Computational approaches, encompassing both physics-based and machine learning (ML) methodologies, have gained substantial traction in drug repurposing efforts targeting specific therapeutic entities. The human dopamine (DA) transporter (hDAT) is the primary therapeutic target of numerous psychiatric medications. However, traditional hDAT-targeting drugs, which interact with the primary binding site, encounter significant limitations, including addictive potential and stimulant effects. In this study, we propose an integrated workflow combining virtual screening based on weighted holistic atom localization and entity shape (WHALES) descriptors with in vitro experimental validation to repurpose novel hDAT-targeting drugs. Initially, WHALES descriptors facilitated a similarity search, employing four benztropine-like atypical inhibitors known to bind hDAT's allosteric site as templates. Consequently, from a compound library of 4,921 marketed and clinically tested drugs, we identified 27 candidate atypical inhibitors. Subsequently, ADMETlab was employed to predict the pharmacokinetic and toxicological properties of these candidates, while induced-fit docking (IFD) was performed to estimate their binding affinities. Six compounds were selected for in vitro assessments of neurotransmitter reuptake inhibitory activities. Among these, three exhibited significant inhibitory potency, with half maximal inhibitory concentration (IC₅₀) values of 0.753 μM, 0.542 μM, and 1.210 μM, respectively. Finally, molecular dynamics (MD) simulations and end-point binding free energy analyses were conducted to elucidate and confirm the inhibitory mechanisms of the repurposed drugs against hDAT in its inward-open conformation. In conclusion, our study not only identifies promising active compounds as potential atypical inhibitors for novel therapeutic drug development targeting hDAT but also validates the effectiveness of our integrated computational and experimental workflow for drug repurposing.

Objective To analyze the situation of blood lipid and blood routine parameters in patients with pneumoconiosis,and construct a column chart diagnostic model to explore their diagnostic value for pneumo-coniosis.Methods A total of 456 patients with pneumoconiosis admitted to the First Affiliated Hospital of Chongqing Medical and Pharmaceutical College from January 2022 to January 2024 were selected as the pneu-moconiosis group,while 462 healthy subjects exposed to dust during the same period were chosen as the con-trol group.Serum lipids and blood routine parameters related to pneumoconiosis were measured and compared between two groups.Univariate and multivariate Logistic regression analyzes were conducted to examine ser-um lipids and blood routine parameters associated with pneumoconiosis.A risk prediction model was construc-ted using logistic regression in machine learning,and the diagnostic efficacy of the column chart diagnostic model was evaluated by calculating the C-index through receiver operating characteristic(ROC)curve and plotting the model calibration curve based on Hosmer Lemeshow goodness of fit.Decision curve analysis(DCA)was used to assess the clinical practicality of the column chart diagnostic model.Results The levels of serum high-density ester protein cholesterol(HDL-C),cholesterol(TC),red blood cell(RBC),hematocrit(HCT),hemoglobin concentration(HGB),lymphocyte number(LYM),and lymphocyte percentage(LYM％)in the pneumoconiosis group were lower than those in the control group(P＜0.05).The levels of neutrophil-lymphocyte ratio(NLR),platelet-to-lymphocyte ratio(PLR),and systemic immune inflammation index(SII)were higher than those in the control group(P＜0.05).Multivariate Logistic regression analysis showed that HDL-C,LYM％,PLR,and SII were independent influencing factors for pneumoconiosis(P＜0.05).A column chart diagnostic model for the occurrence of pneumoconiosis was constructed using HDL-C,TC,LYM％,PLR,and SII as diagnostic factors.The ROC curve C-index of the diagnostic model was 0.84(95％CI:0.81-0.86),with sensitivity for diagnosing pneumoconiosis of 75.29％,specificity of 77.51％,posi-tive predictive value of 83.25％,and negative predictive value of 67.88％.Internal validation was conducted on the constructed column chart diagnostic model,with a validation set ROC curve C-index of 0.84(95％CI:0.80-0.87),sensitivity of 80.91％,specificity of 72.62％,positive diagnostic value of 79.46％,and negative diagnostic value of 74.39％.The calibration positive curve slope of the diagnostic model was close to 1,and in the fit test P＞0.05.DCA analysis showed that the diagnostic model had clinical practical value for risk diag-nosis of pneumoconiosis.Conclusion HDL-C,TC,LYM％,PLR and SII are independent influencing factors for pneumoconiosis.A column chart diagnostic model for the occurrence of pneumoconiosis is successfully constructed based on machine learning principles,and it has been verified to have high diagnostic efficiency.

Objective:To evaluate the myocardial work of patients with different degrees of coronary artery stenosis with normal left ventricular ejection fraction and no segmental ventricular wall motion abnormality by left ventricular pressure-strain ring (PSL), and to explore the clinical value of myocardial work parameters in predicting severe coronary artery stenosis.Methods:The data of 238 patients undergoing coronary angiography (CAG) in the Affiliated Hospital of Jining Medical University from December 2020 to August 2021 was prospectively collected. According to the results of CAG, the patients were divided into control group, moderate stenosis group, severe stenosis (1-2 branches) group, severe stenosis (complex multiple branches) group. Global longitudinal strain (GLS), global work index (GWI), global constructive work (GCW), global work waste (GWW) and global work efficiency (GWE) were measured by PSL. Univariate and multivariate logistics regression were used to analyze the influencing factors of severe coronary artery stenosis. The receiver operating characteristic (ROC) curve was constructed to analyze the predictive value of GLS, GWI, GCW, GWW and GWE for severe coronary artery stenosis.Results:The GLS, GWI, GCW and GWE in severe stenosis group were lower than those in control group and moderate stenosis group (all P<0.05), while GWW was higher than those in control group and moderate stenosis group (all P<0.05); the GWI, GCW and GWE in severe stenosis (complex multiple branches) group were lower than those in severe stenosis (1-2 branches) group (all P<0.05), while GWW was higher than those in severe stenosis (1-2 branches) group (all P<0.05). Multivariate logistic regression analysis showed that GWE was an independent influencing factor for severe coronary stenosis ( OR=0.266, P<0.05). Compared with GLS, GWI, GCW and GWW, GWE had the largest area under the curve (0.920) to predict severe coronary stenosis, with sensitivity of 92.24% and specificity of 73.77%. The intra observer and inter observer correlation coefficients of GWI, GCW, GWW and GWE analyzed by two ultrasound physicians were 0.916 and 0.907, 0.989 and 0.981, 0.932 and 0.955, 0.931 and 0.937, respectively, which showed good repeatability. Conclusions:PSL provides a new method for quantitative evaluation of left ventricular systolic function in patients with coronary artery stenosis. GWE can be used as a sensitive indicator to predict patients with severe coronary artery stenosis, and is worth to be popularized and applied in the clinical.