The ambiguity of symptom information in traditional Chinese medicine （TCM） syndrome differentiation can be amplified in the direct reasoning process from symptoms to syndromes in the absence of constraints， which affects the accuracy and stability of intelligent syndrome differentiation. TCM disease concepts， while historically rational， are structurally ambiguous in both their connotation and extension， making it difficult to serve as stable prior knowledge in intelligent modeling. In contrast， modern medical diseases， based on objective testing and quantifiable indicators， have relatively clear boundaries and reproducible standards. This study proposes a disease-syndrome combination model， adopting modern medical diseases as structural prior variables to reconstruct the hierarchical relationships among disease， symptoms， and syndromes. By applying disease constraints， effective screening of information from the four examinations and compressing the reasoning space are achieved. Furthermore， by integrating artificial intelligence technologies， such as multimodal fusion and knowledge graphs， an intelligent syndrome differentiation model driven by both prior knowledge and clinical data is constructed， providing a feasible path to enhance the accuracy of syndrome differentiation and realize the intelligentization of TCM diagnosis and treatment.

Emerging evidence suggests that dysbiosis of the gut microbiota is associated with the pathogenesis of Parkinson's disease (PD), a prevalent neurodegenerative disorder. The microbiota-gut-brain axis plays a crucial role in the development and progression of PD, and numerous studies have demonstrated the potential therapeutic benefits of modulations in the intestinal microbiota. This review provides insights into the characterization of the gut microbiota in patients with PD and highlights associations with clinical symptoms and underlying mechanisms. The discussion underscores the increased influence of the gut microbiota in the pathogenesis of PD. While the relationship is not fully elucidated, existing research demonstrates a strong correlation between changes in the composition of gut microbiota and disease development, and further investigation is warranted to explain the specific underlying mechanisms.
Humans ; Parkinson Disease/microbiology* ; Gastrointestinal Microbiome/physiology* ; Dysbiosis/microbiology*

T-cell acute lymphoblastic leukemia (T-ALL) is a highly aggressive hematologic malignancy with a poor prognosis, despite advancements in treatment. Many patients struggle with relapse or refractory disease. Investigating the role of the super-enhancer (SE) regulated gene ubiquitin-specific protease 20 (USP20) in T-ALL could enhance targeted therapies and improve clinical outcomes. Analysis of histone H3 lysine 27 acetylation (H3K27ac) chromatin immunoprecipitation sequencing (ChIP-seq) data from six T-ALL cell lines and seven pediatric samples identified USP20 as an SE-regulated driver gene. Utilizing the Cancer Cell Line Encyclopedia (CCLE) and BloodSpot databases, it was found that USP20 is specifically highly expressed in T-ALL. Knocking down USP20 with short hairpin RNA (shRNA) increased apoptosis and inhibited proliferation in T-ALL cells. In vivo studies showed that USP20 knockdown reduced tumor growth and improved survival. The USP20 inhibitor GSK2643943A demonstrated similar anti-tumor effects. Mass spectrometry, RNA-Seq, and immunoprecipitation revealed that USP20 interacted with hypoxia-inducible factor 1 subunit alpha (HIF1A) and stabilized it by deubiquitination. Cleavage under targets and tagmentation (CUT&Tag) results indicated that USP20 co-localized with HIF1A, jointly modulating target genes in T-ALL. This study identifies USP20 as a therapeutic target in T-ALL and suggests GSK2643943A as a potential treatment strategy.

OBJECTIVES: To explore the role of C1q, the promoter of the classical pathway of the complement system, in regulating postpartum depressive-like behaviors in mice and the therapeutic mechanism of C1q-neutralizing antibodies. METHODS: Female C57BL/6 mouse models of postpartum depression established by hormone-simulated pregnancy (HSP) were evaluated for depression-like behaviors, and peripheral blood levels and hippocampal expressions of C1q were detected using ELISA and Western blotting. Immunofluorescence staining was used for detecting co-labeling of C1q and microglia, and the differentially expressed mRNAs in the hippocampus of HSP mice were analyzed using RNA sequencing. The Edinburgh Postnatal Depression Scale was used to screen patients with postpartum depression, from whom peripheral blood mononuclear cells were extracted for detecting C1q expression levels with Western blotting. The HSP mice were subjected to stereotactic injection of C1q-neutralizing antibody or a control IgG in the hippocampus, and the changes in depressive-like behaviors and hippocampal expression of C3 were examined. RESULTS: The HSP mice exhibited obvious depressive behaviors, demonstrated by significantly decreased preference for sugar water and increased forced swimming and tail suspension time. The mouse models showed significantly increased peripheral blood C1q level and hippocampal expression level of C1q, accompanied by an increase in Iba1 and C1q co-labeling in the hippocampus. The expression level of C1q in peripheral monocytes was also significantly increased in patients with postpartum depression. In HSP mice, stereotactic injection of C1q-neutralizing antibody, but not the control IgG, obviously alleviated depressive-like behaviors, shown by significantly increased preference for sugar water and decreased forced swimming and tail suspension time, resulting also in decreased expression of C3 in the hippocampus and lowered serum levels of IL-6 and TNF-α. CONCLUSIONS C1q-neutralizing antibodies improve postpartum depressive-like behaviors in mice possibly by regulating the C1q/C3 signaling pathway.
Animals ; Female ; Depression, Postpartum ; Complement C1q/metabolism* ; Antibodies, Neutralizing/pharmacology* ; Mice, Inbred C57BL ; Mice ; Hippocampus/metabolism* ; Pregnancy ; Disease Models, Animal

The biliary microbiota is an important component of the biliary system, the structure changes and dysbiosis of the biliary microbiota are closely related to the occurrence and development of biliary diseases. Recent studies have found that the dysbiosis of the biliary microbiota can lead to the occurrence of various biliary diseases such as cholelithiasis, cholangitis, and biliary tract cancer. However, the specific mechanisms of some of these diseases remain unclear. In addition, the alternation of biliary microbiota may also lead to the occurrence of postoperative complications of biliary diseases, such as the risk of infection increased after laparoscopic cholecystectomy (LC) and endoscopic retrograde cholangiopancreatography (ERCP). Although some studies have begun to explore the connection between biliary microbiota and biliary diseases, most research have mainly focused on a single disease type or specific microorganisms. There has been no comprehensive and systematic review of the overall changes in biliary microbiota across different diseases and their association with post biliary surgery infection.Therefore, this article presents a systematic review of current advances linking the alternations of biliary microbiota to the development of biliary diseases and their postoperative infections, as well as the changes in the biliary microbiota before and after various biliary diseases and their surgeries, aiming to provide insightful methods for the early diagnosis, prevention and treatment of biliary diseases and management of postoperative infections.

Objective:To explore the application and effectiveness evaluation of the "six thinking hats" thinking mode in nursing teaching rounds.Methods:A total of 65 surgical nursing interns from a tertiary hospital were selected as the research subjects. The control group received routine teaching rounds, while the observation group received teaching rounds based on the "six thinking hats" thinking mode intervention. General information, assessment results, critical thinking ability, job competence, and satisfaction with teaching effectiveness were compared between the two groups of nursing students. SPSS 22.0 statistical software was used for t-test and chi-square test. Results:Compared with the control group, the observation group showed significantly higher scores in operational skills [(92.50±2.20) vs. (89.53±2.08)] and theoretical knowledge [(89.21±1.51) vs. (86.81±2.71)] and medical record writing score [(93.17±4.05) vs. (85.20±3.19)]. The critical thinking ability score was significantly higher in the observation group than in the control group [(319.97±45.13) vs. (283.57±41.57)]. The competency score was significantly higher in the observation group than in the control group [(182.62±14.07) vs. (164.43±14.50)]. The observation group demonstrated a higher overall evaluation of teaching satisfaction.Conclusions:The "six thinking hats" thinking mode improves the ability of nursing students to analyze problems, stimulates their subjective initiative in learning, and has a positive impact on the cultivation of their comprehensive abilities, making it a feasible teaching mode.

Objective To evaluate the predictive value of a nomogram model developed by integrating clinical and ultrasound multiparameters for HER-2-positive breast cancer.Methods This study retrospectively enrolled 343 patients with pathologically confirmed breast cancer from three medical centers and randomly divided them into training and validation cohorts.Univariate analysis,LASSO regression,and multivariate logistic regres-sion were conducted on the training set to identify independent prognostic factors and construct a nomogram model.Bootstrap resampling with 1000 iterations was performed to evaluate the model's robustness.Model calibration was assessed using calibration curves and the Hosmer-Lemeshow goodness-of-fit test.Receiver operating characteristic(ROC)curves were generated to evaluate model discrimination,and the area under the curve(AUC)along with other performance metrics were calculated.Decision curve analysis was employed to assess the clinical utility of the model,and the validation cohort was used for external validation.Results Univariate,LASSO,and multivariate regression analyses demonstrated that age,TTP(time to peak),and the presence of a filling defect sign were independent predictors of HER-2-positive breast cancer(all P<0.05).Based on these independent predictors,a nomogram model was constructed.Bootstrap validation with 1,000 resamples indicated that the model's predictive performance was stable.The Hosmer-Lemeshow test confirmed satisfactory model calibration,while the calibration curve illustrated accurate prediction probabilities.The area under the curve(AUC)for the training set was 0.863(95%CI:0.806～0.920),and for the validation set,it was 0.846(95%CI:0.764～0.929),indicating strong discriminative and generalization capabilities.Additionally,the clinical decision curve analysis demonstrated favor-able clinical utility.Conclusion A nomogram model integrating clinical and multimodal ultrasound parameters demonstrates potential utility in predicting HER-2-positive breast cancer.

Objective:To explore the application and effectiveness evaluation of the "six thinking hats" thinking mode in nursing teaching rounds.Methods:A total of 65 surgical nursing interns from a tertiary hospital were selected as the research subjects. The control group received routine teaching rounds, while the observation group received teaching rounds based on the "six thinking hats" thinking mode intervention. General information, assessment results, critical thinking ability, job competence, and satisfaction with teaching effectiveness were compared between the two groups of nursing students. SPSS 22.0 statistical software was used for t-test and chi-square test. Results:Compared with the control group, the observation group showed significantly higher scores in operational skills [(92.50±2.20) vs. (89.53±2.08)] and theoretical knowledge [(89.21±1.51) vs. (86.81±2.71)] and medical record writing score [(93.17±4.05) vs. (85.20±3.19)]. The critical thinking ability score was significantly higher in the observation group than in the control group [(319.97±45.13) vs. (283.57±41.57)]. The competency score was significantly higher in the observation group than in the control group [(182.62±14.07) vs. (164.43±14.50)]. The observation group demonstrated a higher overall evaluation of teaching satisfaction.Conclusions:The "six thinking hats" thinking mode improves the ability of nursing students to analyze problems, stimulates their subjective initiative in learning, and has a positive impact on the cultivation of their comprehensive abilities, making it a feasible teaching mode.

Objective To evaluate the predictive value of a nomogram model developed by integrating clinical and ultrasound multiparameters for HER-2-positive breast cancer.Methods This study retrospectively enrolled 343 patients with pathologically confirmed breast cancer from three medical centers and randomly divided them into training and validation cohorts.Univariate analysis,LASSO regression,and multivariate logistic regres-sion were conducted on the training set to identify independent prognostic factors and construct a nomogram model.Bootstrap resampling with 1000 iterations was performed to evaluate the model's robustness.Model calibration was assessed using calibration curves and the Hosmer-Lemeshow goodness-of-fit test.Receiver operating characteristic(ROC)curves were generated to evaluate model discrimination,and the area under the curve(AUC)along with other performance metrics were calculated.Decision curve analysis was employed to assess the clinical utility of the model,and the validation cohort was used for external validation.Results Univariate,LASSO,and multivariate regression analyses demonstrated that age,TTP(time to peak),and the presence of a filling defect sign were independent predictors of HER-2-positive breast cancer(all P<0.05).Based on these independent predictors,a nomogram model was constructed.Bootstrap validation with 1,000 resamples indicated that the model's predictive performance was stable.The Hosmer-Lemeshow test confirmed satisfactory model calibration,while the calibration curve illustrated accurate prediction probabilities.The area under the curve(AUC)for the training set was 0.863(95%CI:0.806～0.920),and for the validation set,it was 0.846(95%CI:0.764～0.929),indicating strong discriminative and generalization capabilities.Additionally,the clinical decision curve analysis demonstrated favor-able clinical utility.Conclusion A nomogram model integrating clinical and multimodal ultrasound parameters demonstrates potential utility in predicting HER-2-positive breast cancer.

Objective:To evaluate the role of histogram analysis based on amide proton transfer weighted (APTw) and apparent diffusion coefficient (ADC) imaging in predicting alpha-thalassemia/mental retardation syndrome X-linked ( ATRX) mutation in isocitrate dehydrogenase ( IDH)-mutant WHO grading 2/3 gliomas. Methods:Seventy-eight patients with IDH-mutant WHO grading 2/3 gliomas, admitted to and confirmed by surgical pathology in Department of Functional Neurosurgery, Neurosurgery Center, Zhujiang Hospital, Southern Medical University from June 2017 to October 2023, including 52 with ATRX wild and 26 with ATRX mutant-type, were selected. Preoperative 3D-APTw and ADC imaging data were collected; after post-processing, the lesions were segmented using lesion outlining method based on inclusion of peri-tumor edema and lesion outlining method based on tumor entity, respectively; after that, the histogram features (the 10 th percentile, 90 th percentile, maximum, mean, median, minimum, skewness, kurtosis, entropy, range, uniformity, and variance) were extracted from 3D-APTw and ADC imaging, respectively. Univariate Logistic regression was used to compare the differences in histogram features between patients in the ATRX mutant group and ATRX wild-type group, and multivariate Logistic regression was used to screen the independent predictors for ATRX mutation (a Logistic regression prediction model was constructed). Predictive values of independent predictors and Logistic regression prediction models in ATRX mutation were evaluated by receiver operating characteristic (ROC) curve. Results:(1) With lesion outlining method based on inclusion of peri-tumor edema, univariate analysis indicated significant difference between ATRX mutant group and ATRX wild-type group in 9 histogram features: relative 3D-APTw minimum, 3D-APTw skewness, relative ADC 90 th percentile, relative ADC mean, relative ADC median, ADC kurtosis, ADC skewness, ADC uniformity, and ADC entropy ( P<0.05). With lesion outlining method based on tumor entity, univariate analysis indicated significant difference between ATRX mutant group and ATRX wild-type group in 9 histogram features: relative 3D-APTw 90 th percentile, 3D-APTw skewness, relative ADC 90 th percentile, relative ADC mean, relative ADC median, ADC kurtosis, ADC skewness, ADC uniformity and ADC entropy ( P<0.05). (2) With lesion outlining method based on inclusion of peri-tumor edema, multivariate Logistic regression showed that 3D-APTw skewness and ADC kurtosis were the independent predictor for ATRX mutation in IDH mutant WHO grading 2/3 glioma patients ( OR=0.168, 95% CI: 0.034-0.800, P=0.025; OR=0.508, 95% CI: 0.319-0.807, P=0.004). The constructed Logistic regression prediction model was P(Y=1|X)=1/1+e -(1.827-1.785×3D-APTw skewness-0.678×ADC kurtosis). With lesion outlining method based on tumor entity, multivariate Logistic regression showed that 3D-APTw skewness and ADC kurtosis were independent predictors for ATRX mutation in IDH mutant WHO grading 2/3 glioma patients ( OR=0.164, 95% CI: 0.034-0.791, P=0.024; OR=0.496, 95% CI: 0.312-0.788, P=0.003); the constructed Logistic regression prediction model was P(Y=1|X)=1/1+e -(1.585-1.810×3D-APTw skewness-0.702×ADC kurtosis). (3) ROC curve analysis showed that, with lesion outlining method based on inclusion of peri-tumor edema, area under ROC curve (AUC) of 3D-APTw skewness and ADC kurtosis was 0.725 (95% CI: 0.608-0.842, P=0.001) and 0.794 (95% CI: 0.685-0.904), respectively ( P<0.001); AUC of Logistic regression prediction model was 0.836 (95% CI: 0.729-0.942, P<0.001), and its sensitivity and specificity were 73.10% and 90.40% when the best threshold was 0.505. ROC curve showed that, with lesion outlining method based on tumor entity, AUC of 3D-APTw skewness and ADC kurtosis was 0.705 (95% CI: 0.587-0.823, P=0.003) and 0.808 (95% CI: 0.704-0.913), respectively ( P<0.001); AUC of Logistic regression prediction model was 0.844 (95% CI: 0.739-0.949, P<0.001), and its sensitivity and specificity were 84.60% and 80.80% when the best threshold was 0.399. Conclusion:Histogram analysis based on 3D-APTw and ADC imaging can predict ATRX mutation in IDH mutant WHO grading 2/3 gliomas to a certain extent.