The Pharmacovigilance Guidelines for Clinical Application of Traditional Chinese Medicine Injections （hereinafter referred to as the Guidelines） were released by the China Association of Chinese Medicine， with the standard number T/CACM 1563.4—2024. It is the first specialized guideline in China on the approach to pharmacovigilance activities for the clinical application of traditional Chinese medicine injections （TCMIs）. The Guidelines were jointly developed by the Institute of Basic Research in Clinical Medicine， China Academy of Chinese Medical Sciences， along with 30 experts in TCM pharmacovigilance， clinical practice （TCM， as well as integrated traditional Chinese and Western medicine），and evidence-based medicine from across the country. This publication filled the gap in standard documents in this field， both domestically and internationally. The Guidelines were formulated according to GB/T1.1—2020 Directives for standardization—Part 1： Rules for the structure and drafting of standardizing documents， the WHO Handbook for Guideline Development，and other methodological norms. Based on international norms，national laws and regulations，and scientific research results in the field of pharmacovigilance， methods adopted included expert interviews，literature research，nominal group technique， and Delphi method. Then， key points for pharmacovigilance for TCM injections were summarized and clarified in the four critical sections of "monitoring"，"identification"，"assessment"，and "control". The development process of the Guidelines included project initiation， international registration， expert interviews， literature search， and evaluation. Based on the research results of these steps，a draft was formed and revised through multiple rounds of in-group expert discussion and peer evaluations by 56 external experts. After revisions by the working group based on the feedback， the final version was formed. The Guidelines came into effect on January 8，2024，providing suggestions and reference norms for pharmacovigilance in the clinical application of TCMIs. To further promote the application and popularization of the Guidelines and help pharmacovigilance personnel better understand the development process，this study elucidates the background，methodological framework，and key development steps of the Guidelines.

The Pharmacovigilance Guidelines for Clinical Application of Traditional Chinese Medicine Injections （hereinafter referred to as the Guidelines） were released by the China Association of Chinese Medicine， with the standard number T/CACM 1563.4—2024. It is the first specialized guideline in China on the approach to pharmacovigilance activities for the clinical application of traditional Chinese medicine injections （TCMIs）. The Guidelines were jointly developed by the Institute of Basic Research in Clinical Medicine， China Academy of Chinese Medical Sciences， along with 30 experts in TCM pharmacovigilance， clinical practice （TCM， as well as integrated traditional Chinese and Western medicine），and evidence-based medicine from across the country. This publication filled the gap in standard documents in this field， both domestically and internationally. The Guidelines were formulated according to GB/T1.1—2020 Directives for standardization—Part 1： Rules for the structure and drafting of standardizing documents， the WHO Handbook for Guideline Development，and other methodological norms. Based on international norms，national laws and regulations，and scientific research results in the field of pharmacovigilance， methods adopted included expert interviews，literature research，nominal group technique， and Delphi method. Then， key points for pharmacovigilance for TCM injections were summarized and clarified in the four critical sections of "monitoring"，"identification"，"assessment"，and "control". The development process of the Guidelines included project initiation， international registration， expert interviews， literature search， and evaluation. Based on the research results of these steps，a draft was formed and revised through multiple rounds of in-group expert discussion and peer evaluations by 56 external experts. After revisions by the working group based on the feedback， the final version was formed. The Guidelines came into effect on January 8，2024，providing suggestions and reference norms for pharmacovigilance in the clinical application of TCMIs. To further promote the application and popularization of the Guidelines and help pharmacovigilance personnel better understand the development process，this study elucidates the background，methodological framework，and key development steps of the Guidelines.

Ischemia-reperfusion injury (IRI) can lead to organ dysfunction and tissue necrosis in the liver, kidney, myocardium and spinal cord, and there is currently a lack of effective treatment options. Human umbilical cord mesenchymal stem cell (HUC-MSC) and their derivatives have anti-inflammatory, anti-apoptotic, reactive oxygen species scavenging, mitochondrial and endothelial function improvement properties, and are ideal gene therapy carrier cells, providing new possibilities for the treatment of IRI in different organs. This article reviews the concept and mechanisms of IRI, the biological characteristics of HUC-MSC and their derivatives and their comparison with mesenchymal stem cells from other sources, and the mechanisms of HUC-MSC in treating IRI in different organs. It also summarizes and analyzes the advantages and disadvantages of HUC-MSC in protecting different organs from IRI, and prospects future research directions to explore more valuable research paths.

Objective To compare the diagnostic performance of a multi-marker panel(copeptin,cardiac troponin T[cTnT],and heart-type fatty acid-binding protein[HFABP])with the single marker cTnT in the diagnosis of type 4a acute myocardial infarction(AMI),and explore the application value of combined detectionmodel with the multiple markers.Methods The enrolled non-AMI pa-tients underwent elective percutaneous coronary intervention(PCI)at Nanjing Medical University First Affiliated Hospital during the period from March to December 2022 and were assessed as postoperative elevation of cTnT above the 99th percentile upper reference limit(URL).According to the Fourth Universal Definition of Myocardial Infarction,the patients were divided into non-type 4a AMI group and type 4a AMI group based on whether type 4a AMI occurred after surgery.The concentrations of AMI biomarkers were meas-ured using a chemiluminescent immuno-gold nanoassembly immunosensor array(chemiluminescent immuno-Gold,ciGold).Receiver operating characteristic(ROC)curves were used to analyze the performance of the diagnostic models with single and combined cardiac biomarkers.The sensitivity and specificity were also obtained from the ROC curves,and the area under the ROC curve(AUCROC)was calculated to evaluate respective diagnostic value.Kappa analysis was used to assess the consistency between the results combined de-tection model of multiple biomarkers and the diagnosis based on the Fourth Universal Definition of Myocardial Infarction.Results In this study,a total of 65 patients were included in whom females accounted for 23.1％.The ROC curve indicated that the combined de-tection model of multiple cardiac biomarkers showed specificity of 96.5％,sensitivity of 92.3％,agreement rate of 94.6％,positive pre-dictive value of 92.3％,negative predictive value of 96.2％,and AUCROC of 0.979.The single cTnT diagnostic model showed specificity of 94.2％,sensitivity of 100％,agreement rate of 95.7％,positive predictive value of 100％,negative predictive value of 94.9％,and AUCROC of 0.987.Although the combined detection model of multiple biomarkers had lower sensitivity(P=0.011),it showed higher specificity(P=0.016).The analysis of AUCROC differences between the two diagnostic models showed P＞0.05,indicating no signifi-cantly statistical difference for the diagnostic accuracy.Kappa analysis demonstrated a strong consistency between the combined detec-tion model of multiple cardiac biomarkers and the diagnosis of type 4a AMI based on the Fourth Universal Definition of Myocardial In-farction with a Cohen's Kappa coefficient of 0.818.Conclusion The multi-marker combined detection model showed similar perform-ance of cTnT in diagnos of type 4a AMI with strong diagnostic consistency.However,the combined detection model exhibited an advan-tage of higher specificity.

Objective To explore the predictive value of serum uric acid levels for the occurrence of sarcopenia after hepatectomy in patients with primary liver cancer(PLC).Method A convenience sampling method was used to prospectively include 161 PLC patients who underwent liver resection surgery at Baoding NO.2 Central Hospital of Hebei Province from January 2019 to December 2021.They were divided into occurrence group and non occurrence group based on whether they had muscle deficiency.The clinical data,serum uric acid and other blood biochemical examination results were compared between the two groups,and the predictive value and influence of serum uric acid level on sarcopenia after hepatectomy in PLC patients were analyzed.Results Among the 158 PLC patients who underwent hepatectomy in the final inclusion of this study,34 patients developed postoperative sarcopenia,with an incidence rate of approximately 21.52％.The serum uric acid level(311.79±35.32)μmol/L in the occurrence group was higher than that in the non-occurrence group(280.52±31.15)μmol/L,the ALB level(31.59±5.73)g/L was lower than that in the non-occurrence group(35.63±5.13)g/L,and the proportion of postoperative adjuvant hepatic arterial infusion chemotherapy(HAIC)(38.24％)was higher than that in the non-occurrence group(20.16％),with statistical significant differences(P＜0.05).Multiple Logistic regression analysis showed that serum uric acid、ALB、postoperative adjuvant HAIC were associated with sarcopenia after hepatectomy in PLC patients(OR=0.853,1.035,11.189,95％CI:0.770-0.945,1.018-1.052,3.533-35.433,P＜0.05).The receiver operating characteristic curve(ROC)showed that the area under the curve(AUC)of serum uric acid in predicting sarcopenia after hepatectomy in PLC patients was 0.754(95％CI:0.657-0.850),which had certain predictive value.The nomogram showed that the C-index of the prediction model constructed by serum uric acid assisted other major clinical indicators to predict the occurrence of sarcopenia after hepatectomy in PLC patients was 0.847(95％CI:0.782-0.913),suggesting that the model had certain predictive value.The results of the decision curve showed that when the threshold was in the range of 0.00-1.00,the actual clinical net benefit rate of the model was always greater than 0,and the maximum net benefit rate was 0.215,suggesting that the model had good clinical application value.Conclusion The increase of serum uric acid level in PLC patients is a risk factor for postoperative sarcopenia.The detection of serum uric acid level is helpful to assist in the early prediction of the risk of sarcopenia.

Langerhans cell histiocytosis (LCH) is a rare disease characterized by the proliferation of Langerhans cells and the destruction of local tissue. LCH large occurs in children, whilst incidence of the elderly population is extremely low, and there are few related studies. LCH lesions can involve multiple organs and systems, including bone tissue, lymph nodes, skin, liver, and spleen. However, it is rare that multiple soft tissues are implicated for eldly patients with LCH and present with soft tissue mass as the main manifestation. Here is a report on the clinical features, treatment and prognosis of an elderly LCH with multiple soft tissue masses as the main manifestation, in order to provide clinical reference.

Objective:To investigate the value of number of negative lymph nodes (NLNs) in predicting the prognosis of patients with esophageal cancer after neoadjuvant therapy and the construction of nomogram prodiction model.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 924 patients with esophageal cancer after neoadjuvant therapy uploaded to the Surveillance, Epidemiology, and End Results Database of the National Cancer Institute from 2004 to 2015 were collected. There were 1 624 males and 300 females, aged 63 (range, 23?85)years. All 1 924 patients were randomly divided into the training dataset of 1 348 cases and the validation dataset of 576 cases with a ratio of 7:3 based on random number method in the R software (3.6.2 version). The training dataset was used to constructed the nomogram predic-tion model, and the validation dataset was used to validate the performance of the nomogrram prediction model. The optimal cutoff values of number of NLNs and number of examined lymph nodes (ELNs) were 8, 14 and 10, 14, respectively, determined by the X-tile software (3.6.1 version), and then data of NLNs and ELNs were converted into classification variables. Observation indicators: (1) clinicopathological characteristics of patients in the training dataset and the validation dataset; (2) survival of patients in the training dataset and the validation dataset; (3) prognostic factors analysis of patients in the training dataset; (4) survival of patients in subgroup of the training dataset; (5) prognostic factors analysis in subgroup of the training dataset; (6) construction of nomogram prediction model and calibration curve. Measurement data with normal distribution were represented as Mean± SD, and comparison between groups was conducted using the t test. Measurement data with skewed distribution were represented as M(range), and comparison between groups was conducted using the Mann-Whitney U test. Count data were described as absolute numbers, and comparison between groups was conducted using the chi-square test. The Kaplan-Meier method was used to draw survival curve and Log-Rank test was used for survival analysis. The COX proportional hazard model was used for univariate and multivariate analyses. Based on the results of multivariate analysis, the nomogram prediction model was constructed. The prediction efficacy of nomogram prediction model was evaluated using the area under curve (AUC) of the receiver operating characteristic curve and the Harrell′s c index. Errors of the nomogram prediction model in predicting survival of patients for the training dataset and the validation dataset were evaluated using the calibration curve. Results:(1) Clinicopathological characteristics of patients in the training dataset and the validation dataset. There was no significant difference in clinicopatholo-gical characteristics between the 1 348 patients of the training dataset and the 576 patients of the validation dataset ( P>0.05). (2) Survival of patients in the training dataset and the validation dataset. All 1 924 patients were followed up for 50(range, 3?140)months, with 3-year and 5-year cumulative survival rate as 59.4% and 49.5%, respectively. The 3-year cumulative survival rate of patients with number of NLNs as <8, 8?14 and >14 in the training dataset was 46.7%, 62.0% and 66.0%, respectively, and the 5-year cumulative survival rate was 38.1%, 52.1% and 59.7%, respectively. There was a significant difference in the survival of these patients in the training dataset ( χ2=33.70, P<0.05). The 3-year cumulative survival rate of patients with number of NLNs as <8, 8?14 and >14 in the validation dataset was 51.1%, 54.9% and 71.2%, respectively, and the 5-year cumulative survival rate was 39.3%, 42.5% and 55.7%, respectively. There was a significant difference in the survival of these patients in the validation dataset ( χ2=14.49, P<0.05). The 3-year cumulative survival rate of patients with number of ELNs as <10, 10?14 and >14 in the training dataset was 53.9%, 60.0% and 62.7%, respectively, and the 5-year cumulative survival rate was 44.7%, 49.1% and 56.9%, respectively. There was a significant difference in the survival of these patients in the training dataset ( χ2=9.88, P<0.05). The 3-year cumulative survival rate of patients with number of ELNs as <10, 10?14 and >14 in the validation dataset was 56.2%, 47.9% and 69.3%, respectively, and the 5-year cumula-tive survival rate was 44.9%, 38.4% and 51.9%, respectively. There was a significant difference in the survival of these patients in the validation dataset ( χ2=9.30, P<0.05). (3) Prognostic factors analysis of patients in the training dataset. Results of multivariate analysis showed that gender, neoadjuvant pathological (yp) T staging, ypN staging (stage N1, stage N2, stage N3) and number of NLNs (8?14, >14) were independent influencing factors for the prognosis of patients with esophageal cancer after neoadjuvant therapy ( hazard ratio=0.65, 1.44, 1.96, 2.41, 4.12, 0.69, 0.56, 95% confidence interval as 0.49?0.87, 1.17?1.78, 1.59?2.42, 1.84?3.14, 2.89?5.88, 0.56?0.86, 0.45?0.70, P<0.05). (4) Survival of patients in subgroup of the training dataset. Of the patients with NLNs in the training dataset, the 3-year cumulative survival rate of patients with number of NLNs as <8, 8?14 and >14 was 61.1%, 71.6% and 76.8%, respectively, and the 5-year cumulative survival rate was 50.7%, 59.9% and 70.1%, respectively. There was a significant difference in the survival of these patients in the training dataset ( χ2=12.66, P<0.05). Of the patients with positive lymph nodes in the training dataset, the 3-year cumulative survival rate of patients with number of NLNs as <8, 8?14 and >14 was 26.1%, 42.9% and 44.7%, respectively, and the 5-year cumulative survival rate was 20.0%, 36.5% and 39.3%, respectively. There was a significant difference in the survival of these patients in the training dataset ( χ2=20.39, P<0.05). (5) Prognostic factors analysis in subgroup of the training dataset. Results of multivariate analysis in patients with NLNs in the training dataset showed that gender, ypT staging and number of NLNs (>14) were independent influencing factors for the prognosis of patients with esophageal cancer after neoadju-vant therapy ( hazard ratio=0.67, 1.44, 0.56, 95% confidence interval as 0.47?0.96, 1.09?1.90, 0.41?0.77, P<0.05). Results of multi-variate analysis in patients with positive lymph nodes in the training dataset showed that race as others, histological grade as G2, ypN staging as stage N3 and number of NLNs (8?14, >14) were independent influencing factors for the prognosis of patients with esophageal cancer after neoadjuvant therapy ( hazard ratio=2.73, 0.70, 2.08, 0.63, 0.59, 95% confidence interval as 1.43?5.21, 0.54?0.91, 1.44?3.02, 0.46?0.87, 0.44?0.78, P<0.05). (6) Construction of nomogram prediction model and calibration curve. Based on the multivariate analysis of prognosis in patients of the training dataset ,the nomogram prediction model for the prognosis of patients with esophageal cancer after neoadju-vant treatment was constructed based on the indicators of gender, ypT staging, ypN staging and number of NLNs. The AUC of nomogram prediction model in predicting the 3-, 5-year cumulative survival rate of patients in the training dataset and the validation dataset was 0.70, 0. 70 and 0.71, 0.71, respectively. The Harrell′s c index of nomogram prediction model of patients in the training dataset and the validation dataset was 0.66 and 0.63, respectively. Results of calibration curve showed that the predicted value of the nomogram prediction model of patients in the training dataset and the validation dataset was in good agreement with the actual observed value. Conclusion:The number of NLNs is an independent influencing factor for the prognosis of esophageal cancer patients after neoadjuvant therapy, and the nomogram prediction model based on number of NLNs can predict the prognosis of esophageal cancer patients after neoadjuvant therapy.

Objective:To optimize the extraction process of Shangke Huoxue Granule.Methods:Taking the factors of extraction solvent multiple, extraction time and extraction times as investigation factors, and extraction amount of ferulic acid, paeoniflorin and the ratio of extraction as comprehensive evaluation indices, one-factor experimental design and central composite design-response surface methodology were adopted to optimize the extraction process of Shangke Huoxue Granule.Results:The binomial fitting equation was Y=96.16+2.42 A+0.63 B-3.76 AB-1.57 A2-1.87 B2 ( P<0.01). The optimal extraction process parameters were confirmed to be adding 16 times of water, 64 minutes each time, twice. The deviation rates between the measured values of three verification experiments and the predicted value were 2.00%, 3.23% and 0.66%. Conclusion:The established model of central composite design-response surface methodology has high predictability and the optimized extraction process is stable and feasible.

Objective:To evaluate the performance of machine learning (ML) based on automated breast volume scanner (ABVS) radiomics in distinguishing benign and malignant BI-RADS 4 lesions.Methods:Between May to December 2020, patients with BI-RADS 4 lesions from the Affiliated Hospital of Southwest Medical University (Center 1) and Guangdong Provincial Hospital of Traditional Chinese Medicine (Center 2) were prospectively collected and divided into training cohort (Center 1) and external validation cohort (Center 2). The radiomics features of BI-RADS 4 lesions were extracted from the axial, sagittal and coronal ABVS images by MaZda software. In the training cohort, 7 feature selection methods and thirteen ML algorithms were combined in pairs to construct different ML models, and the 6 models with the best performance were verified in the external validation cohort to determine the final ML model. Finally, the diagnostic performance and confidence (5-point scale) of radiologists (R1, R2 and R3, with 3, 6 and 10 years of experience, respectively) with or without the model were evaluated.Results:①A total of 251 BI-RADS 4 lesions were enrolled, including 178 lesions (91 benign, 87 malignant) in the training cohort and 73 lesions (44 benign, 29 malignant) in the external validation cases. ②In the training cohort, the 6 ML models (DNN-RFE, AdaBoost-RFE, LR-RFE, LDA-RFE, Bagging-RFE and SVM-RFE) had the best diagnostic performance, and their AUCs were 0.972, 0.969, 0.968, 0.968, 0.967 and 0.962, respectively. ③In the external validation cohort, the DNN-RFE still had the best performance with the AUC, accuracy, sensitivity, specificity, PPV and NPV were 0.886, 0.836, 0.934, 0.776, 86.8% and 82.5%, respectively. ④Before model assistance, R1 had the worst diagnostic performance with the accuracy, sensitivity, specificity, PPV and NPV were 0.680, 0.750, 0.640, 57% and 81%, respectively. After model assistance, the diagnostic performance of R1 was significantly improved ( P=0.039), and its diagnostic sensitivity, specificity, accuracy, PPV and NPV improved to 0.730, 0.810, 0.930, 68% and 94%; while the improvement of R2 and R3 were not significantly ( P=0.811, 0.752). Meanwhile, the overall diagnostic confidence of the 3 radiologists increased from 2.8 to 3.3 ( P<0.001). Conclusions:The performance of ML based on ABVS radiomics in distinguishing between benign and malignant BI-RADS 4 lesions is good, which may improve the diagnostic performance of inexperienced radiologists and enhance diagnostic confidence.

Hepatocellular carcinoma is a common malignant disease in clinical practice, and portal vein tumor thrombosis (PVTT) is one of the important factors affecting the prognosis of hepatocellular carcinoma. PVTT has strong oncologic characteristics and is highly susceptible to extrahepatic metastasis, complicating portal hypertension, leading to gastrointestinal bleeding or liver failure and causing death. In this paper, we review the formation mechanism of hepatocellular carcinoma combined with PVTT in terms of local anatomy, hemodynamics, molecular biology and tumor microenvironment to provide effective reference for clinical treatment.