Objective:To investigate the value of breast MRI imaging features combined with serological indicators in predicting the metastatic burden of axillary lymph nodes (ALN) in breast cancer.Methods:This cross-sectional study retrospectively enrolled 146 female patients diagnosed with breast cancer at the First Affiliated Hospital of Shihezi University from January 2020 to November 2024. Patients′ pre-treatment clinical data, serological indices, breast MRI image features, and post-surgical pathologic features were analyzed. Patients were divided into low-burden (<3 metastatic lymph nodes) group and high-burden (≥3 metastatic lymph nodes) group based on pathological ALN confirmation. Group comparisons of clinical variables were analyzed using independent samples t-tests, Mann-Whitney U tests, or χ2 tests. Indicators with statistically significant differences were included in a multivariable logistic regression analysis to screen for independent influences predicting high ALN load and construct multiple logistic regression models. The performance of these models was evaluated using receiver operating characteristic curves and area under the curve (AUC), while net clinical benefit was assessed using decision curve analysis (DCA). Results:Significant differences were observed between low and high ALN burden groups in carcinoembryonic antigen levels, CA153 levels, tumor diameter, margins, enhancement characteristics, number of peritumoral thick blood vessels (TBVs), MRI-reported ALN loading status (MRI-ALN), and lymphovascular invasion status ( P<0.05). Multivariable logistic regression analysis showed that serum CA153 level ( OR=1.056, 95% CI 1.007-1.108, P=0.024), tumor margins ( OR=3.977, 95% CI 1.561-10.131, P=0.004), TBVs ( OR=3.058, 95% CI 1.217-7.684, P=0.017), and MRI-ALN ( OR=9.424, 95% CI 3.531-25.155, P<0.001) were independent risk factors predicting high ALN load in breast cancer ( P<0.05). The logistic regression model incorporating these four risk factors yielded optimal predictive performance for high ALN burden in breast cancer (AUC=0.854). DCA demonstrated optimal net clinical benefit within the threshold probability range of 13.3% to 72.7%. Conclusions:Tumor margins, TBVs, MRI-ALN, and CA153 levels are significantly associated with high ALN metastatic burden in breast cancer. Constructing a predictive model incorporating these features can significantly improve the accuracy of identifying high ALN burden.

Objective:To construct a risk prediction model for early post-injury respiratory failure in patients with traumatic cervical spinal cord injury (TCSCI) and validate its efficacy.Methods:A retrospective cohort study was conducted to analyze the clinical data of 393 TCSCI patients admitted to Zhengzhou Central Hospital Affiliated to Zhengzhou University from January 2020 to October 2024, including 294 males and 99 females, aged 18-82 years [59(45, 72)years]. Among them, 76 patients had respiratory failure (19.3%). The patients were randomly divided into the training set ( n=275) and validation set ( n=118) at a ratio of 7∶3. According to the presence of respiratory failure within one week after admission, 275 patients in the training set were divided into respiratory failure group ( n=53) and non-respiratory failure group ( n=222). The demographic data, injury characteristics, laboratory test results, and imaging findings of the patients were collected. Risk factors were determined through univariate analysis and multivariate Logistic regression analysis and a nomogram prediction model was constructed. The area under the receiver operating characteristic (ROC) curve (AUC) and Hosmer-Lemeshow test were used to evaluate the discrimination and calibration of the model. Decision curve analysis (DCA) was plotted to evaluate the clinical effectiveness of the prediction model. Results:The results of the univariate analysis showed that there were significant differences in history of respiratory diseases, causes of injury, Glasgow coma scale (GCS), American Spinal Injury Association (ASIA) classification, ASIA-motor score (AMS), injury severity score (ISS), clinical pulmonary infection score (CPIS), hypoproteinemia and cervical vertebra fracture and dislocation between the respiratory failure group and non-respiratory failure group in the training set ( P<0.05). The results of multivariate Logistic regression analysis indicated that GCS, ASIA classification, CPIS, and hypoproteinemia were independent risk factors for early post-injury respiratory failure in TCSCI patients ( P<0.05). Based on the above four variables, a Logistic regression equation was constructed: Logit( P)=2.361-0.675×ASIA classification+0.419×CPIS-0.358×GCS+0.854×hypoproteinemia. In the prediction model established based on this equation, the AUC was 0.96 (95% CI 0.94, 0.99) in the training set and 0.89 (95% CI 0.82, 0.96) in the validation set. In the calibration curves of the training set and validation set, the prediction curve and reference curve were approximately overlapping, with the average absolute errors of 0.04 and 0.03. DCA results demonstrated that both the training and validation sets exhibited positive net benefits when threshold probabilities fell within ranges of 0%-78% and 0%-87%, respectively. Conclusion:The risk prediction model for early post-injury respiratory failure in TCSCI patients based on GCS, ASIA classification, CPIS and hypoproteinemia has good predictive efficacy and clinical practicability.

Objective:To construct a risk prediction model for early post-injury respiratory failure in patients with traumatic cervical spinal cord injury (TCSCI) and validate its efficacy.Methods:A retrospective cohort study was conducted to analyze the clinical data of 393 TCSCI patients admitted to Zhengzhou Central Hospital Affiliated to Zhengzhou University from January 2020 to October 2024, including 294 males and 99 females, aged 18-82 years [59(45, 72)years]. Among them, 76 patients had respiratory failure (19.3%). The patients were randomly divided into the training set ( n=275) and validation set ( n=118) at a ratio of 7∶3. According to the presence of respiratory failure within one week after admission, 275 patients in the training set were divided into respiratory failure group ( n=53) and non-respiratory failure group ( n=222). The demographic data, injury characteristics, laboratory test results, and imaging findings of the patients were collected. Risk factors were determined through univariate analysis and multivariate Logistic regression analysis and a nomogram prediction model was constructed. The area under the receiver operating characteristic (ROC) curve (AUC) and Hosmer-Lemeshow test were used to evaluate the discrimination and calibration of the model. Decision curve analysis (DCA) was plotted to evaluate the clinical effectiveness of the prediction model. Results:The results of the univariate analysis showed that there were significant differences in history of respiratory diseases, causes of injury, Glasgow coma scale (GCS), American Spinal Injury Association (ASIA) classification, ASIA-motor score (AMS), injury severity score (ISS), clinical pulmonary infection score (CPIS), hypoproteinemia and cervical vertebra fracture and dislocation between the respiratory failure group and non-respiratory failure group in the training set ( P<0.05). The results of multivariate Logistic regression analysis indicated that GCS, ASIA classification, CPIS, and hypoproteinemia were independent risk factors for early post-injury respiratory failure in TCSCI patients ( P<0.05). Based on the above four variables, a Logistic regression equation was constructed: Logit( P)=2.361-0.675×ASIA classification+0.419×CPIS-0.358×GCS+0.854×hypoproteinemia. In the prediction model established based on this equation, the AUC was 0.96 (95% CI 0.94, 0.99) in the training set and 0.89 (95% CI 0.82, 0.96) in the validation set. In the calibration curves of the training set and validation set, the prediction curve and reference curve were approximately overlapping, with the average absolute errors of 0.04 and 0.03. DCA results demonstrated that both the training and validation sets exhibited positive net benefits when threshold probabilities fell within ranges of 0%-78% and 0%-87%, respectively. Conclusion:The risk prediction model for early post-injury respiratory failure in TCSCI patients based on GCS, ASIA classification, CPIS and hypoproteinemia has good predictive efficacy and clinical practicability.

Objective:To investigate the value of breast MRI imaging features combined with serological indicators in predicting the metastatic burden of axillary lymph nodes (ALN) in breast cancer.Methods:This cross-sectional study retrospectively enrolled 146 female patients diagnosed with breast cancer at the First Affiliated Hospital of Shihezi University from January 2020 to November 2024. Patients′ pre-treatment clinical data, serological indices, breast MRI image features, and post-surgical pathologic features were analyzed. Patients were divided into low-burden (<3 metastatic lymph nodes) group and high-burden (≥3 metastatic lymph nodes) group based on pathological ALN confirmation. Group comparisons of clinical variables were analyzed using independent samples t-tests, Mann-Whitney U tests, or χ2 tests. Indicators with statistically significant differences were included in a multivariable logistic regression analysis to screen for independent influences predicting high ALN load and construct multiple logistic regression models. The performance of these models was evaluated using receiver operating characteristic curves and area under the curve (AUC), while net clinical benefit was assessed using decision curve analysis (DCA). Results:Significant differences were observed between low and high ALN burden groups in carcinoembryonic antigen levels, CA153 levels, tumor diameter, margins, enhancement characteristics, number of peritumoral thick blood vessels (TBVs), MRI-reported ALN loading status (MRI-ALN), and lymphovascular invasion status ( P<0.05). Multivariable logistic regression analysis showed that serum CA153 level ( OR=1.056, 95% CI 1.007-1.108, P=0.024), tumor margins ( OR=3.977, 95% CI 1.561-10.131, P=0.004), TBVs ( OR=3.058, 95% CI 1.217-7.684, P=0.017), and MRI-ALN ( OR=9.424, 95% CI 3.531-25.155, P<0.001) were independent risk factors predicting high ALN load in breast cancer ( P<0.05). The logistic regression model incorporating these four risk factors yielded optimal predictive performance for high ALN burden in breast cancer (AUC=0.854). DCA demonstrated optimal net clinical benefit within the threshold probability range of 13.3% to 72.7%. Conclusions:Tumor margins, TBVs, MRI-ALN, and CA153 levels are significantly associated with high ALN metastatic burden in breast cancer. Constructing a predictive model incorporating these features can significantly improve the accuracy of identifying high ALN burden.

Objective To observe the curative effect of Rougan Granules on rats hepatocirrhosis induced by carbon tetra- chloride(CCl_4)with 10 % alcohol and high fat feed.Method The rat model was established by gastric garage of CCh (CCl_4:salad oil=1:1)1 mL/kg twice a week for two weeks,and with 10 % alcohol as drinking water,high fat feed as food.After administration of CCl_4,the rats were divided into 5 groups:the model group,high-,medium-and low-dosage of Rougan Granules groups,the eolchicine group.And normal control group was also set up.After adminis- tration for 6 weeks,hyaluronic acid,typeⅣcollagen and typeⅢprecollagen in serum were measured and liver histopathol- ogy examination was done.Result The contents of hyaluronic acid,typeⅣcollagen and typeⅢprecollagen in model rats were increased significantly,while Rougan Granules could decrease those increase significantly.Liver histopathology exami- nation showed medium or high fatty degeneration of hepatic cell,obvious hyperplasia of fibrous tissue with fibrous distance broaden and pseudolobuli formed in model group,but in Rougan Granules group the general pathological changes of liver were less.Conclusion Rougan Granules have an effect of experimental anti-hepatocirrhosis in rats.