Viewed by 3217

Abstract

Apixaban and rivaroxaban are the two most prescribed direct factor Xa inhibitors. With the increased use of DOACs in real-world settings, safety and efficacy concerns have emerged, particularly regarding their concomitant use with other drugs. Increasing evidence highlights drug–drug interactions with CYP3A/P-gp modulators [...] Read more.

Apixaban and rivaroxaban are the two most prescribed direct factor Xa inhibitors. With the increased use of DOACs in real-world settings, safety and efficacy concerns have emerged, particularly regarding their concomitant use with other drugs. Increasing evidence highlights drug–drug interactions with CYP3A/P-gp modulators leading to adverse events. However, current recommendations for dose adjustment do not consider CYP3A/P-gp genotype and phenotype. We aimed to determine their impact on apixaban and rivaroxaban blood exposure. Three-hundred hospitalized patients were included. CYP3A and P-gp phenotypic activities were assessed by the metabolic ratio of midazolam and AUC_0–6h of fexofenadine, respectively. Relevant CYP3A and ABCB1 genetic polymorphisms were also tested. Capillary blood samples collected at four time-points after apixaban or rivaroxaban administration allowed the calculation of pharmacokinetic parameters. According to the developed multivariable linear regression models, P-gp activity (p < 0.001) and creatinine clearance (CrCl) (p = 0.01) significantly affected apixaban AUC_0–6h. P-gp activity (p < 0.001) also significantly impacted rivaroxaban AUC_0–6h. The phenotypic switch (from normal to poor metabolizer) of P-gp led to an increase of apixaban and rivaroxaban AUC_0–6h by 16% and 25%, respectively, equivalent to a decrease of 38 mL/min in CrCl according to the apixaban model. CYP3A phenotype and tested SNPs of CYP3A/P-gp had no significant impact. In conclusion, P-gp phenotypic activity, rather than known CYP3A/P-gp polymorphisms, could be relevant for dose adjustment. Full article

(This article belongs to the Special Issue Cardiovascular Disease Prevention in the Era of Personalized Medicine)

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24 pages, 1096 KiB

Open AccessFeature PaperEditor’s ChoiceReview

Artificial Intelligence-Driven Prediction Modeling and Decision Making in Spine Surgery Using Hybrid Machine Learning Models

by Babak Saravi, Frank Hassel, Sara Ülkümen, Alisia Zink, Veronika Shavlokhova, Sebastien Couillard-Despres, Martin Boeker, Peter Obid and Gernot Michael Lang

J. Pers. Med. 2022, 12(4), 509; https://doi.org/10.3390/jpm12040509 - 22 Mar 2022

Cited by 59 | Viewed by 8018

Abstract

Healthcare systems worldwide generate vast amounts of data from many different sources. Although of high complexity for a human being, it is essential to determine the patterns and minor variations in the genomic, radiological, laboratory, or clinical data that reliably differentiate phenotypes or [...] Read more.

Healthcare systems worldwide generate vast amounts of data from many different sources. Although of high complexity for a human being, it is essential to determine the patterns and minor variations in the genomic, radiological, laboratory, or clinical data that reliably differentiate phenotypes or allow high predictive accuracy in health-related tasks. Convolutional neural networks (CNN) are increasingly applied to image data for various tasks. Its use for non-imaging data becomes feasible through different modern machine learning techniques, converting non-imaging data into images before inputting them into the CNN model. Considering also that healthcare providers do not solely use one data modality for their decisions, this approach opens the door for multi-input/mixed data models which use a combination of patient information, such as genomic, radiological, and clinical data, to train a hybrid deep learning model. Thus, this reflects the main characteristic of artificial intelligence: simulating natural human behavior. The present review focuses on key advances in machine and deep learning, allowing for multi-perspective pattern recognition across the entire information set of patients in spine surgery. This is the first review of artificial intelligence focusing on hybrid models for deep learning applications in spine surgery, to the best of our knowledge. This is especially interesting as future tools are unlikely to use solely one data modality. The techniques discussed could become important in establishing a new approach to decision-making in spine surgery based on three fundamental pillars: (1) patient-specific, (2) artificial intelligence-driven, (3) integrating multimodal data. The findings reveal promising research that already took place to develop multi-input mixed-data hybrid decision-supporting models. Their implementation in spine surgery may hence be only a matter of time. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Personalized Medicine)

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11 pages, 599 KiB

Open AccessEditor’s ChoiceArticle

Individualized Biological Age as a Predictor of Disease: Korean Genome and Epidemiology Study (KoGES) Cohort

by Seokyung An, Choonghyun Ahn, Sungji Moon, Eun Ji Sim and Sue-Kyung Park

J. Pers. Med. 2022, 12(3), 505; https://doi.org/10.3390/jpm12030505 - 21 Mar 2022

Cited by 12 | Viewed by 3169

Abstract

Chronological age (CA) predicts health status but its impact on health varies with anthropometry, socioeconomic status (SES), and lifestyle behaviors. Biological age (BA) is, therefore, considered a more precise predictor of health status. We aimed to develop a BA prediction model from self-assessed [...] Read more.

Chronological age (CA) predicts health status but its impact on health varies with anthropometry, socioeconomic status (SES), and lifestyle behaviors. Biological age (BA) is, therefore, considered a more precise predictor of health status. We aimed to develop a BA prediction model from self-assessed risk factors and validate it as an indicator for predicting the risk of chronic disease. A total of 101,980 healthy participants from the Korean Genome and Epidemiology Study were included in this study. BA was computed based on body measurements, SES, lifestyle behaviors, and presence of comorbidities using elastic net regression analysis. The effects of BA on diabetes mellitus (DM), hypertension (HT), combination of DM and HT, and chronic kidney disease were analyzed using Cox proportional hazards regression. A younger BA was associated with a lower risk of DM (HR = 0.63, 95% CI: 0.55–0.72), hypertension (HR = 0.74, 95% CI: 0.68–0.81), and combination of DM and HT (HR = 0.65, 95% CI: 0.47–0.91). The largest risk of disease was seen in those with a BA higher than their CA. A consistent association was also observed within the 5-year follow-up. BA, therefore, is an effective tool for detecting high-risk groups and preventing further risk of chronic diseases through individual and population-level interventions. Full article

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14 pages, 613 KiB

Open AccessEditor’s ChoiceReview

Focus on Sex and Gender: What We Need to Know in the Management of Rheumatoid Arthritis

by Beatrice Maranini, Alessandra Bortoluzzi, Ettore Silvagni and Marcello Govoni

J. Pers. Med. 2022, 12(3), 499; https://doi.org/10.3390/jpm12030499 - 20 Mar 2022

Cited by 35 | Viewed by 9336

Abstract

Rheumatoid arthritis (RA) is a chronic inflammatory disease, affecting mostly women with a female/male ratio of 3:1. It is characterized by symmetrical polyarthritis, leading to progressive joint damage. Sex differences have been reported in terms of disease course and characteristics, influencing patients reported [...] Read more.

Rheumatoid arthritis (RA) is a chronic inflammatory disease, affecting mostly women with a female/male ratio of 3:1. It is characterized by symmetrical polyarthritis, leading to progressive joint damage. Sex differences have been reported in terms of disease course and characteristics, influencing patients reported outcome measures (PROMs) and pain perception, ultimately leading to male–female disparities in treatment response. Notwithstanding, sex and gender discrepancies are still under-reported in clinical trials. Therefore, there is a consistent need for a precise reference of sex and gender issues in RA studies to improve treat-to-target achievement. This narrative review explores the above-mentioned aspects of RA disease, discussing the latest core principles of RA recommendations, from safety issues to early arthritis concept and management, treat-to-target and difficult-to-treat notions, up to the most recent debate on vaccination. Our final purpose is to evaluate how sex and gender can impact current management guidelines and how this issue can be integrated for effective disease control. Full article

(This article belongs to the Special Issue Personalized Medicine Interdisciplinary Aspects and Sex Gender-Specific Differences)

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36 pages, 1280 KiB

Open AccessEditor’s ChoiceReview

Towards Machine Learning-Aided Lung Cancer Clinical Routines: Approaches and Open Challenges

by Francisco Silva, Tania Pereira, Inês Neves, Joana Morgado, Cláudia Freitas, Mafalda Malafaia, Joana Sousa, João Fonseca, Eduardo Negrão, Beatriz Flor de Lima, Miguel Correia da Silva, António J. Madureira, Isabel Ramos, José Luis Costa, Venceslau Hespanhol, António Cunha and Hélder P. Oliveira

J. Pers. Med. 2022, 12(3), 480; https://doi.org/10.3390/jpm12030480 - 16 Mar 2022

Cited by 23 | Viewed by 5077

Abstract

Advancements in the development of computer-aided decision (CAD) systems for clinical routines provide unquestionable benefits in connecting human medical expertise with machine intelligence, to achieve better quality healthcare. Considering the large number of incidences and mortality numbers associated with lung cancer, there is [...] Read more.

Advancements in the development of computer-aided decision (CAD) systems for clinical routines provide unquestionable benefits in connecting human medical expertise with machine intelligence, to achieve better quality healthcare. Considering the large number of incidences and mortality numbers associated with lung cancer, there is a need for the most accurate clinical procedures; thus, the possibility of using artificial intelligence (AI) tools for decision support is becoming a closer reality. At any stage of the lung cancer clinical pathway, specific obstacles are identified and “motivate” the application of innovative AI solutions. This work provides a comprehensive review of the most recent research dedicated toward the development of CAD tools using computed tomography images for lung cancer-related tasks. We discuss the major challenges and provide critical perspectives on future directions. Although we focus on lung cancer in this review, we also provide a more clear definition of the path used to integrate AI in healthcare, emphasizing fundamental research points that are crucial for overcoming current barriers. Full article

(This article belongs to the Special Issue Personalized Medicine in Lung Cancer: Diagnosis, Treatment and Prognosis)

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22 pages, 2248 KiB

Open AccessEditor’s ChoiceReview

Dental and Skeletal Side Effects of Oral Appliances Used for the Treatment of Obstructive Sleep Apnea and Snoring in Adult Patients—A Systematic Review and Meta-Analysis

by Ioannis A. Tsolakis, Juan Martin Palomo, Stefanos Matthaios and Apostolos I. Tsolakis

J. Pers. Med. 2022, 12(3), 483; https://doi.org/10.3390/jpm12030483 - 16 Mar 2022

Cited by 15 | Viewed by 4595

Abstract

Background: Mandibular advancement devices for obstructive sleep apnea treatment are becoming increasingly popular among patients who do not prefer CPAP devices or surgery. Our study aims to evaluate the literature regarding potential dental and skeletal side effects caused by mandibular advancement appliances used [...] Read more.

Background: Mandibular advancement devices for obstructive sleep apnea treatment are becoming increasingly popular among patients who do not prefer CPAP devices or surgery. Our study aims to evaluate the literature regarding potential dental and skeletal side effects caused by mandibular advancement appliances used for adult OSA treatment. Methods: Electronic databases were searched for published and unpublished literature along with the reference lists of the eligible studies. Randomized clinical trials and non-randomized trials assessing dental and skeletal changes by comparing cephalometric radiographs were selected. Study selection, data extraction, and risk of bias assessment were performed individually and in duplicate. Fourteen articles were finally selected (two randomized clinical trials and 12 non-randomized trials). Results: The results suggest that mandibular advancement devices used for OSA treatment increase the lower incisor proclination by 1.54 ± 0.16°, decrease overjet by 0.89 ± 0.04 mm and overbite by 0.68 ± 0.04 mm, rotate the mandible downward and forward, and increase the SNA angle by to 0.06 ± 0.03°. The meta-analysis revealed high statistical heterogeneity. Conclusions: The MADs affect the lower incisor proclination, overjet, overbite, the rotation of the mandible and the SNA angle. More randomized clinical trials providing high-quality evidence are needed to support those findings. Full article

(This article belongs to the Special Issue Respiratory and Critical Care)

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12 pages, 272 KiB

Open AccessEditor’s ChoiceArticle

Physical Frailty and Oral Frailty Associated with Late-Life Depression in Community-Dwelling Older Adults

by Ying-Chun Lin, Shan-Shan Huang, Cheng-Wei Yen, Yuji Kabasawa, Chien-Hung Lee and Hsiao-Ling Huang

J. Pers. Med. 2022, 12(3), 459; https://doi.org/10.3390/jpm12030459 - 14 Mar 2022

Cited by 14 | Viewed by 2778

Abstract

Late-life depression is a major mental health problem and constitutes a heavy public health burden. Frailty, an aging-related syndrome, is reciprocally related to depressive symptoms. This study investigated the associations of physical frailty and oral frailty with depression in older adults. This large-scale [...] Read more.

Late-life depression is a major mental health problem and constitutes a heavy public health burden. Frailty, an aging-related syndrome, is reciprocally related to depressive symptoms. This study investigated the associations of physical frailty and oral frailty with depression in older adults. This large-scale cross-sectional study included 1100 community-dwelling older adults in Taiwan. The participants completed a dental examination and questionnaires answered during personal interviews. The 15-item Geriatric Depression Scale was used to assess depression, and information on physical conditions and oral conditions was collected. Multivariable logistical regression analysis was conducted to examine associations of interest. Significant factors associated with depression were pre-physical frailty (adjusted odds ratio (aOR) = 3.61), physical frailty (aOR = 53.74), sarcopenia (aOR = 4.25), insomnia (aOR = 2.56), pre-oral frailty (aOR = 2.56), oral frailty (aOR = 4.89), dysphagia (aOR = 2.85), and xerostomia (aOR = 1.10). Depression exerted a combined effect on physical frailty and oral frailty (aOR = 36.81). Physical frailty and oral frailty were significantly associated with late-life depression in community-dwelling older adults in a dose–response manner. Developing physical and oral function interventions to prevent depression among older adults is essential. Full article

15 pages, 618 KiB

Open AccessEditor’s ChoiceReview

Sex Differences in Response to Treatment with Glucagon-like Peptide 1 Receptor Agonists: Opportunities for a Tailored Approach to Diabetes and Obesity Care

by Elpiniki Rentzeperi, Stavroula Pegiou, Theocharis Koufakis, Maria Grammatiki and Kalliopi Kotsa

J. Pers. Med. 2022, 12(3), 454; https://doi.org/10.3390/jpm12030454 - 13 Mar 2022

Cited by 40 | Viewed by 7480

Abstract

The available data suggest differences in the course of type 2 diabetes mellitus (T2DM) between men and women, influenced by the distinguishing features of the sex. Glucagon-like peptide 1 receptor agonists (GLP-1 RAs) are a relatively new class of antidiabetic drugs that act [...] Read more.

The available data suggest differences in the course of type 2 diabetes mellitus (T2DM) between men and women, influenced by the distinguishing features of the sex. Glucagon-like peptide 1 receptor agonists (GLP-1 RAs) are a relatively new class of antidiabetic drugs that act by mimicking the function of endogenous glucagon-like peptide 1. They constitute valuable agents for the management of T2DM as, in addition to exerting a strong hypoglycemic action, they present cardiorenal protective properties, promote weight loss, and have a good safety profile, particularly with respect to the risk of hypoglycemia. Due to the precedent of studies having identified sexual dimorphic elements regarding the action of other antidiabetic agents, ongoing research has attempted to examine whether this is also the case for GLP-1 RAs. Until now, sex differences have been observed in the impact of GLP1-RAs on glycemic control, weight reduction, and frequency of adverse events. On the contrary, the question of whether these drugs differentially affect the two sexes with respect to cardiovascular risk and incidence of major adverse cardiovascular events remains under investigation. Knowledge of the potential sex-specific effects of these medications is extremely useful for the implementation of individualized therapeutic plans in the treatment of T2DM. This narrative review aims to present the available data regarding the sex-specific action of GLP-1 RAs as well as to discuss the potential pathophysiologic mechanisms explaining these dissimilarities. Full article

(This article belongs to the Special Issue Obesity and Diabetes: Genetic Markers and Pharmacological Interventions)

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15 pages, 1507 KiB

Open AccessEditor’s ChoiceArticle

Artificial Intelligence-Enabled Electrocardiography Predicts Left Ventricular Dysfunction and Future Cardiovascular Outcomes: A Retrospective Analysis

by Hung-Yi Chen, Chin-Sheng Lin, Wen-Hui Fang, Yu-Sheng Lou, Cheng-Chung Cheng, Chia-Cheng Lee and Chin Lin

J. Pers. Med. 2022, 12(3), 455; https://doi.org/10.3390/jpm12030455 - 13 Mar 2022

Cited by 15 | Viewed by 3484

Abstract

BACKGROUND: The ejection fraction (EF) provides critical information about heart failure (HF) and its management. Electrocardiography (ECG) is a noninvasive screening tool for cardiac electrophysiological activities that has been used to detect patients with low EF based on a deep learning model (DLM) [...] Read more.

BACKGROUND: The ejection fraction (EF) provides critical information about heart failure (HF) and its management. Electrocardiography (ECG) is a noninvasive screening tool for cardiac electrophysiological activities that has been used to detect patients with low EF based on a deep learning model (DLM) trained via large amounts of data. However, no studies have widely investigated its clinical impacts. OBJECTIVE: This study developed a DLM to estimate EF via ECG (ECG-EF). We further investigated the relationship between ECG-EF and echo-based EF (ECHO-EF) and explored their contributions to future cardiovascular adverse events. METHODS: There were 57,206 ECGs with corresponding echocardiograms used to train our DLM. We compared a series of training strategies and selected the best DLM. The architecture of the DLM was based on ECG12Net, developed previously. Next, 10,762 ECGs were used for validation, and another 20,629 ECGs were employed to conduct the accuracy test. The changes between ECG-EF and ECHO-EF were evaluated. The primary follow-up adverse events included future ECHO-EF changes and major adverse cardiovascular events (MACEs). RESULTS: The sex-/age-matching strategy-trained DLM achieved the best area under the curve (AUC) of 0.9472 with a sensitivity of 86.9% and specificity of 89.6% in the follow-up cohort, with a correlation of 0.603 and a mean absolute error of 7.436. In patients with accurate prediction (initial difference < 10%), the change traces of ECG-EF and ECHO-EF were more consistent (R-square = 0.351) than in all patients (R-square = 0.115). Patients with lower ECG-EF (≤35%) exhibited a greater risk of cardiovascular (CV) complications, delayed ECHO-EF recovery, and earlier ECHO-EF deterioration than patients with normal ECG-EF (>50%). Importantly, ECG-EF demonstrated an independent impact on MACEs and all CV adverse outcomes, with better prediction of CV outcomes than ECHO-EF. CONCLUSIONS: The ECG-EF could be used to initially screen asymptomatic left ventricular dysfunction (LVD) and it could also independently contribute to the predictions of future CV adverse events. Although further large-scale studies are warranted, DLM-based ECG-EF could serve as a promising diagnostic supportive and management-guided tool for CV disease prediction and the care of patients with LVD. Full article

(This article belongs to the Special Issue Artificial Intelligence Application in Health Care System)

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14 pages, 984 KiB

Open AccessEditor’s ChoiceReview

Current Status, Issues and Future Prospects of Personalized Medicine for Each Disease

by Yuichi Yamamoto, Norihiro Kanayama, Yusuke Nakayama and Nobuko Matsushima

J. Pers. Med. 2022, 12(3), 444; https://doi.org/10.3390/jpm12030444 - 11 Mar 2022

Cited by 23 | Viewed by 6286

Abstract

In recent years, with the advancement of next-generation sequencing (NGS) technology, gene panel tests have been approved in the field of cancer diseases, and approaches to prescribe optimal molecular target drugs to patients are being developed. In the field of rare diseases, whole-genome [...] Read more.

In recent years, with the advancement of next-generation sequencing (NGS) technology, gene panel tests have been approved in the field of cancer diseases, and approaches to prescribe optimal molecular target drugs to patients are being developed. In the field of rare diseases, whole-genome and whole-exome analysis has been used to identify the causative genes of undiagnosed diseases and to diagnose patients’ diseases, and further progress in personalized medicine is expected. In order to promote personalized medicine in the future, we investigated the current status and progress of personalized medicine in disease areas other than cancer and rare diseases, where personalized medicine is most advanced. We selected rheumatoid arthritis and psoriasis as the inflammatory disease, in addition to Alzheimer’s disease. These diseases have high unmet needs for personalized medicine from the viewpoints of disease mechanisms, diagnostic biomarkers, therapeutic drugs with diagnostic markers and treatment satisfaction. In rheumatoid arthritis and psoriasis, there are many therapeutic options; however, diagnostic methods have not been developed to select the best treatment for each patient. In addition, there are few effective therapeutic agents in Alzheimer’s disease, although clinical trials of many candidate drugs have been conducted. In rheumatoid arthritis and psoriasis, further elucidation of the disease mechanism is desired to enable the selection of appropriate therapeutic agents according to the patient profile. In the case of Alzheimer’s disease, progress in preventive medicine is desired through the establishment of an early diagnosis method as well as the research and development of innovative therapeutic agents. To this end, we hope for further research and development of diagnostic markers and new drugs through progress in comprehensive data analysis such as comprehensive genomic and transcriptomic information. Furthermore, new types of markers such as miRNAs and the gut microbiome are desired to be utilized in clinical diagnostics. Full article

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21 pages, 1084 KiB

Open AccessEditor’s ChoiceReview

A Scoping Review of Attitudes and Experiences with Pharmacogenomic Testing among Patients and the General Public: Implications for Patient Counseling

by Josiah D. Allen, Amy L. Pittenger and Jeffrey R. Bishop

J. Pers. Med. 2022, 12(3), 425; https://doi.org/10.3390/jpm12030425 - 9 Mar 2022

Cited by 14 | Viewed by 3063

Abstract

The use of pharmacogenomic (PGx) tests is increasing, but there are not standard approaches to counseling patients on their implications or results. To inform approaches for patient counseling, we conducted a scoping review of published literature on patient experiences with PGx testing and [...] Read more.

The use of pharmacogenomic (PGx) tests is increasing, but there are not standard approaches to counseling patients on their implications or results. To inform approaches for patient counseling, we conducted a scoping review of published literature on patient experiences with PGx testing and performed a thematic analysis of qualitative and quantitative reports. A structured scoping review was conducted using Joanna Briggs Institute guidance. The search identified 37 articles (involving n = 6252 participants) published between 2010 and 2021 from a diverse range of populations and using a variety of study methodologies. Thematic analysis identified five themes (reasons for testing/perceived benefit, understanding of results, psychological response, impact of testing on patient/provider relationship, concerns about testing/perceived harm) and 22 subthemes. These results provide valuable context and potential areas of focus during patient counseling on PGx. Many of the knowledge gaps, misunderstandings, and concerns that participants identified could be mitigated by pre- and post-test counseling. More research is needed on patients’ PGx literacy needs, along with the development of a standardized, open-source patient education curriculum and the development of validated PGx literacy assessment tools. Full article

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14 pages, 1499 KiB

Open AccessEditor’s ChoiceArticle

Real-World Impact of a Pharmacogenomics-Enriched Comprehensive Medication Management Program

by Joseph P. Jarvis, Arul Prakasam Peter, Murray Keogh, Vince Baldasare, Gina M. Beanland, Zachary T. Wilkerson, Steven Kradel and Jeffrey A. Shaman

J. Pers. Med. 2022, 12(3), 421; https://doi.org/10.3390/jpm12030421 - 8 Mar 2022

Cited by 22 | Viewed by 13168

Abstract

The availability of clinical decision support systems (CDSS) and other methods for personalizing medicine now allows evaluation of their real-world impact on healthcare delivery. For example, addressing issues associated with polypharmacy in older patients using pharmacogenomics (PGx) and comprehensive medication management (CMM) is [...] Read more.

The availability of clinical decision support systems (CDSS) and other methods for personalizing medicine now allows evaluation of their real-world impact on healthcare delivery. For example, addressing issues associated with polypharmacy in older patients using pharmacogenomics (PGx) and comprehensive medication management (CMM) is thought to hold great promise for meaningful improvements across the goals of the Quadruple Aim. However, few studies testing these tools at scale, using relevant system-wide metrics, and under real-world conditions, have been published to date. Here, we document a reduction of ~$7000 per patient in direct medical charges (a total of $37 million over 5288 enrollees compared to 22,357 non-enrolled) in Medicare Advantage patients (≥65 years) receiving benefits through a state retirement system over the first 32 months of a voluntary PGx-enriched CMM program. We also observe a positive shift in healthcare resource utilization (HRU) away from acute care services and toward more sustainable and cost-effective primary care options. Together with improvements in medication risk assessment, patient/provider communication via pharmacist-mediated medication action plans (MAP), and the sustained positive trends in HRU, we suggest these results validate the use of a CDSS to unify PGx and CMM to optimize care for this and similar patient populations. Full article

(This article belongs to the Special Issue Precision Medicine in Clinical Practice)

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14 pages, 1395 KiB

Open AccessEditor’s ChoiceArticle

Effectiveness of Human–Artificial Intelligence Collaboration in Cephalometric Landmark Detection

by Van Nhat Thang Le, Junhyeok Kang, Il-Seok Oh, Jae-Gon Kim, Yeon-Mi Yang and Dae-Woo Lee

J. Pers. Med. 2022, 12(3), 387; https://doi.org/10.3390/jpm12030387 - 3 Mar 2022

Cited by 16 | Viewed by 3752

Abstract

Detection of cephalometric landmarks has contributed to the analysis of malocclusion during orthodontic diagnosis. Many recent studies involving deep learning have focused on head-to-head comparisons of accuracy in landmark identification between artificial intelligence (AI) and humans. However, a human–AI collaboration for the identification [...] Read more.

Detection of cephalometric landmarks has contributed to the analysis of malocclusion during orthodontic diagnosis. Many recent studies involving deep learning have focused on head-to-head comparisons of accuracy in landmark identification between artificial intelligence (AI) and humans. However, a human–AI collaboration for the identification of cephalometric landmarks has not been evaluated. We selected 1193 cephalograms and used them to train the deep anatomical context feature learning (DACFL) model. The number of target landmarks was 41. To evaluate the effect of human–AI collaboration on landmark detection, 10 images were extracted randomly from 100 test images. The experiment included 20 dental students as beginners in landmark localization. The outcomes were determined by measuring the mean radial error (MRE), successful detection rate (SDR), and successful classification rate (SCR). On the dataset, the DACFL model exhibited an average MRE of 1.87 ± 2.04 mm and an average SDR of 73.17% within a 2 mm threshold. Compared with the beginner group, beginner–AI collaboration improved the SDR by 5.33% within a 2 mm threshold and also improved the SCR by 8.38%. Thus, the beginner–AI collaboration was effective in the detection of cephalometric landmarks. Further studies should be performed to demonstrate the benefits of an orthodontist–AI collaboration. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Personalized Medicine)

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16 pages, 329 KiB

Open AccessEditor’s ChoiceReview

Incorporating Biomarkers in COPD Management: The Research Keeps Going

by Ioannis Pantazopoulos, Kalliopi Magounaki, Ourania Kotsiou, Erasmia Rouka, Fotis Perlikos, Sotirios Kakavas and Konstantinos Gourgoulianis

J. Pers. Med. 2022, 12(3), 379; https://doi.org/10.3390/jpm12030379 - 1 Mar 2022

Cited by 13 | Viewed by 3923

Abstract

Globally, chronic obstructive pulmonary disease (COPD) remains a major cause of morbidity and mortality, having a significant socioeconomic effect. Several molecular mechanisms have been related to COPD including chronic inflammation, telomere shortening, and epigenetic modifications. Nowadays, there is an increasing need for novel [...] Read more.

Globally, chronic obstructive pulmonary disease (COPD) remains a major cause of morbidity and mortality, having a significant socioeconomic effect. Several molecular mechanisms have been related to COPD including chronic inflammation, telomere shortening, and epigenetic modifications. Nowadays, there is an increasing need for novel therapeutic approaches for the management of COPD. These treatment strategies should be based on finding the source of acute exacerbation of COPD episodes and estimating the patient’s own risk. The use of biomarkers and the measurement of their levels in conjunction with COPD exacerbation risk and disease prognosis is considered an encouraging approach. Many types of COPD biomarkers have been identified which include blood protein biomarkers, cellular biomarkers, and protease enzymes. They have been isolated from different sources including peripheral blood, sputum, bronchoalveolar fluid, exhaled air, and genetic material. However, there is still not an exclusive biomarker that is used for the evaluation of COPD but rather a combination of them, and this is attributed to disease complexity. In this review, we summarize the clinical significance of COPD-related biomarkers, their association with disease outcomes, and COPD patients’ management. Finally, we depict the various samples that are used for identifying and measuring these biomarkers. Full article

(This article belongs to the Special Issue Respiratory and Critical Care)

14 pages, 3068 KiB

Open AccessEditor’s ChoiceArticle

Effectiveness of Platelet-Rich Plasma Therapy in Androgenic Alopecia—A Meta-Analysis

by Simona Roxana Georgescu, Andreea Amuzescu, Cristina Iulia Mitran, Madalina Irina Mitran, Clara Matei, Carolina Constantin, Mircea Tampa and Monica Neagu

J. Pers. Med. 2022, 12(3), 342; https://doi.org/10.3390/jpm12030342 - 24 Feb 2022

Cited by 14 | Viewed by 8710

Abstract

Platelet-rich plasma (PRP) represents a novel therapy tested and is used more and more frequently in dermatology and cosmetic surgery for a variety of conditions, including androgenic alopecia (AGA), a common condition with a complex pathogenesis involving genetic factors, hormonal status and inflammation. [...] Read more.

Platelet-rich plasma (PRP) represents a novel therapy tested and is used more and more frequently in dermatology and cosmetic surgery for a variety of conditions, including androgenic alopecia (AGA), a common condition with a complex pathogenesis involving genetic factors, hormonal status and inflammation. We performed an extensive literature search which retrieved 15 clinical trials concerning the use in AGA of PRP therapy, alone or in combination, in male, female or mixed patient groups. A quantitative statistical meta-analysis of n = 17 trial groups proved significant increases in hair density from 141.9 ± 108.2 to 177.5 ± 129.7 hairs/cm² (mean ± SD) following PRP (p = 0.0004). To the best of our knowledge, this is the first meta-analysis that proved a statistically significant correlation between the number of PRP treatments per month and the percentage change in hair density (r = 0.5, p = 0.03), as well as a negative correlation between the mean age of treatment group and the percentage change in hair density (r = −0.56, p = 0.016). Other factors considered for analysis were the PRP preparation method, amount used per treatment, hair diameter, terminal hairs and pull test. We conclude that PRP represents a valuable and effective therapy for AGA in both males and females if patients are rigorously selected. Full article

(This article belongs to the Special Issue Personalized Medicine in the Field of Inflammatory Skin Disorders)

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27 pages, 9665 KiB

Open AccessEditor’s ChoiceArticle

Artificial Intelligence-Enabled Electrocardiogram Estimates Left Atrium Enlargement as a Predictor of Future Cardiovascular Disease

by Yu-Sheng Lou, Chin-Sheng Lin, Wen-Hui Fang, Chia-Cheng Lee, Ching-Liang Ho, Chih-Hung Wang and Chin Lin

J. Pers. Med. 2022, 12(2), 315; https://doi.org/10.3390/jpm12020315 - 19 Feb 2022

Cited by 14 | Viewed by 2993

Abstract

Background: Left atrium enlargement (LAE) can be used as a predictor of future cardiovascular diseases, including hypertension (HTN) and atrial fibrillation (Afib). Typical electrocardiogram (ECG) changes have been reported in patients with LAE. This study developed a deep learning model (DLM)-enabled ECG system [...] Read more.

Background: Left atrium enlargement (LAE) can be used as a predictor of future cardiovascular diseases, including hypertension (HTN) and atrial fibrillation (Afib). Typical electrocardiogram (ECG) changes have been reported in patients with LAE. This study developed a deep learning model (DLM)-enabled ECG system to identify patients with LAE. Method: Patients who had ECG records with corresponding echocardiography (ECHO) were included. There were 101,077 ECGs, 20,510 ECGs, 7611 ECGs, and 11,753 ECGs in the development, tuning, internal validation, and external validation sets, respectively. We evaluated the performance of a DLM-enabled ECG for diagnosing LAE and explored the prognostic value of ECG-LAE for new-onset HTN, new-onset stroke (STK), new-onset mitral regurgitation (MR), and new-onset Afib. Results: The DLM-enabled ECG achieved AUCs of 0.8127/0.8176 for diagnosing mild LAE, 0.8587/0.8688 for diagnosing moderate LAE, and 0.8899/0.8990 for diagnosing severe LAE in the internal/external validation sets. Notably, ECG-LAE had higher prognostic value compared to ECHO-LAE, which had C-indices of 0.711/0.714 compared to 0.695/0.692 for new-onset HTN, 0.676/0.688 compared to 0.663/0.677 for new-onset STK, 0.696/0.695 compared to 0.676/0.673 for new-onset MR, and 0.800/0.806 compared to 0.786/0.760 for new-onset Afib in internal/external validation sets, respectively. Conclusions: A DLM-enabled ECG could be considered as a LAE screening tool and provide better prognostic information for related cardiovascular diseases. Full article

(This article belongs to the Special Issue Artificial Intelligence Application in Health Care System)

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26 pages, 5604 KiB

Open AccessEditor’s ChoiceSystematic Review

The Role of Robotic Visceral Surgery in Patients with Adhesions: A Systematic Review and Meta-Analysis

by Marco Milone, Michele Manigrasso, Pietro Anoldo, Anna D’Amore, Ugo Elmore, Mariano Cesare Giglio, Gianluca Rompianesi, Sara Vertaldi, Roberto Ivan Troisi, Nader K. Francis and Giovanni Domenico De Palma

J. Pers. Med. 2022, 12(2), 307; https://doi.org/10.3390/jpm12020307 - 18 Feb 2022

Cited by 12 | Viewed by 2837

Abstract

Abdominal adhesions are a risk factor for conversion to open surgery. An advantage of robotic surgery is the lower rate of unplanned conversions. A systematic review was conducted using the terms “laparoscopic” and “robotic”. Inclusion criteria were: comparative studies evaluating patients undergoing laparoscopic [...] Read more.

Abdominal adhesions are a risk factor for conversion to open surgery. An advantage of robotic surgery is the lower rate of unplanned conversions. A systematic review was conducted using the terms “laparoscopic” and “robotic”. Inclusion criteria were: comparative studies evaluating patients undergoing laparoscopic and robotic surgery; reporting data on conversion to open surgery for each group due to adhesions and studies including at least five patients in each group. The main outcomes were the conversion rates due to adhesions and surgeons’ expertise (novice vs. expert). The meta-analysis included 70 studies from different surgical specialities with 14,329 procedures (6472 robotic and 7857 laparoscopic). The robotic approach was associated with a reduced risk of conversion (OR 1.53, 95% CI 1.12–2.10, p = 0.007). The analysis of the procedures performed by “expert surgeons” showed a statistically significant difference in favour of robotic surgery (OR 1.48, 95% CI 1.03–2.12, p = 0.03). A reduced conversion rate due to adhesions with the robotic approach was observed in patients undergoing colorectal cancer surgery (OR 2.62, 95% CI 1.20–5.72, p = 0.02). The robotic approach could be a valid option in patients with abdominal adhesions, especially in the subgroup of those undergoing colorectal cancer resection performed by expert surgeons. Full article

(This article belongs to the Special Issue Update on Robotic Gastrointestinal Surgery)

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12 pages, 2130 KiB

Open AccessEditor’s ChoiceArticle

Intra- and Early Postoperative Evaluation of Malperfused Areas in an Irradiated Random Pattern Skin Flap Model Using Indocyanine Green Angiography and Near-Infrared Reflectance-Based Imaging and Infrared Thermography

by Wibke Müller-Seubert, Patrick Ostermaier, Raymund E. Horch, Luitpold Distel, Benjamin Frey, Aijia Cai and Andreas Arkudas

J. Pers. Med. 2022, 12(2), 237; https://doi.org/10.3390/jpm12020237 - 8 Feb 2022

Cited by 16 | Viewed by 3924

Abstract

Background: Assessment of tissue perfusion after irradiation of random pattern flaps still remains a challenge. Methods: Twenty-five rats received harvesting of bilateral random pattern fasciocutaneous flaps. Group 1 served as nonirradiated control group. The right flaps of the groups 2–5 were irradiated with [...] Read more.

Background: Assessment of tissue perfusion after irradiation of random pattern flaps still remains a challenge. Methods: Twenty-five rats received harvesting of bilateral random pattern fasciocutaneous flaps. Group 1 served as nonirradiated control group. The right flaps of the groups 2–5 were irradiated with 20 Gy postoperatively (group 2), 3 × 12 Gy postoperatively (group 3), 20 Gy preoperatively (group 4) and 3 × 12 Gy preoperatively (group 5). Imaging with infrared thermography, indocyanine green angiography and near-infrared reflectance-based imaging were performed to detect necrotic areas of the flaps. Results: Analysis of the percentage of the necrotic area of the irradiated flaps showed a statistically significant increase from day 1 to 14 only in group 5 (p < 0.05). Indocyanine green angiography showed no differences (p > 0.05) of the percentage of the nonperfused area between all days in group 1 and 3, but a decrease in group 2 in both the left and the right flaps. Infrared thermography and near-infrared reflectance-based imaging did not show evaluable differences. Conclusion: Indocyanine green angiography is more precise in prediction of necrotic areas in random pattern skin flaps when compared to hyperspectral imaging, thermography or clinical impression. Preoperative fractional irradiation with a lower individual dose but a higher total dose has a more negative impact on flap perfusion compared to higher single stage irradiation. Full article

(This article belongs to the Special Issue Plastic and Reconstructive Surgery in Personalized Medicine)

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19 pages, 3451 KiB

Open AccessEditor’s ChoiceArticle

Explainable Machine Learning Model for Predicting First-Time Acute Exacerbation in Patients with Chronic Obstructive Pulmonary Disease

by Chew-Teng Kor, Yi-Rong Li, Pei-Ru Lin, Sheng-Hao Lin, Bing-Yen Wang and Ching-Hsiung Lin

J. Pers. Med. 2022, 12(2), 228; https://doi.org/10.3390/jpm12020228 - 7 Feb 2022

Cited by 17 | Viewed by 4150

Abstract

Background: The study developed accurate explainable machine learning (ML) models for predicting first-time acute exacerbation of chronic obstructive pulmonary disease (COPD, AECOPD) at an individual level. Methods: We conducted a retrospective case–control study. A total of 606 patients with COPD were screened for [...] Read more.

Background: The study developed accurate explainable machine learning (ML) models for predicting first-time acute exacerbation of chronic obstructive pulmonary disease (COPD, AECOPD) at an individual level. Methods: We conducted a retrospective case–control study. A total of 606 patients with COPD were screened for eligibility using registry data from the COPD Pay-for-Performance Program (COPD P4P program) database at Changhua Christian Hospital between January 2017 and December 2019. Recursive feature elimination technology was used to select the optimal subset of features for predicting the occurrence of AECOPD. We developed four ML models to predict first-time AECOPD, and the highest-performing model was applied. Finally, an explainable approach based on ML and the SHapley Additive exPlanations (SHAP) and a local explanation method were used to evaluate the risk of AECOPD and to generate individual explanations of the model’s decisions. Results: The gradient boosting machine (GBM) and support vector machine (SVM) models exhibited superior discrimination ability (area under curve [AUC] = 0.833 [95% confidence interval (CI) 0.745–0.921] and AUC = 0.836 [95% CI 0.757–0.915], respectively). The decision curve analysis indicated that the GBM model exhibited a higher net benefit in distinguishing patients at high risk for AECOPD when the threshold probability was <0.55. The COPD Assessment Test (CAT) and the symptom of wheezing were the two most important features and exhibited the highest SHAP values, followed by monocyte count and white blood cell (WBC) count, coughing, red blood cell (RBC) count, breathing rate, oral long-acting bronchodilator use, chronic pulmonary disease (CPD), systolic blood pressure (SBP), and others. Higher CAT score; monocyte, WBC, and RBC counts; BMI; diastolic blood pressure (DBP); neutrophil-to-lymphocyte ratio; and eosinophil and lymphocyte counts were associated with AECOPD. The presence of symptoms (wheezing, dyspnea, coughing), chronic disease (CPD, congestive heart failure [CHF], sleep disorders, and pneumonia), and use of COPD medications (triple-therapy long-acting bronchodilators, short-acting bronchodilators, oral long-acting bronchodilators, and antibiotics) were also positively associated with AECOPD. A high breathing rate, heart rate, or systolic blood pressure and methylxanthine use were negatively correlated with AECOPD. Conclusions: The ML model was able to accurately assess the risk of AECOPD. The ML model combined with SHAP and the local explanation method were able to provide interpretable and visual explanations of individualized risk predictions, which may assist clinical physicians in understanding the effects of key features in the model and the model’s decision-making process. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Personalized Medicine)

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13 pages, 19991 KiB

Open AccessEditor’s ChoiceArticle

Effectiveness of Virtual Reality-Based Training on Oral Healthcare for Disabled Elderly Persons: A Randomized Controlled Trial

by Ai-Hua Chang, Pei-Chen Lin, Pei-Chao Lin, Yi-Ching Lin, Yuji Kabasawa, Cheng-Yu Lin and Hsiao-Ling Huang

J. Pers. Med. 2022, 12(2), 218; https://doi.org/10.3390/jpm12020218 - 4 Feb 2022

Cited by 14 | Viewed by 3351

Abstract

(1) Background: Virtual reality (VR) technology is a widely used training tool in medical education. The present study aimed to evaluate the effectiveness of VR training of oral hygiene students on providing oral healthcare to disabled elderly persons. (2) Methods: A randomized controlled [...] Read more.

(1) Background: Virtual reality (VR) technology is a widely used training tool in medical education. The present study aimed to evaluate the effectiveness of VR training of oral hygiene students on providing oral healthcare to disabled elderly persons. (2) Methods: A randomized controlled trial was conducted. In 2021, oral hygiene students were randomly assigned to a VR experimental group (EG; n = 11) and a control group (CG; n = 12). The EG received two-hour, thrice-repeated VR-based training interventions at 2-week, 4-week, and 6-week follow-ups. The CG received no VR-based interventions. Data were collected using a self-administered questionnaire before and immediately after each intervention. We performed generalized estimating equations to compare the responses. (3) Results: The EG exhibited a more significant improvement in oral care-related knowledge, attitude, self-efficacy, and intention at the 6-week follow-up than the CG. The students’ intention to assist the elderly in using interdental brushes (β = 0.91), with soft tissue cleaning (β = 0.53), and with oral desensitization (β = 0.53), and to have regular dental visits (β = 0.61) improved significantly at the 6-week follow-up. (4) Conclusions: VR training positively affected students’ knowledge, attitude, self-efficacy, and intentions on providing oral healthcare to disabled elderly persons. Full article

(This article belongs to the Topic eHealth and mHealth: Challenges and Prospects)

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15 pages, 2104 KiB

Open AccessEditor’s ChoiceReview

A Revised Stem Cell Theory for the Pathogenesis of Endometriosis

by Tetsuo Maruyama

J. Pers. Med. 2022, 12(2), 216; https://doi.org/10.3390/jpm12020216 - 4 Feb 2022

Cited by 18 | Viewed by 6031

Abstract

During the past decade, a stem cell-based hypothesis has emerged (among many others) to explain the pathogenesis of endometriosis. The initial hypothesis proposed that endometriosis arose from a single or a few specific cells with stem cell properties, including self-renewal and multi-lineage cell [...] Read more.

During the past decade, a stem cell-based hypothesis has emerged (among many others) to explain the pathogenesis of endometriosis. The initial hypothesis proposed that endometriosis arose from a single or a few specific cells with stem cell properties, including self-renewal and multi-lineage cell differentiation. The origins of the endometriosis-initiating stem cells were thought to be the bone marrow, uterine endometrium, and other tissues. Based on the implantation or metastatic theory in combination with the initial stem cell theory, one or a few multipotent stem/progenitor cells present in the eutopic endometrium or bone marrow translocate to ectopic sites via fallopian tubes during menstruation, vasculolymphatic routes, or through direct migration and invasion. Subsequently, they give rise to endometriotic lesions followed by differentiation into various cell components of endometriosis, including glandular and stromal cells. Recent somatic mutation analyses of deep infiltrating endometriosis, endometrioma, and eutopic normal endometrium using next-generation sequencing techniques have redefined the stem cell theory. It is now proposed that stem/progenitor cells of at least two different origins—epithelium and stroma—sequentially, differentially, but coordinately contribute to the genesis of endometriosis. The dual stem cell theory on how two (or more) stem/progenitor cells differentially and coordinately participate in the establishment of endometriotic lesions remains to be elucidated. Furthermore, the stem/progenitor cells involved in this theory also remain to be identified. Given that the origin of endometriosis is eutopic endometrium, the candidate cells for endometriotic epithelium-initiating cells are likely to be endometrial epithelial cells positive for either N-cadherin or SSEA-1 or both. The candidate cells for endometriotic stroma-initiating cells may be endometrial mesenchymal stem cells positive for SUSD2. Endometrial side population cells are also a possible candidate because they contain unipotent or multipotent cells capable of behaving as endometrial epithelial and stromal stem/progenitor cells. Full article

(This article belongs to the Special Issue Endometrial Stem/Progenitor Cell Biology: Prospects and Challenges)

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24 pages, 10578 KiB

Open AccessEditor’s ChoiceReview

Ion Channel Involvement in Tumor Drug Resistance

by Concetta Altamura, Paola Gavazzo, Michael Pusch and Jean-François Desaphy

J. Pers. Med. 2022, 12(2), 210; https://doi.org/10.3390/jpm12020210 - 3 Feb 2022

Cited by 17 | Viewed by 3458

Abstract

Over 90% of deaths in cancer patients are attributed to tumor drug resistance. Resistance to therapeutic agents can be due to an innate property of cancer cells or can be acquired during chemotherapy. In recent years, it has become increasingly clear that regulation [...] Read more.

Over 90% of deaths in cancer patients are attributed to tumor drug resistance. Resistance to therapeutic agents can be due to an innate property of cancer cells or can be acquired during chemotherapy. In recent years, it has become increasingly clear that regulation of membrane ion channels is an important mechanism in the development of chemoresistance. Here, we review the contribution of ion channels in drug resistance of various types of cancers, evaluating their potential in clinical management. Several molecular mechanisms have been proposed, including evasion of apoptosis, cell cycle arrest, decreased drug accumulation in cancer cells, and activation of alternative escape pathways such as autophagy. Each of these mechanisms leads to a reduction of the therapeutic efficacy of administered drugs, causing more difficulty in cancer treatment. Thus, targeting ion channels might represent a good option for adjuvant therapies in order to counteract chemoresistance development. Full article

(This article belongs to the Special Issue Ion Channels as Targets of Personalized Medicine)

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20 pages, 2349 KiB

Open AccessEditor’s ChoiceArticle

CRIDECO Anticholinergic Load Scale: An Updated Anticholinergic Burden Scale. Comparison with the ACB Scale in Spanish Individuals with Subjective Memory Complaints

by Hernán Ramos, Lucrecia Moreno, Jordi Pérez-Tur, Consuelo Cháfer-Pericás, Gemma García-Lluch and Juan Pardo

J. Pers. Med. 2022, 12(2), 207; https://doi.org/10.3390/jpm12020207 - 3 Feb 2022

Cited by 33 | Viewed by 8380

Abstract

The increase in life expectancy has also been accompanied by an increase in the use of medication to treat chronic diseases. Polypharmacy is associated with medication-related problems such as the increase in the anticholinergic burden. Older people are more susceptible to anticholinergic effects [...] Read more.

The increase in life expectancy has also been accompanied by an increase in the use of medication to treat chronic diseases. Polypharmacy is associated with medication-related problems such as the increase in the anticholinergic burden. Older people are more susceptible to anticholinergic effects on the central nervous system and this, in turn, may be related to cognitive impairment. In this paper, we develop an updated anticholinergic burden scale, the CRIDECO Anticholinergic Load Scale (CALS) via a systematic review of the literature and compare it with the currently most used Anticholinergic Burden Scale (ACB). Our new scale includes 217 different drugs with anticholinergic properties, 129 more than the ACB. Given the effect that anticholinergic medications have on cognitive performance, we then used both scales to investigate the relationship between anticholinergic burden and cognitive impairment in adult Spanish subjects with subjective memory complaint. In our population, we observed an association between cognitive impairment and the anticholinergic burden when measured by the new CALS, but not when the ACB was applied. The use of a more comprehensive and upgraded scale will allow better discrimination of the risk associated with the use of anticholinergic medications on cognitive impairment. CALS can help raise awareness among clinicians of the problems associated with the use of medications, or combinations of them, with large anticholinergic effect, and promote a better personalized pharmacological approach for each patient. Full article

(This article belongs to the Section Methodology, Drug and Device Discovery)

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20 pages, 1471 KiB

Open AccessEditor’s ChoiceReview

Personalized Dosimetry in Targeted Radiation Therapy: A Look to Methods, Tools and Critical Aspects

by Rachele Danieli, Alessia Milano, Salvatore Gallo, Ivan Veronese, Alessandro Lascialfari, Luca Indovina, Francesca Botta, Mahila Ferrari, Alessandro Cicchetti, Davide Raspanti and Marta Cremonesi

J. Pers. Med. 2022, 12(2), 205; https://doi.org/10.3390/jpm12020205 - 2 Feb 2022

Cited by 17 | Viewed by 4665

Abstract

Targeted radiation therapy (TRT) is a strategy increasingly adopted for the treatment of different types of cancer. The urge for optimization, as stated by the European Council Directive (2013/59/EURATOM), requires the implementation of a personalized dosimetric approach, similar to what already happens in [...] Read more.

Targeted radiation therapy (TRT) is a strategy increasingly adopted for the treatment of different types of cancer. The urge for optimization, as stated by the European Council Directive (2013/59/EURATOM), requires the implementation of a personalized dosimetric approach, similar to what already happens in external beam radiation therapy (EBRT). The purpose of this paper is to provide a thorough introduction to the field of personalized dosimetry in TRT, explaining its rationale in the context of optimization and describing the currently available methodologies. After listing the main therapies currently employed, the clinical workflow for the absorbed dose calculation is described, based on works of the most experienced authors in the literature and recent guidelines. Moreover, the widespread software packages for internal dosimetry are presented and critical aspects discussed. Overall, a selection of the most important and recent articles about this topic is provided. Full article

(This article belongs to the Special Issue The Role of Radiation in Cancer Treatment: New Insights towards Personalized Therapies)

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22 pages, 3565 KiB

Open AccessEditor’s ChoiceReview

Personalized Management of Myocarditis and Inflammatory Cardiomyopathy in Clinical Practice

by Agata Tymińska, Krzysztof Ozierański, Aleksandra Skwarek, Agnieszka Kapłon-Cieślicka, Anna Baritussio, Marcin Grabowski, Renzo Marcolongo and Alida LP Caforio

J. Pers. Med. 2022, 12(2), 183; https://doi.org/10.3390/jpm12020183 - 30 Jan 2022

Cited by 15 | Viewed by 4837

Abstract

Myocarditis is an inflammatory heart disease induced by infectious and non-infectious causes frequently triggering immune-mediated pathologic mechanisms leading to myocardial damage and dysfunction. In approximately half of the patients, acute myocarditis resolves spontaneously while in the remaining cases, it may evolve into serious [...] Read more.

Myocarditis is an inflammatory heart disease induced by infectious and non-infectious causes frequently triggering immune-mediated pathologic mechanisms leading to myocardial damage and dysfunction. In approximately half of the patients, acute myocarditis resolves spontaneously while in the remaining cases, it may evolve into serious complications including inflammatory cardiomyopathy, arrhythmias, death, or heart transplantation. Due to the large variability in clinical presentation, unpredictable course of the disease, and lack of established causative treatment, myocarditis represents a challenging diagnosis in modern cardiology. Moreover, an increase in the incidence of myocarditis and inflammatory cardiomyopathy has been observed in recent years. However, there is a growing potential of available non-invasive diagnostic methods (biomarkers, serum anti-heart autoantibodies (AHA), microRNAs, speckle tracking echocardiography, cardiac magnetic resonance T1 and T2 tissue mapping, positron emission tomography), which may refine the diagnostic workup and/or noninvasive follow-up. Personalized management should include the use of endomyocardial biopsy and AHA, which may allow the etiopathogenetic subsets of myocarditis (infectious, non-infectious, and/or immune-mediated) to be distinguished and implementation of disease-specific therapies. In this review, we summarize current knowledge on myocarditis and inflammatory cardiomyopathy, and outline some practical diagnostic, therapeutic, and follow-up algorithms to facilitate comprehensive individualized management of these patients. Full article

(This article belongs to the Special Issue Personalized Cardiovascular Medicine)

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24 pages, 2124 KiB

Open AccessEditor’s ChoiceReview

Computational Models for Clinical Applications in Personalized Medicine—Guidelines and Recommendations for Data Integration and Model Validation

by Catherine Bjerre Collin, Tom Gebhardt, Martin Golebiewski, Tugce Karaderi, Maximilian Hillemanns, Faiz Muhammad Khan, Ali Salehzadeh-Yazdi, Marc Kirschner, Sylvia Krobitsch, EU-STANDS4PM consortium and Lars Kuepfer

J. Pers. Med. 2022, 12(2), 166; https://doi.org/10.3390/jpm12020166 - 26 Jan 2022

Cited by 29 | Viewed by 10193

Abstract

The future development of personalized medicine depends on a vast exchange of data from different sources, as well as harmonized integrative analysis of large-scale clinical health and sample data. Computational-modelling approaches play a key role in the analysis of the underlying molecular processes [...] Read more.

The future development of personalized medicine depends on a vast exchange of data from different sources, as well as harmonized integrative analysis of large-scale clinical health and sample data. Computational-modelling approaches play a key role in the analysis of the underlying molecular processes and pathways that characterize human biology, but they also lead to a more profound understanding of the mechanisms and factors that drive diseases; hence, they allow personalized treatment strategies that are guided by central clinical questions. However, despite the growing popularity of computational-modelling approaches in different stakeholder communities, there are still many hurdles to overcome for their clinical routine implementation in the future. Especially the integration of heterogeneous data from multiple sources and types are challenging tasks that require clear guidelines that also have to comply with high ethical and legal standards. Here, we discuss the most relevant computational models for personalized medicine in detail that can be considered as best-practice guidelines for application in clinical care. We define specific challenges and provide applicable guidelines and recommendations for study design, data acquisition, and operation as well as for model validation and clinical translation and other research areas. Full article

(This article belongs to the Special Issue Systems Medicine and Bioinformatics)

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15 pages, 992 KiB

Open AccessEditor’s ChoiceReview

Thyroid Diseases and Breast Cancer

by Enke Baldini, Augusto Lauro, Domenico Tripodi, Daniele Pironi, Maria Ida Amabile, Iulia Catalina Ferent, Eleonora Lori, Federica Gagliardi, Maria Irene Bellini, Flavio Forte, Patrizia Pacini, Vito Cantisani, Vito D’Andrea, Salvatore Sorrenti and Salvatore Ulisse

J. Pers. Med. 2022, 12(2), 156; https://doi.org/10.3390/jpm12020156 - 25 Jan 2022

Cited by 16 | Viewed by 6603

Abstract

Epidemiological studies aimed at defining the association of thyroid diseases with extra-thyroidal malignancies (EM) have aroused considerable interest in the possibility of revealing common genetic and environmental factors underlying disease etiology and progression. Over the years, multiple lines of evidence indicated a significant [...] Read more.

Epidemiological studies aimed at defining the association of thyroid diseases with extra-thyroidal malignancies (EM) have aroused considerable interest in the possibility of revealing common genetic and environmental factors underlying disease etiology and progression. Over the years, multiple lines of evidence indicated a significant relationship between thyroid carcinomas and other primary EM, especially breast cancer. For the latter, a prominent association was also found with benign thyroid diseases. In particular, a meta-analysis revealed an increased risk of breast cancer in patients with autoimmune thyroiditis, and our recent work demonstrated that the odds ratio (OR) for breast cancer was raised in both thyroid autoantibody-positive and -negative patients. However, the OR was significantly lower for thyroid autoantibody-positive patients compared to the negative ones. This is in agreement with findings showing that the development of thyroid autoimmunity in cancer patients receiving immunotherapy is associated with better outcome and supports clinical evidence that breast cancer patients with thyroid autoimmunity have longer disease-free interval and overall survival. These results seem to suggest that factors other than oncologic treatments may play a role in the initiation and progression of a second primary malignancy. The molecular links between thyroid autoimmunity and breast cancer remain, however, unidentified, and different hypotheses have been proposed. Here, we will review the epidemiological, clinical, and experimental data relating thyroid diseases and breast cancer, as well as the possible hormonal and molecular mechanisms underlying such associations. Full article

(This article belongs to the Special Issue Personalized Diagnosis and Treatment of Breast Cancer)

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15 pages, 320 KiB

Open AccessEditor’s ChoiceArticle

Nurses’ Practices in the Peripheral Intravenous Catheterization of Adult Oncology Patients: A Mix-Method Study

by Paulo Santos-Costa, Filipe Paiva-Santos, Liliana B. Sousa, Rafael A. Bernardes, Filipa Ventura, William David Fearnley, Anabela Salgueiro-Oliveira, Pedro Parreira, Margarida Vieira and João Graveto

J. Pers. Med. 2022, 12(2), 151; https://doi.org/10.3390/jpm12020151 - 24 Jan 2022

Cited by 11 | Viewed by 5589

Abstract

A significant number of adult oncology patients require at least one peripheral intravenous catheter to fulfill their therapeutic plan. Recent evidence indicates that catheter failure rates are high in this cohort, impacting care outcomes and patient experience during cancer treatment. This reality represents [...] Read more.

A significant number of adult oncology patients require at least one peripheral intravenous catheter to fulfill their therapeutic plan. Recent evidence indicates that catheter failure rates are high in this cohort, impacting care outcomes and patient experience during cancer treatment. This reality represents a challenge to nurses worldwide since in most international settings they are responsible for delivering quality care during the insertion and maintenance of such devices. This study aims to explore current nursing practices regarding the insertion, maintenance, and surveillance of peripheral intravenous catheters in oncology patients. A two-phase mix-method study was conducted with the nursing team from the surgical ward of a large oncology hospital in Portugal. In phase one (observational prospective study), nurses’ practices during catheter insertion and maintenance were observed by the research team and recorded using standardized instruments and validated scales. In phase two, three online focus groups were conducted with the nursing team to present the results observed in phase one and explore their perceptions of current practices. All ethical principles were assured throughout the study. Significant divergent practices were observed and identified by the nurses, especially concerning patient involvement, nurses’ adherence to the aseptic, non-touch technique, catheter stabilization and dressing, and catheter flushing and locking. Such practices may partially explain the high complication rate found (26%) and substantiate the need for future intervention in this field. Full article

(This article belongs to the Special Issue Advances in Personalized Nursing Care)

14 pages, 2199 KiB

Open AccessEditor’s ChoiceArticle

Deep Learning Application to Clinical Decision Support System in Sleep Stage Classification

by Dongyoung Kim, Jeonggun Lee, Yunhee Woo, Jaemin Jeong, Chulho Kim and Dong-Kyu Kim

J. Pers. Med. 2022, 12(2), 136; https://doi.org/10.3390/jpm12020136 - 20 Jan 2022

Cited by 18 | Viewed by 3660

Abstract

Recently, deep learning for automated sleep stage classification has been introduced with promising results. However, as many challenges impede their routine application, automatic sleep scoring algorithms are not widely used. Typically, polysomnography (PSG) uses multiple channels for higher accuracy; however, the disadvantages include [...] Read more.

Recently, deep learning for automated sleep stage classification has been introduced with promising results. However, as many challenges impede their routine application, automatic sleep scoring algorithms are not widely used. Typically, polysomnography (PSG) uses multiple channels for higher accuracy; however, the disadvantages include a requirement for a patient to stay one or more nights in the lab wearing uncomfortable sensors and wires. To avoid the inconvenience caused by the multiple channels, we aimed to develop a deep learning model for use in clinical decision support systems (CDSSs) and combined convolutional neural networks and a transformer for the supervised learning of three classes of sleep stages only with single-channel EEG data (C4-M1). The data for training, validation, and test were derived from 1590, 341, and 343 polysomnography recordings, respectively. The developed model yielded an overall accuracy of 91.4%, comparable with that of human experts. Based on the severity of obstructive sleep apnea, the model’s accuracy was 94.3%, 91.9%, 91.9%, and 90.6% in normal, mild, moderate, and severe cases, respectively. Our deep learning model enables accurate and rapid delineation of three-class sleep staging and could be useful as a CDSS for application in real-world clinical practice. Full article

(This article belongs to the Section Methodology, Drug and Device Discovery)

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14 pages, 1869 KiB

Open AccessEditor’s ChoiceArticle

Detecting Blastocyst Components by Artificial Intelligence for Human Embryological Analysis to Improve Success Rate of In Vitro Fertilization

by Muhammad Arsalan, Adnan Haider, Jiho Choi and Kang Ryoung Park

J. Pers. Med. 2022, 12(2), 124; https://doi.org/10.3390/jpm12020124 - 18 Jan 2022

Cited by 14 | Viewed by 4946

Abstract

Morphological attributes of human blastocyst components and their characteristics are highly correlated with the success rate of in vitro fertilization (IVF). Blastocyst component analysis aims to choose the most viable embryos to improve the success rate of IVF. The embryologist evaluates blastocyst viability [...] Read more.

Morphological attributes of human blastocyst components and their characteristics are highly correlated with the success rate of in vitro fertilization (IVF). Blastocyst component analysis aims to choose the most viable embryos to improve the success rate of IVF. The embryologist evaluates blastocyst viability by manual microscopic assessment of its components, such as zona pellucida (ZP), trophectoderm (TE), blastocoel (BL), and inner cell mass (ICM). With the success of deep learning in the medical diagnosis domain, semantic segmentation has the potential to detect crucial components of human blastocysts for computerized analysis. In this study, a sprint semantic segmentation network (SSS-Net) is proposed to accurately detect blastocyst components for embryological analysis. The proposed method is based on a fully convolutional semantic segmentation scheme that provides the pixel-wise classification of important blastocyst components that help to automatically check the morphologies of these elements. The proposed SSS-Net uses the sprint convolutional block (SCB), which uses asymmetric kernel convolutions in combination with depth-wise separable convolutions to reduce the overall cost of the network. SSS-Net is a shallow architecture with dense feature aggregation, which helps in better segmentation. The proposed SSS-Net consumes a smaller number of trainable parameters (4.04 million) compared to state-of-the-art methods. The SSS-Net was evaluated using a publicly available human blastocyst image dataset for component segmentation. The experimental results confirm that our proposal provides promising segmentation performance with a Jaccard Index of 82.88%, 77.40%, 88.39%, 84.94%, and 96.03% for ZP, TE, BL, ICM, and background, with residual connectivity, respectively. It is also provides a Jaccard Index of 84.51%, 78.15%, 88.68%, 84.50%, and 95.82% for ZP, TE, BL, ICM, and background, with dense connectivity, respectively. The proposed SSS-Net is providing a mean Jaccard Index (Mean JI) of 85.93% and 86.34% with residual and dense connectivity, respectively; this shows effective segmentation of blastocyst components for embryological analysis. Full article

(This article belongs to the Special Issue Artificial Intelligence Application in Health Care System)

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18 pages, 1049 KiB

Open AccessEditor’s ChoiceReview

Adipose-Derived Stem Cells for Facial Rejuvenation

by Agnieszka Surowiecka and Jerzy Strużyna

J. Pers. Med. 2022, 12(1), 117; https://doi.org/10.3390/jpm12010117 - 16 Jan 2022

Cited by 25 | Viewed by 7577

Abstract

The interest in regenerative medicine is increasing, and it is a dynamically developing branch of aesthetic surgery. Biocompatible and autologous-derived products such as platelet-rich plasma or adult mesenchymal stem cells are often used for aesthetic purposes. Their application originates from wound healing and [...] Read more.

The interest in regenerative medicine is increasing, and it is a dynamically developing branch of aesthetic surgery. Biocompatible and autologous-derived products such as platelet-rich plasma or adult mesenchymal stem cells are often used for aesthetic purposes. Their application originates from wound healing and orthopaedics. Adipose-derived stem cells are a powerful agent in skin rejuvenation. They secrete growth factors and anti-inflammatory cytokines, stimulate tissue regeneration by promoting the secretion of extracellular proteins and secrete antioxidants that neutralize free radicals. In an office procedure, without cell incubation and counting, the obtained product is stromal vascular fraction, which consists of not only stem cells but also other numerous active cells such as pericytes, preadipocytes, immune cells, and extra-cellular matrix. Adipose-derived stem cells, when injected into dermis, improved skin density and overall skin appearance, and increased skin hydration and number of capillary vessels. The main limitation of mesenchymal stem cell transfers is the survival of the graft. The final outcomes are dependent on many factors, including the age of the patient, technique of fat tissue harvesting, technique of lipoaspirate preparation, and technique of fat graft injection. It is very difficult to compare available studies because of the differences and multitude of techniques used. Fat harvesting is associated with potentially life-threatening complications, such as massive bleeding, embolism, or clots. However, most of the side effects are mild and transient: primarily hematomas, oedema, and mild pain. Mesenchymal stem cells that do not proliferate when injected into dermis promote neoangiogenesis, that is why respectful caution should be taken in the case of oncologic patients. A longer clinical observation on a higher number of participants should be performed to develop reliable indications and guidelines for transferring ADSCs. Full article

(This article belongs to the Special Issue Adult Stem Cells in Aging)

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13 pages, 752 KiB

Open AccessEditor’s ChoiceSystematic Review

The Contribution of “Individual Participant Data” Meta-Analyses of Psychotherapies for Depression to the Development of Personalized Treatments: A Systematic Review

by Pim Cuijpers, Marketa Ciharova, Soledad Quero, Clara Miguel, Ellen Driessen, Mathias Harrer, Marianna Purgato, David Ebert and Eirini Karyotaki

J. Pers. Med. 2022, 12(1), 93; https://doi.org/10.3390/jpm12010093 - 11 Jan 2022

Cited by 30 | Viewed by 4520

Abstract

While randomized trials typically lack sufficient statistical power to identify predictors and moderators of outcome, ”individual participant data” (IPD) meta-analyses, which combine primary data of multiple randomized trials, can increase the statistical power to identify predictors and moderators of outcome. We conducted a [...] Read more.

While randomized trials typically lack sufficient statistical power to identify predictors and moderators of outcome, ”individual participant data” (IPD) meta-analyses, which combine primary data of multiple randomized trials, can increase the statistical power to identify predictors and moderators of outcome. We conducted a systematic review of IPD meta-analyses on psychological treatments of depression to provide an overview of predictors and moderators identified. We included 10 (eight pairwise and two network) IPD meta-analyses. Six meta-analyses showed that higher baseline depression severity was associated with better outcomes, and two found that older age was associated with better outcomes. Because power was high in most IPD meta-analyses, non-significant findings are also of interest because they indicate that these variables are probably not relevant as predictors and moderators. We did not find in any IPD meta-analysis that gender, education level, or relationship status were significant predictors or moderators. This review shows that IPD meta-analyses on psychological treatments can identify predictors and moderators of treatment effects and thereby contribute considerably to the development of personalized treatments of depression. Full article

(This article belongs to the Special Issue Personalized Medicine Approaches to Depression Prevention, Diagnosis, and Therapeutics)

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15 pages, 2609 KiB

Open AccessEditor’s ChoiceArticle

Precision Medicine for Hypertension Patients with Type 2 Diabetes via Reinforcement Learning

by Sang Ho Oh, Su Jin Lee and Jongyoul Park

J. Pers. Med. 2022, 12(1), 87; https://doi.org/10.3390/jpm12010087 - 11 Jan 2022

Cited by 13 | Viewed by 3037

Abstract

Precision medicine is a new approach to understanding health and disease based on patient-specific data such as medical diagnoses; clinical phenotype; biologic investigations such as laboratory studies and imaging; and environmental, demographic, and lifestyle factors. The importance of machine learning techniques in healthcare [...] Read more.

Precision medicine is a new approach to understanding health and disease based on patient-specific data such as medical diagnoses; clinical phenotype; biologic investigations such as laboratory studies and imaging; and environmental, demographic, and lifestyle factors. The importance of machine learning techniques in healthcare has expanded quickly in the last decade owing to the rising availability of vast multi-modality data and developed computational models and algorithms. Reinforcement learning is an appealing method for developing efficient policies in various healthcare areas where the decision-making process is typically defined by a long period or a sequential process. In our research, we leverage the power of reinforcement learning and electronic health records of South Koreans to dynamically recommend treatment prescriptions, which are personalized based on patient information of hypertension. Our proposed reinforcement learning-based treatment recommendation system decides whether to use mono, dual, or triple therapy according to the state of the hypertension patients. We evaluated the performance of our personalized treatment recommendation model by lowering the occurrence of hypertension-related complications and blood pressure levels of patients who followed our model’s recommendation. With our findings, we believe that our proposed hypertension treatment recommendation model could assist doctors in prescribing appropriate antihypertensive medications. Full article

(This article belongs to the Special Issue Personalized Medicine for Hypertension: Diagnosis, Prevention and Treatment)

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28 pages, 2058 KiB

Open AccessEditor’s ChoiceReview

Multi-Omics Profiling Approach to Asthma: An Evolving Paradigm

by Yadu Gautam, Elisabet Johansson and Tesfaye B. Mersha

J. Pers. Med. 2022, 12(1), 66; https://doi.org/10.3390/jpm12010066 - 7 Jan 2022

Cited by 34 | Viewed by 5435

Abstract

Asthma is a complex multifactorial and heterogeneous respiratory disease. Although genetics is a strong risk factor of asthma, external and internal exposures and their interactions with genetic factors also play important roles in the pathophysiology of asthma. Over the past decades, the application [...] Read more.

Asthma is a complex multifactorial and heterogeneous respiratory disease. Although genetics is a strong risk factor of asthma, external and internal exposures and their interactions with genetic factors also play important roles in the pathophysiology of asthma. Over the past decades, the application of high-throughput omics approaches has emerged and been applied to the field of asthma research for screening biomarkers such as genes, transcript, proteins, and metabolites in an unbiased fashion. Leveraging large-scale studies representative of diverse population-based omics data and integrating with clinical data has led to better profiling of asthma risk. Yet, to date, no omic-driven endotypes have been translated into clinical practice and management of asthma. In this article, we provide an overview of the current status of omics studies of asthma, namely, genomics, transcriptomics, epigenomics, proteomics, exposomics, and metabolomics. The current development of the multi-omics integrations of asthma is also briefly discussed. Biomarker discovery following multi-omics profiling could be challenging but useful for better disease phenotyping and endotyping that can translate into advances in asthma management and clinical care, ultimately leading to successful precision medicine approaches. Full article

(This article belongs to the Special Issue Precision Medicine in Childhood Asthma)

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15 pages, 3775 KiB

Open AccessEditor’s ChoiceArticle

Microbiome Changes in Humans with Parkinson’s Disease after Photobiomodulation Therapy: A Retrospective Study

by Brian Bicknell, Ann Liebert, Craig S. McLachlan and Hosen Kiat

J. Pers. Med. 2022, 12(1), 49; https://doi.org/10.3390/jpm12010049 - 5 Jan 2022

Cited by 14 | Viewed by 7152

Abstract

There is a paucity of information on the effect of photobiomodulation therapy on gut microbiome composition. Parkinson’s disease is a progressive neurological disorder with few management options, although the gut microbiome has been suggested as a potential avenue of treatment. We retrospectively analysed [...] Read more.

There is a paucity of information on the effect of photobiomodulation therapy on gut microbiome composition. Parkinson’s disease is a progressive neurological disorder with few management options, although the gut microbiome has been suggested as a potential avenue of treatment. We retrospectively analysed the microbiome from human stool samples from a previously published study, which had demonstrated the efficacy of photobiomodulation to treat Parkinson’s patients’ symptoms. Specifically, we have observed changes in the microbiome of Parkinson’s patients after a 12-week treatment regimen with photobiomodulation to the abdomen, neck, head and nose. Noted were positive changes in the Firmicutes to Bacteroidetes (F:B) ratio, which is often interpreted as a proxy for gut health. Full article

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16 pages, 1834 KiB

Open AccessEditor’s ChoiceArticle

Utilization of Decision Tree Algorithms for Supporting the Prediction of Intensive Care Unit Admission of Myasthenia Gravis: A Machine Learning-Based Approach

by Che-Cheng Chang, Jiann-Horng Yeh, Hou-Chang Chiu, Yen-Ming Chen, Mao-Jhen Jhou, Tzu-Chi Liu and Chi-Jie Lu

J. Pers. Med. 2022, 12(1), 32; https://doi.org/10.3390/jpm12010032 - 2 Jan 2022

Cited by 19 | Viewed by 3048

Abstract

Myasthenia gravis (MG), an acquired autoimmune-related neuromuscular disorder that causes muscle weakness, presents with varying severity, including myasthenic crisis (MC). Although MC can cause significant morbidity and mortality, specialized neuro-intensive care can produce a good long-term prognosis. Considering the outcomes of MG during [...] Read more.

Myasthenia gravis (MG), an acquired autoimmune-related neuromuscular disorder that causes muscle weakness, presents with varying severity, including myasthenic crisis (MC). Although MC can cause significant morbidity and mortality, specialized neuro-intensive care can produce a good long-term prognosis. Considering the outcomes of MG during hospitalization, it is critical to conduct risk assessments to predict the need for intensive care. Evidence and valid tools for the screening of critical patients with MG are lacking. We used three machine learning-based decision tree algorithms, including a classification and regression tree, C4.5, and C5.0, for predicting intensive care unit (ICU) admission of patients with MG. We included 228 MG patients admitted between 2015 and 2018. Among them, 88.2% were anti-acetylcholine receptors antibody positive and 4.7% were anti-muscle-specific kinase antibody positive. Twenty clinical variables were used as predictive variables. The C5.0 decision tree outperformed the other two decision tree and logistic regression models. The decision rules constructed by the best C5.0 model showed that the Myasthenia Gravis Foundation of America clinical classification at admission, thymoma history, azathioprine treatment history, disease duration, sex, and onset age were significant risk factors for the development of decision rules for ICU admission prediction. The developed machine learning-based decision tree can be a supportive tool for alerting clinicians regarding patients with MG who require intensive care, thereby improving the quality of care. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Personalized Medicine)

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