OBJECTIVE To study the pharmacokinetic characteristics of antidepressant active components from Jiaotai pills in healthy subjects. METHODS Eight healthy subjects （3 males and 5 females） were recruited and given a single oral dose of 8.55 g of Jiaotai pills. Venous blood samples were collected before administration （0 h） and at intervals from 0.25 to 36.0 hours post- administration. After treating the plasma samples with protein precipitation， the blood concentrations of the antidepressant active ingredients （coptisine， berberine， magnoflorine， and palmatine） in Jiaotai pills were determined using liquid chromatography- tandem mass spectrometry （LC-MS/MS） method. DAS 2.0 software was employed to calculate the pharmacokinetic parameters of healthy subjects [half-life （t1/2）， peak concentration （cmax）， time to peak concentration （tmax）， area under the concentration-time curve （AUC）， and mean residence time （MRT）] using a non-compartmental model. RESULTS After healthy subjects took Jiaotai pills， the drug-time curve of the four antidepressant active ingredients conforms to a two-compartment model and tmax values were similar， with all reaching peak blood concentrations within 2.00 to 4.00 hours post-administration. However， the t1/2 and MRT of coptisine and berberine were significantly longer than that of magnoflorine and palmatine. There were also significant differences in the AUC and cmax among the four antidepressant active ingredients， with magnoflorine exhibiting markedly higher AUC0-t and cmax compared to the other three components. CONCLUSIONS In this study，LC-MS/MS is used to analyze the pharmacokinetic characteristics of the antidepressant active ingredients from Jiaotai pills in healthy subjects， can provide valuable references for the clinical application of Jiaotai pills.

OBJECTIVE:Based on different algorithms of machine learning,the prediction model of lumbar disc herniation has become a trend and hot spot in the development of precision medicine.However,there is limited evidence on the reporting quality and methodological quality of prediction models of lumbar disc herniation outcomes using machine learning.This article is aimed to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation by comprehensively analyzing the report quality and risk of bias of previous studies that developed and validated prognosis prediction models based on machine learning through a comprehensive literature search,in order to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation.METHODS:The databases of CNKI,WanFang,VIP,SinOMED,PubMed,Web of Science,Embase,and The Cochrane Library were searched by computer.Studies on the use of machine learning to develop(and/or validate)prognostic prediction models for lumbar disc herniation were collected from the inception of the database to December 31,2023.Two researchers independently screened the literature,extracted data,and assessed the risk of bias of the included studies.The reporting quality and risk of bias of the included studies were assessed by the Multivariable Transparent Reporting of Predictive Models(TRIPOD)statement and the Predictive Model Risk of Bias Assessment Tool(PROBAST).The results of the evaluation were analyzed using descriptive statistics and visual charts.RESULTS:(1)A total of 23 articles were included,and the TRIPOD compliance of each study ranged from 11％to 87％,with a median compliance of 54％.The quality of reporting of titles,detailed descriptions of treatment measures,blinding of predictors,handling of missing data,details of risk stratification,specific procedures for enrollment,model interpretation,and model performance was mostly poor,with TRIPOD adherence rates ranging from 4％to 35％.(2)Of all included studies,61％had a high risk of bias and 39％had an unclear overall risk of bias.The area under the curve,accuracy,sensitivity and specificity were used to evaluate the performance of the model.The areas under the curve of 20 models were reported,ranging from 0.561 to 0.999.Three models reported the accuracy of the model,ranging from 82.07％to 89.65％.(3)Among all included studies,the statistical analysis domain was most often assessed as having a high risk of bias,mainly due to the small number of valid samples,the selection of predictors based on univariate analysis and the lack of calibration and discrimination assessment of the model in the study.CONCLUSION:These results indicate that machine learning can achieve good predictive ability in the development and validation of prognostic models for lumbar disc herniation.The commonly used algorithms include regression algorithm,support vector machine,decision tree,random forest,artificial neural network,naive Bayes and other algorithms.Reasonable algorithms combined with clinical practice can improve the accuracy of prognosis prediction of lumbar disc herniation.However,the reporting and methodological quality of prognosis prediction models based on machine learning are poor,the prediction performance of different models varies greatly,and the generalization and extrapolation of research models are unclear.There is an urgent need to improve the design,implementation and reporting of such studies.To promote the application of machine learning in the clinical practice of lumbar disc herniation prediction models,it is necessary to comprehensively consider various predictors related to the prognosis of the disease before modeling,and strictly follow the relevant standards of PROBAST tool during modeling.

OBJECTIVE:Based on different algorithms of machine learning,the prediction model of lumbar disc herniation has become a trend and hot spot in the development of precision medicine.However,there is limited evidence on the reporting quality and methodological quality of prediction models of lumbar disc herniation outcomes using machine learning.This article is aimed to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation by comprehensively analyzing the report quality and risk of bias of previous studies that developed and validated prognosis prediction models based on machine learning through a comprehensive literature search,in order to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation.METHODS:The databases of CNKI,WanFang,VIP,SinOMED,PubMed,Web of Science,Embase,and The Cochrane Library were searched by computer.Studies on the use of machine learning to develop(and/or validate)prognostic prediction models for lumbar disc herniation were collected from the inception of the database to December 31,2023.Two researchers independently screened the literature,extracted data,and assessed the risk of bias of the included studies.The reporting quality and risk of bias of the included studies were assessed by the Multivariable Transparent Reporting of Predictive Models(TRIPOD)statement and the Predictive Model Risk of Bias Assessment Tool(PROBAST).The results of the evaluation were analyzed using descriptive statistics and visual charts.RESULTS:(1)A total of 23 articles were included,and the TRIPOD compliance of each study ranged from 11％to 87％,with a median compliance of 54％.The quality of reporting of titles,detailed descriptions of treatment measures,blinding of predictors,handling of missing data,details of risk stratification,specific procedures for enrollment,model interpretation,and model performance was mostly poor,with TRIPOD adherence rates ranging from 4％to 35％.(2)Of all included studies,61％had a high risk of bias and 39％had an unclear overall risk of bias.The area under the curve,accuracy,sensitivity and specificity were used to evaluate the performance of the model.The areas under the curve of 20 models were reported,ranging from 0.561 to 0.999.Three models reported the accuracy of the model,ranging from 82.07％to 89.65％.(3)Among all included studies,the statistical analysis domain was most often assessed as having a high risk of bias,mainly due to the small number of valid samples,the selection of predictors based on univariate analysis and the lack of calibration and discrimination assessment of the model in the study.CONCLUSION:These results indicate that machine learning can achieve good predictive ability in the development and validation of prognostic models for lumbar disc herniation.The commonly used algorithms include regression algorithm,support vector machine,decision tree,random forest,artificial neural network,naive Bayes and other algorithms.Reasonable algorithms combined with clinical practice can improve the accuracy of prognosis prediction of lumbar disc herniation.However,the reporting and methodological quality of prognosis prediction models based on machine learning are poor,the prediction performance of different models varies greatly,and the generalization and extrapolation of research models are unclear.There is an urgent need to improve the design,implementation and reporting of such studies.To promote the application of machine learning in the clinical practice of lumbar disc herniation prediction models,it is necessary to comprehensively consider various predictors related to the prognosis of the disease before modeling,and strictly follow the relevant standards of PROBAST tool during modeling.

Objective:To investigate the impact of postoperative complications on adverse outcomes following curative-intent resection for gallbladder cancer (GBC).Methods:The multi-center real-world study was conducted. The clinicopathological data of 629 patients with GBC, who were admitted to 14 medical centers including The First Affiliated Hospital of Army Medical University from the national multicenter database of Biliary Surgery Group of Elite Group of Chinese Journal of Digestive Surgery, from April 2020 to April 2024 were collected. There were 225 males and 404 females, aged (64±10)years. Patients underwent open curative-intent resection for GBC. Observation indicators: (1)surgery, postoperative complica-tions and adverse outcomes; (2) analysis of risk factors affecting postoperative adverse outcomes in patients and population attributable fraction (PAF). Missing data in predictor variables were addressed using multiple imputation with chained equations, while cases with missing outcome variables were addressed using the "multiple imputation then deletion (MID)" strategy. The severity of multicollinearity among independent variables was assessed using the variance inflation factor (VIF) test. Multivariable possion regression models with log link and robust error variance were construc-ted incorporating restricted cubic splines (3 knots) to address nonlinear relationships in continuous variables, calculating adjusted relative risk ( RR) with corresponding 95% confidence interval ( CI). Adjusted PAF was calculated for each imputed dataset using the AF package of R software, with subsequent pooling performed according to Rubin's rules. Results:(1) Surgery, postoperative complications and adverse outcomes. All 629 patients underwent curative-intent resection for GBC, of which 143 cases had postoperative complications, including 68 cases of intra-abdominal ascites, 39 cases of pulmonary infection, 21 cases of bile leakage, 12 cases of intra-abdominal hemorrhage, 11 cases of liver failure, 10 cases of pan-creatic fistula, 10 cases of wound infection, 10 cases of gastroparesis, 7 cases of cholangitis, 7 cases of sepsis. The same patient could have more than one kind of complication. Of 629 patients, there were 19 cases of postoperative 90-day death and 11 cases of missing data, 42 cases with post-operative 90-day reoperation and 7 cases with missing data, 44 cases with postoperative 90-day readmission and 3 cases with missing data, 155 cases with prolonged postoperative hospital stay and 3 cases with missing data. (2) Analysis of risk factors affecting the postoperative adverse outcomes in patients and PAF. Results of multivariate analysis showed that pulmonary infection and liver failure were independent risk factors for postoperative 90-day mortality ( RR=3.74, 12.15, 95% CI as 1.18-11.83, 1.98-74.48, P<0.05). Pulmonary infection demons-trated the highest PAF as 4.61% (95% CI as 3.94%-5.28%, P<0.05). Intra-abdominal ascites, pulmonary infection, bile leakage, and intra-abdominal hemorrhage were independent risk factors for post-operative 90-day reoperation ( RR=4.80, 3.62, 3.46, 4.99, 95% CI as 2.49-9.26, 1.42-9.21, 1.34-8.92, 1.55-16.06, P<0.05). Intra-abdominal ascites demonstrated the highest PAF as 8.65% (95% CI as 8.22%-9.08%, P<0.05). Intra-abdominal ascites, bile leakage, and liver failure were independent risk factors for postoperative 90-day readmission ( RR=6.20, 3.33, 14.33, 95% CI as 3.21-11.95, 1.33-8.35, 3.72-55.28, P<0.05). Intra-abdominal ascites demonstrated the highest PAF as 9.11% (95% CI as 8.85%-9.37%, P<0.05). Intra-abdominal ascites, pulmonary infection, bile leakage, liver failure, and wound infection were independent risk factors for prolonged postoperative hospital stay ( RR=2.29, 2.21, 2.26, 2.14, 3.35, 95% CI as 1.63-3.23, 1.41-3.46, 1.32-3.86, 1.11-4.13, 1.70-6.60, P<0.05). Intra-abdominal ascites demonstrated the highest PAF as 6.03% (95% CI as 5.71%-6.35%, P<0.05). Conclusion:Pulmonary infection is the most significant risk factor for postoperative 90-day mortality after curative-intent resection for GBC, while intra-abdominal ascites is the most significant risk factor for postoperative 90-day reoperation, postoperative 90-day readmission, and prolonged postoperative hospital stay.

Objective To analyze the current status and influencing factors of occupational stress, job burnout and sleep quality among workers in the secondary industry in Jinshan District, Shanghai City. Methods A total of 1 418 workers from six key industries in Jinshan District, Shanghai City were selected as the study subjects by the stratified cluster sampling method. The Occupational Stress Core Scale, Maslash Burnout Inventory General Survey and Pittsburgh Sleep Quality Index were used to investigate occupational stress, job burnout and sleep quality of the workers. Results The detection rates of occupational stress, job burnout and sleep disturbance among the study subjects were 33.6%, 65.4% and 23.3%, respectively. Multivariate logistic regression analysis showed that the workers with a monthly income <5 000 yuan had a higher risk of occupational stress than those with a monthly income ≥5 000 yuan (P<0.01). The workers with ≥5.0 years of service had a higher risk than those with <1.0 year (P<0.05). Lack of physical exercise, employment in medium- and large-sized enterprises, and shift work were risk factors of occupational stress in the workers (all P<0.01). The workers aged 18-<30 years had a higher risk of job burnout than those aged 45-<60 years (P<0.05). The workers monthly income <5 000 yuan was associated with a higher risk of job burnout than those with ≥9 000 yuan (P<0.05). The workers with 1.0-<10.0 years or ≥15.0 years of service had higher job burnout risks than those with <1.0 year (all P<0.05). Being unmarried, lack of physical exercise, and employment in medium- and large-sized enterprises were risk factor of job burnout in the workers (all P<0.05). The workers with an educational level of high school or above had a higher risk of sleep disturbance than those with junior school or below (P<0.05). The workers who work >56 hours per week had a higher risk than those working ≤40 hours per week (P<0.01). Conclusion There is a high detection rate of occupational stress, job burnout, and sleep disturbance in the secondary industry workers in Jinshan District, Shanghai City. Special attention should be given to workers with low income, lack of physical exercise, employment in medium- and large-sized enterprises, shift work, long service duration, and long weekly working hours to protect their physical and mental health.

Objective:To investigate the factors influencing the prognosis of critically ill patients and the predictive value of the C-reactive protein-albumin-lymphocyte (CALLY) index.Methods:A retrospective analysis was conducted on the clinical data of 122 critically ill patients admitted to Guoyao Dongfeng General Hospital affiliated with Hubei University of Medicine from June 2022 to December 2023. Patients were divided into a death group and a survival group based on their 28-day prognosis. Clinical data were compared between the two groups to analyze the factors influencing prognosis and assess the predictive value of various indicators. Normally distributed measurement data were expressed as Mean±SD, and intergroup comparisons were performed by independent samples t-test; non-normally distributed measurement data were expressed as M( Q1,Q3), and intergroup comparisons were performed by the Mann-Whitney U test. Counting data were expressed as case (%), and intergroup comparisons were performed by the χ2 test. Multivariate logistic regression was used to analyze factors influencing patient prognosis, and receiver operating characteristic (ROC) curves were plotted to analyze the predictive value of each indicator. Results:The Acute Physiology and Chronic Health Evaluation Ⅱ (APACHE Ⅱ) score, blood lactate, CRP, and B-type natriuretic peptide levels in the death group were higher than those in the survival group [22 (17, 30) points vs. 17 (14, 22) points, (4.8±1.4) mmol/L vs. (3.3±1.0) mmol/L, 134 (83, 2 381) mg/L vs. 13 (10, 27) mg/L, 259 (111, 592) ng/L vs. 108 (40, 247) ng/L; Z=3.04, P=0.002; t=5.79, P<0.001; Z=8.57, P<0.001; Z=3.28, P=0.001, respectively]. Albumin, neutrophil count, lymphocyte count (LYC), and the CALLY index were lower in the death group than in the survival group [(31±5) g/L vs. (37±6) g/L, (58±9)×10 9/L vs. (63±10)×10 9/L, 0.6 (0.4, 0.8)×10 9/L vs. 1.3 (0.8, 1.7)×10 9/L, 0.03 (0.02, 0.11) vs. 0.26 (0.13, 0.49); t=6.05, P<0.001; t=3.04, P=0.003; Z=5.82, P<0.001; Z=6.52, P<0.001, respectively]. Multivariate logistic regression analysis indicated that the APACHE Ⅱ score and CRP were risk factors for poor prognosis in critically ill patients ( OR=1.349, 95% CI: 1.004-1.821, P=0.048; OR=1.006, 95% CI: 1.003-1.010, P=0.001, respectively), while LYC and the CALLY index were protective factors ( OR=0.297, 95% CI: 0.111-0.795, P=0.016; OR=0.989, 95% CI: 0.955-0.999, P=0.001, respectively). The area under the ROC curve for the CALLY index predicting 28-day mortality in critically ill patients was 0.872 (95% CI: 0.800-0.926), which was higher than that of the APACHE Ⅱ score, LYC, and CRP [0.673 (95% CI: 0.582-0.756), 0.664 (95% CI: 0.573-0.748), 0.576 (95% CI: 0.482-0.665), respectively]. The cut-off values were 0.06, 20 points, 0.8×10 9/L, and 50 mg/L, respectively. When the CALLY index was 0.06, the specificity was 97.65%, the sensitivity was 72.97%, and the Youden index was 0.706. Conclusions:The APACHE Ⅱ score, CRP, LYC, and CALLY index are all factors influencing the prognosis of critically ill patients. The CALLY index has certain predictive value, but its false negative rate is relatively high. Further combination with other indicators is needed to improve its predictive value.

Objective:To investigate the effects of pulsed electromagnetic field(PEMF)on sevoflurane-induced cognitive dysfunction in elderly rats and also explore its related mechanism.Methods:Thirty elderly male rats were randomly divided into the control group,sevoflurane treatment group(SEV),and sevoflurane+PEMF treatment group(SEV+PEMF).Rats in the sevoflurane group and sevoflurane+PEMF group passively inhaled 2.5％sevoflurane for 4 h,while rats in the SEV+PEMF group were stimulated with 2 mT,15 Hz PEMF for 14 d(2 h/day).The cognitive function of rats was evaluated via the Morris water maze testing.The serum concentrations of tumor necrosis factor-α(TNF-α),interleukin-1 β(IL-1β),IL-6,neuron specific enolase(NSE),and β amyloid protein(Aβ),as well as the levels of nerve growth factor(NGF)and brain-derived neurotrophic factor(BDNF)in hippocampal tissue,were de-termined via ELISA.Western blot was used to detect the expression of autophagy-related biomarkers in rat hippocampal tissue.Secondly,30 elderly male rats were randomly divided into three groups:SEV group,SEV+PEMF group,and SEV+3-MA(the autophagy inhibitor)+PEMF group.The Morris water maze experiment was used to evaluate the change of PEMF-induced improvement of cognitive function sevoflurane-inhaled elderly rats following the autophagy inhi-bition.Results:PEMF inhibited sevoflurane-induced increase in escape latency and overall swimming distance,as well as the decrease in the number of crossing target quadrant(P＜0.05);PEMF decreased the levels of serum Aβ and NSE in elderly rats inhaled with sevoflurane(P＜0.05),decreased the levels of TNF-α,IL-1β,and IL-6(P＜0.05),increased the levels of NGF and BDNF in hippocampal tissue(P＜0.05),inhibited neuronal apoptosis in hip-pocampal tissue and increased its autophagy level(P＜0.05).Following inhibition of autophagy with 3-MA,the im-provement of PEMF on the decreased learning and memory ability induced by sevoflurane in elderly rats was significantly inhibited(P＜0.05).Conclusion:PEMF can effectively inhibit sevoflurane-induced cognitive dysfunction in elderly rats by regulating the autophagy of hippocampal neuronal cells.

Objective To determine the effect of limb dominance on landing biomechanics and lower limb functional outcomes in patients with anterior cruciate ligament reconstruction(ACLR).Methods Forty-nine participants were recruited and divided into the ACLR on dominant limb(ACLR-D)group,ACLR on nondominant limb(ACLR-ND)group and healthy control group.Single-leg jump landing,knee isokinetic muscle strength,Y balance,and single-leg hop distance were tested on both limbs of all participants.Kinematics and kinetics data during the single-leg jump landing were collected by an infrared motion capture system and a force platform,and knee joint muscle strength was collected using the isokinetic muscle strength testing system.Two-way mixed-design ANOVAs were used to observe the effects of limb and group on the outcomes of each test.Results The non-surgical limbs had greater knee valgus,knee external rotation angles and knee valgus moments during single-leg jump landing in the ACLR-D group compared with those in the ACLR-ND group,and the ACLR-D group had significantly smaller bilateral knee flexion angles than the control group.There were no differences in knee muscle strength,Y-balance composite scores and single-leg hop distance between ACLR-D and ACLR-ND groups,but the Y balance scores in the ACLR-ND group were smaller than those in the control group.Conclusions Limb dominance has no effects on knee muscle strength,dynamic postural control,and single-leg hop function in ACLR patients.The non-surgical limbs of ACLR-D patients are at a higher risk of ACL injury due to the presence of greater knee valgus and external rotation angles and knee valgus moments.

Objective:To investigate the impact of postoperative complications on adverse outcomes following curative-intent resection for gallbladder cancer (GBC).Methods:The multi-center real-world study was conducted. The clinicopathological data of 629 patients with GBC, who were admitted to 14 medical centers including The First Affiliated Hospital of Army Medical University from the national multicenter database of Biliary Surgery Group of Elite Group of Chinese Journal of Digestive Surgery, from April 2020 to April 2024 were collected. There were 225 males and 404 females, aged (64±10)years. Patients underwent open curative-intent resection for GBC. Observation indicators: (1)surgery, postoperative complica-tions and adverse outcomes; (2) analysis of risk factors affecting postoperative adverse outcomes in patients and population attributable fraction (PAF). Missing data in predictor variables were addressed using multiple imputation with chained equations, while cases with missing outcome variables were addressed using the "multiple imputation then deletion (MID)" strategy. The severity of multicollinearity among independent variables was assessed using the variance inflation factor (VIF) test. Multivariable possion regression models with log link and robust error variance were construc-ted incorporating restricted cubic splines (3 knots) to address nonlinear relationships in continuous variables, calculating adjusted relative risk ( RR) with corresponding 95% confidence interval ( CI). Adjusted PAF was calculated for each imputed dataset using the AF package of R software, with subsequent pooling performed according to Rubin's rules. Results:(1) Surgery, postoperative complications and adverse outcomes. All 629 patients underwent curative-intent resection for GBC, of which 143 cases had postoperative complications, including 68 cases of intra-abdominal ascites, 39 cases of pulmonary infection, 21 cases of bile leakage, 12 cases of intra-abdominal hemorrhage, 11 cases of liver failure, 10 cases of pan-creatic fistula, 10 cases of wound infection, 10 cases of gastroparesis, 7 cases of cholangitis, 7 cases of sepsis. The same patient could have more than one kind of complication. Of 629 patients, there were 19 cases of postoperative 90-day death and 11 cases of missing data, 42 cases with post-operative 90-day reoperation and 7 cases with missing data, 44 cases with postoperative 90-day readmission and 3 cases with missing data, 155 cases with prolonged postoperative hospital stay and 3 cases with missing data. (2) Analysis of risk factors affecting the postoperative adverse outcomes in patients and PAF. Results of multivariate analysis showed that pulmonary infection and liver failure were independent risk factors for postoperative 90-day mortality ( RR=3.74, 12.15, 95% CI as 1.18-11.83, 1.98-74.48, P<0.05). Pulmonary infection demons-trated the highest PAF as 4.61% (95% CI as 3.94%-5.28%, P<0.05). Intra-abdominal ascites, pulmonary infection, bile leakage, and intra-abdominal hemorrhage were independent risk factors for post-operative 90-day reoperation ( RR=4.80, 3.62, 3.46, 4.99, 95% CI as 2.49-9.26, 1.42-9.21, 1.34-8.92, 1.55-16.06, P<0.05). Intra-abdominal ascites demonstrated the highest PAF as 8.65% (95% CI as 8.22%-9.08%, P<0.05). Intra-abdominal ascites, bile leakage, and liver failure were independent risk factors for postoperative 90-day readmission ( RR=6.20, 3.33, 14.33, 95% CI as 3.21-11.95, 1.33-8.35, 3.72-55.28, P<0.05). Intra-abdominal ascites demonstrated the highest PAF as 9.11% (95% CI as 8.85%-9.37%, P<0.05). Intra-abdominal ascites, pulmonary infection, bile leakage, liver failure, and wound infection were independent risk factors for prolonged postoperative hospital stay ( RR=2.29, 2.21, 2.26, 2.14, 3.35, 95% CI as 1.63-3.23, 1.41-3.46, 1.32-3.86, 1.11-4.13, 1.70-6.60, P<0.05). Intra-abdominal ascites demonstrated the highest PAF as 6.03% (95% CI as 5.71%-6.35%, P<0.05). Conclusion:Pulmonary infection is the most significant risk factor for postoperative 90-day mortality after curative-intent resection for GBC, while intra-abdominal ascites is the most significant risk factor for postoperative 90-day reoperation, postoperative 90-day readmission, and prolonged postoperative hospital stay.

Hepatic encephalopathy is a difficult and critical disease with rapid progression and limited treatment methods in the field of liver disease， and it is urgently needed to make breakthroughs in its pathogenesis. Selection of appropriate prevention and treatment strategies is of great importance in delaying disease progression and reducing the incidence and mortality rates. This article reviews the theory of “turbid toxin pathogenesis” and related prevention and treatment strategies for hepatic encephalopathy in traditional Chinese medicine/Zhuang medicine， proposes a new theory of “turbid toxin pathogenesis”， analyzes the scientific connotations of “turbid”， “toxin”， and the theory of “turbid toxin pathogenesis”， and constructs the “four-step” prevention and treatment strategies for hepatic encephalopathy， thereby establishing the new clinical prevention and treatment regimen for hepatic encephalopathy represented by “four prescriptions and two techniques” and clarifying the effect mechanism and biological basis of core prescriptions and techniques in the prevention and treatment of hepatic encephalopathy， in order to provide a reference for the prevention and treatment of hepatic encephalopathy.