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 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 explore the effect of home-based pharmaceutical care for patients with stable chronic obstruc-tive pulmonary diseases(COPD)based on a digital remote management applet.Methods A total of 237 patients with stable COPD from a hospital pharmaceutical outpatient service from March 2022 to March 2023 were divided into a control group,a home visit group,and a remote management group according to the random number table method.Patients in the home visit and re-mote management groups received home-based pharmaceutical interventions such as health science popularization,medication con-sultation,medication guidance,effect evaluation of pharmacotherapy,prescription simplification,and reorganization.Such interven-tions were not provided in the control group.Regular follow-up was performed for 12 months.Results After a pharmaceutical intervention,the operating scores of the inhalation device and medication compliance scores of the home visit and remote manage-ment groups were significantly better than the control group(P＜0.05).The improvement in medication compliance was greater in the remote management group than in the home visit group(54.3％ vs.44.6％).In the three groups between enrollment and 12 months follow-up,CAT scores decreased by 0.78,6.16,and 7.30 points in the control group,home visit group,and remote manage-ment group,respectively.The mean scores of SGRQ symptom decreased by 1.19,4.24,and 6.10 points,the mean activity scores decreased by 1.65,3.56,4.80 points,the impact mean score decreased by 1.08,4.19,5.16 points,and the mean score of the total score decreased by 1.29,4.00,4.80 points in the control group,home visit group,and remote management group,respectively.The remote management group showed dia better decline in CAT score and SGRQ score than the home visit group,and there were sig-nificant differences between the two groups compared with the control group after intervention(P＜0.05).Conclusions Digital remote management of home-based pharmaceutical care mode can effectively improve medication compliance,operation accuracy of inhalation devices,clinical symptoms,and the patient quality of life.This is an effective and efficient pharmaceutical care mode for the long-term home medication management of stable COPD patients.

Forced oscillation technique (FOT) enables early diagnosis of chronic obstructive pulmonary disease (COPD) by quantifying the impedance of COPD patients during normal breathing and reflecting the airway obstruction and distribution of the patients. The flow sensors using in FOT for diagnosis of COPD mainly include differential pressure flow sensors, ultrasonic flow sensors, hot wire gas flow sensors, fiber-optic flow sensors and flow sensors based on friction nanoelectricity technology. In this review, the principles, characteristics, and current application status of these five types of flow sensors were summarized, and their future development prospects were prospected.

In recent years, a variety of innovative fast algorithm techniques have emerged in the field of transcranial magnetic stimulation (TMS) electric field solving, which show great potential to meet the requirements of real-time clinical applications. In this review, the crucial processes of TMS electric field modeling were summarized, focusing on two prominent fast algorithm techniques, namely the basis function method and the deep neural network (DNN)-based method. The advantages and limitations of these two techniques were analyzed in detail. Commonly used software tools for electric field modeling were discussed, and a prospective discussion of future developments was offered, aiming to provide a reference for the further development of TMS electric field modeling technology.

Alzheimer′s disease (AD) is a degenerative disorder of the central nervous system. In the early stages of AD, i.e. when cognitive impairment is mild or absent but biomarkers are present, patients show abnormal neuroelectric activity. Repetitive transcranial magnetic stimulation (rTMS) is an important method of non-invasively regulating neuroelectric activity in the brain. It does so by inducing microcurrents to change the membrane potential of nerve cells in the brain based on electromagnetic induction. In this review, the characteristics of neuroelectric activity of AD patients and AD model mice in the early stage of AD were mainly introduced, and the regulation of rTMS on the neuroelectric activity of early AD was discussed.

Objective:To explore the diagnosis and treatment strategy of emphysematous pyelonephritis (EPN).Methods:The clinical data of 11 cases patients with EPN admitted to 3 hospitals from March 2016 to June 2022 were retrospectively analyzed, among them, 4 cases from Qianhai Shekou Free Trade Zone Hospital, 5 cases from the Second Hospital Affiliated to Kunming Medical University, 2 cases from the Second Affiliated Hospital of Hubei University of Scinece and Technology. Among the 11 patients, 2 were males and 9 were females, aged 50-82 years; the lesions were located on the left side in 6 cases, right side in 4 cases and bilateral in 1 case; all patients had type 2 diabetes and poor glycemic control. The clinical manifestations at admission including back pain in 8 cases, fever in 11 cases, nausea and vomiting in 5 cases, disturbance of consciousness in 3 cases, septic shock in 3 cases, accompanied with ureteral or kidney stones in 5 cases. The pathogenic bacteria were Escherichia coli in 8 cases, Klebsiella pneumoniae in 2 cases and Proteus mirabilis in 1 case. All patients received minimally invasive surgery, anti-infection, subcutaneous injection of insulin, fluid rehydration and nutritional support after admission. 2 cases had a combination of an initial ureteral stenting and second stage percutaneous drainage, 3 patients underwent ureteral stent implantation, 6 patients underwent percutaneous drainage. According to the CT classification of EPN, there were 1 case of type Ⅰ, 3 cases of type Ⅱ, 2 cases of type ⅢA, 4 cases of type ⅢB, and 1 case of type Ⅳ. Results:All 11 cases were cured, 4 cases were admitted to intensive care unit for 2-7 days, 1 case underwent nephrectomy during hospitalization, and 1 case underwent nephrectomy due to renal atrophy during follow-up. After 12 to 18 months of follow-up with urinary CT or B-ultrasound, there were no recurrence cases.Conclusions:EPN is a rare and serious renal parenchymal necrotic infection. Early urinary CT examination is necessary for the diagnosis, and positive minimally invasive surgery combined with comprehensive medical treatment is the preferred treatment strategy. If those above treatment does not work, nephrectomy should be performed.

Objective:To investigate the rate-limiting steps of hand-assisted laparoscopic donor nephrectomy, analyze the relevant factors affecting surgical difficulty, and subsequently construct a mathematical model to predict the difficulty of the procedure preoperatively.Methods:A retrospective study was conducted on 100 kidney donors who underwent hand-assisted laparoscopic donor nephrectomy performed by the same surgeon at Beijing Friendship Hospital, Capital Medical University from January 2021 to January 2024. Preoperative demographic data, imaging findings, general condition, donor kidney size, and postoperative complications were collected and analyzed. The surgeon′s subjective rating (1-3 points) was used as a quantitative measure of surgical difficulty. ANOVA and Chi-square tests were employed to explore the differences in postoperative complications, recovery, operative time, and intraoperative blood loss among groups with varying levels of difficulty. The main procedure was divided into four steps (excluding abdominal closure): Trocar placement, renal hilar dissection, perinephric dissection, and kidney retrieval. The time for each step and the total operative time were recorded. Pearson correlation test was used to analyze the relationship between each step and the total operative time, and ANOVA test was used to assess the time differences between steps and to determine if the time for the same step varied across different difficulty subgroups, thereby identifying the rate-limiting step of hand-assisted laparoscopic donor nephrectomy. In terms of the risk factors influencing the difficulty of surgery, Pearson and Spearman correlation tests were used to investigate the relationship between preoperative donor data and surgical difficulty scores, and a predictive model was constructed using multiple linear regression. Finally, the model was internally and externally validated to confirm its accuracy and effectiveness.Results:As the surgical difficulty increased (groups 1, 2, and 3), the postoperative drainage tube duration was correspondingly prolonged [(5.92±1.48) d, (8.00±1.75) d, and (11.88±4.45) d, respectively, P<0.05], and the severity of postoperative complications also significantly increased (the incidence of Clavien-Dindo grade ≥2 was 5.66%, 31.82% and 64.00%, respectively, P<0.01). In the analysis of rate-limiting steps, the time taken for all steps, except for Trocar placement, showed significant differences among the difficulty subgroups ( P<0.001). However, the average time for renal hilar dissection was (19.82±5.65) min, which was significantly longer than the other steps ( P<0.001). Therefore, renal hilar dissection was identified as the rate-limiting step of hand-assisted laparoscopic donor nephrectomy. In terms of the influencing factors of surgical difficulty, donor obesity, kidney width, abdominal anteroposterior sagittal diameter, number of renal arteries, distance from renal artery bifurcation to the abdominal aorta, degree of renal artery calcification, and mayo adhesive probability (MAP) score were all correlated with the surgical difficulty score ( P<0.05). However, multiple linear regression analysis revealed that only the number of renal arteries and the MAP score were the independent risk factors for higher surgical difficulty of hand-assisted laparoscopic donor nephrectomy. The predictive equation was: surgical difficulty=0.649×number of renal arteries+ 0.770×MAP score. Both internal and external validation confirmed the model's good accuracy. Conclusions:This study established a reliable and objective predictive model for the difficulty of hand-assisted laparoscopic donor nephrectomy based on the number of renal arteries and the MAP score. Renal hilar dissection was identified as the rate-limiting step of the procedure. This provides a reference for selecting an appropriate surgeon based on the predicted surgical difficulty.