Objective To investigate the relationship between collagen type Ⅰ alpha 1 chain(COL1A1)and mucin 4(MUC4)expression and clinicopathological features and prognosis in patients with nasopharynge-al carcinoma.Methods A total of 269 suspected patients with nasopharyngeal carcinoma admitted to a hospi-tal from December 2020 to June 2021 were selected as the study subjects,123 patients with nasopharyngeal carcinoma confirmed by pathological examination were selected as the study group,and 146 patients with na-sopharyngeal carcinoma ruled out by pathological examination were selected as the control group.Real-time fluorescence quantitative polymerase chain reaction was used to detect the mRNA expression of COL1A1 and MUC4,and immunohistochemical staining was used to detect the protein expression of COL1A1 and MUC4.Survival curves were drawn by Kaplan-Meier method to analyze the relationship between COL1A1 and MUC4 protein expression and survival of nasopharyngeal carcinoma patients.COX regression analysis of nasopharyn-geal carcinoma patients with adverse prognostic factors.Receiver operating characteristic(ROC)curve was drawn to analyze the value of COL1A1 and MUC4 mRNA expression in predicting poor prognosis of patients with nasopharyngeal carcinoma.Results Compared with the control group,the mRNA expressions of COL1A1 and MUC4 in the study group were increased,and the difference was statistically significant(P＜0.05).The positive expression rate of COL1A1 protein in the study group was 58.54％(72/123)higher than that in the control group[41.10％(60/146)],and the difference was statistically significant(P＜0.05).The positive expression rate of MUC4 protein was 63.41％(78/123)higher than that of control group[33.56％(49/146)],and the difference was statistically significant(P＜0.05).The expressions of COL1A1 and MUC4 protein were correlated with TNM stage,depth of tumor invasion and lymph node metastasis(P＜0.05).Compared with the patients with good prognosis,the expressions of COL1A1 and MUC4 mRNA were in-creased in the patients with poor prognosis,and the differences were statistically significant(P＜0.05).The 3-year cumulative survival rate of patients with positive expression of COL1A1 protein was significantly lower than that of patients with negative expression of COL1A1 protein,and the difference was statistically signifi-cant(P＜0.05).The 3-year cumulative survival rate of patients with positive expression of MUC4 protein was significantly lower than that of patients with negative expression of MUC4 protein,and the difference was sta-tistically significant(P＜0.05).TNM stage Ⅲ/Ⅳ,tumor invasion stage T3/T4,lymph node metastasis,posi-tive expression of COL1A1 and MUC4 protein were risk factors for poor prognosis(P＜0.05).ROC curve a-nalysis results showed that the area under the curve(AUC)of COL1A1 and MUC4 mRNA combined to pre-dict poor prognosis of nasopharyngeal carcinoma patients was significantly greater than the AUC of MUC4 mRNA alone,and the difference was statistically significant(Z=2.248,P=0.025).Conclusion The expres-sion of COL1A1 and MUC4 in patients with nasopharyngeal carcinoma is increased,which is closely related to the clinicopathological features and prognosis of patients with nasopharyngeal carcinoma,and has important reference value for the prognosis evaluation of patients with nasopharyngeal carcinoma.

Sepsis is an immune disorder caused by infection,which can lead to multiple organ dysfunction,immune response and immune cells play key role in the onset and progression of sepsis.As an immunoglobulin,triggering receptor expressed on mye-loid cells-1(TREM-1)has immunomodulatory effects and participates in the pathophysiological process of many diseases.TREM-1 can regulate inflammatory mediators and immune cells in sepsis,playing an important role in its diagnosis,treatment and prognosis.This article summarizes the role and possible mechanism of TREM-1 in occurrence and development of sepsis,and provides a theoreti-cal basis for the research direction and treatment strategy of the disease.

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.

Sepsis is an immune disorder caused by infection,which can lead to multiple organ dysfunction,immune response and immune cells play key role in the onset and progression of sepsis.As an immunoglobulin,triggering receptor expressed on mye-loid cells-1(TREM-1)has immunomodulatory effects and participates in the pathophysiological process of many diseases.TREM-1 can regulate inflammatory mediators and immune cells in sepsis,playing an important role in its diagnosis,treatment and prognosis.This article summarizes the role and possible mechanism of TREM-1 in occurrence and development of sepsis,and provides a theoreti-cal basis for the research direction and treatment strategy of the disease.

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.

ECG signals and sleep monitoring parameters complement each other and can be used for qualitative diagnosis of sleep apnea syndrome and cardio-related diseases.However,due to the limitations of the instrument volume and the detection environment,it is often challenging to integrate these two functions in practical applications.In this paper,a 12-lead dynamic electrocardiograph integrated with sleep monitoring is designed.The system's volume is reduced by combining the integrated ECG simulation front end with a miniature sensor.The system achieves the extraction,conditioning,and calculation of 12-lead ECG signals and sleep-related parameters and writes the data to a memory card in real time,which offers convenience for users and doctors in the diagnostic process.

Cardiopulmonary exercise testing(CPET)refers to a method of measuring various indicators of the human body under gradually increasing exercise loads to objectively evaluate cardiopulmonary reserve function and exercise endurance.Currently,CPET detection systems primarily measure subjects'ECG,respiratory flow,oxygen(O2),and carbon dioxide(CO2)parameters.This paper introduces a non-invasive multi-parameter exercise cardiopulmonary function evaluation system that incorporates impedance cardiography monitoring.The system integrates impedance cardiography monitoring with conventional CPET detection parameters and detects changes in the hemodynamic parameters of the body during exercise,aiding in the evaluation of exercise capacity.Additionally,the system features a portable design with Wi-Fi wireless transmission,which enhances its applicability.

Objective To prepare Zishen pills as gel plaster according to the prescription,and investigate its transdermal absorption characteristics in vitro.Methods Based on preliminary experiments,the matrix prescription of the gel plaster was optimized by single-factor tests and the Box-Behnken design.Evaluation indicators included initial viscosity,viscosity retention and sensory scores.The modified Franz diffusion cell was used to investigate the effect of penetration enhancers on the transdermal characteristics of gel plaster in vitro,with the permeability of neomangiferin,phellodendrine hydrochloride,mangiferin and berberine hydrochloride as evaluation indicators.Results The prescription dosage of the preferred matrix for the Zishen gel plaster was sodium polyacrylate NP700 2.55 g,glycerin 11.04 g,polyvinylpyrrolidone K90 1.13 g,tartaric acid 0.1 g,glycyrrhizin 0.1 g,kaolin 0.3 g,and distilled water 15 g.Among different types and concentrations of permeation enhancers,5%aminoketone showed the best permeation performance.The permeation rates for neomangiferin,phellodendrine hydrochloride,mangiferin,and berberine hydrochloride were 1.5338,1.7809,2.3247 and 20.0899 μg·(cm2)-1·h-1,and the penetration rates were 2.4319,1.9408,1.9604 and 1.4701,respectively.The percutaneous absorption curve of the drug conformed to the zero-order kinetic equation.Conclusion The preparation process of the obtained gel plaste is stable and feasible,with good adhesive properties,sustained drug release,and favorable in vitro percutaneous permeability,indicating potential clinical application value.