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.

ObjectiveTo explore the effect and toxicity change rule of Aconiti Lateralis Radix Praeparata（ALRP） and Zingiberis Rhizoma（ZR） before and after compatibility， and to reveal the compatibility connotation of them. MethodSixty SD rats were randomly divided into blank group， blank-ALRP group， blank-ALRP-ZR group， model group， model-ALRP group and model-ALRP-ZR group， the latter three groups were injected with adriamycin via tail vein to establish the model of heart failure， and the former three groups were injected with the same amount of physiological saline via tail vein. The effects of ALRP single decoction and ALRP-ZR mixed decoction on biochemical indexes and myocardial histopathological morphology of normal rats and model rats were compared. Metabolomics analysis was performed on rat serum samples， principal component analysis（PCA） and orthogonal partial least squares-discriminant analysis（OPLS-DA） were used to screen the differential metabolites between groups， and the differential metabolic pathways were analyzed. Combined with network pharmacology technology， the metabolites and their associated targets and pathways related to enhancing anti-heart failure efficacy and reducing cardiotoxicity were screened before and after the compatibility of ALRP and ZR， the screened representative pathways were verified by Western blot. ResultCompared with the blank group， the model group showed significant increases in the contents of brain natriuretic peptide（BNP）， creatine kinase（CK）， lactate dehydrogenase（LDH） and cardiac troponin（cTn）-T（P<0.01）， the blank-ALRP group showed obvious increases in CK， LDH， and cTn-T contents（P<0.05， P<0.01）， while the normal-ALRP-ZR group showed a significant increase in CK content（P<0.01）. Compared with the blank-ALRP group， the blank-ALRP-ZR group showed a obvious decrease in LDH content（P<0.05）， and pathological sections showed that both decoctions could lead to myocardial histopathological damage in normal rats. Compared with the model group， the model-ALRP-ZR group showed obvious decreases in BNP， CK， LDH and cTn-T contents（P<0.05， P<0.01）， and the model-ALRP group showed obvious decreases in BNP， LDH and cTn-T contents（P<0.05， P<0.01）. Compared with the model-ALRP group， the model-ALRP-ZR group showed a significant decrease in CK content（P<0.01）， and both decoctions could improve the pathological morphology of myocardial tissue in the model rats. Metabolomics results showed that ALRP single decoction and ALRP-ZR mixed decoction could recover 422 and 459 metabolites in model rats， respectively. And the metabolic disruption of ALRP-ZR mixed decoction on normal rats was weaker than that of ALRP single decoction. The results of network pharmacological association analysis showed that in the aspect of ZR enhancing the anti-heart failure efficacy of ALRP， 3 metabolites such as deoxyuridylic acid were correlated to 56 metabolites， 82 targets and 13 pathways， including calcium signaling pathway， renin secretion， renin-angiotensin system， etc. In the aspect of ZR reducing the cardiotoxicity of ALRP， 3 metabolites such as tyrosol were associated with 24 metabolites， 55 targets and 14 pathways， including adrenergic signaling in cardiomyocytes and carbon metabolism and so on. Western blot results showed that the expression of angiotensin-converting enzyme（ACE）， angiotensin-converting enzyme 2（ACE2） and angiotensin Ⅱ（Ang Ⅱ） in myocardial tissues of rats from the model group was significantly elevated by comparing with the blank group（P<0.01）. Compared with the model group， the model-ALRP group and the model-ALRP-ZR group showed significantly decreased expression of ACE， ACE2 and Ang Ⅱ（P<0.01）. Compared with the model-ALRP group， the expression of ACE2 and AngⅡ was significantly decreased in the model-ALRP-ZR group. Compared with the blank group， the expression of extracellular signal regulated kinase（ERK）， protein kinase B（Akt） and cTn-I3 was significantly elevated in the blank-ALRP group and blank-ALRP-ZR group（P<0.01）. Compared with the blank-ALRP group， the blank-ALRP-ZR group showed decreased expression of ERK， Akt and cTn-I3， but there was no statistical significance. ConclusionTo a certain extent， the combination of ALRP and ZR shows synergistic relationship under pathological state， and attenuated effect of compatibility under normal physiological state， and the pharmacodynamic characteristics and compatibility relationship of ALRP and ZR are closely related to the physiological state.

The Omicron family of SARS-CoV-2 variants are currently driving the COVID-19 pandemic. Here we analyzed the clinical laboratory test results of 9911 Omicron BA.2.2 sublineages-infected symptomatic patients without earlier infection histories during a SARS-CoV-2 outbreak in Shanghai in spring 2022. Compared to an earlier patient cohort infected by SARS-CoV-2 prototype strains in 2020, BA.2.2 infection led to distinct fluctuations of pathophysiological markers in the peripheral blood. In particular, severe/critical cases of COVID-19 post BA.2.2 infection were associated with less pro-inflammatory macrophage activation and stronger interferon alpha response in the bronchoalveolar microenvironment. Importantly, the abnormal biomarkers were significantly subdued in individuals who had been immunized by 2 or 3 doses of SARS-CoV-2 prototype-inactivated vaccines, supporting the estimation of an overall 96.02% of protection rate against severe/critical disease in the 4854 cases in our BA.2.2 patient cohort with traceable vaccination records. Furthermore, even though age was a critical risk factor of the severity of COVID-19 post BA.2.2 infection, vaccination-elicited protection against severe/critical COVID-19 reached 90.15% in patients aged ≽ 60 years old. Together, our study delineates the pathophysiological features of Omicron BA.2.2 sublineages and demonstrates significant protection conferred by prior prototype-based inactivated vaccines.
Humans ; Aged ; Middle Aged ; COVID-19/prevention & control* ; SARS-CoV-2 ; Pandemics/prevention & control* ; China/epidemiology* ; Disease Outbreaks/prevention & control* ; Vaccination

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 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 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.