Heliyon

Cancer-related genomic instability (GI) may cause genetic alterations in spermatozoa, implying health issues not only in cancer survivors, but also in their children [1, 2]. We therefore studied Loss of Y chromosome (LOY), considered as hallmark of GI [3-15], in spermatozoa and blood from survivors of childhood and testicular cancer (CC, TC), and controls (CTRL). We found that LOY was statistically significantly more frequent in spermatozoa from cancer survivors than in controls (Odds Ratio [OR]=2.2 for CC vs. CTRL and OR=2.4 for TC vs. CTRL). Furthermore, LOY was about an order of magnitude more prevalent in spermatozoa than in blood among 18-53-year-old males within all cohorts. Our findings suggest that LOY in spermatozoa might be a clinically useful marker of GI, reduced fertility and disease predisposition in males. Introducing LOY in spermatozoa as a biomarker opens a new research avenue into disease prevention and the causes and consequences of LOY.

Food costs are more significantly impacted by climate change as countries grow. It is well known that climate change has an impact on the productivity of most agricultural goods, but it is unclear how specifically it will affect food costs. The present research explores how the North Atlantic Oscillation (NAO) index, a widely used climate indicator, affects food prices around the world. This is achieved by applying a robust bivariate Hurst exponent (robust bHe). The research creates a color map of this coefficient using a window-sliding technique over various intervals of time, displaying an illustration that changes overtime. Additionally, the NAO index and global food prices are examined for causal connections using variable-lag transfer entropy using a window-sliding technique. The results show that notable rises in a number of international food prices for long as well as short periods are associated with significant increases in the NAO index. Furthermore, the causative function of the NAO index in influencing global food costs is confirmed by variable-lag transfer entropy. Is highly recommended as it directly connects the research to actionable outcomes for policymakers and the overarching goal of sustainability and food security. This study provides the first direct evidence of a robust, long-range cross-correlation and causal link between the North Atlantic Oscillation (NAO) index and key global food prices. It introduces a novel, robust methodological framework to visualize this time-varying relationship, offering a critical tool for policymakers and forecasting models.

This study aimed to evaluate the prognostic value of quantitative analysis of {superscript 1}F-FDG positron emission tomography (PET)/computed tomography (CT) metabolic parameters in patients with pancreatic ductal adenocarcinoma (PDAC) after neoadjuvant chemotherapy (NACT). A retrospective analysis was conducted on the clinical and imaging data of 44 patients with pathologically confirmed PDAC who received NACT. All patients completed standard chemotherapy regimens and underwent {superscript 1}F-FDG PET/CT examinations within 2 weeks before and after chemotherapy. Multiple metabolic parameters of lesions were extracted, their percentage changes were calculated, and the optimal cut-off values for each parameter were determined. Kaplan-Meier survival analysis and Cox proportional hazards regression analysis were applied to explore the prognostic value of the metabolic parameters, and the prognostic stratification performance of PET Response Criteria in Solid Tumors (PERCIST) 1.0 was compared with that of Response Evaluation Criteria in Solid Tumors (RECIST) 1.1. PERCIST 1.0 demonstrated significantly superior prognostic stratification compared with RECIST 1.1. A peak standardized uptake value corrected for lean body mass (SULpeak2) > 3.07 and a percentage change in SULpeak between pre- and post-treatment scans ({Delta}SULpeak%) [≤] 37.66% were identified as independent risk factors for poor prognosis. Furthermore, SUL-related parameters exhibited markedly better predictive efficacy than traditional metabolic parameters such as the standardized uptake value and metabolic tumor volume. Quantitative analysis of {superscript 1}F-FDG PET/CT metabolic parameters can effectively predict prognosis in PDAC after NACT, and PERCIST 1.0 is a more optimal criterion for efficacy and prognostic assessment. A post-NACT SULpeak > 3.07 and {Delta}SULpeak% [≤] 37.66% were core independent indicators for predicting poor prognosis in these patients.

Mass drug administration (MDA) of praziquantel is an intervention used in the treatment and prevention of schistosomiasis. Its effectiveness and sustainability require identifying subpopulations that are at risk of infection. A longitudinal survey was conducted among 3,810 subjects aged 5-19 years old recruited at baseline across ten council wards in Katsina Ala, Benue, Nigeria, to determine the prevalence, intensity, and index of potential contamination of urogenital schistosomiasis for three successive phases: three months, six months, and nine months post-treatment periods. Urine samples were processed using microscopy and reagent strips (Medi Test Combi 9). Prevalence of infection was recorded in all the phases of the surveys, with the first having the highest prevalence (12.30%), followed by the third phase (9.12%) and the second phase (7.60%), the difference being significant (P < 0.05). The highest intensity of infection (16 ova/10 ml urine) was observed in the first phase, followed by the third phase (15.10 ova/10 ml urine) and the second phase (11 ova/10 ml urine). Peak prevalence, intensity, and relative index of potential contamination (Rel. IPC) occurred among pupils between the ages of 10 and 14 years old in both sexes. The result of the relative contribution of each age group in polluting the snail habitat with Schistosoma eggs, thus enabling transmission, showed to a greater extent that children aged 10-14 years old were responsible for contaminating the environment with a bulk of S. haematobium eggs and for the transmission and maintenance of the disease in the area.

Introduction: Solitary plasmacytomas (SP) are rare neoplasm of localised proliferation of clonal plasma cells. It can be classified based on site of involvement and bone marrow involvement. It is an indolent disease in the majority of patients. Primary modality of treatment is radiotherapy and surgical excision. Materials and methods: This was a retrospective audit of SP who were treated and followed up at a tertiary care center in eastern India from January 2012 to December 2025. Patients who has solitary plasma cytoma with more than 10% plasma cells, POEMS syndrome, have been excluded from analysis. Results: We identified 46 patients of SP. The median age of the studied population was 53 years (23-75 years). Males were more commonly affected than females (M:F=2.2:1). Most common chief complaints were bony pain (67.4%). SBP was seen in 39 (84.8%) cases whereas SEP was seen in 7 (15.2%) cases. Vertebra was the most common site of involvement (61.4%). Median M band concentration 0.24 g/dL (0.1 to 1.95 gm/dL). IgG was the most common isotype accounting for 60.6% cases. Six cases (13%) had minimal bone marrow involvement. The majority of the patients received local radiotherapy (89.1%). With a median follow up of 5.4 years (95% CI: 1.8 - 9.0), median OS was not reached, median PFS was 9.22 years (95% CI: 5.8-12.6), median time to next treatment (TTNT) was 9.86 years (95% CI: 6.8 - 12.9). Conclusion: Solitary plasmacytoma commonly affects young males. Bones are more commonly affected than extramedullary sites. SP has a lower rate of progression and excellent prognosis when treated with local radiotherapy.

Road traffic accidents represent a global public safety crisis, necessitating advanced computational tools for accurate injury severity prediction and effective decision support. This study evaluates high-performing ensemble machine learning models, including AdaBoost, XGBoost, LightGBM, HistGBRT, CatBoost, Gradient Boosting, NGBoost, and Random Forest, using a comprehensive National Highway Traffic Safety Administration (NHTSA) dataset from 2018 to 2022. While all models demonstrated exceptional predictive accuracy, with HistGBRT achieving the highest overall accuracy of 92.26%, a defining achievement of this work is the perfect classification (100% precision and recall) of fatal injuries across all ensemble architectures. To bridge the gap between predictive performance and actionable intelligence, this research integrates SHapley Additive exPlanations (SHAP) to provide both global insights into dataset-wide risk factors and local, instance-specific rationales for individual crash events. The global analysis identified ethnicity, airbag deployment, and harmful event type as primary drivers of injury severity, while local force and waterfall plots revealed the precise "push and pull" of variables for specific incidents. The results offer a robust, interpretable framework for stakeholders tasked with improving traffic safety and mitigating crash-related harm.

Recent Artificial Intelligence (AI) models have matched or exceeded human experts in several benchmarks of biomedical task performance, but have lagged behind on surgical image-analysis benchmarks. Since surgery requires integrating disparate tasks --- including multimodal data integration, human interaction, and physical effects --- generally-capable AI models could be particularly attractive as a collaborative tool if performance could be improved. On the one hand, the canonical approach of scaling architecture size and training data is attractive, especially since there are millions of hours of surgical video data generated per year. On the other hand, preparing surgical data for AI training requires significantly higher levels of professional expertise, and training on that data requires expensive computational resources. These trade-offs paint an uncertain picture of whether and to-what-extent modern AI could aid surgical practice. In this paper, we explore this question through a case study of surgical tool detection using state-of-the-art AI methods available in 2026. We demonstrate that even with multi-billion parameter models and extensive training, current Vision Language Models fall short in the seemingly simple task of tool detection in neurosurgery. Additionally, we show scaling experiments indicating that increasing model size and training time only leads to diminishing improvements in relevant performance metrics. Thus, our experiments suggest that current models could still face significant obstacles in surgical use cases. Moreover, some obstacles cannot be simply ``scaled away'' with additional compute and persist across diverse model architectures, raising the question of whether data and label availability are the only limiting factors. We discuss the main contributors to these constraints and advance potential solutions.

Developing scientific reading skills is critical for undergraduate STEM students due to scientific literatures unique formatting and use of specialized jargon. Generative AI tools such as ChatGPT offer students the ability to ask questions about what they are reading interactively. Previously, we reported the development of a ChatGPT-assisted reading guide that combined structured, active reading strategies with using ChatGPT to clarify unfamiliar words and concepts in real time. In the initial study, undergraduates found the use of the ChatGPT-assisted reading guide helpful in their understanding of an abstract and introduction of a journal article. Here, the ChatGPT-assisted reading guide was used in a journal club assignment for an undergraduate chemistry course. ChatGPT transcripts were analyzed for common types of interactions, and students were surveyed about their experience. Overall, students reported that using the ChatGPT-assisted reading guide was helpful in understanding the article and helped them have more productive class discussions. However, some students also expressed skepticism about using AI tools, citing concerns about accuracy of AI-generated information and the effect of using AI on their own learning.

Malawis HIV treatment monitoring system faces serious challenges because of a shortage of experts and reliance on viral load testing every 3 to 12 months. The process causes dangerous delays in identifying treatment failure. This leads to a higher risk of disease progression, transmission, and death. To tackle this issue, this study used a machine learning model based on association rules and combined it with clustering analysis to create a machine learning framework to identify key factors and risk profiles for virological failure among children living with HIV (CLHIV) in Malawi. The methodology combines a Random Forest classifier for feature importance, association rule mining to find predictive rules, and k-Prototype clustering for risk profiling among CLHIV. The random forest feature importance results show that Body Mass Index (BMI), CD4 count, TB status, ART regimen, gender, ART adherence, and treatment duration are major drivers of virological failure. In addition to these individual factors, the analysis produced highly reliable association rules with over 90% confidence. This establishes a framework for identifying complex risk profiles and informing focused clinical interventions. The high lift values of 4.9 across the most significant rules demonstrate the models effectiveness by revealing strong, non-random associations. Clustering analysis also identified two distinct risk profiles associated with virological failure. The k-prototype clustering model performed optimally with a cluster purity of 100% and a silhouette score of 79%.

BackgroundOur study aimed to investigate the relationship between phenotypic age acceleration (PAA), bowel dysfunction (constipation, diarrhea), and depression severity, and examine whether phenotypic age acceleration can play a mediating role in bowel dysfunction and depression severity. MethodsThe data analysis of our study was conducted from the National Health and Nutrition Examination Survey (2005-2010). Participants with bowel dysfunction were identified on the questionnaire of bowel health. Depression was determined based on the Patient Health Questionnaire-9 (PHQ-9). The calculation of PAA is based on 9 test indicators and actual age; a higher PAA means accelerated aging. In this study, a weighted linear regression model was used to analyze the associations among defecation disorders, PAA, and depression. Restricted Cubic Spline (RCS) curves were applied to explore the potential non-linear relationships between the aforementioned variables. Additionally, a mediation effect model was employed to verify whether PAA could function as a mediating variable in the relationship between defecation disorders and depression. ResultA total of 11,808 participants were included in this study. Linear regression analysis showed that both diarrhea ({beta}=3.73, 95% Confidence Interval (CI): 1.69-8.22, P=1.60x10-3) and depression severity ({beta}=1.08, 95%CI: 1.06-1.09, P=4.61x10-16) were positively correlated with PAA. In addition, both constipation ({beta}=2.76, 95%CI: 1.89-4.04, P=2.28x10-6) and diarrhea ({beta}=4.29, 95%CI: 2.65-6.95, P=2.11x10-7) were positively correlated with depression severity. Further mediation effect analysis revealed that PAA may play a mediating role in the association between diarrhea and depression severity (the proportion of mediation effect in the total population was 7.2285%). When exploring whether PAA exerts a mediating role in the association between constipation and depression severity, it was found that PAA played a mediating role in female participants and participants aged <60 years, except for male participants and those aged [&ge;]60 years (the proportion of mediation effect was 9.8417% in females and 8.4512% in the population aged <60 years, with all relevant P-values <0.005)

IntroductionYouth are challenged globally by sexual and reproductive health problems such as unwanted pregnancy, unsafe abortion and sexually transmitted infections. While family planning methods are a safe and effective way for individuals to responsibly control their sexual and reproductive needs, their use amongst sexually active youth is very low. Of urgency, is to understand factors influencing youth to adopt family planning methods so as to inform future strategies aiming at increasing the use of modern methods of contraception. This study aimed at determining the prevalence and factor associated with utilization of family planning methods among youth in northwestern Tanzania. Materials and MethodsA cross sectional analytical study was conducted among youth aged 15-24 years in selected secondary schools and university Colleges in Nyamagana district, Mwanza Tanzania. Participating institutions and the participants were selected using a multistage sampling method. Data was collected using self-administered questionnaires and later analyzed using SPSS version 25. Univariate and multivariable logistic regression was used to analyze factors associated with the use of family planning; with significance considered at p-value<0.05. ResultsA total of 349 participants were enrolled. The prevalence of utilization of family planning methods among sexually active youth was 83.2%. Factors associated with FP use were being female (AOR 2.84; CI: 1.05, 7.67) and not having a peer who is using the method (AOR 0.31; 95% CI: 0.12, 0.82). Poor awareness on availability of FP services at nearby facility was found to be significant (cOR 0.38; 95% CI: 0.16-0.90) during crude analysis but become insignificant when adjusted for other factors ConclusionMajority of sexually active youth were utilizing FP methods. Sex and peer pressure were significantly associated with family planning use. Therefore, initiatives for advocating comprehensive sexuality education and strengthening youth friendly health clinics are highly proposed to increase consistent contraceptive use among youth.

BackgroundWorking equids are fundamental to the socioeconomic structure of Mexicos small-scale agricultural sector, which accounts for 71.2% of the countrys active Agricultural Production Units (APUs). Despite their critical role in human rural livelihoods, food security, and sustainable development, these animals face systemic "statistical invisibility" within national and international productive frameworks. This study evaluates the long-term population dynamics and geographical distribution of working equids to analyze their current status amidst agricultural modernization. MethodsA retrospective analysis was conducted using national census data from 1970 to 2022 provided by the National Institute of Statistics, Geography, and Informatics (INEGI). Population trends for horses, donkeys, and mules were calculated using the Average Annual Variation Rate (AAVR). The severity of population declines was classified according to an adaptation of the International Union for Conservation of Nature (IUCN) criteria. Finally, national census records from INEGI, Agri-food and Fisheries Information Service (SIAP) and The Ministry of Agriculture and Rural Development (SADER) were contrasted with FAOSTAT database estimates to identify reporting discrepancies. ResultsBetween 1970 and 2022, the total equine population in Mexico decreased by 76.5%, falling from 6.8 to 1.6 million. However, a "paradox of modernization" was identified: while total numbers plummeted, the proportion of equids used specifically for work reached a historical peak of 81% in 2022, effectively having doubled from the 44% recorded in 2007. While donkeys and mules have suffered drastic total reductions (87% and 88%, respectively), working horses experienced a resilient 37% recovery between 2007 and 2022 (+3.71% AAVR). Furthermore, a staggering 710.8% discrepancy was found between national census data and FAOSTAT estimates, representing an overestimation of 11.3 million animals in international records. ConclusionsThe persistence and recent recovery of working equids reflect a "resilience of necessity" for approximately 500,000 APUs that depend exclusively on animal traction and packing due to economic constraints and complex topography. These findings challenge the narrative of total agricultural mechanization and highlight an urgent need for evidence-based public policies that address the statistical invisibility of working equids as indispensable drivers of rural sustainability and food security.

Anemia, particularly iron-deficiency anemia, is a critical global health concern, with a high prevalence among children under six years of age. Early and non-invasive detection can significantly improve health outcomes. This study proposes a computer vision and machine learning framework for anemia screening and hemoglobin (Hb) level prediction using palmar images from pediatric subjects. The region of interest (palm) was segmented using a U-Net model, achieving a Dice coefficient of 0.96. Images were processed across RGB, CIELab, and HSV color spaces to extract key color features, including red fraction, erythema index, and normalized a-component. For anemia classification, multiple machine learning models were evaluated, with Logistic Regression, Gradient Boosting, and a custom Convolutional Neural Network (CNN) achieving the highest test accuracies of approximately 94.5% and 95.53%, respectively. For hemoglobin regression, a Random Forest model in the CIELab color space achieved a coefficient of determination (R2) of 0.95. The Pearson correlation coefficient in the Lab color space was 0.98 for the Random Forest algorithm and 0.94 for the Linear Regression algorithm. The analysis, supported by SHAP values, identified red-related color features as the most significant predictors. The model demonstrated robust performance across different skin tones, with particularly high accuracy (R2 = 0.9926) in darker-skinned individuals, who constituted the majority of the studied Iranian population. The results confirm that pallor analysis of palmar images using artificial intelligence techniques offers a reliable, non-invasive, and effective tool for pediatric anemia screening and hemoglobin estimation, with strong potential for point-of-care applications.

BackgroundSnakebite envenoming is a significant global health crisis that has been long neglected as a global health priority. It is a huge problem for rural communities of low and middle-income countries, India accounts for the largest proportion of snakebite deaths globally. Timely identification of venomous snakebite and its syndromic pattern is essential for effective administration of antivenom and supportive treatment. Expert identification of snake species and syndromes is not always available in peripheral healthcare settings. This leads to delays, unnecessary referrals, or improper treatment choices. Additionally, diverse snake species distribution and venom variations across regions pose challenges. AI-powered image classification methods can help overcome these barriers. We propose a clinically oriented deep learning pipeline for binary classification of venomous and non-venomous snake species of India using real-world imagery data. This pipeline would serve as a baseline step towards aiding snakebite management at peripheral healthcare setups with scarce resources. MethodsThe selected dataset consisted of 20 medically important Indian species. MobileViT-S, ConvNeXt-Tiny, EfficientNet-V2-S and ResNeXt-50 (32x4d) were trained under same conditions for comparison of results. Model interpretability was evaluated using Grad-CAM ++ to ensure that classification was not performed based on background but on features like head shape and stripes present on body. For reliable implementation we connected it to a web interface with human in loop expert verification. Experts can confirm or override predictions in real time. ResultsAmong the evaluated architectures, ResNeXt-50 (32x4d) showed the most reliable and consistent performance in classifying venomous and non-venomous snakes. It achieved the highest test accuracy, sensitivity, specificity, and F1-score. The model also had strong discriminative ability, with a ROC-AUC of 0.9950 and PR-AUC of 0.9959. These results indicate dependable performance in safety-critical screening situations. Grad-CAM++ visualizations confirmed that predictions were based on anatomically relevant features, especially in the head and body contour areas. This supports model interpretability and reduces background bias. ConclusionsAlthough the dataset size and single-institution source limit how widely the results can be applied, the proposed framework shows that its possible to create a clinically oriented, ready-to-use deep learning system for snakebite triage support. This system is intended as a scalable tool to help rural healthcare workers, emergency responders, and telemedicine platforms in areas where snakebites are common. Author SummarySnakebite is a major public health concern that disproportionally affects the rural population. Delays in identifying whether a snake is venomous often lead to delayed treatment, unnecessary use of antivenom, or inappropriate referrals. In many rural settings, access to expert snake identification is limited. To address this gap, authors have developed an artificial intelligence (AI)-based image classification system that distinguishes snakes into two clinically relevant categories: venomous or non-venomous. Unlike many previous studies that focused on ideal, high-quality wildlife images, our model was trained using real-world photographs captured in emergency situations, including images taken by patients and field responders under variable lighting and background conditions. This approach improves the models relevance to practical healthcare settings. The system achieved high accuracy and was further strengthened by visual interpretability tools and expert verification to ensure reliability. By combining AI-assisted classification with human oversight, this work provides a scalable decision-support tool that may improve early triage, rational antivenom use, and surveillance in snakebite-endemic regions

IntroductionAnaemia is one of the most prevalent global public health challenges, particularly among women of reproductive age and children. According to the World Health Organization, anaemia is defined as a hemoglobin concentration below 13.0 g/dL in adult men, 12.0 g/dL in non-pregnant women, and 11.0 g/dL in pregnant women. Hemoglobin measurement therefore plays a critical role in diagnosis, classification, and monitoring of anaemia at both clinical and public health levels. Hemoglobin estimation allows early identification and intervention in at-risk populations. MethodologyA cross-sectional study was conducted at Aniniwaa Medical Centre, Kumasi, involving 100 participants who visited the laboratory for a complete blood count. Venous blood samples were collected aseptically into EDTA tubes and analysed first with the fully automated analyser, followed by the two Hb meters. Data were analysed using Chi-square tests, Bland-Altman plots, and descriptive statistics. ResultsResults showed that the prevalence of anaemia varied across methods: 28% by the analyser, 60% by Urit, and 64% by Mission. Both meters demonstrated 100% sensitivity but lower specificities (55.6% for Urit and 50.0% for Mission). Bland-Altman analysis indicated negative biases (Urit = -1.665 g/dL; Mission = -1.55 g/dL), suggesting both underestimated hemoglobin values compared to the reference. ConclusionThe study revealed that while Hb meters offer convenience and portability for field screening, the fully automated analyser remains more accurate and reliable for diagnosing anaemia in clinical settings.

Despite the remarkable progress of artificial intelligence represented by large language models, how AI technologies can contribute to the construction of evidence in evidence-based medicine (EBM) remains an overlooked issue. Now, we need an AI that can be compatible with EBM. In the present paper, we aim to propose an example analysis that may contribute to this approach using variable Vision Transformer.

Natural extracts could improve sperm storage and artificial insemination (AI). This study, for the first time, evaluates the suitability of a blueberry extract (Vaccinium corymbosum) obtained from pomace using a sustainable methodology as a supplement for bull semen extenders. Cryopreserved semen doses from eight bulls were combined in 9 pools (3 bulls/pool), supplemented with 0%, 1%, 5%, or 10% extract, and incubated up to 5 h at 38 {degrees}C. Motility was assessed hourly using OpenCASA, and the effects of treatment and time were evaluated using linear mixed-effects models. Motility was significantly better preserved with 1% extract (total and progressive motility, improved linear velocity and linearities, and decreased BCF and fractal dimension, related to hyperactivation). The effect of 5% was overall positive, but it was below 1%, whereas 10% mostly showed a negative effect. These results show that this natural extract could safely supplement bull semen extenders at least between 1% to 5%, and even help improve sperm motility. Therefore, this extract offers an opportunity to enhance cattle semen extenders using a sustainable approach, potentially improving reproductive outcomes.

BackgroundDengue outbreaks have become a severe threat to Bangladesh as the infections and mortality numbers are skyrocketing in recent years. Favorable environmental and anthropogenic conditions have established the capital of Bangladesh, Dhaka city as the epicenter of dengue outbreak. Studies have showed that climate change induced extreme weather events are exacerbating Aedes mosquito breeding and dengue virus transmission conditions. Methodology/Principal FindingsIn this study, short-term (0-6 weeks) associations of maximum temperature and heatwave days on dengue cases in Dhaka city were examined through Distributed Lag Non-linear Model (DLNM) methodology for weekly measurement of 2016-2024, taking into account relative humidity, cumulative rainfall, seasonality and hospital closure effect. Two separate negative binomial models were constructed. The maximum temperature model rendered an overall inverted U-shaped association, where the maximum temperature range of 31.5-33.2{degrees}C showed a sustained elevated dengue risk, with highest risk estimate at 33.2{degrees}C [relative risk (RR): 1.186, 95% CI: 1.002, 1.403]. Whereas, results of weekly heatwave days showed an overall protective effect (RR<1) for dengue cases. The lowest risk of infection was found at 3 heatwave days per week, with RR 0.275 (95% CI: 0.178, 0.423). Multiple sensitivity analyses were conducted for both models to evaluate their robustness. Lastly, the optimized models were analyzed under three distinct sub-periods, to capture the association of exposure variables with predominant circulating serotypes. Conclusions/SignificanceThe findings of the study aim to support public health policymakers and healthcare authorities in designing and implementing effective vector control interventions under emerging climatic emergencies. Author SummaryDengue disease is one of the most buringing issue in Bangladesh in recent years. This vector-borne disease is inherently influenced by climatic variables, i.e., temperature, rainfall, humidity, etc. Moreover, these relations are complex and non-linearly associated. Due to shift in climatic conditions, the occurance of extreme weather events are becoming frequent, with increased magnitude and longer duration. In this study, the nonlinear and delayed association of dengue infections due to the exposure of extreme temperature events were assessed in climate-change vulnerable Dhaka city. To do this, a statistical method was used, called distributed lag nonlinear methodology (DLNM). The results showed that dengue infections had an inverted U-shaped (parabolic) relationship with maximum temperature, while compared to mean maximum temperature, and a suppressive association with heatwaves relative to days without heatwaves. The findings aim to work as an early warning system, and support to policymakes and healthcare authorities to tackle the dengue surge in the changing climate.

IntroductionTo propose an improved U-Net-based segmentation model for colorectal polyp segmentation, aiming to address the challenges of variable lesion morphology, ambiguous boundaries, complex background interference, and insufficient cross-level feature fusion in endoscopic images [5,12]. MethodsAn improved network termed MCA-UNet was developed based on U-Net [5]. The model incorporates a multi-scale context convolution block (MCCB) to enhance multi-scale feature extraction and an attention-guided feature fusion module (AGFF) to optimize skip-feature selection and fusion in the decoder. Experiments were conducted on publicly available colorectal polyp image datasets, including Kvasir-SEG and CVC-ClinicDB [13-15]. Four models, including U-Net, U-Net+MCCB, U-Net+AGFF, and MCA-UNet, were compared, and all models were trained for 100 epochs. Dice, intersection over union (IoU), and mean absolute error (MAE) were used as the main evaluation metrics [20]. ResultsOn the mixed validation set, the Dice scores of U-Net, U-Net+MCCB, U-Net+AGFF, and MCA-UNet were 0.742, 0.771, 0.754, and 0.783, respectively; the corresponding IoU values were 0.603, 0.635, 0.618, and 0.649; and the MAE values were 0.102, 0.090, 0.097, and 0.086. Compared with the baseline U-Net, MCA-UNet improved Dice and IoU by 5.53% and 7.63%, respectively, while reducing MAE by 15.69%. Comparisons on the Kvasir-SEG and CVC-ClinicDB validation subsets further demonstrated the more stable performance of the proposed model. ConclusionBy jointly integrating multi-scale contextual modeling and attention-guided feature fusion, MCA-UNet effectively improves the accuracy and robustness of colorectal polyp segmentation and may provide useful support for intelligent endoscopic image analysis [12,17,18].

Immunotherapy with immune checkpoint inhibitors and immunotherapy combined with chemotherapy have represented promising treatments for NSCLC patients leading to prolonged survival. However, the majority of patients with advanced NSCLC have a poor prognosis. The identification and development of biomarkers for stratifying responders and non responders to immune checkpoint inhibitors contribute to unravel the mechanism of immune checkpoint pathway and the immune tumor interaction underlying the responses and are urgently needed to improve clinical outcomes of immune checkpoint inhibitor treatment. In this study, we analyzed the clinical and gene mutation data of NCSLC patients treated with nivolumab containing immunotherapy or nivolumab containing immunotherapy combined with chemotherapy (the immunotherapy treated group, n=119) and chemotherapy alone (the chemotherapy alone treated group, n=991) extracted from the MSK CHORD dataset. A DeevSurv model, a deep learning based extension of the Cox proportional hazards model was trained to generate survival risk score of each patient with binary statuses of thirty one gene mutations as input features into the model. The thirty one genes were selected based on population level mutation frequency, patient level variance in mutation status, and univariate Cox proportional hazards analyses evaluating the association between the presence or absence of each gene mutation and overall survival. The performance of the trained DeepSurv model was evaluated on the test set of the immunotherapy treated group using the concordance indexes (C index). The trained model was subsequently applied without retraining to the entire chemotherapy alone treated group as a control. The resulting C indexes for the immunotherapy treated group and chemotherapy alone treated group were 0.789 and 0.483, respectively. All patients within each group were divided into high and low risk groups according to the median predicted risk score. Kaplan Meier survival curves of high and low risk groups (n=43 vs n=70) in the immunotherapy treated group revealed a significant separation (log rank p<0.001), whereas no separation was observed in chemotherapy alone treated group (p=0.62). In the combined cohort of the immunotherapy treated group and chemotherapy alone treated group, the interaction between the DeepSurv derived risk score and treatment modality was significant (HR for interaction 1.47, 95% CI from 1.32 to 1.65, p<0.005), suggesting the DeepSurv derived risk score predictive value specific to the immunotherapy. Principal component analysis and permutation importance analysis were performed as complementary analyses to assess individual genes associated with the DeepSurv derived risk score and identified ZFHX3, SMARCA4, ALK, BTK, and NOTCH2 as major contributors to survival risk stratification. Collectively. we suggested that nonlinear coupling pattern of 31 tumor gene mutation statuses in the DeepSurv model captures the heterogeneity of survival risk among nivolumab containing immunotherapy or nivolumab containing immunotherapy combined with chemotherapy treated patients with NSCLC which was visualized as clear separation between high risk and low risk groups divided by the median value of the risk scores.