Ovarian cancer(OC)is a prevalent malignant gynecological tumor with a high mortality rate.The tumor immune microenvironment,consisting of immune components within the tumor microenvironment,plays a significant role in tumor devel-opment and metastasis.OC is typically categorized as a"cold tumor"due to its immunosuppressive microenvironment,resulting in limited efficacy of immunotherapy for this disease.Specifically,the expression of family with sequence similarity 111 member B(FAM111B)protein has been found to be correlated with the immune microenvironment of various tumors,including its associ-ation with the expression of programmed death ligand-1(PD-L1)in ovarian cancer tissues.This review provides an overview of recent research developments concerning the impact of the FAM111B gene on the immune microenvironment of ovarian cancer.

Objective To evaluate the changes in serum bilirubin levels in children with IgA vasculitis(IgAV)nephritis(IgAVN)and its role in the diagnosis of IgAVN.Methods The clinical data of 134 patients with IgAV who were admitted to the First Affiliated Hospital of Xinxiang Medical College from October 2021 to July 2023 were retrospectively analyzed.They were divided into IgAV group(89 cases)and IgAVN group(45 cases)according to the presence or absence of renal involvement.During the same period,40 children with idiopathic short stature who were hospitalized for examination were selected as the control group.Variance analysis was used to statis-tically analyze the differences in clinical parameters and serum total bilirubin levels among the groups.Spearman correlation analysis and multivariate Logistic regression analysis were used to evaluate the correlation of the parameters.ROC curve was used to evaluate the sensi-tivity and specificity of diagnosis.Results The levels of TBIL and IBIL in the IgAV and IgAVN groups were significantly lower than those in the control group(P＜0.05),and the levels of TBIL,IBIL and DBIL in the IgAVN group were significantly lower than those in the IgAV group(P＜0.05).TBIL and IBIL levels were negatively correlated with IgAVN.The specificity of TBIL and IBIL for diagnosing IgAVN was 86.5％and 68.5％,with sensitivities of 46.7％and 51.1％,respectively.When combined with other variables,the serum bilirubin prediction model achieved an AUC of 0.817(95％CI:0.744-0.891,P＜0.001),with a sensitivity of 79.8％and specificity of 66.7％.Conclusion The determination of serum bilirubin level has a certain clinical value in predicting the occurrence of renal in-volvement(IgAVN)in children with IgAV.

Objective To investigate the predictive value of combined radiomic features derived from chest CT scans with clinical characteristics for epidermal growth factor receptor(EGFR)gene mutations in non-small cell lung cancer(NSCLC).Methods A multi-center case-control study was conducted on the clinical data and CT images of 1 070 NSCLC patients from the radiology departments of the 3 medical institutions between January 2013 and October 2023.The 719 NSCLC patients from the First Affiliated Hospital of Army Medical University were randomly divided into a training set and an internal validation set in a ratio of 7∶3;The 173 patients in the Eastern Theatre General Hospital and the 178 patients in Army Medical Centre of PLA were assigned into the external validation set 1 and 2,respectively.Least absolute shrinkage and selection operator(LASSO)regression was employed to identify the optimal radiomic features,which were subsequently used to construct a radiomics model.Univariate and multivariate logistic regression analyses were applied to identify clinical features associated with EGFR mutation,thereby developing a clinical model.The radiomic and clinical features were subsequently combined to develop a comprehensive model.All the 3 classification models were built using random forest(RF)machine learning.The area under curve(AUC),accuracy,sensitivity and specificity were utilized to evaluate the predictive performance of the models.Calibration curve was plotted to assess the goodness of fit of the comprehensive model,while decision curve analysis was performed to assess the clinical utility of the model.Results The AUC value of the radiomics model was 0.762 4(95%CI:0.692 4～0.825 1),0.745 4(95%CI:0.671 1～0.814 3),and 0.724 7(95%CI:0.639 7～0.801 6),respectively,in the internal validation set,external validation set 1,and external validation set 2;The AUC value of the clinical prediction model was 0.691 7(95%CI:0.627 9～0.757 6),0.652 5(95%CI:0.576 7～0.729 1),and 0.779 2(95%CI:0.712 5～0.847 3),respectively in the above sets in turn;The comprehensive model constructed based on clinical features and radiomic features showed the best predictive efficacy,with an AUC value of 0.818 0(95%CI:0.757 7～0.874 3),0.782 4(95%CI:0.703 1～0.848 2),and 0.796 6(95%CI:0.718 1～0.868 6),respectively in the above sets.Calibration curve analysis indicated that the comprehensive model had a good fit,while decision curve analysis revealed that the model provided a favorable net benefit.Conclusion Our comprehensive model constructed based on chest CT radiomic features and clinical characteristics shows superior predictive performance for EGFR gene mutations in NSCLC across multiple center datasets,which may be helpful for clinical decision-making for treatment strategies.

Objective To investigate the common diseases and the causes of diseases in Chinese Antarctic researchers,so as to provide reference for the medical support.Methods Medical records of 1 127 people with injuries and diseases who participated in four Antarctic scientific expeditions(the 31st,35th,36th,and 39th time)were retrospectively analyzed.The causes of the injuries and diseases as well as the implications for nursing were investigated.Results The top 10 diseases in the four Antarctic expeditions were acute soft tissue injury,dermatomycosis,pharyngitis,insomnia,periodontitis,gastroenteritis,motion sickness,acute upper respiratory infection,primary hypertension,and frostbite.The causes of the diseases in the four Antarctic expeditions were analyzed,and the nursing of different diseases was proposed.Conclusion It is necessary to take preventive measures based on the characteristics of injuries and diseases during Antarctic expeditions,so as to effectively prevent and treat these diseases and provide more comprehensive medical support for Antarctic scientific expeditions.

Objective To evaluate the predictive value of deep learning features from chest CT images combined with clinical and radiomics features for epidermal growth factor receptor(EGFR)mutations in non-small cell lung cancer(NSCLC).Methods This case-control study retrospectively analyzed clinical and imaging data of 1 070 NSCLC patients from radiology departments at three hospitals(January 2013 to October 2023).Patients were divided into:a training set(n=502)and internal validation set(n=217)via 7∶3 randomization of 719 cases from the First Affiliated Hospital of Army Medical University;external validation set 1(n=173)from General Hospital of Eastern Theater Command;external validation set 2(n=178)from Daping Hospital of Army Medical University.Deep learning features were extracted using a 2.5D convolutional neural network(CNN)with ResNet101 backbone,radiomics features were derived from CT images,and clinical risk factors were identified to construct models.An integrated model combined deep learning,clinical,and radiomics features.All four models were developed using random forest(RF)classifiers.Calibration curves assessed goodness-of-fit,and decision curve analysis(DCA)evaluated clinical utility.Results The deep learning model achieved AUCs of 0.833 7(95%CI:0.770 6～0.884 7),0.815 1(0.741 6～0.882 8),and 0.810 1(0.745 2～0.873 6)in the internal and two external validation sets,respectively.Clinical models yielded AUCs of 0.731 0(0.660 2～0.802 1),0.746 0(0.666 4～0.824 9),and 0.813 4(0.743 1～0.883 6);radiomics models showed AUCs of 0.762 4(0.692 4～0.825 1),0.745 4(0.671 1～0.814 3),and 0.724 7(0.639 7～0.801 6).The integrated model demonstrated optimal performance with AUCs of 0.905 5(0.857 0～0.945 4),0.832 7(0.763 3～0.896 4),and 0.889 0(0.834 4～0.934 3).DCA indicated significant net benefit for EGFR prediction at threshold probabilities of 0.15～0.85 using the integrated model.Conclusion Deep learning features from CT images effectively predict EGFR mutation status in NSCLC.The integrated model combining deep learning,clinical,and radiomics features further enhances predictive performance.

Objective To evaluate the changes in serum bilirubin levels in children with IgA vasculitis(IgAV)nephritis(IgAVN)and its role in the diagnosis of IgAVN.Methods The clinical data of 134 patients with IgAV who were admitted to the First Affiliated Hospital of Xinxiang Medical College from October 2021 to July 2023 were retrospectively analyzed.They were divided into IgAV group(89 cases)and IgAVN group(45 cases)according to the presence or absence of renal involvement.During the same period,40 children with idiopathic short stature who were hospitalized for examination were selected as the control group.Variance analysis was used to statis-tically analyze the differences in clinical parameters and serum total bilirubin levels among the groups.Spearman correlation analysis and multivariate Logistic regression analysis were used to evaluate the correlation of the parameters.ROC curve was used to evaluate the sensi-tivity and specificity of diagnosis.Results The levels of TBIL and IBIL in the IgAV and IgAVN groups were significantly lower than those in the control group(P＜0.05),and the levels of TBIL,IBIL and DBIL in the IgAVN group were significantly lower than those in the IgAV group(P＜0.05).TBIL and IBIL levels were negatively correlated with IgAVN.The specificity of TBIL and IBIL for diagnosing IgAVN was 86.5％and 68.5％,with sensitivities of 46.7％and 51.1％,respectively.When combined with other variables,the serum bilirubin prediction model achieved an AUC of 0.817(95％CI:0.744-0.891,P＜0.001),with a sensitivity of 79.8％and specificity of 66.7％.Conclusion The determination of serum bilirubin level has a certain clinical value in predicting the occurrence of renal in-volvement(IgAVN)in children with IgAV.

Ovarian cancer(OC)is a prevalent malignant gynecological tumor with a high mortality rate.The tumor immune microenvironment,consisting of immune components within the tumor microenvironment,plays a significant role in tumor devel-opment and metastasis.OC is typically categorized as a"cold tumor"due to its immunosuppressive microenvironment,resulting in limited efficacy of immunotherapy for this disease.Specifically,the expression of family with sequence similarity 111 member B(FAM111B)protein has been found to be correlated with the immune microenvironment of various tumors,including its associ-ation with the expression of programmed death ligand-1(PD-L1)in ovarian cancer tissues.This review provides an overview of recent research developments concerning the impact of the FAM111B gene on the immune microenvironment of ovarian cancer.

Objective:To explore the self-management cognition and inner experience of adult fixed orthodontic patients, so as to provide evidence support for subsequent research.Methods:This was a qualitative study. By using the purposive sampling method, a total of 12 adult patients who underwent orthodontic treatment in the Affiliated Stomatology Hospital of Guangzhou Medical University from June to July 2022 were selected for face-to-face semi-structured in-depth interviews. Using Nvivo 12 software, the interview data was analyzed and summarized using the Colaizzi 7-step analysis method.Results:Self-management cognition and inner experience of adult fixed orthodontics patients were extracted into 3 core themes and 7 sub-themes. They were the multiple psychological feelings in the face of orthodontic treatment (positive psychological feelings, negative psychological feelings) , impediments to orthodontic treatment (lack of support from family members, obstacles to medical follow-up) , and differences in self-management behaviours (positive self-management, negative self-management, and phased changes in behaviours) .Conclusions:Adult fixed orthodontic patients suffer from negative psychological feelings, obstruction of medical follow-up and insufficient self-management ability, etc. Nursing staff should focus on the feelings and experiences of such patients during the treatment process, conduct a dynamic assessment of the patient's psychology and behaviours throughout the treatment phase, and adopt patient-centred nursing interventions in conjunction with the information technology platform, to improve the patient's negative emotions and enhance their oral health self-management ability.

Objective: To analyze influencing factors for depressive symptoms in the elderly aged 65 years and older in 8 longevity areas in China. Methods: We recruited 2 180 participants aged 65 years and older in 8 longevity areas from Healthy Aging and Biomarkers Cohort Study, a sub-cohort of the Chinese Longitudinal Healthy Longevity Survey in 2017. Multivariate logistic regression analysis was performed to evaluate the relationships of socio-demographic characteristics, behavioral lifestyle, chronic disease prevalence, functional status, family and social support with depressive symptoms in the elderly. Results: The detection rate of depression symptoms was 15.0% in the elderly aged 65 years and older in 8 longevity areas of China, and the detection rate of depression symptoms was 11.5% in men and 18.5% in women. Multivariate logistic regression analysis results showed that the detection rate of depressive symptoms was lower in the elderly who had regular physical exercises (OR=0.44, 95%CI: 0.26-0.74), frequent fish intakes (OR=0.57, 95%CI: 0.39-0.83), recreational activities (OR=0.65, 95%CI: 0.44-0.96), social activities (OR=0.28, 95%CI: 0.11-0.73) and community services (OR=0.68, 95%CI: 0.50-0.93). The elderly who were lack of sleep (OR=2.04, 95%CI: 1.49-2.80), had visual impairment (OR=1.54, 95%CI: 1.08-2.18), had gastrointestinal ulcer (OR=2.97, 95%CI: 1.53-5.77), had arthritis (OR=2.63, 95%CI: 1.61-4.32), had higher family expenditure than income (OR=1.80, 95%CI: 1.17-2.78) and were in poor economic condition (OR=4.58, 95%CI: 2.48-8.47) had higher detection rate of depressive symptoms. Conclusion The status of doing physical exercise, fish intake in diet, social activity participation, sleep quality or vision, and the prevalence of gastrointestinal ulcers and arthritis were associated with the detection rate of depressive symptoms in the elderly.

Objective: To establish a prediction model for 6-year incidence risk of chronic kidney disease (CKD) in the elderly aged 65 years and older in China. Methods: In this prospective cohort study, we used the data of 3 742 participants collected during 2008/2009-2014 and during 2012-2017/2018 from Healthy Aging and Biomarkers Cohort Study, a sub-cohort of the Chinese Longitudinal Healthy Longevity Survey. Two follow up surveys for renal function were successfully conducted for 1 055 participants without CKD in baseline survey. Lasso method was used for the selection of risk factors. The risk prediction model of CKD was established by using Cox proportional hazards regression models and visualized through nomogram tool. Bootstrap method (1 000 resample) was used for internal validation, and the performance of the model was assessed by C-index and calibration curve. Results: The mean age of participants was (80.8±11.4) years. In 4 797 person years of follow up, CKD was found in 262 participants (24.8%). Age, BMI, sex, education level, marital status, having retirement pension or insurance, hypertension prevalence, blood uric acid, blood urea nitrogen and total cholesterol levels and estimated glomerular filtration rate in baseline survey were used in the model to predict the 6-year incidence risk of CKD in the elderly. The corrected C-index was 0.766, the calibration curve showed good consistence between predicted probability and observed probability in high risk group, but relatively poor consistence in low risk group. Conclusion The incidence risk prediction model of CKD established in this study has a good performance, and the nomogram can be used as visualization tool to predict the 6-year risk of CKD in the elderly aged 65 years and older in China.