Tag: pubmed

Sci Rep. 2025 Dec 29;15(1):44883. doi: 10.1038/s41598-025-28804-x.

ABSTRACT

Climate change poses significant risks to food security, especially in agriculture-dependent countries like Bangladesh. This study analyzes temperature trends from 1961 to 2023 using data from the Bangladesh Meteorological Department (BMD) across three climatic regions: Barind, Coastal, and Haor. The Mann-Kendall test revealed statistically significant warming trends in both maximum and minimum temperatures, with the most pronounced increase in the Haor region. Moran’s analysis detected clear spatial clustering of high-risk zones, with Barind districts facing severe maximum temperature risks (> 40 °C) and Sylhet showing heightened minimum temperature risks. The MLP model achieved the lowest errors across ecosystems, with MSEs of 0.82 (Barind), 1.47 (Coastal), and 1.50 (Haor) for maximum temperature and with MSEs of 0.48 (Barind), 0.44 (Coastal), and 0.48 (Haor) for minimum temperature, outperforming SVM, CNN, LSTM, ANN, RF, and Ensemble models. This is the first region-specific application of machine learning models along with Mann-Kendall trend analysis, Moran’s I spatial statistics for rice production in Bangladesh which provides a multidimensional framework that is rarely applied in Bangladesh. These findings underscore the urgent need for region-specific climate adaptation strategies, as rising temperatures threaten rice production and agricultural resilience.

PMID:41461780 | DOI:10.1038/s41598-025-28804-x

Sci Rep. 2025 Dec 29;15(1):44835. doi: 10.1038/s41598-025-28995-3.

ABSTRACT

Following uveal melanoma (UM) affected treatment using ruthenium-106 brachytherapy, tumor thickness patterns fall into one of four categories: decrease (regression), increase (recurrence), stop (stable), or other, which are assessed in follow-up A-mode and B-mode images. These patterns are critical indicators of the tumor’s response to therapy. This study aims to apply deep learning (DL) models for predicting post-brachytherapy tumor response patterns. A cohort of 192 patients participated in this study. B-Mode images taken at the time of diagnosis were collected, and the ophthalmologists labeled the images into four response patterns based on the results of the treatment. DenseNet121 and ResNet34 models were trained and evaluated using performance metrics. DenseNet121 achieved a macro-average AUC of 0.933 (0.95% CI [0.905-0.957]), compared to 0.916 (95%CI [0.884-0.945]) for the ResNet34. The per-class evaluation showed that DenseNet121 excelled in predicting all categories, providing superior predictive accuracy. This difference in classification performance was statistically significant based on the DeLong test (p < 0.05). The ablation study revealed that the best performance was achieved without pretrained weights, using dropout layers and a batch size of 32. Both models demonstrated strong classification capabilities, with DenseNet121 providing the highest overall accuracy. This study highlights the potential of DL models in predicting response patterns in UM patients undergoing brachytherapy. Further validation and exploration of their integration into clinical practice are warranted.

PMID:41461779 | DOI:10.1038/s41598-025-28995-3

Sci Rep. 2025 Dec 29;15(1):44806. doi: 10.1038/s41598-025-28939-x.

ABSTRACT

Aerosols are often hypothesized to invigorate deep convective clouds (DCCs), but observational evidence remains limited and inconclusive. Clarifying this hypothesis is critical for regions vulnerable to thunderstorms and flooding, particularly highly polluted coastal cities. Leveraging a novel causal discovery-inference pipeline and high-resolution observations near Houston, TX, we identify multiple causal pathways among aerosols (mostly organic and sulfate), DCCs, and meteorological factors. However, a direct causal link from aerosols to DCCs is found to be uncommon, occurring in less than 35% of analyzed scenarios, and is characterized by strong conditionality and nonlinearity. When aerosol impacts on DCCs do occur, they can be substantial, enhancing DCC core heights by approximately 1.7 km, with 92% of this effect concentrated in warmer-phase cloud regions. Notably, the presence of sea breezes and the inclusion of all measured aerosol particles each enhance DCCs in over 95% of aerosol-sensitive cases.

PMID:41461776 | DOI:10.1038/s41598-025-28939-x

Sci Rep. 2025 Dec 29;15(1):44813. doi: 10.1038/s41598-025-28908-4.

ABSTRACT

In clinical practice for the diagnosis of pulmonary tuberculosis (PTB), bronchoscopy is typically performed under airway surface anaesthesia. The effectiveness of this anaesthesia is closely associated with the smoothness of bronchoscopy diagnosis, as well as the incidence and severity of related adverse events. To enhance the efficacy of airway surface anaesthesia, the modified oxygen nebulized inhalation (MONI) method is developed. Derived from the traditional oxygen nebulized inhalation (ONI) procedure, this modified method improves the refined selection of a nebulizer and precise control of the oxygen flowing speed.To validate the advantages of the MONI method, this study compared it with the traditional ONI method through data experiments. Patients undergoing bronchoscopic operation were divided into two groups: one group received MONI for anaesthesia, and the other received ONI. Six key clinical items were recorded during the procedure. A comparative analysis was then conducted on the grouped data using machine learning models. A parameter-scalable broad learning system (BLS) architecture is proposed for feature extraction from raw data, with the optimal analytical model determined by minimizing the loss function value. Both the number of virtual input nodes and the number of neurons in the hidden layer are set as tunable parameters to optimize the model. Scoring data for the target clinical items were input into the system, transformed for BLS network training, and then used to generate predictions. Comparative analysis of the BLS output predictions showed that the data recorded from the MONI group performed better than that from the ONI group.Furthermore, the optimal models were validated to be significant for prediction and could explain how the network output data correlates with each of the six clinical items. Thus, we conclude that the proposed MONI method can practically enhance the effect of airway surface anaesthesia, which will facilitate the diagnosis of pulmonary tuberculosis (PTB). The scalable BLS model is prospective to provide advanced artificial intelligence support to detection procedures, thereby contributing to the effective prevention of infectious diseases.

PMID:41461769 | DOI:10.1038/s41598-025-28908-4

Sci Rep. 2025 Dec 29;15(1):44915. doi: 10.1038/s41598-025-28570-w.

ABSTRACT

Enzootic bovine leukosis (EBL) is an economically important disease of cattle caused by the bovine leukemia virus (BLV). Although BLV-seropositive dairy cattle were previously reported in the Emirate of Dhabi, UAE (EAD), molecular characterization of circulating BLV strains has not been undertaken. Therefore, the objectives of this study were to reassess the seroprevalence along with evaluating the genetic diversity of BLV strains circulating in dairy cattle in the EAD. Sera from 782 dairy cattle distributed across 11 farms were ELISA-screened and RT-qPCR testing of seropositive samples was followed by Sanger sequencing of the partial BLV env-gp51 gene (~ 423 bp) and phylogenetic analysis. The overall BLV herd seroprevalence was 27.3% (CI: 6.03%-61.00%), mean animal seroprevalence 33.5% (CI:30.20%-36.93%), and individual farm seroprevalence 28.00% (CI:19.00%-36.00%), 70.00% (CI:56.00%-84.00%), and 64.00% (59.00%-70.00%) for Farms 7, 11, and 14 respectively. Viral RNA was detected in 107 of 205 (52.2%) seropositive cattle, and phylogenetic analysis revealed a high genetic relatedness (~ 99.3-100.0%) among the BLV strains from the EAD. Additionally, study BLV isolates cluster under BLV-genotype 4 along with strains from Belgium, Russia and Vietnam. BLV infection is confirmed in EAD cattle, with circulating genotype 4 strains closely related to those from Europe and Asia, suggesting potential transboundary connections and underscoring the need for coordinated regional control measures. Future studies should focus on characterizing BLV infection risk factors in dairy cattle farms in the EAD. In the meantime, UAE livestock health authorities should urgently consider developing a national EBL control policy.

PMID:41461764 | DOI:10.1038/s41598-025-28570-w

Sci Rep. 2025 Dec 29;15(1):44771. doi: 10.1038/s41598-025-28672-5.

ABSTRACT

Polyherbal formulations are increasingly investigated for their synergistic antioxidant potential against oxidative stress-related disorders. This study evaluated a polyherbal ethanol extract derived from Abelmoschus esculentus pods and Telfairia occidentalis leaves (AETO-PHF) through integrated in vitro and in silico approaches. GC-MS analysis identified 37 compounds, with dodecanoic acid (16.24%) and 9-octadecenoic acid (Z)-,2,3-dihydroxypropyl ester (16.41%) as predominant constituents. Antioxidant assays revealed potent dose-dependent radical scavenging, with IC₅₀ values of 61.39 ± 0.17 µg/mL (DPPH), 11.15 ± 0.15 µg/mL (H₂O₂ scavenging), 61.75 ± 0.00 µg/mL (FRAP), and 38.97 ± 2.66 µg/mL (NO inhibition). These results were statistically comparable (p > 0.05) to ascorbic acid (61.38 ± 0.58, DPPH; 61.71 ± 0.20, FRAP; and 38.94 ± 0.00, NO inhibition µg/mL) and gallic acid (11.30 ± 0.84 µg/mL, H₂O₂ scavenging). Molecular docking against cytochrome c peroxidase showed strong interactions of dodecanoic acid (- 5.7 kcal/mol) and 9-octadecenoic acid ester (- 6.2 kcal/mol), both surpassing the binding affinity of the reference antioxidant ascorbic acid (- 5.5 kcal/mol). Molecular dynamics simulations confirmed stable protein-ligand complexes with favorable RMSD, RMSF, and hydrogen-bond interaction profiles. These findings validate the traditional use of A. esculentus and T. occidentalis, demonstrate synergistic antioxidant efficacy of their polyherbal blend, and provide molecular-level insights into their mechanism of action. AETO-PHF represents a promising candidate for nutraceutical and therapeutic applications against oxidative stress-related diseases, meriting further in vivo and clinical studies.

PMID:41461761 | DOI:10.1038/s41598-025-28672-5

Sci Rep. 2025 Dec 29;15(1):44751. doi: 10.1038/s41598-025-28750-8.

ABSTRACT

In multi-objective particle swarm optimization (MOPSO), challenges persist, including low diversity in external archives, ambiguous individual optimal choice mechanisms, high sensitivity to parameter settings, and the arduous task of balancing global exploration and local exploitation capabilities. To address these issues, this paper introduces a novel multi-objective particle swarm optimization algorithm named HCRMOPSO. The proposed algorithm innovatively leverages hierarchical clustering based on Ward’s linkage to generate the center of mass as reference points, which are then combined with the ideal point and crowding distance. This effectively maintains the external archive, thereby resolving the diversity deficiency commonly found in traditional MOPSO archives. Additionally, HCRMOPSO fuses multiple particles to update the personal best positions. It also adaptively tunes the flight parameters according to the diversity information within each particle’s neighborhood, enhancing the algorithm’s adaptability. Notably, a new strategy is designed for two specific types of particles, further optimizing the search process. The performance of HCRMOPSO is rigorously evaluated against ten existing algorithms on 22 standard test problems. Experimental results demonstrate that HCRMOPSO outperforms its counterparts on multiple benchmarks, showcasing superior effectiveness in handling multi-objective optimization tasks.

PMID:41461751 | DOI:10.1038/s41598-025-28750-8

Sci Rep. 2025 Dec 29;15(1):44812. doi: 10.1038/s41598-025-28584-4.

ABSTRACT

Under-five child mortality remains a significant public health issue in Bangladesh and other developing countries. Identifying key risk factors within a hierarchical data structure is crucial for improving health system performance. This study employed the Fine-Gray Frailty (FGF) model to Bangladesh Demographic and Health Survey (BDHS) data to assess child mortality from disease-related causes, treating non-disease-related deaths as competing risks in hierarchical framework. The model was also applied by considering non-disease deaths as the main event in a subsequent analysis. Findings reveal that maternal age, child sex, birth order, and regional variation remain significant determinants of mortality after adjusting for the hierarchical data structure. Children of mothers older than 30 years face a significantly higher risk of non-disease deaths compared with those aged 20-30 years. Male children experience higher mortality than females for both disease and non-disease causes. Higher birth order is associated with a lower risk of non-disease mortality. Regionally, Khulna shows a significantly increasing hazard of non-disease deaths than Barisal. This study underscores the value of hierarchical competing risk models for guiding targeted public health interventions.

PMID:41461736 | DOI:10.1038/s41598-025-28584-4

Sci Rep. 2025 Dec 29;15(1):44755. doi: 10.1038/s41598-025-28835-4.

ABSTRACT

When passing through an intersection section, the relationship between driving behavior and energy consumption of pure Electric Buses (E-Bus) is unclear. In this study, natural driving data on two Bus Rapid Transit (BRT) routes were collected to quantify and analyze the implied relationship between driving behavior and energy consumption when entering an intersection point. Furthermore, it is proposed that predicting energy consumption on the basis of distinguishing whether an intersection is stopping or non-stopping would be a more accurate scenario. Concerning the working method, firstly, statistical analysis is used to observe the difference between the energy consumption of the stopping and non-stopping samples; secondly, correlation analysis and linear regression are used to analyze the significant parameters related to whether to stop or not, and energy consumption; finally, machine learning method is used to establish the classification model of whether to stop or not at an intersection as well as a prediction model the energy consumption of the intersection.The results show that the model accuracy of XGBoost-KNN is higher than that of KNN and XGBoost in predicting whether to stop or not, which is 84.4%. For predicting energy consumption, the GBDT has the lowest prediction accuracy; as for XGBoost and SVM, which have a higher prediction accuracy, distinguishing whether to stop or not helps to enhance the model’s prediction accuracy. Furthermore, after distinguishing whether to stop or not, SVM outperformed XGBoost in R², MAE, and RMSE. Research results provide a new perspective for studying the relationship between the driving behavior and energy consumption of pure electric buses at intersections. Meanwhile, they also offer the possibility for further research on the applicability of energy consumption when expanding from the BRT to more complex mixed traffic environments.

PMID:41461728 | DOI:10.1038/s41598-025-28835-4

Sci Rep. 2025 Dec 29;15(1):44854. doi: 10.1038/s41598-025-28421-8.

ABSTRACT

Pediatric infections are often closely linked to infections in families, schools, and communities, illustrating the importance of developing a holistic model of pediatric respiratory infection prevention. Research purposes were to construct a new preventive model for pediatric respiratory infection prevention and to clarify the relationships among impact factors in this model. Research method was a cross-sectional survey. A structured questionnaire was used to measure model variables, including “parental prevention measures (PPM),” “concern about pediatric vaccination (CPV),” “school precautionary measures (SPM),” and “children’s self-protection practices (CSPP).” Structural equation modeling analysis was performed to test four proposed hypotheses and identify the relationships among these variables. Research participants were 2420 parents with one or more 3-16-year-old children. Results identified five paths in research model. (1) “Parental prevention measures, PPM” directly affects “concerns about pediatric vaccination, CPV” [direct effect: 0.354], “school precautionary measures, SPM” [direct effect: 0.354], and “children’s self-protection practices, CSPP” [direct effect: 0.354]. (2) PPM affects CPV through the mediating effect of SPM (indirect effect: 0.04), resulting in a total effect of 0.394. (3) PPM affects CSPP through the mediating effect of SPM (indirect effect: 0.3), resulting in a total effect of 0.655. All these effects were statistically significant. Results strongly suggested that coordinating prevention strategies between families and schoolteachers is most effective in equipping children with the knowledge and behaviors to avoid infectious disease. Results confirmed that the newly constructed model for preventing pediatric respiratory infection was well fitted as a double mediation model. Further studies are needed to pursue the family-school health education model in the prevention of pediatric infectious disease. Key words: Parental prevention measures; Concern about pediatric vaccination; School precautionary measures; Children’s self-protection practices; Pediatric Respiratory Infection.

PMID:41461686 | DOI:10.1038/s41598-025-28421-8