Objective To explore the comparative study of constructing prediction models for muscle invasive of bladder cancer based on different machine learning algorithms combined with MRI radiomic.Methods A total of 187 bladder cancer patients who underwent MRI examination and were confirmed by pathology were retrospectively selected.Patients were randomly divided into a training set and a test set in a 7∶3 ratio.The patients were divided into muscle invasive bladder cancer(MIBC)group and non-muscle invasive bladder cancer(NMIBC)group according to the surgical pathology results.Tumor volume of interest(VOI)was outlined on the images of T2 WI,diffusion weighted imaging(DWI),and apparent diffusion coefficient(ADC),and the radiomic features were extracted by A.K software,and dimensionality reduction was performed using the maximum relevance minimum redundancy(mRMR)algorithm combined with least absolute shrinkage and selection operator(LASSO).Six machine learning algorithms,including K-nearest neighbor(KNN),decision tree(DT),support vector machine(SVM),logistic regression(LR),random forest(RF),and explainable boosting machine(EBM)were used to construct the radiomic model and calculate the corresponding area under the curve(AUC),accuracy,sensitivity,and specificity,respectively.Results Six machine learning algorithms,including KNN,DT,SVM,LR,RF,and EBM were used to construct the radiomic model,and the AUC values for predicting MIBC in the training set were 0.863,0.838,0.853,0.866,0.977,0.997,and in the test set were 0.748,0.833,0.860,0.868,0.870,0.900.Among them,the MRI radiomic model constructed based on EBM had the highest predictive efficacy for MIBC,with AUC values,accuracy,sensitivity and specificity of 0.997,0.977,0.957 and 0.981 in the training set,and 0.900,0.877,0.800,and 0.894 in the test set,respectively.Conclusion Multiple machine learning algorithms combined with MRI radiomic to construct models have good predictive efficacy for MIBC,and the model constructed based on EBM shows the highest predictive value.

Objective To analyze the predictive value of dynamic changes in key factors of the toll-like receptor (TLR)/nuclear factor-κB (NF-κB) signaling axis during the perioperative period for bone infection inpatients with open fractures. Methods A total of 55 patients with open fractures who developed bone infections during the perioperative period were selected as infection group, and 110 patients with open fractures who did not develop infections during the same period were selected as non-infection group. Clinical data, pre-and post-operative serum levels of routine inflammatory markers [C-reactive protein (CRP), interleukin-6 (IL-6) and procalcitonin (PCT)] and key factors of the TLR/NF-κB signaling axis (TLR4, NF-κB) were compared between the two groups. Logistic multivariate regression analysis was used to identify risk factors for bone infection during the perioperative period in patients with open fractures. Receiver operating characteristic (ROC) curves were plotted to evaluate the predictive value of the absolute change (the absolute value of the changes was expressed as △) in the levels of key factors of the TLR/NF-κB signaling axis before and after surgery for bone infection, and these results were compared with the predictive value of routine inflammatory markers. A nomogram prediction model was developed based on the identified risk factors, and its value in predicting perioperative bone infection was analyzed. Results The time from fracture to surgery and the duration of surgery were significantly longer, and the proportion of Gustilo type Ⅲ fractures and wounds with a depth ≥2 cm was significantly higher in the infection group compared to the non-infection group (P < 0.05). At 24 h after surgery, serum CRP, IL-6, PCT, TLR4 and NF-κB levels in two groups were significantly higher than before surgery, and serum CRP, IL-6, PCT, TLR4 as well as NF-κB levels and their changes in bone infection group were significantly higher than those in the non-infection group (P < 0.05). Logistic regression analysis indicated that time from fracture to surgery, surgical duration, Gustilo type Ⅲ and wound depth ≥2 cm, and △CRP, △IL-6, △PCT, △TLR4 as well as △NF-κB were risk factors for perioperative bone infection in patients with open fractures (P < 0.05). ROC results showed that the area under the curve (AUC) of △CRP, △IL-6, △PCT, △TLR4 and △NF-κB for predicting bone infection were 0.786, 0.833, 0.772, 0.826 and 0.736, respectively. ROC curve showed that the AUC of the nomogram prediction model for perioperative bone infection was 0.893(95%CI, 0.834 to 0.952), indicating high predictive efficacy. The decision curve showed that the nomogram prediction model had a significant positive net benefit, and it had good clinical utility in predicting the risk of bone infection. Conclusion The dynamic changes of key factors of TLR/NF-κB signal axis in perioperative period of patients with open fracture have certain predictive value for postoperative bone infection. The nomogram prediction model based on the above influencing factors has good predictive value and positive clinical net benefit.

The pathogeneses of oral squamous cell carcinoma and most oral mucosal diseases are unclear. Therefore, establishing animal models with similar pathogeneses is significant for clinical prevention, diagnosis, and treatment of related diseases. At present, scholars have established animal models for different focuses. This paper aims to introduce the methods for establishing animal models of oral squamous cell carcinoma and common oral mucosal diseases, compare their advantages and disadvantages, and provide evidence for related basic research.

Objective:To construct a targeted and accurate evaluation system for facial and cervical wounds and scars of burn patients.Methods:The method combining literature analysis and survey research was adopted, and the basic principles of item system construction were followed. From June to August 2020, based on the aesthetic standards of facial and cervical plastic surgery, the topographic map assessment system for facial and cervical wounds and scars of burn patients was preliminarily formed, focusing on the assessment of wounds and scars in the necks and faces of patients after burns. In September 2020, 38 experts in the relevant fields were consulted in advance and the questionnaire was revised according to the experts' opinions. From December 2020 to March 2021, the Delphi method was applied to conduct inquiry by correspondence with 35 experts in relevant fields from Guangzhou, Shenzhen, Shanghai, Beijing, and other cities, who met the inclusion criteria, and the items were screened and established. The effective recovery rate of inquiry questionnaire was calculated to determine the level of enthusiasm of experts, the average authority coefficient of all items was calculated to determine the level of expert authority, the average importance expert score, the average coefficient of variation, and the average full score rate of all the third-level items were calculated to determine the concentration of expert opinions, the average coefficients of variation and Kendall's harmony coefficients of the importance, sensitivity, and operability expert scores of all the third-level items were calculated to determine the degree of coordination of expert opinions. The Kendall's harmony coefficients for the importance, sensitivity, and operability expert scores of all the third-level items were statistically analyzed with chi-square test.Results:Among the 35 experts consulted by Delphi method, mainly were male, aged (48±10) years, with 8-38 years of working experience, mainly with associate senior titles and above, all with a bachelor's degree or above education background, and of whom 11 were burn experts, 7 were wound repair experts, 4 were plastic surgery experts, and 13 were rehabilitation medicine experts. Finally, a topographic map assessment system for facial and cervical wounds and scars of burn patients was formed, including 4 first-level items, 21 second-level items, 40 third-level items, and 1 mask. The effective recovery rate of inquiry questionnaire was 100% (35/35). The average authority coefficient of all items was 0.89. The average importance expert score was 4.67, the average coefficient of variation of importance expert score was 0.01, and the average full score rate of all the third-level items was 86.3%. The average coefficients of variation of the importance, sensitivity, and operability expert scores of all the third-level items were 0.01, 0.01, and 0.02, respectively. The Kendall's harmony coefficients for the importance, sensitivity, and operability expert scores of all the third-level items were statistically significant (with χ2 values of 1 201.53, 745.67, and 707.07, respectively , P<0.05). Conclusions:The established topographic map assessment system for facial and cervical wounds and scars of burn patients has high scientificity and reliability, which can be used for the evaluation of facial and neck wounds or scars in burn patients.

Alcoholic liver disease(ALD)is the most frequent liver disease worldwide,resulting in severe harm to personal health and posing a serious burden to public health.Based on the reported antioxidant and anti-inflammatory capacities of scutellarin(SCU),this study investigated its protective role in male BALB/c mice with acute alcoholic liver injury after oral administration(10,25,and 50 mg/kg).The results indicated that SCU could lessen serum alanine aminotransferase(ALT)and aspartate aminotransferase(AST)levels and improve the histopathological changes in acute alcoholic liver;it reduced alcohol-induced malondialdehyde(MDA)content and increased glutathione peroxidase(GSH-Px),catalase(CAT),and superoxide dismutase(SOD)activity.Furthermore,SCU decreased tumor necrosis factor-α(TNF-α),interleukin-6(IL-6),and IL-1β messenger RNA(mRNA)expression levels,weakened inducible nitric oxide synthase(iNOS)activity,and inhibited nucleotide-binding oligomerization domain(NOD)-like receptor protein 3(NLRP3)inflammasome activation.Mechanistically,SCU suppressed cytochrome P450 family 2 subfamily E member 1(CYP2E1)upregulation triggered by alcohol,increased the expression of oxidative stress-related nuclear factor erythroid 2-related factor 2(Nrf2)and heme oxygenase-1(HO-1)pathways,and suppressed the inflammation-related degradation of inhibitor of nuclear factor-κB(NF-κB)-α(IκBα)as well as activation of NF-κB by mediating the protein kinase B(AKT)and p38 mitogen-activated protein kinase(MAPK)pathways.These findings demonstrate that SCU protects against acute alcoholic liver injury via inhibiting oxidative stress by regulating the Nrf2/HO-1 pathway and suppressing inflammation by regulating the AKT,p38 MAPK/NF-κB pathways.

Objective: To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables. Materials and Methods: Clinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients. The data of the cohort were randomly divided into training, validation, and test sets using a 7:1:2 ratio. A ML pipeline consisting of a model to predict severity and time-to-event model to predict progression to critical illness were trained on radiomics features and clinical variables. The receiver operating characteristic area under the curve (ROC-AUC), concordance index (C-index), and time-dependent ROC-AUC were calculated to determine model performance, which was compared with consensus CT severity scores obtained by visual interpretation by radiologists. Results: Among 981 patients with confirmed COVID-19, 274 patients developed critical illness. Radiomics features and clinical variables resulted in the best performance for the prediction of disease severity with a highest test ROC-AUC of 0.76 compared with 0.70 (0.76 vs. 0.70, p = 0.023) for visual CT severity score and clinical variables. The progression prediction model achieved a test C-index of 0.868 when it was based on the combination of CT radiomics and clinical variables compared with 0.767 when based on CT radiomics features alone (p < 0.001), 0.847 when based on clinical variables alone (p = 0.110), and 0.860 when based on the combination of visual CT severity scores and clinical variables (p = 0.549). Furthermore, the model based on the combination of CT radiomics and clinical variables achieved time-dependent ROC-AUCs of 0.897, 0.933, and 0.927 for the prediction of progression risks at 3, 5 and 7 days, respectively. Conclusion CT radiomics features combined with clinical variables were predictive of COVID-19 severity and progression to critical illness with fairly high accuracy.

Objective: To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables. Materials and Methods: Clinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients. The data of the cohort were randomly divided into training, validation, and test sets using a 7:1:2 ratio. A ML pipeline consisting of a model to predict severity and time-to-event model to predict progression to critical illness were trained on radiomics features and clinical variables. The receiver operating characteristic area under the curve (ROC-AUC), concordance index (C-index), and time-dependent ROC-AUC were calculated to determine model performance, which was compared with consensus CT severity scores obtained by visual interpretation by radiologists. Results: Among 981 patients with confirmed COVID-19, 274 patients developed critical illness. Radiomics features and clinical variables resulted in the best performance for the prediction of disease severity with a highest test ROC-AUC of 0.76 compared with 0.70 (0.76 vs. 0.70, p = 0.023) for visual CT severity score and clinical variables. The progression prediction model achieved a test C-index of 0.868 when it was based on the combination of CT radiomics and clinical variables compared with 0.767 when based on CT radiomics features alone (p < 0.001), 0.847 when based on clinical variables alone (p = 0.110), and 0.860 when based on the combination of visual CT severity scores and clinical variables (p = 0.549). Furthermore, the model based on the combination of CT radiomics and clinical variables achieved time-dependent ROC-AUCs of 0.897, 0.933, and 0.927 for the prediction of progression risks at 3, 5 and 7 days, respectively. Conclusion CT radiomics features combined with clinical variables were predictive of COVID-19 severity and progression to critical illness with fairly high accuracy.

To understand the development of medical social work in China, and provide reference and basis for promoting medical social work in the next stage.
 Methods: A random sampling method was used to survey and analyze the data from questionnaires distributed to hospitals at or above the second level in China.
 Results: Medical social work had been carried out in all parts of the country, but the development was not balanced with the establishment of specialized agencies accounting for about 7.9% of the total survey. Only 17.5% of the hospitals carried out medical social work as a routine work. The medical social work service mainly included volunteer operation and management, patient psychological counseling, and so on.
 Conclusion: The development of medical social work in hospitals in China is still in its infancy, and the regional development is not balanced. Lack of professionals, unclear responsibilities of medical social workers and low social identity of medical social work are the main factors restricting development.
Asian Continental Ancestry Group ; China ; Hospitals ; Humans ; Social Work ; Surveys and Questionnaires

Objective To learn the diagnosis and treatment of brucellosis patients in Suide County of Shaanxi Province in 2015,and to provide a scientific basis for making accurate prevention and control measures.Methods All the cases diagnosed as brucellosis in Suide County from January 1 to December 31 in 2015 and lived in this county were retrospectively investigated by case questionnaire survey,including basic information,medical procedures,and epidemiological contacts.Descriptive statistical analysis was performed using SPSS 18.0 software.Results In 2015,75 cases of brucellosis were diagnosed in Suide County,75 questionnaires were distributed,with 53 completed questionnaires returned.The average age of respondents was (49 ± 14) years old,of which 84.9％ (45/53) were male and 94.3％ (50/53) were farmers,except one case was actively monitored by Suide County Centre for Disease Control and Prevention then treated,the rest of the patients were treated after the onset of the disease.Of the 52 patients,one patient was diagnosed after one visit,accounting for 25.0％ (13/52);one patient was diagnosed after at least 4 visits,accounting for 3.8％ (2/52).The cumulative diagnosis rate at provincial-level hospitals was 1/3 and that at disease prevention and control institutions was 92.7％ (51/55),there were no confirmed cases in municipal hospitals,county hospitals,township hospitals,village clinics and individual clinics.For the first reason to see a doctor,65.4％ (34/52) of the patients were fever or accompanied by sweating,fatigue,arthralgia and waist and leg pain.The medians from onset to treatment between the first diagnosis,and 2,3,4 visits were 15,18,27,45 days,respectively;the median from onset to diagnosis was 21 days,ranging from 2 to 182 days.Totally 84.9％ (45/53) patients had a history of exposure to animals,of which 97.8％ (44/45) had contacted the sheep.Conclusions The cases in Suide County are mainly adult male farmers,and the diagnosis rate at hospitals below municipal level is low.It is recommended to strengthen the health intervention of high-risk groups and improve the level of diagnosis and treatment of primary medical staff.

Objective To investigate the expression of apolipoprotein (Apo) F in hepatocellular carcinoma (HCC) and its application value in the prognosis of patients with HCC. Methods 50 HCC samples were procured from patients undergoing surgical resection in the Third Affiliated Hospital of Sun Yat-sen University between September 2015 and September 2016, and all the samples were confirmed by postoperative pathological examination. The informed consents of all patients were obtained and the local ethical committee approval was received. There were 37 males and 13 females, aged from 31-67 with a median age of 53 years old. The expression of ApoF mRNA in HCC tissues was detected by RT-PCR. The expression profile was analyzed by using data from the Gene Expression Omnibus (GEO). The expression of ApoF between two groups were compared by t test. Correlation analysis of clinical related parameter was conducted by Chi-square test, and survival prognosis was analyzed by Kaplan-Meier test and Log rank test. Results The average relative expression of ApoF mRNA in HCC tissues was 0.15±0.07, significantly lower than 0.55±0.09 in the adjacent tissues (t=-6.26, P<0.05). GEO online analysis showed that expression of ApoF was significantly correlated with the status of liver cirrhosis, and most HCC patients with liver cirrhosis presented low expression of ApoF (χ2=4.626, P<0.05). The 5-year disease-free survival was respectively 55.9% and 32.0% in ApoF high expression group and low expression group, where significant difference was observed (χ2=3.939, P<0.05). Conclusions Low expression of ApoF exists in HCC tissues, and it is related to the liver cirrhosis status of patients. Patients with low ApoF expression present poorer prognosis. ApoF plays a role in inhibiting the cancer.