Category: Statistics

PLoS One. 2025 Jul 3;20(7):e0327488. doi: 10.1371/journal.pone.0327488. eCollection 2025.

ABSTRACT

In this manuscript, the existence and uniqueness of solutions to equations associated with climate change are discussed. For this purpose, we utilize some results from the existing literature to investigate the behavior of these equations. Additionally, the role of fixed point theory in emphasizing the importance of proving the stability and consistency of the models is explored. Several definitions and results, such as the F-contraction, [Formula: see text]-F-contraction, rational type [Formula: see text]-contraction, and Geraghty type contraction, are recalled from the existing literature to illustrate their theoretical foundations and practical applications.

PMID:40608847 | DOI:10.1371/journal.pone.0327488

PLoS Negl Trop Dis. 2025 Jul 3;19(7):e0013201. doi: 10.1371/journal.pntd.0013201. eCollection 2025 Jul.

ABSTRACT

BACKGROUND: While only a limited number of scorpion species are classified as dangerous to humans, the potentially life-threatening effects of their stings classify scorpionism as a global health concern. Iran, with its high scorpion diversity, reported more than 63,000 scorpion sting cases in 2023. This study aims to elucidate the epidemiological characteristics of scorpion envenomation in northwest Iran.

METHODS: This retrospective descriptive cross-sectional study was conducted over a period of two years (2022-2023) in northwest Iran. The research focused on scorpion sting cases that required treatment at 25 scorpion sting treatment centers (SSTCs) across the East Azerbaijan Province. Data were collected from scorpion sting cases presenting for treatment. Statistical analyses were performed, using Chi² and Mann-Whitney tests for both descriptive and analytical evaluations. Geographic distribution maps were generated to illustrate the locations of sting incidents relative to treatment facilities.

RESULT: During two years, 3,154 scorpion sting cases were reported in East Azerbaijan Province, Iran. Most patients were aged 31 to 40 years, with 54.9% being male. Most stings occurred in urban areas (48.7%) and primarily indoors (75%). Remarkably, 99.96% of cases resulted in full recovery, with only one death reported. Treatment methods included wound cleaning (50.8%) and the administration of antivenom (53.2%). The results indicate scorpion stings peak during the summer months, with the highest frequency occurring between midnight and 2 AM.

CONCLUSION: This study highlights the public health challenge posed by scorpion stings in East Azerbaijan Province. While recovery rates are high, further efforts are needed to improve public health interventions, including educational programs for vulnerable groups such as farmers and children. Enhancing access to medical care and timely treatment is essential to reducing morbidity and mortality. Future research should focus on local scorpion species and develop tailored prevention strategies to mitigate scorpionism.

PMID:40608821 | DOI:10.1371/journal.pntd.0013201

PLoS One. 2025 Jul 3;20(7):e0327615. doi: 10.1371/journal.pone.0327615. eCollection 2025.

ABSTRACT

A fundamental question in word learning is how, given only evidence about what objects a word has previously referred to, children are able to generalize to the correct class. How does a learner end up knowing that “poodle” only picks out a specific subset of dogs rather than the broader class and vice versa? Numerous phenomena have been identified in guiding learner behavior such as the “suspicious coincidence effect” (SCE)-that an increase in the sample size of training objects facilitates more narrow (subordinate) word meanings. While SCE seems to support a class of models based in statistical inference, such rational behavior is, in fact, consistent with a range of algorithmic processes. Notably, the broadness of semantic generalizations is further affected by the temporal manner in which objects are presented-either simultaneously or sequentially. First, I evaluate the experimental evidence on the factors influencing generalization in word learning. A reanalysis of existing data demonstrates that both the number of training objects and their presentation-timing independently affect learning. This independent effect has been obscured by prior literature’s focus on possible interactions between the two. Second, I present a computational model for learning that accounts for both sets of phenomena in a unified way. The Naïve Generalization Model (NGM) offers an explanation of word learning phenomena grounded in category formation. Under the NGM, learning is local and incremental, without the need to perform a global optimization over pre-specified hypotheses. This computational model is tested against human behavior on seven different experimental conditions for word learning, varying over presentation-timing, number, and hierarchical relation between training items. Looking both at qualitative parameter-independent behavior and quantitative parameter-tuned output, these results support the NGM and suggest that rational learning behavior may arise from local, mechanistic processes rather than global statistical inference.

PMID:40608813 | DOI:10.1371/journal.pone.0327615

PLoS One. 2025 Jul 3;20(7):e0326324. doi: 10.1371/journal.pone.0326324. eCollection 2025.

ABSTRACT

INTRODUCTION: Despite being the world’s highest tuberculosis (TB) burden country, India still misses millions of TB cases annually. To address this issue, the India National Strategic Plan, following WHO strategy, promotes combining active case finding (ACF) with passive case finding (PCF) activities. National TB Elimination Programme (NTEP) began ACF campaigns thrice a year, targeting vulnerable populations. However, states like Manipur faced challenges in implementing and sustaining ACF activities due to resource constraints.

OBJECTIVE: To assess the impact of engaging student and women organizations (SAWOs) in improving TB case notifications, treatment adherence, and completion rate in NTEP, as well as to estimate the cost-effectiveness of the ACF intervention.

METHOD: A quasi-experimental pre-post study is being conducted among individuals ≥15 years residing in Senapati District, Manipur, having two phases: preparatory and enhanced case finding and implementation of the ACF. Data is being collected and compared on TB case notification, treatment adherence, and outcomes beforeand after the intervention. Chi-square test will be used to test the statistical significance and logistic regression to identify the factors independently associated with the impact of intervention. Potential confounders at both patient and facility levels will be identified based on expert opinion and bivariate analysis. A multi-level logistic regression model will be used to control the confounding, with sensitivity analysis to ensure result robustness.Cost analysis will cover direct, indirect, medical, and non-medical costs for patients and health system. Incremental cost-effectiveness ratio per quality-adjusted life years gained will be evaluated.

DISCUSSION: This study introduces a novel community-led model involving SAWOsto improve TB case detection and treatment support, comprehensively addressing allfour pillars of ‘END TB’ strategy. The intervention is a community-based participatory research, emphasizing collaboration between researchers andcommunity to address TB control. The main activities of this intervention include community TB sensitization, ECF, ACF, treatment support and monitoring. This model could significantly impact TB control efforts, especially in resource-constrained settings like Manipur, offering valuable insights into ACF implementation and its economic implications.

PMID:40608799 | DOI:10.1371/journal.pone.0326324

PLoS One. 2025 Jul 3;20(7):e0327304. doi: 10.1371/journal.pone.0327304. eCollection 2025.

ABSTRACT

Understanding the dynamics of cancer cell growth, the interplay between tumor and immune cells, and the efficacy of chemotherapy are pivotal areas of focus in cancer research. In this regard, mathematical modeling can provide significant insights. This study re-examines a classical two-dimensional model of tumor-immune cell interactions where the tumor’s growth rate is assumed to adhere to von Bertalanffy’s model instead of the logistic model. We investigate the model both without chemotherapy and with treatment. The equilibrium points are identified, classified, and their stability analyzed. Our results reveal that the model can demonstrate a broad spectrum of behaviors, including bi-stability and multi-stability as well as regions of stable periodic behavior. We establish analytical conditions for the existence of Hopf points. Furthermore, we assess the impact of model parameters on the various behavior predicted by the model. This mathematical investigation can provide general guidance on treatment strategies.

PMID:40608796 | DOI:10.1371/journal.pone.0327304

PLoS One. 2025 Jul 3;20(7):e0327241. doi: 10.1371/journal.pone.0327241. eCollection 2025.

ABSTRACT

Smart beds have become increasingly accepted, and are concurrently reshaping their lifestyles. Addressing the limited ability of smart beds to cater to health requirements, this study investigated smart bed comfort across diverse typical conditions. Objective body pressure distribution and participant-reported perceived comfort were recorded during typical smart bed usage. Statistical analysis was utilized to investigate overall and local comfort variations across different conditions and the correlation between perceived comfort and body pressure distribution. Statistical analysis highlighted the importance of equalizing forces and minimizing peak pressures. Alongside mean pressure, peak pressure-particularly in the buttock, thigh, and shank areas-played a pivotal role in comfort evaluation. Optimal bed board partitioning and interlinked mechanisms between adjacent boards enhance body curve fit and overall comfort. Balancing body forces and preventing feelings of weightlessness significantly improve user comfort and health. This analysis has been used to develop a comfort prediction model for smart bed design.

PMID:40608784 | DOI:10.1371/journal.pone.0327241

PLoS One. 2025 Jul 3;20(7):e0327537. doi: 10.1371/journal.pone.0327537. eCollection 2025.

ABSTRACT

Identifying critically ill patients who are likely to improve their respiratory physiology following RBC transfusion is dynamic and difficult. Current decision tools are over-reliant on hemoglobin transfusion thresholds, without considering respiratory measures that may reflect physiologic effects of anemia and functional responses to RBC transfusion. Further, routine clinical measures to determine transfusion efficacy beyond hemoglobin increment are lacking to identify patients as responders or non-responders. We present a two-center retrospective cohort study aiming to determine a potential biomarker to assess the physiologic response of RBC transfusion for non-traumatic ICU patients. The study was performed with 13,274 eligible patients at the first center. Another 3,757 from the second center were used as a validation population. We introduced a comparative analysis of two respiratory measures, SpO2 and SpO2/FiO2 (SF) ratio, in addition to hemoglobin, to assess individual patient responses to RBC transfusion. A statistical study was performed to compare these markers before and after the transfusion interval. Based on quantitative statistical analyses, we found SF ratio to be a more effective biomarker than hemoglobin alone for revealing RBC transfusion efficacy. There existed an inverse correlation between pre-transfusion SF and transfusion efficacy. The results were consistent across both centers, revealing generalizability. With the SF data from both the centers, we also developed a random forest-based regression model that significantly evaluated the level of transfusion effectiveness (p < 0.001).

PMID:40608780 | DOI:10.1371/journal.pone.0327537

Anal Chem. 2025 Jul 3. doi: 10.1021/acs.analchem.5c01672. Online ahead of print.

ABSTRACT

We present a comprehensive map of the metabolomics research landscape, synthesizing insights from over 80,000 publications. Using PubMedBERT, we transformed abstracts into 768-dimensional embeddings that capture the nuanced thematic structure of the field. Dimensionality reduction with t-SNE revealed distinct clusters corresponding to key domains, such as analytical chemistry, plant biology, pharmacology, and clinical diagnostics. In addition, a neural topic modeling pipeline refined with GPT-4o mini reclassified the corpus into 20 distinct topics─ranging from “Plant Stress Response Mechanisms” and “NMR Spectroscopy Innovations” to “COVID-19 Metabolomic and Immune Responses.” Temporal analyses further highlight trends including the rise of deep learning methods post-2015 and a continued focus on biomarker discovery. Integration of metadata such as publication statistics and sample sizes provides additional context to these evolving research dynamics. An interactive web application (https://metascape.streamlit.app/) enables the dynamic exploration of these insights. Overall, this study offers a robust framework for literature synthesis that empowers researchers, clinicians, and policymakers to identify emerging research trajectories and address critical challenges in metabolomics while also sharing our perspectives on key trends shaping the field.

PMID:40608399 | DOI:10.1021/acs.analchem.5c01672

Epilepsia Open. 2025 Jul 3. doi: 10.1002/epi4.70072. Online ahead of print.

ABSTRACT

OBJECTIVE: The gut microbiota (GM) plays a role in epilepsy development via the microbiota-gut-brain axis. However, its relationship with various epilepsy subtypes and its mediating role through immune cells remain unclear. Thus, identifying the GM linked to specific epilepsy subtypes and investigating immune mechanisms to predict epilepsy risk, tailor treatments, and monitor outcomes are crucial.

METHODS: We performed a two-sample Mendelian randomization (MR) study focused on the relationships between different epilepsy subtypes associated with the GM and the mediating role of immune cells between different epilepsy subtypes and the GM. Genome-wide association analysis summary statistics of 412 GM species (GCST90027446-GCST90027857) and 731 immune cell phenotypes (GCST90001391-GCST90002121), along with summary statistics of different subtypes of epilepsy, were used in a publicly available genome-wide association analysis. Significantly associated single-nucleotide polymorphism (SNP) loci were extracted as instrumental variables according to preset thresholds, with an inverse variance weighted (IVW) model being the main model. Additionally, MR-Egger regression, weighted median, weighted, and simple models were also used for analysis.

RESULTS: MR analyses revealed the relationships of the GM and immune cells with diverse epilepsy subtypes, with no statistically significant effect of different epilepsy subtypes on the GM after correction for multiple testing via the false discovery rate (FDR) approach. Notably, one bacterial species, Gordonibacter pamelaeae, with an uncorrected low p-value (OR: 1.0136, 95% CI: 1.0048-1.0225, p = 0.0025), was positively related to childhood absence epilepsy (CAE). Among immune cells, CD4+ ACs (OR: 1.0152, 95% CI: 1.0067-1.0238, p = 0.0005) were strongly related to CAE. Additionally, mediated effect analysis revealed that seven types of GM mediate the effects of eight immune cells on epilepsy, with Bacteroides caccae mediating CD33br HLA DR+ CD14dim AC cells producing the greatest effect on generalized epilepsy.

SIGNIFICANCE: The above results demonstrate the close association between specific GM and specific immune cells in epilepsy and can be used to inform the treatment of different epilepsy subtypes by modulating the GM and immune cells.

PLAIN LANGUAGE SUMMARY: This study investigated the relationships between the gut microbiota and different epilepsy subtypes and the mediating role of immune cells. These findings emphasize that there is a close association between specific gut microbiota and specific immune cells in epilepsy and that they can be used to inform the treatment of different epilepsy subtypes by modulating the gut microbiota and immune cells.

PMID:40608392 | DOI:10.1002/epi4.70072

JAMA Netw Open. 2025 Jul 1;8(7):e2518906. doi: 10.1001/jamanetworkopen.2025.18906.

ABSTRACT

IMPORTANCE: Tumor-infiltrating lymphocytes (TILs) are a provocative biomarker in melanoma, influencing diagnosis, prognosis, and immunotherapy outcomes; however, traditional pathologist-read TIL assessment on hematoxylin and eosin-stained slides is prone to interobserver variability, leading to inconsistent clinical decisions. Therefore, development of newer TIL scoring approaches that produce more reliable and consistent readouts is important.

OBJECTIVE: To evaluate the analytical and clinical validity of a machine learning algorithm for TIL quantification in melanoma compared with traditional pathologist-read methods.

DESIGN, SETTING, AND PARTICIPANTS: This multioperator, global, multi-institutional prognostic study compared TIL scoring reproducibility between traditional pathologist-read methods and an artificial intelligence (AI)-driven approach. The study was conducted using retrospective cohorts of patients with melanoma between January 2022 and June 2023 across 45 institutions, with tissue evaluated by participants from academic, clinical, and research institutions. Participants were selected to ensure diverse expertise and professional backgrounds.

MAIN OUTCOMES AND MEASURES: Intraclass correlation coefficient (ICC) values were calculated for the manual and AI-assisted arms using log-transformed data. Kendall W values were calculated for Clark scores (brisk = 3, nonbrisk = 2, and sparse = 1). Reliabilities of ICC and W values were classified as moderate (0.40-0.60), good (0.61-0.80), or excellent (>0.80). AI TIL measurements were dichotomized using the 16.6 and median cutoffs. Univariable and multivariable Cox regression analyses assessed the prognostic value of TIL scores adjusted for clinicopathologic variables.

RESULTS: There were 111 patients with melanoma in the independent testing cohort (median [range] age at diagnosis, 61.0 [25.0-87.0] years; 56 [50.5%] male) who contributed melanoma whole tissue sections. A total of 98 participants evaluated TILs on 60 hematoxylin and eosin-stained melanoma tissue sections. All 40 participants in the manual arm were pathologists, while the AI-assisted arm included 11 pathologists and 47 nonpathologists (scientists). The AI algorithm demonstrated superior reproducibility, with ICCs higher than 0.90 for all machine learning TIL variables, significantly outperforming manual assessments (ICC, 0.61 for AI-derived stromal TILs vs Kendall W, 0.44 for manual Clark TIL scoring). AI-based TIL scores showed prognostic associations with patient outcomes (n = 111) using the median cutoff approach with a hazard ratio (HR) of 0.45 (95% CI, 0.26-0.80; P = .005), and using the cutoff of 16.6, with an HR of 0.56 (95% CI, 0.32-0.98; P = .04).

CONCLUSIONS AND RELEVANCE: In this prognostic study of TIL quantification in melanoma, the AI algorithm demonstrated superior reproducibility and prognostic associations compared with traditional methods. Although the retrospective nature of the cohorts limits demonstration of clinical utility, the publicly available dataset and open-source AI tool offer a foundation for future validation and integration into melanoma management.

PMID:40608341 | DOI:10.1001/jamanetworkopen.2025.18906