BackgroundThe violent aggressive behaviors of patients with schizophrenia usually have the characteristics of suddenness， unpredictability， high severity， and great difficulty in prevention. Early identification and accurate assessment of their risk of violent aggression have significant clinical significance. ObjectiveTo construct a predictive model for the violence risk in hospitalized patients with schizophrenia， to identify the key factors influencing the occurrence of violent behavior in these patients， so as to provide references for clinical precise quantitative assessment and early intervention. MethodsA total of 200 patients with schizophrenia who were hospitalized at Taiyuan Psychiatric Hospital from March 2022 to September 2024 and met the diagnostic criteria of the International Classification of Diseases， eleventh edition （ICD-11） were collected to form the modeling cohort. They were randomly divided into a training set （n=140） and a test set （n=60） at a ratio of 7∶3. Based on the least absolute shrinkage and selection operator （LASSO） regression algorithm， the feature variables were screened and dimension-reduced. The support vector machine （SVM） from machine learning was selected for model training and prediction. The discrimination efficacy of the model was evaluated by the area under the receiver operating characteristic （ROC） curve （AUC）， accuracy， precision， sensitivity， specificity， F1 value， and Brier value. ResultsLASSO regression screening identified 16 feature variables. Pearson correlation analysis revealed a positive correlation between prior violent behavior frequency and clinical psychiatric symptom scores （r=0.580， P<0.01）， a positive correlation between hospitalization compliance and current disease status （r=0.550， P=0.003）， and a positive correlation between educational level and family per capita monthly income （r=0.367， P<0.01）. The SVM model achieved an AUC of 0.853， accuracy of 0.800， precision of 0.810， sensitivity of 0.895， specificity of 0.636， F1 value of 0.850， and Brier value of 0.168. ConclusionThe SVM model has a relatively high level of applicability and overall predictive performance in the assessment of violent risk in schizophrenia patients， which is helpful for the early identification of violent risks in such patients. ［Funded by Specialized Research Project for Enhancing the Competence of Health Professionals in Taiyuan City （number， Y2023006）］

Resveratrol(Res) is a kind of polyphenolic compound, possessing multiple biological activities such as antioxidant, anti-inflammatory, cardioprotective, and anticancer effects. In recent years, the cardiovascular protective mechanism of Res has become a research hotspot. Studies have shown that Res has a protective effect on the cardiovascular system through various pathways, such as inhibiting oxidative stress, regulating ferroptosis of cells, improving ischemia-reperfusion(I/R) injury, regulating lipid metabolism, suppressing inflammatory responses, and enhancing endothelial function. It can also alleviate cardiotoxicity caused by drugs and chemicals. In terms of oxidative stress, Res reduces the level of intracellular reactive oxygen species(ROS) by enhancing the expression of proteins such as silent information regulator 1(SIRT1) and regulating mitochondrial function, thereby alleviating myocardial cell damage. Regarding ferroptosis, Res inhibits the occurrence of ferroptosis by regulating the expression of proteins related to iron metabolism. Res can also improve I/R injury through mechanisms such as activating autophagy and the mitochondrial quality control network. In regard to improving endothelial function, Res protects the function of endothelial cells by regulating multiple signaling pathways, such as downregulating the PREP1-mediated pathway. Res can also regulate lipid metabolism and inhibit the progression of atherosclerosis. In terms of inflammatory responses, Res exerts anti-inflammatory effects through mechanisms such as inhibiting the nuclear factor-kappa B(NF-κB) signaling pathway. In addition, Res has an improving effect on cardiotoxicity caused by different drugs or environmental factors. However, the clinical application of Res still faces limitations such as poor pharmacokinetic properties. In the future, in-depth exploration is needed at multiple levels from basic research to clinical application to clarify the dose-response relationship and standardize the standards of medication regimens with the expectation of providing more effective strategies for the prevention and treatment of cardiovascular diseases.
Humans ; Resveratrol/pharmacology* ; Animals ; Cardiotonic Agents/pharmacology* ; Oxidative Stress/drug effects* ; Cardiovascular Diseases/genetics* ; Cardiovascular System/metabolism* ; Signal Transduction/drug effects*

As a new type of production factor that can empower the development of new quality productivity, the data element is an important engine to promote the high quality development of the industry. Traditional Chinese medicine(TCM) dispensing is the most basic work of TCM clinical pharmacy, and its quality directly affects the clinical efficacy of TCM. The integration of data elements and TCM dispensing can stimulate the innovation and vitality of the TCM dispensing industry and promote the high-quality and sustainable development of the industry. A large-scale, detailed, and systematic study on TCM dispensing was conducted. The innovative practice path of data fusion construction in the whole process of TCM dispensing was investigated by integrating the digital resources "nine full activities" of TCM dispensing, creating the digital dictionary of "TCM clinical information data elements", and exploring innovative applications of TCM dispensing driven by data and technology, so as to promote the standardized, digital, and intelligent development of TCM dispensing in medical health services. The research content of this project was successfully selected as the second batch of "Data element×" typical cases of National Data Administration in 2024, which is the only selected case in the field of TCM.
Medicine, Chinese Traditional/methods* ; Drugs, Chinese Herbal ; Humans

Real-time acquisition of pulmonary ventilation and perfusion information through thoracic electrical impedance tomography (EIT) holds significant clinical value. This study proposes a novel method based on the rime (RIME) algorithm-optimized variational mode decomposition (VMD) to separate lung ventilation and perfusion signals directly from raw voltage data prior to EIT image reconstruction, enabling independent imaging of both parameters. To validate this approach, EIT data were collected from 16 healthy volunteers under normal breathing and inspiratory breath-holding conditions. The RIME algorithm was employed to optimize VMD parameters by minimizing envelope entropy as the fitness function. The optimized VMD was then applied to separate raw data across all measurement channels in EIT, with spectral analysis identifying relevant components to reconstruct ventilation and perfusion signals. Results demonstrated that the structural similarity index (SSIM) between perfusion images derived from normal breathing and breath-holding states averaged approximately 84% across all 16 subjects, significantly outperforming traditional frequency-domain filtering methods in perfusion imaging accuracy. This method offers a promising technical advancement for real-time monitoring of pulmonary ventilation and perfusion, holding significant value for advancing the clinical application of EIT in the diagnosis and treatment of respiratory diseases.
Humans ; Electric Impedance ; Algorithms ; Tomography/methods* ; Pulmonary Ventilation/physiology* ; Lung/diagnostic imaging* ; Image Processing, Computer-Assisted/methods* ; Adult

Gene regulation relies on the precise binding of transcription factors (TFs) at regulatory elements, but simultaneously detecting hundreds of TFs on chromatin is challenging. We developed cFOOT-seq, a cytosine deaminase-based TF footprinting assay, for high-resolution, quantitative genome-wide assessment of TF binding in both open and closed chromatin regions, even with small cell numbers. By utilizing the dsDNA deaminase SsdAtox, cFOOT-seq converts accessible cytosines to uracil while preserving genomic integrity, making it compatible with techniques like ATAC-seq for sensitive and cost-effective detection of TF occupancy at the single-molecule and single-cell level. Our approach enables the delineation of TF footprints, quantification of occupancy, and examination of chromatin influences on TF binding. Notably, cFOOT-seq, combined with FootTrack analysis, enables de novo prediction of TF binding sites and tracking of TF occupancy dynamics. We demonstrate its application in capturing cell type-specific TFs, analyzing TF dynamics during reprogramming, and revealing TF dependencies on chromatin remodelers. Overall, cFOOT-seq represents a robust approach for investigating the genome-wide dynamics of TF occupancy and elucidating the cis-regulatory architecture underlying gene regulation.
Transcription Factors/genetics* ; Humans ; Chromatin/genetics* ; Animals ; Binding Sites ; Mice ; DNA Footprinting/methods*

ObjectiveTo investigate the potential mechanism of action of the Qufeng Tongqiao Cough-relieving Decoction （祛风通窍止咳方， QTCD） in the treatment of upper airway cough syndrome （UACS）. MethodsTwenty-four guinea pigs were randomly divided into blank group， model group， traditional Chinese medicine （TCM） group， and inhibitor group， with six guinea pigs in each group. Except for the blank group， guinea pigs were sensitized with ovalbumin and aluminum hydroxide via intraperitoneal injection， followed by ovalbumin nasal drops combined with smoke exposure to establish the UACS model. After modeling， the TCM group was administered QTCD 0.9 g/（100 g·d） by gavage， the inhibitor group received the transient receptor potential vanilloid receptor 4 （TRPV4） inhibitor GSK2193874 1 mmol/L， 5 min by nebulisation， and the blank group and model group were given 2 ml/（100 g·d） normal saline by gavage once daily. After 7 days of treatment， a cough provocation test was performed using 0.4 mol/L citric acid. The levels of IgE in serum and inflammatory cytokines， including interleukin-6 （IL-6）， interleukin-8 （IL-8） in serum， bronchoalveolar lavage fluid （BALF）， and nasal lavage fluid （NLF） were detected by enzyme-linked immunosorbent assay （ELISA）. Histopathological changes in lung and nasal mucosal tissues were observed by hematoxylin-eosin （HE） staining. Immunohistochemistry was used to detect the protein levels of TRPV4， substance P （SP）， and calcitonin gene-related peptide （CGRP） in lung and nasal mucosal tissues. Real-time polymerase chain reaction （Real-time PCR） was used to detect the mRNA expression of TRPV4， SP， and CGRP in lung tissues. ResultsHE staining showed significant structural damage and infiltration of inflammatory cells in the lung and nasal mucosal tissues in the model group， while the TCM group and inhibitor group showed improved pathological changes. Compared with the blank group， the model group showed increased cough frequency， serum IgE level， and IL-6 and IL-8 levels in serum， BALF， and NLF. The protein levels of TRPV4， SP， and CGRP in lung and nasal mucosal tissues and their mRNA expression were elevated （P＜0.05 or P＜0.01）. Compared with the model group， the TCM group and inhibitor group showed reduced cough frequency， serum IgE level， and TRPV4 and SP mRNA expression in lung tissues. The TCM group showed reduced IL-6 and IL-8 levels in serum， BALF， and NLF， and reduced TRPV4 and CGRP protein levels in lung and nasal mucosal tissues. The inhibitor group showed reduced IL-6 and IL-8 levels in serum， BALF， and NLF， reduced IL-6 in BALF， reduced IL-8 in NLF， and decreased TRPV4， SP， and CGRP protein levels in lung tissues and SP and CGRP protein levels in nasal mucosal tissues （P＜0.05 or P＜0.01）. Compared with the TCM group， the inhibitor group had increased serum IgE， IL-6， and IL-8 levels， increased IL-6 level in BALF， and increased IL-8 levle in NLF， but decreased SP protein level in lung tissues and increased TRPV4 and SP mRNA expression in lung tissues （P＜0.01）. ConclusionQTCD effectively reduces cough frequency in the UACS guinea pig model. Its mechanism may involve inhibiting the activation of the TRPV4 pathway， improving airway neurogenic inflammation， alleviating inflammatory responses， and reducing cough hypersensitivity.

ObjectiveTo explore the construction of a machine learning model for the diagnosis of traditional Chinese medicine （TCM） syndromes in chronic cough and the optimization of this model using the Voting ensemble algorithm. MethodsA retrospective analysis was conducted using clinical data from 921 patients with chronic cough treated at the Respiratory Department of Dongfang Hospital， Beijing University of Chinese Medicine. After standardized processing， 84 clinical features were extracted to determine TCM syndrome types. A specialized dataset for TCM syndrome diagnosis in chronic cough was formed by selecting syndrome types with more than 50 cases. The synthetic minority over-sampling technique （SMOTE） was employed to balance the dataset. Four base models， logistic regression （LR）， decision tree （dt）， multilayer perceptron （MLP）， and Bagging， were constructed and integrated using a hard voting strategy to form a Voting ensemble model. Model performance was evaluated using accuracy， recall， precision， F1-score， receiver operating characteristic （ROC） curve， area under the curve （AUC）， and confusion matrix. ResultsAmong the 921 cases， six syndrome types had over 50 cases each， phlegm-heat obstructing the lung （294 cases）， wind pathogen latent in the lung （103 cases）， cold-phlegm obstructing the lung （102 cases）， damp-heat stagnating in the lung （64 cases）， lung yang deficiency （54 cases）， and phlegm-damp obstructing the lung （53 cases）， yielding a total of 670 cases in the specialized dataset. High-frequency symptoms among these patients included cough， expectoration， odor-induced cough， throat itchiness， itch-induced cough， and cough triggered by cold wind. Among the four base models， the MLP model showed the best diagnostic performance （test accuracy： 0.9104； AUC： 0.9828）. Compared with the base models， the Voting ensemble model achieved superior performance with an accuracy of 0.9289 on the training set and 0.9253 on the test set， showing a minimal overfitting gap of 0.0036. It also achieved the highest AUC （0.9836） in the test set， outperforming all base models. The model exhi-bited especially strong diagnostic performance for damp-heat stagnating in the lung （AUC： 0.9984） and wind pathogen latent in the lung （AUC： 0.9970）. ConclusionThe Voting ensemble algorithm effectively integrates the strengths of multiple machine learning models， resulting in an optimized diagnostic model for TCM syndromes in chronic cough with high accuracy and enhanced generalization ability.

Background/Aims: Metabolic dysfunction–associated steatotic liver disease (MASLD) is a chronic liver disease characterized by hepatic steatosis. Ubiquitin-specific protease 29 (USP29) plays pivotal roles in hepatic ischemiareperfusion injury and hepatocellular carcinoma, but its role in MASLD remains unexplored. Therefore, the aim of this study was to reveal the effects and underlying mechanisms of USP29 in MASLD progression. Methods: USP29 expression was assessed in liver samples from MASLD patients and mice. The role and molecular mechanism of USP29 in MASLD were assessed in high-fat diet-fed and high-fat/high-cholesterol diet-fed mice and palmitic acid and oleic acid treated hepatocytes. Results: USP29 protein levels were significantly reduced in mice and humans with MASLD. Hepatic steatosis, inflammation and fibrosis were significantly exacerbated by USP29 deletion and relieved by USP29 overexpression. Mechanistically, USP29 significantly activated the expression of genes related to fatty acid β-oxidation (FAO) under metabolic stimulation, directly interacted with long-chain acyl-CoA synthase 5 (ACSL5) and repressed ACSL5 degradation by increasing ACSL5 K48-linked deubiquitination. Moreover, the effect of USP29 on hepatocyte lipid accumulation and MASLD was dependent on ACSL5. Conclusions USP29 functions as a novel negative regulator of MASLD by stabilizing ACSL5 to promote FAO. The activation of the USP29-ACSL5 axis may represent a potential therapeutic strategy for MASLD.

Background/Aims: Metabolic dysfunction–associated steatotic liver disease (MASLD) is a chronic liver disease characterized by hepatic steatosis. Ubiquitin-specific protease 29 (USP29) plays pivotal roles in hepatic ischemiareperfusion injury and hepatocellular carcinoma, but its role in MASLD remains unexplored. Therefore, the aim of this study was to reveal the effects and underlying mechanisms of USP29 in MASLD progression. Methods: USP29 expression was assessed in liver samples from MASLD patients and mice. The role and molecular mechanism of USP29 in MASLD were assessed in high-fat diet-fed and high-fat/high-cholesterol diet-fed mice and palmitic acid and oleic acid treated hepatocytes. Results: USP29 protein levels were significantly reduced in mice and humans with MASLD. Hepatic steatosis, inflammation and fibrosis were significantly exacerbated by USP29 deletion and relieved by USP29 overexpression. Mechanistically, USP29 significantly activated the expression of genes related to fatty acid β-oxidation (FAO) under metabolic stimulation, directly interacted with long-chain acyl-CoA synthase 5 (ACSL5) and repressed ACSL5 degradation by increasing ACSL5 K48-linked deubiquitination. Moreover, the effect of USP29 on hepatocyte lipid accumulation and MASLD was dependent on ACSL5. Conclusions USP29 functions as a novel negative regulator of MASLD by stabilizing ACSL5 to promote FAO. The activation of the USP29-ACSL5 axis may represent a potential therapeutic strategy for MASLD.

Objective To evaluate the performance of the artificial intelligence (AI)-enabled snail identification system for recognition of Oncomelania hupensis robertsoni and Tricula in schistosomiasis-endemic areas of Yunnan Province. Methods Fifty O. hupensis robertsoni and 50 Tricula samples were collected from Yongbei Township, Yongsheng County, Lijiang City, a schistosomiasis-endemic area in Yunnan Province in May 2024. A total of 100 snail sample images were captured with smartphones, including front-view images of 25 O. hupensis robertsoni and 25 Tricula samples (upward shell opening) and back-view images of 25 O. hupensis robertsoni and 25 Tricula samples (downward shell opening). Snail samples were identified as O. hupensis robertsoni or Tricula by schistosomiasis control experts with a deputy senior professional title and above according to image quality and morphological characteristics. A standard dataset for snail image classification was created, and served as a gold standard for recognition of snail samples. A total of 100 snail sample images were recognized with the AI-enabled intelligent snail identification system based on a WeChat mini program in smartphones. Schistosomiasis control professionals were randomly sampled from stations of schistosomisis prevention and control and centers for disease control and prevention in 18 schistosomiasis-endemic counties (districts, cities) of Yunnan Province, for artificial identification of 100 snail sample images. All professionals are assigned to two groups according the median years of snail survey experiences, and the effect of years of snail survey experiences on O. hupensis robertsoni sample image recognition was evaluated. A receiver operating characteristic (ROC) curve was plotted, and the sensitivity, specificity, accuracy, Youden’s index and the area under the curve (AUC) of the AI-enabled intelligent snail identification system and artificial identification were calculated for recognition of snail sample images. The snail sample image recognition results of AI-enabled intelligent snail identification system and artificial identification were compared with the gold standard, and the internal consistency of artificial identification results was evaluated with the Cronbach’s coefficient alpha. Results A total of 54 schistosomiasis control professionals were sampled for artificial identification of snail sample image recognition, with a response rate of 100% (54/54), and the accuracy, sensitivity, specificity, Youden’s index, and AUC of artificial identification were 90%, 86%, 94%, 0.80 and 0.90 for recognition of snail sample images, respectively. The overall Cronbach’s coefficient alpha of artificial identification was 0.768 for recognition of snail sample images, and the Cronbach’s coefficient alpha was 0.916 for recognition of O. hupensis robertsoni snail sample images and 0.925 for recognition of Tricula snail sample images. The overall accuracy of artificial identification was 90% for recognition of snail sample images, and there was no significant difference in the accuracy of artificial identification for recognition of O. hupensis robertsoni (86%) and Tricula snail sample images (94%) (χ² = 1.778, P > 0.05). There was no significant difference in the accuracy of artificial identification for recognition of snail sample images with upward (88%) and downward shell openings (92%) (χ² = 0.444, P > 0.05), and there was a significant difference in the accuracy of artificial identification for recognition of snail sample images between schistosomiasis control professionals with snail survey experiences of 6 years and less (75%) and more than 6 years (90%) (χ² = 7.792, P < 0.05). The accuracy, sensitivity, specificity and AUC of the AI-enabled intelligent snail identification system were 88%, 100%, 76% and 0.88 for recognition of O. hupensis robertsoni snail sample images, and there was no significant difference in the accuracy of recognition of O. hupensis robertsoni snail sample images between the AI-enabled intelligent snail identification system and artificial identification (χ² = 0.204, P > 0.05). In addition, there was no significant difference in the accuracy of artificial identification for recognition of snail sample images with upward (90%) and downward shell openings (86%) (χ² = 0.379, P > 0.05), and there was a significant difference in the accuracy of artificial identification for recognition of snail sample images between schistosomiasis control professionals with snail survey experiences of 6 years and less and more than 6 years (χ² = 5.604, P_adjusted < 0.025). Conclusions The accuracy of recognition of snail sample images is comparable between the AI-enabled intelligent snail identification system and artificial identification by schistosomiasis control professionals, and the AI-enabled intelligent snail identification system is feasible for recognition of O. hupensis robertsoni and Tricula in Yunnan Province.