Objective:To present an evaluation indicator system for access to cancer screening services.Methods:The evaluation indicator pool was constructed through a scoping review. The theoretical framework was constructed based on the multi-source indicators, and the qualitative expert consultation method was employed to form the initial version of the three-level evaluation indicator system. Delphi expert consultation method was conducted in two rounds to evaluate the relevance, importance, and availability of the proposed evaluation indicator system. The expert positive coefficient, authority coefficient, coordination degree of expert opinions, and concentration of expert opinions were subjected to analysis. Subsequently, the three-level evaluation indicator system for access to cancer screening services was adjusted and determined based on the boundary value method and the open opinions of experts. Finally, the combination weight method was employed to determine the weight.Results:The initial version of the indicator system comprised 3 primary (first-level) indicators, 11 secondary (second-level) indicators, and 46 tertiary (third-level) indicators. Delphi expert consultation was conducted for the initial version, and 17 experts ultimately completed it, exhibiting a positive coefficient of 100% and an authority coefficient of 0.87. In comparison to the initial round of consultation, Kendall's W coefficient ranges (0.15-0.43, all P<0.05) of relevance, importance, and availability scores for each tertiary indicator in the second round exhibited an improvement. The analysis of the importance dimension indicates that expert opinions are also more concentrated, as evidenced by an increase of 8.5% and 7.0% in the proportion of the tertiary indicators with an arithmetic mean above 8 and a full mark ratio above 0.5, respectively. The final evaluation indicator system comprises three primary indicators, with the weights of structure evaluation, process evaluation, and outcome evaluation being 0.338, 0.378, and 0.285, respectively. It also comprises 11 secondary indicators and 45 tertiary indicators. Conclusions:The evaluation indicator system developed in this article can be an effective evaluation tool for quantitative comparison of access to cancer screening services across different populations, cancer types, and before and after intervention. Furthermore, it is recommended that the system undergo continuous optimization concerning its application.

Objective To establish a rat model of venous thrombosis in a plateau hypobaric hypoxic environment and to investigate the effect of this environment on venous thrombosis.Methods A total of 144 healthy male SD rats were assigned randomly to four groups(n=36 rats per group):a plains sham operation(A)group,plains operation(B)group,plateau altitude 6000 m+sham operation(C)group,and plateau altitude 6000 m+surgery(D)group.Rats in A and B groups were maintained in a plains normoxic environment,while rats in C and D groups C and D were subjected to a plateau environment.Rats in the surgical groups underwent quantitative constriction to incompletely obstruct the inferior vena cava blood flow.Each group was further divided into subgroups based on time:1,3,5,7,14,and 21 d(n=6 rats per group).Regular vascular ultrasound monitoring was conducted,and blood samples were taken for whole blood viscosity testing and the assessment of inflammatory indicators,including endothelin-1(ET-1),interleukin-6(IL-6)and tissue factor(TF).Coagulation function was evaluated through the activated partial thromboplastin time(APTT),prothrombin time(PT),thrombin time(TT),fibrinogen(FIB)and D-dimer.After the observation period,the experimental animals were sacrificed and the limbs were removed.Thrombus samples were stained with hematoxylin/eosin(HE),and the thrombus wet mass was measured.Results The thrombosis incidence was significantly higher in the plateau D group than in B group,accompanied by a marked increase in blood viscosity and hematocrit(P＜0.01).Additionally,levels of ET-1,IL-6,and TF were significantly elevated(P＜0.05),indicating a coagulation disorder.Conclusions A plateau hypoxic environment model can be successfully simulated by quantitative coarctation of the inferior vena cava,combined with a specialized environmental chamber.The findings of this study suggest that a plateau hypoxic environment promotes venous thrombosis.

Objective:To present an evaluation indicator system for access to cancer screening services.Methods:The evaluation indicator pool was constructed through a scoping review. The theoretical framework was constructed based on the multi-source indicators, and the qualitative expert consultation method was employed to form the initial version of the three-level evaluation indicator system. Delphi expert consultation method was conducted in two rounds to evaluate the relevance, importance, and availability of the proposed evaluation indicator system. The expert positive coefficient, authority coefficient, coordination degree of expert opinions, and concentration of expert opinions were subjected to analysis. Subsequently, the three-level evaluation indicator system for access to cancer screening services was adjusted and determined based on the boundary value method and the open opinions of experts. Finally, the combination weight method was employed to determine the weight.Results:The initial version of the indicator system comprised 3 primary (first-level) indicators, 11 secondary (second-level) indicators, and 46 tertiary (third-level) indicators. Delphi expert consultation was conducted for the initial version, and 17 experts ultimately completed it, exhibiting a positive coefficient of 100% and an authority coefficient of 0.87. In comparison to the initial round of consultation, Kendall's W coefficient ranges (0.15-0.43, all P<0.05) of relevance, importance, and availability scores for each tertiary indicator in the second round exhibited an improvement. The analysis of the importance dimension indicates that expert opinions are also more concentrated, as evidenced by an increase of 8.5% and 7.0% in the proportion of the tertiary indicators with an arithmetic mean above 8 and a full mark ratio above 0.5, respectively. The final evaluation indicator system comprises three primary indicators, with the weights of structure evaluation, process evaluation, and outcome evaluation being 0.338, 0.378, and 0.285, respectively. It also comprises 11 secondary indicators and 45 tertiary indicators. Conclusions:The evaluation indicator system developed in this article can be an effective evaluation tool for quantitative comparison of access to cancer screening services across different populations, cancer types, and before and after intervention. Furthermore, it is recommended that the system undergo continuous optimization concerning its application.

Objective To establish a rat model of venous thrombosis in a plateau hypobaric hypoxic environment and to investigate the effect of this environment on venous thrombosis.Methods A total of 144 healthy male SD rats were assigned randomly to four groups(n=36 rats per group):a plains sham operation(A)group,plains operation(B)group,plateau altitude 6000 m+sham operation(C)group,and plateau altitude 6000 m+surgery(D)group.Rats in A and B groups were maintained in a plains normoxic environment,while rats in C and D groups C and D were subjected to a plateau environment.Rats in the surgical groups underwent quantitative constriction to incompletely obstruct the inferior vena cava blood flow.Each group was further divided into subgroups based on time:1,3,5,7,14,and 21 d(n=6 rats per group).Regular vascular ultrasound monitoring was conducted,and blood samples were taken for whole blood viscosity testing and the assessment of inflammatory indicators,including endothelin-1(ET-1),interleukin-6(IL-6)and tissue factor(TF).Coagulation function was evaluated through the activated partial thromboplastin time(APTT),prothrombin time(PT),thrombin time(TT),fibrinogen(FIB)and D-dimer.After the observation period,the experimental animals were sacrificed and the limbs were removed.Thrombus samples were stained with hematoxylin/eosin(HE),and the thrombus wet mass was measured.Results The thrombosis incidence was significantly higher in the plateau D group than in B group,accompanied by a marked increase in blood viscosity and hematocrit(P＜0.01).Additionally,levels of ET-1,IL-6,and TF were significantly elevated(P＜0.05),indicating a coagulation disorder.Conclusions A plateau hypoxic environment model can be successfully simulated by quantitative coarctation of the inferior vena cava,combined with a specialized environmental chamber.The findings of this study suggest that a plateau hypoxic environment promotes venous thrombosis.

Objective:To analyze the application value of non-invasive inflammation diagnosis model in the diagnosis of liver fibrosis in patients with non-alcoholic fatty liver disease (NAFLD) combined with hepatitis B virus (HBV) infection.Methods:A total of 98 patients with NAFLD complicated with HBV infection admitted to some coastal China Coast Guard Hospital of People′s Armed Police from June 2019 to October 2021 were selected. Their liver stiffness (LSM), aspartate aminotransferase to platelet ratio (APRI), γ-glutamyltranspeptidase to platelet ratio (GPR), and fibrosis index based on factor 4 (FIB-4) were measured, The receiver operating characteristic (ROC) curve was used to analyze its clinical diagnostic efficacy for liver fibrosis caused by NAFLD combined with HBV infection.Results:Among 98 patients, there were 7 cases in S0 stage, 47 cases in S1 stage, 21 cases in S2 stage, 14 cases in S3 stage, and 9 cases in S4 stage; Including 35 cases of obvious liver fibrosis and 9 cases of cirrhosis. There was no statistically significant difference in gender and body mass index (BMI) among patients in different stages (all P>0.05). Age: the S0 group

Background and purpose:Currently,for patients with mid-to-low locally advanced rectal cancer and potentially resectable T4bM0 colon cancer,guidelines recommend neoadjuvant therapy strategies to enhance the response rate and increase the likelihood of conversion surgery.Among these patients,ypⅢ stage colorectal cancer(CRC)is assessed using the Union for International Cancer Control(UICC)/American Joint Committee on Cancer(AJCC)TNM staging system for postoperative pathological features.However,neoadjuvant therapy can lead to lymph node regression in the surgical area,resulting in an insufficient number of detected lymph nodes(less than 12),preventing classification according to conventional TNM staging.Thus,TNM staging often fails to predict the prognosis of ypⅢ patients who have undergone neoadjuvant therapy.This study aimed to evaluate the prognostic value of the positive lymph node ratio(LNR)in ypⅢ stage CRC patients treated with neoadjuvant therapy.Methods:Retrospective data was collected from ypⅢ stage CRC patients who received neoadjuvant therapy and underwent radical surgery at Fudan University Shanghai Cancer Center between 2008 and 2018.Collect clinical pathological characteristics such as age,gender,primary tumor location,tumor differentiation grade,pathological staging,and whether the patient has relapsed or died during follow-up at the time of surgery.Inclusion criteria:CRC patients who have received neoadjuvant therapy and surgery and have been confirmed to be stage Ⅲ by postoperative pathological examination.Exclusion criteria:① Preoperative imaging examination or intraoperative exploration reveals distant organ metastasis;② History of malignant tumors in the past;③ Multiple primary CRC.This study was approved by the medical ethics committee of Fudan University Shanghai Cancer Center(ethics number:050432-4-2108*).The R software survminer package(surv_cutpoint algorithm)was used to calculate the optimal cutoff value for LNR relative to disease-free survival(DFS),and patients were divided into low and high LNR groups accordingly.Clinical pathological characteristics and DFS were compared between the two groups.COX proportional hazards regression models were employed to identify adverse pathological features,and survival plots along with prediction models for DFS were generated using the survival and rms packages.Results:A total of 489 patients were included,comprising 289 males and 200 females,with a median age of 56 years(23-80 years)and a median follow-up time of 1 062 d.During the follow-up period,164 patients(33.5%)died.In the entire cohort,204(41.7%)patients had fewer than 12 lymph nodes detected.The optimal cutoff value for LNR was 0.29,classifying 317 patients into the low LNR group(LNR≤0.29)and 172 patients into the high LNR group(LNR＞0.29).The high LNR group exhibited shorter DFS compared to the low LNR group[hazard ratio(HR)=2.103,95%CI:1.582-2.796,P＜0.000 1].Multivariate COX regression indicated that LNR was an independent prognostic factor for DFS(HR=1.825,95%CI:1.391-2.394,P＜0.001).The inclusion of LNR in a multicategory DFS nomogram prediction model effectively assessed DFS in stage Ⅲ CRC patients who had undergone neoadjuvant therapy.Conclusion:LNR is an independent prognostic factor for ypⅢ stage CRC patients,showing good predictive power for DFS when combined with other adverse pathological features.Therefore,incorporating LNR as a supplement to TNM staging can improve the accuracy of CRC prognosis assessment.

Objective To explore the role and value of thromboelastography(TEG)combined with white matter hyperintensity(WMH)in predicting the functional prognosis of patients with non-cardiogenic acute ischemic stroke(AIS)through machine learning.Methods This study included 130 patients with non-cardiogenic AIS from August 2022 to February 2024.General clinical data,TEG and WMH information of all patients were collected.Three months later,functional outcomes were followed up using the modified Rankin scale(mRS),with an mRS score of≥2 indicating a poor prognosis.The prediction models were divided into four feature sets according to different ranges of predictors:set A(general clinical data+TEG indicators+WMH score),set B(general clinical data+TEG indicators),set C(general clinical data+WMH score),and set D(general clinical data).For each feature set,three machine learning algorithms,traditional logistic regression(LR)model,random forests(RF),neural network(NNET),and K-nearest neighbors(KNN),were used to construct models for predicting the 3-month neurological function outcome of patients with non-cardiogenic AIS.Bootstrap resampling internal validation was used to compare the performance of prediction models.Results The training and testing of the model were performed on 130 patient samples,and the AUC value and its confidence interval of the model were corrected by the 0.632+method(optimism correction).For the LR,NNET,and KNN models,the corrected AUC values of feature set A were significantly better than those of feature set D(DeLong test,P<0.05).For all models,the corrected AUC value of feature set A was higher than that of other feature sets.For feature set A,the corrected AUC value(0.830)of the NNET model was higher than that of other models.Among the 19 features of feature set A,six features with important associations with functional prognosis were selected including National Institute of Health stroke scale(NIHSS)score,stroke history,small artery occlusion subtype,periventricular white matter hyperintensities(PWMH)score,and TEG indicators maximum amplitude(MA)and LY30.Conclusion Combining TEG indicators and WMH information on the basis of general clinical data can significantly improve the accuracy of predicting poor functional prognosis in patients with non-cardiogenic AIS.The prediction models established by machine learning-based NNET and KNN algorithms have high predictive value.

Objective　To analyze the identification of pathogenic bacteria in municipal sewage of Hangzhou City, Zhejiang Province from January to October 2023, to understand the serotype and drug resistance of Vibrio cholerae isolated from sewage, as well as to predict the virulence gene and drug resistance gene carrying situation. Methods From January to October 2023, urban sewage samples from various environments in Hangzhou City, Zhejiang Province, were continuously collected for bacterial isolation and culture. The serotypes of 27 Vibrio cholerae strains were identified by slide agglutination method, and the drug sensitivity test was conducted by micro broth dilution method. Whole genome sequence and bioinformatics software were used to predict drug resistance genes and virulence genes. Results The sewage identification results showed 26 bacteria genera, including Salmonella, Vibrio, Enterobacter, and Aeromonas, among 82 bacterial species. All 27 strains of Vibrio cholerae were non-O1 and non-O139 serogroups. The strains showed high resistance rates to ampicillin, ampicillin/sulbactam and cotrimoxazole at 59.26%, 14.81%, and 14.81%, respectively), exhibited high sensitivity to cefotaxime, tetracycline, chloramphenicol, meropenem, ertapenem, ceftazidime, ceftazidime/avibactam, amikacin, ciprofloxacin, tigecycline, and azithromycin. A total of 21 different drug resistance genes were detected in 11 strains of Vibrio cholerae, among which the highest carriage rate of β-lactam resistance genes reached 63.64%, and 1 strain carried 17 kinds of drug resistance genes. None of the 11 strains of Vibrio cholerae carried ctxA, ctxB, ace, zot, or chxA genes, but all the strains carried rtxB, rtxC, and rtxD genes; 90.91% of the strains carried hlyA, and 36.36% carried rtxA. Conclusions All Vibrio cholerae isolated from sewage in this study are non-O1 and non-O139 serogroups, showing good sensitivity to most antibiotics, but also they carry a certain number of drug-resistance genes and virulence genes. This suggests that future monitoring efforts should focus on these strains, providing certain data support for the study of the characteristics of Vibrio cholerae isolated from sewage.

Objective To explore the role and value of thromboelastography(TEG)combined with white matter hyperintensity(WMH)in predicting the functional prognosis of patients with non-cardiogenic acute ischemic stroke(AIS)through machine learning.Methods This study included 130 patients with non-cardiogenic AIS from August 2022 to February 2024.General clinical data,TEG and WMH information of all patients were collected.Three months later,functional outcomes were followed up using the modified Rankin scale(mRS),with an mRS score of≥2 indicating a poor prognosis.The prediction models were divided into four feature sets according to different ranges of predictors:set A(general clinical data+TEG indicators+WMH score),set B(general clinical data+TEG indicators),set C(general clinical data+WMH score),and set D(general clinical data).For each feature set,three machine learning algorithms,traditional logistic regression(LR)model,random forests(RF),neural network(NNET),and K-nearest neighbors(KNN),were used to construct models for predicting the 3-month neurological function outcome of patients with non-cardiogenic AIS.Bootstrap resampling internal validation was used to compare the performance of prediction models.Results The training and testing of the model were performed on 130 patient samples,and the AUC value and its confidence interval of the model were corrected by the 0.632+method(optimism correction).For the LR,NNET,and KNN models,the corrected AUC values of feature set A were significantly better than those of feature set D(DeLong test,P<0.05).For all models,the corrected AUC value of feature set A was higher than that of other feature sets.For feature set A,the corrected AUC value(0.830)of the NNET model was higher than that of other models.Among the 19 features of feature set A,six features with important associations with functional prognosis were selected including National Institute of Health stroke scale(NIHSS)score,stroke history,small artery occlusion subtype,periventricular white matter hyperintensities(PWMH)score,and TEG indicators maximum amplitude(MA)and LY30.Conclusion Combining TEG indicators and WMH information on the basis of general clinical data can significantly improve the accuracy of predicting poor functional prognosis in patients with non-cardiogenic AIS.The prediction models established by machine learning-based NNET and KNN algorithms have high predictive value.