OBJECTIVE To formulate Guidelines for the standardized implementation of pharmacist-managed clinics （ 2026 edition ） in response to the challenges faced by such clinics in China， including uneven development， large discrepancies in service specifications， insufficient patient awareness， and limited medical insurance coverage. METHODS Led by the Pharmaceutical Affairs Professional Committee of the Chinese Hospital Association， the Evidence-based Pharmacy Professional Committee of the Chinese Pharmaceutical Association， and the Hospital Pharmacy Professional Committee of the Cross-strait Medical and Health Exchange Association， a total of 19 domestic hospital pharmacy experts were organized. Through a systematic review of national policies and literature research， current practical experience was summarized. Consensus on the contents of the guidelines was reached after in-depth discussions. RESULTS &CONCLUSIONS The guidelines covered five sections： definition and connotation of pharmacist-managed clinics， establishment requirements， implementation and management， post competency， and practical research. Firstly， the definition and connotation included three operational forms of pharmacist-managed clinics （independent mode， physician-pharmacist joint mode， and online pharmacist-managed clinic mode） and classified service modes （specialty-specific， drug-specific， and disease-specific pharmacist-managed clinics）. The establishment requirements were further refined， covering system construction （pharmaceutical service management system， quality control and assessment mechanism）， personnel qualifications （professional credentials， continuing education and professional training， etc）， service recipients， as well as service venues and facilities. Subsequently， the implementation and management of pharmacist-managed clinics were proposed， involving service procedures， intervention measures， documentation and records， patient education and follow-up， humanistic care， as well as risk management and quality control. Finally， post competency encompassed the competency requirements for pharmacists providing services in pharmacist-managed clinics， as well as the suggestions on teaching methods； practical research encouraged the conduct of high-quality pharmaceutical practice in the setting of pharmacist-managed clinics. The guidelines provide valuable guidance for the standardized implementation of pharmacist-managed clinics in China in terms of establishment， management， teaching， and research， fill the guideline gap in this field， and can promote the high-quality development of pharmacist-managed clinics.

OBJECTIVE To provide standardized guidance for the rational clinical use of antibody-based drugs for the treatment of myasthenia gravis， and to enhance the evidence-based system of guidelines and consensus in this field. METHODS The consensus expert team consisted of 71 multidisciplinary experts from 28 provinces/autonomous regions/municipalities directly under the Central Government. Evidence was systematically retrieved through multiple databases， drug package inserts， and official websites of international and national health administrative authorities， drug regulatory agencies， healthcare security departments， and related industry associations， up to April 30， 2025. Evidence was graded according to the 2014 version of JBI pre-grading system for evidence from intervention studies. Based on full consideration of the current best evidence and multidisciplinary expert experience， the expert consensus recommendations were formulated using a modified Delphi method. RESULTS The Evidence-based expert consensus on the clinical application and pharmaceutical management of antibody-based drugs for the treatment of myasthenia gravis standardized the key points of whole-process pharmaceutical management for four antibody-based drugs approved for marketing in the mainland of China for the treatment of myasthenia gravis （efgartigimod alfa， efgartigimod alfa/hyaluronidase， eculizumab， and rozanolixizumab）. It formulated 37 expert consensus recommendations covering nine pharmaceutical management aspects： drug suitability selection， medication in special populations， administration methods， drug storage， therapeutic drug monitoring and pharmacogenetic testing， immunization management， drug interactions， pharmaceutical care， and off-label drug use. CONCLUSIONS Based on the current best evidence and multidisciplinary expert experience， this consensus establishes a whole-process management framework for antibody-based drugs for the treatment of myasthenia gravis， from clinical application to pharmaceutical management. It provides a scientific basis for the rational and precise use of these drugs in clinical practice， effectively promotes the enhancement of pharmaceutical management efficiency， and helps improve the overall therapeutic benefits for patients.

OBJECTIVE To further standardize the technical operations and management processes throughout therapeutic drug monitoring （TDM）， clarify the clinical value of TDM implementation， improve the scientific validity and reliability of monitoring results， and provide a solid reference basis for the formulation and optimization of clinical individualized precision dosing regimens. METHODS The Guidelines for the Management of Therapeutic Drug Monitoring were formulated in accordance with the latest definition of guidelines by the Institute of Medicine of the National Academies and the standard guideline development methodology of the World Health Organization， and in compliance with the requirements of the appraisal of guidelines for research and evaluation. A modified Delphi method was adopted to establish the research question system； evidence-based medicine research methods were applied to systematically search multiple databases to screen the latest and most comprehensive evidence. Evidence was graded and evaluated based on the evidence grading system of the Chinese Evidence-Based Medicine Center， and the grading criteria for recommendation strength from the Oxford Centre for Evidence-Based Medicine were used to determine the recommendation strength. The recommendation opinions were formed through multidisciplinary expert consensus. RESULTS The Guidelines for the Management of Therapeutic Drug Monitoring cover four core modules， including TDM application indications， technical procedures， result interpretation and clinical application， and quality control， involving 18 primary research questions， 34 secondary research questions， and yield 82 recommendations. CONCLUSIONS The guidelines systematically standardize the key technical links and management requirements of the whole TDM process， provide scientific and operable standardized tools， help improve the standardization level of TDM work， promote the translation of monitoring results into clinical decision-making， and provide strong support for precision personalized medicine and ensuring the safety and rationality of medication use.

OBJECTIVE To standardize the main processes and related technical links of the clinical comprehensive evaluation of drugs， and provide guidance and reference for improving the quality of comprehensive evaluation evidence and its transformation and application value. METHODS The construction of Guideline for the Workflow of Clinical Comprehensive Evaluation of Drugs was based on the standard guideline formulation method of the World Health Organization （WHO）， strictly followed the latest definition of guidelines by the Institute of Medicine of the National Academy of Sciences of the United States， and conformed to the six major areas of the Guideline Research and Evaluation Tool Ⅱ. Delphi method was adopted to construct the research questions； research evidence was established by applying the research methods of evidence-based medicine. The evidence quality classification system of the Chinese Evidence-Based Medicine Center was adopted for evidence classification and evaluation. The recommendation strength was determined by the recommendation strength classification standard formulated by the Oxford University Evidence-Based Medicine Center， and the recommendation opinions were formed through the expert consensus method. RESULTS & CONCLUSIONS The Guideline for the Workflow of Clinical Comprehensive Evaluation of Drugs covers 4 major categories of research questions， including topic selection， evaluation implementation， evidence evaluation， and application and transformation of results. The formulation of this guideline has standardized the technical links of the entire process of clinical comprehensive evaluation of drugs， which can effectively guide the high-quality and high-efficient development of this work， enhance the standardized output and transformation application value of evaluation evidence， and provide high-quality evidence support for the scientific decision-making of health and the rationalization of clinical medication.

Objective:To construct a network calculator based on interpretable machine learning models to predict the risk of prolonged length of stay in patients with acute ischemic stroke (AIS), and to provide a tool for the development of individualized intervention plans for patients.Methods:Adopting a retrospective analysis method. The 537 patients with AIS admitted to the Nanyang Central Hospital from July 2022 to July 2024 were selected. Length of stay exceeding the median length of stay was defined as length of stay prolongation. Length of stay prolongation risk profile variables were screened by Boruta algorithm. The 537 patients were randomly divided into a training set (322 cases) and a test set (215 cases) in a 3:2 ratio according to the random number table method to construct and train nine machine learning models and perform tenfold cross-validation. The best predictive performance model was assessed using receiver operating characteristic (ROC) curve analysis and calculating the area under the curve (AUC). The predictive accuracy and clinical utility of the model was assessed using calibration curve, clinical impact curve and decision curve analyses. Additional interpretation and visualisation of the machine learning model using Shapley additive explanations (SHAP) bar charts, summary, dependency and force diagrams. A network calculator for predicting the risk of length of stay prolongation in patients with AIS was constructed using the corresponding R package.Results:Among 537 AIS patients, there were 312 males and 225 females with an age of (58.40 ± 9.00) years old. The incidence of length of stay prolongation was 43.0% (231/537). Boruta algorithm screened 10 characteristic variables. The results of ROC curve analysis showed that the AUC of the extreme gradient boosting (XGBoost) model was at the highest among the 9 machine learning models in 10 random samples. In the training and test sets, the calibration curve and clinical impact curve analysis showed that the C-index was 0.815 and 0.816, respectively, indicating high consistency between the predicted results of the XGBoost model and the actual observations. Decision curve analysis showed that the net clinical benefit was>0 when the risk threshold was>0.18 and >0.22, respectively, indicating that the model had a high application value in actual clinical decision-making. The SHAP bar graph showed the order of importance as pneumonia, urinary tract infection, age, myoglobin, triacylglycerol, neuron-specific enolase (NSE), hemoglobin, total cholesterol, homocysteine (HCY), and National Institute of Health Stroke Scale (NIHSS) scores. SHAP summary charts visualised the contribution of the 10 characteristic variables, which showed a 'bipolar distribution' phenomenon. SHAP dependency plots showed the dependency between the observed values of the 10 variables and the SHAP values, with the trend being most significant for patients with pneumonia. SHAP seeks to provide a local interpretation for individual samples, making the XGBoost model more transparent and interpretable. A web-based calculator (https://nomogram1203.shinyapps.io/LOS_web/) based on the interpretable XGBoost model dynamically quantifies the risk of prolonged length of stay in patients with AIS.Conclusions:Pneumonia, urinary tract infection, age, myoglobin, hemoglobin, total cholesterol, NSE, HCY, triacylglycerol, and NIHSS can effect length of stay prolongation in AIS patients, and a network calculator constructed based on the interpretable XGBoost model dynamically predicts the risk of length of stay prolongation in patients with AIS, which can help to achieve an accurate risk assessment for individual patients.

Objective:To construct a network calculator based on interpretable machine learning models to predict the risk of prolonged length of stay in patients with acute ischemic stroke (AIS), and to provide a tool for the development of individualized intervention plans for patients.Methods:Adopting a retrospective analysis method. The 537 patients with AIS admitted to the Nanyang Central Hospital from July 2022 to July 2024 were selected. Length of stay exceeding the median length of stay was defined as length of stay prolongation. Length of stay prolongation risk profile variables were screened by Boruta algorithm. The 537 patients were randomly divided into a training set (322 cases) and a test set (215 cases) in a 3:2 ratio according to the random number table method to construct and train nine machine learning models and perform tenfold cross-validation. The best predictive performance model was assessed using receiver operating characteristic (ROC) curve analysis and calculating the area under the curve (AUC). The predictive accuracy and clinical utility of the model was assessed using calibration curve, clinical impact curve and decision curve analyses. Additional interpretation and visualisation of the machine learning model using Shapley additive explanations (SHAP) bar charts, summary, dependency and force diagrams. A network calculator for predicting the risk of length of stay prolongation in patients with AIS was constructed using the corresponding R package.Results:Among 537 AIS patients, there were 312 males and 225 females with an age of (58.40 ± 9.00) years old. The incidence of length of stay prolongation was 43.0% (231/537). Boruta algorithm screened 10 characteristic variables. The results of ROC curve analysis showed that the AUC of the extreme gradient boosting (XGBoost) model was at the highest among the 9 machine learning models in 10 random samples. In the training and test sets, the calibration curve and clinical impact curve analysis showed that the C-index was 0.815 and 0.816, respectively, indicating high consistency between the predicted results of the XGBoost model and the actual observations. Decision curve analysis showed that the net clinical benefit was>0 when the risk threshold was>0.18 and >0.22, respectively, indicating that the model had a high application value in actual clinical decision-making. The SHAP bar graph showed the order of importance as pneumonia, urinary tract infection, age, myoglobin, triacylglycerol, neuron-specific enolase (NSE), hemoglobin, total cholesterol, homocysteine (HCY), and National Institute of Health Stroke Scale (NIHSS) scores. SHAP summary charts visualised the contribution of the 10 characteristic variables, which showed a 'bipolar distribution' phenomenon. SHAP dependency plots showed the dependency between the observed values of the 10 variables and the SHAP values, with the trend being most significant for patients with pneumonia. SHAP seeks to provide a local interpretation for individual samples, making the XGBoost model more transparent and interpretable. A web-based calculator (https://nomogram1203.shinyapps.io/LOS_web/) based on the interpretable XGBoost model dynamically quantifies the risk of prolonged length of stay in patients with AIS.Conclusions:Pneumonia, urinary tract infection, age, myoglobin, hemoglobin, total cholesterol, NSE, HCY, triacylglycerol, and NIHSS can effect length of stay prolongation in AIS patients, and a network calculator constructed based on the interpretable XGBoost model dynamically predicts the risk of length of stay prolongation in patients with AIS, which can help to achieve an accurate risk assessment for individual patients.

In recent years,in order to meet urgent clinical needs alleviate the shortage of rare disease drugs in China,a series of policies encouraging the research and development of rare disease drugs has been introduced.Enzyme replacement therapy(ERT)is a standard treatment for rare congenital metabolic disorders,especially for enzyme deficiency diseases.Due to the complexity and heterogeneity of drug molecules for rare enzyme replacement therapy,the evaluation of animal models is a major challenge in non-clinical development and evaluation.This article introduces the latest research progress in the development of rare enzyme replacement therapy drugs at home and abroad,put forward some thoughts and suggestions on the non-clinical evaluation(pharmacology,pharmacokinetics,and toxicology)of such products based on representative literature,combined with review practice.

Objective: To evaluate the therapeutic efficacy of acupuncture plus Dang Gui Bu Xue Qu Feng Tang for benign essential blepharospasm (BEB). Methods: A prospective randomized controlled trial was performed. A total of 105 participants were randomized 1:1:1 into an acupuncture group, a herbal medicine group and an acupuncture plus herbal medicine group. Participants in the acupuncture group received manual acupuncture treatment, twice a week. Participants in the herbal medicine group received Dang Gui Bu Xue Qu Feng Tang, oral administration, once a day. Participants in the acupuncture plus herbal medicine group received both treatments. The therapeutic effects of the three groups were evaluated after four weeks of treatment. The primary outcome was the Jankovic rating scale (JRS) score, and the secondary outcome was the blepharospasm disability index (BSDI) score. Results: After four weeks of treatment, the JRS total scores significantly decreased in all three groups versus baseline (P<0.05). A greater reduction in the JRS total score was reported in participants in the acupuncture plus herbal medicine group (P<0.05), but there was no significant difference between the acupuncture group and the herbal medicine group (P>0.05). The acupuncture plus herbal medicine group had a greater decrease in the JRS severity score than the herbal medicine group (P<0.05). The reduction in the JRS frequency score was not significantly different among the three groups (P>0.05). The BSDI scores significantly decreased in all three groups versus baseline (P<0.05), but the reduction in the BSDI score was insignificantly different among the three groups (P>0.05). Conclusion: It is effective in the treatment of BEB either to use acupuncture and Dang Gui Bu Xue Qu Feng Tang alone or in combination. The combination therapy shows a more significant effect than either of the treatment alone.