Objective To investigate the influence of plasma infusion during orthotopic liver transplantation on the incidence of acute kidney injury (AKI) in recipients. Methods Cinical data of 473 liver transplant recipients who underwent orthotopic liver transplantation at Beijing Chaoyang Hospital Affiliated to Capital Medical University from January 2016 to December 2020 were retrospectively collected. The study included 354 recipients who received plasma infusion during the operation (plasma group) and 119 recipients who did not receive plasma infusion during the operation (control group). Preoperative conditions, donor conditions, intraoperative conditions, main outcome indicators and secondary outcome indicators of the two groups were analyzed and compared. Receiver operating characteristic curve was drawn to calculate the maximum cut-off value of intraoperative plasma infusion volume that affected the occurrence of AKI within 7 days after surgery. Logistic regression analysis was performed to analyze the correlation between intraoperative plasma infusion volume and the incidence of AKI within 7 days after surgery. Results Before propensity score matching, the incidence of AKI within 7 days after surgery and the incidence of grade Ⅲ AKI in the plasma group were higher than those in the control group (both P<0.05). After propensity score matching, 62 recipients were included in each group. There was no statistically significant difference in the incidence of AKI within 7 days after surgery between the plasma group and the control group, but the incidence of grade Ⅲ AKI within 7 days after surgery in the plasma group was higher than that in the control group (P=0.041). Logistic regression analysis showed that intraoperative plasma infusion volume >900 mL was a potential risk factor for AKI within 7 days after surgery (odds ratio=1.936, 95% confidence interval 1.193-3.142, P=0.007). There were no statistically significant differences in the incidence of 365-day postoperative fatality, reperfusion syndrome, and postoperative 30-day complications between the two groups before and after propensity score matching. In addition, the postoperative albumin, fibrinogen levels, and international normalized ratio in the plasma group were better than those in the control group before and after matching (all P<0.05). Conclusions Large amount of intraoperative plasma infusion is associated with an increased risk of grade Ⅲ AKI after orthotopic liver transplantation. Intraoperative plasma infusion volume >900 mL may increase the risk of AKI within 7 days after surgery.

ObjectiveTo explore the data standard system of electronic medical records in the field of rehabilitation, focusing on the terminology and coding standards, data structure, and key content categories of rehabilitation electronic medical records. MethodsBased on the Administrative Norms for the Application of Electronic Medical Records issued by the National Health Commission of China, the electronic medical record standard architecture issued by the International Organization for Standardization and Health Level Seven (HL7), the framework of the World Health Organization Family of International Classifications (WHO-FICs), Basic Architecture and Data Standards of Electronic Medical Records, Basic Data Set of Electronic Medical Records, and Specifications for Sharing Documents of Electronic Medical Records, the study constructed and organized the data structure, content, and data standards of rehabilitation electronic medical records. ResultsThe data structure of rehabilitation electronic medical records should strictly follow the structure of electronic medical records, including four levels (clinical document, document section, data set and data element) and four major content areas (basic information, diagnostic information, intervention information and cost information). Rehabilitation electronic medical records further integrated information related to rehabilitation needs and characteristics, emphasizing rehabilitation treatment, into clinical information. By fully applying the WHO-FICs reference classifications, rehabilitation electronic medical records could establish a standardized framework, diagnostic criteria, functional description tools, coding tools and terminology index tools for the coding, indexing, functional description, and analysis and interpretation of diseases and health problems. The study elaborated on the data structure and content categories of rehabilitation electronic medical records in four major categories, refined the granularity of reporting rehabilitation content in electronic medical records, and provided detailed data reporting guidance for rehabilitation electronic medical records. ConclusionThe standardization of rehabilitation electronic medical records is significant for improving the quality of rehabilitation medical services and promoting the rehabilitation process of patients. The development of rehabilitation electronic medical records must be based on the national and international standards. Under the general electronic medical records data structure and standards, a rehabilitation electronic medical records data system should be constructed which incorporates core data such as disease diagnosis, functional description and assessment, and rehabilitation interventions. The standardized rehabilitation electronic medical records scheme constructed in this study can support the improvement of standardization of rehabilitation electronic medical records data information.

Objective To construct large medical model named by "Huaxi HongYi"and explore its application effectiveness in assisting medical record generation. Methods By the way of a full-chain medical large model construction paradigm of "data annotation - model training - scenario incubation", through strategies such as multimodal data fusion, domain adaptation training, and localization of hardware adaptation, "Huaxi HongYi" with 72 billion parameters was constructed. Combined with technologies such as speech recognition, knowledge graphs, and reinforcement learning, an application system for assisting in the generation of medical records was developed. Results Taking the assisted generation of discharge records as an example, in the pilot department, after using the application system, the average completion times of writing a medical records shortened (21 min vs. 5 min) with efficiency increased by 3.2 time, the accuracy rate of the model output reached 92.4%. Conclusion It is feasible for medical institutions to build independently controllable medical large models and incubate various applications based on these models, providing a reference pathway for artificial intelligence development in similar institutions.

Objective To study the spatial distribution of the incidence of pulmonary tuberculosis in Hubei Province and its influencing factors, so as to improve the theoretical basis for scientific development of tuberculosis prevention and control measures in the future. Methods The data of reported incidence of tuberculosis and related influencing factors in various counties and districts of Hubei Province in 2020 were collected. Global Moran's I index, hotspot analysis and geographically weighted regression (GWR) model analysis were used to calculate the spatial autocorrelation of the incidence of tuberculosis, and to analyze the influencing factors affecting the incidence rate of tuberculosis. Results There were obvious regional differences in the space distribution of the incidence rate of tuberculosis. Hot spot analysis showed positive spatial correlation and obvious clustering. The GWR model (AICc=784.251) in this study had higher AICc value compared to the ordinary least squares regression (OLS) model (AICc=804.2585). The GWR model showed that the increase in the proportion of the population aged 65 and above and the proportion of the ethnic minority population had a significant promoting effect on the increase of the incidence rate of tuberculosis, and there was significant spatial heterogeneity. The effect of PM_2.5 concentration on the incidence rate of pulmonary tuberculosis varied in different regions, and the degree of effect was also different. Conclusion The proportion of people aged 65 and above and the proportion of ethnic minorities may significantly influence the incidence of pulmonary tuberculosis. The effect of PM_2.5 concentration varies in different regions, so targeted measures should be formulated according to the situation in different regions.

Objective To examine the effect of temperature on the risk of varicella in Jiangjin District, Chongqing, and to provide reference for formulating varicella prevention and control strategies. Methods Data on varicella cases from 2015 to 2020 were collected through the China Disease Prevention and Control Information System, while meteorological data for the same period were collected through the China Meteorological Data Sharing Service Network. A distributed lag non-linear model was employed to analyze the correlation between different lag days (lag) and temperature and the incidence of varicella, as well as their impact on sensitive population. Using the lowest risk temperature as a reference, the relative risk of extreme low temperature 8°C (P5) and extreme high temperature 31.5°C (P95) on the onset of varicella was estimated. Results Over the six-year period, Jiangjin District reported 7 547 varicella cases, predominantly among children aged 5-9, accounting for 40.64% (3 067 cases) of the total. The incidence of varicella exhibited obvious seasonality and a bimodal distribution pattern. The exposure-response curve between daily varicella cases and temperature was U-shaped. At a low temperature of 8°C with a 3-day lag, the general population exhibited the highest risk, with a relative risk (RR) of 1.43 (95% CI: 1.22-1.67). After a 7-day lag, the cumulative relative risk (CRR) escalated to 2.79 (95% CI: 1.95-3.98). Conversely, at a high temperature of 31.5°C with no lag, the peak risk yielded an RR of 1.77 (95% CI: 1.23-2.37), and a 7-day lag resulted in a CRR of 2.05 (95% CI: 1.47-2.86). The temperature had the greatest impact on the incidence of varicella in preschool children. At 8°C with a 7-day lag, the CRR of varicella incidence in preschool children was 2.34 (95% CI: 1.89-2.91), with an attributable fraction (AF) of temperature reaching 38.49% (95% CI: 34.45%-42.06%). Conclusion Exposure to both low and high temperature in Jiangjin District, Chongqing can increase the risk of varicella, and the effect is more pronounced in preschool children.

ObjectiveTo investigate the clinical features and outcomes of recompensation in patients with autoimmune hepatitis （AIH）-related decompensated cirrhosis， to identify independent predictive factors， and to construct a nomogram prediction model for the probability of recompensation. MethodsA retrospective cohort study was conducted among the adult patients with AIH-related decompensated cirrhosis who were admitted to The Fifth Medical Center of PLA General Hospital from January 2015 to August 2023 （n=211）. The primary endpoint was achievement of recompensation， and the secondary endpoint was liver-related death or liver transplantation. According to the outcome of the patients at the end of the follow-up， the patients were divided into the recompensation group （n=16） and the persistent decompensation group（n=150）.The independent-samples t test was used for comparison of normally distributed continuous data with homogeneity of variance， and the Mann-Whitney U rank sum test was used for comparison of non-normally distributed continuous data with heterogeneity of variance； the chi-square test or the Fisher’s exact test was used for comparison of categorical data between groups； the Kaplan-Meier method was used for survival analysis； the Cox proportional-hazards regression model was used to identify independent predictive factors， and a nomogram model was constructed and validated. ResultsA total of 211 patients were enrolled， with a median age of 55.0 years and a median follow-up time of 44.0 months， and female patients accounted for 87.2%. Among the 211 patients， 61 （with a cumulative proportion of 35.5%） achieved recompensation. Compared with the persistent decompensation group， the recompensation group had significantly higher white blood cell count， platelet count （PLT）， total bilirubin （TBil）， alanine aminotransferase （ALT）， aspartate aminotransferase （AST）， total bile acid， prothrombin time， international normalized ratio （INR）， SMA positive rate， Model for End-Stage Liver Disease （MELD） score， Child-Pugh score， and rate of use of glucocorticoids （all P0.05）， as well as significantly lower age at baseline， number of complications， and death/liver transplantation rate （all P0.05）. At 3 and 12 months after treatment， the recompensation group had continuous improvements in AST， TBil， INR， IgG， MELD score， and Child-Pugh score， which were significantly lower than the values in the persistent decompensation group （all P0.05）， alongside with continuous increases in PLT and albumin， which were significantly higher than the values in the persistent decompensation group （P0.05）. The multivariate Cox regression analysis showed that baseline ALT （hazard ratio ［HR］=1.067， 95% confidence interval ［CI］： 1.010 — 1.127， P=0.021）， IgG （HR=0.463，95%CI：0.258 — 0.833， P=0.010）， SMA positivity （HR=3.122，95%CI：1.768 — 5.515， P0.001）， and glucocorticoid therapy （HR=20.651，95%CI：8.744 — 48.770， P0.001） were independent predictive factors for recompensation， and the nomogram model based on these predictive factors showed excellent predictive performance （C-index=0.87，95%CI：0.84 — 0.90）. ConclusionAchieving recompensation significantly improves clinical outcomes in patients with AIH-related decompensated cirrhosis. Baseline SMA positivity， a high level of ALT， a low level of IgG， and corticosteroid therapy are independent predictive factors for recompensation. The predictive model constructed based on these factors can provide a basis for decision-making in individualized clinical management.

Continuous manufacturing, as an innovative pharmaceutical production model, offers advantages such as high production efficiency and ease of control compared to traditional batch production, aligning with the future trend of drug production moving toward greater efficiency and intelligence. However, the development of continuous manufacturing technology in wet granulation has been slow. On one hand, this is closely related to its high technical complexity, substantial equipment investment costs, and stringent process control requirements. On the other hand, the long-term use of the traditional batch production model has created strong path dependence, and the lack of mature standardized processes further increases the difficulty of technological transformation. To promote the deep integration of wet granulation technology with continuous manufacturing, this review systematically outlines the current application of wet granulation in continuous manufacturing. It focuses on the development of key technologies such as online detection, process modeling, and process scale-up, with the aim of providing a reference for process innovation and application in wet granulation.
Drug Compounding/instrumentation* ; Technology, Pharmaceutical/methods* ; Drugs, Chinese Herbal/chemistry* ; Models, Theoretical

[Objective] To establish a method for detecting the potency of recombinant human coagulation factor Ⅶa for injection. [Methods] By adding the sample and factor Ⅶ deficient plasma to the sample cup and activating the reaction with prothrombin time assay reagent (PT reagent), the coagulation time of the sample was determined by the change in magnetic bead swing amplitude in the sample cup. The logarithm of coagulation time was inversely proportional to the logarithm of human factor Ⅶa potency. [Results] Under the experimental conditions, the specificity of the methodology was evaluated through spiked recovery, and the recovery rates ranged from 90.0% to 110.0%. Within the range from 0.125 to 1.000 IU/mL, there was a good linear response between the potency and coagulation time of the standard and sample, with correlation coefficients r>0.99. As for the accuracy and repeatability, the recovery rates of various concentrations detected in the stock solution were 101.0%, 100.0% and 112.0%, respectively, with RSD values of 2.6%, 4.0% and 0.0%, respectively. The recovery rates of various concentrations in finished product testing were 104.0%, 94.7% and 112.0%, respectively, with RSD values of 1.9%, 2.4% and 0.0%, respectively. As for the intermediate precision, the RSD were 4.5% and 3.7%, respectively. After treated with sample diluent, the sample was tested at room temperature for 6 hours and still exhibited relatively stable biological activity. [Conclusion] This detection method is accurate, stable, easy to operate and highly automated, and is suitable for detecting the potency of recombinant human coagulation factor Ⅶa for Injection.

OBJECTIVE To construct and evaluate nomogram prediction model for refractory chemotherapy-induced nausea and vomiting （CINV）. METHODS The data of malignant tumor patients who received chemotherapy at the Third People’s Hospital of Zhengzhou from January 2017 to December 2023 were collected. These patients were categorized into the occurrence group and the non-occurrence group according to the occurrence of refractory CINV. Multivariate Logistic regression analysis was employed to screen predictive factors for refractory CINV and constructing a nomogram prediction model. Model performance was assessed via receiver operating characteristic curve analysis. Model calibration was evaluated using Bootstrap resampling. Decision curve analysis （DCA） was used to determine the clinical net benefit of three strategies under different risk thresholds. Clinical impact curves were utilized to assess the clinical value of the model at different risk thresholds. Shapley additive explanations （SHAP） analysis was performed to evaluate individual factor contributions to the predictive model. RESULTS A total of 388 patients were included， with 219 experiencing refractory CINV. Multivariate Logistic regression identified 11 predictive factors for refractory CINV， including gastrointestinal disease history， anticipated nausea and vomiting， chemotherapy-induced emetic risk classification， and electrolyte levels， etc. The model’s area under the curve was 0.80 [95% confidence interval （0.76， 0.84）]， with a mean error of 0.036. DCA demonstrated the prediction model had higher clinical net benefit when the risk threshold was between 0.05 and 0.85. SHAP analysis revealed the top three predictive factors as gastrointestinal disease history （0.924）， chemotherapy- induced emetic risk classification （0.866）， and electrolyte levels （0.581）. CONCLUSIONS Eleven factors， including gastrointestinal disease history， anticipated nausea and vomiting， chemotherapy-induced emetic risk classification， and electrolyte levels， are identified as predictors of refractory CINV. The model based on these factors has good predictive ability， which can be used to predict the risk of refractory CINV.