Search Results (1,934)

Human exploitation has been a major driver of marine turtle population declines, particularly affecting naturally scarce species such as the pantropical hawksbill turtle. Although hawksbill sea turtles have been documented in the Gulf of California since the early 20th century, data on their historical demography during periods of high exploitation in this region are nonexistent. We investigated the size structure of hawksbill turtles from the Western Central Gulf of California by examining a unique sample of decorative taxidermies, corresponding to 31 specimens captured during fishing operations near Santa Rosalía, Baja California Sur, Mexico, between 1980 and 1990. An analysis of the curved carapace measures revealed a length range (nuchal notch to posterior of supracaudals) of 29.5–59.5 cm (mean = 38.75 ± 6.67 cm) and a width range of 25.0–51.5 cm (mean = 33.63 ± 5.66 cm), with 87% of specimens having lengths between 30 and 45 cm. Based on the carapace length measurements, we estimated the ages to be between 7 and 20 years, indicating that the population included juveniles. Our findings provide baseline data for an understudied period and region, suggesting that this area previously served as an important juvenile habitat. These results contribute essential historical demographic information for conservation planning. Full article

(1) Background: Comparison of clinical outcomes between patients with moderate-severe aortic valve stenosis and those with mild or no aortic valve stenosis undergoing surgical revascularization for critical limb threating ischemia (CLTI). (2) Methods: Single center retrospective analysis of consecutive patients undergoing surgical lower limb revascularization with femoro-distal bypass for critical ischemia between 2016 and 2022. All patients were evaluated preoperatively by echocardiographic examination and divided into two cohorts: group A with moderate-severe aortic valve stenosis (AVA-cm² < or =1.5 cm²) and group B with mild or absent stenosis (AVA-cm² > 1.5 cm²). Primary outcomes were major limb amputation and mortality between the two groups. The rate of major cardiovascular events (stroke, myocardial infarction, sudden cardiac death) and change in “preoperative functional status” were the secondary outcomes. Descriptive statistics for continuous variables were performed by calculating means, standard deviation (SD) medians, and interquartile range (IQR) while, for categorical variables, frequencies and percentages were performed. Intergroup comparison tests, for continuous variables, were performed by t-test or corresponding nonparametric tests (Mann-Whitney test) while, for categorical variables, Chi-square test was used. Evaluation of cut-offs for the variable AVA-fx-cm², in terms of predictive of outcome outcomes, was calculated by ROC curves. Comparison between clinical and outcome variables was performed using logistic regression models. A total of 316 patients were analyzed and divided in two groups: 50 (16%) patients with moderate or severe aortic valve stenosis (group A) and 266 (84%) with no or mild aortic valve stenosis (AVA > 1.5 cm²). Patients in group A were significantly older than those in group B (78 years vs. 74 years, p value = 0.005); no other significant comorbidity differences were found between the two groups. The mean follow-up was 1178 days (SD 991 days; 2–3869 days). There were no statistically significant differences between group A and group B in terms of major amputation rate (20% vs. 16.5%; p = 0.895) and overall mortality (48.0% vs. 40.6%; p = 0.640). In the total cohort, the statistically significant variables associated with the major amputation were systemic perioperative complication (OR 5.83, 95% CI: 2.36, 14.57, p < 0.001), bypass-related complication within 30 days of surgery (OR 2.74, 95% CI: 1.17, 6.45, p = 0.020), surgical revascularization below the knee (OR 7.72, 95% CI: 1.53, 140.68, p = 0.049), and the presence of a previous cardiovascular event (OR 2.65, 95% CI: 1.14, 6.26, p = 0.024). In patients undergoing surgical revascularization for CLTI, no significant difference in major amputation rate and overall mortality was found between subjects with mild or no aortic valve stenosis and those with moderate/severe stenosis. As expected, overall mortality was higher in older patients with worse functional status. A significantly higher rate of limb amputation was found in those subjects undergoing subgenicular revascularization, early bypass failure, or previous cardiovascular event. Full article

Background: Oxidative stress is a critical factor contributing to male infertility, impairing spermatogonial stem cells (SSCs) and disrupting normal spermatogenesis. This study aimed to isolate and characterize human SSCs and to investigate oxidative stress-related gene expression, protein interaction networks, and developmental trajectories involved in SSC function. Methods: SSCs were enriched from human orchiectomy samples using CD49f-based magnetic-activated cell sorting (MACS) and laminin-binding matrix selection. Enriched cultures were assessed through morphological criteria and immunocytochemistry using VASA and SSEA4. Transcriptomic profiling was performed using microarray and single-cell RNA sequencing (scRNA-seq) to identify oxidative stress-related genes. Bioinformatic analyses included STRING-based protein–protein interaction (PPI) networks, FunRich enrichment, weighted gene co-expression network analysis (WGCNA), and predictive modeling using machine learning algorithms. Results: The enriched SSC populations displayed characteristic morphology, positive germline marker expression, and minimal fibroblast contamination. Microarray analysis revealed six significantly upregulated oxidative stress-related genes in SSCs—including CYB5R3 and NDUFA10—and three downregulated genes, such as TXN and SQLE, compared to fibroblasts. PPI and functional enrichment analyses highlighted tightly clustered gene networks involved in mitochondrial function, redox balance, and spermatogenesis. scRNA-seq data further confirmed stage-specific expression of antioxidant genes during spermatogenic differentiation, particularly in late germ cell stages. Among the machine learning models tested, logistic regression demonstrated the highest predictive accuracy for antioxidant gene expression, with an area under the curve (AUC) of 0.741. Protein oxidation was implicated as a major mechanism of oxidative damage, affecting sperm motility, metabolism, and acrosome integrity. Conclusion: This study identifies key oxidative stress-related genes and pathways in human SSCs that may regulate spermatogenesis and impact sperm function. These findings offer potential targets for future functional validation and therapeutic interventions, including antioxidant-based strategies to improve male fertility outcomes. Full article

Background: A precise radiographic evaluation of adolescent idiopathic scoliosis (AIS) is essential for effective treatment planning and follow-up. The pelvic axial rotation (PAR) and horizontal balance of the pelvis are critical factors to consider throughout the treatment and monitoring of AIS. While some previous studies have examined spinal curvature in relation to PAR direction and the direction of the major curve (DMC) in AIS patients, this study aims to explore the relationship between scoliosis morphology, pelvic axial rotation (PAR), and the direction of the major curve in patients with adolescent idiopathic scoliosis. Methods: Radiographic images of 397 patients diagnosed with AIS between 2023 and 2024 at a Tertiary Referral Hospital were retrospectively evaluated. Morphological and morphometric measurements, including sex, Lenke and Risser classifications, lower leg discrepancy, Cobb angle, PAR direction, and major curvature direction, were performed. Results: The mean age of the 397 patients (246 female, 151 male) was 14.47 ± 2.29. There is no significant correlation between PAR and DMC (p = 0.919). No significant differences were found in terms of sex (p = 0.603). Regardless of the PAR direction, major curvature was more common on the left side (57.7%). Furthermore, a positive correlation was noted between the Cobb angle and LLD. Conclusions: Our study contributes to a growing body of literature questioning the deterministic role of PAR in AIS. While previous reports have emphasized the directional correlation between the pelvis and spinal curvature, our findings suggest that pelvic rotation may not be a reliable indicator of curve direction in all patients. This highlights the complexity of AIS biomechanics and underscores the need for individualized radiographic and clinical evaluation rather than a reliance on generalized compensatory models. Full article

A critical gap exists between coastal communities’ need for accessible flood risk assessment tools and the availability of sophisticated modeling, which remains limited by technical barriers and computational demands. This study introduces three key innovations through Coastal Defense Pro: (1) the first operational web-based AI ensemble for coastal flood risk assessment integrating real-time multi-agency data, (2) an automated regional calibration system that corrects systematic model biases through machine learning, and (3) browser-accessible implementation of research-grade modeling previously requiring specialized computational resources. The system combines Bayesian neural networks with optional LSTM and attention-based models, implementing automatic regional calibration and multi-source elevation consensus through a modular Python architecture. Real-time API integration achieves >99% system uptime with sub-3-second response times via intelligent caching. Validation against Hurricane Isabel (2003) demonstrates correction from 197% overprediction (6.92 m predicted vs. 2.33 m observed) to accurate prediction through automated identification of a Chesapeake Bay-specific reduction factor of 0.337. Comprehensive validation against 15 major storms (1992–2024) shows substantial improvement over standard methods (RMSE = 0.436 m vs. 2.267 m; R² = 0.934 vs. −0.786). Economic assessment using NACCS fragility curves demonstrates 12.7-year payback periods for flood protection investments. The open-source Streamlit implementation democratizes access to research-grade risk assessment, transforming months-long specialist analyses into immediate browser-based tools without compromising scientific rigor. Full article

As cyber threats continue to grow in complexity and persistence, resilience has become a critical requirement for cyber–physical systems (CPSs). Resilience quantitative assessment is essential for supporting secure system design and ensuring reliable operation. Although various methods have been proposed for evaluating CPS resilience, major challenges remain in accurately modeling the interaction between cyber and physical domains and in providing structured guidance for resilience-oriented design. This study proposes an integrated CPS resilience assessment framework that combines cyber-layer anomaly modeling based on Markov chains with mathematical modeling of performance degradation and recovery in the physical domain. The framework establishes a structured evaluation process through parameter normalization and cyber–physical coupling, enabling the generation of resilience curves that clearly represent system performance changes under adverse conditions. A case study involving an industrial controller equipped with a diversity-redundancy architecture is conducted to demonstrate the applicability of the proposed method. Modeling and simulation results indicate that the framework effectively reveals key resilience characteristics and supports performance-informed design optimization. Full article

Background/Objectives: Peripheral artery disease (PAD) impacts more than 200 million individuals globally and leads to mortality and morbidity secondary to progressive limb dysfunction and amputation. However, clinical management of PAD remains suboptimal, in part because of the lack of standardized biomarkers to predict patient outcomes. Growth differentiation factor 15 (GDF15) is a stress-responsive cytokine that has been studied extensively in cardiovascular disease, but its investigation in PAD remains limited. This study aimed to use explainable statistical and machine learning methods to assess the prognostic value of GDF15 for limb outcomes in patients with PAD. Methods: This prognostic investigation was carried out using a prospectively enrolled cohort comprising 454 patients diagnosed with PAD. At baseline, plasma GDF15 levels were measured using a validated multiplex immunoassay. Participants were monitored over a two-year period to assess the occurrence of major adverse limb events (MALE), a composite outcome encompassing major lower extremity amputation, need for open/endovascular revascularization, or acute limb ischemia. An Extreme Gradient Boosting (XGBoost) model was trained to predict 2-year MALE using 10-fold cross-validation, incorporating GDF15 levels along with baseline variables. Model performance was primarily evaluated using the area under the receiver operating characteristic curve (AUROC). Secondary model evaluation metrics were accuracy, sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV). Prediction histogram plots were generated to assess the ability of the model to discriminate between patients who develop vs. do not develop 2-year MALE. For model interpretability, SHapley Additive exPlanations (SHAP) analysis was performed to evaluate the relative contribution of each predictor to model outputs. Results: The mean age of the cohort was 71 (SD 10) years, with 31% (n = 139) being female. Over the two-year follow-up period, 157 patients (34.6%) experienced MALE. The XGBoost model incorporating plasma GDF15 levels and demographic/clinical features achieved excellent performance for predicting 2-year MALE in PAD patients: AUROC 0.84, accuracy 83.5%, sensitivity 83.6%, specificity 83.7%, PPV 87.3%, and NPV 86.2%. The prediction probability histogram for the XGBoost model demonstrated clear separation for patients who developed vs. did not develop 2-year MALE, indicating strong discrimination ability. SHAP analysis showed that GDF15 was the strongest predictive feature for 2-year MALE, followed by age, smoking status, and other cardiovascular comorbidities, highlighting its clinical relevance. Conclusions: Using explainable statistical and machine learning methods, we demonstrated that plasma GDF15 levels have important prognostic value for 2-year MALE in patients with PAD. By integrating clinical variables with GDF15 levels, our machine learning model can support early identification of PAD patients at elevated risk for adverse limb events, facilitating timely referral to vascular specialists and aiding in decisions regarding the aggressiveness of medical/surgical treatment. This precision medicine approach based on a biomarker-guided prognostication algorithm offers a promising strategy for improving limb outcomes in individuals with PAD. Full article

Background: Metabolic syndrome (MS) is a major public health concern linked to an elevated risk of type 2 diabetes and cardiovascular disease. Simple, reliable screening tools are needed for early identification, especially in working populations. Objective: To compare the diagnostic accuracy of body mass index (BMI), waist-to-height ratio (WtHR), triglyceride–glucose index (TyG), and waist–triglyceride index (WTI) for detecting MS based on NCEP ATP III and IDF criteria in a large cohort of Spanish workers. Methods: This cross-sectional study analyzed data from 386,924 Spanish workers. MS was diagnosed using NCEP ATP III and IDF definitions. The four indexes were evaluated by sex using a receiver operating characteristic (ROC) curve analysis. Area under the curve (AUC), optimal cut-off points, and Youden’s index were calculated. Results: TyG and WTI had the highest AUC values in men (0.911 and 0.901, respectively) for NCEP ATP III-defined MS, while WtHR and WTI achieved the best performance in women (0.955 and 0.953, respectively). WtHR outperformed BMI in all subgroups. Optimal cut-off values were identified according to sex and the definition of MS: TyG (8.95 men, 8.51 women), WtHR (0.54 men, 0.51 women), and WTI (170.6 men, 96.5 women), supporting their practical implementation in occupational health programs. All indexes showed significant discriminatory capacity (p < 0.001). Conclusions: TyG, WtHR, and WTI are more effective than BMI in detecting MS among Spanish workers, with sex-specific patterns. Their ease of use and diagnostic strength support their adoption in occupational health programs for early cardiometabolic risk detection. Full article

Background/Objectives: Colorectal cancer (CRC) is one of the major epidemiological oncological confronts with established risk factors, including male sex. Still, CRC is reported among the leading malignancies in the female population. The necessity for possible, easily accessible prognostic factors is required to improve patient outcomes. This study aimed to assess sex-related differences in nine-month four-stage CRC results of palliative systemic therapy. Methods: A total of 67 patients (39 males) with a median age of 70 (64–76) years were referred for first-line palliative chemotherapy due to end-stage colorectal cancer diagnosis. The CRC advancement was evaluated by computed tomography (CT) before and 9 months after chemotherapy. The demographical and clinical characteristics were evaluated for nine-month therapy outcomes, including mortality risk and CT scan results. Results: The nine-month mortality risk in female and male groups was indifferent, reaching 21% (6 patients) and 21% (8 patients), respectively (p = 0.935). Among survivors, therapy response was observed in 6 (21%) female and 20 (51%) male patients (p = 0.056). In multivariable analysis, the male sex (OR: 3.91, 95% CI: 1.09–14.05, p = 0.037) and RDW (OR: 0.61, 95% CI: 0.42–0.88, p = 0.008) were found to be significant for disease response to systemic therapy based on CT scan results. The ROC curve for predictive role yields a sensitivity of 71.1%, specificity of 57.8%, and an area under the curve (AUC) of 0.726. Conclusions: Our analysis points out the possible favorable role of the male sex on nine-month systemic therapy response in palliative CRC. The RDW-CV can be regarded as a possible indicator of chemotherapy response in colorectal cancer. The mortality risk within 9 months of systemic therapy is comparable between males and females. Full article

Triple-negative breast cancer (TNBC) remains a major clinical challenge due to its aggressive behavior and lack of targeted therapies. Accurate early prediction of response to neoadjuvant chemotherapy (NACT) is essential for guiding personalized treatment strategies and improving patient outcomes. In this study, we present an attention-based multiple instance learning (MIL) framework designed to predict pathologic complete response (pCR) directly from pre-treatment hematoxylin and eosin (H&E)-stained biopsy slides. The model was trained on a retrospective in-house cohort of 174 TNBC patients and externally validated on an independent cohort (n = 30). It achieved a mean area under the curve (AUC) of 0.85 during five-fold cross-validation and 0.78 on external testing, demonstrating robust predictive performance and generalizability. To enhance model interpretability, attention maps were spatially co-registered with multiplex immunohistochemistry (mIHC) data stained for PD-L1, CD8+ T cells, and CD163+ macrophages. The attention regions exhibited moderate spatial overlap with immune-enriched areas, with mean Intersection over Union (IoU) scores of 0.47 for PD-L1, 0.45 for CD8+ T cells, and 0.46 for CD163+ macrophages. The presence of these biomarkers in high-attention regions supports their biological relevance to NACT response in TNBC. This not only improves model interpretability but may also inform future efforts to identify clinically actionable histological biomarkers directly from H&E-stained biopsy slides, further supporting the utility of this approach for accurate NACT response prediction and advancing precision oncology in TNBC. Full article

Background/Objectives: Atrial fibrillation (AF) is a prevalent arrhythmia associated with a high risk of major adverse cardiovascular events (MACEs). This study aimed to compare the predictive ability of an ML model and the CHA₂DS₂-VASc score in predicting MACEs in AF patients using machine learning (ML) techniques. Methods: A cohort of 40,297 individuals aged 65–95 from the Terres de l’Ebre region (Catalonia, Spain) and diagnosed with AF between 2015 and 2016 was analyzed. ML algorithms, particularly AdaBoost, were used to predict MACEs, and the performance of the models was evaluated through metrics such as recall, area under the ROC curve (AUC), and accuracy. Results: The AdaBoost model outperformed CHA₂DS₂-VASc, achieving an accuracy of 99.99%, precision of 0.9994, recall of 1, and an AUC of 99.99%, compared to CHA₂DS₂-VASc’s AUC of 81.71%. A statistically significant difference was found using DeLong’s test (p = 0.0034) between the models, indicating the superior performance of the AdaBoost model in predicting MACEs. Conclusions: The AdaBoost model provides significantly better prediction of MACE in AF patients than the CHA₂DS₂-VASc score, demonstrating the potential of ML algorithms for personalized risk assessment and early detection in clinical settings. Further validation and computational resources are necessary to enable broader implementation. Full article

Background/Objectives: Obesity, a major risk factor for cardiometabolic traits, is influenced by both genetic and environmental factors. Genetic studies have identified multiple single-nucleotide polymorphisms (SNPs) associated with obesity and related traits. This study aimed to examine the association between genetic risk score (GRS) and obesity-associated traits, while incorporating SNPs with established gene–diet interactions to explore their potential role in precision nutrition (PN) strategies. Methods: A total of 4279 participants were stratified into low- and intermediate-/high-GRS groups based on 18 SNPs linked to obesity and cardiometabolic traits. This study followed a case–control design, where cases included individuals with overweight/obesity, T2DM-positive (+), or CVD-positive (+) individuals and controls, which comprised individuals free of these traits. Logistic regression area under the curve (AUC) models were used to assess the predictive power of the GRS and traditional risk factors on BMI, T2DM and CVD. Results: Individuals in the intermediate-/high-GRS group had higher odds of being overweight or obese (OR = 1.23, CI: 1.03–1.48, p = 0.02), presenting as T2DM+ (OR = 1.56, CI: 1.03–2.49, p = 0.03) and exhibiting CVD-related traits (OR = 1.56, CI: 1.25–1.95, p < 0.0001), compared to the low-GRS group. The GRS was the second most predictive factor after age for BMI (AUC = 0.515; 95% CI: 0.462–0.538). The GRS also demonstrated a predictive power of 0.528 (95% CI: 0.508–0.564) for CVD and 0.548 (95% CI: 0.440–0.605) for T2DM. Conclusions: This study supports the potential utility of the GRS in assessing obesity and cardiometabolic risk, while emphasizing the potential of PN approaches in modulating genetic susceptibility. Incorporating gene–diet interactions provides actionable insights for personalized dietary strategies. Future research should integrate multiple gene–diet and gene–gene interactions to enhance risk prediction and targeted interventions. Full article

Purpose: The identification of cardiovascular risk factors in metastatic prostate cancer (PCa) patients prior to the initiation of androgen receptor pathway inhibitors (ARPIs) is important yet challenging. Methods and Results: A nationwide cohort study was conducted utilizing data from the National Health Insurance Research Database containing the Taiwan Cancer Registry. The study population comprised 4739 PCa patients who received abiraterone or enzalutamide between 1 January 2014, and 28 February 2022. The cohort was divided into a training set (n = 3318) and a validation set (n = 1421). Machine learning techniques with random survival forest (RSF) model incorporating 16 variables was developed to predict major adverse cardiovascular events (MACEs). Over a mean follow-up period of 2.1 years, MACEs occurred in 10.9% and 11.3% of the training and validation cohorts, respectively. The RSF model identified five key predictive indicators: age < 65 or ≥75 years, heart failure, stroke, hypertension, and myocardial infarction. The model exhibited robust performance, achieving an area under the curve (AUC) of 85.1% in the training set and demonstrating strong external validity with an AUC of 85.5% in the validation cohort. A positive correlation was observed between the number of risk factors and the incidence of MACEs. Conclusions: This machine learning approach identified five predictors of MACEs in PCa patients receiving ARPIs. These findings highlight the need for comprehensive cardiovascular risk assessment and vigilant monitoring in this patient population. Full article

The depletion of global fish stocks poses a major challenge to sustainable development, particularly in economies where marine resources are critical to livelihoods and food security. In this study, the relationship between economic development and the sustainability of fish stocks is examined using the Environmental Kuznets Curve (EKC). We use panel data from 32 economies between 2002 and 2020 and analyze the fish stock status indicator (EPI_FSS) from the Environmental Performance Index, which captures the proportion of national catches from overfished or collapsed stocks. Using a dynamic panel approach and the generalized method of moments (GMM), we investigate how the human development index (HDI) and other socio-economic factors influence changes in the state of fish stocks. Our results show a statistically significant inverted-U-shaped (∩-shaped) relationship between the HDI and the state of fish stocks, suggesting that the deterioration of fish stocks increases at lower levels of development, but improves beyond a certain threshold. In addition, higher levels of foreign direct investment (FDI), education, and research and development (R&D) spending are associated with better outcomes for fish stocks. These results suggest that while early economic growth may put pressure on marine resources, sustained investment in human capital, innovation, and global integration is critical to promoting long-term marine sustainability. Full article

Background/Objectives: The clinical value of KRAS mutations in lung adenocarcinoma, alone or in combination with other mutations, has been assessed especially in advanced stages. This study evaluates how KRAS and the presence of co-mutations could affect survival in early-stage lung. Methods: We analyzed a real-world cohort including all staged NSCLC patients diagnosed and treated from 2018 to 2022 at our Institute with availability of NGS molecular data. Statistical analyses were made using log-rank test, the two-tailed Fisher’s exact test and Kaplan-Meier survival curves. Results: KRAS mutations were observed in 179/464 cases (38.6%). The majority of KRAS co-mutations were in TP53 (74%) and STK11 (14.3%) genes. KRAS+TP53 co-mutations were more frequent compared to KRAS-only tumors in stage IV NSCLC (p = 0.01). In early stage and locally advanced cases (stage I-III), better prognosis was associated to KRAS-only mutated NSCLC and to KRAS+STK11 mutated cases compared to KRAS+TP53 (p = 0.008). In particular, patients carrying KRAS+TP53 in stage I and II displayed a shorter survival, similar to patients diagnosed at stage III. Conclusions: Routine NGS provides important information for potential actionable mutations but also for the prognostic and predictive role of the presence of co-occurring mutations. In particular, the presence of KRAS+TP53 in stage I and II NSCLC may be considered an unfavorable prognostic marker possibly leading to adapt the perioperative chemo-immunotherapy. Full article