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 causes and management strategies for lower limb ischemic necrosis following xenogeneic heterotopic heart transplantation from a multigene-edited pig to a rhesus monkey. Methods A xenogeneic heterotopic heart transplantation was performed on December 16, 2023, at the Institute of Experimental Animals of Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, using a quintuple-gene-edited pig as the donor and a rhesus monkey as the recipient. On postoperative day (POD) 9, the recipient monkey underwent left lower limb amputation due to ischemic necrosis. Blood samples were collected at various time points after transplantation for analysis of hematologic parameters, liver and renal function, myocardial enzymes, and coagulation profiles. Ultrasound and computed tomography (CT) were used to evaluate anastomotic patency and cardiac structure. Immunological assays, including complement-dependent cytotoxicity (CDC) and IgG/IgM antibody detection, combined with clinical observations, were employed to assess rejection type and therapeutic response. Results The recipient monkey survived for 46 days after transplantation. Echocardiography demonstrated preserved biventricular systolic function in the recipient’s native heart, with left ventricular ejection fraction (LVEF) consistently exceeding 50%. In the donor pig heart, left ventricular endocardial thickening was noted on POD 9, followed by right ventricular endocardial thickening on POD 24, while LVEF remained around 35%. No hyperacute or acute rejection was detected immunologically. CDC positivity ranged between 3.4% and 5.1%, with IgG/IgM antibody binding trends consistent with CDC results. Following amputation, the recipient exhibited elevated inflammatory markers, coagulopathy, and reactive thrombocytosis, which later normalized. Immunohistochemical staining of the necrotic limb revealed arterial and venous thrombosis; however, no T-cell or B-cell infiltration was observed in vascular structures, thrombi, nerves, muscles, fascia, or skin tissues, with CD3 and CD20 staining both negative. Conclusion Limb ischemia after xenogeneic heart transplantation may be associated with lower extremity vascular thrombosis triggered by local trauma in the context of transplantation-induced inflammatory activation and coagulation dysfunction. While no clear lymphocyte-mediated rejection was observed, further studies are needed to explore the potential role of non-lymphocyte-mediated immune mechanisms.

ObjectiveTo investigate the neuroprotective effect of glycosides of cistanches （GCs） in 1-methyl-4-phenyl-1，2，3，6-tetrahydropyridine （MPTP）-induced Parkinson’s disease （PD） model mice and its potential mechanism. MethodsForty mice were randomly divided into control group， PD group， GCs group and levodopa （L-DOPA） group， with 10 mice in each group. The control group received intraperitoneal injection of normal saline， while the other groups were intraperitoneally injected with MPTP （30 mg/kg） once daily for 5 consecutive days. After model establishment， the control group and PD group were intragastrically administered normal saline， and the GCs group and L-DOPA group received intragastric administration once daily for 28 consecutive days， at doses of 100 mg/kg and 50 mg/kg， respectively. The open field test and rotarod test were used to evaluate motor function. Immunohistochemistry was performed to detect the number of tyrosine hydroxylase （TH）-positive neurons in the substantia nigra of the midbrain. Transmission electron microscopy was adopted to observe the ultrastructure of mitochondria in the substantia nigra. Biochemical assays were used to determine the levels of glutathione （GSH）， malondialdehyde （MDA）， total superoxide dismutase （T-SOD） and reactive oxygen species （ROS） in substantia nigra tissues. Western blot was applied to detect the protein expression of TH， nuclear factor erythroid 2-related factor 2 （Nrf2）， Kelch-like ECH-associated protein 1 （Keap-1） and heme oxygenase-1 （HO-1） in substantia nigra tissues. ResultsCompared with the control group， mice in the PD group exhibited markedly impaired motor function， significantly decreased T-SOD activity， obvious reductions in the number of TH-positive neurons and GSH content， as well as elevated MDA and ROS levels in the substantia nigra （P<0.01）. Compared with the PD group， mice in the GCs group showed improved motor function， increased T-SOD activity， higher number of TH-positive neurons and elevated GSH content， accompanied by decreased MDA and ROS levels （P<0.05）. In the control group， mitochondria in the substantia nigra displayed normal morphology without obvious blurred membrane structures or vacuolar degeneration. The PD group presented mitochondrial swelling， disordered cristae and vacuolar changes， whereas these mitochondrial pathological alterations were alleviated in the GCs group. Compared with the control group， the protein levels of TH， Nrf2 and HO-1 were significantly downregulated， while Keap-1 protein expression was markedly upregulated in the PD group （P<0.01）. Compared with the PD group， the GCs group showed significant upregulation of TH， Nrf2 and HO-1 protein levels and a notable reduction in Keap-1 protein level （P<0.05）. ConclusionGCs ameliorate motor dysfunction and attenuate dopaminergic neuronal and mitochondrial damage in MPTP-induced PD model mice. These effects may be associated with modulation of Nrf2/HO-1 pathway-related protein expression， enhancement of antioxidant capacity， and alleviation of oxidative stress.

ObjectiveTo investigate the effects of Hei Xiaoyaosan on neuroinflammation in APP/PS1 mice and decipher the mechanism based on the C-X3-C chemokine ligand 1 （CX3CL1）-CX3CL1 receptor 1 （CX3CR1） signaling axis. MethodsSixty 16-week-old male APP/PS1 mice were selected and acclimatized for one week， and they were then randomly allocated into a model group， a CX3CL1 group （bilateral hippocampal injection of CX3CL1 at 0.5 g·L^-1）， and high-， medium-， and low-dose （22.10， 11.05， and 5.53 g·kg^-1， respectively） Hei Xiaoyaosan groups， with 12 mice in each group. Twelve age-matched male C57BL/6J mice served as the blank group （bilateral hippocampal injection of an equal volume of PBS）. The high-， medium-， and low-dose Hei Xiaoyaosan groups received corresponding drugs via gavage， while the blank group received an equal volume of normal saline， with an intervention period of 90 days. Morris water maze was used to assess learning and memory abilities. Nissl staining was performed to observe the pathological changes in neurons of the hippocampal CA1 region. Immunohistochemistry was adopted to detect amyloid-β_1-42 （Aβ_1-42） deposition in the hippocampal CA1 region. The immunofluorescence assay was employed to detect the expression of M1 and M2 microglial markers， including cluster of differentiation 86 （CD86）/ionized calcium-binding adapter molecule 1 （Iba-1） and arginase-1 （Arg1）/Iba1. Enzyme-linked immunosorbent assay （ELISA） was employed to measure the levels of interleukin-8 （IL-8） and interleukin-13 （IL-13） in the hippocampal tissue. Western blot was employed to determine the protein levels of CX3CL1， CX3CR1， CREB-binding protein （CBP）， cyclic AMP response element-binding protein （CREB）， phosphorylated （p）-CREB， and brain-derived neurotrophic factor （BDNF） in the hippocampal tissue. ResultsCompared with the blank group， the model group exhibited prolonged escape latency on day 5 and a reduced swimming distance in the target quadrant （P<0.01）， disarrangement of hippocampal neurons， with a reduced number of cells and uneven Nissl body staining， increased CD86/Iba1 expression， decreased Arg1/Iba1 expression （P<0.01）， enhanced Aβ expression （P<0.01）， an elevated IL-8 level and a declined IL-13 level （P<0.01）， and downregulated protein levels of CX3CL1， CX3CR1， CBP， p-CREB， and BDNF （P<0.01）. Compared with the model group， all treatment groups showed shortened escape latency on day 5 and an increased swimming distance in the target quadrant （P<0.05， P<0.01）， regular arrangement of neurons with an increased number and increased Nissl bodies， reduced CD86/Iba1 expression， increased Arg1/Iba1 and decreased Aβ expression （P<0.01）， and a declined IL-8 level and an elevated IL-13 level （P<0.05， P<0.01）. In the CX3CL1 group and the high- and medium-dose Hei Xiaoyaosan groups， the relative expression levels of CX3CL1， CX3CR1， CBP， and p-CREB were upregulated （P<0.01）. In the low-dose Hei Xiaoyaosan group， the relative expression levels of CX3CL1， CX3CR1， CBP， p-CREB/CREB， and BDNF were upregulated （P<0.05， P<0.01）. ConclusionHei Xiaoyaosan can improve learning and cognitive abilities in APP/PS1 mice. Specifically， it may regulate the CX3CL1/CX3CR1 signaling axis to activate the CREB/BDNF signaling pathway for the subsequent release of BDNF， modulation of microglial polarization， reduction of inflammatory cytokine levels， and attenuation of Aβ deposition.

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.