ObjectiveTo explore the feasibility of constructing a programmed death receptor-1（PD-1） molecular probe for non-invasive micro-positron emission tomography/computed tomography （Micro-PET/CT） imaging of PD-1 protein in mouse S180 sarcoma. MethodsA transgenic PD-1 C57 S180 sarcoma mouse model was established using the S180 sarcoma cell injection. Furthermore， ¹²⁴I-anti-PD-1 monoclonal antibody probe was synthesized. 18.5 MBq of the ¹²⁴I-anti-PD-1 probe was injected into the tail vein of transgenic PD-1 C57 mice. Subsequently， S180 sarcoma was imaged using Micro-PET/CT. ResultsStudy successfully established a transgenic PD-1 C57 S180 sarcoma mouse model. Immunohistochemical （IHC） results showed PD-1 protein expression in S180 sarcoma. Micro-PET/CT imaging successfully visualized the PD-1 protein receptor in S180 sarcoma at different time points （20， 48， 72， and 120 h） after probe injection. ConclusionThe ¹²⁴I-anti-PD-1 monoclonal antibody molecular probe successfully targets the PD-1 receptor in S180 sarcoma of transgenic PD-1 C57 mice， and presents clear Micro-PET/CT immunoassay results， thus it potentially enables the non-invasive screening of patients with PD-1 positive malignant tumors.

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.

AIM:This study aims to investigate of perodic expression,distribution and interaction between es-trogen receptor α(ERα)and ClC-3 chloride channel,and their relevance to the cell cycle specificity of tamoxifen(TAM)in anti-breast cancer treatment.METHODS:We utilized a web database to analyze the correlation between ERα and ClC-3 expression.Three-dimentional molecular simulation software and co-immunoprecipitation were employed to detect and analyze the interactions between these two proteins.To assess cell cycle dynamics,we performed thymidine(TdR)double-blocking release assay and used nocodazole to block the cell cycle,with subsequent analysis via flow cytometry.Cell viability was measured by MTT assay.Western blot was conducted to evaluate the protein expression levels of ERα and ClC-3,while immunofluorescence staining was utilized to assess their subcellular distribution.RESULTS:(1)Anal-ysis from the web database revealed a significant correlation between ERα and ClC-3 expression,and co-immunoprecipita-tion confirmed their interaction.(2)We successfully obtained human breast cancer T47D cells at different cycle stages us-ing the TdR double-blocking release method and nocodazole treatment.(3)Treatment with TAM primarily inhibited T47D cell viability during G2/M phase.(4)Both ERα and ClC-3 exhibited cyclic variations in protein expression,with their sub-cellular distributions showing periodicity and co-localization.(5)Protein interactions between ERα and ClC-3 were ob-served across all cell cycle phases;(6)After TAM treatment,ERα expression peaked in G2/M phase,while ClC-3 expres-sion remained unaffected.CONCLUSION:Our findings demonstrate cyclic differences in the expression and distribution of ERα and ClC-3 in human breast cancer T47D cells,along with confirmed interactions between these two proteins.The cyclic properties of ERα may play a role in mediating the cell cycle specificity of TAM's anti-breast cancer effect.

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 explore the influencing factors of the subjective well-being of elderly individuals and improve the accuracy of well-being predictions.Methods:A multi-stage stratified sampling method was conducted in Guangdong province and Guangxi Zhuang autonomous region from August 2019 to October 2020. The health status and subjective well-being of older adults were assessed using a self-administered multidimensional health questionnaire, while overall well-being was evaluated with a single-item 5-point scale. The statistical analysis was performed using R software (version 4.3.2), with analytical methods including χ2 tests, univariate analysis, quantile regression and random forest model. Results:A total of 2 620 participants were recruited, and 70.9% of whom reported feeling happy.The depression symptom score of the elderly was 3.00(1.00, 6.00), emotional status score was (28.26±4.14), activities of daily living (ADL) score was 69.00(52.00, 81.00), and the body mass index(BMI) was (22.68±3.81)kg/m 2.The quantile regression results indicated that marital status (married)(Q 10: β=0.049; Q 50: β=0.014; Q 90: β=0.005) and depressive symptoms (no depression, depressive tendency)(Q 10: β=1.000; Q 50: β=1.000; Q 90: β=0.025) had varying effects on older adults' subjective well-being across three quantiles (Q 10, Q 50, Q 90)(all P<0.05). In contrast, social activity (no social activity)(Q 10: β=-0.072; Q 50: β=-0.011; Q 90: β=-0.006), ADL (severe disability, moderate disability, mild disability)(Q 10: β=-0.103, Q 50: β=-0.006, Q 90: β=-0.048; Q 10: β=-0.063, Q 50: β=-0.012, Q 90: β=-0.005; Q 10: β=-0.078, Q 50: β=-0.016, Q 90: β=-0.009), and emotional status (poor emotion, fair emotion)(Q 10: β=-0.261, Q 50: β=-0.009, Q 90: β=-0.025; Q 10: β=-0.140, Q 50: β=-0.017, Q 90: β=-0.029) had a negative impact on elderly subjective well-being at the three quantile points(all P<0.05). Random forest results indicated that depressive symptoms, ADL, emotional status, household monthly income per capita, BMI, social activity, multimorbidity status, and marital status were the most important determinants of subjective well-being. Conclusion:Depressive symptoms and emotional status have a significant impact on the subjective well-being of older adults. Additionally, ADL, household income per capita, BMI, and social engagement also play important roles in influencing well-being, while the emotional support provided by marriage should not be overlooked.

Objective To develop and translate the Chinese listening self-efficacy questionnaire(C-LSEQ)and to test its reliability and validity.Methods A total of 172 subjects aged≥60 years with age-related hearing loss completed the C-LSEQ questionnaire via direct interviews.Pure tone hearing threshold test,Mandarin hearing in noise test,and the hearing handicap inventory for elderly-screening(HHIE-S)were evaluated.Twenty subjects were randomly selected from the original group to complete the C-LSEQ questionnaire in 2 weeks after the initial evaluation.Results ① Reliability:The Cronbach's a coefficients of the three sub-dimensions and the overall ques-tionnaire were all＞0.8,and the test-retest reliability coefficients of the three sub-dimensions and the overall ques-tionnaire were all＞0.9(P＜0.001).② Validity:The experts collectively evaluated the representativeness of the statements with good content validity.The convergent validity test showed that the composite reliability(CR)of the 2 sub-dimensions and the overall questionnaire were all＞0.7,and the average variance extracted(AVE)were all＞0.5.The CR value of the complex listening dimension was 0.655,and the AVE value was 0.937.The criterion validity test showed that the overall questionnaire and the three sub-dimensions of the C-LSEQ were significantly correlated with the pure-tone average,speech recognition thresholds in noise,and HHIE-S(P＜0.001).Conclusion The C-LSEQ exhibits stable structure,with good reliability and validity.It can be used to assess listen-ing self-efficacy in adults with hearing loss,especially in the elderly with age-related hearing loss.

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:The quality of systematic reviews/meta-analyses directly affects the reliability of clinical decision-making basis.Currently,there is no literature quality research on the systematic reviews/meta-analyses of platelet rich plasma versus hyaluronic acid in the treatment of knee osteoarthritis both domestically and internationally.This article will comprehensively evaluate the methodological quality,reporting quality,and evidence quality of the systematic reviews/meta-analyses of platelet rich plasma versus hyaluronic acid in the treatment of knee osteoarthritis.METHODS:Computer searches were conducted on CNKI,WanFang Data,VIP,CBM,PubMed,Embase,and The Cochrane Library.From the establishment of the database until January 24,2024,all systematic reviews/meta-analyses treated with platelet rich plasma versus hyaluronic acid for knee osteoarthritis were collected.Two evaluators independently conducted literature screening and data extraction,and used the AMSTAR 2,PRISMA 2020,and GRADE systems to evaluate and summarize the methodological,reporting,and evidence quality of the included systematic reviews/meta-analyses.RESULTS:A total of 18 qualified systematic reviews/meta-analyses were included,and the results showed that the efficacy and safety of platelet rich plasma might be better than that of hyaluronic acid.The methodological quality of all 18 studies was extremely low;4 reports had poor quality and relatively serious information defects,while 14 reports had moderate quality and some information defects.Among the 275 outcome measures of 16 systematic reviews/meta-analyses,there were 9 medium quality evidence,90 low-quality evidence,and 176 extremely low-quality evidence,with no high-quality evidence.CONCLUSION:At present,the quality of systematic reviews/meta-analyses literature on the treatment of knee osteoarthritis with platelet rich plasma versus hyaluronic acid is relatively low.In the future,the authors of the systematic reviews need to strictly follow the entries of quality evaluation tools such as AMSTAR 2 and PRISMA 2020 in terms of plan registration,research type explanation,retrieval strategy,exclusion list,research site and funding source,bias risk analysis,publication bias evaluation,and public information acquisition,and conduct evidence quality evaluation on the combined results of the systematic reviews/meta-analyses to provide more reliable and rigorous evidence-based basis for clinical practice.

Hand degloving injury represents one of the most severe forms of hand trauma, characterised by challenging treatment and a complex prognostic outcome. It is crucial to effectively utilise the degloved tissues in emergency or primary repair of a hand degloving injury. This consensus provides a comprehensive review of the existing literature on definition, classification, emergency assessment, debridement, judgment of skin viability, in situ repair of the degloved skin, and adjunctive treatment for degloving injury of hand. Based on conclusion of both domestic and international experiences, this expert consensus on the classification of hand degloving injury and the emergency repair with the avulsed skin is established, aiming to provide a guidance to surgeons on standardised treatment strategy and improve the management of hand degloving injury.

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.