Objective To establish the method of thin layer chromatography （TLC） for identification and content quantitative determination of Shugantongluo capsules, and provide methodological reference for the quality control of Shugantongluo capsules. Methods Bupleuriradix, polygala tenuifolia yuanzhi, and acorus tatarinowii rhizoma were identified by TLC qualitatively. The content of Saikosaponin-a was determined by HPLC method. The chromatography was performed on Venusil XBP C₁₈（L） （4.6 mm×150 mm, 5 μm） with a stable temperature of 30℃. The mobile phase in isocratic elution consisted of acetonitrile-water （40∶60, V/V） at the flow rate of 1.0 ml/min with an injection volume of 20 μl. The detection wavelength was set at 210 nm. Results Clear spots were obtained with good separation in TLC identification which was highly specific. The Saikosaponin-a at the range of 25.00-500.0 μg/ml was linear with peak area （r=0.999 9）, the average recovery rate was 97.00%, and RSD was 2.60%. Conclusion These methods was easy to operate with accurate results, with good specificity and reproducibility, which could be uesd for the quality control for Shugantongluo capsules.

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 effect of local histopathological changes of thyroid nodules after ultrasound-guided fine needle aspiration biopsy(US-FNBA)on laparoscopic thyroid surgery.Methods The clinical data of 120 patients with thyroid malignant tumors admitted to Xiaogan Hospital of Wuhan University of Science and Technology from January 2020 to December 2022 were retrospectively analyzed,and according to whether received US-FNAB before surgery they were divided into the observation group(with US-FNAB)and the control group(without US-FNAB),with 60 cases in each group.The operation time,change of parathyroid hormone(ΔPTH)before and after surgery,change of serum Ca2+concentration(serum ΔCa2+)before and after surgery,drainage volume 24 hours after surgery and incidence of complications were compared between the two groups.According to fine needle aspiration-to-surgery(FTS),the patients in the observation group were further divided into the group A(FTS≤3 days)and the group B(FTS>3 days),and the effect of US-FNAB on laparoscopic thyroid surgery was explored combined with the pathological changes of the thyroid puncture capsule of the two groups.Results The operation time of the observation group was longer than that of the control group(P<0.05),and there was no significant difference in the drainage volume 24 hours after surgery,ΔPTH,serum ΔCa2+,or proportions of facial or limb numbness,hypoparathyroidism,recurrent laryngeal nerve integrity,and hypocalcemia between the two groups(P>0.05).There was no significant difference in the operation time,ΔPTH,serum ΔCa2+,drainage volume 24 hours after surgery,hypocalcemia,recurrent laryngeal nerve integrity or hypothyroidism between the group A and group B(P>0.05).The proportion of patients with facial or limb numbness after surgery in the group A was significantly lower than that in the group B(P<0.05).Conclusion Preoperative US-FNAB for thyroid nodules may prolong the operation time in patients undergoing laparoscopic thyroid surgery,and surgical treatment in early aspiration can help decrease this effect and reduce the incidences of postoperative hypocalcemia and hypothyroidism.

Objective To explore the correlation between serum homocysteine(Hcy),25-hydroxyvitamin D[25(OH)D]and frailty with type 2 diabites mellitus(T2DM)complicated with sarcopenia.Methods From September 2021 to March 2023,210 elderly T2DM patients were selected from the Department of Geriatrics of The First People's Hospital of Yunnan Province,and divided into simple T2DM(n=99)group,mild sarcopenia(M-Sar,n=59)group and severe sarcopenia(S-Sar,n=52)group.The"Elderly Comprehensive Assessment System"was used to evaluate subjects.The influencing factors of T2DM complicated with sarcopenia were analyzed by Logistic regression.The receiver operating characteristic(ROC)curve was used to analyze the predictive value of Hcy,25(OH)D combined with frailty in evaluating T2DM with sarcopenia.Results In T2DM,M-Sar and S-Sar groups,the age,Hcy,the risk rate of balance gait work falling and the rate of weakness increased in turn(P<0.05),while BMI,hemoglobin,25(OH)D,the rate of good nutrition,the normal rate of basic daily living,the low risk rate of falling,the rate of good balance gait function and the rate of no weakness decreased in turn(P<0.05).Logistic regression analysis showed that serum Hcy,frailty and 25(OH)D were the influencing factors of senile T2DM complicated with sarcopenia.Hcy,25(OH)D and frailty combined to predict T2DM with sarcopenia had an area under ROC carve of 0.815,with a sensitivity of 0.811 and a specificity of 0.717.Conclusions Serum Hcy,25(OH)D and frailty are closely related to T2DM combined with sarcopenia.Detection of Hcy and 25(OH)D combined with frailty score is helpful for early diagnosis of sarcopenia in primary hospitals.

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 gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid(Gd-EOB-DTPA)enhanced MRI radiomics and signal intensity in hepatobiliary phase(HBP)in predicting the pathological differentiation degree of hep-atocellular carcinoma(HCC).Methods The clinical and imaging data of 224 patients pathologically confirmed with HCC were col-lected.All patients were randomly divided into test group(68 cases)and training group(156 cases)at a ratio of 7︰3.The ITK-SNAP software was used to delineate region of interest(ROI)on arterial phase(AP),portal venous phase(PVP)and HBP,the radiomics features of the tumor tissues were extracted and the radiomics models were established using the FAE software.Logistic regression analysis was used to determine the clinical independent predictors associated with the pathological differentiation degree of HCC and to construct clinical model and clinical-radiomics model.Receiver operating characteristic(ROC)curve was plotted for each model and the area under the curve(AUC)was calculated to compare the diagnostic efficacy of the models.Results Age,alpha-fetoprotein(AFP),and r-glutamyltransferase(r-GT)were independent risk factors for predicting the degree of pathological differentiation of HCC.The AUC of the clinical-radiomics model in the training group and test group were 0.825 and 0.779,respectively,which were higher than those of the radiomics model(0.812 and 0.771)and the clinical model(0.687 and 0.666).Conclusion Gd-EOB-DTPA enhanced MRI radiomics have certain value in predicting the degree of pathological differentiation of HCC,while the predictive value of the signal intensity on HBP and the signal intensity ratio(SIR)on HBP is limited.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Objective: To investigate the urban and rural differences in adverse outcomes of pulmonary tuberculosis (PTB) in patients with pulmonary tuberculosis-diabetes mellitus comorbidity (PTB-DM), so as to provide insights into improving the prevention and treatment measures for PTB-DM. Methods: Patients with PTB-DM who were admitted and discharged from 14 designated tuberculosis hospitals in Hangzhou City from 2018 to 2022 were selected. Basic information, and history of diagnosis and treatment were collected through hospital information systems. The adverse outcomes of PTB were defined as endpoints, and the proportions of adverse outcomes of PTB in urban and rural patients with PTB-DM were analyzed. Factors affecting the adverse outcomes of PTB were identified using a multivariable Cox proportional hazards regression model. Results: A total of 823 patients with PTB-DM were enrolled, including 354 (43.01%) urban and 469 (56.99%) rural patients. There were 112 (13.61%) patients with adverse outcomes of PTB. The proportions of adverse outcomes of PTB in urban and rural patients were 14.41% and 13.01%, respectively, with no statistically significant difference (P>0.05). Multivariable Cox proportional hazards regression analysis identified first diagnosed in county-level hospitals or above (HR=2.107, 95%CI: 1.181-3.758) and drug resistance (HR=3.303, 95%CI: 1.653-6.600) as the risk factors for adverse outcomes of PTB in urban patients with PTB-DM, while the treatment/observed management throughout the process (HR=0.470, 95%CI: 0.274-0.803) and fixed-dose combinations throughout the process (HR=0.331, 95%CI: 0.151-0.729) as the protective factors for adverse outcomes in rural patients with PTB-DM. Conclusions There are differences in influencing factors for adverse outcomes of PTB in urban and rural patients with PTB-DM. The adverse outcomes of PTB are associated with first diagnosed hospitals and drug resistance in urban patients, and are associated with the treatment/observed management and fixed-dose combinations throughout the process in rural patients.