Dynamic multicolor emissions of multimodal phosphors by Mn2+ trace doping in self-activated CaGa4O7.

The manipulation of excitation modes and resultant emission colors in luminescent materials holds pivotal importance for encrypting information in anti-counterfeiting applications. Despite considerable achievements in multimodal and multicolor luminescent materials, existing options generally suffer from static monocolor emission under fixed external stimulation, rendering them vulnerability to replication. Achieving dynamic multimodal luminescence within a single material presents a promising yet challenging solution. Here, we report the development of a phosphor exhibiting dynamic multicolor photoluminescence (PL) and photo-thermo-mechanically responsive multimodal emissions through the incorporation of trace Mn2+ ions into a self-activated CaGa4O7 host. The resulting phosphor offers adjustable emission-color changing rates, controllable via re-excitation intervals and photoexcitation powers. Additionally, it demonstrates temperature-induced color reversal and anti-thermal-quenched emission, alongside reproducible elastic mechanoluminescence (ML) characterized by high mechanical durability. Theoretical calculations elucidate electron transfer pathways dominated by intrinsic interstitial defects and vacancies for dynamic multicolor emission. Mn2+ dopants serve a dual role in stabilizing nearby defects and introducing additional defect levels, enabling flexible multi-responsive luminescence. This developed phosphor facilitates evolutionary color/pattern displays in both temporal and spatial dimensions using readily available tools, offering significant promise for dynamic anticounterfeiting displays and multimode sensing applications.

High-throughput sequencing-based analysis of the composition and diversity of the endophyte community in roots of Stellera chamaejasme.

Stellera chamaejasme (S. chamaejasme) is an important medicinal plant with heat-clearing, detoxifying, swelling and anti-inflammatory effects. At the same time, it is also one of the iconic plants of natural grassland degradation in northwest China, playing a key role in the invasion process. Plant endophytes live in healthy plant tissues and can synthesize substances needed for plant growth, induce disease resistance in host plants, and enhance plant resistance to environmental stress. Therefore, studying the root endophytes of S. chamaejasme is of great significance for mining beneficial microbial resources and biological prevention and control of S. chamaejasme. This study used Illumina MiSeq high-throughput sequencing technology to analyze the composition and diversity of endophytes in the roots of S. chamaejasme in different alpine grasslands (BGC, NMC and XGYZ) in Tibet. Research results show that the main phylum of endophytic fungi in the roots of S. chamaejasme in different regions is Ascomycota, and the main phyla of endophytic bacteria are Actinobacteria, Proteobacteria and Firmicutes (Bacteroidota). Overall, the endophyte diversity of the NMC samples was significantly higher than that of the other two sample sites. Principal coordinate analysis (PCoA) and permutational multivariate analysis of variance (PERMANOVA) results showed significant differences in the composition of endophytic bacterial and fungal communities among BGC, NMC and XGYZ samples. Co-occurrence network analysis of endophytes showed that there were positive correlations between fungi and some negative correlations between bacteria, and the co-occurrence network of bacteria was more complex than that of fungi. In short, this study provides a vital reference for further exploring and utilizing the endophyte resources of S. chamaejasme and an in-depth understanding of the ecological functions of S. chamaejasme endophytes.

Triglyceride-glucose index predicts sepsis-associated acute kidney injury and length of stay in sepsis: A MIMIC-IV cohort study.

Background: Inflammation and stress response may be related to the occurrence of sepsis-associated acute kidney injury (SA-AKI) in patients with sepsis.Insulin resistance (IR) is closely related to the stress response, inflammatory response, immune response and severity of critical diseases. We assume that the triglyceride-glucose (TyG) index, an alternative indicator for IR, is associated with the occurrence of SA-AKI in patients with sepsis. Methods: Data were obtained from The Medical Information Mart for Intensive Care-IV(MIMIC-IV) database in this retrospective cohort study. Univariate and multivariate logistic regression analysis and multivariate restricted cubic spline(RCS) regression were conducted to evaluate the association between TyG index and SA-AKI, length of stay (LOS). Subgroup and sensitivity analyses were performed to verify the robustness of the results. Results: The study ultimately included data from 1426 patients with sepsis, predominantly of white ethnicity (59.2%) and male sex (56.4%), with an SA-AKI incidence rate of 78.5%. A significant linear association was observed between the TyG index and SA-AKI (OR, 1.40; 95% confidence interval(CI) [1.14-1.73]). Additionally, the TyG index demonstrated a significant correlation with the length of stay (LOS) in both the hospital (ß, 1.79; 95% CI [0.80-2.77]) and the intensive care unit (ICU) (ß, 1.30; 95% CI [0.80-1.79]). Subgroup and sensitivity analyses confirmed the robustness of these associations. Conclusion: This study revealed a strong association between the TyG index and both SA-AKI and length of stay in patients with sepsis. These findings suggest that the TyG index is a potential predictor of SA-AKI and the length of hospitalization in sepsis cases, broadening its application in this context. However, further research is required to confirm whether interventions targeting the TyG index can genuinely enhance the clinical outcomes of patients with sepsis.

IGF-1 rs6218 polymorphisms modulate the susceptibility to age-related cataract.

Background: Single nucleotide polymorphisms (SNPs), as the most abundant form of DNA variation in the human genome, contribute to age-related cataracts (ARC) development. Apoptosis of lens epithelial cells (LECs) is closely related to ARC formation. Insulin-like growth factor 1 (IGF1) contributes to cell apoptosis regulation. Moreover, IGF1 was indicated to exhibit a close association with cataract formation. Afterward, an investigation was conducted to examine the correlation between polymorphisms in IGF1 and the susceptibility to ARC. Methods: The present investigation was a case-control study. Venous blood draws were collected from the participants for DNA genotyping. Lens capsule samples were collected to detect mRNA and apoptosis. TaqMan RT-PCR was used to detect IGF1 polymorphism genotypes and qRT PCR was used to detect IGF1 mRNA levels in LECs. LEC apoptosis was evaluated through flow cytometry. The chi-square test was used to compare differences between ARCs and controls of each SNP. Results: We found that the G allele frequency in the IGF1-rs6218 was higher in the ARCs than in the controls. Furthermore, it was observed that the rs6218 GG genotype exhibited a positive correlation to elevated levels of IGF1 mRNA in LECs. The IGF1 mRNA in the LECs and the apoptosis of LECs in nuclear type of ARCs (ARNC) was higher than the controls. Conclusion: The susceptibility to ARC was related to IGF1-rs6218 polymorphism, and this polymorphism is associated with IGF1 expression at the mRNA level. Moreover, apoptosis in LECs of ARNCs was found to be increased.

Key Roles of Gli1 and Ihh Signaling in Craniofacial Development.

The Hedgehog (Hh) signaling pathway orchestrates its influence through a dynamic interplay of Hh proteins, the cell surface receptor Ptch1, Smo, and Gli transcription factors, contributing to a myriad of developmental events. Indian Hedgehog (Ihh) and Gli zinc finger transcription factor 1 (Gli1) play crucial roles in developmental regulation within the Hh signaling pathway. Ihh regulates chondrocyte proliferation, differentiation, and bone formation, impacting the development of cranial bones, cartilage, and the temporomandibular joint (TMJ). Losing Ihh results in cranial bone malformation, decreased ossification, and affects the formation of cranial base cartilage unions, TMJ condyles, and joint discs. Gli1 is predominantly expressed during early craniofacial development, and Gli1+ cells are identified as the primary mesenchymal stem cells (MSCs) for craniofacial bones, crucial for cell differentiation and morphogenesis. Additionally, a complex mutual regulatory mechanism exists between Gli1 and Ihh, ensuring the normal function of the Hh signaling pathway by directly or indirectly regulating each other's expression levels. And the interaction between Ihh and Gli1 significantly impacts the normal development of craniofacial tissues.This review summarizes the pivotal roles of Gli1 and Ihh in the intricate landscape of mammalian craniofacial development, and outlines the molecular regulatory mechanisms and intricate interactions governing the growth of bone and cartilage exhibited by Gli1 and Ihh, which Provides new insights into potential therapeutic strategies for related diseases or researches of tissue regeneration.

Li5.5PS4.5Cl1.5-Based All-Solid-State Battery with a Silver Nanoparticle-Modified Graphite Anode for Improved Resistance to Overcharging and Increased Energy Density.

All-solid-state lithium batteries (ASSLBs) are attracting tremendous attention due to their improved safety and higher energy density. However, the use of a metallic lithium anode poses a major challenge due to its low stability and processability. Instead, the graphite anode exhibits high reversibility for the insertion/deinsertion of lithium ions, giving ASSLBs excellent cyclic stability but a lower energy density. To increase the energy density of ASSLBs with the graphite anode, it is necessary to lower the negative/positive (N/P) capacity ratio and to increase the charging voltage. These strategies bring new challenges to lithium metal plating and dendrite growth. Here, a nano-Ag-modified graphite composite electrode (Ag@Gr) is developed to overcome these shortcomings for Li5.5PS4.5Cl1.5-based ASSLBs. The Ag@Gr composite exhibits a strong ability to inhibit lithium metal plating and fast lithium-ion transport kinetics. Ag nanoparticles can accommodate excess Li, and the as-obtained Li-Ag alloy enhances the kinetics of the composite electrode. The ASSLB with the Li(Ni0.8Co0.1Mn0.1)O2 cathode and Ag@Gr anode achieves an energy density of 349 W h kg-1. The full cell using Ag@Gr with an N/P ratio of 0.6 also highlights the rate performance. This work provides a simple and effective method to regulate the charge transport kinetics of graphite anodes and improve the cyclic performance and energy density of ASSLBs.

Therapeutic trends of priming mesenchymal stem cells: A bibliometric analysis.

Mesenchymal stem cells (MSCs) have gained substantial attention in regenerative medicine due to their multilineage differentiation potential and immunomodulatory capabilities. MSCs have demonstrated therapeutic promise in numerous preclinical and clinical studies across a variety of diseases, including neurodegenerative disorders, cardiovascular diseases, and autoimmune conditions. Recently, priming MSCs has emerged as a novel strategy to enhance their therapeutic efficacy by preconditioning them for optimal survival and function in challenging in vivo environments. This study presented a comprehensive bibliometric analysis of research activity in the field of priming mesenchymal stem cells (MSCs) from 2003 to 2023. Utilizing a dataset of 585 documents, we explored research trends, leading authors and countries, productive journals, and frequently used keywords. We also explored priming strategies to augment the therapeutic efficacy of MSCs. Our findings show increasing research productivity with a peak in 2019, identified the United States as the leading contributor, and highlighted WANG JA as the most prolific author. The most published journal was Stem Cell Research & Therapy. Keyword analysis revealed core research areas emerging hotspots, while coword and cited sources visualizations elucidated the conceptual framework and key information sources. Further studies are crucial to advance the translation of primed MSCs from bench to bedside, potentially revolutionizing the landscape of regenerative medicine.

Federated attention consistent learning models for prostate cancer diagnosis and Gleason grading.

Artificial intelligence (AI) holds significant promise in transforming medical imaging, enhancing diagnostics, and refining treatment strategies. However, the reliance on extensive multicenter datasets for training AI models poses challenges due to privacy concerns. Federated learning provides a solution by facilitating collaborative model training across multiple centers without sharing raw data. This study introduces a federated attention-consistent learning (FACL) framework to address challenges associated with large-scale pathological images and data heterogeneity. FACL enhances model generalization by maximizing attention consistency between local clients and the server model. To ensure privacy and validate robustness, we incorporated differential privacy by introducing noise during parameter transfer. We assessed the effectiveness of FACL in cancer diagnosis and Gleason grading tasks using 19,461 whole-slide images of prostate cancer from multiple centers. In the diagnosis task, FACL achieved an area under the curve (AUC) of 0.9718, outperforming seven centers with an average AUC of 0.9499 when categories are relatively balanced. For the Gleason grading task, FACL attained a Kappa score of 0.8463, surpassing the average Kappa score of 0.7379 from six centers. In conclusion, FACL offers a robust, accurate, and cost-effective AI training model for prostate cancer pathology while maintaining effective data safeguards.

Orchestrated codelivery of peptide antigen and adjuvant to antigen-presenting cells by using an engineered chimeric peptide enhances antitumor T-cell immunity.

The intrinsic pharmacokinetic limitations of traditional peptide-based cancer vaccines hamper effective cross-presentation and codelivery of antigens and adjuvants, which are crucial for inducing robust antitumor CD8+ T-cell responses. Here, we report the development of a versatile strategy that simultaneously addresses the different pharmacokinetic challenges of soluble subunit vaccines composed of antigens and CpG to modulate vaccine efficacy via translating an engineered chimeric peptide, eTAT, as an intramolecular adjuvant. Linking antigens to eTAT enhanced cytosolic delivery of the antigens. This, in turn, led to improved activation and lymph node-trafficking of antigen-presenting cells and antigen cross-presentation, thus promoting antigen-specific T-cell immune responses. Simple mixing of eTAT-linked antigens and CpG significantly enhanced codelivery of antigens and CpG to the antigen-presenting cells, and this substantially augmented the adjuvant effect of CpG, maximized vaccine immunogenicity and elicited robust and durable CD8+ T-cell responses. Vaccination with this formulation altered the tumor microenvironment and exhibited potent antitumor effects, with generally further enhanced therapeutic efficacy when used in combination with anti-PD1. Altogether, the engineered chimeric peptide-based orchestrated codelivery of antigen and adjuvant may serve as a promising but simple strategy to improve the efficacy of peptide-based cancer vaccines.

Analyzing and engineering of the biosynthetic pathway of mollemycin A for enhancing its production.

Mollemycin A (MOMA) is a unique glyco-hexadepsipeptide-polyketide that was isolated from a Streptomyces sp. derived from the Australian marine environment. MOMA exhibits remarkable inhibitory activity against both drug-sensitive and multidrug-resistant malaria parasites. Optimizing MOMA through structural modifications or product enhancements is necessary for the development of effective analogues. However, modifying MOMA using chemical approaches is challenging, and the production titer of MOMA in the wild-type strain is low. This study identified and characterized the biosynthetic gene cluster of MOMA for the first time, proposed its complex biosynthetic pathway, and achieved an effective two-pronged enhancement of MOMA production. The fermentation medium was optimized to increase the yield of MOMA from 0.9 mg L-1 to 1.3 mg L-1, a 44% boost. Additionally, a synergistic mutant strain was developed by deleting the momB3 gene and overexpressing momB2, resulting in a 2.6-fold increase from 1.3 mg L-1 to 3.4 mg L-1. These findings pave the way for investigating the biosynthetic mechanism of MOMA, creating opportunities to produce a wide range of MOMA analogues, and developing an efficient strain for the sustainable and economical production of MOMA and its analogues.

MFEM-CIN: A Lightweight Architecture Combining CNN and Transformer for the Classification of Pre-Cancerous Lesions of the Cervix.

Goal: Cervical cancer is one of the most common cancers in women worldwide, ranking among the top four. Unfortunately, it is also the fourth leading cause of cancer-related deaths among women, particularly in developing countries where incidence and mortality rates are higher compared to developed nations. Colposcopy can aid in the early detection of cervical lesions, but its effectiveness is limited in areas with limited medical resources and a lack of specialized physicians. Consequently, many cases are diagnosed at later stages, putting patients at significant risk. Methods: This paper proposes an automated colposcopic image analysis framework to address these challenges. The framework aims to reduce the labor costs associated with cervical precancer screening in undeserved regions and assist doctors in diagnosing patients. The core of the framework is the MFEM-CIN hybrid model, which combines Convolutional Neural Networks (CNN) and Transformer to aggregate the correlation between local and global features. This combined analysis of local and global information is scientifically useful in clinical diagnosis. In the model, MSFE and MSFF are utilized to extract and fuse multi-scale semantics. This preserves important shallow feature information and allows it to interact with the deep feature, enriching the semantics to some extent. Conclusions: The experimental results demonstrate an accuracy rate of 89.2% in identifying cervical intraepithelial neoplasia while maintaining a lightweight model. This performance exceeds the average accuracy achieved by professional physicians, indicating promising potential for practical application. Utilizing automated colposcopic image analysis and the MFEM-CIN model, this research offers a practical solution to reduce the burden on healthcare providers and improve the efficiency and accuracy of cervical cancer diagnosis in resource-constrained areas.

Immunogenicity and safety of an ORF7-deficient skin-attenuated and neuro-attenuated live vaccine for varicella: a randomised, double-blind, controlled, phase 2a trial.

BACKGROUND: The Oka varicella vaccine strain remains neurovirulent and can establish lifelong latent infection, raising safety concerns about vaccine-related herpes zoster. In this study, we aimed to evaluate the immunogenicity and safety of a skin-attenuated and neuro-attenuated varicella vaccine candidate (v7D vaccine). METHODS: We did this randomised, double-blind, controlled, phase 2a clinical trial in Jiangsu, China. Healthy children aged 3-12 years with no history of varicella infection or vaccination were enrolled and randomly assigned (1:1:1:1) to receive a single subcutaneous injection of the v7D vaccine at 3·3 log10 plaque forming units (PFU; low-dose v7D group), 3·9 log10 PFU (medium-dose v7D group), and 4·2 log10 PFU (high-dose v7D group), or the positive control varicella vaccine (vOka vaccine group). All the participants, laboratory personnel, and investigators other than the vaccine preparation and management staff were masked to the vaccine allocation. The primary outcome was assessment of the geometric mean titres (GMTs) and seroconversion rates of anti-varicella zoster virus immunoglobulin G (IgG) induced by different dose groups of v7D vaccine at 0, 42, 60, and 90 days after vaccination in the per-protocol set for humoral immune response analysis. Safety was a secondary outcome, focusing on adverse events within 42 days post-vaccination, and serious adverse events within 6 months after vaccination. This study was registered on Chinese Clinical Trial Registry, ChiCTR2000034434. FINDINGS: On Aug 18-21, 2020, 842 eligible volunteers were enrolled and randomly assigned treatment. After three participants withdrew, 839 received a low dose (n=211), middle dose (n=210), or high dose (n=210) of v7D vaccine, or the vOka vaccine (n=208). In the per-protocol set for humoral immune response analysis, the anti-varicella zoster virus IgG antibody response was highest at day 90. At day 90, the seroconversion rates of the low-dose, medium-dose, and high-dose groups of v7D vaccine and the positive control vOka vaccine group were 100·0% (95% CI 95·8-100·0; 87 of 87 participants), 98·9% (93·8-100·0; 87 of 88 participants), 97·8% (92·4-99·7; 91 of 93 participants), and 96·4% (89·8-99·2; 80 of 83 participants), respectively; the GMTs corresponded to values of 30·8 (95% CI 26·2-36·0), 31·3 (26·7-36·6), 28·2 (23·9-33·2), and 38·5 (31·7-46·7). The v7D vaccine, at low dose and medium dose, elicited a humoral immune response similar to that of the vOka vaccine. However, the high-dose v7D vaccine induced a marginally lower GMT compared with the vOka vaccine at day 90 (p=0·027). In the per-protocol set, the three dose groups of the v7D vaccine induced a similar humoral immune response at each timepoint, with no statistically significant differences. The incidence of adverse reactions in the low-dose, medium-dose, and high-dose groups of v7D vaccine was significantly lower than that in the vOka vaccine group (17% [35 of 211 participants], 20% [41 of 210 participants], and 13% [27 of 210 participants] vs 24% [50 of 208 participants], respectively; p=0·025), especially local adverse reactions (10% [22 of 211 participants], 14% [30 of 210 participants] and 9% [18 of 210 participants] vs 18% [38 of 208 participants], respectively; p=0·016). None of the serious adverse events were vaccine related. INTERPRETATION: The three dose groups of the candidate v7D vaccine exhibit similar humoral immunogenicity to the vOka vaccine and are well tolerated. These findings encourage further investigations on two-dose vaccination schedules, efficacy, and the potential safety benefit of v7D vaccine in the future. FUNDING: The National Natural Science Foundation of China, CAMS Innovation Fund for Medical Sciences, the Fundamental Research Funds for the Central Universities, and Beijing Wantai. TRANSLATION: For the Chinese translation of the abstract see Supplementary Materials section.

Characterization and validation of fatty acid metabolism-related genes predicting prognosis, immune infiltration, and drug sensitivity in endometrial cancer.

Endometrial cancer is considered to be the second most common tumor of the female reproductive system, and patients diagnosed with advanced endometrial cancer have a poor prognosis. The influence of fatty acid metabolism in the prognosis of patients with endometrial cancer remains unclear. We constructed a prognostic risk model using transcriptome sequencing data of endometrial cancer and clinical information of patients from The Cancer Genome Atlas (TCGA) database via least absolute shrinkage and selection operator regression analysis. The tumor immune microenvironment was analyzed using the CIBERSORT algorithm, followed by functional analysis and immunotherapy efficacy prediction by gene set variation analysis. The role of model genes in regulating endometrial cancer in vitro was verified by CCK-8, colony formation, wound healing, and transabdominal invasion assays, and verified in vivo by subcutaneous tumor transplantation in nude mice. A prognostic model containing 14 genes was constructed and validated in 3 cohorts and clinical samples. The results showed differences in the infiltration of immune cells between the high-risk and low-risk groups, and that the high-risk group may respond better to immunotherapy. Experiments in vitro confirmed that knockdown of epoxide hydrolase 2 (EPHX2) and acyl-CoA oxidase like (ACOXL) had an inhibitory effect on EC cells, as did overexpression of hematopoietic prostaglandin D synthase (HPGDS). The same results were obtained in experiments in vivo. Prognostic models related to fatty acid metabolism can be used for the risk assessment of endometrial cancer patients. Experiments in vitro and in vivo confirmed that the key genes HPGDS, EPHX2, and ACOXL in the prognostic model may affect the development of endometrial cancer.

Exploring deep learning radiomics for classifying osteoporotic vertebral fractures in X-ray images.

Purpose: To develop and validate a deep learning radiomics (DLR) model that uses X-ray images to predict the classification of osteoporotic vertebral fractures (OVFs). Material and methods: The study encompassed a cohort of 942 patients, involving examinations of 1076 vertebrae through X-ray, CT, and MRI across three distinct hospitals. The OVFs were categorized as class 0, 1, or 2 based on the Assessment System of Thoracolumbar Osteoporotic Fracture. The dataset was divided randomly into four distinct subsets: a training set comprising 712 samples, an internal validation set with 178 samples, an external validation set containing 111 samples, and a prospective validation set consisting of 75 samples. The ResNet-50 architectural model was used to implement deep transfer learning (DTL), undergoing -pre-training separately on the RadImageNet and ImageNet datasets. Features from DTL and radiomics were extracted and integrated using X-ray images. The optimal fusion feature model was identified through least absolute shrinkage and selection operator logistic regression. Evaluation of the predictive capabilities for OVFs classification involved eight machine learning models, assessed through receiver operating characteristic curves employing the "One-vs-Rest" strategy. The Delong test was applied to compare the predictive performance of the superior RadImageNet model against the ImageNet model. Results: Following pre-training separately on RadImageNet and ImageNet datasets, feature selection and fusion yielded 17 and 12 fusion features, respectively. Logistic regression emerged as the optimal machine learning algorithm for both DLR models. Across the training set, internal validation set, external validation set, and prospective validation set, the macro-average Area Under the Curve (AUC) based on the RadImageNet dataset surpassed those based on the ImageNet dataset, with statistically significant differences observed (P<0.05). Utilizing the binary "One-vs-Rest" strategy, the model based on the RadImageNet dataset demonstrated superior efficacy in predicting Class 0, achieving an AUC of 0.969 and accuracy of 0.863. Predicting Class 1 yielded an AUC of 0.945 and accuracy of 0.875, while for Class 2, the AUC and accuracy were 0.809 and 0.692, respectively. Conclusion: The DLR model, based on the RadImageNet dataset, outperformed the ImageNet model in predicting the classification of OVFs, with generalizability confirmed in the prospective validation set.

Influence of moderate-to-high intensity physical activity on depression levels: a study based on a health survey of Chinese university students.

OBJECTIVE: The study aims to examine how moderate-to-vigorous physical activity (MVPA) affects the severity of depression symptoms among Chinese college students. Additionally, it seeks to analyze the mediating mechanisms involving self-rated health and general self-efficacy. METHODS: The study utilized data from the 2023 Chinese College Health Tracking Survey and employed multiple linear regression and structural equation modeling techniques to investigate the impacts of MVPA on depression levels and its underlying mediating mechanisms among college students. The primary cohort comprised 49,717 enrolled college students from 106 universities in China. RESULTS: A total of 41,620 valid questionnaires were collected (response rate: 83.7%), with females accounting for 58.6%. In the past month, approximately 30.2% of college students engaged in MVPA. Self-rated health (B = - 0.282, P < 0.001) and general self-efficacy (B = - 0.133, P < 0.001) significantly influenced college students' depression scores. Even after controlling for other variables, participating in MVPA remained significantly associated with reduced depression scores (B = - 0.062, P = 0.002). The results of the structural equation model showed that MVPA not only directly decreased college students' depression scores but also indirectly reduced the likelihood of depression occurrence by improving their physical health status and general self-efficacy. CONCLUSION: The lack of physical activity among Chinese college students is evident. Engaging in MVPA can reduce the likelihood of depression among college students. MVPA achieves this reduction by enhancing college students' general self-efficacy and improving their physical health. The factors influencing depression levels among college students are multifaceted. For future interventions targeting college students' mental health, comprehensive approaches that incorporate behavioral and psychological factors should be emphasized.

Intelligent Fault Diagnosis of Rolling Bearing Based on Gramian Angular Difference Field and Improved Dual Attention Residual Network.

With the rapid development of smart manufacturing, data-driven deep learning (DL) methods are widely used for bearing fault diagnosis. Aiming at the problem of model training crashes when data are imbalanced and the difficulty of traditional signal analysis methods in effectively extracting fault features, this paper proposes an intelligent fault diagnosis method of rolling bearings based on Gramian Angular Difference Field (GADF) and Improved Dual Attention Residual Network (IDARN). The original vibration signals are encoded as 2D-GADF feature images for network input; the residual structures will incorporate dual attention mechanism to enhance the integration ability of the features, while the group normalization (GN) method is introduced to overcome the bias caused by data discrepancies; and then the model is trained to complete the classification of faults. In order to verify the superiority of the proposed method, the data obtained from Case Western Reserve University (CWRU) bearing data and bearing fault experimental equipment were compared with other popular DL methods, and the proposed model performed optimally. The method eventually achieved an average identification accuracy of 99.2% and 97.9% on two different types of datasets, respectively.

Cardiac Multi-Frequency Vibration Signal Sensor Module and Feature Extraction Method Based on Vibration Modeling.

Cardiovascular diseases pose a long-term risk to human health. This study focuses on the rich-spectrum mechanical vibrations generated during cardiac activity. By combining Fourier series theory, we propose a multi-frequency vibration model for the heart, decomposing cardiac vibration into frequency bands and establishing a systematic interpretation for detecting multi-frequency cardiac vibrations. Based on this, we develop a small multi-frequency vibration sensor module based on flexible polyvinylidene fluoride (PVDF) films, which is capable of synchronously collecting ultra-low-frequency seismocardiography (ULF-SCG), seismocardiography (SCG), and phonocardiography (PCG) signals with high sensitivity. Comparative experiments validate the sensor's performance and we further develop an algorithm framework for feature extraction based on 1D-CNN models, achieving continuous recognition of multiple vibration features. Testing shows that the recognition coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) of the 8 features are 0.95, 2.18 ms, and 4.89 ms, respectively, with an average prediction speed of 60.18 us/point, meeting the re-quirements for online monitoring while ensuring accuracy in extracting multiple feature points. Finally, integrating the vibration model, sensor, and feature extraction algorithm, we propose a dynamic monitoring system for multi-frequency cardiac vibration, which can be applied to portable monitoring devices for daily dynamic cardiac monitoring, providing a new approach for the early diagnosis and prevention of cardiovascular diseases.

[Element relationship and extension path of clinical evidence knowledge map with Chinese patent medicine].

This study aims to construct the element relationship and extension path of clinical evidence knowledge map with Chinese patent medicine, providing basic technical support for the formation and transformation of the evidence chain of Chinese patent medicine and providing collection, induction, and summary schemes for massive and disorganized clinical data. Based on the elements of evidence-based PICOS, the conventional construction methods of knowledge graph were collected and summarized. Firstly, the data entities related to Chinese patent medicine were classified, and entity linking was performed(disambiguation). Secondly, the study associated and classified the attribute information of the data entity. Finally, the logical relationship between entities was constructed, and then the element relationship and extension path of the knowledge map conforming to the characteristics of clinical evidence of Chinese patent medicine were summarized. The construction of the clinical evidence knowledge map of Chinese patent medicine was mainly based on process design and logical structure, and the element relationship of the knowledge map was expressed according to the PICOS principle and evidence level. The extension path crossed three levels(model layer, data layer application, and new evidence application), and the study gradually explored the path from disease, core evaluation indicators, Chinese patent medicine, core prescriptions, syndrome and treatment rules, and medical case comparison(evolution law) to new drug research and development. In this study, the top-level design of the construction of the clinical evidence knowledge map of Chinese patent medicine has been clarified, but it still needs the joint efforts of interdisciplinary disciplines. With the continuous improvement of the map construction technology in line with the characteristics of TCM, the study can provide necessary basic technical support and reference for the development of the TCM discipline.

[Guidelines for post-marketing research on clinical effectiveness of Chinese patent medicines].

Due to the lack of specialized guidance, the post-marketing research on clinical effectiveness of Chinese patent medicines demonstrates varied quality and lacks high-quality evidence, failing to meet the demands of policy-making, clinical decision-making, and industrial decision-making. To address this issue, this project gathered experts in clinical medicine, clinical pharmacy, evidence-based medicine, drug epidemiology, medical ethics, and policy and regulation in China. They referred to the model of international post-marketing research on medicines and developed Guidelines for post-marketing research on clinical effectiveness of Chinese patent medicines under the framework of relevant laws and regulations and technical guidance documents in China. The guidelines were developed with consideration to the characteristics of Chinese patent medicines, China's national conditions, and all the stakeholders including marketing authorization holders, clinical researchers, drug administration, and users. The development of the guidelines followed the requirements for developing group standards set by the China Association of Chinese Medicine. The guidelines fully implement the concept of full life-cycle research, emphasizing the combination of traditional Chinese medicine(TCM) theory, human use experience, and clinical trials and pay attention to the compliance, scientificity, and ethics of research. The guidelines clarify the topic selection and decision-making path of the post-marketing research on effectiveness of Chinese patent medicines through six steps: determining research purpose, analyzing drug characteristics, evaluating research basis, proposing clinical orientation, clarifying research purpose, and implementing classified research. The general principles of research design and implementation were clarified from eight aspects: research type, research objects, sample size, efficacy indicators, bias, missing data, evidence level, and practicality. It focuses on the research on the TCM syndrome-based efficacy evaluation, clinical value-oriented mechanism of action, and the effectiveness of Chinese patent medicines with different routes of administration. The guidelines provide a universal methodological basis for the post-marketing research on clinical effectiveness of Chinese patent medicines.