ObjectiveTo analyze the policy texts regarding the inclusion of 44 assisted reproductive medical service projects into basic medical insurance in China and to provide recommendations for the formulation and improvement of subsequent policies. MethodsMethods such as textual analysis， qualitative research， and descriptive statistics were employed. Based on the theory of policy instruments， a two-dimensional analytical framework integrating policy instruments with policy objectives was constructed to systematically examine the current status and existing issues in the inclusion of assisted reproductive medical service projects in basic medical insurance. ResultsDemand-based and environment-based policy instruments were more frequently utilized， accounting for 42.11% and 37.89%， respectively. Supply-based policy instruments accounted for only 20.00%， indicating limited use. In terms of policy objectives， standardized service management constituted the largest proportion （27.65%）， whereas integrating service resources accounted for the smallest share （10.00%）， showing an imbalanced and insufficient utilization of policy instruments. ConclusionTo better advance and implement the inclusion of assisted reproductive medical service projects in basic medical insurance and foster the construction of a fertility-friendly society， it is essential to strengthen the use of supply-based policy instruments， promote internal structural balance， expand the medical insurance catalog of assisted reproductive technologies， increase reimbursement rates， intensify publicity efforts， formulate scientific planning， establish rigorous qualification evaluations， enhance the synergy of policy instruments， and optimize the structure of policy instruments.

OBJECTIVE:Nowadays,machine learning algorithms are gradually being applied to predict stroke and cardiovascular disease.Compared with traditional regression models,machine learning can learn from data to achieve high prediction accuracy by exploring the flexible relationship between a large number of predictive features and outcome variables,providing a new method for the formulation of individualized treatment and rehabilitation programs.This study aims to systematically evaluate stroke functional recovery and prognosis prediction models based on machine learning,comprehensively assessing their predictive performance and clinical application potential to provide references for the development,application,and promotion of related predictive models.METHODS:This review was conducted following the PRISMA(Preferred Reporting Items for Systematic Reviews and Meta-Analyses)guidelines.Relevant literature on stroke prognosis prediction using machine learning methods was selected by searching PubMed,EMbase,Web of Science Core Collection,CNKI,WanFang,and the China Biomedical Literature Database,with the search period from January 1,2014,to July 1,2024.Two researchers independently screened the literature and extracted data based on inclusion and exclusion criteria,using the Prediction model Risk Of Bias ASsessment Tool(PROBAST)to assess model quality.RESULTS:(1)A total of 3 126 articles were obtained in the preliminary search.After screening and exclusion,18 articles were finally included.150 prediction models were constructed using 13 machine learning methods.The three most frequently used methods are Logistic Regression,Random Forest,and Extreme Gradient Boosting(XGBoost).Only one study was externally validated.Eight studies reported how the missing data were handled.(2)In terms of outcome indicators,8 studies used the combination of clinical data and imaging data to build models,9 studies only used clinical data to build models,and 1 study only used imaging data to build models.(3)Each of the 18 studies gave the most important characteristics of the study,with the most mentioned being the National Institute of Health Stroke Scale and age.All studies reported area under curve values ranging from 0.74 to 0.96,with the highest area under curve being 0.96.The overall risk of bias in all models was high.The high risk of bias in the field of model analysis was the main reason for the high risk of overall bias in all models.(4)The results of meta-analysis showed that age and National Institute of Health Stroke Scale score had significant influence on stroke prognosis,with age[MD=8.49,95％CI(6.24,10.75),P＜0.01]and National Institute of Health Stroke Scale score[MD=4.78,95％CI(2.56,7.00),P＜0.01].CONCLUSION:This study systematically evaluated the predictive model of functional recovery and prognosis of stroke based on machine learning,and all the models have good predictive potential.However,future studies should increase the sample size of the included model,adopt prospective studies,and add external validation of the model to improve the stability and prediction accuracy of the model,control the risk of bias,and contribute to the validation and promotion of the model in practical clinical applications.At the same time,the interpolation of missing values is more transparent and accurate.Although existing machine learning models show good predictive performance,it is also important to focus on the functionality and usability of the model,and the inclusion of features will reduce ease of use.We should develop easy to use model interfaces and user-friendly clinical tools to enable medical staff to better apply the model for clinical decision.

Objective To establish a liquid chromatography method for the simultaneous determination of 13 aromatic amine compounds (AAs) in workplace air. Methods A total of 13 AAs in both vapor and aerosol phases were collected in workplace air using a new GDH-6 sampling tube. Samples were desorbed and eluted with methanol, separated using a Symmetry Shield™ RP₁₈ reversed-phase liquid chromatography column, and detected with a diode array detector. Quantification was performed using an external standard method. Results The linear range of the 13 AAs measured by this method was 0.02-373.60 μg/L with the correlation coefficients greater than 0.999 0. The minimum detection concentration was 0.09-14.37 μg/m³, and the minimum quantitative concentration was 0.31-47.90 μg/m³ (both calculated based on sampling 15.0 L of air and 3.0 mL of elution volume). The average desorption and elution efficiency ranged from 97.46% to 101.23%. The within-run relative standard deviation (RSD) was 0.10%-5.99%, and the between-run RSD was 0.17%-2.71%. Samples could be stably stored in sealed conditions at 2-8 ℃ for more than seven days. Conclusion This method is suitable for the simultaneous determination of 13 AAs in workplace air, including both vapor and aerosol phases.

Objective To establish a method for simultaneous determination of four high-molecular-weight thiol derivatives (TDs) in workplace air by gas chromatography. Methods The four kinds of vapor-phase macromolecular TDs (1-pentanethiol, 1-hexanethiol, 1-benzyl mercaptan, and n-octanethiol) in the workplace air were collected using the GDH-1 air sampling tubes, desorbed with anhydrous ethanol, separated on a DB-FFAP capillary column, and determined by flame ionization detector. Results The quantitation range of the four TDs was 0.30-207.37 mg/L, with the correlation coefficients greater than 0.999 00. The minimum detection mass concentrations and minimum quantitation mass concentrations were 0.18-0.32 and 0.60-1.05 mg/m³, respectively (both calculated based on the 1.5 L sample and 3.0 mL desorption solvent). The mean desorption efficiencies ranged from 87.07% to 103.59%. The within-run and between-run relative standard deviations were 1.92%-8.22% and 1.89%-8.45%, respectively. The samples can be stored at room temperature or 4 ℃ for three days and up to 7 days at -18 ℃. Conclusion This method is suitable for the simultaneous determination of four vapor-phase TDs in workplace air.

By consulting relevant literature of ancient herbal books and processing specifications， this paper made a systematic research and analysis of Ostreae Concha， including the name， producing area， harvesting， quality， historical evolution of processing， relevant processing specifications， modern processing technology， and changes in chemical composition and pharmacological effects before and after processing， in order to provide documentary evidence for the research on processing technology and the establishment of quality standards. According to the textual research， it is known that Ostreae Concha has a long history of being used in medicine， and there have been many aliases and local names in each historical period. Shennong's Classic of the Materia Medica（Shennong Bencaojing） began to use Muli as the correct name， which has continued to use to today， and there were also aliases such as Muge， Zuogu Muli and Haoke. Ostreae Concha has a wide range of localities and irregular harvesting periods. The ancients believed that its left shell was of superior quality， but this has not been seen in modern. And there were many kinds of processing methods of Ostreae Concha， such as grinding， roasting， calcining， frying， simmering， quenching and so on， and the calcining was still in use. The different editions of Chinese Pharmacopoeia from 1963 to 2020 contain only calcined Ostreae Concha， and the local processing specifications mainly include three kinds of processed products（calcined products， salt-soaked products and vinegar-soaked products）. Modern processing research mainly focuses on process optimization， changes in chemical composition and pharmacological effects， and the research methods are relatively single. Overall， there are currently issues such as inconsistent processing standards， unclear process parameters and imperfect quality standards， which are not conducive to the quality control and standardized clinical use of Ostreae Concha. Therefore， it is necessary to further investigate the pharmacological substance basis of Ostreae Concha and its processed products in order to elucidate the processing mechanism， standardize the processing technology and improve the quality standard.

Background: Apolipoprotein A-I (ApoA-I) is a key cardioprotective lipoprotein. Nevertheless, it remains unclear how ApoA-I relates to ischemic risk across glucose metabolism statuses in patients with coronary artery disease (CAD). This study investigated whether glucose metabolism status influences the association between ApoA-I and ischemic risk in CAD patients. Methods: This cohort study included 10,724 consecutive CAD patients undergoing percutaneous coronary intervention, who were classified into diabetes mellitus (DM), pre-DM, and normal glucose regulation (NGR) groups. The primary clinical endpoint was major adverse cardiac and cerebrovascular event (MACCE), defined as a composite of all-cause death, myocardial infarction, revascularization, and stroke. Results: Of the 10,232 patients ultimately included, 2,139 (20.9%) experienced MACCE over 5 years. A significant interaction was observed between ApoA-I levels and glucose metabolism status (P for interaction=0.041). In the DM group, an L-shaped association between ApoA-I and MACCE was found, with lower ApoA-I levels linked to a higher risk of MACCE (P for non-linearity= 0.044). Multivariate Cox regression analysis showed that patients in the lowest quintile of ApoA-I had a 1.327-fold increased risk of MACCE compared to those at the lowest risk (hazard ratio, 1.327; 95% confidence interval, 1.097 to 1.604). However, no significant association was observed in the pre-DM or NGR groups (both P>0.05). Conclusion This large-scale, 5-year follow-up study is the first to demonstrate that lower ApoA-I levels are associated with increased MACCE risk in CAD patients with DM, highlighting the potential value of ApoA-I in risk stratification and as a therapeutic target.

OBJECTIVE:Nowadays,machine learning algorithms are gradually being applied to predict stroke and cardiovascular disease.Compared with traditional regression models,machine learning can learn from data to achieve high prediction accuracy by exploring the flexible relationship between a large number of predictive features and outcome variables,providing a new method for the formulation of individualized treatment and rehabilitation programs.This study aims to systematically evaluate stroke functional recovery and prognosis prediction models based on machine learning,comprehensively assessing their predictive performance and clinical application potential to provide references for the development,application,and promotion of related predictive models.METHODS:This review was conducted following the PRISMA(Preferred Reporting Items for Systematic Reviews and Meta-Analyses)guidelines.Relevant literature on stroke prognosis prediction using machine learning methods was selected by searching PubMed,EMbase,Web of Science Core Collection,CNKI,WanFang,and the China Biomedical Literature Database,with the search period from January 1,2014,to July 1,2024.Two researchers independently screened the literature and extracted data based on inclusion and exclusion criteria,using the Prediction model Risk Of Bias ASsessment Tool(PROBAST)to assess model quality.RESULTS:(1)A total of 3 126 articles were obtained in the preliminary search.After screening and exclusion,18 articles were finally included.150 prediction models were constructed using 13 machine learning methods.The three most frequently used methods are Logistic Regression,Random Forest,and Extreme Gradient Boosting(XGBoost).Only one study was externally validated.Eight studies reported how the missing data were handled.(2)In terms of outcome indicators,8 studies used the combination of clinical data and imaging data to build models,9 studies only used clinical data to build models,and 1 study only used imaging data to build models.(3)Each of the 18 studies gave the most important characteristics of the study,with the most mentioned being the National Institute of Health Stroke Scale and age.All studies reported area under curve values ranging from 0.74 to 0.96,with the highest area under curve being 0.96.The overall risk of bias in all models was high.The high risk of bias in the field of model analysis was the main reason for the high risk of overall bias in all models.(4)The results of meta-analysis showed that age and National Institute of Health Stroke Scale score had significant influence on stroke prognosis,with age[MD=8.49,95％CI(6.24,10.75),P＜0.01]and National Institute of Health Stroke Scale score[MD=4.78,95％CI(2.56,7.00),P＜0.01].CONCLUSION:This study systematically evaluated the predictive model of functional recovery and prognosis of stroke based on machine learning,and all the models have good predictive potential.However,future studies should increase the sample size of the included model,adopt prospective studies,and add external validation of the model to improve the stability and prediction accuracy of the model,control the risk of bias,and contribute to the validation and promotion of the model in practical clinical applications.At the same time,the interpolation of missing values is more transparent and accurate.Although existing machine learning models show good predictive performance,it is also important to focus on the functionality and usability of the model,and the inclusion of features will reduce ease of use.We should develop easy to use model interfaces and user-friendly clinical tools to enable medical staff to better apply the model for clinical decision.

Objective To improve the national standardized method for determining biphenyl in workplace air, which was based on activated carbon tube sampling, carbon disulfide desorption, and gas chromatography, by developing a method using GDX-502 tubes for sampling, toluene for desorption, and gas chromatography. Methods Workplace air samples were collected using GDX-502 sampling tubes and desorbed with toluene, followed by determination with gas chromatography. Results The improved method demonstrated good linearity for biphenyl concentrations ranging from 0.33 to 330.00 mg/L, with a correlation coefficient of 0.999 9. The detection limit and lower limit of quantification were 0.06 and 0.21 mg/L, and the minimum detection concentration and minimum quantification concentration were 0.04 and 0.14 mg/m³ (based on 1.5 L air sample volume), respectively. The average desorption efficiency ranged from 96.6% to 101.1%. The within-run and between-run relative standard deviations were 0.6%-1.4% and 1.4%-3.3%, respectively, with 100.0% sampling efficiency. Samples remained stable for at least 14 days at room temperature. Conclusion The improved method for biphenyl detection demonstrates rapid and accurate performance, with the advantages of low detection limits and high sampling and desorption efficiency.

ObjectiveTo investigate the potential mechanism of Liangxue Tuizi Formula （凉血退紫方， LXTZF） in treating Henoch-Schönlein Purpura （HSP） by examining its regulatory effect on neutrophil extracellular trap （NETs） dysregulation via the rapidly accelerated fibrosarcoma kinase （RAF）/mitogen-activated protein kinase （MEK）/extracellular signal-regulated kinase （ERK） signaling pathway. MethodsSeventy Wistar rats were randomly allocated into a blank control group （n=14） and a modeling group （n=56）. Rats in the modelling group underwent an eight-week modelling period to establish HSP rat models with blood-heat syndrome via modified ovalbumin （OVA） induction method combined with oral administration of heat-property Chinese herbal medicine. Fifty successfully modeled rats were subsequently randomly divided into five groups （n=10 per group）， model group， compound glycyrrhizin group， LXTZF group， RAF inhibitor group， and LXTZF + RAF agonist group. Additionally， 10 rats were selected from the original blank control group for the final experiment. From the 11th week of modelling， rats in the blank control group and the model group received 1 ml/（100 g·d） ultrapure water via oral administration， in addition to 0.5 ml/（kg·d） 0.9% sodium chloride solution via intraperitoneal injection. The LXTZF group and the compound glycyrrhizin group received 7.5 g/（kg·d） LXTZF granule suspension via gavage， 13.5 mg/（kg·d） compound glycyrrhizin suspension via gavage， respectively. The RAF inhibitor group received 1 mg/（kg·d） GW5074 suspension via intraperitoneal injection and ultrapure water via oral administration； the LXTZF + RAF agonist group received 7.5 g/（kg·d） LXTZF granule suspension via gavage and 1 mg/（kg·d） paclitaxel suspension via intraperitoneal injection. All administrations were performed once daily for 4 weeks. After intervention， skin tissue histopathology was examined by hematoxylin and eosin （H&E） staining， immunoglobulin A （IgA） deposition was assessed via immunofluorescence， serum levels of neutrophil elastase （NE）， tumor necrosis factor-α （TNF-α）， and vascular cell adhesion molecule-1 （VCAM-1） were measured using enzyme-linked immunosorbent assay （ELISA）， serum myeloperoxidase （MPO） level was determined by a colorimetric assay； the mRNA expression levels of RAF， MEK， and ERK in skin tissue were detected by real-time quantitative polymerase chain reaction （RT-qPCR）； and the protein expression of RAF， MEK， ERK， as well as phosphorylated MEK （p-MEK） and phosphorylated ERK （p-ERK）， were analyzed by Western Blot. ResultsSkin tissue in the blank control group rats remained normal， whereas the model group exhibited neutrophil infiltration and haemorrhage with red blood cell rupture. In all drug intervention groups， neutrophil infiltration and haemorrhagic exudation reduced markedly， with LXTZF group demonstrating the most pronounced improvement. Compared with the blank control group， rats in the model group exhibited enhanced IgA fluorescence intensity in skin tissue， elevated serum levels of NE， MPO， TNF-α and VCAM-1， increased mRNA expression of RAF， MEK， ERK1 and ERK2， as well as heightened RAF protein levels and p-MEK/MEK and p-ERK/ERK ratios （P＜0.05）. Compared with the model group， the drug intervention groups exhibited reduced IgA fluorescence intensity in skin tissue， along with decreased serum levels of NE， MPO， TNF-α， and VCAM-1 （P＜0.05）. In LXTZF group and RAF inhibition groups， reduced mRNA expression of RAF， MEK， ERK1， and ERK2 was observed in rat skin tissue， alongside decreased RAF protein levels and reduced p-MEK/MEK and p-ERK/ERK ratios （P＜0.05）. Compared with LXTZF + RAF agonist group， the compound glycyrrhizin group， LXTZF group， and RAF inhibitior group exhibited reduced IgA fluorescence intensity in skin tissue， decreased serum NE， MPO， TNF-α， and VCAM-1 levels， and decreased MEK mRNA expression and p-MEK/MEK ratio （P＜0.05）. ConclusionThe potential mechanism by which LXTZF treats Henoch-Schönlein purpura with blood heat syndrome may involve blocking the RAF/MEK/ERK signaling pathway in skin tissue， and suppressing excessive formation of NETs， thereby reducing IgA deposition in dermal microvessels and attenuating systemic inflammatory responses.

Objective To develop a solvent desorption-gas chromatography method for quantifying malononitrile in workplace air. Methods Malononitrile in workplace air was collected using a silica gel tube and desorbed with methanol. Separation was performed using DB-FFAP capillary column, and detection was performed by hydrogen flame ionization detector. Results The linear ranges of malononitrile were 4.00-600.00 mg/L, with the correlation coefficient of 0.999 92. The detection limit was 0.54 mg/L. The minimum detection concentration was 0.36 mg/m³ (based on a collected air volume of 1.5 L). The average desorption efficiency was 95.0%-101.0%. The within-run and between-run relative standard deviations were 0.9%-5.3% and 1.0%-6.8%, respectively. The sampling efficiency was 100.0%. The samples were stable at 4 ℃ for at least 14 days. Conclusion The solvent desorption-gas chromatography method established in this study for the determination of malononitrile in workplace air is simple to use, with high accuracy and good precision, and is suitable for the determination of malononitrile in workplace air.