Object:To explore the feasibility of using artificial intelligence for quality control of echocardiographic images.Methods:Retrospectively，5 000 two-dimensional echocardiographic video images within the period from 2021 to 2023 were randomly retrieved from the echocardiography database of Nanjing Drum Tower Hospital，Affiliated Hospital of Medical School，Nanjing University. Among these selected images，1 559 of them were apical views. The physician team formulated the scoring rules，which specifically included four scoring criteria：gain，scaling ratio，cardiac axis angle，and structure. Subsequently，the data were labeled with view classification and image quality scores. The labeled data were further partitioned into the training set（ n = 643），the validation set（ n = 276），and the test set（ n = 640）. The training and validation sets were utilized for constructing the models for view classification and quality assessment，while the test set was employed to verify the models' effectiveness. The view classification module was implemented using the SlowFast model，and the quality assessment module involved algorithms such as ResNet，Video Swin Transformer，SSD，and U-Net. Results:The average accuracy，precision，recall rate and F1 score of the classification model in identifying each apical view were 0.987 1，0.983 0，0.987 1 and 0.984 9 respectively，and the inference time was（333.4 ± 105.4）ms. The average accuracies of the quality assessment module in terms of gain，scaling ratio，cardiac axis angle and display of main structures were 0.915 1，0.928 2，0.938 7 and 0.965 6 respectively，and the overall scoring accuracy was 0.912 7.Conclusions:The echocardiogram quality control system developed in this research can effectively classify and evaluate the quality of two-dimensional images of the apical views in echocardiograms. Moreover，it guarantees the objectivity，timeliness and high-efficiency of quality control，which has reference value for the establishment of the echocardiogram quality control system.

Low-intensity extracorporeal shock wave therapy (LiESWT), as a non-invasive physical therapeutic modality, is increasingly recognized for its potential in treating lower urinary tract dysfunction (LUTD). This article systematically explores the potential molecular mechanisms through which LiESWT acts on various types of LUTD, including the downregulation of pro-inflammatory cytokine expression, induction of neovascularization, mobilization and activation of tissue stem cells, regulation of neural regeneration and repair processes, and restoration of lower urinary tract muscle function. It also elaborates the latest therapeutic advances in the application of LiESWT for overactive bladder, underactive bladder, stress urinary incontinence, chronic prostatitis/chronic pelvic pain syndrome, and interstitial cystitis/bladder pain syndrome. This review also provides an in-depth analysis of the therapeutic efficacy differences associated with various treatment parameters (energy density, number of shocks, treatment frequency, and total sessions), summarizing the challenges currently faced in clinical translation and proposes future research directions and perspectives, so as to offer new feasible ideas for the treatment of LUTD and provide a theoretical basis for clinical practice.

OBJECTIVE To explore the effect of Astragali Radix-Curcuma Zedoaria-Paridis Rhizoma(Qi-Zhu-Zao)combina-tion on inhibiting the growth and metastasis of colon cancer based on the PINK1/Parkin/EMT signaling pathway.METHODS Thirty male BALB/c mice were randomly assigned to five groups:sham operation group,model group,positive control group,high-dose Qi-Zhu-Zao group(5.85 g·kg-1),and low-dose Qi-Zhu-Zao group(2.925 g·kg-1),with six mice in each group.An orthotopic colon cancer model was established in the mice using CT26.WT cells.After 15 days of treatment,tumor and liver tissues were collected from each group.Hematoxylin and eosin(HE)staining was performed to assess tumor metastasis,and transmission electron microscopy was used to observe mitochondrial autophagy in tumor tissues.The expression of mitochondrial autophagy-related proteins PINK1,Parkin,p62,and LC3-Ⅱ/LC3-Ⅰ was analyzed using Western blot and immunohistochemistry(IHC).Additionally,the expression levels of epithelial-mesenchymal transition(EMT)-related proteins and mRNA,including E-cadherin,N-cadherin,Vimentin,and Snail,were detected using Western blot,qPCR,and IHC staining.RESULTS Compared to the model group,mice in the treatment groups exhibited significantly reduced tumor volumes and fewer metastatic foci.Additionally,liver tissues showed pathological changes,and the overall growth condition of the mice was markedly improved;the tumor tissues in the treatment groups displayed selective mitochon-drial autophagy,accompanied by the formation of autophagosomes.The treatment influenced the PINK1/Parkin pathway-mediated mi-tochondrial autophagy biological process,with PINK1,Parkin,p62,and LC3-Ⅱ/LC3-Ⅰ levels being significantly upregulated(P<0.05,P<0.01),the high-dose group exhibited a more significant impact than the low-dose group(P<0.05,P<0.01).Furthermore,the treatment groups also showed significant reductions in the protein and mRNA levels of N-cadherin,Vimentin,and Snail(P<0.05,P<0.01),along with significant increases in the protein and mRNA levels of E-cadherin(P<0.05,P<0.01),these effects were more pronounced in the high-dose group compared to the low-dose group(P<0.05,P<0.01).CONCLUSION The herbal combination of Qi-Zhu-Zao inhibits tumor growth and metastasis to a certain extent in a mouse model of orthotopic transplantation of colon cancer.The underlying mechanism may involve the restoration of mitochondrial function through the PINK1/Parkin signaling pathway and the inhibition of the epithelial-mesenchymal transition(EMT)process,thereby achieving a therapeutic effect on colon cancer.

OBJECTIVE To explore the effect of Astragali Radix-Curcuma Zedoaria-Paridis Rhizoma(Qi-Zhu-Zao)combina-tion on inhibiting the growth and metastasis of colon cancer based on the PINK1/Parkin/EMT signaling pathway.METHODS Thirty male BALB/c mice were randomly assigned to five groups:sham operation group,model group,positive control group,high-dose Qi-Zhu-Zao group(5.85 g·kg-1),and low-dose Qi-Zhu-Zao group(2.925 g·kg-1),with six mice in each group.An orthotopic colon cancer model was established in the mice using CT26.WT cells.After 15 days of treatment,tumor and liver tissues were collected from each group.Hematoxylin and eosin(HE)staining was performed to assess tumor metastasis,and transmission electron microscopy was used to observe mitochondrial autophagy in tumor tissues.The expression of mitochondrial autophagy-related proteins PINK1,Parkin,p62,and LC3-Ⅱ/LC3-Ⅰ was analyzed using Western blot and immunohistochemistry(IHC).Additionally,the expression levels of epithelial-mesenchymal transition(EMT)-related proteins and mRNA,including E-cadherin,N-cadherin,Vimentin,and Snail,were detected using Western blot,qPCR,and IHC staining.RESULTS Compared to the model group,mice in the treatment groups exhibited significantly reduced tumor volumes and fewer metastatic foci.Additionally,liver tissues showed pathological changes,and the overall growth condition of the mice was markedly improved;the tumor tissues in the treatment groups displayed selective mitochon-drial autophagy,accompanied by the formation of autophagosomes.The treatment influenced the PINK1/Parkin pathway-mediated mi-tochondrial autophagy biological process,with PINK1,Parkin,p62,and LC3-Ⅱ/LC3-Ⅰ levels being significantly upregulated(P<0.05,P<0.01),the high-dose group exhibited a more significant impact than the low-dose group(P<0.05,P<0.01).Furthermore,the treatment groups also showed significant reductions in the protein and mRNA levels of N-cadherin,Vimentin,and Snail(P<0.05,P<0.01),along with significant increases in the protein and mRNA levels of E-cadherin(P<0.05,P<0.01),these effects were more pronounced in the high-dose group compared to the low-dose group(P<0.05,P<0.01).CONCLUSION The herbal combination of Qi-Zhu-Zao inhibits tumor growth and metastasis to a certain extent in a mouse model of orthotopic transplantation of colon cancer.The underlying mechanism may involve the restoration of mitochondrial function through the PINK1/Parkin signaling pathway and the inhibition of the epithelial-mesenchymal transition(EMT)process,thereby achieving a therapeutic effect on colon cancer.

Object:To explore the feasibility of using artificial intelligence for quality control of echocardiographic images.Methods:Retrospectively，5 000 two-dimensional echocardiographic video images within the period from 2021 to 2023 were randomly retrieved from the echocardiography database of Nanjing Drum Tower Hospital，Affiliated Hospital of Medical School，Nanjing University. Among these selected images，1 559 of them were apical views. The physician team formulated the scoring rules，which specifically included four scoring criteria：gain，scaling ratio，cardiac axis angle，and structure. Subsequently，the data were labeled with view classification and image quality scores. The labeled data were further partitioned into the training set（ n = 643），the validation set（ n = 276），and the test set（ n = 640）. The training and validation sets were utilized for constructing the models for view classification and quality assessment，while the test set was employed to verify the models' effectiveness. The view classification module was implemented using the SlowFast model，and the quality assessment module involved algorithms such as ResNet，Video Swin Transformer，SSD，and U-Net. Results:The average accuracy，precision，recall rate and F1 score of the classification model in identifying each apical view were 0.987 1，0.983 0，0.987 1 and 0.984 9 respectively，and the inference time was（333.4 ± 105.4）ms. The average accuracies of the quality assessment module in terms of gain，scaling ratio，cardiac axis angle and display of main structures were 0.915 1，0.928 2，0.938 7 and 0.965 6 respectively，and the overall scoring accuracy was 0.912 7.Conclusions:The echocardiogram quality control system developed in this research can effectively classify and evaluate the quality of two-dimensional images of the apical views in echocardiograms. Moreover，it guarantees the objectivity，timeliness and high-efficiency of quality control，which has reference value for the establishment of the echocardiogram quality control system.

Objective To investigate the risk factors of in-hospital mortality and establish a risk prediction model for patients receiving venoarterial extracorporeal membrane oxygenation(VA-ECMO).Methods We retrospectively collected the data of 302 patients receiving VA-ECMO in ICU of 3 hospitals in Guangdong Province between January,2015 and January,2022 using a convenience sampling method.The patients were divided into a derivation cohort(201 cases)and a validation cohort(101 cases).Univariate and multivariate logistic regression analyses were used to analyze the risk factors for in-hospital death of these patients,based on which a risk prediction model was established in the form of a nomogram.The receiver operator characteristic(ROC)curve,calibration curve and clinical decision curve were used to evaluate the discrimination ability,calibration and clinical validity of this model.Results The in-hospital mortality risk prediction model was established based the risk factors including hypertension(OR=3.694,95%CI:1.582-8.621),continuous renal replacement therapy(OR=9.661,95%CI:4.103-22.745),elevated Na2+ level(OR=1.048,95%CI:1.003-1.095)and increased hemoglobin level(OR=0.987,95%CI:0.977-0.998).In the derivation cohort,the area under the ROC curve(AUC)of this model was 0.829(95%CI:0.770-0.889),greater than those of the 4 single factors(all AUC<0.800),APACHE Ⅱ Score(AUC=0.777,95%CI:0.714-0.840)and the SOFA Score(AUC=0.721,95%CI:0.647-0.796).The results of internal validation showed that the AUC of the model was 0.774(95%CI:0.679-0.869),and the goodness of fit test showed a good fitting of this model(χ2=4.629,P>0.05).Conclusion The risk prediction model for in-hospital mortality of patients on VA-ECMO has good differentiation,calibration and clinical effectiveness and outperforms the commonly used disease severity scoring system,and thus can be used for assessing disease severity and prognostic risk level in critically ill patients.

Compared with simple stress urinary incontinence (SUI), complicated female SUI is more diverse in etiology and more difficult to treat.Based on domestic and foreign literature and our diagnosis and treatment experience, this paper makes a detailed introduction to the definition, diagnosis, evaluation and clinical treatment of complicated female SUI, so as to provide guidance and reference for the treatment of this disease.

Objective To investigate the risk factors of in-hospital mortality and establish a risk prediction model for patients receiving venoarterial extracorporeal membrane oxygenation(VA-ECMO).Methods We retrospectively collected the data of 302 patients receiving VA-ECMO in ICU of 3 hospitals in Guangdong Province between January,2015 and January,2022 using a convenience sampling method.The patients were divided into a derivation cohort(201 cases)and a validation cohort(101 cases).Univariate and multivariate logistic regression analyses were used to analyze the risk factors for in-hospital death of these patients,based on which a risk prediction model was established in the form of a nomogram.The receiver operator characteristic(ROC)curve,calibration curve and clinical decision curve were used to evaluate the discrimination ability,calibration and clinical validity of this model.Results The in-hospital mortality risk prediction model was established based the risk factors including hypertension(OR=3.694,95%CI:1.582-8.621),continuous renal replacement therapy(OR=9.661,95%CI:4.103-22.745),elevated Na2+ level(OR=1.048,95%CI:1.003-1.095)and increased hemoglobin level(OR=0.987,95%CI:0.977-0.998).In the derivation cohort,the area under the ROC curve(AUC)of this model was 0.829(95%CI:0.770-0.889),greater than those of the 4 single factors(all AUC<0.800),APACHE Ⅱ Score(AUC=0.777,95%CI:0.714-0.840)and the SOFA Score(AUC=0.721,95%CI:0.647-0.796).The results of internal validation showed that the AUC of the model was 0.774(95%CI:0.679-0.869),and the goodness of fit test showed a good fitting of this model(χ2=4.629,P>0.05).Conclusion The risk prediction model for in-hospital mortality of patients on VA-ECMO has good differentiation,calibration and clinical effectiveness and outperforms the commonly used disease severity scoring system,and thus can be used for assessing disease severity and prognostic risk level in critically ill patients.

Objective To compare the principles and performance of three Hough transform algorithms(standard Hough transform,gradient based Hough transform,and random Hough transform)in order to establish a suitable control basis for precise and rapid recognition of targets and acquisition of target center coordinates for craniocerebral puncture robots.Methods A simulation environment in MATLAB software was built to study and analyze image feature recognition,filtering,edge detection,cumulative voting and other processing engineering.Contour recognition and fitting of target circles were achieved in multiple scenarios before their center coordinates were obtained.The recognition and fitting performance of these algorithms was quantitatively compared.Finally,a better detection algorithm based on the actual environment of the craniocerebral puncture robot was determined.Results The standard Hough transform algorithm had the largest error between the mark circle and the target circle,and the running time of this algorithm was the longest due to large computation.The detection speed of the random Hough transform algorithm was lower than that of the gradient-based Hough transform algorithm,but the fitting accuracy was slightly better than that of the standard Hough transform algorithm.The speed and accuracy of circle fitting based on the gradient Hough transform algorithm had significant advantages over the other two.Conclusion The gradient based Hough transform algorithm is more suitable for obtaining the target center coordinates of the craniocerebral puncture robot system.

OBJECTIVE: To explore the clinical characteristics and genetic basis of a child with autism spectrum disorder (ASD) in conjunct with congenital heart disease (CHD). METHODS: A child who was hospitalized at the Third People's Hospital of Chengdu on April 13, 2021 was selected as the study subject. Clinical data of the child were collected. Peripheral blood samples of the child and his parents were collected and subjected to whole exome sequencing (WES). A GTX genetic analysis system was used to analyze the WES data and screen candidate variants for ASD. Candidate variant was verified by Sanger sequencing and bioinformatics analysis. Real-time fluorescent quantitative PCR (qPCR) was carried out to compare the expression of mRNA of the NSD1 gene between this child and 3 healthy controls and 5 other children with ASD. RESULTS: The patient, an 8-year-old male, has manifested with ASD, mental retardation and CHD. WES analysis revealed that he has harbored a heterozygous c.3385+2T>C variant in the NSD1 gene, which may affect the function of its protein product. Sanger sequencing showed that neither of his parent has carried the same variant. By bioinformatic analysis, the variant has not been recorded in the ESP, 1000 Genomes and ExAC databases. Analysis with Mutation Taster online software indicated it to be disease causing. Based on the guidelines from the American College of Medical Genetics and Genomics (ACMG), the variant was predicted to be pathogenic. By qPCR analysis, the expression level of mRNA of the NSD1 gene in this child and 5 other children with ASD was significantly lower than that of the healthy controls (P < 0.001). CONCLUSION The c.3385+2T>C variant of the NSD1 gene can significantly reduce its expression, which may predispose to ASD. Above finding has enriched the mutational spectrum the NSD1 gene.
Male ; Child ; Humans ; Autism Spectrum Disorder/genetics* ; Heart Defects, Congenital/genetics* ; Computational Biology ; Genomics ; Mutation ; RNA, Messenger/genetics* ; Histone-Lysine N-Methyltransferase/genetics*