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.

Osteoarthritis （OA） and stroke are common clinical diseases that reduce patients' quality of life and place a burden on families and society. Ruyi Zhenbaowan， a classic prescription in Tibetan medicine， have the functions of clearing heat， awakening the brain and opening orifices， relaxing tendons and promoting meridian circulation， and eliminating yellow water. Clinically， they are used to treat osteoarthritis， post-stroke sequelae， neuropathic pain， and other related conditions. Modern pharmacological studies have demonstrated their anti-inflammatory， analgesic， and nerve-repairing effects. However， current research remains insufficient regarding the appropriate indications， timing， and efficacy of this medicine in treating relevant diseases. To enhance clinicians' understanding of this medicine and promote its standardized and rational clinical use， a panel of national experts， including clinical specialists， Tibetan medicine practitioners， pharmacologists， and methodologists， formulated this consensus based on clinical experience and evidence-based practice. The Cochrane systematic review framework， the Grading of Recommendations Assessment， Development and Evaluation （GRADE） system， and the nominal group method were employed to generate seven graded recommendations and 19 consensus-based suggestions. These recommendations clearly define the key points in the clinical application of Ruyi Zhenbaowan， including therapeutic indications， dosage and administration， treatment duration， and medication safety. The consensus specifically addresses the clinical efficacy， appropriate timing of administration， dosage strategies， treatment cycles， and combination medication strategies for treating osteoarthritis and stroke and provides an overview of safety considerations. The aim is to provide standardized guidance for hospitals and healthcare institutions nationwide to ensure the rational application of Ruyi Zhenbaowan in the treatment of osteoarthritis and stroke， reduce medication-related risks， and further leverage its clinical advantages. This consensus has been approved and issued by the China Association of Chinese Medicine， with the standard number GS/CACM 369-2024.

Chemotherapy is currently the mainstay of systemic management for triple-negative breast cancer (TNBC), but chemoresistance significantly impacts patient outcomes. Our research indicates that Doxorubicin (Dox)-resistant TNBC cells exhibit increased glycolysis and ATP generation compared to their parental cells, with this metabolic shift contributing to chemoresistance. We discovered that ALKBH3, an m¹A demethylase enzyme, is crucial in regulating the enhanced glycolysis in Dox-resistant TNBC cells. Knocking down ALKBH3 reduced ATP generation, glucose consumption, and lactate production, implicating its involvement in mediating glycolysis. Further investigation revealed that aldolase A (ALDOA), a key enzyme in glycolysis, is a downstream target of ALKBH3. ALKBH3 regulates ALDOA mRNA stability through m¹A demethylation at the 3'-untranslated region (3'UTR). This methylation negatively affects ALDOA mRNA stability by recruiting the YTHDF2/PAN2-PAN3 complex, leading to mRNA degradation. The ALKBH3/ALDOA axis promotes Dox resistance both in vitro and in vivo. Clinical analysis demonstrated that ALKBH3 and ALDOA are upregulated in breast cancer tissues, and higher expression of these proteins is associated with reduced overall survival in TNBC patients. Our study highlights the role of the ALKBH3/ALDOA axis in contributing to Dox resistance in TNBC cells through regulation of ALDOA mRNA stability and glycolysis.

Objective To investigate the influence of herbal compatibility on the chemical composition of Banxia Houpo Decoction(BHD)using ultra-performance liquid chromatography-tandem mass spectrometry(UPLC-MS)coupled with multivariate statistical analysis,and to evaluate the neuroprotective effects of key differential components against neuroinflammation and neuronal injury using cellular models.Methods(1)UPLC-MS analysis of chemical constituents in co-decoction and separated decoction(individual herbs decocted separately then combined)of Banxia Houpo Decoction,followed by orthogonal partial least squares-discriminant analysis(OPLS-DA)to identify differential components before and after herbal compatibility(2)BV2 microglia were stimulated with lipopolysaccharide(LPS)to establish a neuroinflammation model.Cell viability was assessed using the Cell Counting Kit 8(CCK-8)assay.Nitric oxide(NO)levels were measured by the Griess method,while TNF-α and IL-1β concentrations were quantified via enzyme-linked immunosorbent assay(ELISA).(3)SH-SY5Y neuronal cells were co-cultured with conditioned medium from LPS-stimulated BV2 cells(LPS-CM)to model neuronal injury.Cell viability was evaluated using the CCK-8 assay.Results UPLC-MS/OPLS-DA identified 11 differential components between compatibility methods,with honokiol and magnolol showing significant post-compatibility increases.In the neuroinflammation model,LPS stimulation elevated NO,TNF-α and IL-1 β levels in BV2 cells,which were suppressed by 5,10 μg/mL honokiol or magnolol.In the neuronal injury model,LPS-CM induced SH-SY5Y apoptosis,while 5,10 μg/mL honokiol or magnolol attenuated this damage.Conclusion Herbal compatibility significantly enhances honokiol and magnolol content in BHD.These components inhibit microglial inflammatory responses and neuronal apoptosis,suggesting their role as primary active constituents mediating BHD's neuroprotective effects.

Purpose To evaluate the value of tumors invasion in the central airway(TICA)in predicting the severe immune checkpoint inhibitor-related pneumonitis(S-CIP)in lung cancer patients using propensity score matching(PSM).Materials and Methods The intact data of 162 consecutive lung cancer patients who received treatment with immune checkpoint inhibitors in Xi'an International Medical Center Hospital from September 2019 to March 2022 were retrospectively collected.Patients were divided into S-CIP group(23 cases)and non-S-CIP group(139 cases)according to the presence of S-CIP.The demographic information of the patients,including gender,age,history of smoking,thoracic radiotherapy histology,baseline lung diseases,classification,TNM stage,tumor location as well as TICA were collected.A binary Logistic regression was used to analyze the confounding factors and independent risk factors of S-CIP and to predict the development of S-CIP.A 1:1 matching was performed by the nearest neighbor method for PSM.The PSM was used to pair the two groups,and the value of TICA in predicting S-CIP before and after PSM was compared.The receiver operating characteristic curve and the area under the curve were used for model performance based on TICA.Results Before PSM,the proportion of baseline lung diseases(78.3%vs.32.4%,OR=6.802,P=0.001),thoracic radiotherapy history(69.6%vs.30.2%,OR=5.300,P=0.002)and TICA(65.2%vs.27.3%,OR=5.882,P=0.001)in the S-CIP group was higher than those in the non-S-CIP group,and were independent risk factor for predicting S-CIP.After PSM,20 patients were included in each group.The presence of TICA was higher in S-CIP group than that in the non-S-CIP group(60.0%vs.20.0%,OR=6.000,P=0.013).The area under the curves of Logistic regression model based on TICA was 0.700(95%CI 0.534-0.866).Conclusion TICA is an independent risk factor for development of S-CIP,which has moderate degree of accuracy in predicting S-CIP,can be used for risk prediction and early intervention to reduce the poor prognosis of S-CIP patients.

Objective:To establish the predictive model for major adverse cardiovascular events(MACE) following endovascular repair in elderly patients with abdominal aortic aneurysm(AAA).Methods:The clinical data and postoperative MACE were retrospectively collected from elderly patients with AAA who underwent their first endovascular aneurysm repair(EVAR)in Beijing Hospital and Tibet Autonomous Region People's Hospital between January 2016 and December 2023.Patients were randomly divided into training and validation cohorts at a ratio of 7∶3.Predictive models were using logistic regression, LASSO regression, random forest, linear discriminant analysis, na?ve Bayes, k-nearest neighbor algorithm, support vector machine, decision tree, and AdaBoost.Models were evaluated using receiver operating characteristic(ROC)curves.Results:A total of 171 elderly AAA patients were enrolled, aged 60 to 94 years(mean 73.0 ± 7.5 years), of whom 145 were male.MACE occurred after EVAR in 30 patients(17.5%). LASSO regression identified monocyte count, history of coronary artery disease, the ratio of maximum AAA diameter to body mass index(DBR), neutrophil-lymphocyte count ratio(NLR), and age as significant predictors, yielding an area under the ROC curve(AUC)of 0.816.Logistic regression achieved an AUC of 0.813 in the training cohort and 0.772 in the validation cohort.Among all models, AdaBoost demonstrated the best performance, with an AUC of 0.92 in the validation cohort.Conclusions:Age, monocyte count, DBR, NLR and creatinine could predict the occurrence of MACE after EVAR in AAA patients.The AdaBoost model provides the most accurate prediction of postoperative MACE.

Purpose To evaluate the value of tumors invasion in the central airway(TICA)in predicting the severe immune checkpoint inhibitor-related pneumonitis(S-CIP)in lung cancer patients using propensity score matching(PSM).Materials and Methods The intact data of 162 consecutive lung cancer patients who received treatment with immune checkpoint inhibitors in Xi'an International Medical Center Hospital from September 2019 to March 2022 were retrospectively collected.Patients were divided into S-CIP group(23 cases)and non-S-CIP group(139 cases)according to the presence of S-CIP.The demographic information of the patients,including gender,age,history of smoking,thoracic radiotherapy histology,baseline lung diseases,classification,TNM stage,tumor location as well as TICA were collected.A binary Logistic regression was used to analyze the confounding factors and independent risk factors of S-CIP and to predict the development of S-CIP.A 1:1 matching was performed by the nearest neighbor method for PSM.The PSM was used to pair the two groups,and the value of TICA in predicting S-CIP before and after PSM was compared.The receiver operating characteristic curve and the area under the curve were used for model performance based on TICA.Results Before PSM,the proportion of baseline lung diseases(78.3%vs.32.4%,OR=6.802,P=0.001),thoracic radiotherapy history(69.6%vs.30.2%,OR=5.300,P=0.002)and TICA(65.2%vs.27.3%,OR=5.882,P=0.001)in the S-CIP group was higher than those in the non-S-CIP group,and were independent risk factor for predicting S-CIP.After PSM,20 patients were included in each group.The presence of TICA was higher in S-CIP group than that in the non-S-CIP group(60.0%vs.20.0%,OR=6.000,P=0.013).The area under the curves of Logistic regression model based on TICA was 0.700(95%CI 0.534-0.866).Conclusion TICA is an independent risk factor for development of S-CIP,which has moderate degree of accuracy in predicting S-CIP,can be used for risk prediction and early intervention to reduce the poor prognosis of S-CIP patients.

Objective:To establish the predictive model for major adverse cardiovascular events(MACE) following endovascular repair in elderly patients with abdominal aortic aneurysm(AAA).Methods:The clinical data and postoperative MACE were retrospectively collected from elderly patients with AAA who underwent their first endovascular aneurysm repair(EVAR)in Beijing Hospital and Tibet Autonomous Region People's Hospital between January 2016 and December 2023.Patients were randomly divided into training and validation cohorts at a ratio of 7∶3.Predictive models were using logistic regression, LASSO regression, random forest, linear discriminant analysis, na?ve Bayes, k-nearest neighbor algorithm, support vector machine, decision tree, and AdaBoost.Models were evaluated using receiver operating characteristic(ROC)curves.Results:A total of 171 elderly AAA patients were enrolled, aged 60 to 94 years(mean 73.0 ± 7.5 years), of whom 145 were male.MACE occurred after EVAR in 30 patients(17.5%). LASSO regression identified monocyte count, history of coronary artery disease, the ratio of maximum AAA diameter to body mass index(DBR), neutrophil-lymphocyte count ratio(NLR), and age as significant predictors, yielding an area under the ROC curve(AUC)of 0.816.Logistic regression achieved an AUC of 0.813 in the training cohort and 0.772 in the validation cohort.Among all models, AdaBoost demonstrated the best performance, with an AUC of 0.92 in the validation cohort.Conclusions:Age, monocyte count, DBR, NLR and creatinine could predict the occurrence of MACE after EVAR in AAA patients.The AdaBoost model provides the most accurate prediction of postoperative MACE.

In the field of dental aesthetics,digital aesthetic design plays a crucial role in helping dentists to predict treatment outcomes vis-ually,as well as in enhancing the consistency of knowledge and understanding of aesthetic goals between dentists and patients.It serves as the foundation for achieving ideal aesthetic effects.However,there is no clear standard for this digital process currently in China and abroad.Many dentists lack of systematic understanding of how to carry out digital aesthetic design for treatment.To establish standardized processes for dental aesthetic design and to improve the homogeneity of treatment outcomes,Chinese Society of Digital Dental Industry(CSD-DI)convened domestic experts in related field to compile this consensus.This article elaborates on the key aspects of digital aesthetic data collection,integration steps,and the digital aesthetic design process.It also formulates a decision tree for dental aesthetics at macro level and outlines corresponding workflows for various clinical scenarios,serving as a reference for clinicians.