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Enhancing group lifestyle intervention for depression with ecological momentary assessment: a pilot randomized controlled trial

Abstract To investigate the impact of smartphone-delivered ecological momentary assessment (EMA) as a self-monitoring tool to complement a 6-week group face-to-face delivered multicomponent lifestyle medicine (LM) intervention for improving depressive symptoms. 56 Chinese Hong Kong adults with at least a moderate level of depressive symptoms were randomized to the EMA-supported intervention (LM/S; n  = 18), pure intervention (PLM; n  = 20), or care-as-usual (CAU; n  = 18) groups. Data were collected at baseline, immediate post-intervention, and 3-month follow-up. LM/S only showed significantly greater vigorous physical activity than PLM at Week 19. At Week 7, PLM demonstrated a marginal reduction in depressive symptoms and significant improvements in insomnia symptoms, physical health-related quality of life (QoL), overall lifestyle, nutrition, and stress management compared to CAU, while LM/S improved only environmental health-related QoL. At Week 19, both intervention groups showed large improvements in depressive, anxiety, and insomnia symptoms, environmental health-related QoL, overall lifestyle, and stress management compared to CAU. Additional gains were observed for LM/S in nutrition, spiritual growth, and vigorous activity, and for PLM in physical and psychological health-related QoL, and interpersonal relationships. No significant differences in study attrition and intervention attendance were found between groups. Despite a low EMA compliance of 27.1%, the LM/S exhibited a higher, though not significantly different, full intervention adherence rate (66.67%) compared to the PLM (38.89%). A group-based, multicomponent LM intervention could potentially improve depressive symptoms, and smartphone-delivered EMA might enhance full intervention adherence despite modest compliance. A future adequately powered trial is warranted.

Correction: The validity of mid-upper arm circumference as an indicator of underweight, overweight and obesity adults in Bangladesh

The effect of different types of drugs on balance and reaction ability of female abstainers

Whole genome sequence of multidrug-resistant Staphylococcus haemolyticus and Enterococcus faecalis isolates from public gymnasium equipment reveals evolving infection potential and resistance

Whole genome sequences (WGSs) of Enterococcus fa ecalis S3 and S taphylococ cus haemolyticus S5, isolated from gymnasium equipment in Tennessee, USA, were analyzed. The genome sizes of E. faecalis S3 and S. haemolyticus S5 were approximately 3.0 Mb and 2.5 Mb, respectively. Both isolates were found to harbor genes conferring resistance to multiple antibiotics, including tetracycline, fluoroquinolone, and macrolide. Gene cluster analysis revealed a cyclic lactone inducer cluster in both strains, which is critical for quorum sensing-mediated pathogenicity. Multilocus sequence typing (MLST) identified E. faecalis S3 as ST40 and S. haemolyticus S5 as ST52. Notably, evolutionary analysis of gene contraction and expansion in these isolates revealed an expansion of genes associated with horizontal gene transfer. This expansion likely represents an evolutionary strategy to facilitate the spread of antibiotic resistance genes to other isolates. These findings offer valuable insights into the genomic apparatus responsible for antibiotic resistance and potential transmission mechanisms in human-associated environments.

Multimodal property video neuroanalytics

Evaluating the sustainability and long-term outcomes of the Home Care Support Intervention Program (HoSIP) to reduce loneliness among community-dwelling older adults: A two-year follow-up study

Introduction Understanding the long-term effects of home care support programs on loneliness in older adults is crucial for optimizing service delivery and improving the quality of life and care. This research explores the Sustainability and Long-Term Outcomes of the Home Care Support Intervention Program (HoSIP) to Reduce Loneliness among Community-dwelling Older Adults: A two-year follow-up study. Method and materials This concurrent nested mixed-method study investigated the impact of HoSIP on older adults two years post-implementation. Quantitative data were collected on loneliness, social networks, perceived social support, quality of life, self-care ability, and general health. RAMNOVA analysis was used to analyze the results of univariate tests conducted at different points of measurement using SPSS version 23. Sixteen participants completed semi-structured individual interviews in-person and virtually. Conventional content analysis was undertaken using MAXQDA version 20. Results Sixteen older adults remained in the HoSIP program at the two-year post-test assessment (mean age 73.5 years  +  6.6 years). The participants were predominantly female (81.3%). Over two years compared to baseline, a significant decline was observed in loneliness, social network, perceived social support, quality of life, self-care ability ( p  < 0.05) while no significant changes were observed for general health ( p  > 0.05). Three main categories, along with forth sub-categories, emerged from the data analysis. Discussion This study explored how a community-based program helped reduce loneliness in older adults. The results highlight the importance of involving older adults in designing programs to improve their overall well-being. These findings can guide future interventions to enhance the quality of life for older adults, potentially lowering healthcare costs and benefiting both individuals and governments. This program provides a framework for the development and implementation of sustained, community-based interventions directed by older adults. Given the potential impacts of sociocultural factors on the efficacy and longevity of such programs, these elements warrant careful consideration during the design phase of the similar interventions.

Luteoloside alleviates bleomycin-induced pulmonary fibrosis in mice via SIRT1-mediated protective effect against alveolar epithelial cell senescence

Perceived ease of use of telehealth services and associated factors in Saudi Arabia: A cross-sectional study

This paper aimed to explore the impact of disability along with other factors on telehealth usage, examining the degree of ease people feel while using telehealth services in Saudi Arabia. A cross-sectional study collected data from 428 Saudi adult participants via an online survey between October and November 2024. The Extended Unified Theory of Acceptance and Use of Technology (UTAUT) model was adopted to design the questionnaire. The paper utilized the binary Logistic Regression and the random forest algorithm to predict the participants’ attitudes towards the ease of using Telehealth services. The ease of use of the telehealth system was assessed using the effort expectancy index, which measures participants’ perceptions about feasibility, clarity, simplicity, and comfort related to the usage of telehealth services. The results showed perceptions supporting the ease of using telehealth services decreased for disabled individuals by 80% (p = 0.04) compared to non-disabled individuals. In contrast, availability of facilitating conditions (OR=9.18, p < 0.001), performance expectancy (OR=4.70, p = 0.006), perceived safety (OR=3.33, p = 0.044), and social influence (OR=3.82, p = 0.016) were positively and significantly associated with perceived ease of use. The presence of perceived barriers also had a positive effect (OR=3.62, p = 0.024). The random forest algorithm outperformed logistic regression in terms of classification accuracy and AUC (0.774 versus 0.758 in the test set). Classification models indicated that factors related to telehealth technology were the most influential in perceptions of ease of use. These findings underscore the need for policymakers to develop inclusive telehealth strategies that specifically address barriers faced by disabled individuals, ensuring equitable and accessible digital health services for all.

Stage-specific responses of Spergularia marina to salinity reveal strategies of tolerance and restoration potential

Correction: Epidemiology of heart failure and long-term follow-up outcomes in a north-African population: Results from the NAtional TUnisian REgistry of Heart Failure (NATURE-HF)

Interplay between irrigation and genotype determines biofortification and flavonoid enrichment in wheat and triticale leaves

The impact of urban low-carbon incentive policy on enterprise transformation and upgrading: Evidence from a quasi-natural experiment in China

As carbon emissions in China continue to rise and the cost advantage in the global value chain diminishes, enterprise transformation and upgrading has emerged as a new engine for economic growth. By implementing low carbon incentive policies, the government aims to spur corporate self‑innovation and phase out obsolete capacity, thereby boosting resource use efficiency and curbing environmental pollution. This paper examines the impact of China’s low carbon incentive policies on enterprise transformation and upgrading, with a particular focus on the role and mechanisms of urban environmental policy in this process. Employing a multi-period difference in differences approach, we analyze how the low carbon city policy affects the transformation and upgrading of Chinese listed firms. The results show that the low carbon city policy significantly enhances enterprise transformation and upgrading at the 1% level: participation in the low carbon city policy raises the composite index of enterprise transformation and upgrading by 0.012. We further explore the moderating role of enterprise green development level by incorporating it into our model of low carbon city policy effects. The findings reveal that firms exhibiting higher green total factor productivity, as well as those adopting green innovation and green management practices, display stronger adaptability to the low carbon city policy. Finally, both heterogeneity and dynamic analyses indicate that, over the medium to long term, the low carbon city policy continues to promote enterprise transformation and upgrading. In sum, the low carbon city policy not only provides exogenous momentum for enterprise transformation and upgrading but also interacts synergistically with firms’ green development to guide them toward more efficient and sustainable transformation and upgrading.

A high-throughput drug screening assay for anti-tau aggregation using split GFP and flow cytometry

Abstract Tau protein aggregation is a hallmark of neurodegenerative diseases, including Alzheimer’s disease, making the development of anti-aggregation therapeutics a critical area of research. Progress in drug discovery has been hindered by the lack of efficient screening methods that accurately reflect cellular conditions. We present a high-throughput cell-based assay utilizing split GFP technology to monitor tau aggregation in living cells. Our system employs suspension-adapted HEK293 cells co-transfected with tau proteins fused to complementary GFP fragments, producing fluorescent signals upon tau aggregation. Notably, our system demonstrates tau aggregation without external aggregation inducers, likely due to the enhanced protein expression in suspension-adapted cells. Validation with a known urea-based tau aggregation inhibitor showed dose-dependent reduction in fluorescence, corresponding to decreased tau aggregation. The assay’s flow cytometry compatibility enables rapid, quantitative analysis of large sample sets while allowing simultaneous assessment of compound efficacy and cytotoxicity. This method advances tau aggregation monitoring and drug discovery by providing a physiologically relevant platform for identifying novel anti-tau aggregation therapeutics.

Smart sensors, smarter players: The role of real-time monitoring in football training

Objectives This study aimed to develop and evaluate a real-time sensor-based monitoring and feedback system for enhancing four core football performance metrics, passing accuracy, sprint speed, agility, and shot power, each defined and quantified using validated wearable sensors and baseline‐referenced improvement thresholds. Methods A randomized controlled trial (RCT) was conducted over eight weeks with 30 university-level male football players (aged 21.70 ± 1.28 years) from Zhengzhou University. Participants were randomly assigned to an experimental group (n = 15), which trained using the real-time monitoring system, or a control group (n = 15), which followed traditional training methods. The wearable system integrated accelerometers, gyroscopes, and magnetometers to provide real-time, skill-specific feedback during drills. Performance data were collected weekly and analyzed using repeated measures ANOVA with effect sizes calculated via partial eta squared (η²p). Results The results demonstrated statistically and practically significant improvements in the experimental group across all measured parameters. Notably, the effect sizes ranged from large to very large (η²p = .59 to.89), indicating that the improvements were not only statistically reliable but also substantial enough to have meaningful impact on the players’ performance. Passing accuracy increased by 10.21% (F(1,27) = 210.02, p < 0.001, η²p = .88), sprint speed improved by 17.33% (F(1,27) = 92.00, p < 0.001, η²p = .76), agility improved by 10.90% (F(1,27) = 41.75, p < 0.001, η²p = .59), and shot power increased by 10.75% (F(1,27) = 247.32, p < 0.001, η²p = .89). The control group showed negligible or negative changes in all performance metrics. Performance improvements in the experimental group were progressive and sustained across the 8-week training period, with weekly data showing steady gains in passing accuracy, sprint speed, agility, and shot power. No performance regressions or plateaus were observed during the intervention period. Conclusion By delivering instantaneous, sensor-validated feedback on precisely defined performance metrics, the system accelerated improvements in both technical and physical skills. These findings support the integration of wearable sensor technology into football training to achieve data-driven, individualized skill development. Future work should explore AI-driven personalization and long-term retention of gains.

Meropenem dosing optimization on day 1 and steady state in critically ill patients without significant renal impairment

Feminization of the precarious at the UNAM: Examining obstacles to gender equality

The STEM workforce is marked by the persistent underrepresentation of women. Herein, we seek a better understanding of this gender gap in different science disciplines within Latin America. Specifically, we analyzed a case study: the professional development of women in science research institutes of the National Autonomous University of Mexico (UNAM). This interdisciplinary work analyzed quantitative and qualitative data through an intersectional philosophical lens, employing specific analytical tools drawn from feminist epistemology. We examined the interplay between horizontal and vertical segregation, symbolic and structural obstacles, and economic labor precariousness within the framework of gender norms. Shared trends in the Global North were analyzed to understand the perpetuation of gender stereotypes in the production of scientific knowledge. Additionally, we examined the relationship between the values embedded in gender norms and the cultural capital--here defined as encompassing both economic status and social legitimacy--associated with each discipline. Our findings indicate that, although women are underrepresented in pSTEM, they experience less vertical segregation than their counterparts in STEM related to the Natural Sciences. This suggests that knowledge areas currently associated with the highest cultural capital (pSTEM) may impose primarily symbolic rather than structural barriers for women. By contrast, in fields characterized by less masculine-coded values, women appear to face predominantly structural obstacles, as evidenced by the vertical segregation observed. These results contribute to a deeper understanding of the gender biases that exclude women from STEM disciplines.

Innovative green niosomal piperlongumine as a novel topical treatment for dermatophytosis in guinea pigs model

SCI-YOLO11: An improved defect detection algorithm for transmission line insulators based on YOLO11

The detection of insulator defects in transmission lines is of paramount importance for the safe operation of power systems. However, small object detection faces numerous challenges, such as significant difficulty, substantial interference from complex backgrounds, and inconsistent annotation quality. These factors continue to constrain the performance of existing methods. To address these issues, this paper proposes an improved object detection algorithm named SCI-YOLO11, which optimizes the YOLO11 framework from three aspects: feature extraction, attention mechanism, and loss function. Specifically, to tackle the difficulties associated with small object detection, we replace conventional convolutions in the Backbone with SPDConv modules to enhance feature capture capabilities for small targets and low-resolution images while reducing computational overhead. To improve model accuracy further, we introduce the SE attention mechanism that adaptively adjusts the weights of feature channels to enhance the discriminative ability of insulator defect features. In response to the adverse effects caused by inconsistent annotation quality on defect image detection performance, we incorporate Wise-IoU-V3 loss function to optimize boundary box regression performance effectively mitigating negative impacts stemming from uneven annotation quality. Experimental results demonstrate that SCI-YOLO11 achieves a 3.2% improvement over baseline models in terms of MAP@0.5 metric; precision and recall rates increase by 2.6% and 3.7%, respectively. Additionally, its parameter count and floating-point operations decrease by 6% and 7.9%, respectively. These experimental findings substantiate the substantial improvements in detection accuracy, lightweight design, and robustness provided by SCI-YOLO11. This framework offers an effective technical solution for identifying defects in transmission line insulators.

LyricEmotionNet for robust emotion recognition with hybrid CapsNet-memory network architecture

Analysis of disaster-affected population mobility through grid-aggregated mobile location data: The 2017 Jiuzhaigou earthquake, China

In disaster research, individual-level mobile phone location data is considered highly valuable for assessing population mobility and disaster impacts. However, due to privacy regulations in China, only spatially aggregated mobile data with a resolution of 1 km × 1 km are available. These data do not contain explicit population individual population movement, which poses challenges for analyzing population movement patterns in disaster research. To using this grid-based mobile data to describe population movement, we applied an empirical orthogonal function (EOF) method to the post-disaster phase of the 2017 Jiuzhaigou earthquake. The first EOF mode (EOF1) primarily exhibits positive anomalies centered over the Jiuzhaigou Valley. The principal components for the EOF1 show a decreasing trend from midnight to 20:00, indicating a continuous outflow of population from the Jiuzhaigou Valley during this period. The second mode (EOF2) exhibits negative anomalies at the Jiuzhaigou Valley and along the road to the southwest of the Valley, while positive anomalies appear along two roads, i.e., one extending from the Jiuzhaigou Valley to Shuanghe, and the other from the Chuanzhusi Town government square to western Chuanzhusi. The primary components of EOF2 reveal that, from midnight to 10:00, population increased along these two roads while decreasing over the Jiuzhaigou Valley and the road leading southward to the Chuanzhusi Town government square. After 10:00, this population change pattern diminished between 10:00–15:00. Based on the EOF2 results, two evacuation routes were identified: Path 1 extended northwest from the Chuanzhusi Town government square; Path 2 led southeast from Jiuzhaigou Valley through Shuanghe Town. In comparison, the BBAC_I clustering method identifies clusters with similar temporal trends but fails to pinpoint the most affected areas or infer evacuation directions. In contrast, EOF analysis overcomes these limitations by revealing key impact zones and evacuation patterns, even in the absence of trajectory data.