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.

Colorectal cancer is one of the most common malignant tumors, which is a great threat to human life and health. The change of bile acid homeostasis can activate their corresponding receptors to regulate the immune functions, which is closely related to the occurrence of colorectal cancer. In addition, some bile acids can directly induce colorectal cancer and play an important role in the development of colorectal cancer. In this paper, the metabolic process of bile acids in vivo and the immunomodulatory role of bile acid receptors were reviewed, and the evidence of associations between bile acids and colorectal cancer were summarized, which showed the rebalancing the bile acid levels might play a role in the prevention or treatment of colorectal cancer.

Objective:To explore the risk factors of adverse events during the perioperative period of transurethral resection of bladder tumor(TURBT)in bladder cancer patients with coronary atherosclerotic heart disease(CAD).Methods:We retrospectively analyzed the clinical data of bladder cancer patients who underwent TURBT in Beijing Anzhen Hospital from June 2022 to September 2024.All patients with bladder cancer and CAD underwent coronary computed tomography angiography(CCTA)for diagnosis and assessment of CAD before surgery.Based on the CCTA results,the patients with bladder cancer and CAD were divided into two groups:those with mild to moderate coronary stenosis and those with severe coronary stenosis.The severe coronary stenosis group was further divided into two subgroups based on whether they received low-molecular-weight heparin(LMWH)bridging therapy or continued their anti-platelet treatment before surgery.Perioperative anticoagulation and antiplatelet strategies were adjusted according to the opinions of the specialists.The incidence of adverse events within 30 days postoperative-ly was followed up and analyzed.Results:A total of 80 bladder cancer patients with CAD who underwent TURBT were included in the study.Among the 80 patients with CAD,55(68.8％)had mild to moder-ate coronary stenosis,and 25(31.2％)had severe coronary stenosis.Compared with those had mild to moderate coronary stenosis,the patients who had severe coronary stenosis had a higher incidence of post-operative bleeding and pulmonary embolism,although the differences were not statistically significant(P＞0.05).However,the incidence of postoperative myocardial infarction was significantly higher in the patients who had severe coronary stenosis(P=0.034).Among the patients with severe coronary stenosis,8(32.0％)received LMWH bridging therapy before TURBT,and 17(68.0％)continued their previous antiplatelet treatment.Compared with those who continued antiplatelet treatment,the patients who re-ceived LMWH bridging therapy had a higher incidence of postoperative bleeding and pulmonary embo-lism,although the differences were not statistically significant(P＞0.05).However,the incidence of postoperative myocardial infarction was significantly higher in the LMWH bridging group(P=0.032).Conclusion:Patients with mild-to-moderate coronary stenosis demonstrate relatively low perioperative risk during TURBT procedures and may safely undergo TURBT following antiplatelet therapy discontinuation.Conversely,those with severe coronary stenosis exhibit significantly higher perioperative risk and require intensive monitoring.In bladder cancer patients with concomitant severe coronary stenosis,perioperative LMWH bridging therapy is associated with increased myocardial infarction risk,whereas continued anti-platelet therapy does not elevate postoperative bleeding risk.Current evidence therefore supports maintai-ning antiplatelet therapy in these patients,with appropriate bleeding risk assessment.

Perception of recurrence risk in cardiovascular disease (CVD) patients plays a significant role in aspects such as their quality of life and treatment adherence. This paper reviews the theoretical foundations of recurrence risk perception, the conceptual origins and developmental process, measurement tools, influencing factors of recurrence risk perception in CVD patients, and research progress of recurrence risk perception in CVD management. The aim is to provide a basis for developing scientifically effective intervention measures for CVD patients in the future.

Objective To evaluate the clinical efficacy of hip-knee-ankle active and passive exercise therapy in patients with early-to mid-stage knee osteoarthritis(KOA).Methods A total of 180 patients with early to mid-stage knee osteoarthritis(KOA)were recruited from the First Affiliated Hospital of Hebei University of Tradi-tional Chinese Medicine between March 2023 and March 2024.Patients were randomly assigned to one of four groups:active movement group,passive movement group,combined movement group,and control group,with 45 patients in each group.The active movement group received hip-knee-ankle active movement therapy daily until the end of follow-up.The passive movement group underwent hip-knee-ankle passive movement therapy three times per week for two weeks.The combined movement group received both active and passive therapies.The control group was administered oral celecoxib capsules(200 mg once daily for two weeks).Joint function was assessed in all four groups before treatment,at two weeks post-treatment,and at 14 weeks post-treatment.The primary outcome measure was the WOMAC joint function score,while secondary outcomes included the WOMAC pain score,stiffness score,and quality of life score(SF-12).Results A total of 160 patients completed the trial,with 39 in the active group,42 in the passive group,40 in the combined group,and 39 in the control group.There were no significant differences in baseline characteristics among the groups(P>0.05).Compared to baseline,the WOMAC scores for function,pain,and stiffness in the passive,combined,and control groups decreased significantly at both 2 and 14 weeks post-treatment(P<0.05),while the SF-12 scores increased significantly(P<0.05).Between 2 and 14 weeks post-treat-ment,the active and combined groups showed further significant decreases in WOMAC function,pain,and stiffness scores(P<0.05)and increases in SF-12 scores(P<0.05).At 2 weeks post-treatment,compared to the control group,the passive and combined groups exhibited significantly lower WOMAC function scores(P<0.05),with no significant difference between the passive and combined groups(P>0.05).By 14 weeks post-treatment,the active and combined groups demonstrated significantly lower WOMAC function scores(P<0.05),with the combined group showing a significantly lower score than the active group(P<0.05).Conclusion The four therapeutic approaches demonstrate a certain degree of efficacy in improving joint function for patients with early and mid-stage KOA.The passive therapy group exhibits superior short-term outcomes,while the active therapy group shows better long-term benefits.The combined therapy group presents notable advantages in both short-term and long-term effi-cacy,although its short-term effectiveness does not surpass that of the passive therapy group.It is recommended for patients with early and mid-stage KOA who have underlying gastrointestinal and cardiovascular conditions.

Objective:To identify the key epithelial cell characteristics that can accurately diagnose eosinophilic chronic sinusitis with nasal polyps（ECRSwNP） through nasal brush sampling and comparing with the pathological results of nasal polyp tissue sections. Methods:Ninety-one patients underwent surgery in the Ophthalmology and ENT Department of the Second People's Hospital of Longgang District, Shenzhen, from January 2022 to July 2024 were selected. The cohort comprised 58 males and 33 females（mean age: 41.4 years; range: 12.0-71.0）. The clinical characteristics of the patients, including gender, age, disease duration, smoking and drinking history, asthma history, subjective symptoms, sinus CT, and nasal endoscopy scores, were recorded. Nasal brush sampling of nasal polyps and inferior turbinate mucosa was performed before surgery to obtain cytological specimens, and nasal polyp tissues were collected during surgery. The demographic and clinical characteristics of patients with eosinophilic and non-eosinophilic nasal polyps were compared, as well as the relationship between nasal brush cytology of nasal polyps and inferior turbinate and nasal polyp histopathology. Statistical analysis was performed using SPSS 23.0 software. Results:Among the 91 patients, no significant differences were observed between ECRSwNP and NECRSwNP patients in terms of age, gender, smoking status, alcohol consumption, and disease duration. The nasal brush cell population in ECRSwNP patients was more likely to contain eosinophils（P<0.001） and less likely to contain lymphocytes and plasma cells（P<0.001）. Additionally, the ciliated cells in ECRSwNP patients exhibited larger widths（P=0.036）, shorter cilium lengths（P<0.001）, and more disordered arrangements（P<0.001） compared to NECRSwNP patients. In nasal brush cells from the inferior turbinate, ECRSwNP patients also showed shorter cilium lengths（P<0.001） and shorter cilia（P=0.024） compared to NECRSwNP patients. Conclusion:There are significant differences in obtaining epithelial cytological information from nasal polyps or inferior turbinates through nasal brush sampling between ECRSwNP and NECRSwNP patients.
Humans ; Male ; Female ; Middle Aged ; Adult ; Nasal Polyps/complications* ; Sinusitis/complications* ; Aged ; Chronic Disease ; Adolescent ; Nasal Mucosa/pathology* ; Young Adult ; Rhinitis/complications* ; Eosinophilia/pathology* ; Child ; Eosinophils/pathology* ; Rhinosinusitis

Stearoyl-coenzyme A desaturase 1 (SCD1) catalyzes the rate-limiting step of de novo lipogenesis and modulates lipid homeostasis. Although numerous SCD1 inhibitors were tested for treating metabolic disorders both in preclinical and clinic studies, the tissue-specific roles of SCD1 in modulating obesity-associated metabolic disorders and determining the pharmacological effect of chemical SCD1 inhibition remain unclear. Here a novel role for intestinal SCD1 in obesity-associated metabolic disorders was uncovered. Intestinal SCD1 was found to be induced during obesity progression both in humans and mice. Intestine-specific, but not liver-specific, SCD1 deficiency reduced obesity and hepatic steatosis. A939572, an SCD1-specific inhibitor, ameliorated obesity and hepatic steatosis dependent on intestinal, but not hepatic, SCD1. Mechanistically, intestinal SCD1 deficiency impeded obesity-induced oxidative stress through its novel function of inducing metallothionein 1 in intestinal epithelial cells. These results suggest that intestinal SCD1 could be a viable target that underlies the pharmacological effect of chemical SCD1 inhibition in the treatment of obesity-associated metabolic disorders.

Prostate cancer is the most prevalent malignant tumor among men, ranking first in incidence and second in mortality globally. Novel hormone therapies (NHT) targeting the androgen receptor (AR) pathway have become the standard of care for metastatic prostate cancer. This review offers a comprehensive overview of NHT, including abiraterone, enzalutamide, apalutamide, darolutamide, and rezvilutamide, which have demonstrated efficacy in delaying disease progression and improving patient survival and quality of life. Nevertheless, resistance to NHT remains a critical challenge. The mechanisms underlying resistance are complex, involving AR gene amplification, mutations, splice variants, increased intratumoral androgens, and AR-independent pathways such as the glucocorticoid receptor, neuroendocrine differentiation, DNA repair defects, autophagy, immune evasion, and activation of alternative signaling pathways. This review discusses these resistance mechanisms and examines strategies to counteract them, including sequential treatment with novel AR-targeted drugs, chemotherapy, poly ADP-ribose polymerase inhibitors, radionuclide therapy, bipolar androgen therapy, and approaches targeting specific resistance pathways. Future research should prioritize elucidating the molecular basis of NHT resistance, optimizing existing therapeutic strategies, and developing more effective combination regimens. Additionally, advanced sequencing technologies and resistance research models should be leveraged to identify novel therapeutic targets and improve drug delivery efficiencies. These advancements hold the potential to overcome NHT resistance and significantly enhance the management and prognosis of patients with advanced prostate cancer.