Objective:To investigate the local hemodynamic spatial distribution of internal carotid artery stenosis and its correlation with plaque characteristics using high-resolution vessel wall imaging (HR-VWI) combined with computational fluid dynamics.Methods:This was a cross-sectional study. A retrospective analysis was conducted on the clinical and imaging data of 70 patients with moderate to severe stenosis at the initiation of the internal carotid artery in First Teaching Hospital of Tianjin University of Traditional Chinese Medicine and Tianjin First Central Hospital from March 2018 to June 2020. All patients underwent HR-VWI and CT angiography examinations. The parameters related to plaque characteristics, such as plaque length, maximum wall thickness, plaque volume, wall volume percentage and intraplaque hemorrhage (IPH) were measured and evaluated on HR-VWI images. CT angiography images were used to construct a local hemodynamic vascular model to measure various wall shear stress (WSS) derived parameters, such as time-averaged wall shear stress (TAWSS), oscillatory shear index (OSI), relative residence time (RRT), and transverse wall shear stress (transWSS), at the narrowest, proximal, and distal parts of the lesion. The Friedman test was used to analyze the difference of hemodynamic parameters in different parts of the lesion. Spearman correlation analysis was performed to assess the correlation between plaque burden and local hemodynamic parameters. Univariate and multivariate logistic regression analyses were used to explore the independent risk factors for predicting IPH.Results:Among the 70 patients, 25 patients with IPH and 45 patients without IPH. The overall differences in TAWSS, OSI, RRT and transWSS at the narrowest, proximal, and distal parts of the lesion in 70 patients were statistically significant ( P<0.05). The TAWSS and transWSS at the narrowest parts were significantly higher than those at the proximal and distal parts ( P<0.05). The OSI at the distal part was significantly higher than that at the narrowest and the proximal parts ( P<0.05). The RRT at the proximal part was significantly lower than that at the narrowest and the distal parts ( P<0.05). Spearman correlation analysis showed RRT at the distal part was correlated with plaque volume ( r s=0.249, P=0.044) and wall volume percentage ( r s=0.286, P=0.016), respectively. In a multivariate logistic regression showed plaque length ( OR=1.315, 95% CI 1.073-1.612, P=0.008) and TAWSS at the narrowest part ( OR=1.631, 95% CI 1.308-1.854, P=0.008) were independent risk factors for predicting IPH. Conclusions:The spatial distribution of local hemodynamics of moderate to severe stenosis at the initiation of the internal carotid artery is different, and the WSS parameters in different parts of the lesion have different effects on plaque volume, wall volume percentage and IPH.

Objective To investigate the feasibility and accuracy of an ultrasound-based transfer learning artificial intelligence model in predicting the malignancy probability of partially cystic thyroid nodules(PCTN).Methods A retrospective analysis was conducted on 246 patients with PCTN who had definitive pathological results and were admitted to Weihai Municipal Hospital,Cheeloo College of Medicine,Shandong University from January 2021 to December 2023.Patients were randomly divided into training and test cohorts at a ratio of 7:3.Ultrasonic image features of PCTN were evaluated,and independent risk factors were identified using multivariate logistic regression analysis,with the area under the curve(AUC)subsequently calculated.Additionally,five different pre-trained models-Inception_v3,EfficientNet,VGG19,ResNet50,and DenseNet121-were selected for transfer learning after data preprocessing using the PyTorch framework in Python.The AUC values of these models were calculated and compared.Results Solid portion greater than 50%,eccentric acute angle,ill-defined margin,spiculated or microlobulated margin,rim calcification,and microcalcification exhibited statistically significant differences(P<0.05)in distinguishing between benign and malignant PCTN.The AUC value derived from these independent risk factors was 0.843.Furthermore,among the five transfer learning models evaluated,the ResNet50 model demonstrated the highest diagnostic efficiency,achieving an AUC value of 0.903 2.Conclusion The ultrasound-based transfer learning artificial intelligence model demonstrated superior performance compared to traditional ultrasound image evaluation methods,enabling accurate prediction of the nature of PCTN and thereby reducing unnecessary ultrasound-guided fine needle biopsies.

Objective:To construct a prediction model for the risk of sarcopenia in middle-aged and elderly patients with knee osteoarthritis (KOA) based on machine learning, and to provide a basis for carrying out the prevention of sarcopenia in patients with KOA.Methods:Clinical data of KOA patients from three tertiary hospitals in Guangdong Province were collected between December 2023 and September 2024 using a convenience sampling method. The data were randomly split into training and test sets at an 8:2 ratio, with the occurrence of sarcopenia as the outcome variable. Risk prediction models for sarcopenia were constructed using eight machine learning algorithms: logistic regression, K-nearest neighbors, support vector machine, decision tree, neural network, random forest, gradient boosting machine (GBM), and eXtreme gradient boosting. Model performance was evaluated based on metrics including the area under the receiver operating characteristic curve (AUC), accuracy, precision, sensitivity, specificity, and F1 score. The optimal model was selected, and feature importance was visualized using the Shapley Additive exPlanations (SHAP) method.Results:Data from 640 KOA patients were analyzed, 143 males and 497 females, (67.51± 7.72) years, with 136 cases (21.25%) developing sarcopenia. All eight prediction models showed high AUC values, with the GBM model demonstrating the best performance. Its metrics included an AUC of 0.926 (95% CI 0.874 - 0.965), accuracy of 0.852, precision of 0.611, sensitivity of 0.815, specificity of 0.861, and F1 score of 0.698. SHAP analysis identified body mass index, calf circumference, body fat percentage, WOMAC score, and age as the most important predictive features. Conclusions:The GBM-based risk prediction model for sarcopenia in middle- aged and elderly KOA patients demonstrated optimal performance, enabling healthcare professionals to accurately and promptly identify high-risk groups among these patients and to develop effective, evidence-based intervention strategies.

Objective To investigate the feasibility and accuracy of an ultrasound-based transfer learning artificial intelligence model in predicting the malignancy probability of partially cystic thyroid nodules(PCTN).Methods A retrospective analysis was conducted on 246 patients with PCTN who had definitive pathological results and were admitted to Weihai Municipal Hospital,Cheeloo College of Medicine,Shandong University from January 2021 to December 2023.Patients were randomly divided into training and test cohorts at a ratio of 7:3.Ultrasonic image features of PCTN were evaluated,and independent risk factors were identified using multivariate logistic regression analysis,with the area under the curve(AUC)subsequently calculated.Additionally,five different pre-trained models-Inception_v3,EfficientNet,VGG19,ResNet50,and DenseNet121-were selected for transfer learning after data preprocessing using the PyTorch framework in Python.The AUC values of these models were calculated and compared.Results Solid portion greater than 50%,eccentric acute angle,ill-defined margin,spiculated or microlobulated margin,rim calcification,and microcalcification exhibited statistically significant differences(P<0.05)in distinguishing between benign and malignant PCTN.The AUC value derived from these independent risk factors was 0.843.Furthermore,among the five transfer learning models evaluated,the ResNet50 model demonstrated the highest diagnostic efficiency,achieving an AUC value of 0.903 2.Conclusion The ultrasound-based transfer learning artificial intelligence model demonstrated superior performance compared to traditional ultrasound image evaluation methods,enabling accurate prediction of the nature of PCTN and thereby reducing unnecessary ultrasound-guided fine needle biopsies.

Objective:To systematic evaluate the correlation between arsenic and Alzheimer's disease (AD).Methods:Literature search was conducted using China National Knowledge Infrastructure, Wanfang Data Knowledge Service Platform, PubMed, Science Direct, Web of Science, and Cochrane Library to collect relevant literature on the correlation between arsenic and AD published domestically and internationally. The time limit was from the establishment of the database to February 1, 2024. Meta-analysis was conducted using RevMan 5.3 software, heterogeneity test was conducted using I2 statistic and Q-test, and random effects model or fixed effects model were used for comprehensive quantitative analysis based on heterogeneity results. Standard mean difference ( SMD) was used as the effect indicator and subgroup analysis was conducted based on detection markers and disease progression. Results:The final results included 10 articles (including 1 235 AD patients and 1 293 healthy controls), all of which were in English. After heterogeneity testing, significant heterogeneity was found in the results of each study ( I2 = 78%, P < 0.001). A random effects model was used for meta-analysis, and the combined SMD value (95% CI) was 0.58 (0.45, 0.70). The sub group analysis of detection markers showed that compared with the healthy controls the SMD values (95% CI) of urine arsenic, blood arsenic, ventricular fluid arsenic, frontal cortex arsenic, hair arsenic, nail arsenic, groundwater arsenic, and brain arsenic in AD patients were 5.02 (- 2.09, 12.14), 0.40 (- 0.02, 0.83), 5.00 (- 0.35, 10.35), - 14.00 (- 27.92, - 0.08), 0.75 (0.21, 1.29), 1.42 (0.48, 2.35), - 0.10 (- 0.48, 0.28), and 0.57 (0.36, 0.78), respectively. The sub group analysis of disease progression showed that that compared with the healthy controls the SMD values (95% CI) of arsenic content in mild, moderate, and severe AD patients were 0.88 (0.52, 1.24), 0.57 (0.24, 0.91), and 2.13 (0.26, 4.00), respectively. Conclusion:There is a correlation between arsenic and AD, and hair arsenic and nail arsenic can serve as potential important indicators for evaluating the correlation between arsenic and AD.

Objective:To systematic evaluate the correlation between arsenic and Alzheimer's disease (AD).Methods:Literature search was conducted using China National Knowledge Infrastructure, Wanfang Data Knowledge Service Platform, PubMed, Science Direct, Web of Science, and Cochrane Library to collect relevant literature on the correlation between arsenic and AD published domestically and internationally. The time limit was from the establishment of the database to February 1, 2024. Meta-analysis was conducted using RevMan 5.3 software, heterogeneity test was conducted using I2 statistic and Q-test, and random effects model or fixed effects model were used for comprehensive quantitative analysis based on heterogeneity results. Standard mean difference ( SMD) was used as the effect indicator and subgroup analysis was conducted based on detection markers and disease progression. Results:The final results included 10 articles (including 1 235 AD patients and 1 293 healthy controls), all of which were in English. After heterogeneity testing, significant heterogeneity was found in the results of each study ( I2 = 78%, P < 0.001). A random effects model was used for meta-analysis, and the combined SMD value (95% CI) was 0.58 (0.45, 0.70). The sub group analysis of detection markers showed that compared with the healthy controls the SMD values (95% CI) of urine arsenic, blood arsenic, ventricular fluid arsenic, frontal cortex arsenic, hair arsenic, nail arsenic, groundwater arsenic, and brain arsenic in AD patients were 5.02 (- 2.09, 12.14), 0.40 (- 0.02, 0.83), 5.00 (- 0.35, 10.35), - 14.00 (- 27.92, - 0.08), 0.75 (0.21, 1.29), 1.42 (0.48, 2.35), - 0.10 (- 0.48, 0.28), and 0.57 (0.36, 0.78), respectively. The sub group analysis of disease progression showed that that compared with the healthy controls the SMD values (95% CI) of arsenic content in mild, moderate, and severe AD patients were 0.88 (0.52, 1.24), 0.57 (0.24, 0.91), and 2.13 (0.26, 4.00), respectively. Conclusion:There is a correlation between arsenic and AD, and hair arsenic and nail arsenic can serve as potential important indicators for evaluating the correlation between arsenic and AD.

Objective:To investigate the local hemodynamic spatial distribution of internal carotid artery stenosis and its correlation with plaque characteristics using high-resolution vessel wall imaging (HR-VWI) combined with computational fluid dynamics.Methods:This was a cross-sectional study. A retrospective analysis was conducted on the clinical and imaging data of 70 patients with moderate to severe stenosis at the initiation of the internal carotid artery in First Teaching Hospital of Tianjin University of Traditional Chinese Medicine and Tianjin First Central Hospital from March 2018 to June 2020. All patients underwent HR-VWI and CT angiography examinations. The parameters related to plaque characteristics, such as plaque length, maximum wall thickness, plaque volume, wall volume percentage and intraplaque hemorrhage (IPH) were measured and evaluated on HR-VWI images. CT angiography images were used to construct a local hemodynamic vascular model to measure various wall shear stress (WSS) derived parameters, such as time-averaged wall shear stress (TAWSS), oscillatory shear index (OSI), relative residence time (RRT), and transverse wall shear stress (transWSS), at the narrowest, proximal, and distal parts of the lesion. The Friedman test was used to analyze the difference of hemodynamic parameters in different parts of the lesion. Spearman correlation analysis was performed to assess the correlation between plaque burden and local hemodynamic parameters. Univariate and multivariate logistic regression analyses were used to explore the independent risk factors for predicting IPH.Results:Among the 70 patients, 25 patients with IPH and 45 patients without IPH. The overall differences in TAWSS, OSI, RRT and transWSS at the narrowest, proximal, and distal parts of the lesion in 70 patients were statistically significant ( P<0.05). The TAWSS and transWSS at the narrowest parts were significantly higher than those at the proximal and distal parts ( P<0.05). The OSI at the distal part was significantly higher than that at the narrowest and the proximal parts ( P<0.05). The RRT at the proximal part was significantly lower than that at the narrowest and the distal parts ( P<0.05). Spearman correlation analysis showed RRT at the distal part was correlated with plaque volume ( r s=0.249, P=0.044) and wall volume percentage ( r s=0.286, P=0.016), respectively. In a multivariate logistic regression showed plaque length ( OR=1.315, 95% CI 1.073-1.612, P=0.008) and TAWSS at the narrowest part ( OR=1.631, 95% CI 1.308-1.854, P=0.008) were independent risk factors for predicting IPH. Conclusions:The spatial distribution of local hemodynamics of moderate to severe stenosis at the initiation of the internal carotid artery is different, and the WSS parameters in different parts of the lesion have different effects on plaque volume, wall volume percentage and IPH.

Objective:To construct a prediction model for the risk of sarcopenia in middle-aged and elderly patients with knee osteoarthritis (KOA) based on machine learning, and to provide a basis for carrying out the prevention of sarcopenia in patients with KOA.Methods:Clinical data of KOA patients from three tertiary hospitals in Guangdong Province were collected between December 2023 and September 2024 using a convenience sampling method. The data were randomly split into training and test sets at an 8:2 ratio, with the occurrence of sarcopenia as the outcome variable. Risk prediction models for sarcopenia were constructed using eight machine learning algorithms: logistic regression, K-nearest neighbors, support vector machine, decision tree, neural network, random forest, gradient boosting machine (GBM), and eXtreme gradient boosting. Model performance was evaluated based on metrics including the area under the receiver operating characteristic curve (AUC), accuracy, precision, sensitivity, specificity, and F1 score. The optimal model was selected, and feature importance was visualized using the Shapley Additive exPlanations (SHAP) method.Results:Data from 640 KOA patients were analyzed, 143 males and 497 females, (67.51± 7.72) years, with 136 cases (21.25%) developing sarcopenia. All eight prediction models showed high AUC values, with the GBM model demonstrating the best performance. Its metrics included an AUC of 0.926 (95% CI 0.874 - 0.965), accuracy of 0.852, precision of 0.611, sensitivity of 0.815, specificity of 0.861, and F1 score of 0.698. SHAP analysis identified body mass index, calf circumference, body fat percentage, WOMAC score, and age as the most important predictive features. Conclusions:The GBM-based risk prediction model for sarcopenia in middle- aged and elderly KOA patients demonstrated optimal performance, enabling healthcare professionals to accurately and promptly identify high-risk groups among these patients and to develop effective, evidence-based intervention strategies.

Objective To analyze the expression level of YEATS2 in hepatocellular carcinoma(HCC)about its clinical prognosis and therapeutic value based on biological information from the cancer genome atlas(TCGA)and human protein atlas(HPA)databases.Methods The mRNA expression data and clinical information of HCC were downloaded from the TCGA database,the expression of YEATS2 between HCC tissues and normal tissues was analyzed by using the R software,and the protein expression differences were preliminary verified by the HPA database.The expression differences of YEATS2 between various clinical features of HCC were compared,and their effects on the survival of HCC patients by Kaplan-Meier method and COX regression analysis were then evaluated.Receiver operating characteristic(ROC)curves were plotted to evaluate their diagnostic values.The biological functions of YEATS2 in HCC were analyzed using gene ontology(GO)and Kyoto encyclopedia of genes and genomes(KEGG)enrichment analysis.The relationship between YEATS2 expression and tumor microenvironment(TME)was analyzed by the"ESTIMATE"algorithm,and its relationship with tumor-infiltrating immune cells(TIICs)was assessed by CIBERSORT.Analysis of YEATS2 expression levels to immune checkpoints and drug sensitivity was performed using the R package.Results The expression of YEATS2 was increased in HCC tissues(P=4.96e-21),and its expression level was correlated with age,clinical stage,pathological grade and T stage(all P＜0.05).Overall survival(OS)(P＜0.001)and progression-free survival(FPS)(P=0.016)were decreased in HCC patients with high expression of YEATS2,COX regression results showed that the expression level ofYEATS2 was associated with poor prognosis in HCC patients(OS:HR=2.167,95％CI:1.441～3.261,P=2.06e-04),and it was an independent risk factor for predicting poor prognosis in HCC patients(OS:HR=1.891,95％CI:1.243～2.877,P=0.003).The ROC curve suggested the AUCs for 1,3 and 5 years were 0.677,0.622 and 0.612,respectively,indicating good predictive ability.The TCGA database screened a total of 6 764 differential genes in the YEATS2 high and low expression groups,of which 4 094 genes were up-regulated and 2 670 genes were down-regulated in the YEATS2 high expression group.The results of GO and KEGG enrichment analyses showed that the differentially differentiated genes in the YEATS2 high expression group were mainly enriched in immunoregulation,and cell cycle regulation drug resistance pathway.The results of the TME score showed that the YEATS2 high expression group caused a decrease in immunity score(P＜0.01).The correlation between YEATS2 and TIICs showed that YEATS2 expression was positively correlated with the level of M0-type macrophage infiltration levels(r=0.48,P＜0.001)and 23 immune checkpoint genes(r=0.20～0.46,all P＜0.05),and was negatively correlated with the CD8+T-cells,plasma cells and monocyte(r=-0.26,-0.29,-0.30,P=0.021,0.011,0.008).Drug sensitivity analysis showed that the half maximal inhibitory concentration(IC50)of cabozantinib,lincitinib,doxorubicin,and cyclobenzaprine in patients with high expression of YEATS2 was higher than those in patients with low expression(all P＜0.01).Conclusion YEATS2 was highly expressed in HCC,and the expression level was associated with poor prognosis in HCC patients.YEATS2 can be used as a biomarker for the clinical early diagnosis,prognosis and immunotherapy of HCC,which may provide new ideas for clinical diagnosis and treatment.

Objective:To compare the clinical effects of mortice and tenon joint and end-to-end bone contact of zygomatic arch in reduction malarplasty, and to explore the clinical application value of mortice and tenon joint.Methods:The clinical data of patients who underwent reduction malarplasty in Sichuan Friendship Hospital and West China Hospital of Stomatology, Sichuan University from January 2013 to June 2020 were analyzed retrospectively. All patients were treated with classical or modified L-shaped zygomatic arch osteotomy. According to the bone connection mode of the zygomatic arch, the patients were divided into Group Ⅰ and Group Ⅱ. The patients in Group Ⅰ were treated with mortice and tenon joint (from June 2017 to June 2020), and the ones in Group Ⅱ were treated with end-to-end contact (from January 2013 to May 2017). The movement between zygomatic body and zygomatic arch were measured 6-24 months after surgeries. The postoperative satisfaction rate was investigated, and the incidence of complications within 2 years after operation was recorded. The data were statistically analyzed by SPSS 20.0 software. The measurement data were expressed by Mean±SD, and the counting data were expressed by cases (%). T-test was used to compare the age difference, zygomatic body and zygomatic arch movement distance between the two groups. Chi-square test was used to compare the sex composition ratio, postoperative satisfaction rate (sum of very satisfied and satisfied cases/sum of cases in respective group) and incidence of complications between the two groups. P<0.05 was considered statistically significant. Results:A total of 380 patients presenting with zygomatic protrusion were included in this retrospective study. One hundred and ninety patients were included in Group Ⅰ[22 men and 168 women; aged(26.8 ±8.6) years)] and 190 patients in Group Ⅱ [16 men and 174 women; aged (25.2 ±8.8) years)]. There was no significant difference in sex constituent ratio and age between the two groups ( P>0.05). After the operation, the width of the face was narrowed, and the contour of the face was significantly improved. The medial movement distance of zygomatic body in Group Ⅰ and Group Ⅱ was (2.65±0.76) mm versus (2.51±0.78) mm, and the movement distance of zygomatic arch in Group Ⅰ and Group Ⅱ was (4.58 ±0.44) mm versus (2.60±0.28) mm. There was no significant difference in movement distance of zygomatic body between Group Ⅰ and Group Ⅱ ( t=1.77, P=0.077), but there was significant difference in the zygomatic arch ( t=52.33, P<0.001). The satisfaction rate of patients in Group Ⅰ was 75.8% (144/190), which was higher than that in Group Ⅱ (55.3%, 105/190) ( χ2=17.72, P<0.001). There was no significant difference in the incidence of postoperative infection, temporary paresthesia, and hematoma between the two groups ( P>0.05). The incidence of facial asymmetry, sagging cheek, bone suture and nonunion in Group Ⅱ was significantly higher than that in Group Ⅰ ( P<0.05). Conclusion:Compared with end-to-end bone contact in reduction malarplasty, the mortice and tenon joint of the zygomatic arch achieved better facial narrowing effect and long-term stability, higher patient satisfaction rate and less postoperative complications.