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.

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.

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 identify the most significantly compressed areas and the areas with the best recovery effects by analyzing the changes in vertebral height after percutaneous vertebroplasty(PVP)in patients with osteoporotic vertebral compression fractures(OVCF)through lateral radiographs.Methods A retrospective analysis was conducted on the lateral X-rays of 186 injured vertebrae from 142 patients hospitalized in our hospital's intervertebral disc center.The sagittal height of the vertebrae was measured at five different points before and after surgery,and the collected data were statistically analyzed using SPSS software.Results There were statistically significant differences in the heights of the five measured points before and after surgery within OVCF injured vertebrae(P＜0.05),in the ascending order:central＜mid-anterior＜mid-posterior＜anterior edge＜posterior edge.Comparison of the height parameters of the five measured points before and after surgery showed statistically significant differences(P＜0.01).In comparing the height restoration differences of the five measured points after PVP,the differences between central and mid-anterior,central and anterior edge,and mid-posterior and anterior edge were found not to be statistically significant(P＞0.05).The differences in height restoration for the remaining groups were statistically significant(P＜0.05),with the height restoration differences from highest to lowest being:mid-anterior,central,anterior edge,mid-posterior,posterior edge.Conclusion In patients with OVCF,the compression of the injured vertebra is most pronounced in the central part,followed by the mid-anterior part.PVP surgery can effectively restore the height of various parts of the injured vertebra,especially in the mid-anterior and central parts of the vertebral body,where the recovery effect is particularly significant.

The presence of magnetic fields in a magnetic resonance accelerator (MR-linac) can affect the reference dosimetry, and thus the existing Code of Practices (CoPs) are inadequate for MR-linac. In this article, the characteristics of adsorbed dose to water and ionization chamber response in the presence of magnetic fields were introduced and a formalism for reference dosimetry in MR-linac was developed based on the existing CoPs, aiming to provide reference for dosimetric quality control and research work of MR-linac in China.

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 investigate the therapeutic efficacy of artesunate(AS)on polycystic ovary syndrome(PCOS)in mice and explore the potential mechanism primarily.Methods Twenty-five female C57BL/6J mice were randomly divided into Control group,model group(PCOS group),low-and high-dose AS groups(AS15 and AS30 groups)and metformin group(Met group).In addition to the Control group,the mouse model of PCOS was established by subcutaneous injection of dehydroepiandrosterone(DHEA,60 mg/kg)following by a high-fat diet for 21 d.After modeling,AS of 15 and 30 mg/kg was intraperitoneally injected into the mice of the AS 15 and AS30 groups,respectively,and 200 mg/kg Met was given to those of the Met group by gavage,once per day,for 6 weeks.ELISA was used to detect serum testosterone(T),fasting insulin(FINS),luteinizing hormone(LH)and follicle-stimulating hormone(FSH),and the LH/FSH ratio was calculated.The levels of fasting blood glucose(FBG),triglyceride(TG)and total cholesterol(TC)were detected by automatic biochemical analyzer,and the homeostasis model assessment of insulin resistance(HOMA-IR)was calculated.The estrous cycle was observed,and HE staining was performed for pathological changes in the ovary and uterus.Immunofluorescence assay was employed to measure the expression of p-eIF2α,ATF4 and CHOP in the ovarian tissue.After steroidogenic human granulosa-like tumor cell line KGN were exposed to 100 μmol/L DHEA to simulate the hyperandrogen environment of PCOS,and then treated with 5 and 10 μg/mL AS for 24 h,the protein levels of endoplasmic reticulum stress signaling pathway was detected by Western blotting.Results Compared with the Control group,the PCOS mice had disturbed estrous cycle,polycystic changes in the ovaries,and significantly increased serum T level and LH/FSH ratio(P＜0.05),and obviously elevated HOMA-IR,TC and TG levels in terms of metabolism(P＜0.01).The expression levels of p-eIF2α,ATF4 and CHOP were notably up-regulated in the ovarian granulosa cells of PCOS mice and KGN cells after DHEA exposure(P＜0.05).Additionally,AS treatment attenuated the pathological changes of ovary and uterine expression,decreased the serum T level and the LH/FSH ratio(P＜0.05),and reduced HOMA-IR,TC and TG levels(P＜0.05)when compared with the PCOS mice.Moreover,the expression levels of p-eIF2α,ATF4 and CHOP were significantly down-regulated after AS treatment in both ovarian granulosa cells of PCOS mice and KGN cells(P＜0.05).Conclusion AS significantly improves glycolipid metabolic disorder and reproductive dysfunction in PCOS mice,which may be associated with its suppressing endoplasmic reticulum stress by inhibiting the PERK/eIF2α/ATF4/CHOP pathway.

Objective To investigate the effect of dexmedetomidine(Dex)on bone loss in tail-suspended rats and primarily explore its regulatory mechanism on bone metabolism.Methods A total of 30 male rats were randomly divided into a control group,a model group,and a Dex group,with 10 animals in each group.Rat model of osteoporosis was established by hind limb suspension for 4 weeks.Dex at a dose of 10 μg/kg was given intraperitoneally,once every other day from the day of tail suspension.And equal amount of normal saline was given to the control and model group.Bone histological staining was used to observe the trabecular bone area fraction.Biomechanical three-point bending test was employed to measure the maximum load,stiffness,and fracture energy.Dual calcein/alizarin red fluorescence labeling and tartrate resistant acid phosphatase(TRAP)staining were applied respectively to detect the mineral apposition rate and bone formation rate as well as the number of osteoclasts on bone surfaces.Secondly,after primary osteoblasts were isolated from the tibiae of tail-suspended rats and then treated with 1 nmol/L Dex,the proportion of alkaline phosphatase(ALP)-positive osteoblasts and the activity of the enzyme were detected by ALP staining and activity test.qRT-PCR was applied to measure the expression of osteogenic activity-related factors,including osteocalcin(Ocn),Runt related transcription factor 2(Runx2),Osterix protein(Osx),and type 1 collagen(Col1).Results The animal experiments revealed that Dex treatment significantly increased the tibial trabecular bone area fraction,inhibited the decrease in bone mechanical strength,and enhanced the mineralization deposition rate and new bone formation rate of trabecular bone in the tail-suspended rats(all P＜0.001).The in vitro experiments showed that Dex treatment obviously improved ALP activity and the number of ALP-positive osteoblasts in primary osteoblasts isolated from tail-suspended rats(P＜0.01),and up-regulated the expression levels of osteogenic differentiation-related genes,such as Ocn,Runx2,Osx and Col1(P＜0.01).Conclusion Dex exerts anti-bone loss effect in tail-suspended rats,which may be associated with its stimulation on osteoblast-mediated bone formation.