OBJECTIVE To systematically investigate the current status and effectiveness of the standardized on-the-job training program for community clinical pharmacists in Shanghai， and to provide a scientific basis for optimizing the training scheme. METHODS A questionnaire survey was conducted to collect the data from trainees and mentor pharmacists who participated in the program between 2016 and 2024. The survey examined their basic information， evaluations of the training scheme， satisfaction with training outcomes， and suggestions for improvement. Statistical analyses were also conducted. RESULTS A total of 420 valid responses were collected， including 340 from trainees and 80 from mentor pharmacists. Before training， only 30.29% of trainees were engaged in clinical pharmacy-related work， whereas this proportion increased to 73.24% after training. Most mentor pharmacists had extensive experience in clinical pharmacy （76.25% with ≥5 years of experience） and mentoring （78.75% with ≥3 teaching sessions）. Totally 65.59% of trainees and 55.00% of mentor pharmacists believed that blended training yielded the best learning outcomes. Over 80.00% of both trainees and mentor pharmacists considered the overall training duration， theoretical study time， and practical training time to be reasonable. More than 95.00% of trainees and mentor pharmacists agreed that the homework and assessment schemes were appropriate. Trainees rated the relevance of training content to their actual work highly （with an average relevance score ＞4.5）， though they perceived the chronic disease medication therapy management module as significantly more challenging than the prescription review and evaluation module and the home-based pharmaceutical care module. The average satisfaction score of trainees and mentor pharmacists with the training effectiveness of each project was above 4 points， indicating a high overall satisfaction. Inadequate provision of teaching resources was unanimously recognized by trainees and mentor pharmacists as the key area requiring improvement. CONCLUSIONS The standardized on-the-job training program for community clinical pharmacists in Shanghai has contributed to improving pharmaceutical services in community healthcare settings. However， ongoing improvements must concentrate on content design， resource development， and faculty cultivation.

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.

Objective: To explore the bidirectional associations among body mass index Z scores (BMI Z scores) and weight stigma with depressive symptoms in adolescents, thereby providing evidence for targeted intervention strategies. Methods: A stratified cluster random sampling method was employed to select 18 301 adolescents aged 12-18 years from all 12 prefectures (103 counties) in the Inner Mongolia Autonomous Region, and two waves of longitudinal surveys were conducted in September 2023 (T1) and September 2024 (T2) among the adolescents. Weight stigma was assessed by using a self developed questionnaire, depressive symptom was measured with the Center for Epidemiologic Studies Depression Scale (CES-D), and BMI Z scores were calculated according to the World Health Organization standards. Pearson correlation analysis was used to examine associations among variables, and cross lagged panel models were constructed to investigate the dynamic bidirectional relationships among the three variables. Results: Adolescents BMI Z scores and weight stigma with depressive symptoms all exhibited autoregressive stability across the two time points (autoregressive paths, all P <0.01). Cross lagged model comparisons indicated that the bidirectional path model achieved the best fit ( χ 2=12.65, RMSEA =0.017, CFI =1.000; △ χ 2=193.39, P <0.01), supporting dynamic bidirectional associations among the three variables. After adjusting for gender, age, subjective social status and only child status, T1 BMI Z scores among adolescents positively predicted T2 weight stigma ( β =0.061), and T1 weight stigma positively predicted T2 depressive symptoms ( β =0.608); in the reverse direction, T1 depressive symptoms predicted T2 weight stigma ( β =0.003), and T1 weight stigma predicted T2 BMI Z scores ( β =0.081) (all P <0.01). Conclusions There is a bidirectional cross lagged relationship among adolescents BMI Z scores and weight stigma with depressive symptoms, suggesting that weight stigma may serve as a key psychological variable linking obesity and depressive symptoms. Greater attention should be paid to the potential threat of weight stigma to adolescents mental health, with intervention strategies expanded from a solely physiological focus to encompass psychosocial dimensions.

ObjectiveTo explore the dose-effect relationship and safety of high， medium， and low doses of raw Astragali Radix in the modified Huangqi Chifengtang （MHCD） for treating proteinuria in immunoglobulin A （IgA） nephropathy， and to provide scientific evidence for the clinical use of high-dose Astragali Radix in the treatment of proteinuria in IgA nephropathy. MethodsA total of 120 patients with IgA nephropathy， diagnosed with Qi deficiency and blood stasis combined with wind pathogen and heat toxicity， were randomly divided into a control group and three treatment groups. The control group received telmisartan combined with a Chinese medicine placebo， while the treatment groups were given telmisartan combined with MHCD containing different doses of raw Astragali Radix （60， 30， 15 g）. Each group contained 30 patients， and the treatment period was 12 weeks. Changes in 24-hour urinary protein （24 hUTP）， traditional Chinese medicine （TCM） syndrome scores， effective rate， and renal function were observed before and after treatment. Safety was assessed by monitoring liver function and blood routine. ResultsAfter 12 weeks of treatment， 24 hUTP significantly decreased in the high， medium， and low-dose groups， as well as the control group （P<0.05， P<0.01）. The TCM syndrome scores in the high， medium， and low-dose groups also significantly decreased （P<0.01）. Comparisons between groups showed that the 24 hUTP in the high-dose group was significantly lower than in the medium， low-dose， and control groups （P<0.05， P<0.01）， and the 24 hUTP in the medium-dose group was significantly lower than in the control group （P<0.05）. The TCM syndrome scores in the high and medium-dose groups were significantly lower than in the low-dose and control groups （P<0.05， P<0.01）. The total effective rates for proteinuria in the high， medium， low-dose， and control groups were 92.59% （25/27）， 85.19% （23/27）， 60.71% （17/28）， and 57.14% （16/28）， respectively. The effective rates in the high and medium-dose groups were significantly higher than in the low-dose and control groups （χ²=13.185， P<0.05， P<0.01）. The effective rates for TCM syndrome scores in the high， medium， low-dose， and control groups were 88.89% （24/27）， 81.48% （22/27）， 71.43% （20/28）， and 46.43% （13/28）， respectively. The efficacy of TCM syndrome scores in the high and medium-dose groups was significantly higher than in the control group （χ²=14.053， P<0.01）. Compared with pre-treatment values， there was no statistically significant difference in eGFR and serum creatinine in the high and medium-dose groups. However， eGFR significantly decreased in the low-dose and control groups after treatment （P<0.05）， and serum creatinine levels increased significantly in the control group （P<0.05）. No statistically significant differences were observed in urea nitrogen， uric acid， albumin， total cholesterol， triglycerides， liver function， and blood routine before and after treatment in any group. ConclusionThere is a dose-effect relationship in the treatment of IgA nephropathy with high， medium， and low doses of raw Astragali Radix in MHCD. The high-dose group exhibited the best therapeutic effect and good safety profile.

ObjectiveTo establish a risk recognition model for coronary artery stenosis by using a machine learning method and to identify the key causative factors. MethodsPatients aged ≥18 years，diagnosed with coronary heart disease through coronary angiography from January 2013 to May 2020 in two prominent hospitals in Shanxi Province， were continuously enrolled. Logistic regression，back propagation neural network （BPNN）， and random forest（RF）algorithms were used to construct models for detecting the causative factors of coronary artery stenosis. Sensitivity （TPR）， specificity （TNR）， accuracy （ACC）， positive predictive value （PV+）， negative predictive value （PV-）， area under subject operating characteristic curve （AUC）， and calibration curve were used to compare the discrimination and calibration performance of the models. The best model was then employed to predict the main risk variables associated with coronary stenosis. ResultsThe RF model exhibited superior comprehensive performance compared to logistic regression and BPNN models. The TPR values for logistic regression，BPNN，and RF models were 75.76%， 74.30%， and 93.70%， while ACC values were 74.05%， 72.30%， and 79.49%， respectively. The AUC values were：logistic regression 0.739 9； BPNN 0.723 1； RF 0.752 2. Manifestations such as chest pains，abnormal ST segments on ECG，ventricular premature beats with hypertension， atrial fibrillation， regional wall motion abnormalities（RWMA） by color echocardiography， aortic regurgitation（AR）， pulmonary insufficiency （PI）， family history of cardiovascular diseases，and body mass index（BMI）were identified as top ten important variables affecting coronary stenosis according to the RF model. ConclusionsRandom forest model shows the best comprehensive performance in identification and accurate assessment of coronary artery stenosis. The prediction of risk factors affecting coronary artery stenosis can provide a scientific basis for clinical intervention and help to formulate further diagnosis and treatment strategies so as to delay the disease progression.

The analysis presented here is based on the blood components of Guanxin Qiwei tablets, the key anti-atherosclerosis pathway of Guanxin Qiwei tablets was screened by network pharmacology, and the anti-atherosclerosis mechanism of Guanxin Qiwei tablets was clarified and verified by cell experiments. HPLC-Q-Exactive-MS/MS technique was used to analyze the components of Guanxin Qiwei tablets into blood, to determine the precise mass charge ratio of the compounds, and to conduct a comprehensive analysis of the components by using secondary mass spectrometry fragments and literature comparison. Finally, a total of 42 components of Guanxin Qiwei tablets into blood were identified. To better understand the interactions, we employed the Swiss Target Prediction database to predict the associated targets. Atherosclerosis (AS) disease targets were searched in disease databases Genecard, OMIM and Disgent, and 181 intersection targets of disease targets and component targets were obtained by Venny 2.1.0 software. Protein interactions were analyzed by String database. The 32 core targets were selected by Cytscape software. Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed in DAVID database. It was found that the anti-atherosclerosis pathways of Guanxin Qiwei tablets mainly include lipid metabolism and atherosclerosis and AGE-RAGE signaling pathway in diabetic complications and other signal pathways. The core targets and the core compounds were interlinked, and it was found that cryptotanshinone and tanshinone ⅡA in Guanxin Qiwei tablets were well bound to TNF, PPARγ, AKT1, PTG2 and other targets. The lipid metabolism and atherosclerotic pathway was verified using human hl-7702 hepatocytes. This study preliminarily identified the potential pharmacodynamic components of Guanxin Qiwei tablets in the treatment of AS diseases and predicted their pathways of action, and verified the relationship between regulating lipid metabolism and atherosclerosis of Guanxin Qiwei tablets in vitro, providing a reference for the further study of the pharmacodynamic material basis and mechanism of action of this prescription. This experiment was approved by the Medical Ethics Committee of Inner Mongolia Medical University (No. YKD202401262).

Membrane proteins are integral components of cellular membranes, accounting for approximately 30% of the mammalian proteome and serving as targets for 60% of FDA-approved drugs. They are critical to both physiological functions and disease mechanisms. Their functional protein-protein interactions form the basis for many physiological processes, such as signal transduction, material transport, and cell communication. Membrane protein interactions are characterized by membrane environment dependence, spatial asymmetry, weak interaction strength, high dynamics, and a variety of interaction sites. Therefore, in situ analysis is essential for revealing the structural basis and kinetics of these proteins. This paper introduces currently available in situ analytical techniques for studying membrane protein interactions and evaluates the characteristics of each. These techniques are divided into two categories: label-based techniques (e.g., co-immunoprecipitation, proximity ligation assay, bimolecular fluorescence complementation, resonance energy transfer, and proximity labeling) and label-free techniques (e.g., cryo-electron tomography, in situ cross-linking mass spectrometry, Raman spectroscopy, electron paramagnetic resonance, nuclear magnetic resonance, and structure prediction tools). Each technique is critically assessed in terms of its historical development, strengths, and limitations. Based on the authors’ relevant research, the paper further discusses the key issues and trends in the application of these techniques, providing valuable references for the field of membrane protein research. Label-based techniques rely on molecular tags or antibodies to detect proximity or interactions, offering high specificity and adaptability for dynamic studies. For instance, proximity ligation assay combines the specificity of antibodies with the sensitivity of PCR amplification, while proximity labeling enables spatial mapping of interactomes. Conversely, label-free techniques, such as cryo-electron tomography, provide near-native structural insights, and Raman spectroscopy directly probes molecular interactions without perturbing the membrane environment. Despite advancements, these methods face several universal challenges: (1) indirect detection, relying on proximity or tagged proxies rather than direct interaction measurement; (2) limited capacity for continuous dynamic monitoring in live cells; and (3) potential artificial influences introduced by labeling or sample preparation, which may alter native conformations. Emerging trends emphasize the multimodal integration of complementary techniques to overcome individual limitations. For example, combining in situ cross-linking mass spectrometry with proximity labeling enhances both spatial resolution and interaction coverage, enabling high-throughput subcellular interactome mapping. Similarly, coupling fluorescence resonance energy transfer with nuclear magnetic resonance and artificial intelligence (AI) simulations integrates dynamic structural data, atomic-level details, and predictive modeling for holistic insights. Advances in AI, exemplified by AlphaFold’s ability to predict interaction interfaces, further augment experimental data, accelerating structure-function analyses. Future developments in cryo-electron microscopy, super-resolution imaging, and machine learning are poised to refine spatiotemporal resolution and scalability. In conclusion, in situ analysis of membrane protein interactions remains indispensable for deciphering their roles in health and disease. While current technologies have significantly advanced our understanding, persistent gaps highlight the need for innovative, integrative approaches. By synergizing experimental and computational tools, researchers can achieve multiscale, real-time, and perturbation-free analyses, ultimately unraveling the dynamic complexity of membrane protein networks and driving therapeutic discovery.

Diabetic nephropathy (DN) remains a leading cause of end-stage renal disease (ESRD), driven by chronic inflammation, oxidative stress, and apoptosis. Current therapies targeting glycemic and blood pressure control fail to address the underlying molecular mechanisms of DN. This study investigates the therapeutic potential of andrographolide (AD), a diterpenoid lactone from Andrographis paniculata, in mitigating DN by modulating key molecular pathways. Through integrative network pharmacology, molecular docking, and in vivo/in vitro experiments, 107 overlapping DN-related targets were identified, with STAT3, PI3K, and AKT1 emerging as core nodes. Molecular docking revealed high binding affinities between AD and these targets, supporting its modulatory potential. In vivo, AD significantly improved renal function in streptozotocin-induced DN rats, reducing proteinuria, glomerular hypertrophy, and renal fibrosis. AD also attenuated oxidative stress, decreased pro-inflammatory cytokine levels, and enhanced antioxidant enzyme activities, demonstrating systemic anti-inflammatory and antioxidative effects. In vitro studies further confirmed that AD alleviates podocyte oxidative stress and apoptosis under high glucose conditions by suppressing the RAGE-NF-κB and STAT3/PI3K/Akt pathways. Histological analyses revealed substantial improvements in renal architecture, including reductions in fibrosis and mesangial expansion. These results underscore AD’s multi-target mechanism, directly addressing DN’s core pathological drivers, including inflammation, oxidative stress, and apoptosis. As a natural compound with notable safety and efficacy, AD holds promise as an adjunct or standalone therapeutic agent for DN. This study establishes a robust preclinical foundation for AD, warranting further exploration in clinical trials and its potential application in other diabetic complications.

Purpose: It was aimed to clarify the casual connection between prostate cancer (PCa) and arthritis by utilizing two-sample Mendelian randomization (MR) analysis and data from National Health and Nutrition Examination Survey (NHANES) database. Materials and Methods: This study utilized NHANES data. Through association analysis and risk stratification analysis, the association between arthritis and PCa were examined. MR analysis was performed to elucidate the causal relationship between arthritis and PCa. Sensitivity analysis and Steiger directionality test confirmed the reliability of the MR analysis results. Results: A total of 23,608 (PCa:controls=413:23,195) participants after a sample exclusion and variable definition process were screened in NHANES database. Adjustments across three diverse models consistently revealed a notable influence of arthritis on PCa progression. Arthritis was identified as a risk factor for PCa (odds ratio [OR] 1.88, 95% confidence interval [CI] 1.36–2.62, p<0.001). Subsequent analysis indicated that in the arthritis-adjusted model with multiple covariates, the area under the curve of the receiver operating characteristic curve was 0.94. The inverse variance weighting method of MR analysis showed a causal relationship between rheumatoid arthritis (RA) and PCa (OR 1.090, 95% CI 1.053–1.128, p<0.001) as well as osteoar-thritis and PCa (OR 1.002, 95% CI 1.001–1.004, p=0.002). This suggested that RA and osteoarthritis were risk factors for PCa. The heterogeneity (p>0.05), horizontal pleiotropy (p>0.05), leave-one-out and Steiger test confirmed reliability of MR results. Conclusions NHANES database and MR analyses identified arthritis as a risk factor for PCa, offering fresh avenues for preventive and therapeutic approaches.

Diabetic nephropathy (DN) remains a leading cause of end-stage renal disease (ESRD), driven by chronic inflammation, oxidative stress, and apoptosis. Current therapies targeting glycemic and blood pressure control fail to address the underlying molecular mechanisms of DN. This study investigates the therapeutic potential of andrographolide (AD), a diterpenoid lactone from Andrographis paniculata, in mitigating DN by modulating key molecular pathways. Through integrative network pharmacology, molecular docking, and in vivo/in vitro experiments, 107 overlapping DN-related targets were identified, with STAT3, PI3K, and AKT1 emerging as core nodes. Molecular docking revealed high binding affinities between AD and these targets, supporting its modulatory potential. In vivo, AD significantly improved renal function in streptozotocin-induced DN rats, reducing proteinuria, glomerular hypertrophy, and renal fibrosis. AD also attenuated oxidative stress, decreased pro-inflammatory cytokine levels, and enhanced antioxidant enzyme activities, demonstrating systemic anti-inflammatory and antioxidative effects. In vitro studies further confirmed that AD alleviates podocyte oxidative stress and apoptosis under high glucose conditions by suppressing the RAGE-NF-κB and STAT3/PI3K/Akt pathways. Histological analyses revealed substantial improvements in renal architecture, including reductions in fibrosis and mesangial expansion. These results underscore AD’s multi-target mechanism, directly addressing DN’s core pathological drivers, including inflammation, oxidative stress, and apoptosis. As a natural compound with notable safety and efficacy, AD holds promise as an adjunct or standalone therapeutic agent for DN. This study establishes a robust preclinical foundation for AD, warranting further exploration in clinical trials and its potential application in other diabetic complications.