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Nevin Manimala Statistics

Utilizing artificial intelligence to predict and analyze socioeconomic, environmental, and healthcare factors driving tuberculosis globally

Sci Rep. 2025 Apr 19;15(1):13619. doi: 10.1038/s41598-025-96973-w.

ABSTRACT

Tuberculosis (TB) is a major global health issue, contributing significantly to mortality and morbidity rates worldwide. Socioeconomic, environmental, and healthcare factors significantly impact TB trends. Therefore, we aimed to predict TB and identify the determinants of the disease using advanced artificial intelligence (AI). This study employed the advanced machine learning (ML) model, XGBoost (eXtreme gradient boosting), combined with XAI (eXplainable artificial intelligence) and spatial analysis to describe global TB incidence and mortality rates across 194 countries from 2000 to 2022. Spatial autocorrelation analysis utilizing Moran’s I revealed geographical clusters and significant determinants affecting TB incidence. Treatment success rates and MDR-TB treatment initiation were identified as pivotal determinants of TB incidence. The correlation study revealed a substantial positive relationship between TB incidence in HIV-positive patients and overall TB incidence (r = 0.83). Confirmed cases of MDR-TB had the most significant impact on TB incidence (SHAP = 0.874). Additionally, air pollution had a notable impact on TB incidence (SHAP = 1.36). The XGBoost model demonstrated the best predictive performance for TB incidence and mortality, exhibiting the lowest RMSE (0.88), the highest R2 (0.67), and Adjusted R2 (0.65). This study highlights the potential of integrating XGBoost and XAI methodologies as a holistic framework for effectively tackling global tuberculosis incidence and mortality rates, as well as for the proficient application of advanced analytical techniques in public health.

PMID:40253515 | DOI:10.1038/s41598-025-96973-w

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Nevin Manimala Statistics

Automated assessment of simulated laparoscopic surgical skill performance using deep learning

Sci Rep. 2025 Apr 19;15(1):13591. doi: 10.1038/s41598-025-96336-5.

ABSTRACT

Artificial intelligence (AI) has the potential to improve healthcare and patient safety and is currently being adopted across various fields of medicine and healthcare. AI and in particular computer vision (CV) are well suited to the analysis of minimally invasive surgical simulation videos for training and performance improvement. CV techniques have rapidly improved in recent years from accurately recognizing objects, instruments, and gestures to phases of surgery and more recently to remembering past surgical steps. Lack of labeled data is a particular problem in surgery considering its complexity, as human annotation and manual assessment are both expensive in time and cost, and in most cases rely on direct intervention of clinical expertise. In this study, we introduce a newly collected simulated Laparoscopic Surgical Performance Dataset (LSPD) specifically designed to address these challenges. Unlike existing datasets that focus on instrument tracking or anatomical structure recognition, the LSPD is tailored for evaluating simulated laparoscopic surgical skill performance at various expertise levels. We provide detailed statistical analyses to identify and compare poorly performed and well-executed operations across different skill levels (novice, trainee, expert) for three specific skills: stack, bands, and tower. We employ a 3-dimensional convolutional neural network (3DCNN) with a weakly-supervised approach to classify the experience levels of surgeons. Our results show that the 3DCNN effectively distinguishes between novices, trainees, and experts, achieving an F1 score of 0.91 and an AUC of 0.92. This study highlights the value of the LSPD dataset and demonstrates the potential of leveraging 3DCNN-based and weakly-supervised approaches to automate the evaluation of surgical performance, reducing reliance on manual expert annotation and assessments. These advancements contribute to improving surgical training and performance analysis.

PMID:40253514 | DOI:10.1038/s41598-025-96336-5

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Nevin Manimala Statistics

Extreme ambient temperature and emergency healthcare service utilization due to substance use disorders: a systematic review and meta-analysis

Sci Rep. 2025 Apr 19;15(1):13582. doi: 10.1038/s41598-025-98247-x.

ABSTRACT

To synthesize the association between extreme ambient temperatures and the utilization of emergency healthcare services for substance use disorder (SUD). We performed a systematic literature review of original research published between 2000 and 2023 using five databases (PubMed, Embase, CINAHL, WoS, and Scopus) for literature search, and assessed study quality and risk of bias. A random-effects meta-analysis was conducted to calculate the odds ratios (OR) for SUD-related emergency healthcare service utilization during periods of extremely high or low ambient temperatures. Of 709 articles screened, eight studies met the eligibility criteria. Six studies focused on emergency department (ED) visits, while two examined on-site emergency care utilization. The risk of SUD-related ED visits was significantly higher when the mean ambient temperature was in the top 5% of the temperature distribution range (pooled OR = 1.11, 95% confidence interval [CI]: 1.07, 1.15). Conversely, the risk of SUD-related ED visits was lower when the mean temperature was in the bottom 5% of the distribution (pooled OR = 0.94, 95% CI: 0.89, 0.99). Our review showed the extremely high ambient temperature is associated with higher risk of SUD-related emergency healthcare service utilization. However, given the high heterogeneity observed across studies, these results should be interpreted with caution. Differences in study design, population characteristics, geographic region, and substance type may have contributed to this heterogeneity. Despite these differences, this finding highlights the importance of considering environmental factors in the management and prevention of SUD-related health issues.

PMID:40253512 | DOI:10.1038/s41598-025-98247-x

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Nevin Manimala Statistics

Phylogeography of introgression: Spatial and temporal analyses identify two introgression events between brown and American black bears

Heredity (Edinb). 2025 Apr 19. doi: 10.1038/s41437-025-00762-0. Online ahead of print.

ABSTRACT

Brown bears (Ursus arctos) colonized North America from Eurasia in two distinct and temporally separated waves. Once in North America they encountered endemic American black bears (U. americanus) during range expansions from eastern Beringia southwards into the interior of the continent. The establishment of sympatry between these species provided the opportunity for hybridization and introgression, which was previously identified at the species level using D-statistics. Both species have broad spatial ranges that should limit the extent of introgression, such that it is found primarily between sympatric populations. Here, we used range-wide sampling and whole genome sequencing of both bear species to test for spatial variability in introgression. We identified two pulses of introgression between brown and American black bears, and demonstrate the introgressed segments occur across spatially structured lineages in both species. The first pulse occurred 270-120 kya, near the initiation of intraspecific divergence, approximately 99-93 kya, within each species. This pulse occurred as sympatry was established in western North America. The second pulse occurred between western American black bears and North American brown bears and lasted to 9 kya. Introgression was bidirectional and sympatric lineages had more introgressed tracts and a larger proportion of the genome introgressed from the other species. This study advances our phylogeographic understanding of both iconic bear species through investigating the timing of divergence and gene flow as bears expanded and contracted their ranges across North America.

PMID:40253500 | DOI:10.1038/s41437-025-00762-0

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Nevin Manimala Statistics

Evaluating the impact of gut microbiota, circulating cytokines and plasma metabolites on febrile seizure risk in Mendelian randomization study

Sci Rep. 2025 Apr 19;15(1):13603. doi: 10.1038/s41598-025-97759-w.

ABSTRACT

Febrile seizures (FS) is the most common type of convulsion in infants and preschool children. This study aimed to investigate the associations between gut microbiota abundance, plasma metabolites, circulating cytokines, and FS. Summary statistics of 211 gut microbiota traits, 1,400 plasma metabolite traits, 91 circulating cytokine traits, and FS were obtained from publicly available genome-wide association studies. Two-sample Mendelian randomization (MR) analysis and causality was inferred using Inverse variance-weighted (IVW), Weighted median, MR-Egger, simple mode-based estimate and weighted mode-based estimate 5 methods. Several sensitivity analyses were also used to ensure the robustness of the results. Furthermore, mediation analysis was used to determine the pathway from gut microbiota to FS mediated by plasma metabolites and circulating cytokines. MR revealed the associations of 1 gut microbiota (phylum Verrucomicrobia), 4 circulating cytokines and 50 plasma metabolites on FS. Based on the known pathogenic metabolites, we observed that the tryptophan, androgen, and sphingolipids pathways are associated with FS. Mediation analysis revealed 1 strongly documented plasma metabolite (Ascorbic acid 2-sulfate) as a mediator linking “gut microbiota to plasma metabolite to FS”. Sensitivity analysis was represented no heterogeneity or pleiotropy in this study.Our study provides some causal evidence concerning the effects of the gut microbiota, circulating cytokines, and plasma metabolites on FS, which needs to be verified in randomized controlled trials. These biomarkers provide new insights into the underlying mechanisms of FS and contribute to its prevention, diagnosis, and treatment.

PMID:40253491 | DOI:10.1038/s41598-025-97759-w

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Nevin Manimala Statistics

Information technology assists in the innovative development of throwing embroidered balls

Sci Rep. 2025 Apr 19;15(1):13550. doi: 10.1038/s41598-025-96872-0.

ABSTRACT

This study aims to understand the historical and development of throwing embroidered balls, combine information processing and drone technology to innovate throwing techniques, improve the competitive level and competition rules, promote the high-pole throwing embroidered balls to the international sports. Utilizing literature review method to clarify the evolution history of throwing embroidered balls; using mathematical statistics to analyze the results of competition during the 12th and 13th Guangxi Student Games; Using mechanics analysis and animation production methods innovate throwing techniques. The imperfect competition rules, inconsistent specifications of embroidered balls, and outdated throwing techniques have hindered the healthy development of throwing embroidery ball. The optimal throwing angle range for innovative throwing techniques is between 64° < α < 72°, with the range of throwing speed being between 13.04 m/s < v < 13.70 m/s. When α < 66°, v≈13.70m/s, the ball passes through the lower edge of the top of the circle; When α > 64°, v≈13.04m/s, when α < 72°, v≈13.17m/s, the ball passes through the upper edge of the bottom of the circle. Drone aerial photography technology can assist judges; using animation production techniques to form throwing motion models to guide training can improve competitive skills. Improving competition rules, unifying the use of competition balls, and strengthening the integration of information technology with ethnic sports can effectively enhance the development level of the sport of throwing embroidered balls.

PMID:40253481 | DOI:10.1038/s41598-025-96872-0

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Nevin Manimala Statistics

Exposure to sub-zero temperatures down to -11 °C does not impact packed red cells storage quality

Sci Rep. 2025 Apr 19;15(1):13574. doi: 10.1038/s41598-025-98273-9.

ABSTRACT

European guidelines require packed red blood cells (pRBC) to be stored at 2-6 °C. However, negative temperature shifts can occur especially in prehospital transfusion. We investigated the impact of sub-zero temperature exposure on pRBC storage quality. At day 6 post donation (D6), three cohorts (14 pRBC) were put on a supercooled table for 10 h at either – 1 °C, -5 °C, and – 11 °C and compared to a control cohort. Hemolysis, pH and plasma biochemistry were evaluated weekly until D49. Storage-induced micro-erythrocytes (SMEs) were quantified as a surrogate marker for transfusion recovery. The primary endpoint was compliance with European storage standards at D42. The three sub-zero-exposed cohorts met standards at D42. Differences in hemolysis, pH, plasma biochemistry, or SMEs between exposed and control cohorts were non-statistically and/or non-clinically significant. Ten hours exposure to sub-zero temperatures down to -11 °C by conduction maintains storage quality of pRBCs, enabling a wiser risk assessment for potential transfusion use.

PMID:40253464 | DOI:10.1038/s41598-025-98273-9

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Nevin Manimala Statistics

Accuracy of infusion flow rates and bolus doses for portable infusion pump

Sci Rep. 2025 Apr 19;15(1):13517. doi: 10.1038/s41598-025-98533-8.

ABSTRACT

This study aimed to investigate the flow rate accuracy and bolus doses of four portable pumps with different mechanisms. We evaluated two electronic (COOPDECH Amy PCA [Amy] and CADD-Solis) and two elastomeric balloon-type pumps (COOPDECH Balloonjector and Rakurakufuser) under the following conditions: placement with an epidural catheter, operation on a shaking table, warming to 32 °C, and at room temperature (control, 25 °C). Amy maintained consistent flow rates across all conditions. The CADD-Solis also exhibited consistent flow rates, with minor yet statistically significant changes upon epidural catheter placement (- 5.0%, P = 0.005) and motion conditions (4.2%, P = 0.015). The Balloonjector and Rakurakufuser exhibited flow rate variations over time, and temperature increases significantly increased flow rates by 24.3% (P < 0.001) and 20.3% (P < 0.001), respectively. Bolus volume accuracy for the Amy, Balloonjector, and Rakurakufuser was not significantly affected under different conditions. The CADD-Solis showed a slight decrease in bolus volume with an epidural catheter (mean difference, 0.3 mL; P = 0.003). The electronic pumps maintained consistent flow rates across various conditions, whereas elastomeric balloon pumps exhibited variable rates influenced by time and temperature, increasing the risk of medication overdose. Bolus dosing accuracy was clinically satisfactory for all pump mechanisms.

PMID:40253460 | DOI:10.1038/s41598-025-98533-8

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Nevin Manimala Statistics

A text dataset of campaign speeches of the main tickets in the 2020 US presidential election

Sci Data. 2025 Apr 19;12(1):662. doi: 10.1038/s41597-025-04681-x.

ABSTRACT

Unstructured text data have gained popularity in political science, owing to advancements in rigorous ‘text-as-data’ methods that allow extracting insights into election outcomes, candidates’ appeal to voters, ideologies and campaign strategies. Existing datasets on US presidential election campaign speeches are limited in size or source variation, and often contain speeches of different types (debates, rallies, official presidential events, e.g. inauguration), thus lacking consistency in their rhetorical content. The introduced dataset comprises the campaign speeches of the Democratic and Republican tickets for the 2020 US presidential election (1, 056 in total), covering the period between January 2019 and January 2021. Importantly, the dataset dictates specific criteria for the rhetorical structure of the speech ensuring consistency, critical for quantitative analysis. It has been carefully curated, yet only to the necessary extent to still be able to inform studies that require semantic or grammatical/syntactical structure. The provided corpus is hosted on Zenodo and GitHub under the CC BY-NC 4.0 license, and it aims to enhance timely studies on US presidential elections with high-quality text data.

PMID:40253428 | DOI:10.1038/s41597-025-04681-x

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Nevin Manimala Statistics

Circulating DNA methylation-based diagnostic, prognostic, and predictive biomarkers in colorectal cancer

Sci Rep. 2025 Apr 19;15(1):13577. doi: 10.1038/s41598-025-95712-5.

ABSTRACT

Plasma DNA methylation SEPTIN9, Syndecan 2 (SDC2), and Branched Chain Amino Acid Transaminase 1 (BCAT1) tests have served as valuable diagnostic, prognostic, and predictive markers for colorectal cancer (CRC). In this study, we analyzed data including 104 eligible CRC patients, 138 colorectal benign diseases, and 106 healthy subjects in our hospital from January 2019 to May 2023. This study was approved by the Medical Ethics Committee of Henan Cancer Hospital (Approval No.2018156). A real-time polymerase chain reaction-based gene panel was used to detect the methylation of SEPTIN9, SDC2, and BCAT1. The composite score (P) was calculated according to the cycle threshold (Ct) values of the three methylated genes using the logistic regression equation. The consistency of assay and pathological diagnosis were evaluated with kappa analyzed by IBM SPSS Statistics. The median survival time was obtained by Kaplan-Meier survival analysis. Statistical figures were all carried out using Origin software. The three genes were found to be significantly methylated in ctDNA of CRC patients compared to patients with colorectal benign diseases and healthy controls. The sensitivity was 86.1%, the specificity was 97.6%, and the area under the curve of 0.929. Positive predictive value (PPV) was 57.2%, and Negative predictive value (NPV) was 99.5%. No statistically significant differences in diagnostic efficiency were observed in relation to different types of stages. Moreover, there was a significant difference in the expression of composite scores between survival periods greater than 1 year and less than 1 year (p < 0.01). The composite score (P) derived from the ctDNA methylation levels of SEPTIN9, SDC2, and BCAT1 can be used for CRC diagnosis with high sensitivity and specificity. A combination of ctDNA methylation was proved to be an effective diagnostic, prognostic, and predictive biomarker in colorectal cancer.

PMID:40253416 | DOI:10.1038/s41598-025-95712-5