Objective:To establish three machine learning prediction models based on CT imaging characteristics of papillary thyroid carcinoma (PTC) , and use SHAP (shapley additive explanations) analysis to investigate the contribution of each CT image features in the best model.Methods:CT imaging features in 426 cases of 440 PTCs confirmed pathologically from Jan. 2016 to Jan. 2021 at the affiliated Hangzhou First People’s Hospital of Westlake University Medical School were retrospectively analyzed. compared with 467 cases of 528 nodular goiter (NG) , evaluating the distribution of four CT characteristics: cookie bite sign, enhanced range of narrowing/blur (ERNB) , microcalcifications, and irregular shape. We split the data into 8∶2 ratio for training and testing sets, then constructed three machine learning models using XGBoost, RF, and SVM. Based on AUC, accuracy, F1 score, and other metrics, we selected the best model. Lastly, we used SHAP values to assess each CT feature’s contribution and positive/negative effects on the model.Results:Among 440 PTC and 528 NG nodules, CT features like cookie bite sign, ERNB, microcalcifications, and irregular shape occurred in 326 and 30 ( χ 2=483.05, P<0.001) , 363 and 106 ( χ 2=374.45, P<0.001) , 158 and 53 ( χ 2=94.24, P<0.001) , and 354 and 52 ( χ 2=491.34, P<0.001) nodules, respectively. The machine learning models built using XGBoost, RF, and SVM had AUC, accuracy, and F1 scores ranging from 0.884~0.925, 0.867~0.873, and 0.844~0.854 respectively on the training set. On the test set, the scores ranged from 0.869~0.923, 0.845~0.871, and 0.803~0.845. Among them, the XGBoost model demonstrated the highest diagnostic performance on the test set. Among the four CT features, irregular shape had the highest absolute SHAP value, positively contributing to PTC diagnosis. Conclusion:XGBoost model showed the highest PTC diagnostic performance. Irregular shape had the greatest positive impact on PTC diagnosis.

Executive function(EF) is an advanced cognitive function of the central nervous system, and is closely related to an individual's capacity for daily living and adaptation. Patients with attention deficit hyperactivity disorder (ADHD) typically exhibit significant executive dysfunction. While most existing studies on the executive function of individuals with ADHD are cross-sectional, and little is known about the longitudinal maturation process of related brain structures and functional connectivity patterns. The findings indicate that ADHD patients exhibit differential developmental trajectories in brain structural and functional connectivity compared with typically developing group.Furthermore, there is a lifespan association between abnormal brain network development and ADHD symptoms. This article aims to elucidate the characteristics of executive function deficits in ADHD patients across different developmental stages, examining their relationship with the nervous system’s development from a development perspective.

Objective To observe the value of non-contrast CT radiomics extreme gradient boosting(XGBoost)model based on SHAP method for predicting acute necrotic collection(ANC)around acute pancreatitis(AP).Methods A total of 307 patients with initially clinically diagnosed AP were retrospectively enrolled.The optimal radiomics features of peripheral pancreatic tissue volume of interest(VOI)were extracted and screened based on automatic segmentation on the first non-contrast CT,and the evaluation results of modified CT severity index(MCTSI)score of AP severity based on first enhanced CT were recorded.The patients were divided into peripancreatic ANC group(ANC group)and acute peripancreatic fluid collection(APFC)group according to follow-up abdominal CT.XGBoost method was used to construct radiomics model,MCTSI model and combined model for predicting AP ANC based on the optimal radiomics features,MCTSI and their combination,respectively.The diagnostic efficacy of each model was evaluated using 5-fold cross-validation method,and the contribution of each variable to combined model was analyzed with SHAP method.Results Among 307 cases,there were 134 cases in ANC group and 173 in APFC group.Totally 6 optimal radiomics features were screened based on the first non-contrast CT.The area under the receiver operating characteristic curve(AUC)of radiomics model,MCTSI model and combined model was 0.936,0.693 and 0.917,respectively.The AUC of MCTSI model was lower than that of radiomics model and combined model(Z=-3.485,-2.824,both P＜0.01),while no significant difference of AUC was found between radiomics model and combined model(Z=-0.817,P=0.415).The contribution of optimal radiomics features to combined model were all higher than that of MCTSI score.Conclusion Non-contrast CT radiomics XGBoost model could effectively predict AP ANC.

Objective:To establish three machine learning prediction models based on CT imaging characteristics of papillary thyroid carcinoma (PTC) , and use SHAP (shapley additive explanations) analysis to investigate the contribution of each CT image features in the best model.Methods:CT imaging features in 426 cases of 440 PTCs confirmed pathologically from Jan. 2016 to Jan. 2021 at the affiliated Hangzhou First People’s Hospital of Westlake University Medical School were retrospectively analyzed. compared with 467 cases of 528 nodular goiter (NG) , evaluating the distribution of four CT characteristics: cookie bite sign, enhanced range of narrowing/blur (ERNB) , microcalcifications, and irregular shape. We split the data into 8∶2 ratio for training and testing sets, then constructed three machine learning models using XGBoost, RF, and SVM. Based on AUC, accuracy, F1 score, and other metrics, we selected the best model. Lastly, we used SHAP values to assess each CT feature’s contribution and positive/negative effects on the model.Results:Among 440 PTC and 528 NG nodules, CT features like cookie bite sign, ERNB, microcalcifications, and irregular shape occurred in 326 and 30 ( χ 2=483.05, P<0.001) , 363 and 106 ( χ 2=374.45, P<0.001) , 158 and 53 ( χ 2=94.24, P<0.001) , and 354 and 52 ( χ 2=491.34, P<0.001) nodules, respectively. The machine learning models built using XGBoost, RF, and SVM had AUC, accuracy, and F1 scores ranging from 0.884~0.925, 0.867~0.873, and 0.844~0.854 respectively on the training set. On the test set, the scores ranged from 0.869~0.923, 0.845~0.871, and 0.803~0.845. Among them, the XGBoost model demonstrated the highest diagnostic performance on the test set. Among the four CT features, irregular shape had the highest absolute SHAP value, positively contributing to PTC diagnosis. Conclusion:XGBoost model showed the highest PTC diagnostic performance. Irregular shape had the greatest positive impact on PTC diagnosis.

Executive function(EF) is an advanced cognitive function of the central nervous system, and is closely related to an individual's capacity for daily living and adaptation. Patients with attention deficit hyperactivity disorder (ADHD) typically exhibit significant executive dysfunction. While most existing studies on the executive function of individuals with ADHD are cross-sectional, and little is known about the longitudinal maturation process of related brain structures and functional connectivity patterns. The findings indicate that ADHD patients exhibit differential developmental trajectories in brain structural and functional connectivity compared with typically developing group.Furthermore, there is a lifespan association between abnormal brain network development and ADHD symptoms. This article aims to elucidate the characteristics of executive function deficits in ADHD patients across different developmental stages, examining their relationship with the nervous system’s development from a development perspective.

Objective To observe the value of non-contrast CT radiomics extreme gradient boosting(XGBoost)model based on SHAP method for predicting acute necrotic collection(ANC)around acute pancreatitis(AP).Methods A total of 307 patients with initially clinically diagnosed AP were retrospectively enrolled.The optimal radiomics features of peripheral pancreatic tissue volume of interest(VOI)were extracted and screened based on automatic segmentation on the first non-contrast CT,and the evaluation results of modified CT severity index(MCTSI)score of AP severity based on first enhanced CT were recorded.The patients were divided into peripancreatic ANC group(ANC group)and acute peripancreatic fluid collection(APFC)group according to follow-up abdominal CT.XGBoost method was used to construct radiomics model,MCTSI model and combined model for predicting AP ANC based on the optimal radiomics features,MCTSI and their combination,respectively.The diagnostic efficacy of each model was evaluated using 5-fold cross-validation method,and the contribution of each variable to combined model was analyzed with SHAP method.Results Among 307 cases,there were 134 cases in ANC group and 173 in APFC group.Totally 6 optimal radiomics features were screened based on the first non-contrast CT.The area under the receiver operating characteristic curve(AUC)of radiomics model,MCTSI model and combined model was 0.936,0.693 and 0.917,respectively.The AUC of MCTSI model was lower than that of radiomics model and combined model(Z=-3.485,-2.824,both P＜0.01),while no significant difference of AUC was found between radiomics model and combined model(Z=-0.817,P=0.415).The contribution of optimal radiomics features to combined model were all higher than that of MCTSI score.Conclusion Non-contrast CT radiomics XGBoost model could effectively predict AP ANC.

This study was designed to evaluate the correlation between immune antibodies and pelvic inflammatory disease(PID)using retrospective analysis.Cases were selected from 171 patients who met the diagnosis of PID in Liuzhou People's Hospital of Guangxi Province from January 2022 to March 2023,and the PID patients were further divided into simple PID group(53 cases)and in PID combined with reproductive tract infection group(118 cases)according to the presence or absence of reproductive tract infections,while 83 cases of women who did not meet the specific diagnostic criteria of PID and did not have reproductive tract infections were selected as the control group during the same period.The positive rate of immune antibodies in the three groups were observed and compared to explore the relationship between immune antibodies and PID.Data showed that the positive rates of immune antibodies were significantly higher in the PID alone group and the PID combined with reproductive tract infection group than that in the control group.Furthermore,the positive rate of immune antibody TPOAb was significant difference in the PID combined with reproductive tract infection group and the PID alone group(P<0.05).In conclusion,TPOAb is closely associated with reproductive tract infections.

Objective To observe the clinical effect of osteoporotic vertebral compression fracture(OVCF)and analyze the value of magnetic resonance imaging(MRI)and bone metabolism indexes in predicting the poor prognosis.Methods A total of 258 OVCF patients admitted to Hangzhou Ninth Peopl's Hospital from March 2021 to March 2023 were selected as study objects.After percutaneous kyphoplasty(PKP)or percutaneous vertebroplasty(PVP),visual analogue scale(VAS)score and Cobb angle were collected.The patients were divided into poor prognosis group and good prognosis group according to whether the fracture was repeated after surgery.MRI and bone metabolism indexes of patients were collected,and the influencing factors of prognosis were analyzed.Results The VAS scores of OVCF patients decreased with the extension of time(P<0.05).One month and three months after surgery,the Cobb angle of injured vertebrae in OVCF patients was significantly lower than that before surgery(P<0.05).The proportion of vertebral fluid signs in poor prognosis group was significantly higher than that in good prognosis group(P<0.05),and N-terminal midragment of osteocalcin(N-MID)and 25-hydroxyvitamin D[25(OH)D]in poor prognosis group were lower than those in good prognosis group(P<0.05).Vertebral fluid signs,N-MID and 25(OH)D were all associated with poor prognosis in OVCF patients(P<0.05).The area under the curve(AUC)of vertebral fluid signs,N-MID and 25(OH)D alone for predicting poor prognosis of OVCF was 0.744,0.872 and 0.822,the sensitivity was 56.5%,87.0%and 73.9%,and the specificity was 92.3%,74.5%and 80.9%,respectively.Above indicators combined AUC,sensitivity and specificity were 0.967,95.7%and 80.9%.Conclusion PKP/PVP can reduce pain and improve function in OVCF patients.MRI vertebral fluid signs,N-MID and 25(OH)D are all factors affecting the poor prognosis of OVCF patients,and the combination of three factors can predict the poor prognosis of OVCF patients.

HMG-CoA reductase (HMGCR) protein is usually upregulated after statin (HMGCR inhibitor) treatment, which inevitably diminishes its therapeutic efficacy, provoking the need for higher doses associated with adverse effects. The proteolysis targeting chimera (PROTAC) technology has recently emerged as a powerful approach for inducing protein degradation. Nonetheless, due to their bifunctional nature, developing orally bioavailable PROTACs remains a great challenge. Herein, we identified a powerful HMGCR-targeted PROTAC (

Objective:To analyze the multi-slice spiral CT (MSCT) scan signs and the expression of vascular endothelial growth factor (VEGF) in hepatocellular carcinoma (HCC) patients.Methods:A total of 51 patients with HCC admitted to our hospital from April 2015 to December 2018 were enrolled. The VEGF positive rate, microvessel density (MVD) level, HCC VEGF positive and negative MVD levels, and MSCT were compared between HCC and paracancerous tissues. The level of angiogenesis index was scanned and the association between MSCT scan signs and VEGF and MVD was analyzed.Results:The positive rate of VEGF and MVD in HCC tissues were higher than that in adjacent tissues ( P<0.05); MVD in patients with VEGF positive HCC was higher than that in patients with VEGF negative ( P<0.05); There was no significant difference in the positive rate of VEGF and MVD between patients with lesions >5 cm and ≤ 5 cm ( P>0.05). The VEGF positive rate and MVD in patients with pseudocapsule without / incomplete were higher than those in patients with intact capsule; the VEGF positive rate and MVD in patients with high risk invasion were higher than those in patients with low risk; the VEGF positive rate and MVD in arterial blood supply were higher than those in patients with dual supply and insufficient blood supply ( P<0.05); MSCT scan showed that false capsule without / incomplete, high risk of invasion, arterial blood supply and double supply were positively correlated with VEGF and MVD, while less blood supply was negatively correlated with VEGF and MVD ( P<0.05). Conclusions:MSCT scan can accurately evaluate the size of HCC lesions, the presence of false capsule, invasion and metastasis, enhancement type, etc. No/incomplete pseudocapsule, high-risk invasion, arterial blood supply and dual supply are positively correlated with VEGF and MVD, while less blood supply is negatively correlated with VEGF and MVD. This can pro-vide a new rapid, simple and non-invasive examination method for the evaluation of tumor neovascularization and diagnosis and prognosis for HCC.