ObjectiveAbnormal lipids in neurons can cause the accumulation of α-synuclein（α-syn）. This study aimed to explore the mechanism of Acanthopanacis Senticosi Radix et Rhizoma seu Caulis extract （ASH） in treating Parkinson's disease （PD） mice using lipidomics combined with network pharmacology. MethodsMice were divided into the blank group， model group and ASH （45.5 mg·kg^-1） group. Motor ability was evaluated by pole climbing time and autonomous activity count； The oxidative stress indicators were detected by enzyme-linked immunosorbent assay （ELISA）. Lipid biomarkers in brain tissues were screened and identified by ultra-performance liquid chromatography-tandem mass spectrometry （UPLC-MS/MS）， and metabolic pathway analysis was conducted. The key targets of ASH for PD treatment were explored using network pharmacology. The Kyoto Encyclopedia of Genes and Genomes （KEGG） database was used for pathway enrichment analysis， and the "compound-reaction-enzyme-gene" network was constructed using the MetScape plugin. The protein expression levels of glutathione S-transferase P1 （GSTP1）， glutathione S-transferase Mu 2 （GSTM2）， prostaglandin peroxide synthase 1 （PTGS1）， prostaglandin peroxide synthase 2 （PTGS2）， and prostaglandin E synthase （PTGES） were validated by Western blot. ResultsCompared with the blank group， the model group showed significantly prolonged pole climbing time and reduced autonomous activity count （P<0.01）. Compared with the model group， the ASH group demonstrated significantly faster pole climbing and increased autonomous activity count （P<0.01）. The model group exhibited significantly decreased superoxide dismutase （SOD） and glutathione peroxidase （GSH-Px） levels， and increased malondialdehyde （MDA） level in brain tissues compared with the blank group （P<0.01）. The ASH group showed increased SOD and GSH-Px levels and decreased MDA level compared with the model group （P<0.05， P<0.01）. Lipidomics analysis identified 10 differential metabolites and 8 differential metabolic pathways. Network pharmacological analysis revealed 213 intersection targets between ASH components and PD， with KEGG enrichment involving the sphingolipid signaling pathway， lipid arteriosclerosis， phosphoinositide 3-kinase/protein kinase B（PI3K/Akt） signaling pathway， mitogen-activated protein kinase（MAPK） signaling pathway， and hypoxia inducible factor-1（HIF-1） signaling pathway. Integrated lipidomics and network pharmacology analysis highlighted the central role of the arachidonic acid metabolic pathway. The Western blot results showed that ASH effectively up-regulated GSTP1， GSTM2， and PTGS1 protein expression， and down-regulated PTGS2 and PTGES protein expression. ConclusionASH can ameliorate behavioral deficits， exert antioxidant effects， regulate lipid differential metabolites and the arachidonic acid metabolic pathway， thereby exerting therapeutic effects in PD model mice.

Objective To investigate the perioperative safety of patients with myasthenia gravis who take high doses of oral glucocorticoids. Methods A retrospective analysis was conducted on the clinical data of patients with myasthenia gravis who received oral glucocorticoids and underwent thoracoscopic thymectomy at the Department of Thoracic Surgery, the University of Hong Kong-Shenzhen Hospital from April 2013 to October 2019. Patients were divided into a high-dose steroid group and a medium-to-low dose steroid group based on the dosage of oral steroids, and the clinical data of the two groups were compared. Results A total of 102 patients were included, including 19 (18.62%) males and 83 (81.37%) females, with an average age of (32.25±9.83) years. There were 75 patients in the medium-to-low dose steroid group and 27 patients in the high-dose steroid group. All patients in both groups successfully completed the surgery without major intraoperative bleeding, conversion to open chest surgery, delayed extubation, severe infection, or perioperative death. The daily oral steroid dose for the high-dose steroid group was (35.81±4.29) mg, and for the medium-to-low dose steroid group it was (15.29±2.17) mg. There was no statistical difference in the operation time [(124.69±23.51) min vs. (117.89±21.46) min, P=0.172] and intraoperative blood loss [(21.19±3.48) mL vs. (20.56±3.41) mL, P=0.419] between the two groups. Postoperatively, 12 (11.76%) patients developed complications: one patient of myasthenic crisis (the medium-to-low dose steroid group), which was improved after short-term respiratory support and intravenous immunoglobulin treatment; 11 patients of respiratory/swallowing difficulties (9 in the medium-to-low dose steroid group and 2 in the high-dose steroid group), which were improved after anticholinergic treatment to reduce oral secretions and sputum suction, and the patients were discharged smoothly. There was no statistical difference in the incidence of postoperative complications between the two groups (P=0.637). Conclusion On the basis of good perioperative management, it is safe and feasible for patients with myasthenia gravis who take high dose of oral steroids to undergo thymectomy, and they have the same perioperative safety as patients with medium-to-low dose steroids.

Metabolic dysfunction-associated fatty liver disease （MAFLD）， as one of the most common chronic liver diseases in the world， poses a severe challenge to precision diagnosis and treatment due to its complex pathogenesis and highly heterogeneous disease progression. Existing clinical classification systems cannot meet the needs for comprehensively analyzing the complexity of the disease and the heterogeneity of its adverse outcomes. In recent years， data-driven prognostic risk classification methods have gradually emerged， optimizing the ability for predicting adverse outcomes and enhancing the accuracy of identifying different endpoint outcomes. However， such paradigm of “classify first， associate outcomes later” suffers from a “black-box” nature， and there are various indicators for classification， leading to limited stability and generalizability in clinical application. Future research needs to integrate or establish large-scale population cohorts， develop outcome-oriented prognostic risk classification models， incorporate dynamic data， refine classification algorithms， and validate their generalizability across multiple populations， thereby providing reliable support for the precision diagnosis and treatment of MAFLD.

Objective To establish an effective model for distinguishing glandular prodromal lesions(PGL)mixed with ground-glass nodules(mGGN)from minimally invasive adenocarcinoma(MIA)on CT based on artificial intelligence.Methods A retrospective analysis was conducted on the clinical and CT image data of 180 patients with lung adenocarcinoma confirmed by surgical pathology and with CT manifestations of mGGN in the First Affiliated Hospital of Wenzhou Medical University from January 2017 to June 2023,inclu-ding 66 patients with PGL and 114 patients with MIA.Patients were divided into the training set(n=144)and the test set(n=36)in an 8∶2 ratio using a completely random method.The quantitative parameters and radiomics features of the lesions in CT images were automatically extracted using artificial intelligence soft-ware(United Imaging Research Platform uRP).By incorporating the most obvious correlation features of omics through dimensionality reduction,five machine learning classifiers were established,including logistic regression(LR),support vector machine(SVM),Random forest(RF),Gaussian process(GP),and Decision Tree(DT).The classifier with the training set highest area under the curve(AUC)was selected as the best radiomics model,and output the result as radiomics score(Rad-score).The clinical information,CT morpho-logical characteristics and quantitative data of the two groups were included in the multivariate logistic regres-sion analysis to screen the independent influencing factors for effectively differentiating PGL and MIA,and a clinical model was established.Finally,a comprehensive prediction model was constructed based on Rad-score and clinical risk factors.The diagnostic performance of the three models was evaluated by using the AUC,sen-sitivity,specificity and accuracy of receiver operating characteristic(ROC)curve.Results Eleven radiomics features for distinguishing PGL from MIA were obtained through LASSO dimensionality reduction.Among the five machine learning classifiers,GP has the best diagnostic performance,with AUC of 0.865 in the train-ing set and 0.762 in the test set,respectively.Univariate and multivariate logistic regression analyses were used for clinical feature screening.The clinical model was constructed by using the average CT value,average long and short diameter,and solid partial long diameter of mGGN,and the AUCs of the training set and the test set were 0.870 and 0.794,respectively.The comprehensive prediction model demonstrated superior diag-nostic performance,with AUC,sensitivity,specificity,and accuracy in the training set being 0.948,81.1%,91.2%and 87.5%respectively,while 0.883,76.9%,91.3%and 86.1%respectively in the test set.Conclu-sion The comprehensive prediction model established based on the quantitative and omics feature analysis of pulmonary nodules by artificial intelligence can well distinguish mGGN mixed with PGL from MIA on CT,and can be used to guide clinical treatment decisions.

Metastatic dissemination is the major cause of death from breast-cancer (BC). Fusobacterium nucleatum (F.n) is widely enriched in BC and has recently been identified as one of the high-risk factors for promoting BC metastasis. Here, with an experimental model, we demonstrated that intratumoral F.n induced BC aggressiveness by transcriptionally activating Epithelial-mesenchymal transition-associated genes. Therefore, the F.n may be a potential target to prevent metastasis. Given the fact that cancer-associated fibroblasts (CAFs) are abundant in BC and located near blood vessels, we report an optogenetic system that drives CAF to in situ produce human antibacterial peptide LL37, with the characteristics of biosafety and freely intercellular trafficking, for depleting intratumoral F.n, leading to a 72.1% reduction in lung metastatic nodules number without affecting the balance of the systemic flora. Notably, mild photothermal treatment was found that could normalize CAF, contributing to synergistically inhibiting BC metastasis. In addition, the system can also simultaneously encode a gene of TNF-related apoptosis-inducing ligand to suppress the primary tumor. Together, our study highlights the potential of local elimination of tumor pathogenic bacteria to prevent BC metastasis.

Approximately 20% to 30% of the global workforce is engaged in shift work. As a significant cause of circadian disruption, shift work is closely associated with an increased risk for periodontitis. Nevertheless, how shift work-related circadian disruption functions in periodontitis remains unknown. Herein, we employed a simulated shift work model constructed by controlling the environmental light-dark cycles and revealed that shift work-related circadian disruption exacerbated the progression of experimental periodontitis. RNA sequencing and in vitro experiments indicated that downregulation of the core circadian protein brain and muscle ARNT-like protein 1 (BMAL1) and activation of the Gasdermin D (GSDMD)-mediated pyroptosis were involved in the pathogenesis of that. Mechanically, BMAL1 regulated GSDMD-mediated pyroptosis by suppressing NOD-like receptor protein 3 (NLRP3) inflammasome signaling through modulating nuclear receptor subfamily 1 group D member 1 (NR1D1), and inhibiting Gsdmd transcription via directly binding to the E-box elements in its promoter. GSDMD-mediated pyroptosis accelerated periodontitis progression, whereas downregulated BMAL1 under circadian disruption further aggravated periodontal destruction by increasing GSDMD activity. And restoring the level of BMAL1 by circadian recovery and SR8278 injection alleviated simulated shift work-exacerbated periodontitis via lessening GSDMD-mediated pyroptosis. These findings provide new evidence and potential interventional targets for circadian disruption-accelerated periodontitis.
Pyroptosis/physiology* ; ARNTL Transcription Factors/metabolism* ; Animals ; Periodontitis/etiology* ; Mice ; Phosphate-Binding Proteins/metabolism* ; Shift Work Schedule/adverse effects* ; Intracellular Signaling Peptides and Proteins/metabolism* ; Mice, Inbred C57BL ; Male ; Disease Models, Animal ; Gasdermins

Objective:To analyze risk factors associated with false-positive results of quantitative fecal immunochemical testing (FIT), evaluate its performance for detecting advanced colorectal neoplasia across different subgroups, and explore the optimal positivity thresholds for each subgroup.Methods:Individuals who participated in the Zhejiang Colorectal Cancer Screening Program in 2020-2021, completed questionnaire-based risk assessment and quantitative FIT for initial screening, and undertook colonoscopy for confirmed diagnosis were included in this study. The information of individuals, including demographic characteristics, lifestyles, history of diseases, and family history of colorectal cancer (CRC), was collected by using questionnaires. The diagnostic outcomes of the individuals were obtained through colonoscopy and pathological examination. Multivariate logistic regression analyses were conducted to identify factors associated with false-positive FIT results. The optimal threshold of FIT was determined based on the receiver operating characteristic (ROC) curve and 10-fold cross-validation. The effectiveness of FIT screening in different subgroups was compared using the unified threshold of 100 ng/ml or optimal positivity thresholds.Results:There were 25 874 individuals included in the analysis, with 14 694 (56.79%) having fecal hemoglobin concentrations ≥100 ng/ml. A total of 3 830 advanced adenoma cases (14.80%) and 362 CRC cases (1.40%) were identified. Age below 60 years old, females, underweight, smoking, drinking, use of nonsteroidal anti-inflammatory drugs, no family history of CRC, no history of intestinal disease, no history of hypertension, and physical inactivity were associated with an elevated risk of false-positive results in FIT ( P<0.05). Compared to the predetermined threshold of 100 ng/ml, the false positive rate (FPR) of quantitative FIT decreased from 52.3% to 37.3% in all individuals, and decreased by more than 20% in females, individuals with normal weight, smokers, and those without a history of intestinal disease when adopting the optimal threshold (all P<0.001). Conclusion:The risk of false-positive results in quantitative FIT varies across different subgroups. Adopting the optimal thresholds could improve the specificity and reduce the FPR of quantitative FIT for CRC screening.

Neurodegenerative diseases are a group of disorders characterized by chronic progressive degeneration of neurons in the brain and/or spinal cord.Their etiology remains unclear,the pathogenesis is complex,and no effective treatments exist.Importantly,the roles of mitochondria-localized silent information regulator(SIRT)family members,including SIRT3,SIRT4,and SIRT5,in neurodegenerative diseases are attracting increasing attention.Accumulating evidence demonstrates their involvement in critical processes of neuronal degeneration by regulating,for example,mitochondrial function and inflammatory responses.This review summarizes the research advances on mitochondrial SIRTs in neurodegenerative diseases such as Alzheimer's disease,Parkinson's disease,and amyotrophic lateral sclerosis,and aims to provide new insights for elucidating disease pathogenesis and developing prevention/therapeutic strategies.

Objective:To investigate the predictive value of radiomics features derived from dynamic contrast-enhanced MRI (DCE-MRI) based on habitat imaging technology for pathological complete response after neoadjuvant therapy (NAT) for breast cancer.Methods:All patients were female, aged 25-67 years. Patients were stratified into training ( n=83) and validation ( n=36) sets via stratified random sampling (7∶3 ratio). Pathological complete remission (pCR) and non-pathological complete remission (non-pCR) were defined using the Miller-Payne grading system. All patients underwent DCE-MRI before NAT. ITK-Snap software was used to outline the region of interest (ROI), the imaging histological features of the entire tumor region were extracted and screened, a traditional imaging histological model for predicting post-NAT pCR (ROI overall model) was constructed; the tumor region was divided into three subregions using habitat imaging technology, and the imaging histological features within ROI subregion 1, ROI subregion 2, and ROI subregion 3 were extracted and screened, and the habitat imaging model for predicting post-NAT pCR were constructed (ROI subregion 1 model, ROI subregion 2 model, ROI subregion 3 model). Univariate logistic regression identified clinical predictors of pCR for clinical model construction. Combined models integrating clinical predictors and habitat imaging features were established. The efficacy of each model in predicting pCR after NAT in breast cancer was evaluated using receiver operating characteristic curves and area under the curve (AUC), and the efficacy of clinical application of the models was evaluated using decision curve analysis (DCA). Results:Of the 119 patients, 74 were pCR patients, with 52 in the training set and 22 in the validation set, and 45 were non-pCR patients, with 31 in the training set and 14 in the validation set. Logistic regression analysis showed that human epidermal growth factor receptor 2 status ( OR=0.254, 95% CI 0.093-0.697, P=0.008) was an independent predictor of pCR after NAT, and this was used to construct a clinical prediction model. The predictive efficacy of ROI subregion 1 model and ROI subregion 2 model in the habitat model was higher than that of the traditional imaging histology model (ROI overall model), with AUCs of 0.805, 0.748,0.728 for the training set and 0.776,0.718,0.708 for the validation set, respectively. The combined clinical prediction model for predicting pCR after NAT in breast cancer had AUCs of 0.877 and 0.818 for the training and validation sets, respectively. DCA showed a higher net benefit for the combined model than for the traditional imaging histology model and the habitat imaging histology model. Conclusion:Compared with the traditional method of extracting the entire tumor region, extracting radiomics features from DCE-MRI subregions based on habitat imaging technology can improve the predictive performance of NAT efficacy in breast cancer.

Chimeric antigen receptor-modified T(CAR-T)cell therapy,as a new type of cellular immunotherapy,has shown good clinical efficacy in the treatment of malignant hematological tumors,especially B-cell acute lympho-blastic leukemia.However,there are problems such as antigen loss and immune evasion in single-target selection,so multi-target therapy strategies are gradually gaining attention.Multi-target CAR-T can effectively avoid antigen escape caused by a single target by targeting multiple tumor-associated antigens at the same time,reduce the risk of recurrence,and is expected to improve the therapeutic effect.This paper primarily discusses the structural types of multi-target CAR-T cell therapy and its clinical trial applications in the treatment of B-cell acute lymphoblastic leu-kemia(B-ALL),aiming to provide future references for the treatment of B-ALL.