Objective:To develop and validate CT radiomics models based on machine learning for predicting recurrence of chronic subdural hematoma (cSDH) after endoscopic treatment.Methods:A retrospective study was performed; 252 patients with cSDH who underwent endoscopic treatment in Department of Neurosurgery, the Second Affiliated Hospital of Soochow University from October 2016 to October 2024 were selected. The clinical and imaging data of these patients were collected, and these patients were divided into a training set ( n=176) and a validation set ( n=76) at a ratio of 7:3. Patients in both sets were further sub-divided into a recurrence group and a non-recurrence group based on whether they had recurrence within 3 months of discharge. (1) Radiomics features of cSDH on initial non-enhanced CT images were extracted using 3D-Slicer software. Optimal features were selected through univariate analysis and least absolute shrinkage and selection operator (LASSO) regression analysis; based on these optimal features, 3 machine learning algorithms (Logistic, support vector machine [SVM], and K-nearest neighbor [KNN]) were used to construct CT radiomics models. Differences in predictive performance of different radiomics models were compared by analyzing indicators such as sensitivity, specificity, and area under receiver operating characteristic (ROC) curve (AUC), and the best model was selected. (2) Based on the initial non-enhanced CT images, cSDH was classified into homogeneous type, laminar type, septated type, and trabecular type according to Nakaguchi classification system; combined these cSDH typing with clinical features (clinical Markwalder's grade and bilateral hematoma), univariate analysis and multivariate Logistic regression analysis were used to screen the independent risk factors for cSDH recurrence. Based on these factors, the 3 machine learning algorithms (Logistic, SVM, KNN) were used to construct hematoma typing-clinical feature models; differences in predictive performance of different hematoma typing-clinical feature models were compared by analyzing indicators such as sensitivity, specificity, and AUC, and the best model was selected. (3) DeLong's test was used to compare the ROC curve differences between the CT radiomics model and hematoma typing-clinical feature model. Decision curve analysis was used to compare the effective scope of the CT radiomics model and hematoma typing-clinical feature model. Results:(1) Seven optimal CT radiomics features based on wavelet transform were obtained after univariate analysis and LASSO regression: one gray-level dependence matrix feature, one first-order energy feature, two gray-level co-occurrence matrix features, two gray level size zone matrix features, and one gray-level run-length matrix feature. The KNN model constructed based on these 7 optimal features had the best performance in predicting cSDH recurrence, with an AUC of 0.845, a sensitivity of 0.833, a specificity of 0.857, a recall rate of 0.833, and an F1 score of 0.476 in patients from the validation set. (2) Three independent risk factors for cSDH recurrence were screened out through univariate analysis and multivariate Logistic regression analysis: hematoma Nakaguchi classification, Markwalder's grade, and bilateral hematoma. Logistic model constructed based on these 3 factors had the best performance in predicting cSDH recurrence, with an AUC of 0.675, a sensitivity of 0.609, a specificity of 0.654, a recall rate of 0.609, and an F1 score of 0.311 in patients from the validation set. (3) DeLong's test showed that the AUC of the CT radiomics model was significantly greater than that of the hematoma typing-clinical feature model in patients from the training set and validation set ( P=0.027 and P=0.035). Decision curve analysis showed that in the CT radiomics model, the net benefit of the model was >0 when the risk threshold was 0.05-0.95; in the hematoma typing-clinical feature model, the net benefit of the model was >0 when the risk threshold was 0.05-0.55. Conclusion:The KNN model based on 7 CT radiomics features in this study can effectively predict the cSDH recurrence in patients after endoscopic treatment, and its performance is obviously better than that of hematoma typing-clinical feature model constructed in this study.

ObjectiveBased on analytic hierarchy process（AHP）-criteria importance through intercriteria correlation（CRITIC） hybrid weighting method， grey relational analysis and backpropagation artificial neural network（BP-ANN）， to optimize the water extraction process of Qizhi prescription， so as to provide an experimental basis for optimization of the preparation process of this prescription and the establishment of quality standards. MethodsL₉（3⁴） orthogonal test was employed， and the AHP-CRITIC hybrid weighting method was utilized to determine the weight coefficients of the quality fractions of various components， including astragaloside Ⅳ， polygalaxanthone Ⅲ， calycosin-7-O-β-D-glucoside， tenuifolin， and 3，6′-disinapoylsucrose， as well as the dry extract yield. The comprehensive score of each factor level combination in the orthogonal test were calculated as evaluation indicator to select the optimal extraction process parameters. The effects of extraction times， extraction time， and solvent dosage on the aqueous extraction process of the formula were investigated through intuitive analysis， variance analysis， and grey relational analysis. Meanwhile， a BP-ANN model was established to reverse-predict the optimal extraction process parameters of Qizhi prescription， and the optimized process parameters were validated. ResultsThe weight coefficients of the five index components（astragaloside Ⅳ， tenuifolin， calycosin-7-O-β-D-glucoside， polygalaxanthone Ⅲ， and 3，6′-disinapoylsucrose） and dry extract yield were 25.7%， 20.82%， 16.41%， 12.45%， 15.96% and 8.67%， respectively. The optimized extraction process parameters were extracted 3 times with 8， 6， 6 times the amount of water， each time for 1 h. The network prediction results of BP-ANN test samples were consistent with the orthogonal test results， and the mean square error（MSE） of the predicted and measured values of the network was <1%. The water extraction process of Qizhi prescription analyzed and predicted by relevant mathematical models was stable and feasible， which could effectively improve the extraction efficiency of the active ingredients of Astragali Radix and Polygalae Radix， and the average comprehensive score of the validation test was 90.85 with the relative standard deviation（RSD） of 1.55%. ConclusionThis study establishes a water extraction process for compound Qizhi granules， and the optimized extraction process can effectively improve the extraction efficiency of active ingredients， which provides useful references for the optimization of preparation process and the establishment of quality standards for other clinical experience formulas.

Objective To investigate the predictive value of temperature gradients on the mortality of sepsis patients and their correlation with fluid input.Methods By means of a prospective observational method,154 patients with sepsis or septic shock admitted to the Department of Critical Care Medicine at Nanfang Hospital,Southern Medical University from November 2019 to November 2021 were included as research subjects.They were divided into a survivor group(n=118)and a non-survivor group(n=36)according to whether they survived within 28 days.The core-to-toe temperature gradient(CTTG)and toe-to-room temperature gradient(TRTG)were monitored and calculated immediately upon admission to the intensive care unit(ICU)and 6 hours after admission.Receiver operating characteristic(ROC)curve was used to explore the predictive value of temperature gradients on mortality,and multivariate Cox regression analysis was performed to explore the risk factors of 28-day mortality in sepsis patients.The results were verified through survival analysis.Correlation analysis and multivariate analysis of variance were used to explore the correlation between temperature gradients and fluid input,as well as noradrenaline doses.Results Among the 154 patients,118 survived within 28 days(survivor group),and 36 died(non-survivor group).ROC curve and multivariate Cox regression analysis showed that a toe-to-room temperature gradient of≤5.35℃within 6 hours after admission was a risk factor for 28-day mortality.Compared with patients with a high toe-to-room temperature gradient(>5.35℃),patients with a low toe-to-room temperature gradient(≤5.35℃)had a 2.74-fold increase in the risk of 28-day mortality(P=0.004,95%CI 1.54,9.12).The CTTG and TRTG upon admission to the ICU and 6 hours after admission were not significantly associated with fluid input or noradrenaline doses(P>0.05).Conclusions A toe-to-room temperature gradient of less than or equal to 5.35℃within 6 hours after ICU admission is a risk factor for 28-day mortality in sepsis patients.The improvement of temperature gradients at different time points is not associated with fluid input.

Effective axon regeneration is essential for the successful restoration of nerve functions in patients suffering from axon injury-associated neurological diseases. Certain self-regeneration occurs in injured peripheral axonal branches of dorsal root ganglion (DRG) neurons but does not occur in their central axonal branches. By performing rat sciatic nerve or dorsal root axotomy, we determined the expression of the dysbindin domain containing 2 (DBNDD2) in the DRGs after the regenerative peripheral axon injury or the non-regenerative central axon injury, respectively, and found that DBNDD2 is down-regulated in the DRGs after peripheral axon injury but up-regulated after central axon injury. Furthermore, we found that DBNDD2 expression differs in neonatal and adult rat DRGs and is gradually increased during development. Functional analysis through DBNDD2 knockdown revealed that silencing DBNDD2 promotes the outgrowth of neurites in both neonatal and adult rat DRG neurons and stimulates robust axon regeneration in adult rats after sciatic nerve crush injury. Bioinformatic analysis data showed that transcription factor estrogen receptor 1 (ESR1) interacts with DBNDD2, exhibits a similar expression trend as DBNDD2 after axon injury, and may targets DBDNN2. These studies indicate that reduced level of DBNDD2 after peripheral axon injury and low abundance of DBNDD2 in neonates contribute to axon regeneration and thus suggest the manipulation of DBNDD2 expression as a promising therapeutic approach for improving recovery after axon damage.
Animals ; Ganglia, Spinal/metabolism* ; Nerve Regeneration/genetics* ; Rats ; Axons/metabolism* ; Sciatic Nerve/injuries* ; Rats, Sprague-Dawley ; Male

Objective To explore the application and effectiveness of the apprenticeship education model in Chinese herbal medicine teaching at Western medical college.Methods By comparing classic lecture-based teaching with a combined approach integrating apprenticeship education,the study assesses the impact on student learning outcomes.Participants included students from the 2018 cohort of the eight-year clinical medicine program and the 2022 cohort of the"4+4"pilot program at Peking Union Medical College,who received classic teaching methods and apprenticeship case-based teaching methods,respectively.Upon course completion,students completed a 14-item multiple-choice questionnaire covering essential theory of Chinese medicine,as well as specific categories such as qi-regulating,blood-activating herbs,among others.Results The overall accuracy rate in the apprenticeship case-based teaching group was significantly higher than that in the classic teaching group(P＜0.01).Conclusions The apprenticeship education model of Traditional Chinese Medicine has a positive effect on teaching of Chinese herbal medicine at West-ern medical college and warrants further promotion and application.

Inflammatory bowel disease (IBD) is a chronic inflammatory disorder of the gastrointestinal tract, which increases the incidence of colorectal cancer (CRC). In the pathophysiology of IBD, ubiquitination/deubiquitination plays a critical regulatory function. Josephin domain containing 2 (JOSD2), a deubiquitinating enzyme, controls cell proliferation and carcinogenesis. However, its role in IBD remains unknown. Colitis mice model developed by dextran sodium sulfate (DSS) or colon tissues from individuals with ulcerative colitis and Crohn's disease showed a significant upregulation of JOSD2 expression in the macrophages. JOSD2 deficiency exacerbated the phenotypes of DSS-induced colitis by enhancing colon inflammation. DSS-challenged mice with myeloid-specific JOSD2 deletion developed severe colitis after bone marrow transplantation. Mechanistically, JOSD2 binds to the C-terminal of inosine-5'-monophosphate dehydrogenase 2 (IMPDH2) and preferentially cleaves K63-linked polyubiquitin chains at the K134 site, suppressing IMPDH2 activity and preventing activation of nuclear factor kappa B (NF-κB) and inflammation in macrophages. It was also shown that JOSD2 knockout significantly exacerbated increased azoxymethane (AOM)/DSS-induced CRC, and AAV6-mediated JOSD2 overexpression in macrophages prevented the development of colitis in mice. These outcomes reveal a novel role for JOSD2 in colitis through deubiquitinating IMPDH2, suggesting that targeting JOSD2 is a potential strategy for treating IBD.

Booster vaccinations are highly recommended in combating the SARS-CoV-2 Omicron variant and its subvariants. However, the optimal booster vaccination strategies and related immune mechanisms with different prior vaccinations are under-revealed. In this study, we systematically evaluated the immune responses in mice and hamsters with different prime-boost regimens before their protective efficacies against Omicron were detected. We found that boosting with Ad5-nCoV, S_WT-2P or S_Omicron-6P induced significantly higher levels of neutralization activities against Omicron variants than CoronaVac and ZF2001 by eliciting stronger germinal center (GC) responses. Specifically, S_Omicron-6P induced even stronger antibody responses against Omicron variants in CoronaVac and Ad5-nCoV-primed animals than non-Omicron-specific vaccines but with limited differences as compared to Ad5-nCoV and S_WT-2P. In addition, boosting with a specific vaccine has the potential to remodel the existing immune profiles. These findings indicated that adenovirus-vectored vaccines and mRNA vaccines would be more effective than other types of vaccines as booster shots in combating Omicron infections. Moreover, the protective efficacies of the vaccines in booster vaccinations are highly related to GC reactions in secondary lymphatic organs. In summary, these findings provide timely important information on prime-boost regimens and future vaccine design.

Objective:To evaluate the diagnostic performance of resting echocardiography in detecting severe coronary artery stenosis.Methods:A total of 136 patients with suspected coronary artery disease（CAD）who presented to Sichuan Provincial People's Hospital between January 2021 and December 2024 were prospectively enrolled. All patients underwent both coronary computed tomography angiography（CCTA）and transthoracic echocardiography within one week. Based on CCTA results，the patients were divided into non-severe stenosis group（ n=78）and severe stenosis group（ n=58）. Echocardiographic parameters including left atrial maximum volume（LAVmax），left ventricular global longitudinal strain（GLS），left ventricular longitudinal strain of endo-myocardium，mid-myocardium，epi-myocardium（LSendo，LSmid，LSepi），early diastolic mitral inflow velocity（E），early diastolic mitral annular velocity of the lateral and septal walls（e'），and E/e' were measured. Predictive factors for severe coronary stenosis were identified using LASSO regression，and a nomogram model was developed via multivariate Logistic regression. Model performance was evaluated using ROC curves，calibration curves，and decision curve analysis. Results:Multivariate Logistic regression analysis revealed LSendo，LAVmax，and E/e' as independent predictors of severe coronary artery stenosis. The nomogram constructed based on these predictors achieved an area under the curve of 0.798（95% CI=0.723-0.873），with sensitivity and specificity of 0.756 and 0.759，respectively. Conclusions:The resting echocardiography-based nomogram model demonstrates good diagnostic efficacy for severe coronary artery stenosis. It may serve as a noninvasive tool to assist in risk stratification and clinical decision-making in patients with suspected CAD.

Objective To retrospectively analyze the association between metabolic factors and high-risk colorectal adenoma(CRA).Methods The medical records of patients aged 18-75 years who underwent their initial colonoscopy at Karamay Central Hospital of Xinjiang Uygur Autonomous Region from Jul 2000 to Mar 2017 were collected.The comparison between normal colonoscopy(NC)and high-risk CRA patients was conducted using an unpaired t-test,while chi-square test was used for categorical variables.Least absolute shrinkage and selection operator(LASSO)regression and Logistic regression were utilized to analyze the association between metabolic factors and high-risk CRA.Results A total of 1 798 patients meeting the inclusion and exclusion criteria were enrolled and divided into normal colonoscopy(NC)findings group(n=972)and high-risk CRA group(n=826).The high-risk CRA group exhibited significantly lower levels of high-density lipoprotein cholesterol(HDL-C)in comparison to the NC group,while uric acid and fibrosis 4(FIB-4)index levels were significantly higher than those observed in the NC group(all P<0.05).Based on LASSO regression analysis,we identified 12 variables that potentially influence the occurrence of high-risk CRA,including age,gender,smoking history,alcohol consumption history,non-alcoholic fatty liver disease(NAFLD),hypertension,coronary artery disease,hyperglycemia,hypercholesterolemia,low levels of HDL-C,elevated alanine aminotransferase,and elevated gamma-glutamyl transferase.Multivariate analysis revealed that individuals aged over 50 years,male gender,cigarette and alcohol consumption,low HDL-C levels,history of NAFLD and hypertension were identified as independent risk factors associated with high-risk CRA(P<0.05).In addition,without or with adjusting for age,sex,smoking,and drinking history,patients with a high TG/HDL-C ratio(the ratio≥2.68)had a significantly higher risk of high-risk CRA than those with a low TG/HDL-C ratio(the ratio<2.68)[odds ratios(ORs)were1.430 and 1.235 respectively,all P<0.05)].Without or with adjusting variables,the ORs for NAFLD patients with FIB-4 index>2.67 were 1.849(P=0.466)and 1.435(P=0.707),respectively.Conclusion A significant association exists between metabolic factors and high-risk CRA.Independent risk factors for high-risk CRA include older age(≥50 years),male,smoking history,alcohol consumption history,low levels of HDL-C,and a history of NAFLD and hypertension.Individuals exhibiting a TG/HDL-C ratio exceeding 2.68 manifest a significantly heightened susceptibility to the development of high-risk CRA.Therefore,elderly males with one or more aforementioned metabolic abnormalities should be considered a priority population for colorectal screening.