Browse Articles

Rumination and subjective well-being: The chain mediating role of emotion regulation difficulties and problematic social media use

Objectives Based on the Compensatory Internet Use Model and the I-PACE framework, the present study develops a serial mediation model grounded in a cognition-emotion-behavior pathway. Specifically, this study examined whether difficulties in emotion regulation and problematic social media use were associated with the relationship between rumination and subjective well-being among a sample of vocational college students within the context of increasingly digitalized everyday life. Methods A survey was conducted among 402 vocational college students using the Ruminative Responses Scale, the Brief the Version of the Difficulties in Emotion Regulation Scale, the Problematic Social Media Use Questionnaire, the Satisfaction with Life Scale and the Positive and Negative Affect Schedule. Results Rumination was positively associated with difficulties in emotion regulation and problematic social media use, whereas subjective well-being was negatively associated with all three variables. Difficulties in emotion regulation were also positively correlated with problematic social media use. Mediation analyses identified three significant indirect pathways: the mediating role of difficulties in emotion regulation, accounting for 35.49% of the total effect; the mediating role of problematic social media use, accounting for 22.35%; and the serial mediating role of difficulties in emotion regulation and problematic social media use, accounting for 12.16%. Conclusions The findings suggest that difficulties in emotion regulation and problematic social media use are associated with the relationship between rumination and subjective well-being among the sampled vocational college students. These findings may provide preliminary implications for interventions targeting emotion regulation and problematic social media use among vocational college students.

An informed regression-based knowledge distillation framework for simultaneous prediction of physical and mechanical properties of thermoset epoxy polymers

Abstract Epoxy polymers are widely used due to their multifunctional properties, however their complex 3D molecular structure, multi-component nature, and lack of curated datasets have limited the application of machine learning (ML) for these materials.Existing ML studies are largely restricted to simulation data, specific properties, or narrow constituent ranges. To address these limitations, we developed an Informed Regression-based Knowledge Distillation (R-KD) framework for predicting multiple physical (glass transition temperature, density) and mechanical properties (elastic modulus, tensile strength, flexural strength, adhesive strength) of thermoset epoxy polymers. The model was trained on experimental literature data covering diverse monomer classes (9 resins, 37 hardeners). The best-performing single-task regression model per target property serves as teacher model capturing nonlinear feature-property relationships, while a unified neural network student model learns distilled knowledge across all properties simultaneously. By encoding the target property as an input feature, the student model leverages cross-property correlations. Molecular-level descriptors extracted from SMILES representations using RDKit create a physics-informed model. Comparative analysis demonstrates superior or comparable prediction accuracy over multi-task NN baseline model and conventional ML models. Simultaneous multi-property prediction further improves accuracy through information sharing across correlated properties. The proposed framework enables accelerated design of novel epoxy polymers with tailored properties.

Latent classes and predictors of aggression trajectories in Korean adolescents: Implications for targeted prevention

School violence and juvenile crime are emerging social problems in Korea. Adolescent aggression, an important mediator linking risk factors in the developmental environment to more serious deviant behavior, exerts long-term cumulative effects into adulthood. Thus, a longitudinal examination of aggression among adolescents is crucial. This study identified differential longitudinal trajectories of aggression in Korean adolescents and investigated the predictors distinguishing these latent classes. Data were used from 2,016 adolescents from Waves 1–5 (2018–2022) of the Korean Children and Youth Panel Survey. The overall trajectory of adolescent aggression was explored using latent growth curve modeling, and latent class growth modeling was conducted to identify possible heterogeneity in aggression trajectories. Multinomial logistic regression was used to analyze the predictors of each latent class. Latent growth curve modeling revealed an overall decline in aggression across adolescents. Latent class growth modeling identified three latent classes—moderate-decreasing, low-increasing, and high-decreasing—indicating heterogeneous developmental patterns of adolescent aggression. Key predictors of latent class included gender, perceived economic status, impulsivity, depression, smartphone dependency, negative parenting attitude, and negative peer relationships. Adolescent aggression follows heterogeneous developmental pathways shaped by individual, family, and school factors. These findings highlight the importance of identifying distinct aggression trajectory groups and their key predictors and demonstrate the value of a trajectory-based approach to guide targeted prevention strategies.

Learning operational compatibility in distribution networks under multi-step renewable dynamics

Alpine crossing took a heavy toll on Hannibal’s elephants and troops

Durvalumab With Radiation Therapy in Patients With Inoperable Locally Advanced Non–Small Cell Lung Cancer Ineligible for Concurrent Chemoradiotherapy (DART)

PURPOSE Concurrent platinum-based chemotherapy and radiation therapy (cCRT) followed by consolidative durvalumab is the standard of care (SoC) for fit patients with inoperable, locally advanced non–small cell lung cancer (LA-NSCLC). However, no SoC exists for patients who are cCRT-ineligible because of age, comorbidities, or frailty. Here, we investigated the efficacy and adverse events (AEs) of concurrent and consolidative durvalumab with definitive radiation therapy (RT) without chemotherapy. METHODS In this multicenter, single-arm, prospective phase II study, patients received conventionally fractionated RT plus concurrent and consolidative durvalumab (1,500 mg fixed dose once every 4 weeks) for up to 12 months. The primary end point was 2-year progression-free survival (PFS) of 36% compared with historical results of 20% with sequential CRT (86% power). Additional end points included overall survival (OS) and cancer-specific survival (CSS). RESULTS Fifty-eight patients (median age 82 years [IQR, 76-86]; 16 [28%] PD-L1–negative; eight [14%] Eastern Cooperative Oncology Group [ECOG] score 2; 46 [79%] ECOG score 1; four [7%] ECOG score 0) were treated per protocol. The study met its primary end point with a 2-year PFS of 39% (one-sided CI, 29 to 100) and a 2-year OS of 54%. Better performance status and PD-L1 positivity were associated with improved PFS; better ECOG was associated with improved OS; PD-L1 positivity was associated with better CSS. Grade 3/4 treatment-related AEs occurred in 12 (21%) patients. Grade 5 AEs occurred in four (7%) patients (radiation pneumonitis (n = 2) and cardiac arrest (n = 2)). Durvalumab was discontinued early because of AEs in 18 (31%) patients. CONCLUSION Thoracic RT with concurrent and consolidative durvalumab is a promising treatment option for cCRT-ineligible patients with LA-NSCLC, demonstrating better PFS with a favorable safety profile compared with historical controls.

PyPeCT2S: Pythonic paediatric computed tomography to strength with automatic landmarking for the automation of bone strength analysis in children

Quantitative computed tomography (QCT) based finite element analysis (FEA) models have been used to accurately predict bone strength. However, the process is time-consuming and requires a trained professional to provide manual input during several steps. The aim of this work is to automate the FEA processes of the computed tomography to strength (CT2S) pipeline applied to the paediatric femur, so that use of the pipeline requires less training, is more user-friendly and can be run for a large cohort. A deployable application was built using Python and Qt to create a repeatable, extensible, automatic, and contained platform called pythonic paediatric computed tomography to strength (PyPeCT2S), with specific attention to the development of automatic landmarking for the paediatric cohort. In this study, the computed tomography (CT) scans of 69 children were included, and FEA models were created using the PyPeCT2S pipeline. The models were subjected to four-point bending for both landmarking methods (automatic versus manual). The FEA critical moment against age results showed comparable values to existing experimental research that utilises equivalent boundary conditions, with values from 0.19–167.94 Nm. The automatic landmarking methodology was shown to produce minimal differences in location and FEA results, compared to manual landmarking, but was substantially faster to operate. The overall pipeline performance showed a mean time reduction of 49–61% and a maximum of 70% against the native pipeline, reducing completion time from 35 to 13 min. Time savings came from both process optimisations and improved user interaction pathways. The work demonstrates that a pythonic approach is a step change to speed up the prediction of FE-based bone strength while still allowing interaction and substantially limiting the chance of human error. Overall, the pythonic approach provides simpler operation and time efficiencies, allowing the tool to be used by clinicians and deployed in the clinical setting in future.

Illumina and PacBio insights into the metataxonomic enrichment of fish pond sediment microbiota via Candida albicans co-culture

Breast Cancer Follow-Up and Surveillance After Primary Treatment: ASCO Guideline Update

PURPOSE To offer clinicians recommendations concerning breast cancer follow-up and surveillance after primary treatment. METHODS ASCO convened an Expert Panel to develop recommendations based on a systematic review and a formal consensus process. RESULTS One randomized controlled trial (RCT) was identified and formed the evidentiary basis for the surveillance mammography guideline recommendation. No RCTs were identified that evaluated different follow-up approaches by risk of relapse in patients treated for early-stage breast cancer. Given the dearth of evidence identified in the systematic review of the literature, formal modified Delphi consensus-based recommendations were generated. RECOMMENDATIONS The guideline offers recommendations on the role of a risk-based approach to the frequency and intensity of follow-up among patients with breast cancer in the adjuvant setting. Regular history, physical examination, and mammography are recommended for breast cancer follow-up. Either virtual or in-person visits can be offered. Certain patients at high risk of cancer recurrence warrant closer surveillance, such as those patients with locally advanced disease or residual disease following neoadjuvant chemotherapy. Recommendations on the use of blood-based biomarkers and supplemental imaging are provided. Guideline recommendations will be updated once additional evidence-based tools become available to better individualize surveillance. Additional information is available at www.ascopubs.org/topics/asco-guidelines/breast-cancer .

Using nominal group technique to select an HIV status disclosure decision aid for adaptation in Georgia

Introduction HIV status disclosure decision-making is a complex process influenced by stigma, relationship dynamics, and anticipated social consequences. Although decision aids can support individuals in navigating such decisions, no disclosure decision-support interventions have been adapted for use in the Georgian context. This study aimed to identify and prioritize an existing evidence-based intervention for adaptation to support HIV disclosure decision-making among people living with HIV (PLWH) in Georgia. Methods We used the Nominal Group Technique (NGT), a structured consensus method, to elicit and prioritize stakeholder perspectives. Two separate NGT sessions were conducted with HIV care providers (n = 12) and PLWH (n = 10), followed by a joint session to reach consensus. Prior to the sessions, nine evidence-based disclosure decision-support interventions from HIV, mental health, and substance use fields were identified through a desk review and grouped into session-based, paper-based, and digital formats. Participants generated ideas, discussed advantages and limitations, and ranked intervention formats and specific interventions. Descriptive content analysis was used to summarize discussion themes. Results Providers prioritized digital interventions, emphasizing accessibility and scalability, whereas PLWH preferred session-based interventions, highlighting the importance of trust, individualized support, and peer involvement. Within these formats, providers favored a structured digital program, while PLWH selected an individual session-based intervention focused on disclosure to family members. Adaptation priorities included incorporating peer educators, addressing disclosure to different social and healthcare contexts, and including locally relevant content on legal issues, treatment adherence, and available support services. In the joint session, consensus was reached to prioritize the intervention selected by PLWH. Conclusions This study identified a priority disclosure decision-support intervention for adaptation to the Georgian context, emphasizing the importance of patient-centered and contextually tailored approaches. Future research will focus on adapting the selected intervention and identifying appropriate implementation strategies to support pilot testing and integration into HIV care services.

Effect of deep margin elevation with different base materials on periodontal health in a clinical study

Abstract This prospective clinical investigation evaluated the effects of proximal box elevation using three base materials on periodontal tissues in premolar teeth treated with direct composite restorations. In endodontically treated maxillary premolars, gingival crevicular fluid (GCF) samples were collected after restoring cavities using three base materials: resin-modified glass ionomer (RMGI), flowable composite (Tetric N-Flow), and injectable hybrid composite (Beautifil Flow Plus). Periodontal health was assessed by measuring interleukin-1 (IL-1) and tumor necrosis factor (TNF) concentrations, bleeding on probing (BOP), and pocket depth (PD) immediately and at 3 months postoperatively. Data were statistically analyzed using one-way ANOVA to compare groups, Levene’s test to assess homogeneity of variances, and paired t-tests and chi-square tests were used to assess differences within groups. the results revealed that the levels of IL-1, TNF, and PD differed significantly among the three base materials at different time points. The Beautifil Flow Plus group showed the highest TNF levels, the most substantial absolute reduction, and the lowest reduction in IL-1 levels. However, there were no significant differences in BOP among the three materials at any time interval. Within the limitations of this three-month study, all materials were clinically acceptable, though Tetric N-Flow and RM-GI showed a more favorable short-term inflammatory profile than Beautifil Flow Plus. However, due to the limited sample size and short follow-up, no definitive claims regarding long-term periodontal compatibility or clinical superiority can be made.

Efficient and lightweight long-range modeling for 3d point cloud classification and segmentation

3D point clouds, with their compact structural representation and rich geometric information, have become fundamental data sources for visual understanding tasks in computer vision, robotics, and intelligent systems.Despite extensive progress, many existing methods still place strong emphasis on local geometric modeling while exhibiting limited ability to capture global long-range contextual dependencies. Moreover, the increasing architectural complexity of modern models often leads to high computational cost and memory consumption. In this paper, we propose Point BiLSTM, an efficient and lightweight framework for 3D point cloud classification and segmentation. The core of the proposed method is a bidirectional long short-term memory (BiLSTM)-based sequencer module, which models long-range contextual dependencies among points with linear computational complexity, enabling effective global feature learning at a low cost. Considering the unordered nature of point clouds, we further propose a Mixed Sequence Soft Cross-Entropy Loss that jointly supervises fixed-order and randomly permuted point sequences during training. This design explicitly enhances robustness to permutation ambiguity and improves training stability. Extensive experiments conducted on three widely used benchmarks—ModelNet40, ScanObjectNN, and ShapeNet Part—demonstrate that Point BiLSTM achieves highly competitive performance. In particular, the proposed method attains the fastest inference speed on both idealized and real-world datasets, outperforming current state-of-the-art methods by 30.2% and 54.2%, respectively. In addition, Point BiLSTM significantly reduces computational complexity and memory consumption, providing an effective solution for efficient point cloud learning. Our code will be available at https://github.com/wendaodao04/PointBiLSTM .

Spatiotemporal patterns and influencing factors of taxi supply-demand imbalance: A case study of Urumqi, China

Projected climate change effects on suitable habitat for Pseudosuccinea columella and Radix natalensis in uMgungundlovu, South Africa

Introduction Climate change impacts oceanic and atmospheric circulation patterns, precipitation, air and sea surface temperatures leading to altered intermediate host vector distribution, reproduction, and maturation. With temperatures predicted to rise by 1.5°C or more, understanding the influence of environmental characteristics and climatic conditions on distribution of intermediate host vectors, occurrence and habitat suitability is vital in vector borne diseases and their control policies. Hence, the study focused on the predicted changes in habitat suitability of Pseudosuccinea columella and Radix natalensis in uMgungundlovu district under climate change scenarios. Methods Freshwater Pseudosuccinea columella and Radix natalensis were collected from seven localities in uMgungundlovu using stratified random cluster sampling. Snail samples were collected at 45 sites. The sites were selected based on local knowledge and accessibility. The ensemble model assessed climate variability, while Representative Concentration Pathways (RCP) predicted habitat appropriateness for P. columella and R. natalensis . Maps were created using the MaxEnt model, and the model’s performance was assessed using the Area Under the Curve (AUC) of the receiver operating characteristic (ROC) curve. Results Our study indicates that P. columella and R. natalensis under current climatic conditions have suitable habitats in the Mpofana, Impendle, Richmond, and Mkhambathini municipalities within the uMgungundlovu district, but the central and northern regions are considered unsuitable. Furthermore, under the RCP4.5 and RCP8.5 climate scenarios, an estimated 22.31% and 22.57% of areas that are presently suitable are expected to become unsuitable for P. columella in uMgungundlovu, with fragmented areas and unsuitable areas becoming suitable for R. natalensis distribution by 2085. Conclusion Climate change could influence the distribution of fasciola intermediate host snails, potentially shifting the hotspots of fascioliasis transmission. Future studies should take into account shifts in land use, evolving agricultural techniques, and the impact of human activities to ensure effective management strategies.

Inhaling high THC cannabis acutely increases inflammation but not insulin sensitivity: a quasi-randomized trial

Biodegradable Self‐Enhancing Piezoelectric Scaffold for Synchronized Tendon‐to‐Bone Regeneration

ABSTRACT A key challenge in contemporary rotator cuff repair lies in achieving spatiotemporally specific differentiation of stem cells within the intricate tendon–bone interface (TBI) microenvironment, thereby enabling the synchronous regeneration of functionally graded tendon, fibrocartilage, and bone tissues. Existing scaffold designs often fail to adequately replicate the mechanical gradients and biochemical heterogeneity of the natural TBI microenvironment, particularly in reconstructing the precise spatial organization of the tendon–cartilage–bone transitional zone. This study innovatively integrates the in situ electrical stimulation advantages of piezoelectric degradable scaffolds, the topological guidance mediated by electrospun fibers, and biomimetic mineralization capabilities to construct a “mechanical–electrical–biochemical cue” coupled system for structural reconstruction and functional restoration of TBI. The gradient scaffold, designed with structural‐component matching, generates a piezoelectric enhancement effect during stress concentration, creating precise electrical stimulation and mechanical microenvironment gradients within the fiber scaffold. This enhanced electrical signal is three times greater than the piezoelectric performance of traditional poly( l ‐lactic acid) scaffolds. This dynamically controllable electrical signal activates the PI3K/AKT signaling pathway, modulating mesenchymal stem cells differentiation toward tenogenic, chondrogenic, and osteogenic lineages. In a rat rotator cuff tear (RCT) model, this gradient‐structured, fully biodegradable scaffold combined with electrical stimulation therapy was demonstrated to achieve coordinated multi‐tissue regeneration in complex TBI.

Epidemiology and clinical features of Hymenoptera stings in East Azerbaijan, northwestern Iran

Background Stings caused by Hymenoptera including honey bees, wasps, and hornets represent a growing global and national health concern with outcomes ranging from mild local reactions to severe systemic complications. Despite their significance, region-specific epidemiological data from Iran remain limited. This study aimed to investigate the demographic characteristics, anatomical distribution, seasonal patterns, clinical features, and hospitalization-related factors among Hymenoptera sting victims in northwestern Iran. Methods A retrospective descriptive-analytical study was conducted between 2021 and 2024 in East Azerbaijan Province, Iran, using medical records from hospitals affiliated with Tabriz University of Medical Sciences. A total of 225 eligible patients with confirmed or clinically suspected Hymenoptera stings were analyzed through a census-based approach. Demographic (including age, sex, and occupational status), epidemiological, and clinical variables were collected via structured forms. Descriptive statistics were applied. Chi-square or exact tests were used to assess associations between categorical variables, and multivariable logistic regression was performed to identify independent predictors of hospitalization. Statistical significance was set at p < 0.05. Results Among 225 cases, 71.6% were male, and the mean age was 30.99 ± 18.49 years, showing a bimodal distribution peaking in children aged <10 years and adults aged 30–49 years. A highly significant association was found between gender and occupation (p < 0.001); females were predominantly housewives, males were distributed mainly among self-employed individuals, students, farmers, and manual workers. Urban areas accounted for 53.3% of incidents, and summer was the peak season (52.4%). The head and neck were the most affected sites (44.9%), with a significant gender disparity: males were more frequently stung on the head/neck, whereas females were more frequently stung on the limbs (p < 0.001). Hospitalization was required in 28.0% of patients. In multivariable logistic regression, pain (OR = 40.43, 95% CI: 12.56–130.15), severe muscle pain (OR = 77.56, 95% CI: 12.97–463.89), and local swelling/redness (OR = 16.05, 95% CI: 3.96–65.06) were independently associated with hospitalization. Male gender showed a non-significant trend toward higher odds of hospitalization (OR = 3.06, p = 0.067). Conclusion Hymenoptera stings in northwestern Iran are shaped by a complex interplay of ecological, occupational, and socio-cultural factors. The distinct anatomical sting patterns may be influenced by regional dress codes and gender-specific activities, suggest that preventive strategies should address both domestic and occupational exposure settings. Public health interventions should focus on habitat management in residential areas, awareness among households, and protective clothing for outdoor workers to reduce the burden of envenomation.

Using capnographic changes to assess pulmonary perfusion impairment during pulmonary surgery: animal experiments and clinical studies

A novel deep-learning approach for robust identification of plant diseases

Rising temperatures and changing weather conditions are accelerating the spread of plant diseases and increasing the threat to global food security. Reliable detection of leaf diseases is therefore essential to protect crop yields and ensure food quality. Deep learning has proven to be a powerful tool for classifying leaf diseases across various crops. Due to the natural variability of plants, plant diseases often appear in irregular structures. Surface unevenness, folds, or dirt particles are common in field images and can be mistakenly identified as important features by convolutional neural networks (CNNs). This is a challenge that has not been sufficiently addressed in previous studies. This study proposes a novel deep learning approach that takes into account both the specific visual characteristics of plant diseases and potential disturbances in the microstructure, such as surface irregularities or prominent leaf veins, which may mislead the model. Using stratified five-fold cross-validation on a peer-reviewed dataset, which comprises 2,801 images of radish leaves across five classes (healthy, three disease classes: mosaic virus, black leaf spot, and downy mildew, and one pest-affected class: flea beetle), the proposed method achieved an average and balanced accuracy of 99.86%, establishing a new dataset-level benchmark in the field and demonstrating its effectiveness. The results indicate that the proposed approach may provide a promising basis for future applications in agricultural field monitoring, automated sorting and post-harvest quality control, offering potential to reduce both food waste and associated costs.

Repeated heat pain threshold testing in pain-free individuals induces habituation and yields false-positive pre–post comparisons

Abstract Heat pain thresholds (HPTs) are used for clinical assessments, sensory phenotyping, and pre–post comparisons. This usage assumes that HPTs are stable, exchangeable, and unaffected by repetition. Twenty consecutive HPT measures were taken at each volar forearm during three daily sessions in pain-free individuals. Negative exponential growth models were used to characterise HPT series and estimate effects of session, side, and time between sessions. To assess suitability for within-session pre–post comparisons, the mean of the first n repetitions of a series was compared with the mean of the subsequent n repetitions for n = 1–10, and ICC(3,1) calculated for n = 2–20. From 23 participants, 2266 HPT measurements were recorded within 118 series. Strong habituation was observed (Cohen’s d = 1.9), with HPT increasing by a mean of 4.9 °C across all series (95% CI: 4.4 to 5.3 °C, p <0.001). Repeated HPTs followed an asymptotic trajectory; lower initial HPTs were associated with larger repetition-related increases, and right forearms adapted faster than left. Later sessions were associated with higher initial and asymptotic HPT values and faster adaptation. Using single or averaged HPTs for within-series pre–post comparisons consistently yielded false-positive findings, while ICC decreased as n increased.