Jiegeng decoction is a classic prescription composed of two Chinese medicinal herbs： Platycodon grandiflorum and Glycyrrhiza uralensis. It has the efficacy of diffusing lung qi， resolving phlegm， relieving sore throat and discharging pus， and is commonly used in the treatment of respiratory diseases such as cough and pharyngodynia. This article reviews the chemical components， pharmacological mechanisms and clinical applications of Jiegeng decoction. It was found that Jiegeng decoction contains triterpenoid saponins， flavonoids， glycosides， acids， and other components， with platycodin D， platycodin D2， glycyrrhizic acid， glycyrrhetinic acid， liquiritin， etc.， serving as the main active pharmaceutical ingredients. Jiegeng decoction and its chemical constituents exert anti-inflammatory effects by inhibiting signaling pathways such as nuclear factor-κB and mitogen- activated protein kinases， and demonstrate anti-tumor activities through mechanisms like modulating the tumor immune microenvironment and promoting cancer cell apoptosis. Additionally， it exhibits various pharmacological actions including antibacterial， antiviral， and antioxidant effects. Clinically， Jiegeng decoction， its modified prescription and compound combinations are widely used in the treatment of respiratory diseases such as cough， pneumonia， and pharyngitis， as well as digestive system disorders like constipation.

Jiegengtang is a basic formula for treating sore throat and cough. By means of bibliometrics， this study conducted a textual research and analysis on the key information such as formula origin， decocting methods， and clinical application of Jiegengtang. After the research， it can be seen that Jiegengtang is firstly contained in Treatise on Febrile and Miscellaneous Disease， which is also known as Ganjietang， and it has been inherited and innovated by medical practitioners of various dynasties in later times. The origins of Chinese medicines in this formula is basically clear， Jiegeng is the dried roots of Platycodon grandiflorum， Gancao is the dried roots and rhizomes of Glycyrrhiza uralensis， the two medicines are selected raw products. The dosage is 27.60 g of Glycyrrhizae Radix et Rhizoma and 13.80 g of Platycodonis Radix， decocted with 600 mL of water to 200 mL， taken warmly after meals， twice a day， 100 mL for each time. In ancient times， Jiegengtang was mainly used for treating Shaoyin-heat invasion syndrome， with cough and sore throat as its core symptoms. In modern clinical practice， Jiegengtang is mainly used for respiratory diseases such as pharyngitis， esophagitis， tonsillitis and lung abscess， especially for pharyngitis and lung abscess with remarkable efficacy. This paper can provide literature reference basis for the modern clinical application and new drug development of Jiegengtang.

Jiegengtang is a basic formula for treating sore throat and cough. By means of bibliometrics， this study conducted a textual research and analysis on the key information such as formula origin， decocting methods， and clinical application of Jiegengtang. After the research， it can be seen that Jiegengtang is firstly contained in Treatise on Febrile and Miscellaneous Disease， which is also known as Ganjietang， and it has been inherited and innovated by medical practitioners of various dynasties in later times. The origins of Chinese medicines in this formula is basically clear， Jiegeng is the dried roots of Platycodon grandiflorum， Gancao is the dried roots and rhizomes of Glycyrrhiza uralensis， the two medicines are selected raw products. The dosage is 27.60 g of Glycyrrhizae Radix et Rhizoma and 13.80 g of Platycodonis Radix， decocted with 600 mL of water to 200 mL， taken warmly after meals， twice a day， 100 mL for each time. In ancient times， Jiegengtang was mainly used for treating Shaoyin-heat invasion syndrome， with cough and sore throat as its core symptoms. In modern clinical practice， Jiegengtang is mainly used for respiratory diseases such as pharyngitis， esophagitis， tonsillitis and lung abscess， especially for pharyngitis and lung abscess with remarkable efficacy. This paper can provide literature reference basis for the modern clinical application and new drug development of Jiegengtang.

Objective:To investigate the association between blood selenium levels and sex hormones in Chinese men aged 18-79 years.Methods:Data were derived from the China National Human Biomonitoring survey conducted in 2017-2018, with a final sample size of 5 414 men. General demographic characteristics, behavioral habits, and dietary frequency were collected through questionnaires and physical examinations. Fasting blood samples were collected to measure blood lead, serum testosterone, and estradiol levels. Complex sampling linear regression models were used to analyze the associations between blood selenium levels and testosterone, estradiol, and the testosterone/estradiol ratio, adjusting for confounding factors including age, education level, marital status, smoking status, alcohol consumption, seafood intake, soy product intake, protein supplement intake, BMI, and diabetes status.Results:The mean age of the 5 414 participants was (46.85±27.91) years; 4 774 (91.65%) were of Han ethnicity and 4 505 (86.68%) were married. The median ( Q1, Q3) blood selenium concentration in men was 97.80 (80.64, 116.99) μg/L. After adjusting for confounding factors, the complex sampling linear regression model revealed negative associations between blood selenium levels and both testosterone levels and the testosterone/estradiol ratio, with a significant linear trend ( Ptrend<0.05). Compared with the Q1 group, the β (95% CI) values for testosterone in the Q2, Q3, and Q4 groups were -0.02 (-0.06 to 0.02), -0.03 (-0.08 to 0.01), and -0.06 (-0.09 to -0.02), respectively. Similarly, the β (95% CI) values for the testosterone/estradiol ratio in the Q2, Q3, and Q4 groups were -0.01 (-0.03 to 0.02), -0.01 (-0.04 to 0.04), and -0.03 (-0.06 to -0.01), respectively. Subgroup analysis indicated stronger associations between blood selenium levels and testosterone/estradiol levels in non-smoking and obese men (BMI≥28 kg/m2). Conclusion:Blood selenium levels are negatively associated with testosterone levels and the testosterone/estradiol ratio in Chinese adult males.

Objective:To explore the association of blood and urinary cadmium levels with lipid profile levels and dyslipidemia in Chinese adults aged 18 to 79 years.Methods:Based on the China National Human Biomonitoring (CNHBM) program, a cross-sectional survey was conducted from 2017 to 2018 using a multi-stage stratified random sampling method, including a total of 10 713 adults aged 18 to 79 years. Data was obtained through questionnaires, physical examinations, biological sample collection, and laboratory testing. Multiple linear mixed effect model (MLMM) and generalized linear mixed effect model (GLMM) were used to analyze the association of blood and creatinine-corrected urinary cadmium levels with lipid profile levels as well as dyslipidemia among adults.Results:The age of 10 713 participants was (47.23±0.24) years, with 5 372 males accounting for 61.3% of the national population. The weighted mean±standard error (SE) of total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) was (5.21±0.03), (1.86±0.03), (2.96±0.03), and (1.43±0.01) mmol/L, respectively. The prevalence rate of hypercholesterolemia, hypertriglyceridemia, mixed hyperlipidemia, low HDL-C, and high LDL-C was 16.0%, 21.6%, 6.6%, 13.5%, and 10.0%, respectively. MLMM showed that, after adjusting for relevant confounders, log-transformed blood cadmium levels were positively associated with increased levels of TC, TG and LDL-C ( P<0.05). When blood cadmium levels were categorized into quartiles, compared to the lowest exposure group ( Q1), participants in the highest blood cadmium exposure group ( Q4) had increases of 0.19 (95% CI: 0.06, 0.32) mmol/L in TC and 0.25 (95% CI: 0.08, 0.43) mmol/L in TG. GLMM indicated that, after adjusting for confounders, higher blood cadmium exposure levels were associated with increased risks of hypercholesterolemia, hypertriglyceridemia, mixed hyperlipidemia, and high LDL-C ( P<0.05). Further analysis by quartiles showed that, compared to the blood cadmium Q1 exposure group, the OR value (95% CI) for the Q4 group was 1.53 (1.12, 2.08) for hypercholesterolemia, 1.54 (1.09, 2.17) for hypertriglyceridemia, 2.24 (1.47, 3.40) for mixed hyperlipidemia, and 1.49 (1.07, 2.09) for high LDL-C. Conclusion:The cadmium internal exposure levels are associated with blood lipid profile levels as well as the incidence of dyslipidemia in Chinese adults aged 18 to 79.

Objective:To investigate the clinical value of enhanced magnetic resonance imaging (MRI)-based deep learning model in preoperative prediction of proliferative hepatocellular carcinoma (HCC).Methods:The retrospective cohort study was conducted. The clinical data of 906 HCC patients who were admitted to The First Affiliated Hospital of Army Medical University and The Second Affiliated Hospital of Chongqing Medical University from May 2017 to October 2022 were collected. There were 769 males and 137 females, aged (53.2±10.9)years. Of the 906 patients, 815 cases who were admitted to The First Affiliated Hospital of Army Medical University were divided into the training set of 634 patients and the internal validation set of 181 patients using a random number table method with a ratio of 8:2, and 91 patients who were admitted to The Second Affiliated Hospital of Chongqing Medical University were divided into the external validation set. The training set was used to construct the prediction model, while the validation set was used to validate the prediction model. Observation indicators: (1) analysis of factors influencing the pathological classification of HCC patients; (2) deep learning imaging features of HCC patients; (3) evaluation of the efficacy of prediction model for proliferative HCC; (4) validation of the prediction model for proliferative HCC; (5) prognosis of HCC patients. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. Multivariate analysis was conducted using the binary Logistic regression model. The model perfor-mance was evaluated through five-fold cross-validation, and receiver operating characteristic (ROC) curve was plotted to assess the diagnostic value of the model based on the area under curve (AUC), sensitivity, and specificity. The Delong test was used to compare the diagnostic performance of models. The Hosmer-Lemeshow test was employed to evaluate the calibration of models. The optimal cutoff value of the prediction model was determined by the maximum Youden index, with the value >0.175 indicating high-risk patients and value ≤0.175 indicating low-risk patients.The Kaplan-Meier method was used to calculate the survival rate and the Log-rank test was used for survival analysis. Results:(1) Analysis of factors influencing the pathological classification of HCC patients. Of 634 patients in the training set, there were 190 cases of proliferative HCC and 444 cases of non-proliferative HCC. Results of multivariate analysis showed that alpha fetoprotein (AFP) ≥400 μg/L and tumor diameter >5 cm were independent risk factors for pathological type of HCC as proli-ferative [ odds ratio=1.73, 1.88, 95% confidence interval ( CI) as 1.19-2.50, 1.30-2.71, P<0.05]. (2) Deep learning imaging features of HCC patients. In the training set of 634 patients, the probability predicted by MRI-based deep learning model was 84.8%(30.5%,95.4%) for proliferative HCC and 5.8%(3.2%,12.5%) for non-proliferative HCC, showing a significant difference between them ( Z=-16.01, P<0.05). (3) Evaluation of the efficacy of prediction model for proliferative HCC. In the training set, the AUC of clinical prediction model for proliferative HCC was 0.63(95% CI as 0.59-0.68, P<0.05), with sensitivity of 54.74% and specificity of 64.19%. The AUC of MRI-based deep learning prediction model was 0.90(95% CI as 0.87-0.93, P<0.05), with sensitivity of 80.53% and specificity of 86.94%. The AUC of combined MRI-based deep learning with clinical prediction model was 0.90 (95% CI as 0.87-0.93, P<0.05), with sensitivity of 83.16% and specificity of 86.04%. Results of Delong test showed that there was a significant difference between the combined MRI-based deep learning with clinical prediction model and the clinical prediction model ( P<0.05), and there was no signifi-cant difference between the combined MRI-based deep learning with clinical prediction model and the MRI-based deep learning prediction model ( P>0.05). Results of Hosmer-Lemeshow test showed good calibration for the clinical prediction model, the MRI-based deep learning prediction model and the combined MRI-based deep learning with clinical prediction model ( χ2=0.84, 6.38, 3.93, P>0.05), indicating that the predicted probabilities of these three prediction models matched the actual risk well. (4) Validation of the prediction model for proliferative HCC. Results of validation of the prediction model in internal validation set showed the AUC of MRI-based deep learning prediction model for proliferative HCC was 0.84(95% CI as 0.77-0.91, P<0.05), with sensitivity of 82.35% and specificity of 77.69%. Results of validation of the prediction model in external validation set showed the AUC of MRI-based deep learning prediction model for proliferative HCC was 0.81(95% CI as 0.71-0.92, P<0.05), with sensitivity of 70.00% and specificity of 81.69%. (5) Prognosis of HCC patients. Of the 906 patients, the 1-, 3-, and 5-year recurrence-free survival rates for 645 proliferative HCC patients were 56.9%, 31.4%, and 29.1%, respectively, and the 1-, 3-, and 5-year recurrence-free survival rates for 261 non-proliferative HCC patients were 88.8%, 68.6%, and 56.0%, respectively. There were significant differences in recurrence-free survival time between proliferative HCC and non-proliferative HCC patients of the training set, internal validation set and external validation set ( P<0.05). The 1-, 3-, 5-year recurrence-free survival rates for 331 high-risk HCC patients were 64.6%, 50.4%, 43.6%, versus 88.5%, 71.9%, 62.7% for 575 low-risk HCC patients. There were significant differences in recurrence-free survival time between high-risk HCC patients and low-risk HCC patients of the training set, internal validation set and external validation set ( P<0.05). Conclusion:The MRI-based deep learning model can effectively predict proliferative HCC and recurrence-free survival of patients before the surgery.

Purpose To explore the predictive value of CT-enhanced for tumor mutation burden(TMB)in hepatocellular carcinoma(HCC).Materials and Methods A total of 22 patients with pathologically confirmed HCC after undergoing radical resection in the First Affiliated Hospital,Army Medical University(Third Military Medical University)from January 2020 to January 2023 were collected,all of whom were quantified for TMB.Clinical,laboratory tests,CT imaging characteristics and follow-up of patients were recorded.Variables with P<0.2 were screened by stepwise regression analysis for independent risk factors for TMB.The area under the curve of receiver operating characteristic was used to assess the diagnostic efficacy.Results High TMB level was a risk factor for disease-free survival after HCC surgery(HR=1.115,P<0.05).According to the optimal cut-off value,TMB was classified into a high-risk group(>9.25 mutation/Mb)and low-risk group(≤9.25 mutation/Mb).Univariate analysis of intratumor ischemia or necrosis was statistically different between the high-risk and low-risk groups(P=0.005),and only intratumor ischemia or necrosis was an independent risk factor for predicting high TMB level by stepwise regression analysis(P<0.05).The area under the curve for predicting disease-free survival was 0.833(95%CI 0.615-0.956,P<0.001),with a sensitivity of 100.0%and a specificity of 66.7%.Conclusion High TMB level is associated with poor prognosis after HCC resection.Intratumor ischemia or necrosis have certain clinical value in predicting high TMB level,and are expected to provide a reference basis for personalized diagnosis and treatment of HCC patients.

Objective:To evaluate the clinical efficacy of transoral robotic surgery (TORS) in the treatment of malignant tongue base tumors.Methods:A multicenter study was conducted to collect and analyze the clinical data of patients with malignant tongue base tumors who underwent TORS at five otolaryngology-head and neck surgery centers in China, including Eye Ear Nose and Throat Hospital of Fudan University, Sun Yat-sen University Cancer Center, Tongji Hospital of Huazhong University of Science and Technology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, and the First Affiliated Hospital of China Medical University between January 2017 and January 2023. Among the patients, 38 were males and 11 were females, with a mean age of 59.0±8.8 years. Baseline characteristics, complications, and follow-up data were compared between groups. Independent sample t-tests or Mann-Whitney U tests was used for comparisons of continuous variables; chi-square tests or Fisher′s exact tests was applied for categorical variables. Survival analysis was performed using the Kaplan-Meier method to calculate overall survival and disease-free survival, and differences between groups were compared using the log-rank test. Results:Among the 49 patients, 41 (83.7%) were diagnosed with squamous cell carcinoma (SCC), with a p16 positive rate of 51.2% (21/41). There were no statistically significant differences between the p16-positive group ( n=21) and the p16-negative group ( n=20) in age, sex, or postoperative bleeding (all P>0.05). However, there was a significant difference in TNM stage between the two groups ( χ2=14.556, P=0.020), with the p16-positive group predominantly in stage I (66.7%) and the p16-negative group primarily in stages Ⅲ and Ⅳ (40.0% and 30.0%, respectively). The postoperative tracheotomy rate was 30.6% (15/49), and the incidence of postoperative bleeding was 6.1% (3/49). The 1-year and 3-year overall survival rates were 98.0% and 92.5%, respectively, while, the 1-year and 3-year disease-free survival rates were 89.2% and 84.9%, respectively. No significant differences were observed between the p16-positive and p16-negative groups in 3-year overall survival (100% vs. 83.8%, χ2=1.093, P=0.518) or 3-year disease-free survival (68.2% vs. 88.9%, χ2=2.161, P=0.382). Conclusion:TORS for malignant tongue base tumors demonstrates high clinical safety and favorable oncological outcomes.

Objective:To investigate the status quo of occult hepatitis B virus infection (OBI) among blood donors in certain region and to explore the characteristics of amino acid variation between antibody to hepatitis B core antigen (HBcAb) and S region.Methods:The qualified blood samples from January 2019 to December 2022 were collected, and the OBI samples were screened by ELISA and HBV DNA. The OBI samples were quantitatively detected by HBcAb and HBV DNA and analyzed by HBV S region sequencing.Results:A total of 126 OBI positive samples were detected out of 188933 samples, the detection rate was 0.07%, and there was no significant difference in the detection rate of OBI between gender and blood donation times ( P>0.05). The highest OBI detection rate was found in 46-55 years old group (0.15%), other education group (0.35%) and occupational workers group (0.13%). The total detection rate of HBcAb (+ ) in 126 OBI samples was 90.48%. As the S/CO value of HBcAb (+ ) increased, the positive detection rate of HBV DNA also increased, and the correlation coefficient between HBcAb (+ ) and the quantitative value of HBV DNA was 0.782. The proportion of HBV B genotype in OBI blood donors was the highest (73.08%). A total of 15 OBI-related mutation sites were found, and there was no statistically significant difference among the mutation sites. Conclusions:OBI exists in blood donors in certain region, and HBcAb (+ ) is moderately positively correlated with the quantitative value of HBV DNA. HBV infection in OBI blood donors is mainly type B.

Objective:To evaluate the efficacy of gadolinium ethoxybenzyl- diethy-lenetriamine pentaacetic acid (Gd-EOB-DTPA) enhanced MRI features in the preoperative prediction of tertiary lymphoid structures (TLS) within hepatocellular carcinoma (HCC) lesions.Methods:This retrospective cross-sectional study included clinical and pathological data from 297 HCC patients treated at the Southwest Hospital, Army Medical University between June 2021 and November 2022. Based on postoperative pathology, patients were categorized into TLS-negative ( n=93) and TLS-positive ( n=204) groups. MRI features of HCC lesions using Gd-EOB-DTPA enhancement and relevant clinical data were analyzed. Intergroup comparisons of imaging features and laboratory findings were performed using independent sample t-test, Mann-Whitney U test, χ2 test, or Fisher exact test, as appropriate. The logistic regression analysis was conducted to identify independent predictors of TLS positivity. A predictive model was constructed and visualized using a nomogram. The model′s predictive performance and clinical utility were assessed using the receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). The area under the ROC curve (AUC) was compared using the DeLong test. Results:Significant differences were observed between the TLS-negative and TLS-positive groups in alpha-fetoprotein (AFP) levels, intratumoral hemorrhage, and peritumoral satellite nodules in the hepatobiliary phase ( P<0.05). Multivariate logistic regression identified intratumoral hemorrhage ( OR=0.123, 95% CI 0.070-0.216, P<0.001) and peritumoral satellite nodules in the hepatobiliary phase ( OR=0.236, 95% CI 0.093-0.596, P=0.002) as independent predictive factors for TLS-positivity. The imaging model based on these two features yielded an AUC of 0.764 (95% CI 0.709-0.809) for predicting TLS-positivity. When combined with AFP levels, the resulting clinical-imaging model achieved a superior AUC of 0.784 (95% CI 0.732-0.829), which was significantly higher than that of the imaging model alone ( Z=2.20, P=0.028). A nomogram was constructed based on the clinical-imaging model. The calibration curve demonstrated good predictive performance of the nomogram, and the DCA showed that the curve remained above the default line across a range of reasonable threshold probabilities, indicating that patients could derive clinical benefit. Conclusion:A nomogram model based on Gd-EOB-DTPA enhanced MRI features combined with AFP levels can effectively predict the presence of TLS in HCC.