Objective To develop an assessment scale for post-intensive care syndrome in pediatrics(PICS-p)and evaluate its reliability and validity,aiming to provide a scientifically sound and feasible tool for clinical assessment.Methods Based on the PICS-p conceptual framework,combined with literature review,semi-structured interviews,and Delphi expert consultation,a preliminary scale was developed.From June to December 2023,a survey was conducted among 330 pediatric patients who were discharged from a tertiary children's hospital in Hangzhou,followed by a two-week retest with 30 children to assess the reliability and validity of the scale.Results Finally 304 children completed the survey.The Post-Intensive Care Syndrome Scale for Children consists of 4 dimensions of physical dysfunction,cognitive dysfunction,psychological dysfunction and social dysfunction,with a total of 29 entries,with a cumulative variance contribution of 61.705％.The content validity index for individual items ranged from 0.800 to 1.000,and the content validity index for the scale as a whole was 0.98.The dimensions of the scale showed positive correlations with the Functional Status Scale(r=0.438-0.581,P＜0.001).The overall Cronbach's α coefficient of the scale was 0.847;the split-half reliability was 0.868;the test-retest reliability was 0.832.Conclusion The scale demonstrates good reliability and validity,and it is suitable for assessing the severity of PICS-p in pediatric patients discharged from the PICU.

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.

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.

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 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 develop an assessment scale for post-intensive care syndrome in pediatrics(PICS-p)and evaluate its reliability and validity,aiming to provide a scientifically sound and feasible tool for clinical assessment.Methods Based on the PICS-p conceptual framework,combined with literature review,semi-structured interviews,and Delphi expert consultation,a preliminary scale was developed.From June to December 2023,a survey was conducted among 330 pediatric patients who were discharged from a tertiary children's hospital in Hangzhou,followed by a two-week retest with 30 children to assess the reliability and validity of the scale.Results Finally 304 children completed the survey.The Post-Intensive Care Syndrome Scale for Children consists of 4 dimensions of physical dysfunction,cognitive dysfunction,psychological dysfunction and social dysfunction,with a total of 29 entries,with a cumulative variance contribution of 61.705％.The content validity index for individual items ranged from 0.800 to 1.000,and the content validity index for the scale as a whole was 0.98.The dimensions of the scale showed positive correlations with the Functional Status Scale(r=0.438-0.581,P＜0.001).The overall Cronbach's α coefficient of the scale was 0.847;the split-half reliability was 0.868;the test-retest reliability was 0.832.Conclusion The scale demonstrates good reliability and validity,and it is suitable for assessing the severity of PICS-p in pediatric patients discharged from the PICU.

Periodontitis is a critical risk factor for the occurrence and development of diabetes.Porphyromonas gingivalis may participate in insulin resistance(IR)caused by periodontal inflammation,but the functional role and specific mechanisms of P.gingivalis in IR remain unclear.In the present study,clinical samples were analysed to determine the statistical correlation between P.gingivalis and IR occurrence.Through culturing of hepatocytes,myocytes,and adipocytes,and feeding mice P.gingivalis orally,the functional correlation between P.gingivalis and IR occurrence was further studied both in vitro and in vivo.Clinical data suggested that the amount of P.gingivalis isolated was correlated with the Homeostatic Model Assessment for IR score.In vitro studies suggested that coculture with P.gingivalis decreased glucose uptake and insulin receptor(INSR)protein expression in hepatocytes,myocytes,and adipocytes.Mice fed P.gingivalis tended to undergo IR.P.gingivalis was detectable in the liver,skeletal muscle,and adipose tissue of experimental mice.The distribution sites of gingipain coincided with the downregulation of INSR.Gingipain proteolysed the functional insulin-binding region of INSR.Coculture with P.gingivalis significantly decreased the INSR-insulin binding ability.Knocking out gingipain from P.gingivalis alleviated the negative effects of P.gingivalis on IR in vivo.Taken together,these findings indicate that distantly migrated P.gingivalis may directly proteolytically degrade INSR through gingipain,thereby leading to IR.The results provide a new strategy for preventing diabetes by targeting periodontal pathogens and provide new ideas for exploring novel mechanisms by which periodontal inflammation affects the systemic metabolic state.

Immune checkpoint inhibitors(ICIs)mainly promote anti-tumor immunity by relieving tumor immune suppression.Interferon-γ(IFN-γ)plays an important role in increasing tumor antigen presentation,inducing immune cell infiltration,and directly inducing tumor cell apoptosis,becoming a key factor in improving the efficacy of immunotherapy.The abnormal signaling pathway of IFN-γ and its own effect on promoting tumor are the key mechanisms that trigger resistance to ICIs therapy.Immunotherapy,when combined with IFN-γ or IFN-γ inducers,can effectively restore the presentation ability of MHC-I,increase immune cell infiltration,and thus improve the efficacy of immunotherapy.In addition,domestic and foreign scholars have achieved certain therapeutic effects on clinical trials by normalizing the IFN-γ signaling pathway and combining ICIs with JAKi.This review mainly introduces the role of IFN-γ in the treatment of tumor ICIs,providing direction for potential treatment strategies to reverse ICIs resistance and further provi-ding theoretical basis for clinical application.

Immune checkpoint inhibitors(ICIs)mainly promote anti-tumor immunity by relieving tumor immune suppression.Interferon-γ(IFN-γ)plays an important role in increasing tumor antigen presentation,inducing immune cell infiltration,and directly inducing tumor cell apoptosis,becoming a key factor in improving the efficacy of immunotherapy.The abnormal signaling pathway of IFN-γ and its own effect on promoting tumor are the key mechanisms that trigger resistance to ICIs therapy.Immunotherapy,when combined with IFN-γ or IFN-γ inducers,can effectively restore the presentation ability of MHC-I,increase immune cell infiltration,and thus improve the efficacy of immunotherapy.In addition,domestic and foreign scholars have achieved certain therapeutic effects on clinical trials by normalizing the IFN-γ signaling pathway and combining ICIs with JAKi.This review mainly introduces the role of IFN-γ in the treatment of tumor ICIs,providing direction for potential treatment strategies to reverse ICIs resistance and further provi-ding theoretical basis for clinical application.

ObjectiveTo explore the sensory processing characteristics of preschool children with autism spectrum disorder （ASD）， and to provide a theoretical basis for early screening and intervention training of ASD. MethodsA total of 215 preschool children with ASD and170 typically developed （TD） children were investigated with a basic situation questionnaire and sensory processing measure （SPM）. The two groups were stratified according to age and gender， and the differences of scores in sensory domains were compared to analyze the sensory processing characteristics of preschool children with ASD. ResultsThe scores of social participation， vision， hearing， touch， taste and smell， body awareness， balance and motion， planning and ideas， and total sensory system in children with ASD were all higher than those in children with TD （all P<0.01）. The highest degree of abnormality was found in hearing and the lowest degree in taste and smell in children with ASD. The results of Spearman correlation analysis showed that in the 4-year-old and 5-year-old children with ASD， the scores of vision （r_s=-0.200， P= 0.033） and hearing （r_s=-0.194， P=0.040） decreased with age. There was no correlation between the scores of other developmental quotients and age （all P>0.05）. Boys and girls with ASD had higher scores in all developmental quotients than TD children （P<0.01）. However， there was no significant gender difference in any developmental quotients of ASD children （all P>0.05）. ConclusionSensory processing abnormalities are common in preschool children with ASD， which are different from those of TD children in multiple sensory domains. Sensory processing abnormalities may be used as an indicator for early screening of ASD， and it is necessary to conduct corresponding intervention training for sensory processing abnormalities in children with ASD.