Search Results (10,262)

Background/Objectives: Limited accessibility to hypercholesterolemia diagnosis hinders the primary prevention of cardiovascular disease. Therefore, we conducted a prospective, temporal validation study of two diagnostic prediction models, targeting endpoints of elevated low-density lipoprotein cholesterol (LDL-C, ≥160 mg/dL) and non-high-density lipoprotein cholesterol (non-HDL-C, ≥160 mg/dL). Methods: We prospectively recruited workers aged 20–40 years from a single-center, university hospital from March to June 2024 (n = 1099). We determined two diagnostic endpoints: elevated LDL-C and non-HDL-C. The predicted probabilities were derived from the binary logistic regression based on gender, metabolic age, and diastolic blood pressure. We assessed three prediction performances: discrimination from area under the receiver-operating characteristic curve (AuROC); calibration slope (C-slope) and calibration-in-the-large (CITL) from the calibration plot; clinical net benefit from decision curve analysis. Recalibration was based on C-slope and CITL, with a socioeconomic subgroup fairness assessment of AuROC, C-slope, and CITL. Results: From 1099 eligible participants, we identified 135 (12.3%) elevated LDL-C and 251 (22.8%) elevated non-HDL-C cases. The LDL-C model had poor discrimination (AuROC 0.59; 95%-CI, 0.56–0.62), miscalibration (C-slope 0.64; 95%-CI, 0.39–0.88 and CITL −0.14; 95%-CI, −0.27–−0.02), and negligible investigation reduction. The non-HDL-C model had fair discrimination (AuROC 0.67; 95%-CI, 0.64–0.69), miscalibration (C-slope 0.71; 95%-CI, 0.59–0.83 and CITL −0.07; 95%-CI, −0.17–0.03), and 20% investigation reduction at prevalence threshold probability. Updated model fairness improved compared to the original models. Conclusions: Temporal validation demonstrated modest replicability for the elevated non-HDL-C model, with a potential limitation in participants with normal BMI but low muscle and high fat mass. Health practitioners may use updated elevated non-HDL-C models as a non-invasive triage strategy in young adults, with threshold probabilities within the positive clinical net benefit ranges. Further external validation studies in a larger and more diverse population are necessary. Full article

Listeria monocytogenes is a significant foodborne pathogen associated with high rates of hospitalization and death, especially among vulnerable populations. Despite established regulatory standards and available antimicrobial intervention strategies, L. monocytogenes remains as a pathogen of concern in ready-to-eat (RTE) foods. This ultimately can lead to food recalls or listeriosis outbreak, highlighting its ongoing risks to food safety and public health. This review consolidates publicly accessible surveillance case counts and recall data of L. monocytogenes contamination from Australia, Europe, Canada, and the United States to assess the contamination trends in the RTE food supply chain. It also evaluates the effectiveness of antimicrobial intervention strategies, including both those currently implemented in industry and those that have been studied as potential interventions but are not yet widely adopted. Key factors affecting the efficiency of those strategies are identified, including food matrix composition, water activity (a_w), fat content, and strain variability. Emerging multi-hurdle technology that integrates physical, chemical, and biological antimicrobial interventions are highlighted as promising approaches for maintaining both food safety and product quality. It also outlines the role of quantitative microbial risk assessment (QMRA) as a decision-support tool to select appropriate control strategies, predict recall risk and guide evidence-based risk management. Future research directions are proposed to expand the application of QMRA in managing recall risks throughout the RTE food supply chain due to L. monocytogenes. Full article

Preeclampsia (PE) occurs in approximately 2–8% of all pregnancies worldwide and represents one of the primary causes of maternal and fetal morbidity and mortality. Angiogenic growth factors such as placental growth factor (PlGF) and vascular endothelial growth factor (VEGF), along with their tyrosine kinase receptor (Flt-1), play a central role in placental and fetal development. Impaired placentation results in the excessive release of the antiangiogenic soluble fms-like tyrosine kinase-1 (sFlt-1) which is pivotal in the pathogenesis of PE. By binding to and neutralizing angiogenic factors, sFlt-1 disrupts normal angiogenic signaling, creating an imbalance that is often detectable before clinical symptoms of PE appear. Recent studies have highlighted the prognostic potential of the sFlt-1/PlGf ratio as an early indicator of PE risk, since this ratio has demonstrated value in both confirming and excluding PE in the high-risk population. Its incorporation into routine medical care has the potential to reduce unnecessary hospital admissions, intensive management, and premature deliveries, ultimately lowering healthcare costs. The objective of this review is to highlight the clinical utility of the sFlt-1/PlGf ratio in the prediction, diagnosis, and management of preeclampsia and to emphasize the cost-effectiveness of implementing sFlt-1/PlGF ratio measurement in the care of women at risk of developing PE. Full article

This study estimates the number of electric vehicles (EVs) and charging stations in Sakarya Province, Türkiye, for 2030 using advanced artificial intelligence time series methods and statistical approaches. The novelty of the work lies in the application of hyperparameter-optimized LSTM and GRU models alongside Multiple Linear Regression (MLR) to a regional dataset, enabling accurate, data-driven forecasting for regional EV planning. Performance was evaluated using multiple metrics, including R², MAE, MSE, DTW, RMSE, and MAPE, with the GRU model achieving the highest reliability and lowest errors (R² = 0.99, MAE = 0.3, MSE = 2.9, DTW = 123.2, RMSE = 3.1, MAPE = 2.8%) under optimized parameters. The predicted EV counts and charging station numbers from GRU informed a neighborhood-level allocation of charging stations using Google Maps API, considering local population ratios. These results demonstrate the practical applicability of deep learning for regional infrastructure planning and provide a replicable framework for similar studies in other provinces. Full article

Chronic kidney disease (CKD) impacts over 850 million people globally, representing a critical public health issue, yet existing risk assessment methodologies inadequately address the complexity of disease progression trajectories. Traditional machine learning approaches encounter critical limitations including inefficient hyperparameter selection and lack of clinical transparency, hindering their deployment in healthcare settings. This study introduces an innovative computational framework that integrates adaptive Multi-Armed Bandit (MAB) strategies with BorderlineSMOTE sampling techniques to improve CKD risk assessment. The proposed methodology leverages XGBoost within an ensemble learning paradigm enhanced by Upper Confidence Bound exploration strategy, coupled with a comprehensive interpretability system incorporating SHAP and LIME analytical tools to ensure model transparency. To address the challenge of algorithmic interpretability while maintaining clinical utility, a four-level risk categorization framework was developed, employing cross-validated stratification methods and balanced performance evaluation metrics, thereby ensuring fair predictive accuracy across diverse patient populations and minimizing bias toward dominant risk categories. Through rigorous empirical evaluation on clinical datasets, we performed extensive comparative analysis against sixteen established algorithms using paired statistical testing with Bonferroni correction. The MAB-optimized framework achieved superior predictive performance with accuracy of 91.8%, F1-score of 91.0%, and ROC-AUC of 97.8%, demonstrating superior performance within the evaluated cohort of reference algorithms (p-value < 0.001). Remarkably, our optimized framework delivered nearly ten-fold computational efficiency gains relative to conventional grid search methods while preserving robust classification performance. Feature importance analysis identified albumin-to-creatinine ratio, eGFR measurements, and CKD staging as dominant prognostic factors, demonstrating concordance with established clinical nephrology practice. This research addresses three core limitations in healthcare artificial intelligence: optimization computational cost, model interpretability, and consistent performance across heterogeneous clinical populations, offering a practical solution for improved CKD risk stratification in clinical practice. Full article

Background: Dementia refers to a spectrum of clinical conditions characterized by impairments in memory, language, and cognitive function. Alzheimer’s Disease (AD) is the most common cause of dementia and it accounted for 60–70% of the estimated 57 million cases worldwide as of 2021. The exact pathology of this neurodegenerative condition is not fully understood. While it is currently incurable, progression to more critical stages can be slowed, and early diagnosis is crucial to alleviate and manage some of its symptoms. Contemporary diagnostic practices hinder early detection due to the high costs and inaccessibility of advanced neuroimaging tools and specialists, particularly for populations with resource-constrained clinical settings. Methods: This paper addresses this challenge by developing and evaluating computationally efficient lightweight deep learning models, MobileNetV2 and EfficientNetV2B0, for early AD detection from 2D slices sourced from standard structural magnetic resonance imaging (MRI). Results: For the challenging multi-class task of distinguishing between Cognitively Normal (CN), Early Mild Cognitive Impairment (EMCI), and Late Mild Cognitive Impairment (LMCI), our best model, EfficientNetV2B0, achieved 88.0% mean accuracy across a 5-fold stratified cross-validation (std = 1.0%). To enhance clinical interpretability and build trust, we integrated explainability methods, Grad-CAM++ and Guided Grad-CAM++, to visualize the anatomical basis for the models’ predictions. Conclusions: This work delivers an accessible and interpretable neuroimaging tool to support early AD diagnosis and extend expert-level capabilities to routine clinical practice. Full article

Background/Objectives: As population aging garners attention worldwide, there is great significance in communicating information on such measures to countries outside of Japan, which is considered unique in its position as a “super-aging society.” This study objectives to investigate public health measures linked to daily life by clarifying how the role transitions of local residents in their 60s, such as seeking re-employment, looking after grandchildren, and caring for family, affect their health status. Methods: We conducted focus group interviews with 26 residents and analyzed them qualitatively and inductively. Result: The findings suggested that, in predicted role transitions voluntarily chosen by participants, they tended to experience positive changes in health through the transition, although temporary feelings of fatigue were also described in relation to re-employment and grandchild care. Even in anticipated role changes, some participants expressed reluctance to engage in health-promoting activities within the local community. In cases of unavoidable role transition to family caregiving, participants described difficulties in maintaining self-care and feelings of caregiving fatigue that were challenging to manage through personal effort alone. These findings suggest that health support during role transitions in one’s 60s may benefit from including information about community activities and opportunities to build connections with local residents. In addition, support for those transitioning into caregiving roles could focus on facilitating access to social resources and tailoring assistance to individual needs. Conclusions: This study confirmed to specifically target health support for people in their 60s based on the results of this study, the focus on the transition needs to include not only role transition to re-employment but also unavoidable transition to caregiving. Full article

Accurate wildfire prediction and identification of key environmental drivers are critical for effective wildfire management. We propose a spatiotemporally adaptive framework integrating ST-DBSCAN clustering with GTWR-RFR. In this hybrid model, Random Forest captures local nonlinear relationships, while GTWR assigns adaptive spatiotemporal weights to refine predictions. Using historical wildfire records from Hunan Province, China, we first derived wildfire occurrence probabilities via ST-DBSCAN, avoiding the need for artificial non-fire samples. We then benchmarked GTWR-RFR against seven models, finding that our approach achieved the highest accuracy (R² = 0.969; RMSE = 0.1743). The framework effectively captures spatiotemporal heterogeneity and quantifies dynamic impacts of environmental drivers. Key contributing drivers include DEM, GDP, population density, and distance to roads and water bodies. Risk maps reveal that central and southern Hunan are at high risk during winter and early spring. Our approach enhances both predictive performance and interpretability, offering a replicable methodology for data-driven wildfire risk assessment. Full article

MDM hydantoin (MDMH), a formaldehyde-releasing preservative widely used in cosmetics, poses potential health risks due to its conversion to formaldehyde and systemically absorbed metabolites. Current safety assessments lack quantitative exposure data due to rapid degradation of MDMH in biological matrices. In the present study, we developed a validated LC-MS/MS assay for simultaneous determination of MDMH and its stable metabolite DMH in rat plasma, and characterized their toxicokinetics using population modeling following intravenous and transdermal administration. MDMH exhibited extremely rapid elimination (t_1/2 = 0.4 ± 0.1 min) with near-complete conversion to DMH (97.6 ± 9.6%), while DMH demonstrated prolonged retention (t_1/2 = 174.2 ± 12.2 min) and complete bioavailability (100.9 ± 18.0%) after transdermal application. Population modeling estimated that 84% (relative standard error: 42.8%) of applied MDMH undergoes cutaneous absorption and metabolism to DMH and formaldehyde within skin tissues. This study demonstrates that stable metabolite monitoring combined with population modeling enables toxicokinetic characterization of rapidly degrading compounds following dermal exposure. Full article

Healthcare systems worldwide have faced unprecedented pressure during crises such as the COVID-19 pandemic, exposing limits in managing scarce hospital resources. Many predictive models remain static, unable to adapt to new variants, shifting conditions, or diverse patient populations. This work proposes a dynamic prioritization framework that recalculates severity scores in batch mode when new factors appear and applies them instantly through a streaming pipeline to incoming patients. Unlike approaches focused only on fixed mortality or severity risks, our model integrates dual datasets (survivors and non-survivors) to refine feature selection and weighting, enhancing robustness. Built on a big data infrastructure (Spark/Databricks), it ensures scalability and responsiveness, even with millions of records. Experimental results confirm the effectiveness of this architecture: The artificial neural network (ANN) achieved 98.7% accuracy, with higher precision and recall than traditional models, while random forest and logistic regression also showed strong AUC values. Additional tests, including temporal validation and real-time latency simulation, demonstrated both stability over time and feasibility for deployment in near-real-world conditions. By combining adaptability, robustness, and scalability, the proposed framework offers a methodological contribution to healthcare analytics, supporting fair and effective hospitalization prioritization during pandemics and other public health emergencies. Full article

Misiones province covers < 1% of Argentina’s land area yet harbors > 50% of the country’s biodiversity, with a significant remnant of Atlantic Forest, a global biodiversity hotspot. Approximately 540,000 ha of this native forest is protected, with the remaining areas facing threats from ongoing land conversion, an expanding road network, and a growing rural population. A prior study incorporated noninvasive data on five carnivores into a multifaceted cost analysis to define the optimal location for a multispecies biological corridor, with the goal of enhancing landscape connectivity among protected areas. Subsequent analyses, with an updated framework, emphasized management strategies that balanced human–wildlife coexistence and habitat needs. Building on these efforts, our study applied ecological niche modeling to data located by conservation detection dogs, with genetics used to confirm species identity, and two land-use scenarios, to predict potential distributions of three game species—lowland tapir (Tapirus terrestris), white-lipped peccary (Tayassu pecari), and collared peccary (Pecari tajacu)—that are not only threatened by poaching, road mortality, and habitat loss but also serve as essential prey for carnivores. We assessed the suitability of unique and overlapping vegetation types, within and outside of protected areas, as well as within this multispecies corridor, identifying zones of high conservation concern that underscore the need for integrated planning of predators and prey. These results highlight that ensuring the long-term viability of wildlife across the heterogeneous land-use matrices of Misiones requires going beyond protected areas to promote functional connectivity, restore degraded habitats, and balance human–wildlife needs. Full article

Background/Objectives: Cirrhosis is an irreversible state of chronic liver disease. Systemic inflammatory response syndrome (SIRS) is a severe complication and significantly contributes to lethal outcomes in cirrhotic patients. We studied a group of cirrhotic patients with SIRS admitted to our centre, assessing the relationship with in-hospital outcomes. Methods: The study population included 102 patients with alcoholic cirrhosis and SIRS. Laboratory biomarkers, the model for end-stage liver disease, the model for end-stage liver disease—natrium, the Acute Physiology and Chronic Health Evaluation II score, CLIF-C organ failure, the systemic immune-inflammation index score (S II), and the Cirrhosis Acute Gastrointestinal Bleeding (CAGIB) score were tested in relation to the mortality risk using receiver operating characteristic (ROC) curves. Results: Our results demonstrated that values of sodium, chlorides, and albumin significantly correlated with 7-day survival. The area under the curve’s (AUCs) values for sodium, chlorides, and albumin were 0.542, 0.627, and 0.610, respectively, for 7-day mortality prediction. The CAGIB score significantly correlated with 7-day mortality, with the cut-off value of −7.86 (AUC: 0.674, 95% CI (0.555–0.794)). For the assessment of 28-day mortality, the AUC values for sodium, chlorides, and albumin were 0.630, 0.654, and 0.661, respectively. Additionally, the cut-off value of the CAGIB score was found to be −7.84 (AUC: 0.625, 95% CI (0.509–0.740)) in 28-day mortality prediction. Conclusions: Sodium, chlorides, albumin, and the CAGIB score are reliable predictors of 7-day and 28-day in-hospital mortality in patients with advanced alcoholic liver disease and SIRS. Full article

Background: The HPV16 E2 gene plays a crucial role in viral replication and oncogene regulation. This study aimed to assess the genetic variability of the E2 gene and to identify immunogenic epitopes of the E2 protein. Methods: The E2 gene was amplified and sequenced. T-cell epitope prediction and evaluation were performed using IEDB, NetMHCpan v4.0, NetMHCIIpan v4.1, VaxiJen, ToxNet, and pLM4Alg. Results: Phylogenetic analysis of 47 E2 sequences demonstrated co-circulation of the D (n = 4) and A (n = 43) HPV16 lineages in Cajamarca. Twenty-eight Single Nucleotide Polymorphism (SNPs) were identified in E2, 21 of which were nonsynonymous. Seventeen variations were associated with positive Papanicolaou (Pap) test results. Epitope prediction identified 2 MHC class I and 27 MHC class II epitopes classified as potentially antigenic, non-toxic, and non-allergenic, with an overall global population coverage across both MHC classes of 99.78%. Conclusions: The A HPV16 lineage predominated among the women studied. The identified SNPs indicate substantial variability in the E2 gene and a relationship with endocervical lesions. In total, 29 E2-derived T-cell epitopes with immunogenic potential were identified. Full article

Background: Prostate cancer is the most frequent malignancy in men, with an incidence of 21% of all diagnosed tumors in this population in Spain. Between 10 and 20% of patients with prostate cancer develop castration-resistant prostate cancer (CRPC). Abiraterone is widely used in CRPC and metastatic prostate cancer, but data on its renal safety are limited. Methods: We performed a single-center, retrospective observational study including patients with advanced prostate cancer who initiated abiraterone between January 2013 and July 2024 at Hospital Clínico San Carlos (Madrid, Spain). Patients were followed until December 2024. Renal events were defined as acute kidney injury (AKI), electrolyte imbalance, new onset or worsening hypertension (HTN), and/or volume overload. Risk factors and associations with mortality were analyzed using multivariate models. Results: Seventy-nine patients were included (mean age 76 ± 9.5 years; 70.9% CRPC; 89.9% metastatic disease). Median follow-up was 17 months. Renal events occurred in 63.3% of patients. Independent risk factors were metastatic disease (OR 13.335; 95% CI 1.418–124.444; p < 0.0235) and HTN (OR 3.336; 95% CI 1.091–10.206; p < 0.0347). Electrolyte imbalance occurred in 36.7% of patients. AKI developed in 30.4% of patients, of whom 50% progressed to chronic kidney disease. New/worsening HTN occurred in 25.5%, and volume overload occurred in 16.5%. Abiraterone discontinuation due to renal events was rare (4%). At the end of follow-up, 18.9% of patients had died. In a multivariate Cox analysis, AKI was identified as an independent predictor of mortality [HR 3.044 (95% CI 1.001–9.260); p = 0.05]. Conclusions: Renal events are common in patients treated with abiraterone, especially in those with metastatic disease and hypertension. AKI independently predicted mortality. Close monitoring of renal function and blood pressure is essential in this population. Full article

Thyroid nodules, prevalent in 2% to 65% of the general population depending on diagnostic methodology, represent a significant clinical concern despite a low malignancy rate, typically 1% to 5%. A substantial proportion of thyroid cancers are small, indolent lesions, allowing for conservative management with favorable prognoses. Nodule detection commonly occurs via palpation, clinical examination, or incidental radiological findings. Established risk factors include advanced age, female gender, obesity, metabolic syndrome, and estrogen dominance. Despite conservative management potential, a considerable number of thyroid nodules in Europe are unnecessarily referred for surgery, incurring unfavorable risk-to-benefit ratios and increased costs. Minimally invasive techniques (MITs), encompassing ethanol and thermal ablation modalities (e.g., laser, radiofrequency, microwave), offer outpatient, nonsurgical management for symptomatic or cosmetically concerning thyroid lesions. These procedures, performed under ultrasound guidance without general anesthesia, are associated with low complication rates. MITs effectively achieve substantial and sustained nodule volume reduction (57–77% at 5 years), correlating with improved local symptoms. Thermal ablation (TA) is particularly favored for solid thyroid lesions due to its precise and predictable tissue destruction. Optimal TA balances near-complete nodule eradication to prevent recurrence with careful preservation of adjacent anatomical structures to minimize complications. Radiofrequency ablation (RFA) is widely adopted, while microwave ablation (MWA) presents a promising alternative addressing RFA limitations. Percutaneous laser ablation (LA), an early image-guided thyroid ablation technique, remains a viable option for benign, hyperfunctioning, and malignant thyroid pathologies. This review comprehensively evaluates RFA, MWA, and LA for thyroid nodule treatment, assessing current evidence regarding their efficacy, safety, comparative outcomes, side effects, and outlining future research directions. Full article