Search Results (3,551)

Background: Hypertension remains the leading global risk factor for cardiovascular morbidity and mortality, with suboptimal control rates despite guideline-directed therapies. Digital health and artificial intelligence (AI) technologies offer novel approaches for improving diagnosis, monitoring, and individualized treatment of hypertension. Objectives: To critically review the current landscape of AI-enabled digital tools for hypertension management, including emerging applications, implementation challenges, and future directions. Methods: A narrative review of recent PubMed-indexed studies (2019–2024) was conducted, focusing on clinical applications of AI and digital health technologies in hypertension. Emphasis was placed on real-world deployment, algorithmic explainability, digital biomarkers, and ethical/regulatory frameworks. Priority was given to high-quality randomized trials, systematic reviews, and expert consensus statements. Results: AI-supported platforms—including remote blood pressure monitoring, machine learning titration algorithms, and digital twins—have demonstrated early promise in improving hypertension control. Explainable AI (XAI) is critical for clinician trust and integration into decision-making. Equity-focused design and regulatory oversight are essential to prevent exacerbation of health disparities. Emerging implementation strategies, such as federated learning and co-design frameworks, may enhance scalability and generalizability across diverse care settings. Conclusions: AI-guided titration and digital twin approaches appear most promising for reducing therapeutic inertia, whereas cuffless blood pressure monitoring remains the least mature. Future work should prioritize pragmatic trials with equity and cost-effectiveness endpoints, supported by safeguards against bias, accountability gaps, and privacy risks. Full article

Background/Objectives: Cardiac sarcoidosis (CS) is a granulomatous inflammatory cardiomyopathy with heterogeneous presentations, from palpitations to heart failure and sudden cardiac arrest. Despite advances in imaging and immunosuppressive (IS) therapy, relapse patterns and long-term outcomes remain poorly defined. This study aimed to characterize relapse and identify predictors of relapse and major adverse cardiac events (MACE) in a real-world CS cohort. Methods: This retrospective single-center study included 25 adults diagnosed with CS at Geneva University Hospitals between 2016 and 2024, classified per the 2024 American Heart Association diagnostic criteria. Relapse was defined as clinical, arrhythmic, or imaging deterioration requiring treatment escalation. MACE included cardiovascular hospitalization, device therapy, left ventricular assist device, heart transplant, or death. Statistical methods included Kaplan–Meier analysis with log-rank tests and multivariable Cox regression adjusted for age and sex. Results: Relapse occurred in 13 patients (56%), frequently subclinical (61.5%) and detected incidentally on routine PET-CT during IS tapering. In the multivariate model, predictors of relapse included right ventricular FDG uptake (aHR 13.1; 95% CI 1.3–133.7; p = 0.03) and second-line immunosuppression duration ≤24 months (aHR 20.1; 95% CI 1.1–363.8; p = 0.04). Relapse-free patients were more often maintained on dual or triple IS therapy (71.4% vs. 15.4%; p = 0.02) and low-dose prednisone (<10 mg/day) (57.1% vs. 7.7%; p = 0.03). Conclusions: Relapse is common in CS, often subclinical, and associated with PET-CT findings and premature IS tapering. Maintenance therapy may reduce risk. Multimodal imaging remains critical for disease monitoring, though tracers with higher specificity are needed. Further research should refine relapse definitions and support personalized treatment strategies. Full article

Fourier transform-based frequency sweeping interferometry (FT-FSI) is an interferometric technique that enables absolute distance measurement by detecting the beat frequencies from the interference of reflected signals. This method allows robust, simultaneous distance measurements to multiple targets and is largely immune to variations in the reflected optical signal intensity. As a result, FT-FSI maintains accuracy even when measuring reflectors with low reflectance. FT-FSI has recently been integrated into the full remote alignment system (FRAS) developed for the High-Luminosity Large Hadron Collider (HL-LHC) project at CERN. Designed to operate in harsh environments with electromagnetic interference, ionizing radiation and cryogenic temperatures, FRAS employs FT-FSI for the precise monitoring of the alignment of accelerator components. The system includes specialized interferometers and a range of sensors, including inclinometers, distance sensors, and leveling sensors. This paper presents a comprehensive review of the challenges associated with remote measurement and monitoring systems in harsh environments such as those of particle accelerators. It details the development and validation of the FT-FSI-based measurement system, emphasizing its critical role in enabling micrometric alignment accuracy. The developments and results presented in this work can be readily translated to other demanding metrology applications in harsh environments. Full article

Background/Objectives: Surgical site infection (SSI) rates following breast surgical procedures range from 0.8% to 26%. Both prophylactic antibiotics and antimicrobial-coated sutures have been shown to play an important role in reducing these complications. This study aimed to evaluate the impact of antibiotic prophylaxis in mastectomy procedures using triclosan-coated sutures. Methods: This study included 300 consecutive patients who underwent mastectomy for breast cancer over a two-year period, during which triclosan-coated Vicryl Plus sutures were used. Patients were divided into two groups based on the use of antibiotic prophylaxis. The prophylaxis group received 1.5 g cefuroxime intravenously at anesthesia induction (600 mg clindamycin in case of allergy), while the control group received no antibiotics. Endpoints of interest included differences in SSI and specific wound-healing complications at follow-up. Results: There was no significant difference in the overall SSI rates between the two groups: 23.2% in the prophylaxis group vs. 18.8% in the control group [odds ratio (OR): 0.88; 95% confidence interval (CI): 0.69–1.13; vs. OR: 1.16; 95% CI 0.85–1.58; p = 0.343]. No adverse drug reactions were observed. Staphylococcus aureus was the most isolated microorganism in both groups. Multivariate analysis identified prolonged operative time and hematoma formation as significant predictors of postoperative infection. Conclusions: Antibiotic prophylaxis did not reduce the rate of SSI following mastectomy for breast cancer when triclosan-coated sutures were used. Further high-quality, independent studies are warranted, particularly in breast surgery context. Full article

The study of various microorganisms isolated from an Indian Ocean sponge, Scopalina hapalia ML-263, led to the selection of a promising Actinobacteria strain, Micromonospora sp. SH-82. Genomic analysis identified this strain as a new species, revealing the presence of 23 biosynthetic gene clusters (BGCs), some of which are associated with the synthesis of specialized metabolites such as polyketides deriving from polyketide synthases (PKSs). The strain was cultivated under favorable conditions for the production of bioactive molecules, resulting in the isolation and identification of seven microbial metabolites. Three of them are potentially novel, two erythronolides and one erythromycin, all characterized by a rare C10–C11 double bond. Some of these compounds also display atypical conformations, forming hemiacetals or spiroacetals. Their identification was achieved through detailed chemical analyses (NMR and ESI⁺-HRMS). A molecular networking approach was employed to assess the presence of potentially novel molecules in the microbial crude extract, supported by the identification of isolated molecules. Four molecules (1, 2, 3 and 5) were evaluated for their cytotoxic activities against cancer cell lines (HCT-116 and MDA-MB-231) and the immortalized retinal pigment epithelial RPE1 cells. No activity was observed in the latter, suggesting a lack of toxicity toward healthy cells. Moreover, megalomicin C1 (3), one of the isolated compounds, showed interesting antiplasmodial activity against Plasmodium falciparum 3D7, with an IC₅₀ of 6.37 ± 2.99 µM. Full article

This study formulates an efficient, affordable, and low-carbon binder based on locally excavated earth from Yaoundé, offering sufficient mechanical strength and water resistance for rendering applications. Through material characterization, a binary binder composed of Portland cement (PC) and calcined laterite (CL) was developed, reducing the PC content by up to 30%. The mortar used laterite sand with varying fine particle contents in place of river sand, and its mechanical strength and water absorption via capillarity action were evaluated. Due to the porosity of the laterite fines, all mixes were prepared at equivalent workability. The mechanical strength was the same as if the binder solely consisted of PC and reached 11 MPa when the laterite sand contained no fine particles. As the fine particle content increased, the mechanical strength decreased to a minimum value of 4 MPa when raw laterite was used, and the coefficient of water absorption via capillarity action decreased. Overall, the formulated class Wc2 mortar is suitable for rendering applications. The valorization potential of fine particles and coarse aggregates of the crushed mortar was assessed: the crushed mortar fines had pozzolanic properties and could serve as supplementary cementitious materials; the largest particles are suitable for lime stabilization. Full article

Recent advances in artificial intelligence (AI) have transformed ophthalmic diagnostics, particularly for retinal diseases. In this prospective, non-randomized study, we evaluated the performance of an AI-based software system against conventional clinical assessment—both quantitative and qualitative—of optical coherence tomography (OCT) images for diagnosing diabetic macular edema (DME). A total of 700 OCT exams were analyzed across 26 features, including demographic data (age, sex), eye laterality, visual acuity, and 21 quantitative OCT parameters (Macula Map A X-Y). We tested two classification scenarios: binary (DME presence vs. absence) and multiclass (six distinct DME phenotypes). To streamline feature selection, we applied paraconsistent feature engineering (PFE), isolating the most diagnostically relevant variables. We then compared the diagnostic accuracies of logistic regression, support vector machines (SVM), K-nearest neighbors (KNN), and decision tree models. In the binary classification using all features, SVM and KNN achieved 92% accuracy, while logistic regression reached 91%. When restricted to the four PFE-selected features, accuracy modestly declined to 84% for both logistic regression and SVM. These findings underscore the potential of AI—and particularly PFE—as an efficient, accurate aid for DME screening and diagnosis. Full article

Psychedelic substances have experienced a resurgence of clinical interest in recent years, particularly for their promising effects in the treatment of psychiatric disorders such as depression and anxiety. While evidence regarding their role in chronic pain management remains limited, emerging studies suggest potential therapeutic benefits. This case report describes a patient with persistent somatoform pain disorder and recurrent depressive disorder who underwent four sessions of psilocybin-assisted psychotherapy. The intervention was associated with a reduction in the negative impact of pain on daily life, increased pain acceptance, improved quality of life, and reduction in depressive symptoms. These findings contribute to the growing body of literature suggesting that psychedelics, when combined with psychotherapy, may offer a novel and holistic approach to the treatment of chronic pain. Further controlled studies are needed to explore the safety, efficacy, and underlying mechanisms. Full article

Despite rising global reports of Acanthamoeba keratitis (AK), the incidence of AK in Switzerland remains unknown. This investigator-initiated, retrospective, multicenter study assessed the nationwide incidence of PCR- and/or culture-positive Acanthamoeba results from January 2010 to December 2023. Data were collected from all tertiary care and large ophthalmological facilities in Switzerland, fully anonymized, and aggregated by month and year. We considered all corneal scraping results, whereby the detection method was specific to local standards. We identified 271 PCR- or culture-positive Acanthamoeba cases over 14 years. Applying the population data from the Federal Statistical Office in Switzerland, this corresponds to a mean incidence of 2.29 cases per million people annually. Infections were most common in summer (87 cases, 32.1%), followed by autumn (74 cases, 27.3%), spring (60 cases, 22.1%), and winter (50 cases, 18.5%). We found no significant change in incidence across the investigated period, p = 0.47. This nationwide study reveals a low but stable incidence of AK in Switzerland, in line with other industrialized countries but well below levels reported in tropical or densely populated regions such as India or Egypt. Seasonal variation supports the influence of environmental exposure and underscores the importance of preventive measures during warmer months. Full article

Clear aligners (CAs) have emerged as a widely accepted alternative to conventional fixed orthodontic appliances due to their aesthetic appeal, comfort, and removability. Despite their increasing use, the precise biomechanical behavior of CAs—particularly in relation to maxillary arch expansion and torque control—remains incompletely understood. This scoping review aims to synthesize and critically examine the recent body of evidence derived from finite element analysis (FEA) studies investigating the performance of clear aligners in managing transverse discrepancies and controlling tooth movement. It considered studies published up to April 2025. All included FEA studies assumed dental and bone tissues as linearly elastic, homogeneous, and isotropic, unless otherwise specified. Five in silico studies were included, all employing three-dimensional FEA models to assess the influence of various clinical and design parameters, such as aligner thickness, movement sequence, attachment configuration, and torque compensation. The findings consistently show that movement protocols involving alternating activation patterns and specific attachment designs can significantly improve the efficiency of maxillary expansion, while reducing undesired tipping or anchorage loss. Additionally, greater aligner thicknesses were generally associated with increased force delivery and more pronounced tooth displacement. Although FEA provides a powerful tool for visualizing stress distribution and predicting mechanical responses under controlled conditions, the lack of standardized force application and limited clinical validation remain important limitations. These findings underscore the potential of optimized aligner protocols to enhance treatment outcomes, but they also highlight the need for complementary in vivo studies to confirm their clinical relevance and guide evidence-based practice. Full article

Background: Potential advantages for improving plant growth, stress tolerance, and valuable metabolites generation are provided by the implementation of nanotechnology into plant biotechnology. A recently discovered technique with significant promise for agricultural practices is the use of biopolymer-based nanomaterials, like peptidomimetics, as insecticides, growth regulators, and nutrient carriers. This study explores the impact of biopolymer-based organic nanofibers—specifically peptidomimetics formed through the self-assembly of L-valine and nicotinic acid (NA) (denoted as M6) on Stevia rebaudiana in vitro propagation and specialized metabolite production. The central hypothesis was that such nanofibers, particularly when used as hormone carriers, can beneficially influence plant morphology, physiology, and biochemistry, thereby promoting the synthesis of antioxidant compounds with therapeutic potential. Methods: The nanofibers were tested either alone (M6) or as carriers of the plant hormone indole-3-acetic acid (IAA) (M6+IAA), supplemented to the cultivation MS medium at variable concentrations (0, 1, 10, and 50 mg L⁻¹). Results: The results revealed that treatment with 10 mg L⁻¹ M6 significantly enhanced shoot growth parameters, including the highest fresh weight (0.249 g), mean shoot height (9.538 cm), shoot number (1.95), and micropropagation rate. Plants treated with M6 alone outperformed those treated with M6+IAA in terms of shoot growth, total soluble sugars, and steviol glycoside content. Conversely, M6+IAA treatment more effectively promoted root initiation, the increased accumulation of mono- and dicaffeoylquinic acids, and boosted antioxidant enzyme activity. Conclusions: These findings highlight the potential of organic nanofibers, both with and without hormone loading, as novel tools for optimizing micropropagation and metabolite enhancement in Stevia rebaudiana. Full article

Background: Scientific evidence plays a critical role in informing public health decision-making processes. However, the extent, nature, and effectiveness of its use remain uneven across contexts. Despite the increasing volume of literature on the subject, previous syntheses have often suffered from narrow thematic, temporal, or geographic scopes. Objectives: This study undertook a comprehensive systematic literature review spanning 50 years to (i) synthesise current knowledge on the use of scientific evidence in public health decisions, (ii) identify key determinants, barriers, and enablers, (iii) evaluate implementation patterns, and (iv) propose future directions for research and practice. Methods: We adopted the PRISMA model (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). Moreover, we researched three large databases (Web of Science, Embase, and PubMed), and this study focused on articles published in the English and French languages between January 1974 and December 2024. Studies were analysed thematically and descriptively to identify trends, patterns, and knowledge gaps. Results: This review reveals a growing corpus of scholarship with a predominance of qualitative studies mainly published in public health journals. Evidence use is most frequently analysed at the national policy level. Analyses of the evolution of scientific production over time revealed significant shifts beginning as early as 2005. Critical impediments included limited access to reliable and timely data, a lack of institutional capacity, and insufficient training among policy-makers. In contrast, enablers encompass cross-sector collaboration, data transparency, and alignment between researchers and decision-makers. Conclusions: Addressing persistent gaps necessitates a more nuanced appreciation of interdisciplinary and contextual factors. Our findings call for proactive policies aimed at promoting the use of scientific evidence by improving the accessibility of health data (addressing the absence or lack of data, as well as its reliability, timeliness, and accessibility), and by training decision-makers in the use of scientific evidence for decision making. Furthermore, our findings advocate for better alignment between the agendas of healthcare professionals (e.g., data collection), researchers (e.g., the selection of research topics), and decision-makers (e.g., expectations and needs) in order to develop and implement public health policies that are grounded in and informed by scientific evidence. Full article

The increasing complexity in hospital waste management requires innovative solutions that integrate sustainability and regulatory compliance. This study proposes an AI-based decision tool to support the circular management of healthcare waste. The approach combines two key elements: (i) the systematic qualitative analysis of international, European, and national regulations, scientific literature, and best practices aimed at identifying strategic actions; (ii) the prioritization of these actions through machine learning, using a Random Forest classifier. We identified 55 actions, grouped into 13 thematic areas, and used them as input variables to assess their impact on regulatory compliance. The variable importance analysis allowed us to classify actions according to their strategic relevance, guiding the structure of the tool and its user interface. Validation, conducted on four simulated case studies, demonstrated the system’s ability to improve compliance monitoring, operational efficiency, and the implementation of circular economy and Zero-Waste strategies. The proposed model represents a scalable and evidence-based solution capable of supporting the ecological transition of healthcare facilities in line with EU directives and the Sustainable Development Goals. Full article

Increasing antimicrobial resistance (AMR) levels in Enterobacterales from pigs in Ghana prompted us to investigate farm feed, pig slurry, and farm water for Enterobacterales isolates, their antimicrobial resistance patterns, and antimicrobial residues. Between August and November 2024, we collected one sample each of feed, slurry, and water from 14 pig farms for microbiological analysis. Out of 42 samples, Enterobacterales (E. coli and Enterobacter spp.) were isolated from 30 (71.4%) samples, with the highest prevalence found in feed (85.7%), followed by slurry (78.6%) and water (50.0%). The prevalence of AMR to tetracyclines, trimethoprim-sulfamethoxazole, and ampicillin was high, with over 50% of isolates from slurry and water and 40% from feed exhibiting tetracycline resistance. Multi-drug resistance (MDR) was identified in nine (27.3%) isolates of Enterobacterales, with the highest prevalence found in feed (38.5%), then slurry (23.1%), and water (14.3%). Among 42 farm samples screened for colistin-resistant Enterobacterales, 10 (23.8%) exhibited phenotypic colistin resistance. No antimicrobial residues were detected. Risk factors associated with MDR included large farms with high pig turnover (p < 0.05) and the channelling of slurry into both covered and uncovered pits on the farm (p < 0.05). These high resistance levels underscore the urgent need for improved hygiene in feed, water, and slurry management, stricter antibiotic stewardship with veterinary oversight, and better enforcement of existing antibiotic use regulations on pig farms. Full article

Background: The emergency medical services (EMS) sector, as a complex system, presents substantial hurdles in providing excellent treatment while operating within limited resources, prompting greater adoption of artificial intelligence (AI) as a tool for improving operational efficiency. While AI models have proved beneficial in healthcare operations, there is limited explainability and interpretability, as well as a lack of data used in their application and technological advancement. Methods: The scoping review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for scoping reviews, using PubMed, IEEE Xplore, and Web of Science, with a procedure of double screening and extraction. The search included articles published from 2018 to the beginning of 2025. Studies were excluded if they did not explicitly identify an artificial intelligence (AI) component, lacked relevance to emergency department (ED) or prehospital contexts, failed to report measurable outcomes or evaluations, or did not exploit real-world data. We analyzed the data source used, clinical subclasses, AI domains, ML algorithms, their performance, as well as potential roles for large language models (LLMs) in future applications. Results: A comprehensive PRISMA-guided methodology was used to search academic databases, finding 1181 papers on prehospital emergency treatment from 2018 to 2025, with 65 articles identified after an extensive screening procedure. The results reveal a significant increase in AI publications. A notable technological advancement in the application of AI in EMS using different types of data was explored. Conclusions: These findings highlighted that AI and ML have emerged as revolutionary innovations with huge potential in the fields of healthcare and medicine. There are several promising AI interventions that can improve prehospital emergency care, particularly for out-of-hospital cardiac arrest and triage prioritization scenarios. Implications for EMS Practice: Integrating AI methods into prehospital care can optimize the use of available resources, as well as triage and dispatch efficiency. LLMs may have the potential to improve understanding and assist in decision-making under pressure in emergency situations by combining various forms of recorded data. However, there is a need to emphasize continued research and strong collaboration between AI experts and EMS physicians to ensure the safe, ethical, and effective integration of AI into EMS practice. Full article