Objective To explore a deep learning-based automatic bone age estimation model for elbow joint X-ray images of Chinese Han adolescents and children and evaluate its performance.Methods A total of 943(517 males and 426 females)elbow joint frontal view X-ray images of Chinese Han ado-lescents and children aged 6.00 to<16.00 years were collected from East,South,Central and North-west China.Three experimental schemes were adopted for bone age estimation.Scheme 1:Directly in-put preprocessed images into the regression model;Scheme 2:Train a segmentation network using"key elbow joint bone annotations"as labels,then input segmented images into the regression model;Scheme 3:Train a segmentation network using"full elbow joint bone annotations"as labels,then in-put segmented images into the regression model.For segmentation,the optimal model was selected from U-Net,UNet++and TransUNet.For regression,VGG16,VGG19,InceptionV2,InceptionV3,ResNet34,ResNet50,ResNet101 and DenseNet121 models were selected for bone age estimation.The dataset was randomly split into 80%(754 samples)for training and validation for model fitting and hyperparameter tuning,and 20%(189 samples)as an internal test set to test the performance of the trained model.An additional 104 elbow joint X-ray images from the same demographic and age group were col-lected and used as an external test set.Model performance was evaluated by comparing the mean ab-solute error(MAE),root mean square error(RMSE),accuracies within±0.7 years(P±0.7 years)and±1.0 years(P±1.0 years)between the estimated age and the actual age,and by drawing radar charts,scat-ter plots,and heatmaps.Results When segmented with Scheme 3,the UNet++model achieved good segmentation performance with a segmentation loss of 0.000 4 and an accuracy of 93.8%at a learning rate of 0.000 1.In the internal test set,the DenseNet121 model with Scheme 3 yielded the best results with MAE,P±0.7 years and P±1.0 years being 0.83 years,70.03%,and 84.30%,respectively.In the external test set,the DenseNet121 model with Scheme 3 also performed best,with an average MAE of 0.89 years and an average RMSE of 1.00 years.Conclusion When performing automatic bone age estima-tion using elbow joint X-ray images in Chinese Han adolescents and children,it is recommended to use the UNet++model for segmentation.The DenseNet121 model with Scheme 3 achieves optimal per-formance.Using segmentation networks,especially that trained with annotation areas encompassing the full elbow joint including the distal humerus,proximal radius,and proximal ulna,can improve the ac-curacy of bone age estimation based on elbow joint X-ray images.

Objective To explore a deep learning network model suitable for bone age estimation using shoulder joint X-ray images in Chinese Han adolescents.Methods A retrospective collection of 1 286 shoulder joint X-ray images of Chinese Han adolescents aged 12.0 to<18.0 years(708 males and 578 females)was conducted.Using random sampling,approximately 80%of the samples(1 032 cases)were selected as the training and validation sets for model learning,selection and optimization,and the other 20%samples(254 cases)were used as the test set to evaluate the model's generalization ability.The original single-channel shoulder joint X-ray images and dual-channel inputs combining original images with segmentation labels(manually annotated shoulder joint regions multiplied pixel-by-pixel with original images,followed by segmentation via the U-Net++network to retain only key shoulder joint region information)were respectively input into four network models,namely VGG16,ResNet18,ResNet50 and DenseNet121 for bone age estimation.Additionally,manual bone age estimation was con-ducted on the test set data,and the results were compared with the four network models.The mean absolute error(MAE),root mean square error(RMSE),coefficient of determination(R2),and Pear-son correlation coefficient(PCC)were used as main evaluation indicators.Results In the test set,the bone age estimation results of the four models with dual-channel input of shoulder joint X-ray images outperformed those with single-channel input in all four evaluation indicators.Among them,DenseNet121 with dual-channel input achieved best results with MAE of 0.54 years,RMSE of 0.82 years,R2 of 0.76,and PCC(r)of 0.88.Manual estimation yielded an MAE of 0.82 years,ranking second only to dual-channel DenseNet121.Conclusion The DenseNet121 model with dual-channel input combined with original images and segmentation labels is superior to manual evaluation results,and can effectively estimate the bone age of Chinese Han adolescents.

In the study of age estimation in living individuals,a lot of data needs to be analyzed by mathematical statistics,and reasonable medical statistical methods play an important role in data design and analysis.The selection of accurate and appropriate statistical methods is one of the key factors af-fecting the quality of research results.This paper reviews the principles and applicable principles of the commonly used medical statistical methods such as descriptive statistics,difference analysis,consistency test and multivariate statistical analysis,as well as machine learning methods such as shallow learning and deep learning in the age estimation research of living individuals,and summarizes the relevance and application prospects between medical statistical methods and machine learning methods.This paper aims to provide technical guidance for the age estimation research of living individuals to obtain more scientific and accurate results.

Aim To explore the potential mechanism of action of Codonopsis Poria in the treatment of alco-holic liver disease(ALD).Methods TCMSP and Swiss Target Prediction were used to obtain the active ingredients and targets of Codonopsis Poria;OMMI,DisGeNET and GeneCards databases were used to obtain the targets of ALD;STRING database was used to construct the PPI network;and Bioconductor soft-ware was used to analyze the enrichment of GO and KEGG pathways.Cytoscape 3.7.1 software was used to construct the drug-component-target-disease network of Codonopsis Poria for ALD treatment,and key targets were screened for molecular docking;the effects of Codonopsis Poria on ALD rats were verified by experi-ments.Results The removal of duplicate targets ob-tained 36 chemical components and 529 potential ac-tion targets.GO enrichment analysis:2 245 biological processes,74 cellular components,125 molecular functions.KEGG enrichment analysis:159 signaling pathways,mainly involving PI3K-Akt,MAPK,AGE-RAGE signaling pathways.Molecular docking showed that AKT1,MMP9 and other targets may be the key targets of Codonopsis Poria in the treatment of ALD.Experiments showed that Codonopsis Poria could im-prove the inflammation level of hepatocytes in ALD rats and reduce the levels of TC,TG,AST,ALT and GGT in ALD rats,PCR assay concluded that Codonopsis Po-ria could reduce the expression of PI3 K and AKT,and electron microscopy results showed that Codonopsis Po-ria could affect the autophagy of cells.Conclusions It is initially revealed that Codonopsis Poria may atten-uate inflammatory cell infiltration by affecting the ex-pression of AKT,TNF and MAPK,and it is hypothe-sized that Codonopsis Poria may affect autophagy through the PI3K-Akt signaling pathway,thus treating ALD,which is initially verified by PCR assay to pro-vide a basis for in-depth explanation of the molecular mechanism of Codonopsis Poria medicinal pairs in the treatment of ALD.

Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.

Objective: To investigate the relationship between obstructive sleep apnea (OSA), apnea hypopnea index (AHI) and vascular injury in hypertensive patients. Methods: This cross-sectional study enrolled patients admitted to the Hypertension Department of TEDA International Cardiovascular Hospital from April 2020 to April 2023, who finished portable sleep monitoring. Sleep monitoring indicators, flow-mediated vasodilation (FMD), carotid artery ultrasound, carotid intima-media thickness, cervical and femoral pulse wave conduction velocity (cfPWV), brachial and ankle pulse wave conduction velocity (baPWV) were analyzed. OSA was classified into mild (5 times/h≤AHI<15 times/h), moderate (15≤AHI<30 times/h), and severe (AHI≥30 times/h) based on AHI levels. FMD<6.0% was defined as vascular endothelial injury, and cfPWV>10 m/s and/or baPWV>18 m/s was defined as arterial stiffness. Multivariate logistic regression analysis was used to explore the correlation between AHI, OSA severity and vascular injury, and subgroup analysis was performed in young (age≤45 years) and middle-to-old patients (age>45 years). Sensitivity analysis was performed by excluding patients with diabetes, cerebrovascular disease and coronary heart disease. The correlation between AHI and vascular injury index was analyzed by restricted cubic spline. Results: A total of 555 adult hypertensive patients were included, the mean age was (39.7±9.2) years, 422 were males (76.0%), and the prevalence of OSA was 66.7% (370/555). Multivariate logistic regression analysis showed that moderate OSA (OR=2.83, P=0.019) and severe OSA (OR=3.40, P=0.016) were positively correlated with vascular endothelial injury after adjusting for age, sex, body mass index and mean arterial pressure. Subgroup analysis showed that log AHI (OR=1.99, P=0.035), moderate OSA (OR=4.83, P=0.010) and severe OSA (OR=4.64, P=0.015) were associated with vascular endothelial injury in young hypertensive patients. The results of sensitivity analysis were similar to the above results. The results of restricted cubic spline analysis showed that AHI was correlated with FMD (P=0.022), and the slope of the curve was the largest when AHI was between 0 and 10 times/h. There was no correlation between log AHI and OSA severity and carotid intima-media thickening and arterial stiffness (all P<0.05). Conclusions: OSA is associated with vascular endothelial injury in hypertensive patients, especially in young patients.
Male ; Humans ; Adult ; Middle Aged ; Female ; Carotid Intima-Media Thickness ; Vascular System Injuries ; Cross-Sectional Studies ; Hypertension/complications* ; Sleep Apnea, Obstructive/complications* ; Carotid Arteries ; Vascular Stiffness

Objective: To investigate the relationship between obstructive sleep apnea (OSA), apnea hypopnea index (AHI) and vascular injury in hypertensive patients. Methods: This cross-sectional study enrolled patients admitted to the Hypertension Department of TEDA International Cardiovascular Hospital from April 2020 to April 2023, who finished portable sleep monitoring. Sleep monitoring indicators, flow-mediated vasodilation (FMD), carotid artery ultrasound, carotid intima-media thickness, cervical and femoral pulse wave conduction velocity (cfPWV), brachial and ankle pulse wave conduction velocity (baPWV) were analyzed. OSA was classified into mild (5 times/h≤AHI<15 times/h), moderate (15≤AHI<30 times/h), and severe (AHI≥30 times/h) based on AHI levels. FMD<6.0% was defined as vascular endothelial injury, and cfPWV>10 m/s and/or baPWV>18 m/s was defined as arterial stiffness. Multivariate logistic regression analysis was used to explore the correlation between AHI, OSA severity and vascular injury, and subgroup analysis was performed in young (age≤45 years) and middle-to-old patients (age>45 years). Sensitivity analysis was performed by excluding patients with diabetes, cerebrovascular disease and coronary heart disease. The correlation between AHI and vascular injury index was analyzed by restricted cubic spline. Results: A total of 555 adult hypertensive patients were included, the mean age was (39.7±9.2) years, 422 were males (76.0%), and the prevalence of OSA was 66.7% (370/555). Multivariate logistic regression analysis showed that moderate OSA (OR=2.83, P=0.019) and severe OSA (OR=3.40, P=0.016) were positively correlated with vascular endothelial injury after adjusting for age, sex, body mass index and mean arterial pressure. Subgroup analysis showed that log AHI (OR=1.99, P=0.035), moderate OSA (OR=4.83, P=0.010) and severe OSA (OR=4.64, P=0.015) were associated with vascular endothelial injury in young hypertensive patients. The results of sensitivity analysis were similar to the above results. The results of restricted cubic spline analysis showed that AHI was correlated with FMD (P=0.022), and the slope of the curve was the largest when AHI was between 0 and 10 times/h. There was no correlation between log AHI and OSA severity and carotid intima-media thickening and arterial stiffness (all P<0.05). Conclusions: OSA is associated with vascular endothelial injury in hypertensive patients, especially in young patients.
Male ; Humans ; Adult ; Middle Aged ; Female ; Carotid Intima-Media Thickness ; Vascular System Injuries ; Cross-Sectional Studies ; Hypertension/complications* ; Sleep Apnea, Obstructive/complications* ; Carotid Arteries ; Vascular Stiffness

Objective:To evaluate the performance of an artificial intelligent (AI)-based automated digital cell morphology analyzer (hereinafter referred as AI morphology analyzer) in detecting peripheral white blood cells (WBCs).Methods:A multi-center study. 1. A total of 3010 venous blood samples were collected from 11 tertiary hospitals nationwide, and 14 types of WBCs were analyzed with the AI morphology analyzers. The pre-classification results were compared with the post-classification results reviewed by senior morphological experts in evaluate the accuracy, sensitivity, specificity, and agreement of the AI morphology analyzers on the WBC pre-classification. 2. 400 blood samples (no less than 50% of the samples with abnormal WBCs after pre-classification and manual review) were selected from 3 010 samples, and the morphologists conducted manual microscopic examinations to differentiate different types of WBCs. The correlation between the post-classification and the manual microscopic examination results was analyzed. 3. Blood samples of patients diagnosed with lymphoma, acute lymphoblastic leukemia, acute myeloid leukemia, myelodysplastic syndrome, or myeloproliferative neoplasms were selected from the 3 010 blood samples. The performance of the AI morphology analyzers in these five hematological malignancies was evaluated by comparing the pre-classification and post-classification results. Cohen′s kappa test was used to analyze the consistency of WBC pre-classification and expert audit results, and Passing-Bablock regression analysis was used for comparison test, and accuracy, sensitivity, specificity, and agreement were calculated according to the formula.Results:1. AI morphology analyzers can pre-classify 14 types of WBCs and nucleated red blood cells. Compared with the post-classification results reviewed by senior morphological experts, the pre-classification accuracy of total WBCs reached 97.97%, of which the pre-classification accuracies of normal WBCs and abnormal WBCs were more than 96% and 87%, respectively. 2. The post-classification results reviewed by senior morphological experts correlated well with the manual differential results for all types of WBCs and nucleated red blood cells (neutrophils, lymphocytes, monocytes, eosinophils, basophils, immature granulocytes, blast cells, nucleated erythrocytes and malignant cells r>0.90 respectively, reactive lymphocytes r=0.85). With reference, the positive smear of abnormal cell types defined by The International Consensus Group for Hematology, the AI morphology analyzer has the similar screening ability for abnormal WBC samples as the manual microscopic examination. 3. For the blood samples with malignant hematologic diseases, the AI morphology analyzers showed accuracies higher than 84% on blast cells pre-classification, and the sensitivities were higher than 94%. In acute myeloid leukemia, the sensitivity of abnormal promyelocytes pre-classification exceeded 95%. Conclusion:The AI morphology analyzer showed high pre-classification accuracies and sensitivities on all types of leukocytes in peripheral blood when comparing with the post-classification results reviewed by experts. The post-classification results also showed a good correlation with the manual differential results. The AI morphology analyzer provides an efficient adjunctive white blood cell detection method for screening malignant hematological diseases.

Objective Coronavirus disease 2019 (COVID-19) and tuberculosis (TB) are major public health and social issues worldwide. The long-term follow-up of COVID-19 with pulmonary TB (PTB) survivors after discharge is unclear. This study aimed to comprehensively describe clinical outcomes, including sequela and recurrence at 3, 12, and 24 months after discharge, among COVID-19 with PTB survivors. Methods From January 22, 2020 to May 6, 2022, with a follow-up by August 26, 2022, a prospective, multicenter follow-up study was conducted on COVID-19 with PTB survivors after discharge in 13hospitals from four provinces in China. Clinical outcomes, including sequela, recurrence of COVID-19, and PTB survivors, were collected via telephone and face-to-face interviews at 3, 12, and 24 months after discharge. Results Thirty-two COVID-19 with PTB survivors were included. The median age was 52 (45, 59) years, and 23 (71.9％) were men. Among them, nearly two-thirds (62.5％) of the survivors were moderate, three (9.4％) were severe, and more than half (59.4％) had at least one comorbidity (PTB excluded). The proportion of COVID-19 survivors with at least one sequela symptom decreased from 40.6％ at 3 months to 15.8％ at 24 months, with anxiety having a higher proportion over a follow-up. Cough and amnesia recovered at the 12-month follow-up, while anxiety, fatigue, and trouble sleeping remained after 24 months. Additionally, one (3.1％) case presented two recurrences of PTB and no re-positive COVID-19 during the follow-up period. Conclusion The proportion of long symptoms in COVID-19 with PTB survivors decreased over time, while nearly one in six still experience persistent symptoms with a higher proportion of anxiety. The recurrence of PTB and the psychological support of COVID-19 with PTB after discharge require more attention.

Aortic Valve/surgery* ; Aortic Valve Stenosis/surgery* ; China/epidemiology* ; Heart Valve Prosthesis Implantation ; Humans ; Severity of Illness Index ; Treatment Outcome