Myasthenia gravis(MG)is an autoimmune disease characterized primarily by skeletal muscle weakness and,in severe cases,respiratory involvement.Western medical treatment predominantly relies on immunosuppressants,but long-term administration often leads to notable side effects.In contrast,traditional Chinese medicine(TCM)offers the advantage of multi-target interventions.However,the pathogenesis of MG has not been fully elucidated,and the establishment of animal models that accurately reflect the clinical characteristics of both Chinese and Western medicine is essential for mechanism research and new drug development.This paper systematically reviews the etiology and pathogenesis,diagnostic criteria,and progress of animal model research for MG from both Chinese and Western medicine perspectives.In Western medicine,the pathogenesis of MG is closely related to genetic susceptibility,environmental factors,and autoantibody-mediated postsynaptic membrane damage.In TCM,MG is classified under the category of"flaccidity syndrome",attributed to congenital deficiencies and acquired malnourishment.Western diagnostic criteria involve a combination of clinical symptoms,fatigue testing,serum antibody assays,and electrophysiological evaluation.In contrast,TCM diagnosis emphasizes the integration of primary and secondary symptoms with tongue and pulse pattern differentiation.Currently available animal models mainly include experimental autoimmune myasthenia gravis(EAMG)and passive transfer myasthenia gravis(PTMG).The Toredo acetylcholine receptor(AChR)induced EAMG model aligns well with Western diagnostic criteria,but poorly matches secondary symptoms in TCM.The synthetic AChR peptide model is widely used,but shows low conformity with TCM syndromes.Models induced by muscle-specific tyrosine kinase(MuSK),low-density lipoprotein receptor-related protein 4(LRP4),and transgenic models demonstrate high innovation but exhibit low clinical conformity.Evaluation of these models requires integration of behavioral,electrophysiological,and immunological indicators.However,a systematic framework for modelling TCM syndromes is still lacking.Future research should integrate TCM-based etiological modelling methods with the Western pathological mechanisms to construct disease-syndrome combination models.Additionally,it is crucial to establish a TCM syndrome evaluation system based on"validation by prescription",as well as to improve the scientific rigor and practicality of animal models by the incorporation of emerging technologies.This review provides a theoretical foundation for optimizing MG animal model design,advancing the research on the combination of Chinese and Western medicine,and supporting efficacy assessment and mechanism exploration of Chinese herbal prescriptions.

Myasthenia gravis(MG)is an autoimmune disease characterized primarily by skeletal muscle weakness and,in severe cases,respiratory involvement.Western medical treatment predominantly relies on immunosuppressants,but long-term administration often leads to notable side effects.In contrast,traditional Chinese medicine(TCM)offers the advantage of multi-target interventions.However,the pathogenesis of MG has not been fully elucidated,and the establishment of animal models that accurately reflect the clinical characteristics of both Chinese and Western medicine is essential for mechanism research and new drug development.This paper systematically reviews the etiology and pathogenesis,diagnostic criteria,and progress of animal model research for MG from both Chinese and Western medicine perspectives.In Western medicine,the pathogenesis of MG is closely related to genetic susceptibility,environmental factors,and autoantibody-mediated postsynaptic membrane damage.In TCM,MG is classified under the category of"flaccidity syndrome",attributed to congenital deficiencies and acquired malnourishment.Western diagnostic criteria involve a combination of clinical symptoms,fatigue testing,serum antibody assays,and electrophysiological evaluation.In contrast,TCM diagnosis emphasizes the integration of primary and secondary symptoms with tongue and pulse pattern differentiation.Currently available animal models mainly include experimental autoimmune myasthenia gravis(EAMG)and passive transfer myasthenia gravis(PTMG).The Toredo acetylcholine receptor(AChR)induced EAMG model aligns well with Western diagnostic criteria,but poorly matches secondary symptoms in TCM.The synthetic AChR peptide model is widely used,but shows low conformity with TCM syndromes.Models induced by muscle-specific tyrosine kinase(MuSK),low-density lipoprotein receptor-related protein 4(LRP4),and transgenic models demonstrate high innovation but exhibit low clinical conformity.Evaluation of these models requires integration of behavioral,electrophysiological,and immunological indicators.However,a systematic framework for modelling TCM syndromes is still lacking.Future research should integrate TCM-based etiological modelling methods with the Western pathological mechanisms to construct disease-syndrome combination models.Additionally,it is crucial to establish a TCM syndrome evaluation system based on"validation by prescription",as well as to improve the scientific rigor and practicality of animal models by the incorporation of emerging technologies.This review provides a theoretical foundation for optimizing MG animal model design,advancing the research on the combination of Chinese and Western medicine,and supporting efficacy assessment and mechanism exploration of Chinese herbal prescriptions.

Objective To investigate the value of the volume percentage of solid component in differential diagnosis of stage Ⅰ lung adenocarcinoma subtypes,and to predict the optimal critical CT value of solid component.Methods A total of 962 nodules with sur-gical pathological findings confirmed as adenocarcinoma in situ(AIS)or pulmonary adenocarcinoma and manifested as subsolid nod-ules on thin-section CT were analyzed retrospectively,and divided the lesions into:(1)AIS(n=350)or pulmonary adenocarcinoma(n=612)groups;and(2)minimally invasive adenocarcinoma(MIA)(n=213)or invasive adenocarcinoma(IAC)(n=399)groups based on pathological findings.The volume percentage of solid components within pulmonary nodules was measured via CT density histogram analysis at different thresholds,the diagnostic efficacy of different thresholds was analyzed and the optimal critical CT val-ues of solid component was found.Results In the AIS versus pulmonary adenocarcinoma groups,age,mass,and volume percentage of solid component were statistically significant(P＜0.001);the CT threshold of-350 HU had the highest diagnostic efficacy[area under the curve(AUC)0.859,accuracy 81.69％,sensitivity 85.60％,and specificity 74.86％,respetively],and the critical value of volume percentage of solid component determined under this threshold was 8.3％.In the MIA versus IAC groups,age(P=0.017),mass(P＜0.001),and volume percentage of solid component(P＜0.001)were statistically significant;the CT threshold of-350 HU had the highest diagnostic efficacy(AUC 0.857,accuracy 78.27％,sensitivity 82.91％,and specificity 69.48％,respectively),and the critical value of volume percentage of solid component determined under this threshold was 16.9％.Conclusion The volume percentage of solid component measured based on CT density histogram analysis may be helpful in the differential diagnosis of early stage lung ade-nocarcinoma subtypes.

Objective:To establish and validate a prediction model for the risk of major upper gastrointestinal bleeding in patients with varices of liver cirrhosis.Method:This study retrospectively collected the clinical data of patients with esophageal and gastric variceal bleeding who were admitted to the emergency department of Ningxia Medical University General Hospital from October 2019 to October 2022. The patients were divided into modeling group and validation group according to the ratio of 7:3 by random number table method. The observation index was whether the upper gastrointestinal bleeding occurred within 24 hours after admission. The predictors in the logistic regression model were used to construct a prediction model for the risk of major upper gastrointestinal bleeding in patients with varices with liver cirrhosis, and the area under the receiver operating characteristic curve (AUC), the correction curve and the decision curve were analyzed to evaluate the discriminatory ability, accuracy and clinical utility of the prediction model.Results:A total of 305 patients were included, including 215 and 90 in the modeling and validation groups, respectively, and the clinical data of the two groups were comparable. Multivariate logistic regression showed that systolic blood pressure ( OR=0.918, 95% CI: 0.860-0.980, P=0.010), MAP(ASH) score ( OR=1.993, 95% CI: 1.017-3.907, P=0.045), Child-Pugh score ( OR=1.999, 95% CI: 1.139-3.510, P=0.016) and model for end-stage liver disease (MELD) ( OR=1.398, 95% CI: 1.037-1.886, P=0.028) were independent influencing factors for the occurrence of upper gastrointestinal bleeding in patients with liver cirrhosis varices. The AUC of the prediction model in the modeling group was 0.936 (95% CI: 0.895-0.976), and that of the prediction model in the validation group was 0.891 (95% CI: 0.807-0.975), the prediction model had good identification, calibration, and clinical application value. Conclusions:Systolic blood pressure, MAP (ASH) score, Child-Pugh score, and prediction models constructed by end-stage liver disease models are helpful for early prediction of the risk of upper gastrointestinal bleeding in patients with cirrhosis varices in the emergency department.

Objective:To explore the prognostic factors of elderly patients with community-acquired pneumonia-related sepsis and to construct a prediction model.Methods:The clinical data of elderly patients with community-acquired pneumonia-associated sepsis from October 2020 to October 2022 in the General Hospital of Ningxia Medical University from October 2020 to October 2022 were retrospectively included, and the clinical data of the two groups were divided into the modeling population and the validation population in the ratio of 7:3 by random number table method, and the clinical data of the two groups were compared. According to the 30-day outcomes of admission, the patients were divided into survival group and death group, and the independent risk factors for the prognosis of elderly patients with community-acquired pneumonia-related sepsis were screened out by LASSO regression and multivariate logistic regression analysis, and the nomogram prediction model was constructed by R software. The area under the curve (AUC), calibration curve and decision curve of the receiver operating characteristic curve were used to validate the nomogram prediction model in the modeling population and the validation population to judge its discrimination, calibration and clinical practicability.Results:A total of 472 patients were included, with 331 and 141 models and validations, respectively, indicating that the clinical data were comparable between the modeled and validated populations. LASSO regression and multivariate logistic regression analysis showed that pneumonia severity index (PSI) score and sequential organ failure assessment (SOFA) score were independent risk factors for the prognosis of elderly patients with community-acquired pneumonia-associated sepsis. The AUC of the modeled population prediction model was 0.984 (95% CI: 0.975-0.994), and the AUC of the validated population prediction model was 0.961 (95% CI: 0.926-0.996). The nomogram prediction model has good discrimination, calibration and clinical practicability in both the modeled and validated populations. Conclusions:The nomogram prediction model established in the study has high accuracy for early identification and risk of sepsis in elderly patients with CAP and can guide for clinicians to formulate personalized interventions.

Objective:To explore the risk factors of sepsis in patients with multiple trauma and construct a nomogram prediction model.Methods:The data of patients with multiple injuries admitted to the emergency intensive care unit (EICU) of the General Hospital of Ningxia Medical University from January 2021 to April 2022 were respectively collected. Inclusion criteria: (1) meet the diagnostic criteria for multiple injuries; (2) the time from injury to admission ≤ 24 hours; (3) age>18 years old; (4) all examination or rescue measures were approved by the patient or the patient's family; (5) the patient's clinical data were complete. The patients were divided into sepsis group and non-sepsis group according to the definition of Sepsis 3.0 at the 28-day of EICU hospitalization. The receiver operating characteristic curve was drawn. Logistic regression analysis was applied to determine the independent predictors for sepsis, and the nomogram was constructed.Results:A total of 291 patients were included, including 102 in the sepsis group and 189 in the non-sepsis group. Multivariate logistic analysis revealed that age, acute physiology and chronic health status score (APACHE) Ⅱ, Glasgow Coma Scale (GCS), injury severity score (ISS), sequential organ failure assessment (SOFA) within 24 hours after admission, blood transfusion frequency, the application of norepinephrine, mechanical ventilation, pathogenic culture results, and history of diabetes were independent factors influencing the occurrence of sepsis. A nomogram model was constructed by combining these variables (AUC=0.913, 95% CI: 0.847-0.942), and the model had a good fitting calibration curve. Conclusions:The nomogram constructed by age, APACHE-Ⅱ, GCS score, SOFA score, ISS score, number of blood transfusions, mechanical ventilation, norepinephrine drug use, pathogenic culture and diabetes has a good predictive value for sepsis in patients with multiple trauma in the later stage, which is worth promoting.

ObjectiveTo clarify the differences in the efficacy and mechanism of different processed products of Atractylodes chinensis rhizoma by the pharmacodynamics and metabolomics studies of raw， bran-fried and rice water-processed products on rats with spleen deficiency. MethodSixty male SD rats were randomly divided into blank group， model group， raw product group（3.75 g·kg^-1）， bran-fried product group（3.75 g·kg^-1）， rice water-processed product group（3.75 g·kg^-1） and Shenling Baizhusan group（6.7 g·kg^-1）， with 10 rats in each group. The method of excessive fatigue+improper diet was used to establish a spleen deficiency model in rats. After the end of modeling， except for the blank and model groups， each dosing group was given the corresponding drug suspension， the immune organ coefficients of each group of rats were examined， the levels of interleukin-6（IL-6）， tumor necrosis factor-α（TNF-α）， immunoglobulin G（IgG）， amylase（AMS）， motilin（MTL）， gastrin（GAS）， Na⁺-K⁺-adenosine triphosphatase（ATPase）， aquaporin 2（AQP2）， AQP3 and AQP8 in rats were measured by enzyme-linked immunosorbent assay（ELISA）. Ultra high performance liquid chromatography-quadrupole-time-of-flight tandem mass spectrometry（UPLC-Q-TOF-MS） combined with orthogonal partial least squares-discriminant analysis（OPLS-DA） were used to search for biomarkers in the plasma samples of spleen-deficient rats by using two criteria［P<0.05 and variable importance in the projection（VIP） value>1］， and to compare the different modulatory effects of the three decoction pieces on the splenic-deficient biomarkers， and metabolic pathway analysis was conducted through the Kyoto Encyclopedia of Genes and Genomes（KEGG） database. ResultCompared with the blank group， the thymus index and spleen index of rats in the model group were significantly decreased（P<0.05）， the levels of IL-6， TNF-α， IgG and AQP2 were significantly increased（P<0.05）， the levels of AMS， GAS， MTL， AQP3， AQP8 and Na⁺-K⁺-ATPase were significantly decreased（P<0.05）. Compared with the model group， raw products， bran-fried products and rice water-processed products all increased thymus index and spleen index（P<0.05）， decreased IL-6， TNF-α， IgG and AQP2 levels（P<0.05）， and increased AMS， GAS， MTL， AQP3， AQP8 and Na⁺-K⁺-ATPase levels to different degrees. A total of 176 differential metabolites were screened in the model group compared with the blank group， of which 75， 72 and 84 biomarkers were called back by the raw products， bran-fried products and rice water-processed products， respectively（P<0.05， P<0.01）. Raw products of A. chinensis rhizoma mainly affected glycine， serine and threonine metabolism. Bran-fried products mainly affected alanine， aspartate and glutamate metabolism， D-arginine and D-ornithine metabolism. Rice water-processed products mainly affected glycine， serine and threonine metabolism， alanine， aspartate and glutamate metabolism， citrate cycle， thiamine metabolism， D-arginine and D-ornithine metabolism. ConclusionRaw products， bran-fried products and rice water-processed products of A. chinensis rhizoma all have good spleen strengthening effects， among which the effects of bran-fried products and rice water-processed products were stronger. Meanwhile， raw products has the strongest dryness， followed by bran-fried products， and the weakest dryness of rice water-processed products. The three decoction pieces are able to significantly modulate metabolic abnormalities in spleen-deficient rats， and the mechanism may be related to amino acid metabolism such as glycine， serine and threonine metabolism as well as alanine， aspartate and glutamate metabolism.

In recent years,with the gradual maturity of achievable remote collection of digital health technology,more and more clinical research have applied this technology to improve the quality of data collection and reduce the burden on subjects.However,its technological characteristics of detachment from the diagnostic and treatment environment and real-time transmission of sensitive information also pose corresponding risks to the protection of subjects'rights and interests and data standardization management,among which personal information protection and data control permissions are more prominent issues.Based on risk analysis and regulatory review,this paper explored the responsibilities of multiple parties,including the sponsors,researchers,clinical trial institutions,and ethics committee,as well as proposed five elements of special concern for ethical review,with a view to providing a reference for promoting the standardized development of digital health technology in clinical research.

Objective:To explore the prognostic risk factors of patients with multiple injuries and establish a nomogram prediction model.Methods:The clinical data of 291 patients with multiple injuries admitted to the Emergency Intensive Care Unit (EICU) of General Hospital of Ningxia Medical University were collected, including sex, age, open injury, norepinephrine use, mechanical ventilation, time to hospital after injury, distance to hospital, relative lymphocyte value, platelet count, lactic acid, injury severity score (ISS), acute physiology and chronic health evaluationⅡ (APACHE Ⅱ), Glasgow coma scale (GCS), number of blood transfusions, number of operations, and previous history of diabetes, hypertension and smoking within 24 h after admission. According to whether the condition worsened during the hospitalization of EICU, the patients were divided into the deterioration group and improvement group. SPSS26.0 software was used for statistical analysis of the data, univariate and multivariate analysis were used to screen the factors affecting the prognosis of patients with multiple injuries, receiver operating characteristic (ROC) curve and forest chart were drawn, and the influencing factors in binary Logistic regression model were used to make the nomogram.Results:Mechanical ventilation, norepinephrine use, age, relative lymphocyte value, lactic acid, APACHE-II score, GCS score, and number of operations were significant for predicting the prognosis of patients with multiple injuries ( P<0.05). The independent influencing factors obtained by binary Logistic regression model were age, lactic acid, APACHE-Ⅱ score and number of operations. ROC curve analysis showed that the area under the curve was the largest in multi-factor combined prediction, followed by APACHE-Ⅱ score. The diagnostic cut-off value of each index was as follows: age >58 years old, relative lymphocyte value≤ 8.62%, lactic acid >1.72, APACHE-Ⅱ score >16, GCS score≤ 6, and number of operations≤ 0. The R software was used to establish a nomogram of the influencing factors in the binary Logistic regression model, which had good predictive value. Conclusions:The nomogram constructed by age, relative lymphocyte value, lactic acid, APACHE-Ⅱ score, GCS score, number of operations, mechanical ventilation, and norepinephrine use has a good predictive value for the prognosis of patients with multiple injuries, and is worthy of promotion..