ObjectiveThis study aimed to investigate the action mechanism by which Banxia Xiexintang （BXT） inhibits epithelial-mesenchymal transition （EMT） in chronic atrophic gastritis （CAG） rats by regulating the interleukin-17（IL-17）/extracellular regulated protein kinases（ERK）/CCAAT enhancer binding protein β（C/EBPβ）signaling pathway， thereby providing new theoretical evidence for the treatment of CAG with classic traditional Chinese medicine formulas. MethodsA CAG rat model was established by using the combined factor method. After successful modeling， the rats were randomly divided into the model group， low-， medium-， and high-dose groups （0.549， 1.098， 2.196 g·kg^-1， respectively） of BXT， and the positive drug group （vitacoenzyme， 0.3 g·kg^-1）. A normal control group was also set up. After 8 weeks of intervention， the pathological changes of gastric tissue were evaluated. The enzyme-linked immunosorbent assay （ELISA） was used to detect the contents of IL-17， tumor necrosis factor-α （TNF-α）， cyclooxygenase-2 （COX-2）， and C/EBPβ in serum， as well as the contents of EMT markers in gastric mucosal tissue including E-cadherin， N-cadherin， and vimentin. The immunohistochemistry method was employed to determine the localization and protein expression levels of IL-17， p-ERK， and C/EBPβ in gastric mucosal tissue. Western blot was used to detect the protein expressions of C/EBPβ， ERK， and its phosphorylated form （p）-ERK in gastric mucosa. Real-time polymerase chain reaction （Real-time PCR） was applied to measure the mRNA expression levels of ERK， COX-2， and C/EBPβ in gastric mucosa. ResultsCompared with those in the normal control group， the rats in the model group showed gastric mucosal glandular atrophy and inflammatory cell infiltration. The protein and their related mRNA expressions of C/EBPβ， ERK， and p-ERK in gastric mucosa were significantly increased （P<0.05，P<0.01）. The levels of IL-17， TNF-α， COX-2， and C/EBPβ in serum were significantly increased （P<0.01）. The contents of N-cadherin and vimentin in gastric mucosal tissue were significantly increased， while the content of E-cadherin was significantly decreased （P<0.01）. Compared with the model group， after intervention with different doses of BXT， the pathological damage of the gastric mucosa was improved to varying degrees. The protein and mRNA expressions of C/EBPβ， ERK， and p-ERK in gastric mucosa were significantly reduced （P<0.05，P<0.01）. The levels of IL-17， TNF-α， COX-2， and C/EBP β in serum were significantly decreased （P<0.01）. The contents of N-cadherin and vimentin in gastric mucosa tissue were decreased， while the content of E-cadherin was increased （P<0.05，P<0.01）. ConclusionBXT can effectively improve the pathological damage of gastric mucosal tissue in CAG rats. Its action mechanism may be related to reducing the levels of IL-17 and TNF-α in serum， regulating the IL-17/ERK/C/EBPβ signaling pathway and inhibiting the EMT process.

ObjectiveTransfer RNA-derived fragments (tRFs) are a recently characterized and rapidly expanding class of small non-coding RNAs, typically ranging from 13 to 50 nucleotides in length. They are derived from mature or precursor tRNA molecules through specific cleavage events and have been implicated in a wide range of cellular processes. Increasing evidence indicates that tRFs play important regulatory roles in gene expression, primarily by interacting with target messenger RNAs (mRNAs) to induce transcript degradation, in a manner partially analogous to microRNAs (miRNAs). However, despite their emerging biological relevance and potential roles in disease mechanisms, there remains a significant lack of computational tools capable of systematically predicting the interaction landscape between tRFs and their target mRNAs. Existing databases often rely on limited interaction features and lack the flexibility to accommodate novel or user-defined tRF sequences. The primary goal of this study was to develop a machine learning based prediction algorithm that enables high-throughput, accurate identification of tRF:mRNA binding events, thereby facilitating the functional analysis of tRF regulatory networks. MethodsWe began by assembling a manually curated dataset of 38 687 experimentally verified tRF:mRNA interaction pairs and extracting seven biologically informed features for each pair: (1) AU content of the binding site, (2) site pairing status, (3) binding region location, (4) number of binding sites per mRNA, (5) length of the longest consecutive complementary stretch, (6) total binding region length, and (7) seed sequence complementarity. Using this dataset and feature set, we trained 4 distinct machine learning classifiers—logistic regression, random forest, decision tree, and a multilayer perceptron (MLP)—to compare their ability to discriminate true interactions from non-interactions. Each model’s performance was evaluated using overall accuracy, receiver operating characteristic (ROC) curves, and the corresponding area under the ROC curve (AUC). The MLP consistently achieved the highest AUC among the four, and was therefore selected as the backbone of our prediction framework, which we named tRF Prospect. For biological validation, we retrieved 3 high-throughput RNA-seq datasets from the gene expression omnibus (GEO) in which individual tRFs were overexpressed: AS-tDR-007333 (GSE184690), tRF-3004b (GSE197091), and tRF-20-S998LO9D (GSE208381). Differential expression analysis of each dataset identified genes downregulated upon tRF overexpression, which we designated as putative targets. We then compared the predictions generated by tRF Prospect against those from three established tools—tRFTar, tRForest, and tRFTarget—by quantifying the number of predicted targets for each tRF and assessing concordance with the experimentally derived gene sets. ResultsThe proposed algorithm achieved high predictive accuracy, with an AUC of 0.934. Functional validation was conducted using transcriptome-wide RNA-seq datasets from cells overexpressing specific tRFs, confirming the model’s ability to accurately predict biologically relevant downregulation of mRNA targets. When benchmarked against established tools such as tRFTar, tRForest, and tRFTarget, tRF Prospect consistently demonstrated superior performance, both in terms of predictive precision and sensitivity, as well as in identifying a higher number of true-positive interactions. Moreover, unlike static databases that are limited to precomputed results, tRF Prospect supports real-time prediction for any user-defined tRF sequence, enhancing its applicability in exploratory and hypothesis-driven research. ConclusionThis study introduces tRF Prospect as a powerful and flexible computational tool for investigating tRF:mRNA interactions. By leveraging the predictive strength of deep learning and incorporating a broad spectrum of interaction-relevant features, it addresses key limitations of existing platforms. Specifically, tRF Prospect: (1) expands the range of detectable tRF and target types; (2) improves prediction accuracy through multilayer perceptron model; and (3) allows for dynamic, user-driven analysis beyond database constraints. Although the current version emphasizes miRNA-like repression mechanisms and faces challenges in accurately capturing 5'UTR-associated binding events, it nonetheless provides a critical foundation for future studies aiming to unravel the complex roles of tRFs in gene regulation, cellular function, and disease pathogenesis.

Objective: To investigate the mediating role of vaccine literacy between vaccine knowledge and vaccine hesitancy and the moderating role of parental education level, so as to provide references for adjusting vaccination strategies. Methods: From May to December 2024, a stratified random sampling method was used to select 10 community hospitals in Guiyang and Zunyi City, Guizhou Province. A total of 1 401 parents of children aged 0-6 years were surveyed regarding their socio demographic characteristics, vaccine knowledge, vaccine literacy, and vaccine hesitancy levels. Data were analyzed using common method bias tests, Spearman correlation analysis, mediation and moderation effects tests. Results: The mean score for vaccine knowledge, vaccine literacy and vaccine hesitancy were (2.96±1.11, 14.25±2.64, 39.12±4.93) among the 1 401 participants. Mediating effect analysis showed that both parental vaccine knowledge ( β =1.28, 95% CI =1.08-1.49) and vaccine literacy ( β =0.75, 95% CI =0.66-0.84) positively predicted vaccine hesitancy (both P <0.01). Meanwhile, vaccine literacy accounted for 28.1% of the total effect of mediation between knowledge and vaccine hesitancy. In the moderated effects analysis, education level positively predicted vaccine literacy ( β =0.40, 95% CI =0.24-0.57), and education level moderated the pathway of vaccine knowledge on vaccine hesitancy ( β = 0.28 , 95% CI =0.05-0.52) (both P <0.01). Conclusions Vaccine literacy partially mediates the relationship between vaccine knowledge and vaccine hesitancy. Parental education level positively moderates the prediction of vaccine knowledge on vaccine hesitancy score.

Objective To explore the application value of CT-based radiomics in differentiating pneumonia-type mucinous adenocarcinoma(PTMA)from organizing pneumonia(OP).Methods A total of 52 PTMA patients and 102 OP patients were retrospectively included and randomly divided into training set(n=124)and test set(n=30)in an 8∶2 ratio.Eight PTMA patients and 22 OP patients from another hospital during the same period were included as external validation set(n=30).Clinical characteristics and CT signs of the patients were selected to construct the clinical model.Radiomics features were extracted and dimensionality reduction was performed through the least absolute shrinkage and selection operator(LASSO)algorithm.A radiomics model was constructed and the Radiomics score(Radscore)was calculated.The Radscore was combined with clinical factors to establish the combined model and a nomogram was illustrated.The models' fitting degree was analyzed by the calibration curve,while their efficacy was evaluated by the receiver operating characteristic(ROC)curve and decision curve analysis(DCA).Results The clinical model,established based on the border,cystic space and bronchial leafless tree sign,achieved area under the curve(AUC)of 0.850,0.782,and 0.759 in the training set,test set,and external validation set,respectively.Thirteen features were obtained to construct the radiomics model,with AUC of 0.925,0.865,and 0.830,respectively.The AUC of the combined model were 0.970,0.905,and 0.864,respectively,in which the calibration curve demonstrated good model fitting.DCA indicated that the combined model had the greatest clinical net benefit.Conclusion The combined model based on CT radiomics can effectively distinguish PTMA from OP.

Objective:To investigate the feasibility and value of nomogram based on base line clinical and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) characteristics for pretreatment predicting the depth of myometrial invasion and tumor grade of endometrioid endometrial carcinoma (EEC).Methods:Preoperative baseline clinical characteristics and DCE-MRI characteristics of 194 EEC patients were prospectively collected at Obstetrics and Gynecology Hospital, Fudan University from October 2020 to January 2022 and used as a training set. Univariate analysis was conducted to compare baseline clinical characteristics and DCE-MRI quantitative parameters [including tumor volume, and mean, median, and standard deviation of volume transfer constant (K trans), rate constant (K ep), extravascular extracellular volume fraction (V e), and initial area under the enhancement curve (iAUC)] between patients with deep myometrial invasion (DMI) and those with superficial myometrial invasion (SMI), as well as between high-grade and low-grade EEC. Multivariate logistics regression analysis was used to identify independent predictors for the construction of nomogram. An independent external testing set comprising 127 EEC patients was retrospectively collected from Zhongshan Hospital, Fudan University and Zhongshan Hospital, Fudan University (Xiamen Branch). The area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA) were used for evaluating the model′s predictive performance and clinical net benefit, respectively. Results:(1) The depth of myometrial invasion: univariate analysis showed that in the training set, the EEC patients with DMI differed significantly from those with SMI in clinical characteristics including higher proportion of postmenopausal state and overweight [body mass index (BMI)≥25 kg/m2], and abnormal levels of serum cancer antigen (CA) 125, CA 199, and human epididymis protein 4 (HE4), and in DCE-MRI quantitative parameters including tumor volume, and median, mean, and standard deviation of K trans, median of V e, as well as median, mean, and standard deviation of iAUC (all P<0.05). Multivariate analysis showed that the patient′s menstrual status, BMI, CA 199, tumor volume, and mean of iAUC were independent predictors of the depth of myometrial invasion, and constructed the nomogram (recorded as Nomogram_1), achieving an AUC of 0.861 (95% CI: 0.803-0.919) in the training set. In the independent external testing set, the AUC was 0.876 (95% CI: 0.815-0.938), with corresponding sensitivity of 82.0%, specificity of 80.7%, accuracy of 81.1%, positive predictive value (PPV) of 65.3%, and negative predictive value (NPV) of 91.0% for predicting DMI. (2) The EEC grade: univariate analysis showed that in the training set, high-grade EEC patients differed significantly from low-grade EEC in clinical characteristics including patient′s age, the proportion of postmenopausal state and overweight, and abnormal levels of serum CA 125, and in DCE-MRI quantitative parameters including tumor volume, median, mean, and standard deviation of K trans, median and mean of V e, as well as median, mean, and standard deviation of iAUC (all P<0.05). Multivariate analysis showed that the patient′s menstrual status, BMI, tumor volume, and median of V e emerged as independent predictors of EEC grade, and constructed the nomogram (recorded as Nomogram_2), achieving an AUC of 0.845 (95% CI: 0.786-0.893) in the training set. While in the external testing set, the AUC was 0.819 (95% CI: 0.744-0.894), with corresponding sensitivity of 72.4%, specificity of 72.4%, accuracy of 72.4%, PPV of 43.8%, and NPV of 89.9% for predicting high-grade EEC. (3) The DCA curves demonstrated that both Nomogram_1 and Nomogram_2 yielded obvious positive clinical net benefits across a wide range of threshold probabilities. Conclusion:The nomogram based on pretreatment clinical and DCE-MRI characteristics has the potential to noninvasive predict the depth of myometrial invasion and grade of EEC, providing valuable reference information for clinical management decision-making.

Alzheimer's disease(AD)is a neurodegenerative disorder primarily affecting memory,learn-ing,and cognitive functions.It poses a significant health concern for the elderly,but effective treatments are lacking.Its main pathological features are amyloid β(Aβ)deposits forming senile plaques(SPs)and neurofibrillary tangles(NFTs)formed by hyperphosphorylated tau(p-Tau).These pathological changes often induce oxidative stress,which is an important pathological mechanism in AD.Oxidative stress is closely associated with Aβ and Tau deposition and is a potential target for intervention in the treatment of AD.However,the pathological mechanisms leading to AD are multifactorial,and AD oxida-tive stress often interacts with other mechanisms to jointly influence the AD process.Therefore,this paper focuses on the regulatory relationship between mitophagy,neuroinflammation,neuronal apoptosis and nu-clear factor erythroid 2-related factor 2(Nrf2)and oxidative stress.By elucidating the relationship be-tween the pathological features,oxidative stress and its regulatory mechanism of AD,potential effective intervention targets were found.At present,numerous studies have indicated that exercise can alleviate oxidative stress in AD and improve cognitive function,but the underlying molecular mechanisms require further clarification.Therefore,we further discussed the mechanism by which exercise regulates oxidative stress and related molecular signaling pathways,and clarified that exercise may ameliorate AD oxidative stress by affecting these signaling pathways,thereby improving AD-related pathological features and cog-nitive function.It is helpful to understand the pathogenesis of AD from the perspective of molecular mechanism and provide theoretical support for scientific and effective exercise intervention to prevent and cure AD.

Objective To understand the current situation of healthcare-associated infection(HAI)in China,pro-vide data support and decision-making basis for formulating scientific and effective strategies for HAI prevention and control.Methods A nationwide cross-sectional survey on HAI was conducted among various types and levels of medical institutions in China according to a unified protocol of bedside surveys and case investigations.Results In 2024,a total of 5 736 medical institutions and 2 751 765 patients were surveyed.Among them,34 889 HAI cases were identified,with a prevalence rate of 1.27％.The number of HAI episodes was 38 032,and case prevalence rate was 1.38％.The prevalence rate of HAI in medical institutions in different regions of China ranged from 0.66％to 2.35％.Among medical institutions of different scales,those with a bed capacity of ≥900 had the high-est incidence of HAI,reaching 1.65％.The most common infection site was the lower respiratory tract(44.66％),followed by the urinary tract(12.94％),surgical site(9.32％),upper respiratory tract(7.02％),and bloodstream infection(5.78％).The top 3 departments with the highest HAI rates were the general intensive care unit(10.02％),department of neurosurgery(5.51％),and department(group)of hematology(5.34％).A total of 23 238 strains of HAI pathogens were detected,with 10 714 strains(46.10％)from lower respiratory tract speci-mens.The top 5 detected strains were Klebsiella pneumoniae(14.76％),Pseudomonas aeruginosa(13.33％),Escherichia coli(12.79％),Acinetobacter baumannii(9.23％),and Staphylococcus aureus(7.88％).231 944 pa-tients underwent class Ⅰ incision surgery were monitored,with 1 647 cases experienced surgical site infection,and the prevalence rate of surgical site infection was 0.71％.The number of patients who should undergo pathogen de-tection(patients receiving therapeutic and therapeutic combined prophylactic antimicrobial agents)was 715 179,while the actual number was 480 492,with a pathogen detection rate of 67.18％.425 225 patients received patho-genic detection before treatment,with a detection rate of 59.46％.Conclusion The overall HAI prevalence in Chi-na is lower,showing disparities among medical institutions of different regions and scales.Therefore,precise imple-mentation of measures is necessary for HAI prevention and control,with a focus on high-risk institutions and high-risk departments,key areas,and critical procedures.All levels of medical institutions should continuously reduce the incidence of HAI by strengthening monitoring,standardizing the use of antimicrobial agents,and reinforcing basic HAI prevention and control measures.

ObjectiveTo elucidate the critical role of rehabilitation medical records (including electronic records) in rehabilitation medicine's clinical practice and management, comprehensively analyzed the structure, core content and data standards of rehabilitation medical records, to develop a standardized medical record data architecture and core dataset suitable for rehabilitation medicine and to explore the application of rehabilitation data in performance evaluation and payment. MethodsBased on the regulatory documents Basic Specifications for Medical Record Writing and Basic Specifications for Electronic Medical Records (Trial) issued by National Health Commission of China, and referencing the World Health Organization (WHO) Family of International Classifications (WHO-FICs) classifications, International Classification of Diseases (ICD-10/ICD-11), International Classification of Functioning, Disability and Health (ICF), and International Classification of Health Interventions (ICHI Beta-3), this study constructed the data architecture, core content and data standards for rehabilitation medical records. Furthermore, it explored the application of rehabilitation record summary sheets (home page) data in rehabilitation medical statistics and payment methods, including Diagnosis-related Groups (DRG), Diagnosis-Intervention Packet (DIP) and Case Mix Index. ResultsThis study proposed a systematic standard framework for rehabilitation medical records, covering key components such as patient demographics, rehabilitation diagnosis, functional assessment, rehabilitation treatment prescriptions, progress evaluations and discharge summaries. The research analyzed the systematic application methods and data standards of ICD-10/ICD-11, ICF and ICHI Beta-3 in the fields of medical record terminology, coding and assessment. Constructing a standardized data structure and data standards for rehabilitation medical records can significantly improve the quality of data reporting based on the medical record summary sheet, thereby enhancing the quality control of rehabilitation services, effectively supporting the optimization of rehabilitation medical insurance payment mechanisms, and contributing to the establishment of rehabilitation medical performance evaluation and payment based on DRG and DIP. ConclusionStructured rehabilitation records and data standardization are crucial tools for quality control in rehabilitation. Systematically applying the three reference classifications of the WHO-FICs, and aligning with national medical record and electronic health record specifications, facilitate the development of a standardized rehabilitation record architecture and core dataset. Standardizing rehabilitation care pathways based on the ICF methodology, and developing ICF- and ICD-11-based rehabilitation assessment tools, auxiliary diagnostic and therapeutic systems, and supporting terminology and coding systems, can effectively enhance the quality of rehabilitation records and enable interoperability and sharing of rehabilitation data with other medical data, ultimately improving the quality and safety of rehabilitation services.

BackgroundDepression is a prevalent emotional problem in adolescents， and parental psychological control is an important predictor of adolescent depression. However， existing research on the acting mechanism between the two is not adequate. ObjectiveTo explore the pathway of negative perfectionism and academic stress between parental psychological control and depressive symptoms among secondary school students， so as to provide references for reducing the incidence risk of depression in such population. MethodsFrom February to April 2023， 1 100 students across 2 middle schools and 2 high schools in Zhongshan city were selected as subjects. The survey was conducted adopting Parental Psychological Control Questionnaire， Chinese Frost Multidimensional Perfectionism Scale （CFMPS）， sense of academic stress subscale in Mental Health Inventory of Middle School Student （MMHI-60） and Center for Epidemiological Studies-Depression Scale （CES-D）. Spearman correlation analysis was adopted to examine the correlation between scores of all scales above， and Amos 24.0 was used to test the mediating path of negative perfectionism and academic stress between parental psychological control and depressive symptoms among secondary school students. ResultsAmong the 1 009 valid questionnaires withdrew （91.73% of the total）， 261 students were detected to have depressive symptoms （25.87%）. As the results of Spearman correlation analysis showed， the scores of the Parental Psychological Control Questionnaire， score of negative perfectionism dimension in CFMPS， score of sense of academic stress subscale in MMHI-60 and CES-D score were positively correlated with each other （r=0.323~0.644， P<0.05 or 0.01）. The direct effect value of parental psychological control on depressive symptoms in secondary school students was 0.128 （95% CI： 0.061~0.201）， accounting for 31.37% of the total effect. Negative perfectionism and academic stress played independently as intermediatory roles between parental psychological control and depressive symptoms in secondary school students， and the indirect effect values were 0.099 （95% CI： 0.068~0.133） and 0.100 （95% CI： 0.060~0.143）， accounting for 24.27% and 24.51% of the total effect， respectively. Negative perfectionism and academic stress acted combinedly as the chain effect pathway between parental psychological control and depressve symptoms in secondary school students， with the indirect effect value of 0.081 （95% CI： 0.060~0.106） accounting for 19.85% of the total effect. ConclusionParental psychological control can affect the depressive symptoms among secondary school students directly， and through independent or chain paths of negative perfectionism and academic stress indirectly. ［Funded by Zhongshan Social Welfare Technology Research Project （number， 2022B1060）］

Objective To explore the recognition capabilities of electronic nose combined with machine learning in identifying the breath odor map of benign and malignant pulmonary nodules and Traditional Chinese Medicine (TCM) syndrome elements. Methods The study design was a single-center observational study. General data and four diagnostic information were collected from 108 patients with pulmonary nodules admitted to the Department of Cardiothoracic Surgery of Hospital of Chengdu University of TCM from April 2023 to March 2024. The patients' TCM disease location and nature distribution characteristics were analyzed using the syndrome differentiation method. The Cyranose 320 electronic nose was used to collect the odor profiles of oral exhalation, and five machine learning algorithms including random forest (RF), K-nearest neighbor (KNN), logistic regression (LR), support vector machine (SVM), and eXtreme gradient boosting (XGBoost) were employed to identify the exhaled breath profiles of benign and malignant pulmonary nodules and different TCM syndromes. Results (1) The common disease locations in pulmonary nodules were ranked in descending order as liver, lung, and kidney; the common disease natures were ranked in descending order as Yin deficiency, phlegm, dampness, Qi stagnation, and blood deficiency. (2) The electronic nose combined with the RF algorithm had the best efficacy in identifying the exhaled breath profiles of benign and malignant pulmonary nodules, with an AUC of 0.91, accuracy of 86.36%, specificity of 75.00%, and sensitivity of 92.85%. (3) The electronic nose combined with RF, LR, or XGBoost algorithms could effectively identify the different TCM disease locations and natures of pulmonary nodules, with classification accuracy, specificity, and sensitivity generally exceeding 80.00%.Conclusion Electronic nose combined with machine learning not only has the potential capabilities to differentiate the benign and malignant pulmonary nodules, but also provides new technologies and methods for the objective diagnosis of TCM syndromes in pulmonary nodules.