Research indicates that microbe activity within the human body significantly influences health by being closely linked to various diseases. Accurately predicting microbe-disease interactions (MDIs) offers critical insights for disease intervention and pharmaceutical research. Current advanced AI-based technologies automatically generate robust representations of microbes and diseases, enabling effective MDI predictions. However, these models continue to face significant challenges. A major issue is their reliance on complex feature extractors and classifiers, which substantially diminishes the models' generalizability. To address this, we introduce a novel graph autoencoder framework that utilizes decoupled representation learning and multi-scale information fusion strategies to efficiently infer potential MDIs. Initially, we randomly mask portions of the input microbe-disease graph based on Bernoulli distribution to boost self-supervised training and minimize noise-related performance degradation. Secondly, we employ decoupled representation learning technology, compelling the graph neural network (GNN) to independently learn the weights for each feature subspace, thus enhancing its expressive power. Finally, we implement multi-scale information fusion technology to amalgamate the multi-layer outputs of GNN, reducing information loss due to occlusion. Extensive experiments on public datasets demonstrate that our model significantly surpasses existing top MDI prediction models. This indicates that our model can accurately predict unknown MDIs and is likely to aid in disease discovery and precision pharmaceutical research. Code and data are accessible at: https://github.com/shmildsj/MDI-IFDRL.

This study investigated the therapeutic effects and mechanisms of medical ozone on sepsis- associated kidney injury (S-AKI) induced by lipopolysaccharide in mice. Using enzyme-linked immunosorbent assay, renal histopathological evaluation, detection of renal function biochemical indicators, immunofluorescence staining, and Western blot analysis, the effects of intraperitoneal injection of ozone on inflammation, coagulation, and renal tissue in mice were systematically detected.The results demonstrated that ozone treatment significantly reduced circulating levels of the specific markers (citrullinated histone H3 and myeloperoxidase-DNA complexes) from neutrophil extracellular traps (NETs) in S-AKI mice, with a suppression on inflammatory and tissue factor expression in renal tissue. Furthermore, ozone effectively improved microcirculation dysfunction, reduced tubular damage and interstitial inflammatory infiltration, thereby alleviating pathological changes of kidneys of S-AKI mice. Mechanistic studies revealed that ozone enhances phagocytic clearance of tissue factor-rich microparticles (TF-MPs) by activating the 5'-monophosphate-activated protein kinase (AMPK) / scavenger receptor (SR)-A1 signaling pathway in macrophages. In Sr-a1-/- mice, renoprotective effect of ozone was completely abolished, confirming the critical role of SR-A1 in this mechanism. In summary, this study demonstrates that medical ozone promotes macrophage clearance of TF-NETs complexes through the AMPK/SR-A1 signaling axis, exerting dual protective effects on mice through anti-inflammatory action and microcirculation improvement, which provides novel intervention targets and therapeutic strategies for S-AKI treatment.

OBJECTIVE: To explore the effectiveness of arthroscopic release of elbow joint assisted by medial small incision ulnar nerve release in the treatment of non-traumatic elbow stiffness. METHODS: The clinical data of 15 patients with non-traumatic elbow stiffness treated with arthroscopic release of elbow joint assisted by medial small incision ulnar nerve release between April 2019 and September 2023 were retrospectively analyzed. There were 6 males and 9 females with an average age of 46 years ranging from 34 to 56 years. The causes included rheumatoid arthritis in 3 cases, gouty arthritis in 2 cases, loose bodies in 3 cases, and elbow osteoarthritis in 7 cases. There were 4 cases with ulnar neuritis and 3 cases with synovial osteochondromatosis. The duration of elbow stiffness ranged from 6 to 18 months, with an average of 10 months. The operation time and intraoperative blood loss were recorded. The effectiveness was evaluated by visual analogue scale (VAS) score, range of elbow motion (maximum flexion, maximum extension, and total flexion and extension), Mayo score, and Hospital for Special Surgery (HSS) elbow score. RESULTS: The operation time was 60-90 minutes, with an average of 65 minutes, and the intraoperative blood loss was 40-100 mL, with an average of 62 mL. All patients were followed up 13-18 months, with an average of 14 months. There was no complication such as vascular and nerve injury, poor wound healing, collateral ligament injury, elbow joint space narrowing, osteophyte proliferation, or loose body formation around the joint. At last follow-up, the elbow range of motion (maximum flexion, maximum extension, and total flexion and extension), VAS score, and Mayo score significantly improved when compared with those before operation ( P<0.05). The HSS elbow score was 85-95, with an average of 92; 12 cases were excellent, 3 cases were good, and the excellent and good rate was 100%. CONCLUSION Arthroscopic release of elbow joint assisted by medial small incision ulnar nerve release is an effective way to treat non-traumatic elbow stiffness, which has the advantages of small trauma, short operation time, and good effectiveness. It can carry out early elbow rehabilitation training and significantly improve elbow function.
Humans ; Male ; Female ; Arthroscopy/methods* ; Adult ; Middle Aged ; Elbow Joint/physiopathology* ; Retrospective Studies ; Range of Motion, Articular ; Treatment Outcome ; Ulnar Nerve/surgery* ; Operative Time

Objective:To develop and validate risk prediction models for hypertension comorbidity in community-dwelling patients with type 2 diabetes mellitus(T2DM).Methods:The health records of 2 979 T2DM patients from two community health service centers in Fengtai District of Beijing from January 2023 to January 2024 were collected,including 2 591 cases from Fangzhuang Center(model development group) and 388 cases from Youanmen Center(external validation group). Patients in model development group were randomly assigned in a training set( n=1 813) and an internal validation set(778 cases) at a ratio of 7∶3. The risk factors associated with hypertention comorbidity in T2DM patients were identified with LASSO regression analysis,based on which risk prediction models was developed using six machine learning algorithms,including logistic regression(LR),classification and regression tree(CART),random forest(RF),extreme gradient boosting(XGB),support vector machine(SVM) and artificial neural network(ANN). The internal and external validations of the prediction models were conducted. Results:Among 2 979 patients with T2DM,2 158(72.44%) had concurrent hypertension,with 1 572 in the development set,280 in the internal validation set,306 in the external validation set. The LASSO regression identified 14 risk factors: age,educational level,occupation,medical insurance type,alcohol consumption,exercise frequency,BMI,SBP,TG/HDL-C,METS-IR,FBG,eGFR,duration of T2DM,and dyslipidemia. The nomogram model based on 14 predictive factors was constructed with XGB algorithm showed the best performance in predicting risk of hypertention for T2DM patients,showing the highest area under the curve(AUC) of 0.694(95% CI: 0.524-0.810) and effective calibration(Brier Score=0.121). Decision curve analysis confirmed the clinical utility of the predictive model. Conclusion:The risk prediction models based on machine learning algorithms have been developed in the study,which show good prediction perfomance for hiypertention comorbidity in community-dwelling T2DM patients.

Objective:To assess the efficacy and safety profile of antaitasvir phosphate combined with yiqibuvir in the treatment of chronic hepatitis C (CHC) of various genotypes, without cirrhosis or with compensated cirrhosis.Methods:394 cases with CHC from 22 centers were collected from October 2021 to April 2023. They were randomly assigned to receive either the experimental drugs (antaitasvir phosphate 100 mg+yiqibuvir 600 mg) or placebo treatment in a 3∶1 ratio. The patients were administered drugs once a day for 12 consecutive weeks, and then followed up for 24 weeks after treatment cessation. All subjects were unblinded at the four-week follow-up following drug discontinuation, with the experimental drug group continuing to complete subsequent post-discontinuation follow-up. The placebo group was switched to receive the experimental drugs for a repeated 12-week treatment period and followed up for another 24 weeks after discontinuation of the drug (placebo delayed treatment phase).The sustained virologic response rate (SVR12) was observed for subjects in the double-blind phase and the placebo delayed-treatment phase at 12 weeks after treatment cessation.Virological resistance analysis was performed on subjects who failed treatment. The primary efficacy endpoint was SVR12. The number and percentage of subjects who achieved "HCV RNA

Research indicates that microbe activity within the human body significantly influences health by being closely linked to various diseases.Accurately predicting microbe-disease interactions(MDIs)offers critical insights for disease intervention and pharmaceutical research.Current advanced AI-based technologies automatically generate robust representations of microbes and diseases,enabling effec-tive MDI predictions.However,these models continue to face significant challenges.A major issue is their reliance on complex feature extractors and classifiers,which substantially diminishes the models' generalizability.To address this,we introduce a novel graph autoencoder framework that utilizes decoupled representation learning and multi-scale information fusion strategies to efficiently infer po-tential MDIs.Initially,we randomly mask portions of the input microbe-disease graph based on Bernoulli distribution to boost self-supervised training and minimize noise-related performance degradation.Secondly,we employ decoupled representation learning technology,compelling the graph neural network(GNN)to independently learn the weights for each feature subspace,thus enhancing its expressive power.Finally,we implement multi-scale information fusion technology to amalgamate the multi-layer outputs of GNN,reducing information loss due to occlusion.Extensive experiments on public datasets demonstrate that our model significantly surpasses existing top MDI prediction models.This indicates that our model can accurately predict unknown MDIs and is likely to aid in disease discovery and precision pharmaceutical research.Code and data are accessible at:https://github.com/shmildsj/MDI-IFDRL.

Objective:To assess the efficacy and safety profile of antaitasvir phosphate combined with yiqibuvir in the treatment of chronic hepatitis C (CHC) of various genotypes, without cirrhosis or with compensated cirrhosis.Methods:394 cases with CHC from 22 centers were collected from October 2021 to April 2023. They were randomly assigned to receive either the experimental drugs (antaitasvir phosphate 100 mg+yiqibuvir 600 mg) or placebo treatment in a 3∶1 ratio. The patients were administered drugs once a day for 12 consecutive weeks, and then followed up for 24 weeks after treatment cessation. All subjects were unblinded at the four-week follow-up following drug discontinuation, with the experimental drug group continuing to complete subsequent post-discontinuation follow-up. The placebo group was switched to receive the experimental drugs for a repeated 12-week treatment period and followed up for another 24 weeks after discontinuation of the drug (placebo delayed treatment phase).The sustained virologic response rate (SVR12) was observed for subjects in the double-blind phase and the placebo delayed-treatment phase at 12 weeks after treatment cessation.Virological resistance analysis was performed on subjects who failed treatment. The primary efficacy endpoint was SVR12. The number and percentage of subjects who achieved "HCV RNA

Objective:To develop and validate risk prediction models for hypertension comorbidity in community-dwelling patients with type 2 diabetes mellitus(T2DM).Methods:The health records of 2 979 T2DM patients from two community health service centers in Fengtai District of Beijing from January 2023 to January 2024 were collected,including 2 591 cases from Fangzhuang Center(model development group) and 388 cases from Youanmen Center(external validation group). Patients in model development group were randomly assigned in a training set( n=1 813) and an internal validation set(778 cases) at a ratio of 7∶3. The risk factors associated with hypertention comorbidity in T2DM patients were identified with LASSO regression analysis,based on which risk prediction models was developed using six machine learning algorithms,including logistic regression(LR),classification and regression tree(CART),random forest(RF),extreme gradient boosting(XGB),support vector machine(SVM) and artificial neural network(ANN). The internal and external validations of the prediction models were conducted. Results:Among 2 979 patients with T2DM,2 158(72.44%) had concurrent hypertension,with 1 572 in the development set,280 in the internal validation set,306 in the external validation set. The LASSO regression identified 14 risk factors: age,educational level,occupation,medical insurance type,alcohol consumption,exercise frequency,BMI,SBP,TG/HDL-C,METS-IR,FBG,eGFR,duration of T2DM,and dyslipidemia. The nomogram model based on 14 predictive factors was constructed with XGB algorithm showed the best performance in predicting risk of hypertention for T2DM patients,showing the highest area under the curve(AUC) of 0.694(95% CI: 0.524-0.810) and effective calibration(Brier Score=0.121). Decision curve analysis confirmed the clinical utility of the predictive model. Conclusion:The risk prediction models based on machine learning algorithms have been developed in the study,which show good prediction perfomance for hiypertention comorbidity in community-dwelling T2DM patients.

Objective The critical genes associated with exhausted CD8+T cells were screened and validated by mapping the single-cell transcriptome profile of high-grade serous ovarian cancer(HGSOC).Methods The specific subtypes of T cells in the tumor microenvironment were analyzed using the single-cell sequencing data from the early stage of laboratory(SRA database:PRJNA756768)and integrating 5 HGSOC sequencing from the database,and the differentiation trajectory of T cell subsets was ex-plored through pseudotime analysis.Differential gene enrichment was used to determine immunosuppressed CD8+IL-2Low and CD8+IFN-γLow T cell subsets and differential genes,and candidate molecules closely related to exhausted CD8+T cells were screened based on patient prognosis.Flow cytometry was used to analyze the expression of PHLDA1 on CD8+T cells,CD4+T cells and Treg cells dur-ing the activation to exhaustion process of T cells in human PBMCs.ELISA was used to detect the levels of IFN-γ and IL-2 secreted by CD8+T cells in PHLDA1High and PHLDA1Low.Finally,flow cytometry was used to analyze the association between PHLDA1 and ex-hausted markers PD-1 and TIM-3.Results The results showed that T cells were grouped in three ways:(1)IL-2High and IL-2Low;(2)IFN-γHigh and IFN-γLow;and(3)exhausted and cytotoxic CD8+T cells.Subsequently,the intersection of its differentially expressed genes was taken,and the key gene PHLDA1 was ultimately screened.Flow cytometry analysis suggested that during the process of T cell activation to exhaustion,the expression of PHLDA1 continued to increase on CD8+T cells,CD4+T cells and Treg cells;The ELISA results showed that the levels of IFN-γ and IL-2 secreted by CD8+PHLDA1High T cells were significantly lower than those of CD8+PHLDA1Low T cells.Meanwhile,the CD8+PHLDA1High T cell subset could simultaneously cover the exhausted T cell types of CD8+TIM-3+and CD8+PD-1+.Conclusion Based on single-cell sequencing data,this study identified PHLDA1 as a key molecule responsi-ble for CD8+T cell exhaustion in OC,providing new insights for immunotherapy of OC.

Objective:To evaluate the efficacy and safety of antaitasvir phosphate 100 mg or 200 mg combined with yiqibuvir for 12 weeks in patients with various genotypes of chronic hepatitis C, without cirrhosis or compensated stage cirrhosis.Methods:Patients with chronic hepatitis C (without cirrhosis or compensated stage cirrhosis) were randomly assigned to the antaitasvir phosphate 100 mg+yiqibuvir 600 mg group (100 mg group) or the antaitasvir phosphate 200 mg+yiqibuvir 600 mg group (200 mg group) in a 1∶1 ratio. The drugs were continuously administered once a day for 12 weeks and observed for 24 weeks after drug withdrawal. The drug safety profile was assessed concurrently with the observation of the sustained virological response (SVR12) in the two patient groups 12 weeks following the drug cessation. The intention-to-treat concept was used to define as closely as possible a full analysis set, including all randomized cases who received the experimental drug at least once. The safety set was collected from all subjects who received the experimental drug at least once (regardless of whether they participated in the randomization group) in this study. All efficacy endpoints and safety profile data were summarized using descriptive statistics. The primary efficacy endpoint was SVR12. The primary analysis was performed on a full analysis set. The frequency and proportion of cases were calculated in the experimental drug group (antaitasvir phosphate capsules combined with yiqibuvir tablets) that achieved "HCV RNA