Search Results (52,786)

Background: Frailty is a major determinant of adverse outcomes in older adults with cardiovascular disease. Automated digital tools may facilitate routine frailty assessment in hospital settings; however, their validity and prognostic relevance in acutely hospitalized patients remain insufficiently established. Methods: In this prospective cohort study, 113 hospitalized cardiology patients underwent frailty assessment using both manual Short Physical Performance Battery (mSPPB) and an automated electronic SPPB (eSPPB) system. Agreement between methods was evaluated using Pearson correlation, intraclass correlation coefficients (ICCs), and Bland–Altman analysis. Frailty was defined as SPPB < 5. The association between frailty and 30-day mortality was assessed using logistic regression and Kaplan–Meier survival analysis. Results: Seventeen patients (15.0%) were classified as frail. Automated and manual SPPB scores were highly correlated (r = 0.994, p < 0.001) and demonstrated good agreement (ICC = 0.80). Bland–Altman analysis showed a mean difference of −1.63 points (95% limits of agreement −4.41 to 1.16). Frailty was associated with significantly higher 30-day mortality (17.6% vs. 2.1%, p = 0.009), corresponding to a tenfold increase in mortality odds (OR 10.07; 95% CI 1.5–67.5). An exploratory model showed apparent discriminative performance (AUC 0.83; 95% CI 0.71–0.95). Conclusions: Automated eSPPB demonstrated good agreement with manual assessment and was significantly associated with short-term mortality in hospitalized cardiovascular patients. These findings support the validity and potential clinical utility of automated frailty assessment for risk stratification in acute cardiology settings. Full article

Managing blood glucose in type 1 diabetes (T1D) remains a daily clinical challenge, and accurate short-term prediction of glucose levels can meaningfully improve insulin dosing decisions while reducing the risk of dangerous hypoglycaemic episodes. Although numerous machine learning approaches have been proposed for this task, comparing their relative merits is difficult because published studies differ widely in datasets, preprocessing choices, and evaluation criteria. In this work, we address this research gap by benchmarking ten machine learning methods—from a naïve persistence baseline through classical linear regressors, gradient-boosted ensembles, and recurrent neural networks to a novel hybrid that couples LightGBM with stochastic differential equation (SDE)-based glucose–insulin simulation—on two multi-patient datasets comprising 34 T1D subjects, across prediction horizons of 15, 30, 60, and 120 min. Every method is trained and tested under identical preprocessing and temporal splitting conditions to ensure a fair comparison. The proposed Hybrid LightGBM-SDE model consistently outperforms all alternatives, recording RMSE values of 22.42 mg/dL at 15 min, 28.74 mg/dL at 30 min, 33.89 mg/dL at 60 min, and 37.22 mg/dL at 120 min—an improvement of between 13.6% and 27.0% relative to standalone LightGBM. At the clinically important 30 min horizon, 99.7% of predictions lie within the acceptable A and B zones of the Clarke Error Grid. Wilcoxon signed-rank tests confirm that performance differences are statistically significant (p < 10⁻¹⁰), and SHAP-based analysis shows that the SDE-derived simulation features are among the most influential predictors, especially at longer horizons. All source code and evaluation scripts are publicly released to support reproducibility. Due to temporary data access constraints, all experiments reported here use physics-based synthetic datasets generated from the Bergman minimal model, replicating the structural properties of the D1NAMO and HUPA-UCM collections; validation on the original clinical recordings is planned. Among the two synthetic datasets, the D1NAMO-equivalent cohort (nine patients) proves more challenging, with systematically higher per-patient RMSE variance. The clinically acceptable prediction accuracy at the 30 min horizon (99.7% in Clarke zones A + B) suggests potential for integration into insulin dosing decision-support systems. Full article

The accumulated ammonia within the recirculating aquaculture systems threaten fish health, while little is known about the influences during early fish ontogeny. Using larval and juvenile yellow catfish (Pelteobagrus fulvidraco) as a model, a comprehensive experiment exposing fish to varying total ammonia nitrogen concentrations (0, 10, 20 mg/L for larvae; 0, 25, 125 mg/L for juveniles) was conducted to evaluate the effects on gill transcriptome and microbiota along with the serotonergic regulation. First, the serotonin (5-HT) signal, which controls oxygen chemoreception and ventilation, was mainly detected in the surface of the body of the larvae, and then shifted to gill filaments of juveniles, showing a transition from cutaneous to branchial respiration. Both larval and juvenile yellow catfish exhibited reduced survival, damaged gill structure, and elevated 5-HT expression after ammonia exposure, as well as upregulated tph1b, slc6a4b, scgn and lama5 expression with the increased ammonia concentration, indicating the effects on respiratory function via serotonergic regulation. Further transcriptome analysis was conducted in juveniles to identify the differentially expressed genes (DEGs) and thus, to illustrate more detailed responses after ammonia exposure; KEGG enrichment analysis of DEGs indicated the coping strategy shifted from metabolic buffering to metabolic elimination via glutamine synthesis with the increased ammonia level. The qRT-PCR experiment also identified the increased expression of genes involved in the urea cycle—such as ass1, asl and glula—with the increased ammonia level. Considering the potential contributary role of microbiome to gill health, 16S sequencing was conducted on the gill in the control and the 125 mg/L ammonia-exposed group. Ammonia exposure at 125 mg/L induced significant variation in Simpson index and a marked decline in β diversity. Notably, the abundance of opportunistic pathogens such as Pseudomonadota increased, while the abundance of Deinococcota and Deinococcus—which were renowned for exceptional stress resistance capacity—decreased after ammonia exposure. Thus ammonia exposure disrupts the transcriptomic and microecological balance within gill mucosa, which may elevate the risk of pathogenic infection. Overall, our study provided the first evidence of serotonergic regulation on early fish respiration during ammonia exposure, and also offered new theoretical insights into the involvement of microorganisms in ammonia toxicity. Full article

Background/Objectives: Measures of excess body fat are often more informative for predicting health risk than body mass index (BMI) alone. With obesity prevalence increasing among young adults, this study evaluated whether adding dominant handgrip strength improves prediction of body fat percentage (BF%) and BF%-defined obesity in a university population. Methods: Cross-sectional data from 895 students (401 women, 494 men; mean age 19.9 years; fall 2015–spring 2016) in Kentucky, USA were analyzed. BMI was calculated from self-reported height and weight. BF% was estimated using bioelectrical impedance analysis (BIA), and dominant handgrip strength was measured with a hydraulic hand grip dynamometer. Sex-specific linear and logistic regression models assessed associations among BMI, grip strength, relative grip strength, and BF%. Results: BMI was a strong predictor of BF% in linear models (R² = 0.74 in women; 0.68 in men). Grip strength alone was not associated with BF% but showed an inverse association when combined with BMI. For BF%-defined obesity, BMI remained the most influential predictor, with grip strength contributing additional predictive value. Among men, age significantly modified these relationships, with marked differences between those aged 18–19 years versus older participants. Conclusions: BMI strongly predicted BF% and BF%-based obesity in this cross-sectional study of a predominantly white young adult population. Incorporating handgrip strength modestly improved classification, particularly among women, suggesting that a functional measure like hand grip strength may enhance obesity screening and health communication in young adults. Full article

Lung cancer remains the leading cause of cancer-related mortality worldwide, and although immunotherapy has transformed the treatment landscape of advanced non-small cell lung cancer (NSCLC), durable benefit is limited to a subset of patients. PD-L1 immunohistochemistry and tumor mutational burden, while clinically utilized, demonstrate imperfect predictive capacity, underscoring the need for more robust biomarkers. This review highlights emerging multimodal biomarkers—including circulating tumor DNA (ctDNA), the gut microbiome, and artificial intelligence (AI)-driven radiomics—as promising tools to enhance the prediction of immunotherapy response. Longitudinal ctDNA monitoring offers a minimally invasive method to assess tumor burden dynamics, detect early molecular response, distinguish pseudo-progression from true progression, and stratify risk, with ctDNA clearance correlating with improved survival outcomes. The gut microbiome has also been associated with ICI efficacy, as specific bacterial taxa and composite scoring systems correlate with treatment response, though methodological heterogeneity limits clinical translation. Radiomic analyses leveraging CT and PET imaging extract quantitative tumor features that, when integrated with clinical and molecular data, demonstrate improved predictive performance compared to single-modality approaches. Despite promising advances, challenges including assay standardization, external validation, data harmonization, interpretability of AI models, and infrastructure requirements remain barriers to widespread adoption. Multimodal integration of genomic, microbiome, and imaging biomarkers represents a critical step toward precision immuno-oncology, with prospective validation needed to translate these approaches into improved outcomes for patients with advanced NSCLC. Full article

Breast cancer (BRCA) is the most common cancer worldwide, with an incidence exceeding that of lung cancer. Protein lactylation, a newly identified post-translational modification involving the binding of lactic acid to lysine residues, plays an important role in BRCA. However, its role in BRCA progression remains largely unexplored. This study aims to identify and characterize the lactylation-related genes involved in BRCA biology. Transcriptomic and clinical data of BRCA and normal breast tissues were obtained from TCGA and GEO. Lactylation-related genes were curated from literature and intersected with BRCA datasets to identify candidates. A prognostic risk model was constructed using LASSO and Cox regression. Functional enrichment was performed using KEGG, GSVA, and GSEA. Immune correlations were evaluated by ESTIMATE, CIBERSORT. Single-cell RNA-seq data were integrated to assess gene expression heterogeneity across tumor and immune compartments. In vitro, MDA-MB-231 cells were treated with sodium L-lactate and lactylation-inducing agents, and gene expression was validated by Western blot and RT-qPCR, while EdU and wound healing assays evaluated proliferation and migration. We identified six hub genes associated with the immune microenvironment. Notably, S100A4 is significantly underexpressed, suggesting their potential regulatory roles in BRCA. Further analysis demonstrated that lactylation-related genes are closely linked to immune regulation in BRCA, indicating a possible crosstalk between metabolic modification and tumor immunity. Additionally, we found that lactylation significantly influences gene expression patterns and immune infiltration in BRCA. Importantly, lactic acid ions were shown to upregulate lactylation levels in BRCA cells, underscoring the functional impact of metabolic signals on post-translational modifications in tumorigenesis. Our findings indicate a potential mechanism wherein lactylation affects BRCA progression via lactic acid-driven regulation of the immune microenvironment; they also highlight the possible involvement of S100A4 in this process and offer new insights that could contribute to the diagnosis and treatment of BRCA. Full article

Objective: To evaluate overall survival and to identify clinical, pathological, and demographic factors associated with survival in patients with stage III colon cancer. Methods: This retrospective cross-sectional study included 452 patients with stage III colon cancer who were followed at Ankara Bilkent City Hospital between 2005 and 2025. Patient data, including age, sex, ECOG performance status, comorbidities, tumor characteristics, treatment-related toxicities, and recurrence, were analyzed using PASW Statistics 18.0 (SPSS Inc., Chicago, IL, USA). Kaplan–Meier and log-rank tests were used for survival analysis. Prognostic factors, survival, mortality, and recurrence predictions were evaluated using machine learning algorithms, including coarse tree, bagged trees, support vector machines, and k-nearest neighbors. Furthermore, an explainable artificial intelligence framework was incorporated to improve model transparency and reveal clinically meaningful feature contributions. Model performance was assessed using accuracy, sensitivity, specificity, and F-score. Results: According to statistical analyses, older age, ECOG performance score ≥ 2, stage IIIC disease, N2-level lymph node metastasis, and the presence of comorbidities—particularly diabetes mellitus—were significantly associated with worse survival (p < 0.05). Machine learning analyses identified key prognostic factors, including positive surgical margins, rash, mucositis, thrombocytopenia, number of chemotherapy cycles, pathological tumor subtype, diarrhea, age at diagnosis, and anemia. SHAP analysis further demonstrated that treatment-related variables, particularly surgical margin positivity and chemotherapy-associated toxicities, were among the most influential predictors of survival. Several machine learning models outperformed traditional statistical methods in predicting mortality and recurrence, with the highest accuracy observed in ensemble methods such as coarse tree (87%) and bagged trees. Conclusions: This study identifies key prognostic factors influencing survival in stage III colon cancer and demonstrates that machine learning-based approaches can complement conventional statistical methods. The integration of clinical and treatment-related variables may improve individualized risk stratification and support clinical decision-making. These findings may also guide future large-scale, multicenter, and prospective studies. Full article

Climate change has intensified extreme hydrological risks, particularly in basins characterized by frequent seasonal streamflow interruptions and discontinuous hydrological records, where traditional process-based models exhibit limited capability for adaptive water resource management. This study develops a hybrid SWAT-LSTM framework that integrates SWAT-derived hydrological variables with meteorological factors and applies SHAP interpretability analysis to quantify dominant drivers and identify threshold inflection points of runoff variability. Using the upper and middle reaches of the Huolin River Basin as a case study, the coupled model outperformed the standalone SWAT model during the test period (NSE: 0.876 vs. 0.710; R²: 0.884 vs. 0.736) and more accurately reproduced extreme flood and drought events. Future projections (2026–2100), driven by the optimized FGOALS-g3 climate model under SSP2-4.5 and SSP5-8.5 scenarios, indicate increasing precipitation, accelerated minimum temperature rise, and a non-stationary runoff pattern characterized by a mid-century decline followed by a late-century increase. The SHAP results reveal strengthened meteorological dominance, particularly for precipitation and minimum temperature, while soil moisture, evapotranspiration, and percolation remain key hydrological controls. The upward shift in the minimum temperature threshold reflects strengthened temperature control on runoff dynamics under warming. The proposed framework improves extreme runoff prediction and provides a quantitative basis for climate-adaptive basin management. Full article

Background/Objectives: Children and adolescents exposed to criminal victimization are at increased risk for depression; however, the contribution of nutritional status to depressive symptom severity in this vulnerable population remains poorly understood. Therefore, we aimed to examine the associations between depression severity and nutritional parameters in child and adolescent victims of crime. Methods: This cross-sectional study included 72 children and adolescents (aged 10–16 years) referred to a forensic medicine department in Türkiye. Nutritional status was assessed using anthropometric measurements (body weight, body mass index [BMI], BMI-Z score, and body fat percentage), three-day dietary records, and the Mediterranean Diet Quality Index (KIDMED). Depression severity was evaluated using the Kutcher Adolescent Depression Scale (KADS). The associations were analyzed using Pearson’s rho correlation and forward stepwise linear regression. Potential confounding variables, including age, gender, socioeconomic status, and trauma-related characteristics, were recorded and considered during the analysis; however, due to the limited sample size and to avoid model overparameterization, they were not fully adjusted for in the final model. Results: Depression severity was positively correlated with the body weight, BMI, BMI-Z score, body fat percentage, and dietary energy, carbohydrate, protein, and fat intakes (all p < 0.05). In contrast, the vitamin C and dietary fiber intakes, breastfeeding duration, and KIDMED scores were negatively correlated with the KADS scores (p < 0.05). Regression analysis revealed that the lower KIDMED scores, higher body fat percentage, and greater body weight were significantly associated with depression severity, collectively explaining 82.2% of the variance in the KADS scores. Conclusions: Poor diet quality and adverse body composition are strongly associated with depression severity in child and adolescent victims of crime. These findings suggest that nutritional factors may be associated with depression severity in child and adolescent victims of crime; however, the results should be interpreted as preliminary and hypothesis-generating. Full article

Background/Objectives: Cancer patients increasingly present to emergency departments, posing unique clinical and organizational challenges. Data on this population in Poland remain limited. Methods: A retrospective study was conducted in two hospitals in northern Poland (January–March 2023). All adult patients with active cancer presenting to the ED were included (n = 552, 3.1% of visits). Data included demographics, cancer type, presenting complaints, Emergency Severity Index (ESI), disposition, and in-hospital mortality. Multivariable logistic regression models were used to assess predictors of hospitalization, hospice referral, and mortality, reported as odds ratios (ORs) with 95% confidence intervals (CIs). Results: Mean age was 68 years; 51% were female. The most common cancers were lung, breast, colorectal, and prostate. Leading complaints included abdominal pain (15%), trauma (7.5%), and dyspnea (7%). Most patients were triaged as ESI 3–4 (87%). Hospitalization rate was 58%, hospice referral 6%, and in-hospital mortality 7.1%. Lower ESI levels were significantly associated with hospitalization (OR 0.57; 95% CI 0.44–0.73), hospice referral (OR 0.40; 95% CI 0.25–0.63), and in-hospital mortality (OR 0.29; 95% CI 0.18–0.47). Conclusions: Oncology patients represent a small but high-risk ED population. While ESI reflects acute severity, it may not adequately capture palliative care needs. These findings suggest opportunities to improve integration of palliative care in ED settings. Full article

Background: Prenatal vitamin D exposure has been proposed as a potential determinant of immune development and subsequent allergic disease risk in offspring; however, long-term cohort data remain inconsistent. Methods: We analyzed data from the KLOTHO birth cohort, including 98 adolescents with available allergic outcome assessment. A maternal–neonatal sub-cohort of mother–child pairs with available maternal and neonatal serum total 25-hydroxyvitamin D₃ [25(OH)D] measurements at delivery was used for vitamin D analyses. Allergic outcomes included asthma, allergic rhinitis, and eczema in offspring. Associations were evaluated using descriptive statistics, Spearman correlation analyses, and logistic regression models. Results: Maternal 25(OH)D concentrations were not significantly associated with asthma (ρ = 0.075, p = 0.652), allergic rhinitis (ρ = 0.100, p = 0.556), or eczema (ρ = 0.131, p = 0.426). In crude logistic regression models, vitamin D concentrations were not associated with asthma (odds ratio (OR) per 10 nmol/L: 1.07, 95% confidence interval (CI): 0.78–1.48, p = 0.67), allergic rhinitis (OR: 1.05, 95% CI: 0.76–1.45, p = 0.77), or eczema (OR: 1.17, 95% CI: 0.86–1.60, p = 0.31). Adjusted models including maternal age, pre-pregnancy body mass index (BMI), season of delivery, and ultraviolet exposure yielded similar non-significant findings, although analyses were limited by a reduced complete-case sample size. Conclusions: In this prospective cohort with follow-up into early adolescence, vitamin D status at delivery was not associated with asthma, allergic rhinitis, or eczema in offspring. These findings support a lack of statistically significant association; however, potential non-linear relationships should be interpreted cautiously, given the modest sample size. Full article

Background/Objectives: Inguinal hernia and hydrocele are common pediatric surgical conditions resulting from failed obliteration of the processus vaginalis during fetal development. Although prenatal exposure to fine particulate matter (PM2.5) has been linked to adverse perinatal outcomes and congenital anomalies, its role in structurally defined pediatric surgical diseases remains unclear. We examined the association between maternal PM2.5 exposure during pregnancy and the risk of inguinal hernia or hydrocele in offspring. Methods: We performed a retrospective cohort study of 1093 mother–offspring pairs delivering at a tertiary referral center (July 2016–June 2019). Monthly residential PM2.5 levels were estimated at geocoded maternal addresses using kriging interpolation from fixed-site monitoring stations. Offspring diagnosed with inguinal hernia or hydrocele through March 2024 were identified using ICD-10 codes. Perinatal characteristics were compared using t-tests and chi-square tests, and multivariable logistic regression assessed trimester-specific PM2.5 exposure and risk. Results: During follow-up, 53 offspring (4.85%) developed inguinal hernia or hydrocele. Male sex (odds ratio [OR], 24.71; 95% CI, 5.95–102.54; p < 0.001) and second-trimester PM2.5 exposure (OR, 1.07 per µg/m³; 95% CI, 1.01–1.14; p = 0.028) were independent risk factors. A dose–response pattern was observed across quartiles of second-trimester exposure; an interquartile range increase was associated with a 64% higher risk (OR, 1.64). The model showed good discrimination (AUC, 0.804). Conclusions: Elevated maternal PM2.5 exposure during the second trimester was independently associated with increased risk of inguinal hernia or hydrocele in offspring. Prenatal air pollution may contribute to persistence of the processus vaginalis and represents a potentially modifiable environmental risk factor. Full article

Non-muscle-invasive bladder cancer (NMIBC) is characterized by high recurrence rates and necessitates lifelong cystoscopic surveillance, underscoring the need for minimally invasive biomarkers to improve early detection and risk stratification. Therefore, this study aimed to investigate the role of trimethylamine-N-oxide (TMAO) and its precursors as diagnostic biomarkers for NMIBC. A total of 50 male patients with NMIBC (25 pTa and 25 pT1) were included in this study. Additionally, 52 age-matched healthy individuals were included as controls. Serum TMAO and its dietary precursors were quantified using liquid chromatography–tandem mass spectrometry. Group differences were analyzed using nonparametric tests, associations were assessed using Spearman’s correlation, and diagnostic performance was evaluated using receiver operating characteristic (ROC) analysis. Multivariate logistic regression was performed to identify independent predictors, and a composite risk score was generated. Serum TMAO, carnitine, and choline levels were significantly higher in patients with NMIBC than in controls (p ≤ 0.0001), whereas betaine showed a nonsignificant trend toward higher levels (p ≥ 0.05). The pathological stage (pTa vs. pT1) showed the strongest correlation with TMAO levels. The ROC analysis revealed that TMAO had the highest individual diagnostic accuracy (area under the curve [AUC] = 0.875, 95% confidence interval [CI] 0.812–0.939), whereas carnitine and choline provided complementary diagnostic performance. In multivariate models, TMAO, carnitine, and choline remained independent predictors of NMIBC (p ≤ 0.0001). A composite risk score integrating all four metabolites demonstrated excellent discriminatory capacity (AUC = 0.958, 95% CI 0.926–0.991). The TMAO metabolic axis can be used as a minimally invasive biomarker panel for NMIBC. Further large, prospective, multicenter studies integrating metabolomic and microbiome profiling are needed to validate the findings. Full article

Accounting for over 1.2 million new diagnoses worldwide in 2022, non-melanoma skin cancer (NMSC) represents the most common human cancer, predominantly manifesting as basal cell carcinoma (BCC) and squamous cell carcinoma (SCC). NMSC serves as a powerful natural model for studying how environmental exposure, the exposome, reprograms the epigenome to drive carcinogenesis. Chronic ultraviolet radiation (UVR), the dominant risk factor, induces DNA damage and inflammation that dysregulate epigenetic enzymes (e.g., DNMTs, HDACs). These effects are layered with perturbations from β-HPV infection and cutaneous dysbiosis, altering DNA methylation, histone modifications, and non-coding RNA and miRNA expression in a multistep carcinogenic process. This review synthesizes the central role of epigenetic regulation as the critical interface between genetic susceptibility and cumulative exposome factors in NMSC pathogenesis. We integrate how UVR, HPV, and inflammation converge to remodel the keratinocyte epigenome. Finally, we evaluate the translational potential of this knowledge for refined risk stratification through epigenetic biomarkers and discuss emerging therapeutic strategies, including epidrugs, that target these dysregulated pathways for advanced NMSC management. Full article