PLOS ONE Alerts: New Articles

Correction: A new criterion for defining tunnel portal failure using the strength reduction method

2026-06-12T14:00:00Z

by Chengya Hua, Hongzhou Zhang, Chenguang Song, Leihua Yao

Drug-induced gastric motility disorders: A disproportionality analysis from the FAERS and CVARD databases

2026-06-12T14:00:00Z

by Zhiheng Qian, Ni Jiang

Background

Delayed gastric emptying and gastroesophageal reflux represent critical yet frequently underrecognized complications in hospitalized patients, particularly in the context of polypharmacy. While multiple medication classes have been implicated in disrupting gastrointestinal motility, the comprehensive risk spectrum of individual drugs remains poorly characterized. This study aimed to conduct a comprehensive disproportionality analysis to identify drugs associated with delayed gastric emptying and reflux using large-scale pharmacovigilance data.

Methods

We analyzed adverse event reports from the FDA Adverse Event Reporting System (FAERS; 2004–2025; n > 58 million) and validated findings against the Canada Vigilance Adverse Reaction Online Database (CVARD). Disproportionality analysis was performed using Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), and Bayesian Confidence Propagation Neural Network (BCPNN). Weibull time-to-onset analysis was conducted to characterize temporal patterns of adverse event onset.

Results

Among the top 50 drugs screened, 20 demonstrated positive signals across all three algorithms. Glucagon-like peptide-1 (GLP-1) receptor agonists exhibited the strongest associations with gastric motility disorders, with semaglutide showing the highest ROR for impaired gastric emptying (ROR: 80.27; 95% CI: 76.39–84.34), validated in CVARD (ROR: 54.17). Insulin formulations, particularly insulin degludec (ROR: 18.90), bisphosphonates, angiotensin receptor blockers, and trofinetide also demonstrated significant signals. Weibull analysis revealed divergent temporal patterns, ranging from early-onset (trofinetide: median 6.6 days) to late-onset (immunoglobulin G: median 535.1 days).

Conclusion

This study identifies a broad spectrum of drug-associated gastric motility disorders with distinct temporal profiles. These findings provide evidence-based priorities for enhanced pharmacovigilance and inform clinical decision-making to mitigate this preventable cause of morbidity.

Musculoskeletal surgeons use mixed reasoning rather than pure Bayesian strategies in clinical practice

2026-06-12T14:00:00Z

by Robert Parisien, Alexander Drost, Amin Razi, Sina Ramtin, David Ring, Stein J. Janssen

Objectives

To inform efforts to promote regular and normalized Bayesian reasoning, we studied factors associated with the degree to which surgeons use Bayesian reasoning to navigate uncertainty across different clinical scenarios.

Methods

Science of Variation Group members (153; 58% North America, 30% Europe, 69% over 15 years of experience) completed an online survey reading 8 scenarios of test and treatment decisions and chose one of 4 answer options with higher scores indicating more Bayesian reasoning. Internal consistency of the survey was assessed using Cronbach alpha.

Results

The average Bayesian reasoning score across all scenarios was 3.0 (IQR 2.7–3.2) on a 4-point scale, indicating a relative context-dependent variability. Completely non-Bayesian reasoning was selected least often (8.6%, 90 of 1,044) and fully Bayesian reasoning represented 29% (301 of 1,044) of responses. Most surgeons showed mixed patterns (defined as reasoning in which prior probability is acknowledged but underweighted, without explicit probabilistic updating): 85% (121 of 142) used fully Bayesian reasoning at least once (121 of 142) while 42% (60 of 142) used completely non-Bayesian reasoning at least once. The Cronbach alpha was 0.43 suggesting the scenarios measured different aspects of clinical reasoning rather a unified construct.

Conclusions

The finding that surgeons use relatively context-dependent reasoning suggests an opportunity for surgeons to develop and practice Bayesian reasoning strategies both in training programs and in practice.

Long-term trends in height, weight and body mass index of children and adolescents in Macao Special Administrative Region (China), 2005–2020

2026-06-12T14:00:00Z

by Qingyuan Li, Yousong Yue

Objective

To assess long-term trends in height, weight and body mass index (BMI) among children and adolescents from 2005 to 2020 in Macao Special Administrative Region (SAR), China.

Methods

Height, weight and BMI data for Macao children and adolescents aged 6–18 years were obtained from the Physical Fitness Reports of Macao SAR Residents in 2005, 2010, 2015, and 2020. Sex-specific two-way analysis of variance was used to estimate the differences in means. The Bonferroni post hoc test was used for multiple comparisons.

Results

During the entire period, the average height, weight and BMI increased by 2.1 cm (95% confidence interval (CI): 1.6 to 2.6 cm), 4.0 kg (95% CI: 3.2 to 4.8 kg), and 1.1 kg/m² (95% CI: 0.8 to 1.3 kg/m²) for boys and 2.4 cm (95% CI: 1.9 to 2.9 cm), 2.6 kg (95% CI: 1.9 to 3.3 kg), and 0.5 kg/m² (95% CI: 0.3 to 0.8 kg/m²) for girls, respectively (p < 0.001). Boys and girls in most age groups experienced significant increases. The greatest increases in height occurred between 2005 and 2010 in both sexes. The weight and BMI of boys have continued to increase. The weight and BMI of girls continued to increase until 2015, and thereafter declined.

Conclusion

There were positive long-term trends in growth among Macao children and adolescents since 2005. Sex differences in changes of weight and BMI over the past five years may be related to the pandemic, and efforts are needed by governments and public health departments.

Dynamic analysis of critical maternal complications in tertiary hospitals in Wuxi: A study based on four years of monitoring data

2026-06-12T14:00:00Z

by Ye Xu, Peimin Hua, Ye Shen

Background

Severe maternal morbidity (SMM) is a significant public health concern. This study analyzed the incidence, trends, causes, and pregnancy outcomes of SMM in Wuxi to inform future clinical and public health strategies.

Methods

A retrospective analysis was conducted on 315 critical maternal cases identified from 156,435 deliveries in Wuxi between October 1, 2020, and September 30, 2024. Data were extracted from a citywide near-miss maternal surveillance system. Statistical analyses were performed using SPSS 25.0, employing chi-square tests and Cochran-Armitage trend tests to evaluate trends, and chi-square tests for comparisons between groups.

Results

The overall incidence of SMM was 0.20%. Initially, this rate remained stable at 0.19% across the first three cycles (P > 0.05); however, it significantly increased to 0.24% during the cycle from October 2023 to September 2024 (χ² = 5.24, P = 0.02). This increase was closely associated with a rise in the proportion of women of advanced maternal age (≥35 years), which reached 26.03% (χ² = 11.76, P = 0.001). Over time, the distribution of risk levels shifted. Initially, the high-risk group was dominant (63.29%), but in recent cycles, the moderate-risk group became more prominent (64.44%). The moderate-risk group was associated with a higher rate of adverse outcomes (25.00–25.71%) compared to the high-risk group (17.11–20.69%; χ² = 10.83, P = 0.01). Direct obstetric factors were the primary causes, accounting for 79.05% of cases, with obstetric hemorrhage being the most prevalent (53.97%). In contrast, the proportion of cases attributable to indirect obstetric factors increased from 17.81% to 26.67%, primarily due to heart disease and infectious diseases.

Conclusion

Improving maternal safety involves dynamic risk assessments, tiered referrals for moderate-risk pregnancies, better multidisciplinary management of complications, optimized emergency responses in primary care, and refined regional referral systems to reduce preventable SMM and mortality.

Correction: Efficacy and safety of the ayurvedic formulation ‘Trikatu’ as an add-on to standard care in dyslipidemia: Study protocol for a randomized, double-blind, placebo-controlled trial evaluating lipid parameters, and gut microbiota

2026-06-12T14:00:00Z

by Shruti Khanduri, Sophia Jameela, Suchanda Sahu, Sujata Devi, Bhogavalli Chandra Sekhara Rao, Kshirod Ratha, Richa Singhal, Latika Kaushik, Narayanam Srikanth, Rabinarayan Acharya

KYNU in macrophages contributes to the unique immune feature of LUAD via integrating single-cell and bulk RNA sequencing data: an exploratory analysis

2026-06-12T14:00:00Z

by Jie Yao, Changshuai Zhou, Liren Ding

Background

Lung adenocarcinoma (LUAD) is a predominant subtype of lung cancer associated with an unfavorable prognosis. However, the roles of the tumor microenvironment (TME) and Kynureninase (KYNU) in LUAD remain largely unclear. This study aimed to investigate the potential role of KYNU in macrophages and LUAD.

Methods

All LUAD related data were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The expression of KYNU was analyzed across different cell types following dimensionality reduction analysis. Immune cell infiltration and immunotherapy response prediction were performed using CIBERSORT and TIMER, respectively. Gene set variation analysis (GSVA) was employed for functional enrichment.

Results

Among all immune cells in LUAD, KYNU was primarily expressed in monocytes and macrophages. The upregulated genes in KYNU+macrophages group were significantly enriched in in gene ontology (GO) terms related to antigen processing and presentation. There were increased MHC-I/ MHC-II signal interactions between KYNU+macrophages and B cells as well as T cells. In LUAD patients with higher proportions of KYNU+macrophages, a significantly greater number of patients benefited from immunotherapy (p = 0.033). GSVA results indicated that the MHC pathway was significantly activated in high KYNU+macrophage group.

Conclusions

KYNU is primarily in LUAD macrophages, contributing to the distinct immune features and correlating with the enhanced antigen presentation in LUAD. This study preliminarily confirms that KYNU may serve as a potential biomarker for immunotherapy.

Perspectives of community-dwelling older adults with chronic diseases on Baduanjin practice: A qualitative study

2026-06-12T14:00:00Z

by Haixu Ji, Wei Li, Jinhua Zhang, Xuyan Liu, Jing Wang, Guanglei Dong

Background

With the accelerating aging of China’s population, Baduanjin has been promoted as a community-based exercise to enhance public health, particularly among older adults with chronic diseases. As a traditional Chinese exercise with a long history and profound cultural connotations, Baduanjin has attracted a large number of practitioners. However, the factors underlying the sustained practice of Baduanjin remain insufficiently explored.

Objective

This study aims to explore the factors underlying the persistence of older adults with chronic diseases in practicing Baduanjin.

Methods

A qualitative research approach was adopted in this study. 25 practitioners participated in semi‑structured face‑to‑face interviews. Thematic analysis was employed to analyze the data and generate core themes.

Results

The factors driving community-dwelling older adults with chronic diseases to persist in practicing Baduanjin were analyzed across five dimensions: perceived safety and learning-practice ease, improvements in physical health, promotion of mental well-being, enhancement of social functioning, and appreciation of traditional culture.

Conclusion

Community-dwelling older adults with chronic diseases maintain long-term Baduanjin practice not only due to its perceived safety and ease of learning and practice, but also because it embodies the essence of traditional Chinese culture. Moreover, regular practice contributes to improved physical health, promoted mental well-being, and enhanced social functioning in this population. Accordingly, Baduanjin shows considerable potential as a community-based exercise intervention to support health promotion among community-dwelling older adults with chronic diseases.

Identification of candidate sex hormone-associated genes and immune infiltration characteristics in osteoarthritis based on bioinformatics analysis and machine learning

2026-06-12T14:00:00Z

by Yishu Wang, Ling Zhu, Shuna Jin, Yuhan Wang, Zhaoxiang Zeng, Yunzhou Zuo, Xingliang Xiang, Xugui Li, Rongzeng Huang, Chengwu Song

Background

Sex hormones play critical roles in the pathogenesis and progression of osteoarthritis (OA), yet the hormone-related molecular networks remain poorly defined. This study aimed to identify candidate sex hormone-associated genes in OA and to explore their potential functional enrichment and immune-related characteristics using bioinformatics analysis.

Methods

OA gene expression data were obtained from the GEO database and integrated with candidate sex hormone-associated genes retrieved from GeneCards. The R package “limma” was then used to identify differentially expressed genes (DEGs) and sex hormone-associated DEGs (SADEGs). OA-associated SADEGs, termed OA-SADEGs, were selected using weighted gene co-expression network analysis (WGCNA), and their potential biological functions and pathways were explored by GO and KEGG enrichment analyses. Hub genes were identified using three machine learning models. xCell analysis was used to estimate immune infiltration and its associations with hub genes, and hub gene expression was further evaluated in external datasets and peripheral blood samples.

Results

We identified 32 sex hormone-associated genes in OA, enriched in extracellular matrix remodeling, receptor signaling, and antigen presentation pathways. Three candidate hub genes (LOXL1, HLA-DRA, and CYBB) were consistently upregulated in OA and showed significant correlations with immune infiltration scores. xCell analysis identified 13 differentially enriched immune cell types, of which three were associated with hub genes. External dataset analysis and peripheral blood qRT-PCR showed upregulation of LOXL1, HLA-DRA, and CYBB in OA samples.

Conclusion

This study integrated bioinformatics and immune analyses to identify candidate sex hormone-associated genes in OA. These findings provide associative bioinformatics evidence for sex hormone-associated molecular features in OA.

THSD7B promotes tumor progression and is associated with prognosis in gastric adenocarcinoma

2026-06-12T14:00:00Z

by Xinying Quan, Wei Cheng, Yao Pu, Hong Deng

THSD7B (thrombospondin type-1 domain-containing 7B) has been implicated in several malignancies; however, its role in gastric adenocarcinoma remains unclear. This study aimed to investigate the expression pattern, clinical significance, and biological function of THSD7B in gastric adenocarcinoma. Public datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were analyzed to evaluate THSD7B expression and its association with clinical outcomes. Functional enrichment analysis was performed to explore potential biological processes. In vitro assays, including cell proliferation, colony formation, wound healing, and Transwell invasion, were conducted following THSD7B knockdown or overexpression in gastric cancer cell lines. In addition, a xenograft model was established to assess tumor growth in vivo. THSD7B expression was significantly elevated in gastric adenocarcinoma tissues compared with normal controls and was associated with patient survival. Functional analyses suggested that THSD7B-related genes were mainly enriched in cell adhesion and cytoskeleton-associated processes. In vitro experiments showed that THSD7B knockdown suppressed cell proliferation, migration, and invasion, whereas overexpression produced the opposite effects. Consistent with these findings, THSD7B modulation was accompanied by alterations in adhesion-related signaling molecules and phenotype-associated protein expression. In vivo, THSD7B promoted tumor growth in xenograft models. In conclusion, THSD7B is associated with tumor progression and clinical outcomes in gastric adenocarcinoma and may be involved in the regulation of cell motility-related processes. These findings suggest that THSD7B may serve as a potential biomarker in gastric cancer.

Gender differences in the relationship between adult attachment and self-identity: A network analysis research among Chinese college students

2026-06-12T14:00:00Z

by Cui Lyu, Lili Xu, Ying Qian, Jia-Ming Wei

Objective

This study aims to investigate the relationship between adult attachment and self-identity among Chinese college students using network analysis, with a specific focus on examining gender differences in network structure, global strength. and edge strength.

Methods

A convenience sampling method was employed, and a total of 624 university students from China were surveyed using the Experiences in Close Relationships Inventory and the Self-Identity Questionnaire developed by Chinese scholars. Network analysis was conducted to estimate the structure of adult attachment and self-identity, identify central and bridge symptoms, and examine gender differences in network structure, global strength, and edge strength.

Results

The self-identity items formed a tightly connected cluster. Temporal disintegration constituted the core node bridging adult attachment and self-identity networks, followed by identity diffusion. Attachment anxiety was the strongest bridge node connecting adult attachment to self-identity, primarily associating with temporal disintegration. Network invariance test indicated no significant gender differences. Global strength invariance test was significantly higher in females than in males. Edge strength invariance test revealed that there were significant differences in the strength of some edges between males and females.

Conclusion

In the adult attachment and self-identity network, temporal disintegration and identity diffusion serve as core nodes, around which close clusters form. Attachment anxiety serving as the key bridge. Although the network structure is similar across genders, females show significantly stronger overall network connectivity. These findings highlight the importance of examining both local and global metrics when studying group differences in psychological networks.

Dynamic postural stability in individuals with ACL reconstruction versus healthy controls with insights into sex differences: A cross-sectional study

2026-06-12T14:00:00Z

by Wasim Labban, Juan Forero, Lindsey Westover, Mark Sommerfeldt, Stephanie Nathanail, Lauren Beaupre

Objectives

To compare dynamic postural stability, measured by time to stabilization (TTS) and postural stability indices (PSI), after double-leg counter-movement jump (CMJ) landing in individuals 9–24 months following anterior cruciate ligament reconstruction (ACLR) and healthy controls. Additionally, to explore the effect of sex and ACLR status on postural stability.

Methods

This cross-sectional laboratory-based study included 41 participants: 21 individuals (10 females) 9–24 months post-ACLR and 20 healthy controls (10 females). Participants performed double-leg countermovement jumps (CMJs) on force plates, landed, and maintained the landing position for 10 seconds. Time to stabilization (TTS), defined as the time (s) required for the ground reaction force to reach and maintain a stable state following landing, and postural stability index (PSI), a composite measure of the ability to maintain equilibrium during the transition from dynamic to static conditions, were calculated and compared between groups.

Results

The ACLR group exhibited significantly higher TTS values than healthy controls, indicating a longer duration to achieve stability. Specifically, the resultant vector TTS when combined from both force plates (RVTTS-C), and the vertical TTS in the operated leg (VTTS-op) was higher in the ACLR than the healthy controls (p = 0.03, p = 0.02, respectively). Furthermore, males with ACLR demonstrated higher VTTS combined (VTTS-C) and VTTS-op than females post-ACLR (p = 0.03, p < 0.01, respectively) and higher VTTS-op compared to healthy males (p = 0.03). There were no differences in PSIs between groups.

Conclusion

Our study revealed significant deficits in dynamic postural stability in individuals post ACLR, with notable sex differences. The findings suggest a need for targeted neuromuscular rehabilitation to improve landing stability post ACLR and reduce the risk of secondary injury. Further research is needed to understand sex-specific postural stability mechanisms for tailored rehabilitation.

Research on fine-tuning algorithms for Large Language Models integrating Uncertainty Modeling and External Memory Augmentation

2026-06-12T14:00:00Z

by Yumeng Ma, Yue Xing, Di Wu, Yining Zhou, Yun Zi, Ming Wang, Yingnan Deng, Shuaidong Pan

This paper proposes a parameter-efficient fine-tuning framework that integrates uncertainty modeling with external memory augmentation, aiming to improve robustness, confidence calibration, and contextual completeness in downstream natural language processing tasks. From the methodological perspective, the uncertainty modeling module explicitly characterizes uncertainty in inputs and intermediate representations through feature-level estimation, cross-layer propagation, and confidence calibration, thereby enhancing training stability and reducing the influence of noisy signals. Meanwhile, the external memory augmentation module employs key-value retrieval and gated fusion mechanisms to provide reusable contextual support, alleviating information loss caused by limited contextual summarization and improving representation quality under heterogeneous evaluation settings. Extensive experiments and ablation studies were conducted on text classification and named entity recognition tasks across multiple public benchmark datasets, using GPT-2 Small, GPT-2 Medium, and LLaMA3-8B as backbone models. The results demonstrate that the proposed framework consistently outperforms several mainstream fine-tuning methods in terms of accuracy, F1 score, and robustness, while also showing stable behavior under learning-rate sensitivity and missing-information settings. Overall, this study provides a novel perspective for efficient and interpretable fine-tuning paradigms, achieving a favorable balance among performance improvement, parameter efficiency, and deployment feasibility, and offering a practical basis for future extensions to more complex downstream scenarios.

Cost-effectiveness analysis of mammography screening for early detection of breast cancer in Nigeria

2026-06-12T14:00:00Z

by Ifeoma Jovita Nduka, Charles Ebuka Okafor, Obinna Ikechukwu Ekwunife

Mammography still remains the gold standard for breast cancer screening, considering its impact on breast cancer mortality. However, it has a relatively low utilization rate in Nigeria. Although the National Strategic Cancer Control Plan (NSCCP) has a goal of making screening services and early detection of cancer available for all Nigerians, there is currently no national breast cancer screening program implemented in Nigeria. The modelling study aimed to evaluate the cost-effectiveness of mammography screening from the healthcare provider’s perspective and to determine the appropriate screening interval for Nigerian women, aiming to enhance the efficiency and effectiveness of breast cancer detection programs. A state-transition Markov model was adapted to simulate annual and biennial mammography, breast cancer diagnosis, and treatment in a cohort of cancer-free Nigerian women aged 40 years and followed them for a lifetime. The study was conducted from the healthcare provider’s perspective. Disability-adjusted life year (DALY) averted, representing the health outcomes, was used to estimate the incremental cost-effectiveness ratio (ICER). Costs and outcomes were discounted at an annual rate of 5%. Annual mammography screening costs US$238.60, averted a DALY of 1.060, and was the most cost-effective intervention with an ICER of US$207.24 (95% CI US$213.31 – US$216.88)/DALY averted, which was below the willingness-to-pay threshold of $1074. Mammography screening strategies were estimated to be cost-effective from the healthcare payer’s perspective under the model assumptions. Annual screening showed the most favorable cost-effectiveness profile among the strategies evaluated, but this finding is model-dependent and should be interpreted as comparative economic evidence rather than a definitive screening recommendation. These results can inform future research, policy discussions, and consideration of sustainable financing for breast cancer screening in Nigeria.

Retraction: Enhanced heart disease diagnosis and management: A multi-phase framework leveraging deep learning and personalized nutrition

2026-06-12T14:00:00Z

by The PLOS One Editors

Editorial Note: Soil quality indicator-based land productivity modelling for agricultural sustainability

2026-06-12T14:00:00Z

by The PLOS One Editors

Correction: Predicting poor functional outcomes for patients with large computed tomography perfusion core infarctions treated with endovascular thrombectomy

2026-06-12T14:00:00Z

by The PLOS One Editors

Antioxidant, antibacterial, in vitro, and in silico α-glucosidase inhibition activities and chemical profiling of Usnea cornuta Korb

2026-06-12T14:00:00Z

by Deepa Karki, Anuraj Phunyal, Tika Ram Lamichhane, Deepika Karki, Achyut Adhikari

The total phenolic content and flavonoid content of the Usnea cornuta extract were evaluated as 210.31 ± 2.87 mg GAE/g and 22.42 ± 0.78 mg QE/g, respectively. The crude extract exhibited strong antioxidant activity (IC₅₀: 32.91 ± 1.27 µg/mL) and notable anti-diabetic effects via α-glucosidase inhibition, with IC₅₀ values of 2.59 ± 2.23 µg/mL for the dichloromethane extract. LC-MS analysis identified eleven metabolites like D-mannitol (1), galbinic acid (2), conhypoprotocetraric acid (3), roccellaric acid (4), diffractatic acid (5), haemathamnolic acid isomer (6), conprotocetraric acid (7), constictic acid I (8), salazinic acid II (9), menegazziaic acid (10), and one unknown compound (11). Among these, menegazziaic acid exhibited the strongest binding affinity of −9.7 kcal/mol with the target (PDB ID 3A4A), favorable molecular dynamics, binding free energy (MM/GBSA, and pharmacokinetic profiles. Furthermore, the extract showed strong antimicrobial activity, with inhibition zones of 23 mm and 26 mm at 10 mg/mL against Staphylococcus aureus ATCC 29213 and ATCC 245, respectively. These findings highlight the therapeutic potential of Usnea cornuta, specifically for managing oxidative stress, microbial infections, and type 2 diabetes.

Victim framing shapes attitudes across diverse contexts

2026-06-12T14:00:00Z

by Stephen J. Flusberg, Asher Donnelly, J. D. Jarolimek, Esmé Nix, Lili B. Davis, Boshang Yin, Lindsey Anderson, Dylan Ciolfi, Kevin J. Holmes

A person accused of victimizing others may be described as the “real” victim by their defenders to garner empathy and mitigate blame. Recent research shows that this rhetorical strategy, known as “victim framing,” can increase support for a man accused of sexually assaulting a woman. Little is known, however, about its effects in other contexts. Across five experiments (N = 2,941), we investigated whether victim framing generalizes beyond prototypical sexual assault cases. Participants read fictionalized news reports where one party was labeled the victim (or neither was) and expressed support for the individuals involved. We found significant framing effects across diverse scenarios: (a) a man accused of sexual assault who self-described as the victim; (b) a woman accused of sexually assaulting a man; (c) same-sex assault allegations involving men or women; (d) a celebrity or stranger accused of physically assaulting his girlfriend; and (e) a police officer who shot an unarmed civilian. As in prior work, only participants who explicitly cited the victim-related language as influencing their evaluations showed robust and reliable framing effects. Multiple observer characteristics (e.g., gender, political ideology) predicted attitudes in expected ways, yet victim framing effects persisted when controlling for these individual differences. Taken together, these findings are consistent with a social-pragmatic account of victim framing: many people treat a victim label as communicating relevant information and adjust their evaluations accordingly, while others either do not draw this inference or weigh other information more strongly. Our findings highlight the power and limits of explicit forms of linguistic framing.

Unimodal vs. multimodal deep learning for non-invasive MGMT promoter methylation prediction in glioblastoma: A systematic evaluation on the BraTS 2021 dataset

2026-06-12T14:00:00Z

by Freddy Oulia, Philippe Charton, Muhammad Kabir, Fabrice Gardebien, Cédric Damour, Frederic Cadet

Glioblastoma multiforme (GBM) is the most aggressive primary brain tumor in adults, with a median survival of 14.6 months under standard radiotherapy and temozolomide (TMZ) chemotherapy. The methylation status of the O⁶-methylguanine-DNA methyltransferase (MGMT) promoter is a critical biomarker predicting TMZ response; however, its determination currently requires invasive tissue sampling. Non-invasive prediction of MGMT promoter methylation from multiparametric MRI (mpMRI) through deep learning represents a compelling alternative, yet its clinical feasibility remains unresolved. Using the BraTS 2021 dataset (582 patients, four MRI sequences: FLAIR, T1w, T1wCE, T2w), we conducted a systematic comparative study of unimodal and multimodal deep learning approaches based on VGG-16, exploring 1,380 experimental configurations (unimodal: 192; multimodal: 1,188) across three imaging planes, eight slice counts, and three multimodal fusion strategies (early, intermediate, and late fusion). In the unimodal setting, the best model trained on T2w coronal images (32 slices, no transfer learning) achieved an accuracy of 0.6458 and an AUC of 0.6422 on the validation set, but dropped to 0.5586 and 0.5533 on the independent test set, revealing substantial overfitting attributable to limited dataset size. Strikingly, multimodal fusion consistently failed to outperform the best unimodal model, with all three fusion strategies plateauing at ~0.64 accuracy and ~0.64 AUC on validation data. Transfer learning improved generalization across train/test distributions at the cost of peak performance. These findings suggest, for the tested framework in this study, that MGMT methylation status prediction from mpMRI remains fundamentally constrained by dataset heterogeneity and size, irrespective of modality combination strategy, and that T2w coronal acquisitions could be more interesting in future data collection efforts.

Importance of developmental stage and microenvironment control in Zebrafish larvae cardiovascular studies

2026-06-12T14:00:00Z

by Patricia Fiorino, Luigi Fernandes Rosa Cauduro, Danielle Silberspitz Konig, Leonardo Fernandes Rosa Cauduro, Caio de Araujo Santos, Juliana Alves Kavai, Isadora Durigan Duarte, Anna Laura Viacava Américo

Zebrafish (Danio rerio) are widely used as models in cardiovascular research due to their rapid development, optical transparency, and genetic similarity to humans. However, the lack of standardized experimental conditions, particularly regarding developmental stage and microenvironmental parameters, limits reproducibility across studies. This study aimed to characterize cardiovascular function in Zebrafish larvae and evaluate the impact of developmental stage and environmental factors. Wild-type AB embryos were maintained under standard conditions, and heart rate (HR), cardiac output (CO), and ejection fraction (EF) were measured at 24, 30, 48, 52, 56, 72, 78, and 80 hours post-fertilization (hpf). The effects of variations in temperature (27.0, 27.5, and 28.0 °C) and pH (7.0, 7.4, and 8.0) were also assessed. Results showed a progressive increase in HR from 24 to 72 hpf, stabilizing thereafter. CO exhibited two phases of elevation: an early rise between 24–48 hpf and a stronger increase between 48–56 hpf. EF remained generally stable, with a transient reduction at 48 hpf. Cardiovascular performance reached a physiologically stable state after 72 hpf, defining a reliable window for functional studies. Environmental conditions modulated these parameters: temperature variation induced approximately 20% difference in HR and reduced EF, while CO was minimally affected. In contrast, pH variations within the physiological range had no significant impact on HR, CO, or EF. These findings highlight developmental and environmental variables that may influence cardiovascular measurements in Zebrafish larvae and support the development of more consistent experimental approaches in cardiovascular and toxicological research.

Delays in diagnosis and treatment of depressive disorder among young adults: A national online survey-based cross-sectional study

2026-06-12T14:00:00Z

by Rena Xu, David Pletta, Caitlynn Feng, Cassandra Morrow, Maya C. Clark, Badar Omar, Alex S. Keuroghlian, Sari L. Reisner

Background

Depression is highly prevalent among U.S. young adults and associated with long-term functional impairment and increased suicide risk. While delays in diagnosis and treatment of depression are well documented among older adults, the magnitude and predictors of such delays in the younger population are poorly understood.

Objective

To characterize the time to diagnosis and treatment of depressive disorder and predictors of diagnostic and treatment delay among young adults.

Methods

This cross-sectional study used a self-reported survey conducted via the online research platform Prolific in May 2025. Eligible participants were U.S. adults aged 18–35 years with a history of at least one depressive episode. Sociodemographic, clinical, and psychosocial characteristics, including age of depressive symptom onset, degree of social support, and frequency of social group engagement, were assessed. Primary outcomes were probability of not receiving a depressive disorder diagnosis despite symptoms, time from symptom onset to diagnosis, probability of not seeking treatment, and time from symptom onset to treatment. Secondary outcomes were perceived treatment effectiveness and current symptom control.

Results

In total, 871 respondents met inclusion criteria. Of those with one or more lifetime depressive episodes, 46.2% reported never receiving a depressive disorder diagnosis. Median time from symptom onset to diagnosis was 3 years (IQR: 0–7). Over a quarter (27.4%) never sought treatment; among those who did, 93.5% received care, but 31.4% experienced a delay of 1–4 years, and 28.8% experienced a delay of 5 + years. Symptom onset in childhood (ages 0–12) or adolescence (ages 13–17) was associated with longer time to diagnosis and treatment and lower perceived treatment effectiveness. Greater social support was associated with shorter time to diagnosis; lower probability of never receiving a diagnosis, never seeking treatment, or experiencing prolonged treatment delay; and higher perceived treatment effectiveness and current symptom control. Frequent engagement in social groups was also associated with greater perceived treatment effectiveness.

Conclusions

Among U.S. young adults, prolonged delays in depression diagnosis and treatment are common. Early symptom onset is associated with longer delays and worse outcomes, whereas greater social support is associated with shorter delays and more favorable outcomes. These findings highlight the need for further research to clarify causal mechanisms and for interventions to promote timely diagnosis and treatment among young adults at risk for depression.

Altered brain–behavior coupling during inhibitory control in ankylosing spondylitis: ERP evidence from NoGo-P3 component

2026-06-12T14:00:00Z

by Lei Zhang, Fang Lu, Yuxin He, Lin Tang, Li Zhang, Jing Xiang, Chun rong Gu, Su wan Guo, Zheng hong Yu

Background

Cognitive dysfunction has increasingly been recognized in Ankylosing Spondylitis (AS), yet the neural mechanisms underlying inhibitory control in this population remain insufficiently characterized. Rather than reflecting a simple global deficit, cognitive alterations in AS may involve task-dependent changes in the coupling between neural activity and behavioral performance. This study examined executive control in AS using a Go/NoGo paradigm and focus on brain-behavior coupling during inhibitory processing.

Methods

16 male patients and 23 age-matched healthy controls completed a Go/NoGo task while undergoing 32-channel EEG recording. ERP analyses focused on N2 (200–300ms) and the late positive component in the NoGo condition (hereafter termed NoGo-P3; 400–600ms). Mean amplitudes were extracted at fronto-parietal midline electrodes. To directly test group differences in brain–behavior coupling, linear regression models including the ERP × Group interaction term were fitted. Theta-band (4–7 Hz) power within the 400–600 ms window was additionally analyzed using FFT-based spectral estimation.

Results

Behaviorally, AS patients showed lower Go accuracy and longer Go reaction times, together with a tendency toward higher NoGo accuracy. In AS patients, NoGo accuracy positively correlated with NoGo-P3 amplitude at FCz (r = .64, p = .009) and Cz (r = .55, p = .035), whereas these associations were not significant in controls. Direct group comparison showed a significant ERP × Group interaction at FCz (b = 0.084, p = 0.033), indicating that the relationship between NoGo-P3 amplitude and inhibitory accuracy differed between AS patients and healthy controls, while Cz (b = 0.051, p = 0.106) showed a similar but non-significant trend. Complementary theta analyses revealed enhanced post-stimulus theta power in centro-parietal regions during NoGo processing in AS.

Conclusions

The findings suggest altered brain–behavior coupling during inhibitory control in AS, with the most robust evidence emerging from the NoGo-P3 component at FCz. This pattern is consistent with greater reliance on effortful control-related neural recruitment during successful inhibition and may represent a candidate electrophysiological marker of altered executive processing in AS.

A reliability assessment of the basic erosive wear examination and the tooth wear evaluation system 2.0 utilizing intraoral scan data

2026-06-12T14:00:00Z

by Maria Lorens, Iwona Tomaszewska

Objectives

This study assessed the reliability and clinical applicability of two tooth wear screening indices—Basic Erosive Wear Examination (BEWE) and the Tooth Wear Screening module of the Tooth Wear Evaluation System 2.0 (TWES 2.0)—using intraoral scans.

Materials and methods

A total of 246 anonymized intraoral scans from adult patients were independently evaluated by two calibrated examiners. Examiner calibration was performed prior to the study using a representative set of intraoral scans. Calibration was repeated until consensus regarding the application of the scoring criteria was achieved before formal data collection. Scores for all sextants were recorded for BEWE and TWES 2.0. Inter-rater agreement was primarily assessed using weighted kappa coefficients, as BEWE and TWES 2.0 are ordinal, numerically coded indices. Wilcoxon signed-rank tests were additionally used to assess systematic directional differences between paired scores. Statistical significance was set at p < 0.05.

Results

BEWE demonstrated good reliability, with weighted kappa values ranging from 0.760 to 0.851 across sextants, 0.868 for the total BEWE score, and an overall weighted kappa of 0.841. TWES 2.0 showed moderate to good reliability, with weighted kappa values ranging from 0.543 to 0.761 across sextants and an overall weighted kappa of 0.715.

Conclusions

Both BEWE and TWES 2.0 are reliable and practical for screening noncarious tooth wear via intraoral scans. BEWE showed slightly higher inter-rater consistency, whereas TWES 2.0 allows more detailed evaluation of occlusal and palatal surfaces. These indices can support standardized monitoring, early detection, and clinical management of tooth wear. Examiner calibration remains essential, particularly for TWES 2.0.

Clinical trial registration

This was a retrospective reliability study and did not involve an interventional clinical trial; therefore, registration was not applicable.

Cross-dataset benchmarking of machine learning models for marine and atmospheric environmental prediction

2026-06-12T14:00:00Z

by Xuehua Zhou, Hanming Zhang, Tiantian Du, Quanbo Yuan, Huijuan Wang

Accurate prediction of marine and atmospheric environmental variables is important for climate adaptation, ecosystem management, and operational decision-making, yet practitioners still lack clear guidance on which machine-learning models are reliable across heterogeneous environmental tasks. We therefore developed a unified, leakage-aware benchmark across nine datasets, of which seven passed quality checks for modeling, spanning chlorophyll-a, wind speed, hydrographic observations, biotoxins, and bathymetry, and compared representative linear, tree-based, and sequence models under a common evaluation framework. Results show strong heterogeneity across tasks and model classes: tree ensembles are robust baselines for tabular problems, LSTM-based recurrent sequence modeling is most useful when temporal structure is central, and predictive skill depends more on target structure and covariate quality than on model complexity alone. Within the observational settings represented in this benchmark—predominantly Chinese coastal/estuarine and regional marine datasets, plus one atmospheric reanalysis wind task and one global cast archive—quality-controlled chlorophyll-a is comparatively predictable, whereas event-driven biotoxins and bathymetry inversion remain difficult under the current predictors. These findings provide practical guidance for researchers and environmental monitoring practitioners working in similar data regimes, but they should not be assumed to transfer automatically to untested regions such as the North Atlantic, the Mediterranean, or tropical open-ocean systems without further validation.

An optimization method for flexible interconnection planning based on improved CNN-LSTM prediction and tunable relative entropy-driven chaotic evolution

2026-06-12T14:00:00Z

by Xiaoyan Zhao, Xubin Xing, Xiaoyan Guo, Jian Chao, Dapeng Hu, Xiangtao Zhuan

As power systems evolve towards greater intelligence and flexibility, flexible interconnection technology has emerged as a critical means to enhance operational reliability and economic performance. This paper presents a data-model dual-driven planning methodology for flexible interconnection systems, integrating a multi-scale spatio-temporal cross-enhanced CNN-LSTM model for load forecasting with a Chaotic Evolutionary Optimization (CEO) algorithm to optimize system design. The proposed framework first constructs an improved CNN-LSTM hybrid architecture, trained on historical load data and simulated feature sets, to predict future load profiles. A novel Tunable Relative Entropy (TRE) metric is introduced as a complementarity quantification index, forming a multi-objective function that incorporates system balance, reliability, economy, and spatio-temporal complementarity. The CEO algorithm is then employed to solve the optimization model, determining the optimal system configuration and operational parameters. Experimental evaluations demonstrate that the forecasting module achieves high accuracy, with a Mean Squared Error (MSE) of 0.000368 and a Mean Absolute Error (MAE) of 0.006334. Moreover, the TRE index improves complementarity efficiency by 3.8%. By leveraging the predictive capability of the hybrid neural network and the CEO algorithm’s optimization efficacy, the proposed approach not only reduces load fluctuation indices but also enhances planning efficiency and operational economy, offering a viable pathway for intelligent power system development.

Identification of AC025811.3 and AC012354.6 as two critical survival-related lncRNAs for uterine corpus cancer

2026-06-12T14:00:00Z

by Lu Pu, Rui Ou, Zhaomin Deng, Hao Jiang

Uterine corpus endometrial carcinoma (UCEC) ranks as the most frequently diagnosed gynecologic malignancy and the second leading cause of gynecologic cancer-related mortality. Long non-coding RNAs (lncRNAs) have emerged as critical regulators of gene expression and tumor biology; however, their prognostic significance in UCEC remains largely unexplored. To systematically identify survival-associated lncRNA biomarkers, we integrated clinical and transcriptomic data from two independent cohorts: TCGA-UCEC (548 tumor, 35 normal) and CPTAC-Uterus (102 tumor, 15 normal). Following upper quartile normalization and ComBat-based batch effect correction, differentially expressed lncRNAs (FDR < 0.01, |log2FC| > 2) were identified using Student’s t-test. The intersecting set of consistently dysregulated lncRNAs from both cohorts was subjected to Cox proportional hazards regression to identify survival-associated candidates. Functional inference was performed through Spearman correlation with protein-coding genes and Gene Set Enrichment Analysis (GSEA) of KEGG pathways. A total of 550 lncRNAs were consistently downregulated and 148 were upregulated in UCEC across both cohorts. Cox regression identified 30 survival-associated lncRNAs (FDR < 0.01), all with elevated expression correlating with worse overall survival. The top two candidates, AC025811.3 and AC012354.6, showed significant stage-dependent expression patterns across FIGO stages I–IV and were functionally enriched in immune regulation and carbohydrate metabolism pathways. In conclusions, AC025811.3 and AC012354.6 represent novel candidate prognostic lncRNA biomarkers in UCEC. Experimental validation, including FISH-based tissue localization and staged quantification, and functional assays, is warranted to confirm their biological roles.

Empowering rural governance with digital technology: Deep learning models for automated detection of rural buildings using remote sensing images

2026-06-12T14:00:00Z

by Jingling Zhong, Youcai Xie, Lixia Li, Chuanlin Shi

Building detection from drone imagery represents a transformative approach to rural governance by enabling precise spatial data acquisition for critical applications including illegal construction monitoring, disaster assessment, and cadastral mapping. However, automated detection systems face persistent challenges including extreme scale variations in rural buildings, complex background interference from vegetation and shadows leading to boundary ambiguity, and severe scarcity of high-quality annotated datasets that limit model generalization. To overcome these limitations, this study introduces an integrated framework featuring three innovative components: the Multi-scale Hybrid Attention module employs parallel convolutional pathways with channel and spatial attention to dynamically capture multi-scale features while suppressing background noise; the Dynamic Feature Pyramid Network utilizes content-aware routing to adaptively fuse hierarchical features for optimal scale-invariant representation; and the Progressive Contrastive Learning strategy leverages both labeled and unlabeled data through hard sample mining to enhance discriminability under data constraints. Extensive experiments validate the model’s efficacy, achieving a mean Intersection over Union (MIoU) of 87.3%, pixel accuracy (PA) of 94.2%, and mean Average Precision (mAP) of 89.6% on the Massachusetts Buildings Dataset, substantially surpassing benchmarks like U-Net (80.1% MIoU), SegNet (78.9% MIoU), and DeepLabV3+ (82.4% MIoU), with ablation studies confirming critical module contributions (e.g., MIoU drops to 81.5% without MHA). The framework demonstrates robust cross-dataset generalization (72.3% MIoU on Chinese rural data) and effective problem resolution, establishing a scalable solution for intelligent rural governance through accurate building extraction. The dataset and code used in this study have been uploaded to the GitHub website: https://github.com/xiexie1234567890/rural_building_detection/tree/main.

Implementation bottlenecks of near point of care HIV viral load monitoring for children and young people in Tanzania: A Qualitative Study

2026-06-12T14:00:00Z

by Perry Msoka, Alan Mtenga, Rehema Maro, Rhoda Akello, Iraseni Ufoo Swai, Blandina T. Mmbaga, Ria Reis, Marion Sumari-de Boer

Background

Near point-of-care (n)POC Human Immunodeficiency Virus (HIV) viral load (VL) monitoring, consisting of VL testing in laboratories close to HIV treatment facilities, improves turnaround time from sampling to result. Other benefits of using nPOC monitoring are reduced laboratory workload, and limited loss of results, all leading to improved clinic retention and treatment adherence. However, the specific implementation bottlenecks are still unclear. This study aims to investigate the bottlenecks in the implementation of nPOC HIV VL monitoring among children and young people (ages 0–24 years) living with HIV, as experienced by healthcare workers (HCWs) in Tanzania.

Methods

We conducted observations in clinics and in-depth interviews with HCWs from January 2023 to January 2024 at Tanzanian intervention sites within the East Africa Point-of-Care Viral Load Monitoring (EAPOC-VL) study. The EAPOC-VL study was conducted in four countries in East Africa (Tanzania, Kenya, Rwanda, Uganda). It was a cluster-randomised controlled trial, in which participants were tested at three time points (months 0, 6, and 12). We purposively selected 25 HCWs involved in implementing nPOC at the intervention sites for in-depth interviews. We interviewed HCWs at baseline (month 0) and after the initiation of nPOC HIV VL monitoring (months 1 and 6). We conducted deductive thematic framework analysis using the Measurement Instrument for Determinants of Innovations (MIDI), to which an inductive approach was added to identify facilitators and barriers across intervention, provider, organisational, social and political contexts. We used NVivo 12 to organise the data.

Results

A total of 75 interviews were conducted among 43 HCWs across 3 time points: 33 at baseline (T0), 25 at month 1 (T1; 19 participants from T0 and 6 new participants) and 17 at month 6 follow up (T2;6 participants from T1, 7 returning participants from T0 and 4 new participants). Observations and interviews showed that nPOC HIV VL testing improved result turnaround time and enabled same-day counselling, which motivated both HCWs and clients. This data showed that knowledge, confidence, and adherence to procedures after training. Near POC, HIV VL was supported by compatibility with existing practices, strong teamwork, and management commitment. However, challenges included clients waiting at the clinic for over two hours to receive their results, the scarcity of resources, such as rooms and electricity, and staff shortages. Finally, delays were observed when samples had to be transported to nearby laboratories.

Conclusion

Near POC HIV VL monitoring shortens turnaround times and enables immediate counselling. To maximise these benefits, there is a need to prioritise investment in staff training, infrastructure, improving sample handling/turnaround time and guideline alignment. Developing these areas will enhance service delivery and allow for improved outcomes among children and young people living with HIV.

Development and validation of search hedges for Transgender and Gender Diverse (TGD) populations in Ovid MEDLINE and Ovid APA PsycInfo

2026-06-12T14:00:00Z

by Ryn Gagen, Scott Marsalis, Chi Dinh, Milo Applejohn, Rosa Shah, Ophelia Xiner Tong, Zack Marshall, Avery Everhart

Introduction

This paper describes the development and validation of highly sensitive search hedges for Ovid MEDLINE and Ovid APA PsycInfo that effectively identify literature on transgender and gender diverse (TGD) populations.

Methods

Two librarians developed the search hedges using relevant keywords and controlled vocabulary terms, building on previous work on identifying transgender populations in evidence synthesis. The hedges were tested and refined to capture diverse and expansive gender identities across cultures and disciplines. The hedges were validated for sensitivity using a gold standard set of 144 articles from the Knowsy portal of evidence syntheses tagged as Two-Spirit, transgender, or gender non-binary. To assess precision an international research team of subject experts independently screened a randomized sample of search results in a two-stage screening process with an additional screener resolving disputes.

Results

The final search hedges demonstrated 100% sensitivity in both MEDLINE and APA PsycInfo, identifying all 144 relevant articles from the Knowsy gold standard set. The MEDLINE search hedge achieved a 71% precision, and the APA PsycInfo hedge achieved a 67% precision. These results balance comprehensive retrieval while minimizing non-relevant articles for an efficient screening process.

Conclusions

These search hedges in MEDLINE and APA PsycInfo are valuable tools for researchers and librarians to more effectively identify literature on TGD populations. These tools will be crucial for ongoing work in addressing gaps in research and health disparities faced by TGD populations and will be particularly valuable for researchers conducting evidence synthesis projects related to this population.