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 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.

Peptide-based therapies have attracted considerable interest in the treatment of cancer, diabetes, bacterial infections, and neurodegenerative diseases due to their promising therapeutic properties and enhanced safety profiles. This review provides a comprehensive overview of the major trends in peptide drug discovery and development, emphasizing preclinical strategies aimed at improving peptide stability, specificity, and pharmacokinetic properties. It assesses the current applications and challenges of peptide-based drugs in these diseases, illustrating the pharmaceutical areas where peptide-based drugs demonstrate significant potential. Furthermore, this review analyzes the obstacles that must be overcome in the future, aiming to provide valuable insights and references for the continued advancement of peptide-based drugs.
Humans ; Peptides/pharmacology* ; Animals ; Neoplasms/drug therapy* ; Drug Discovery ; Neurodegenerative Diseases/drug therapy* ; Diabetes Mellitus/drug therapy*

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.

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.