Tag: pubmed

Chaos. 2025 Jul 1;35(7):073128. doi: 10.1063/5.0273965.

ABSTRACT

In this paper, based on the traditional prisoner’s dilemma model, we introduce time-varying peer pressure and verify the enhancing effect of this time-varying peer pressure model on cooperation in different types of networks. We decompose peer pressure into two aspects: pressure intensity, reflecting the degree of punishment an individual receives due to strategy inconsistency with neighbors, and pressure sensitivity, indicating the likelihood of an individual being influenced by peer pressure, which can be regarded as an individual characteristic. Considering individuals’ continuous development over time, it is possible for individual characteristics to change over time. Thus, we treat pressure sensitivity as a time-varying function in this paper and construct it based on the widely used Sigmoid function, taking into account the differences in sensitivity among different individual types. We apply the time-varying peer pressure model to Watts-Strogatz (WS) and Barabási-Albert (BA) networks and evaluate its effect from two aspects: the increase in the proportion of cooperators compared to the traditional prisoner’s dilemma model, and the range of b within which there are still cooperators that can survive in the system. Overall, we find that the introduction of the time-varying peer pressure can more significantly enhance the evolution of cooperation in WS networks. Specifically, under the time-varying peer pressure model, the range of b that the system can withstand can be expanded to b≤1.95 in WS networks, and the range expands to b≤2.7 in BA networks, while the network scale is 100.

PMID:40663761 | DOI:10.1063/5.0273965

JMIR Cancer. 2025 Jul 15;11:e52627. doi: 10.2196/52627.

ABSTRACT

BACKGROUND: In the modern era, the use of technology can substantially impact care access. Despite the extent of its influence on several chronic medical conditions related to the heart, lungs, and others, the relationship between one’s access to digital resources and oncologic conditions has been seldom investigated in select pathologies among gastrointestinal and head-neck regions. However, studies on the influence of this “digital inequity” on other cancers pertaining to nasal and paranasal sinus cancer (NPSC) have yet to be performed. This remains in stark contrast to the extent of large data approaches assessing the impact of traditional social determinants/drivers of health (SDoH), such as factors related to one’s socioeconomic status, minoritized race or ethnicity, and housing-transportation status, on prognostic and treatment outcomes.

OBJECTIVE: This study aims to use the Digital Inequity Index (DII), a novel, comprehensive tool that quantifies digital resource access on an area- or community-based level, to assess the relationship between inequities in digital accessibility with NPSC disparities in prognosis and care in the United States.

METHODS: Patients with NPSC from 2008 to 2017 in the Surveillance, Epidemiology, and End Results Program were assessed for significant regression trends in the long-term follow-up period and treatment receipt across NPSCs with increasing overall digital inequity, as measured by DII. DII was based on 17 census-tract level variables derived from the summarized values overlapping that same time period from the US Census/American Community Survey and Federal Communications Commission Annual Broadband Report. Variables were categorized as infrastructure-access (ie, electronic device ownership, internet provider availability, and income-broadband subscription ratio) or sociodemographic (education, income, age, and disability), ranked, and then averaged into a composite score to encompass direct and indirect factors related to digital inequity.

RESULTS: Across 8012 adult patients with NPSC, males (n=5416, 67.6%) and White race (n=4293, 53.6%) were the most represented demographics. With increasing digital inequity, as measured by increasing total DII scores, significant decreases in the length of long-term follow-up were observed with nasopharyngeal (P<.01) and maxillary sinus cancers (P=.02), with decreases as high as 19% (35.2 to 28.5 months, nasopharynx). Electronic device and service availability inequities showcased higher-magnitude contributions to observed associated regression trends, while the income-broadband ratio contributed less. Significantly decreased odds of receiving indicated surgery (lowest odds ratio 0.87, 95% CI 0.80-0.95, maxillary) and radiation (lowest odds ratio 0.78, 95% CI 0.63-0.95, ethmoid) for several NPSCs were also observed.

CONCLUSIONS: Digital inequities are associated with detrimental NPSC care and surveillance trends in the United States, even when accounting for traditional SDoH factors. These results prompt the need to include digital factors into the discussion of contextualizing SDoH-based analyses of cancer care disparities, as well as the specific factors from which prospective implementations and initiatives can invest limited public health resources to alleviate the most pertinent drivers of disparities.

PMID:40663723 | DOI:10.2196/52627

IEEE Trans Pattern Anal Mach Intell. 2025 Jul 15;PP. doi: 10.1109/TPAMI.2025.3588186. Online ahead of print.

ABSTRACT

Self-attention and transformers have been widely used in deep learning. Recent efforts have been devoted to incorporating transformer blocks into different neural architectures, including those with convolutions, leading to various visual transformers for computer vision tasks. In this paper, we propose a novel and compact transformer block, Transformer with Learnable Token Merging (LTM), or LTM-Transformer. LTM-Transformer performs token merging in a learnable scheme. LTM-Transformer is compatible with many popular and compact transformer networks, and it reduces the FLOPs and the inference time of the visual transformers while maintaining or even improving the prediction accuracy. In the experiments, we replace all the transformer blocks in popular visual transformers, including MobileViT, EfficientViT, ViT, and Swin, with LTM-Transformer blocks, leading to LTM-Transformer networks with different backbones. The LTM-Transformer is motivated by reduction of Information Bottleneck, and a novel and separable variational upper bound for the IB loss is derived. The architecture of mask module in our LTM blocks which generate the token merging mask is designed to reduce the derived upper bound for the IB loss. Extensive results on computer vision tasks evidence that LTM-Transformer renders compact and efficient visual transformers with comparable or much better prediction accuracy than the original visual transformers. The code of the LTM-Transformer is available at https://github.com/Statistical-Deep-Learning/LTM.

PMID:40663671 | DOI:10.1109/TPAMI.2025.3588186

J Clin Endocrinol Metab. 2025 Jul 15:dgaf391. doi: 10.1210/clinem/dgaf391. Online ahead of print.

ABSTRACT

CONTEXT: Longer-term survival is possible for some patients with Anaplastic Thyroid Cancer (ATC). However, genomic factors associated with improved survival are poorly characterized.

OBJECTIVE: To develop a mathematical model to predict mutation-based survival risk in ATC.

DESIGN: Retrospective cohort study of 204 ATC samples from the cBioPortal database, divided into 80% training and 20% validation cohorts. Multivariate analysis identified prognostic genes, used to construct a point-based risk model. KEGG pathway enrichment and BRAF subanalyses were performed.

SETTING: Multi-institutional, international genomic database.

PATIENTS OR OTHER PARTICIPANTS: Samples were included if sequencing and survival data were available (N=204).

INTERVENTION(S): Not applicable.

MAIN OUTCOME MEASURE(S): The prespecified primary outcome was overall survival.

RESULTS: Fourteen genes were associated with increased risk – TET1, MAPK12, ATP10A, PIK3CA, MUC4, PNPLA2, PLD4, EGLN2, BSN, FLNC, RADIL, ZMYND8, FRAS1, RECQL4. More aggressive (n=37) and less aggressive cohorts (n=128) were determined using the maximally selected rank statistic, yielding a point threshold of 0.27. The predictive performance of the risk model demonstrated a C-index of 0.74. On Kaplan Meier analysis, 1-year survival differed for more aggressive patients (0%) compared to less aggressive patients (32%). For the validation cohort, survival remained significantly different between risk cohorts and on BRAF subanalysis. Each risk cohort subsequently underwent KEGG pathway enrichment analysis which showed significantly increased enrichment across several pathways for more aggressive tumors.

CONCLUSIONS: This model identifies mutated genes that are associated with the most aggressive ATCs and thus may aid in preoperative risk assessment when evaluating patients for surgery for curative intent.

PMID:40663630 | DOI:10.1210/clinem/dgaf391

Menopause. 2025 Jul 15. doi: 10.1097/GME.0000000000002574. Online ahead of print.

ABSTRACT

OBJECTIVE: To explore potential differences in physical activity and sedentary behavior volumes and patterns among postmenopausal women with and without premenopausal bilateral oophorectomy (PBO).

METHODS: Women with a history of PBO (n = 50) and age-matched postmenopausal referent women (n = 50) were recruited. Participants wore accelerometers on both ankles for 7 days. Volume metrics of sedentary behavior and physical activity, such as step counts, active time, and sedentary time, as well as the sedentary behavior and habitual physical activity distribution, and accumulation patterns, were quantified from the accelerometer data and compared between groups.

RESULTS: Metrics indicating volume of sedentary behavior and physical activity were not statistically different between groups. PBO was significantly associated with higher variability in stepping bout time (P = 0.022), indicating a potentially more complex walking pattern. In addition, PBO was significantly associated with lower variability in sedentary break time (P = 0.012), and lower activity time Gini index (Z = -2.428, P = 0.015). This suggests that women with PBO may have broken up their sedentary time with shorter and less variable activity bouts, and because they had relatively shorter average daily active time, they might be at a higher risk of subsequent adverse health outcomes such as low bone mineral density.

CONCLUSIONS: Although there were no differences in overall activity volume, some differences in activity patterns emerged between women with PBO and referent women. The study highlights the need for longitudinal research to understand how physical activity and sedentary behavior patterns evolve in postmenopausal women with a history of PBO.

PMID:40663581 | DOI:10.1097/GME.0000000000002574

PLoS One. 2025 Jul 15;20(7):e0327214. doi: 10.1371/journal.pone.0327214. eCollection 2025.

ABSTRACT

Despite global efforts, gender-based violence (GBV) remains a problem that affects millions of people, particularly women. The prevalence of GBV in Nigeria has not improved over time; women who experienced physical violence since age 15 increased from 28% in 2008 to 31% in 2018. Intimate partner violence (IPV) constitutes a large proportion of the GBV in Nigeria. Although perceptions of IPV have been studied, differentials in perceptions among the different generations of Nigerians are unknown. People’s perceptions of IPV are influenced by societal shifts and other factors that differ among people of various ages. This research examined inter-generational differentials in the perception of IPV in Nigeria. Data were obtained from the 2021 Nigeria Multiple Indicator Cluster Survey. A sample of 53,706 men and women was analyzed using descriptive statistics, and logistic regression models. The older generations of men and women in Nigeria have significantly better perceptions of IPV than the younger generation, but there is a significant variation at the sub-national level. The observed pattern is worrisome and calls for urgent action by the government to advance more positive perceptions of GBV in Nigeria if the country will make progress in reducing the prevalence of GBV and achieve a violence-free society.

PMID:40663580 | DOI:10.1371/journal.pone.0327214

PLoS One. 2025 Jul 15;20(7):e0326922. doi: 10.1371/journal.pone.0326922. eCollection 2025.

ABSTRACT

This study aimed to develop the Italian version of the Compassionate Engagement and Action Scales (CEAS) and examine its validity and reliability among Italian-speaking adults. A total of 374 (mean age = 23.11) Italian speaking participants took part in the study. All of them completed a questionnaire comprising the CEAS, together with measures of self-compassion, self-criticism, social support, empathy, well-being and general distress, used to estimate the scale’s convergent and criterion-related validity. Confirmatory Factor Analysis (CFA) revealed a satisfactory fit for a model in which three second-order factors (Self-compassion, Compassion for others and Compassion from others) were further articulated in two first-order factors (Engagement and Action). All the scales presented good reliability in terms of internal consistency. Correlations with measures of social support, empathy, self-compassion, self-criticism, well-being, and general distress indicated good convergent and criterion-related validity of the Italian version of the CEAS. Taken together, these results suggest that the CEAS can be properly used with Italian-speaking individuals in order to assess the three compassion flows in terms of both engagement and action.

PMID:40663573 | DOI:10.1371/journal.pone.0326922

PLOS Glob Public Health. 2025 Jul 15;5(7):e0004890. doi: 10.1371/journal.pgph.0004890. eCollection 2025.

ABSTRACT

Although the prevalence of childhood stunting has reduced in Bangladesh over time, it is still considered a major public health issue. While research has determined the risk factors for childhood stunting in Bangladesh, the factors that lead to reductions in stunting have received very little attention. Hence, we examined the factors contributing to the changes in childhood stunting over time in Bangladesh using a decomposition approach. In this study, data on childhood stunting of 41,013 under-5 children (U5C) were utilized from the Multiple Indicator Cluster Survey (MICS) 2012 and 2019, which are nation-wide cross-sectional surveys. Mixed-effect logistic regression analysis was adopted to identify the predictors of childhood stunting, and multivariate decomposition analysis was used to examine the factors contributing to the changes in childhood stunting over time. The prevalence of stunting declined from 41.9% in 2012 to 28.0% in 2019. Regression analysis showed that lower education of household head and mothers, older children, lower wealth status of households, unimproved sanitation facilities, and being urban residents were significant predictors of childhood stunting. The decomposition analysis revealed that around 86% of the overall decline in stunting resulted from the differences in the effect of independent variables. Furthermore, children’s age, maternal education, place of residence, and regions were significant factors contributing to the decline in childhood stunting prevalence over time based on both compositional and behavioral changes in these factors. Although childhood stunting has decreased in Bangladesh over time, the current prevalence remains high. Over 86% of the overall decline in stunting was due to the differences in the effect of independent variables. Interventions targeting children of mothers with lower education, infants, rural children, and children from households with lower wealth status and unimproved sanitation facilities may help to reduce the stunting prevalence in Bangladesh.

PMID:40663564 | DOI:10.1371/journal.pgph.0004890

PLoS One. 2025 Jul 15;20(7):e0328452. doi: 10.1371/journal.pone.0328452. eCollection 2025.

ABSTRACT

The seamless integration of shared bikes and metro systems promotes green and eco-friendly travel, yet the supply-demand imbalance of shared bikes around metro stations remains a critical challenge, making accurate demand prediction particularly crucial. Targeting metro-adjacent areas, this study proposes a method to identify shared bike trips connecting to metro usage, effectively filtering out approximately 24% of non-connecting travel records within the buffer zones. A predictive model integrating a Spatiotemporal Attention Graph Convolutional Network (STAGCN), Long Short-Term Memory (LSTM) network, and Informer is developed to forecast shared bike demand for metro connectivity. Specifically, the Informer model incorporates STAGCN to capture spatial correlations in bike demand and introduces an LSTM module to learn long- and short-term temporal dependencies. The final demand prediction is generated through a multilayer perceptron. Experiments conducted on shared bike and metro datasets in Shenzhen demonstrate that the proposed model achieves a coefficient of determination (R2) of 0.893, outperforming baseline models by 6.7% in prediction accuracy. Additionally, it exhibits lower Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) compared to traditional time-series forecasting methods. The proposed demand prediction model can assist operators in optimizing the allocation of shared bike resources, which is of great significance for improving user experience.

PMID:40663546 | DOI:10.1371/journal.pone.0328452

J Med Internet Res. 2025 Jul 15;27:e74299. doi: 10.2196/74299.

ABSTRACT

BACKGROUND: Perioperative education is crucial for optimizing outcomes in neuroendovascular procedures, where inadequate understanding can heighten patient anxiety and hinder care plan adherence. Current education models, reliant on traditional consultations and printed materials, often lack scalability and personalization. Artificial intelligence (AI)-powered chatbots have demonstrated efficacy in various health care contexts; however, their role in neuroendovascular perioperative support remains underexplored. Given the complexity of neuroendovascular procedures and the need for continuous, tailored patient education, AI chatbots have the potential to offer tailored perioperative guidance to improve patient education in this specialty.

OBJECTIVE: We aimed to develop, validate, and assess NeuroBot, an AI-driven system that uses large language models (LLMs) with retrieval-augmented generation to deliver timely, accurate, and evidence-based responses to patient inquiries in neurosurgery, ultimately improving the effectiveness of patient education.

METHODS: A mixed methods approach was used, consisting of 3 phases. In the first phase, internal validation, we compared the performance of Assistants API, ChatGPT, and Qwen by evaluating their responses to 306 bilingual neuroendovascular-related questions. The accuracy, relevance, and completeness of the responses were evaluated using a Likert scale; statistical analyses included ANOVA and paired t tests. In the second phase, external validation, 10 neurosurgical experts rated the responses generated by NeuroBot using the same evaluation metrics applied in the internal validation phase. The consistency of their ratings was measured using the intraclass correlation coefficient. Finally, in the third phase, a qualitative study was conducted through interviews with 18 health care providers, which helped identify key themes related to the NeuroBot’s usability and perceived benefits. Thematic analysis was performed using NVivo and interrater reliability was confirmed through Cohen κ.

RESULTS: The Assistants API outperformed both ChatGPT and Qwen, achieving a mean accuracy score of 5.28 out of 6 (95% CI 5.21-5.35), with a statistically significant result (P<.001). External expert ratings for NeuroBot demonstrated significant improvements, with scores of 5.70 out of 6 (95% CI 5.46-5.94) for accuracy, 5.58 out of 6 (95% CI 5.45-5.94) for relevance, and 2.70 out of 3 (95% CI 2.73-2.97) for completeness. Qualitative insights highlighted NeuroBot’s potential to reduce staff workload, enhance patient education, and deliver evidence-based responses.

CONCLUSIONS: NeuroBot, leveraging LLMs with the retrieval-augmented generation technique, demonstrates the potential of LLM-based chatbots in perioperative neuroendovascular care, offering scalable and continuous support. By integrating domain-specific knowledge, NeuroBot simplifies communication between professionals and patients while ensuring patients have 24-7 access to reliable, evidence-based information. Further refinement and research will enhance NeuroBot’s ability to foster patient-centered communication, optimize clinical outcomes, and advance AI-driven innovations in health care delivery.

PMID:40663377 | DOI:10.2196/74299