Following the release of the Technical Requirements on Quality Control and Standard Establishment of Traditional Chinese Medicine Formula Granules by the National Medical Products Administration in 2021, Chinese Pharmacopoeia Commission has promulgated 296 national drug standards so far, and most provinces have started the work of establishing provincial standards as supplements. The promulgation of standards fostered high-quality development of the industry. Since the implementation of national and provincial standards for more than three years, enterprises have gained deep understanding and hands-on experiences on the characteristics, technical requirements, production process, and quality control of traditional Chinese medicine(TCM) formula granules. Meanwhile, challenges have emerged restricting the high-quality development of this industry, including how to formulate quality control strategies for medicinal materials and decoction pieces, how to reduce manufacturing costs, and how to improve the pass rate and product stability under high standards. Based on the work experiences from standard management and process research, this article analyzed the distribution of sources, processing methods, dry extract rate ranges, process requirements for volatile oil-containing decoction pieces, control measures of safety indices, characteristics and trends of setting characteristic chromatograms or fingerprints, characteristics and trends of setting content ranges, and main differences between national standards and provincial standards. On the one hand, this article aims to present main characteristics for deeply understanding different indicators in standards and provide basic ideas for establishing quality and process control systems. On the other hand, from the perspective of industrial practice, suggestions are put forward on the important aspects that need to be focused on in the quality and process control of TCM formula granules.
Drugs, Chinese Herbal/chemistry* ; Quality Control ; Medicine, Chinese Traditional/standards* ; China ; Drug Industry/standards*

OBJECTIVE: To investigate the clinical features and prognosis of central nervous system (CNS) invasion in peripheral T-cell lymphoma (PTCL) and construct a risk prediction model for CNS invasion. METHODS: Clinical data of 395 patients with PTCL diagnosed and treated in the First Affiliated Hospital of Zhengzhou University from 1st January 2013 to 31st December 2022 were analyzed retrospectively. RESULTS: The median follow-up time of 395 PTCL patients was 24(1-143) months. There were 13 patients diagnosed CNS invasion, and the incidence was 3.3%. The risk of CNS invasion varied according to pathological subtype. The incidence of CNS invasion in patients with anaplastic large cell lymphoma (ALCL) was significantly higher than in patients with angioimmunoblastic T-cell lymphoma (AITL) (P <0.05). The median overall survival was significantly shorter in patients with CNS invasion than in those without CNS involvement, with a median survival time of 2.4(0.6-127) months after diagnosis of CNS invasion. The results of univariate and multivariate analysis showed that more than 1 extranodal involvement (HR=4.486, 95%CI : 1.166-17.264, P =0.029), ALCL subtype (HR=9.022, 95%CI : 2.289-35.557, P =0.002) and ECOG PS >1 (HR=15.890, 95%CI : 4.409-57.262, P <0.001) were independent risk factors for CNS invasion in PTCL patients. Each of these risk factors was assigned a value of 1 point and a new prediction model was constructed. It could stratify the patients into three distinct groups: low-risk group (0-1 point), intermediate-risk group (2 points) and high-risk group (3 points). The 1-year cumulative incidence of CNS invasion in the high-risk group was as high as 50.0%. Further evaluation of the model showed good discrimination and accuracy, and the consistency index was 0.913 (95%CI : 0.843-0.984). CONCLUSION The new model shows a precise risk assessment for CNS invasion prediction, while its specificity and sensitivity need further data validation.
Humans ; Lymphoma, T-Cell, Peripheral/pathology* ; Prognosis ; Retrospective Studies ; Central Nervous System Neoplasms/pathology* ; Neoplasm Invasiveness ; Male ; Female ; Central Nervous System/pathology* ; Middle Aged ; Adult

The rapid development of artificial intelligence (AI), especially generative AI (GenAI), has already brought, and will continue to bring, revolutionary changes to our daily production and life, as well as create new opportunities and challenges for diagnostic and therapeutic practices in the medical field. Haihe Hospital of Tianjin University collaborates with the National Supercomputer Center in Tianjin, Tianjin University, and other institutions to carry out research in areas such as smart healthcare, smart services, and smart management. We have conducted research and development of a full-process disease diagnosis and treatment assistance system based on GenAI in the field of smart healthcare. The development of this project is of great significance. The first goal is to upgrade and transform the hospital's information center, organically integrate it with existing information systems, and provide the necessary computing power storage support for intelligent services within the hospital. We have implemented the localized deployment of three models: Tianhe "Tianyuan", WiNGPT, and DeepSeek. The second is to create a digital avatar of the chief physician/chief physician's voice and image by integrating multimodal intelligent interaction technology. With generative intelligence as the core, this solution provides patients with a visual medical interaction solution. The third is to achieve deep adaptation between generative intelligence and the entire process of patient medical treatment. In this project, we have developed assistant tools such as intelligent inquiry, intelligent diagnosis and recognition, intelligent treatment plan generation, and intelligent assisted medical record generation to improve the safety, quality, and efficiency of the diagnosis and treatment process. This study introduces the content of a full-process disease diagnosis and treatment assistance system, aiming to provide references and insights for the digital transformation of the healthcare industry.
Artificial Intelligence ; Humans ; Delivery of Health Care ; Generative Artificial Intelligence

Objective:To develop a treatment-related quality of life scale for Chinese patients with multiple myeloma (MM) and to test its reliability and validity.Methods:The initial scale was constructed through a literature search, Delphi expert correspondence, and cognitive testing. This study conducted a preliminary survey of 379 patients with MM and a formal survey of 865 patients from the hematology departments of 155 hospitals nationwide from February 2024 to March 2024. The final scale was obtained after conducting item analysis and reliability and validity tests on the initial scale.Results:The constructed scale contains 36 items covering six domains: physiological, psychological, social, treatment side effects, general health, and others. In the preliminary survey, the Cronbach’s alpha coefficient of each item ranged from 0.597 to 0.939, and the test-retest reliability was 0.747 ( P<0.001). Exploratory factor analysis extracted eight common factors with a cumulative variance contribution of 60.058%. In the formal survey, the Cronbach’s alpha coefficient of each item ranged from 0.484 to 0.930, and the test-retest reliability was 0.835 ( P<0.001). Confirmatory factor analysis revealed a comparative fit index of 0.750, a root-mean-square error of approximation of 0.090, and a root-mean-square residual of 0.067. Conclusion:The treatment-related quality of life scale for Chinese patients with MM designed in this study exhibited good reliability and validity, reflecting the impact of treatment on the quality of life of patients. This scale can provide a reference to clinicians for assessing the disease status of patients.

Objective To explore the relationship between C-reactive protein/albumin ratio(CAR)and the disease severity in patients with severe pneumonia,and its predictive value for 28-day mortality risk.Methods A retrospective analysis was conducted on 152 patients with severe pneumonia admitted to Fuyang People's Hospital from January 2020 to January 2022.They were divided into non-critical illness group(n=51),critical illness group(n=63),and extremely critical illness group(n=38)based on the disease severity.The clinical data such as age and gender of patients was collected,and Pearson correlation analysis was used to explore the correlation between CAR and the severity of illness[determined by Acute Physiology and Chronic Health Evaluation Ⅱ(APACHE Ⅱ)score].Multivariate logistic regression was employed to identify independent influencing factors of the severity of illness.According to the survival status of patients after 28 days of treatment,they were divided into survival group(n=107)and death group(n=45).CAR was categorized into quintiles(Q1-Q5),and multivariate logistic regression analysis was conducted to explore the correlation between CAR and 28-day mortality risk in severe pneumonia patients.A restricted cubic spline(RCS)model was used to analyze the dose-response relationship between CAR and mortality risk.The predictive value of CAR and related indicators for patient mortality risk was evaluated using the receiver operating characteristic curve(ROC).Results CAR was significantly positively correlated with the severity of the disease(APACHE Ⅱ score)(r=0.716,P<0.05).Neutrophil/lymphocyte ratio(NLR),blood lactate(Lac),and high CAR were independent risk factors for the disease severity in patients with severe pneumonia(P<0.05).After adjusting for confounding factors,the mortality risk increased with the increase of CAR(P<0.05).Subgroup analysis of the screened confounding factors revealed that the correlation between CAR and 28-day mortality risk in severe pneumonia patients remained stable across different APACHE Ⅱ scores,GCS scores,SOFA scores,white blood cell counts(WBC),neutrophils(NEU),red cell volume distribution width(RDW),procalcitonin(PCT),and Lac,with interactions observed between NLR and Lac subgroups(P<0.05).The RCS model indicated that there was no non-linear dose-response relationship between CAR and 28-day mortality risk in patients with severe pneumonia of different genders.ROC curve analysis showed that CAR,Lac,and NLR had good predictive value for 28-day mortality in severe pneumonia patients,with the combined predictive efficacy being significantly higher than that of individual indicators.Conclusion There is a close relationship between CAR and the progression and prognosis of severe pneumonia,making it a new approach to assessing the severity of illness and predicting mortality risk in patients.

Objective To discuss the risk factors for impaired fasting glucose(IFG)and construct and validate a predictive model based on column charts of the risk of IFG occurrence.Methods This retrospective study included 3 037 individuals who underwent routine physical examinations at a hospital in Shenyang between August and December 2022.The population was randomly divided into a training group(n=2 126)and a validation group(n=911)in a 7∶3 ratio,and physical examination data were collected.LASSO regression analy-sis was used to screen predictive variables and logistic regression analysis was used to further screen and construct a column chart pre-dictive model.The validation group was used to conduct an internal validation of the feasibility of the model,and the area under the curve(AUC)of receiver operator characteristic(ROC)and goodness of fit tests were used to evaluate the model effectiveness.Results Among the 3 037 included individuals,2 880 did not experience IFG and 157 did.The results showed that age(OR=1.04,95％CI:1.02-1.05),body mass index(BMI,OR=1.10,95％CI:1.05-1.17),systolic blood pressure(SBP,OR=1.01,95％CI:1.00-1.03),triglycerides(TG,OR=1.20,95％CI:0.99-1.51),and a history of hypertension(OR=1.28,95％CI:1.04-1.59)were independent risk factors for IFG occurrence in this population.Based on these variables,a column chart prediction model was constructed.In the training group,the model predicted an AUC of 0.722(95％CI:0.68-0.77)for IFG occurrence,while in the validation group,it predicted an AUC of 0.907(95％CI:0.87-0.94)for IFG occurrence.The results of the Hosmer-Lemeshow goodness of fit test showed that the models of the training and validation groups were not significantly different(P＞0.05);that is,the actual probability was consistent with the prediction probability of the model,and the models calibration was good.Conclusion A risk prediction model for IFG occurrence that included five variables:age,BMI,SBP,TG,and history of hypertension could be construted.This model might help to identify high-risk groups for IFG early and allow for inter-vention in a timely manner.

Objective To discuss the risk factors for impaired fasting glucose(IFG)and construct and validate a predictive model based on column charts of the risk of IFG occurrence.Methods This retrospective study included 3 037 individuals who underwent routine physical examinations at a hospital in Shenyang between August and December 2022.The population was randomly divided into a training group(n=2 126)and a validation group(n=911)in a 7∶3 ratio,and physical examination data were collected.LASSO regression analy-sis was used to screen predictive variables and logistic regression analysis was used to further screen and construct a column chart pre-dictive model.The validation group was used to conduct an internal validation of the feasibility of the model,and the area under the curve(AUC)of receiver operator characteristic(ROC)and goodness of fit tests were used to evaluate the model effectiveness.Results Among the 3 037 included individuals,2 880 did not experience IFG and 157 did.The results showed that age(OR=1.04,95％CI:1.02-1.05),body mass index(BMI,OR=1.10,95％CI:1.05-1.17),systolic blood pressure(SBP,OR=1.01,95％CI:1.00-1.03),triglycerides(TG,OR=1.20,95％CI:0.99-1.51),and a history of hypertension(OR=1.28,95％CI:1.04-1.59)were independent risk factors for IFG occurrence in this population.Based on these variables,a column chart prediction model was constructed.In the training group,the model predicted an AUC of 0.722(95％CI:0.68-0.77)for IFG occurrence,while in the validation group,it predicted an AUC of 0.907(95％CI:0.87-0.94)for IFG occurrence.The results of the Hosmer-Lemeshow goodness of fit test showed that the models of the training and validation groups were not significantly different(P＞0.05);that is,the actual probability was consistent with the prediction probability of the model,and the models calibration was good.Conclusion A risk prediction model for IFG occurrence that included five variables:age,BMI,SBP,TG,and history of hypertension could be construted.This model might help to identify high-risk groups for IFG early and allow for inter-vention in a timely manner.

Objective:To develop a treatment-related quality of life scale for Chinese patients with multiple myeloma (MM) and to test its reliability and validity.Methods:The initial scale was constructed through a literature search, Delphi expert correspondence, and cognitive testing. This study conducted a preliminary survey of 379 patients with MM and a formal survey of 865 patients from the hematology departments of 155 hospitals nationwide from February 2024 to March 2024. The final scale was obtained after conducting item analysis and reliability and validity tests on the initial scale.Results:The constructed scale contains 36 items covering six domains: physiological, psychological, social, treatment side effects, general health, and others. In the preliminary survey, the Cronbach’s alpha coefficient of each item ranged from 0.597 to 0.939, and the test-retest reliability was 0.747 ( P<0.001). Exploratory factor analysis extracted eight common factors with a cumulative variance contribution of 60.058%. In the formal survey, the Cronbach’s alpha coefficient of each item ranged from 0.484 to 0.930, and the test-retest reliability was 0.835 ( P<0.001). Confirmatory factor analysis revealed a comparative fit index of 0.750, a root-mean-square error of approximation of 0.090, and a root-mean-square residual of 0.067. Conclusion:The treatment-related quality of life scale for Chinese patients with MM designed in this study exhibited good reliability and validity, reflecting the impact of treatment on the quality of life of patients. This scale can provide a reference to clinicians for assessing the disease status of patients.

Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.

Objective:To optimize the technical parameters related to the preparation of novel frozen human platelets and formulate corresponding protocol for its preparation.Methods:Novel frozen human platelets were prepared with O-type bagged platelet-rich plasma(PRP),the key technical parameters(DMSO addition,incubation time,centrifugation conditions,etc.)of the preparation process were optimized,and the quality of the frozen platelets was evaluated by routine blood tests,apoptosis rate,platelet activation rate and surface protein expression level.Results:In the preparation protocol of novel frozen human platelets,the operation of centrifugation to remove supernatant was adjusted to before the procedure of platelets freezing,and the effect of centrifugation on platelets was minimal when the centrifugation condition was 800 xg for 8 min.In addition,platelets incubated with DMSO for 30 min before centrifugation exhibited better quality after freezing and thawing.The indexes of novel frozen human platelets prepared with this protocol remained stable after long-term cryopreservation.Conclusion:The preparation technique of novel frozen human platelets was established and the protocol was formulated.It was also confirmed that the quality of frozen platelets could be improved by incubating platelets with DMSO for 30 min and then centrifuging them at 800 ×g for 8 min in the preparation of novel frozen human platelets.