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Depression affects people's daily lives and even leads to suicidal behavior. Text-based depression estimation using natural language processing has emerged as a feasible approach for early mental health screening. However, most existing reviews often included studies with weak depression labels, which affected the reliability of the results and further limited the practical application of the automatic depression estimation models. This review aimed to evaluate the predictive performance of text-based depression models that used standard labels, and to identify text resources, text representation, model architecture, annotation source, and reporting quality contributing to performance heterogeneity. Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines, we systematically searched 4 main databases (PubMed, Scopus, IEEE Xplore, and Web of Science) for studies published between 2014 and 2025. The eligible studies were included: machine learning models were developed based on the text generated by the participants and used validated scales or clinical diagnoses as depression labels. Pooled effect sizes (r) were calculated using random-effects meta-analysis with Hartung-Knapp-Sidik-Jonkman correction, and subgroup and meta-regression analyses were conducted to explore potential moderators. We scanned 3067 articles and finally filtered 15 models from 11 studies for the meta-analysis. The overall pooled effect size was 0.605 (95% CI 0.498-0.693), indicating a large strength of association. Subgroup analyses showed that models using embedding-based text representations achieved higher performance than those using traditional features (r=0.741, 95% CI 0.648-0.812 vs r=0.514, 95% CI 0.385-0.623; P<.001 for subgroup difference), and deep learning architectures outperformed shallow models (r=0.731, 95% CI 0.660-0.789 vs r=0.486, 95% CI 0.352-0.599; P<.001). Models trained with clinician diagnoses also outperformed better than those relying on self-report scales (r=0.688, 95% CI 0.554-0.787 vs r=0.500, 95% CI 0.340-0.631; P=.03). Reporting quality was positively associated with model performance (β=0.085, 95% CI 0.050-0.119; P<.001). Begg-Mazumdar and Egger tests provided no evidence of small-study effects. Begg-Mazumdar test (Kendall τ=0.17143, P=.37) and the Egger test (t =1.13401, 2-tailed P=.28) indicated no evidence of small-study effects. Text-based depression estimation models trained with standard depression labels demonstrate solid predictive performance, with embedding features, deep model architectures, and clinician diagnosis labels showing significantly higher performance. Transparent reporting is also positively associated with model performance. This study highlights the importance of standard labels, feature representation, and reporting quality for improving model reliability. Unlike prior reviews that included weak or heterogeneous depression labels, this study offers more clinically reliable and comparable evidence. Moreover, this review provides clearer methodological guidance for developing more consistent and practically informative text-based depression screening models. PROSPERO CRD420251056902; https://www.crd.york.ac.uk/PROSPERO/view/CRD420251056902.

The glass–glass interfaces (GGIs) are in a unique glass phase, while current knowledge on the interfacial phase has not completely established to explain the unprecedented improvements in the ductility of metallic nanoglasses (NGs). In this work, Co–P NGs prepared through the pulse electrodeposition are investigated, whose GGI regions clearly show elemental segregation with chemical composition dominated by element Co. Such compositional heterogeneity is further verified by molecular dynamics (MD) simulation on the formation of GGIs in Co-P NGs and atomic structures of GGIs with Co segregation are found to be less dense than those of glassy grains. More importantly, Co segregation at GGIs is closely related to the improved ductility observed in Co-P NGs, as demonstrated by nanoindentation measurements and MD simulations. This work facilitates the understanding on the relations between compositional heterogeneity and improved ductility as observed in Co-P NGs, and thus opens a new window for controlling the mechanical properties of NGs through GGI engineering.

Depression is a major psychological disorder with a growing impact worldwide. Traditional methods for detecting the risk of depression, predominantly reliant on psychiatric evaluations and self-assessment questionnaires, are often criticized for their inefficiency and lack of objectivity. Advancements in deep learning have paved the way for innovations in depression risk detection methods that fuse multimodal data. This paper introduces a novel framework, the Audio, Video, and Text Fusion-Three Branch Network (AVTF-TBN), designed to amalgamate auditory, visual, and textual cues for a comprehensive analysis of depression risk. Our approach encompasses three dedicated branches—Audio Branch, Video Branch, and Text Branch—each responsible for extracting salient features from the corresponding modality. These features are subsequently fused through a multimodal fusion (MMF) module, yielding a robust feature vector that feeds into a predictive modeling layer. To further our research, we devised an emotion elicitation paradigm based on two distinct tasks—reading and interviewing—implemented to gather a rich, sensor-based depression risk detection dataset. The sensory equipment, such as cameras, captures subtle facial expressions and vocal characteristics essential for our analysis. The research thoroughly investigates the data generated by varying emotional stimuli and evaluates the contribution of different tasks to emotion evocation. During the experiment, the AVTF-TBN model has the best performance when the data from the two tasks are simultaneously used for detection, where the F1 Score is 0.78, Precision is 0.76, and Recall is 0.81. Our experimental results confirm the validity of the paradigm and demonstrate the efficacy of the AVTF-TBN model in detecting depression risk, showcasing the crucial role of sensor-based data in mental health detection.

Drug–target interaction (DTI) predictions, which aim to predict whether a drug will be bounded to a target, have received wide attention recently. The goal is to automate and accelerate the costly process of drug design. Most of the recently proposed methods use single drug–drug similarity and target–target similarity information for DTI predictions; thus, they are unable to take advantage of the abundant information regarding the various types of similarities between these two types of information. Very recently, some methods have been proposed to leverage multi-similarity information; however, they still lack the ability to take into consideration the rich topological information of all sorts of knowledge bases in which the drugs and targets reside. Furthermore, the high computational cost of these approaches limits their scalability to large-scale networks. To address these challenges, we propose a novel approach named summated meta-path-based probabilistic soft logic (SMPSL). Unlike the original PSL framework, which often overlooks the quantitative path frequency, SMPSL explicitly captures crucial meta-path count information. By integrating summated meta-path counts into the PSL framework, our method not only significantly reduces the computational overhead, but also effectively models the heterogeneity of the network for robust DTI predictions. We evaluated SMPSL against five robust baselines on three public datasets. The experimental results demonstrate that our approach outperformed all of the baselines in terms of the AUPR and AUC scores.

The healing of diabetic skin wounds is a complex process significantly affected by the hyperglycemic environment. In this context, glucose oxidase (GOx), by catalyzing glucose to produce gluconic acid and hydrogen peroxide, not only modulates the hyperglycemic microenvironment but also possesses antibacterial and oxygen-supplying functions, thereby demonstrating immense potential in the treatment of diabetic wounds. Despite the growing interest in GOx-based therapeutic strategies in recent years, a systematic summary and review of these efforts have been lacking. To address this gap, this review article outlines the advancements in the application of GOx and GOx-like nanozymes in the treatment of diabetic wounds, including reaction mechanisms, the selection of carrier materials, and synergistic therapeutic strategies such as multi-enzyme combinations, microneedle structures, and gas therapy. Finally, the article looks forward to the application prospects of GOx in aiding the healing of diabetic wounds and the challenges faced in translating these innovations to clinical practice. We sincerely hope that this review can provide readers with a comprehensive understanding of GOx-based diabetic treatment strategies, facilitate the rigorous construction of more robust multifunctional therapeutic systems, and ultimately benefit patients with diabetic wounds. [Display omitted] •For the first time, the application potential of GOx and GOx-like nanozymes in diabetic wound healing is explored.•Synergistic strategies for diabetic wound healing based on GOx are overviewed.•The challenges and future directions for the clinical application of GOx-based biomaterials are discussed.

Studies of nitric oxide over the past two decades have highlighted the fundamental importance of gaseous signaling molecules in biology and medicine. The physiological role of other gases such as carbon monoxide and hydrogen sulfide (H₂S) is now receiving increasing attention. Here we show that H₂S is physiologically generated by cystathionine γ-lyase (CSE) and that genetic deletion of this enzyme in mice markedly reduces H₂S levels in the serum, heart, aorta, and other tissues. Mutant mice lacking CSE display pronounced hypertension and diminished endothelium-dependent vasorelaxation. CSE is physiologically activated by calcium-calmodulin, which is a mechanism for H₂S formation in response to vascular activation. These findings provide direct evidence that H₂S is a physiologic vasodilator and regulator of blood pressure.

ObjectivesTo investigate the prevalence of depression and anxiety symptoms among older adults in an urban district in China, as well as their associated factors.DesignCross-sectional study.SettingGeneral communities in Shenzhen, Guangdong, China.ParticipantsA total of 5372 community-dwelling older adults aged 65 years or older were initially recruited. Ultimately, 5331 participants met the inclusion criteria and were included in this study.MethodsParticipants completed a sociodemographic questionnaire, along with assessments including the Patient Health Questionnaire-9, Generalised Anxiety Scale-7, UCLA Loneliness Simplification Scale, Insomnia Severity Index Scale (ISI), Community Dementia Brief Screening Scale and the 8-item Dementia Screening Questionnaire. Statistical analyses included the Shapiro-Wilk test, independent t-test, Wilcoxon rank test, χ2 test and univariate and multivariate linear regression analysis.ResultsThe prevalence of depression and anxiety symptoms among older adults in Shenzhen communities was 10.4% and 11.3%, respectively. In multivariate analysis, age (B=−0.01, p<0.05), relatively poor health status in the past year (B=1.00, p<0.01), poor health status in the past year (B=2.40, p<0.01), ISI score (B=0.21, p<0.01), -item Ascertain Dementia Questionnaire (AD8) score (B=0.22, p<0.01), UCLA Loneliness Scale (ULS) score (B=0.24, p<0.01) were significantly associated with the severity of depression symptom, Compared with their respective reference categories, relatively poor health status in the past year (B=0.50, p<0.01), poor health status in the past year (B=1.32, p<0.01), ISI score (B=0.23, p<0.01), sleep duration (B=0.05, p<0.01), AD8 score (B=0.21, p<0.01), Community Screening Instrument for Dementia score (B=0.13, p<0.01), ULS score (B=0.22, p<0.01) were significantly associated with the severity of anxiety symptom.ConclusionsWe observed a high prevalence of depression and anxiety symptoms among older adults in this study. The existing welfare system and infrastructure should remain and targeted mental health programmes addressing the identified risk factors should be proposed.

This study investigates the quality information disclosure and blockchain technology adoption strategies of suppliers on a third-party e-commerce platform. Based on a Stackelberg game model, the impacts of blockchain technology adoption on the quality information disclosure decision and profit of the third-party e-commerce platform and suppliers are explored. The results indicate that whether blockchain adoption benefits suppliers depends on the unit blockchain cost and the reliability of quality information. Counterintuitively, higher information reliability may disadvantage suppliers under certain conditions. With the increase in unit blockchain cost, the incentive of suppliers to adopt blockchain is weakened, and suppliers need to adjust their strategies of quality information disclosure according to the adoption situation and the cost of blockchain. Adopting blockchain technology may be unfavorable to the suppliers but beneficial to the third-party e-commerce platform; the platform can incentivize suppliers to adopt blockchain and achieve a win-win situation. These findings provide some valuable managerial implications for the quality information disclosure decision of suppliers and blockchain adoption in the e-commerce platform supply chain.

The efficient detection of volatile organic compounds (VOCs) is critically important in the domains of environmental protection, healthcare, and industrial safety. The development of metal oxide semiconductor (MOS) heterojunction gas-sensing materials is considered one of the most effective strategies to enhance sensor performance. This review summarizes and discusses the types of heterojunctions and their working principles, enhancement strategies, preparation methodologies, and applications in acetone and ethanol detection. To address the constraints pertaining to low sensitivity, sluggish response/recovery times, and elevated operating temperatures that are inherent in VOC sensors, several improvement methods are proposed, including doping with metals like Ag and Pd, incorporating additives such as MXene and polyoxometalates, optimizing morphologies through a fine design, and self-doping via oxygen vacancies. Furthermore, this work provides insights into the challenges faced by MOSs heterojunction-based gas sensors and outlines future research directions in this field. This review will contribute to foundational theories to overcome existing bottlenecks in MOS heterojunction technology while promoting its large-scale application in disease screening or agricultural food quality assessments.

Bone nonunion poses an urgent clinical challenge that needs to be addressed. Recent studies have revealed that the metabolic microenvironment plays a vital role in fracture healing. Macrophages and bone marrow-derived mesenchymal stromal cells (BMSCs) are important targets for therapeutic interventions in bone fractures. Itaconate is a TCA cycle metabolite that has emerged as a potent macrophage immunomodulator that limits the inflammatory response. During osteogenic differentiation, BMSCs tend to undergo aerobic glycolysis and metabolize glucose to lactate. Copper ion (Cu2+) is an essential trace element that participates in glucose metabolism and may stimulate glycolysis in BMSCs and promote osteogenesis. In this study, we develop a 4-octyl itaconate (4-OI)@Cu@Gel nanocomposite hydrogel that can effectively deliver and release 4-OI and Cu2+ to modulate the metabolic microenvironment and improve the functions of cells involved in the fracture healing process. The findings reveal that burst release of 4-OI reduces the inflammatory response, promotes M2 macrophage polarization, and alleviates oxidative stress, while sustained release of Cu2+ stimulates BMSC glycolysis and osteogenic differentiation and enhances endothelial cell angiogenesis. Consequently, the 4-OI@Cu@Gel system achieves rapid fracture healing in mice. Thus, this study proposes a promising regenerative strategy to expedite bone fracture healing through metabolic reprogramming of macrophages and BMSCs. A 4-OI@Cu@Gel nanocomposite hydrogel is developed to modulate metabolic microenvironment and promote fracture healing. Through timed release of 4-OI and Cu2+, the hydrogel reduces inflammatory response and alleviates oxidative damage at the early stage, and enhances bone formation and vascularization at the later stage, proving promising regenerative strategy for fracture healing based on metabolic reprogramming of macrophages and BMSCs. [Display omitted] •In this study, a 4-octyl itaconate (4-OI)@Cu@Gel nanocomposite hydrogel is developed to delivery 4-OI and Cu2+ for modulation of metabolic microenvironment in the fracture site.•We reveal that Cu2+ promotes BMSC osteogenic differentiation by stimulating glycolysis through activation of HIF-1α-Glut1 pathway.•The hydrogel reduces inflammatory response and alleviates oxidative damage at the early stage, and enhances bone formation and vascularization at the later stage.•This study proposes a promising regenerative strategy to expedite bone fracture healing through metabolic reprogramming of macrophages and BMSCs.