Objective To explore the value of deep learning(DL)model based on grayscale ultrasound for predicting asymptomatic advanced chronic liver disease(cACLD).Methods Totally 258 patients with asymptomatic compensatory chronic liver diseases were retrospectively included,among them 117 with F3 or F4 stage liver fibrosis were classified into cACLD group,while 141 with F1 or F2 stage liver fibrosis were taken as non-cACLD group.The patients were divided into training set(n=180,including 82 cases of cACLD and 98 cases of non-cACLD)and validation set(n=78,including 35 cases of cACLD and 43 cases of non-cACLD)at the ratio of 7∶3.Univariate and multivariate logistic regression were used to screen independent clinical predictors of cACLD and construct a clinical model.Based on liver grayscale ultrasound,optimal DL features were extracted and screened,and Resnet50 network was adopted as framework,na?ve Bayes classifier was used to construct DL model,and a combined model was constructed based on clinical model and DL model.The efficacy and clinical value of each model for predicting asymptomatic cACLD were evaluated.Results Age,gamma-glutamyl transferase and platelet count were all independent clinical predictors of cACLD,and a clinical model was constructed.Totally 38 optimal DL features were screened to build a DL model.The AUC of combined model in training set and validation set was 0.950 and 0.740,of DL model was 0.944 and 0.737,respectively,being not significantly different(both P＞0.05)but all higher than that of clinical model(0.667 and 0.573,all P＜0.05).Taken 0.59-0.90 as the threshold,the net benefits of combined model in both training and validation sets were higher than that of other models.Conclusion DL model based on grayscale ultrasound could be used to effectively predict asymptomatic cACLD.Combining with clinical characteristics might improve clinical net benefit of this model.

Objective:To investigate any effect of aerobic exercise on the functioning of endothelial progenitor cells (EPCs) and on the Akt/eNOS signaling pathway in type 2 diabetes.Methods:Forty-five 6-week-old Sprague-Dawley rats free of specific pathogens were randomly divided into a normal control group, a diabetic model group and an exercise group. Type 2 diabetes mellitus was induced in the model and exercise groups by feeding a high-fat diet and streptozotocin injection. After successful modeling, the exercise group underwent 8 weeks of non-weight-bearing swimming training after which blood was collected from their abdominal aortas to measure EPCs, serum nitric oxide and the level of vascular endothelial growth factor (VEGF). Bone marrow-derived EPCs were isolated from the rats′ femurs and tibias for in vitro culture. The cells′ tube formation capacity was assessed using Matrigel assays, while the expression of protein kinase B (Akt) and endothelial nitric oxide synthase (eNOS) were determined using western blotting.Results:Compared with the normal group, the model group exhibited significantly reduced counts of EPCs in their peripheral blood. Serum NO and VEGF were also significantly lower, on average, and tube formation capacity was significantly impaired. p-Akt and p-eNOS protein expression were significantly downregulated. In contrast, the exercise group showed significantly increased EPC counts, elevated serum NO and VEGF levels, improved tube formation, and upregulated p-Akt and p-eNOS expression compared with the model group.Conclusions:Aerobic exercise improves EPC functioning in diabetic rats, and its mechanism may be associated with the regulation of the Akt/eNOS signaling pathway.

Purpose To develop an integrate model combining deep learning features from contrast-enhanced lung ultrasonography with clinical characteristics for predicting epidermal growth factor receptor mutation status in peripheral non-small cell lung cancer.Materials and Methods This retrospective study included 117 patients with pathologically confirmed non-small cell lung cancer from the First Affiliated Hospital of Guangxi Medical University(July 2021 to February 2024).Patients were randomly divided into training(n=93)and test(n=24)sets at an 8∶2 ratio.Regions of interest were delineated at the peak enhancement phase of contrast-enhanced lung ultrasonography.Various deep learning convolutional neural networks were pretrained,with ResNet18 selected as optimal for feature extraction.Deep learning,clinical,and integrated models were constructed using naive Bayesian algorithm.Performance was evaluated via receiver operating characteristic and calibration curves,while class activation mapping and Shapley additive explanation values provided model interpretability.Results In the training set,the deep learning,clinical and integrated models achieved area under the curve of 0.93(95%CI 0.88-0.98),0.86(95%CI 0.68-1.00),and 0.91(95%CI 0.85-0.97),respectively.Corresponding test set area under the curve were 0.81(95%CI 0.72-0.90),0.56(95%CI 0.33-0.80),and 0.87(95%CI 0.72-1.00).Both deep learning and integrated models significantly outperformed the clinical model in training(Z=2.380,P=0.017;Z=2.597,P=0.009)and test sets(Z=2.034,P=0.042;Z=2.577,P=0.010).The integrated model demonstrated excellent calibration and predictive performance.Conclusion The integrated model combining deep learning features from contrast-enhanced lung ultrasonography with clinical characteristics effectively predicts epidermal growth factor receptor mutation status in peripheral non-small cell lung cancer.

Purpose To construct a comprehensive model of deep learning features and clinical features based on renal ultrasound for early identification of renal impairment in the pregnant women with preeclampsia.Materials and Methods The information of 279 pregnant women in the First Affiliated Hospital of Guangxi Medical University from January 2018 to June 2023 were retrospectively collected,and all pregnant women were divided the into preeclampsia group(151 cases)and normal group(128 cases).The dataset was randomly divided into a training set(195 samples)and a testing set(84 samples)at a ratio of 7∶3.Based on ultrasound images,the deep learning convolutional neural networks Resnet152 was used to extract deep learning features.The non-zero coefficient features were selected from the deep learning features by the least absolute shrinkage and selection operator,and the K-nearest neighbor algorithm was used to establish the deep learning model.Then,the same classifier model was used to construct a comprehensive model based on clinical data.The receiver operating characteristic curve was used to evaluate the prediction effect.To address the interpretability visualization of models using gradient_weighted class activation mapping and SHapley Additive exPlanations(SHAP)values.Results The area under the curve of the composite model was 0.964(95%CI 0.940-0.988)in the training cohort and 0.899(95%CI 0.835-0.963)in the test cohort.SHAP analysis showed that deep learning features contributed the highest value in the prediction model.Conclusion The comprehensive model based on deep learning combined with clinical features of renal ultrasound can be used to identify renal impairment in normal pregnancy and preeclampsia pregnant women at an early stage,which is conducive to early clinical intervention.

Objective:To explore the psychological acceptance mechanism of appropriate Traditional Chinese Medicine (TCM) techniques among patients with abnormal uterine bleeding (AUB) .Methods:Using purposive sampling, AUB patients experiencing menstruation but not yet menopausal were recruited from Zhumadian City, Henan Province, and Jiaxing City, Zhejiang Province, between November 2022 and January 2023. Semi-structured interviews were conducted, and the collected data were analyzed using content analysis.Results:A total of four main themes were identified: individualized disease perception and treatment experience; awareness and treatment experience of appropriate TCM techniques; cultural identity and influence of traditional beliefs; and the need for science communication and safety regarding TCM techniques.Conclusions:While AUB patients show a generally high level of acceptance toward appropriate TCM techniques, their understanding of both AUB and the relevant TCM therapies remains limited. Multiple factors influence patients' choices, and some concerns and doubts still persist during the decision-making process.

Objective:To evaluate the effectiveness of diaphragm ultrasound in predicting the success of extubation from tracheotomy in patients with acquired brain injury.Methods:A retrospective analysis was conducted on 51 brain-injured patients. They were divided into an extubation failure group and an extubation success group. The results of ultrasound examination of the diaphragm in the 2 groups were analyzed by univariate analysis, and the independent variables with significance were further subjected to multivariate logistic regression analysis. R software was applied to build the diaphragm indicators showing significant predictive power into a histogram model. The predictive value of this nomogram model was assessed using the receiver operating characteristics (ROC) curve.Results:The univariate analysis revealed significant differences between the two groups in terms of diaphragm excursion, diaphragm thickening fraction and diaphragm excursion-time index. The multivariate logistic regression analysis and the nomogram showed that those three variables are independent influencing factors predicting the success of decannulation. The areas under the ROC curves confirmed that finding.Conclusions:Diaphragm excursion, diaphragm thickening fraction and the diaphragm excursion-time index are useful independent predictors of the success of decannulation among brain injury patients.

Objective:To evaluate the effect of inspiratory muscle training on autonomic nervous function, respiratory muscle strength, lung function and exercise capacity in patients with chronic obstructive pulmonary disease (COPD).Methods:Sixty COPD patients were randomly divided into an observation group and a control group, each of 30. Both groups received routine rehabilitation management (pharmacotherapy, pursed-lip breathing exercises, and abdominal breathing training), but the observation group also received threshold-loaded inspiratory muscle training at 30% of their maximum inspiration pressure. The regimen was three sessions weekly over a 12-week period. Before and after the intervention, everyone′s lung function and respiratory muscle strength were measured with an electronic spirometer. The 6min walking test (6MWT) was also administered, with the subjects′ heart rate variability (HRV) recorded.Results:After the intervention, no significant change was observed among the control group in any of the measurements except in their average maximum inspiratory pressure and 6MWT distance. In the observation group there was a significant increase in their average maximum inspiratory pressure (97.0±12.8cmH 2O) and 6MWT distance, but a significant decrease in the average heart rate after the 6MWT. Conclusions:Twelve weeks of low-intensity inspiratory muscle training can significantly improve the respiratory muscle strength, functional exercise capacity and cardiac function of stable COPD patients, relieving their risk of cardiovascular disease.

Objective:To evaluate the effectiveness of diaphragm ultrasound in predicting the success of extubation from tracheotomy in patients with acquired brain injury.Methods:A retrospective analysis was conducted on 51 brain-injured patients. They were divided into an extubation failure group and an extubation success group. The results of ultrasound examination of the diaphragm in the 2 groups were analyzed by univariate analysis, and the independent variables with significance were further subjected to multivariate logistic regression analysis. R software was applied to build the diaphragm indicators showing significant predictive power into a histogram model. The predictive value of this nomogram model was assessed using the receiver operating characteristics (ROC) curve.Results:The univariate analysis revealed significant differences between the two groups in terms of diaphragm excursion, diaphragm thickening fraction and diaphragm excursion-time index. The multivariate logistic regression analysis and the nomogram showed that those three variables are independent influencing factors predicting the success of decannulation. The areas under the ROC curves confirmed that finding.Conclusions:Diaphragm excursion, diaphragm thickening fraction and the diaphragm excursion-time index are useful independent predictors of the success of decannulation among brain injury patients.

Objective:To evaluate the effect of inspiratory muscle training on autonomic nervous function, respiratory muscle strength, lung function and exercise capacity in patients with chronic obstructive pulmonary disease (COPD).Methods:Sixty COPD patients were randomly divided into an observation group and a control group, each of 30. Both groups received routine rehabilitation management (pharmacotherapy, pursed-lip breathing exercises, and abdominal breathing training), but the observation group also received threshold-loaded inspiratory muscle training at 30% of their maximum inspiration pressure. The regimen was three sessions weekly over a 12-week period. Before and after the intervention, everyone′s lung function and respiratory muscle strength were measured with an electronic spirometer. The 6min walking test (6MWT) was also administered, with the subjects′ heart rate variability (HRV) recorded.Results:After the intervention, no significant change was observed among the control group in any of the measurements except in their average maximum inspiratory pressure and 6MWT distance. In the observation group there was a significant increase in their average maximum inspiratory pressure (97.0±12.8cmH 2O) and 6MWT distance, but a significant decrease in the average heart rate after the 6MWT. Conclusions:Twelve weeks of low-intensity inspiratory muscle training can significantly improve the respiratory muscle strength, functional exercise capacity and cardiac function of stable COPD patients, relieving their risk of cardiovascular disease.

Purpose To develop an integrate model combining deep learning features from contrast-enhanced lung ultrasonography with clinical characteristics for predicting epidermal growth factor receptor mutation status in peripheral non-small cell lung cancer.Materials and Methods This retrospective study included 117 patients with pathologically confirmed non-small cell lung cancer from the First Affiliated Hospital of Guangxi Medical University(July 2021 to February 2024).Patients were randomly divided into training(n=93)and test(n=24)sets at an 8∶2 ratio.Regions of interest were delineated at the peak enhancement phase of contrast-enhanced lung ultrasonography.Various deep learning convolutional neural networks were pretrained,with ResNet18 selected as optimal for feature extraction.Deep learning,clinical,and integrated models were constructed using naive Bayesian algorithm.Performance was evaluated via receiver operating characteristic and calibration curves,while class activation mapping and Shapley additive explanation values provided model interpretability.Results In the training set,the deep learning,clinical and integrated models achieved area under the curve of 0.93(95%CI 0.88-0.98),0.86(95%CI 0.68-1.00),and 0.91(95%CI 0.85-0.97),respectively.Corresponding test set area under the curve were 0.81(95%CI 0.72-0.90),0.56(95%CI 0.33-0.80),and 0.87(95%CI 0.72-1.00).Both deep learning and integrated models significantly outperformed the clinical model in training(Z=2.380,P=0.017;Z=2.597,P=0.009)and test sets(Z=2.034,P=0.042;Z=2.577,P=0.010).The integrated model demonstrated excellent calibration and predictive performance.Conclusion The integrated model combining deep learning features from contrast-enhanced lung ultrasonography with clinical characteristics effectively predicts epidermal growth factor receptor mutation status in peripheral non-small cell lung cancer.