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.

In order to establish a competitive chemiluminescent enzyme-linked immunoassay for rapid and quantitative detection of Senecavirus A antibodies,the polystyrene plate was coated with inactivated Senecavirus A antigen,and the monoclonal antibodies against Senecavirus A VP2 and VP3 proteins labeled by horseradish peroxidase(HRP)were used as the competitive enzymic anti-bodies of the antibodies in the serum samples.The standard curve of the calibrator prepared by di-lution of positive serum was drawn to achieve quantitative detection.The successfully established SVA competitive CLEIA reported the result within 45 minutes.The maximum dilution of 1∶2 048 for calibrator serum was still detectable with no cross-reaction with the standard positive serum of other five kinds of virus antigens such as foot-and-mouth disease.The coefficient of variation within batches was less than 10％,and the coefficient of variation between batches was less than 15％,which showed good repeatability and stability.The positive and negative coincidence rates were 95.30％and 97.57％,respectively,and the total coincidence rate was 96.88％,showing high consistency.The SVA competitive CLEIA assay established in this study can be used for the rapid quantitative detection of Senecavirus A antibodies,filling the gap in the domestic rapid quantitative detection of SVA antibodies.

Objective To investigate the correlation of vascular senescence indicators,brachial-ankle pulse wave velocity(baPWV),cardio-ankle vascular index(CAVI),ankle-brachial index(ABI)with total burden of MRI in patients with cerebral small vessel disease(CSVD).Methods A total of 200 CSVD patients admitted to our hospital from November 2023 to October 2024 were retrospectively recruited,and based on their total MRI burden,they were divided into a low-burden group(score:0-1,103 cases)and a high-burden group(score:2-4,97 cases).A athero-sclerosis monitoring device(VS-1500A)was used to detect baPWV,CAVI,and ABI values.The relationships of the three indicators with total MRI burden score and their predictive values for the burden score were analyzed.Results The high-burden group had significantly higher BMI,el-evated homocysteine and uric acid levels,and increased baPWV and CAVI,but lower ABI than the low-burden group(P＜0.01).Multivariate logistic analysis showed that baPWV,CAVI and ABI were independent influencing factors for high MRI burden of CSVD patients.Spearman correlation analysis showed that baPWV and CAVI values were positively correlated(r=0.589,P=0.000;r=0.458,P=0.000),and ABI was negatively correlated with the total MRI burden score of CSVD patients(r=-0.352,P=0.000).ROC curve analysis showed that baPWV(AUC=0.816,P=0.000),CAVI(AUC=0.725,P=0.000)and ABI(AUC=0.676,P=0.000)were all predic-tors for high MRI burden score in CSVD patients.Conclusion baPWV and CAVI are positively,and ABI is negatively correlated with the total MRI burden score of CSVD patients.baPWV,CAVI and ABI show higher predictive value for the high burden score,with baPWV most significant.

Computational approaches,encompassing both physics-based and machine learning(ML)methodolo-gies,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 psychi-atric 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 toxi-cological 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 re-uptake 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 con-ducted 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 explore the construction of an evaluation model for aortic root anatomy and calcium burden in patients with bicuspid aortic valve (BAV) stenosis before transcatheter aortic valve replacement (TAVR) based on deep learning (DL) algorithms.Methods:A retrospective collection of 362 BAV stenosis patients who underwent TAVR from September 2023 to May 2024 was performed. All patients underwent cardiac CT angiography. The patients were divided into training group ( n=104), internal validation group ( n=206), and external validation group ( n=52). A DL model was trained on the training dataset to assess aortic root anatomy and calcification burden. The evaluation included the segmentation accuracy of the algorithm, the measurement performance of key anatomical structures (i.e., valve leaflets and type-1 and type-2 fusion raphe), and calcification burden, as well as the measurement efficiency. Overall segmentation performance was assessed using the average Dice coefficient (ADC). The fine-scale segmentation quality was validated by the 95th-percentile Hausdorff distance (HD-95) and the average symmetric surface distance (ASSD). The consistency of the measurement results was assessed using the Pearson correlation coefficient and the intraclass correlation coefficient ( ICC) with a two-way mixed model for absolute agreement. In addition, the total time and total mouse movement distance required for manual assessment versus the DL model on the validation datasets were recorded and compared. Results:The algorithm demonstrated excellent segmentation performance on aortic root anatomical targets, achieving outstanding consistency within both internal and external validation datasets (0.955

Objective:To explore the construction of an evaluation model for aortic root anatomy and calcium burden in patients with bicuspid aortic valve (BAV) stenosis before transcatheter aortic valve replacement (TAVR) based on deep learning (DL) algorithms.Methods:A retrospective collection of 362 BAV stenosis patients who underwent TAVR from September 2023 to May 2024 was performed. All patients underwent cardiac CT angiography. The patients were divided into training group ( n=104), internal validation group ( n=206), and external validation group ( n=52). A DL model was trained on the training dataset to assess aortic root anatomy and calcification burden. The evaluation included the segmentation accuracy of the algorithm, the measurement performance of key anatomical structures (i.e., valve leaflets and type-1 and type-2 fusion raphe), and calcification burden, as well as the measurement efficiency. Overall segmentation performance was assessed using the average Dice coefficient (ADC). The fine-scale segmentation quality was validated by the 95th-percentile Hausdorff distance (HD-95) and the average symmetric surface distance (ASSD). The consistency of the measurement results was assessed using the Pearson correlation coefficient and the intraclass correlation coefficient ( ICC) with a two-way mixed model for absolute agreement. In addition, the total time and total mouse movement distance required for manual assessment versus the DL model on the validation datasets were recorded and compared. Results:The algorithm demonstrated excellent segmentation performance on aortic root anatomical targets, achieving outstanding consistency within both internal and external validation datasets (0.955

In order to establish a competitive chemiluminescent enzyme-linked immunoassay for rapid and quantitative detection of Senecavirus A antibodies,the polystyrene plate was coated with inactivated Senecavirus A antigen,and the monoclonal antibodies against Senecavirus A VP2 and VP3 proteins labeled by horseradish peroxidase(HRP)were used as the competitive enzymic anti-bodies of the antibodies in the serum samples.The standard curve of the calibrator prepared by di-lution of positive serum was drawn to achieve quantitative detection.The successfully established SVA competitive CLEIA reported the result within 45 minutes.The maximum dilution of 1∶2 048 for calibrator serum was still detectable with no cross-reaction with the standard positive serum of other five kinds of virus antigens such as foot-and-mouth disease.The coefficient of variation within batches was less than 10％,and the coefficient of variation between batches was less than 15％,which showed good repeatability and stability.The positive and negative coincidence rates were 95.30％and 97.57％,respectively,and the total coincidence rate was 96.88％,showing high consistency.The SVA competitive CLEIA assay established in this study can be used for the rapid quantitative detection of Senecavirus A antibodies,filling the gap in the domestic rapid quantitative detection of SVA antibodies.

Objective To investigate the correlation of vascular senescence indicators,brachial-ankle pulse wave velocity(baPWV),cardio-ankle vascular index(CAVI),ankle-brachial index(ABI)with total burden of MRI in patients with cerebral small vessel disease(CSVD).Methods A total of 200 CSVD patients admitted to our hospital from November 2023 to October 2024 were retrospectively recruited,and based on their total MRI burden,they were divided into a low-burden group(score:0-1,103 cases)and a high-burden group(score:2-4,97 cases).A athero-sclerosis monitoring device(VS-1500A)was used to detect baPWV,CAVI,and ABI values.The relationships of the three indicators with total MRI burden score and their predictive values for the burden score were analyzed.Results The high-burden group had significantly higher BMI,el-evated homocysteine and uric acid levels,and increased baPWV and CAVI,but lower ABI than the low-burden group(P＜0.01).Multivariate logistic analysis showed that baPWV,CAVI and ABI were independent influencing factors for high MRI burden of CSVD patients.Spearman correlation analysis showed that baPWV and CAVI values were positively correlated(r=0.589,P=0.000;r=0.458,P=0.000),and ABI was negatively correlated with the total MRI burden score of CSVD patients(r=-0.352,P=0.000).ROC curve analysis showed that baPWV(AUC=0.816,P=0.000),CAVI(AUC=0.725,P=0.000)and ABI(AUC=0.676,P=0.000)were all predic-tors for high MRI burden score in CSVD patients.Conclusion baPWV and CAVI are positively,and ABI is negatively correlated with the total MRI burden score of CSVD patients.baPWV,CAVI and ABI show higher predictive value for the high burden score,with baPWV most significant.

Objective:To analyze the disease characteristics, diagnosis and treatment methods of venous pulsatile tinnitus treated by intervention of sigmoid sinus.Methods:Fifty patients (from Shandong Provincial ENT Hospital, Shandong University between February 2014 and July 2020) with venous pulsatile tinnitus treated by sigmoid sinus surgery were analyzed retrospectively. The tinnitus characteristics, imaging findings, surgical methods, intraoperative findings and postoperative tinnitus changes were recorded. The patients were followed up for 6-12 months. The sign rank sum test was used to analyze the difference in tinnitus grading before and after surgery. There were 50 patients with unilateral venous pulsatile tinnitus, including 49 females and 1 male. The age ranged from 17 to 67 years, with a median age of 44 years. There were 45 cases of right tinnitus and 5 cases of left tinnitus. The degree of tinnitus before operation was grade Ⅱ or above, including 4 cases of gradeⅡ, 11 cases of grade Ⅲ, 22 cases of grade Ⅳ and 13 cases of grade Ⅴ.Results:Thirty-seven cases were cured, 8 cases were ineffective (no change in tinnitus), 3 cases were markedly effective (tinnitus grade decreased by 3 in 2 cases, 4 in 1 case), and 2 cases were effective (tinnitus grade decreased by 1). The difference of tinnitus grade before and after operation was statistically significant ( Z=-5.70, P<0.05). Temporal bone CT showed 36 cases of sigmoid diverticulum (including 17 cases with sigmoid sinus dehiscence), 12 cases of sigmoid sinus dehiscence and 2 cases of absence of the temporal bone cortex abutting to sigmoid sinus. Thirty-five cases were performed with closure of sigmoid sinus diverticulum, 4 cases were performed with resurfacing of the sigmoid plate, 5 cases were performed with narrowing of sigmoid sinus, 4 cases were performed with simple opening of pre sigmoid mastoid air chamber, 1 case of opening was performed with pre sigmoid mastoid air chamber combined with narrowing of sigmoid sinus, and 1 case was performed with opening of pre sigmoid mastoid air chamber combined with closure of sigmoid sinus diverticulum. Conclusions:Venous pulsatile tinnitus is common in women. The common causes may be sigmoid sinus wall abnormalities such as sigmoid sinus diverticulum and perisigmoid bone defect. Imaging examinations are helpful for diagnosis. Venous pulsatile tinnitus can be treated with surgery.

Objective:To train a deep convolutional neural networks (CNN) using a labeled data set to classify the metaphase chromosomes and test its accuracy for chromosomal identification.Methods:Three thousand and three hundred individuals undergoing surveillance for chromosomal disorders at the Laboratory for Comprehensive Prevention and Treatment of Birth Defects, Ningbo Maternal and Child Health Care Hospital from January 2013 to July 2019 were enrolled. A total of 3 300×46 chromosome images were included, of which 70% were used as the training set and 30% were used as the test set for the deep CNN. The accuracy of chromosome counting and "cutting + recognition + arrangement + automatic analysis" of the model were respectively evaluated. Another 80 images were collected to record the time and accuracy of chromosome classification by geneticists and the model, respectively, so as to assess the practical value of the model.Results:The CNN model was used to count the chromosomes with an accuracy of 61.81%, and the "cutting + recognition + arrangement + automatic analysis" accuracy of the model was 96.16%. Compared with manual operation, the classification time of the CNN model has been greatly reduced, and its karyotyping accuracy was only 3.58% lower than that of geneticists.Conclusion:The CNN model has a high performance for chromosome classification and can significantly reduce the work load involved with the segmentation and classification and improve the efficiency of chromosomal karyotyping, thereby has a broad application prospect.