Objective To observe the correlations of chest CT quantitative parameters and one-year readmission in patients with acute exacerbation of chronic obstructive pulmonary disease(AECOPD).Methods Totally 225 AECOPD patients were included and divided into the readmission group(group A,n=61)and non-readmission group(group B,n=164)according to whether readmitted due to acute exacerbation within one year or not.Quantitative CT parameters,including the percentage of low attenuation area volume to total lung volume(LAA％)and intrapulmonary vascular volume(IPVV)of the whole lung,left/right lung and each lobe,as well as the bronchial lumen area(LA)and percentage of bronchial wall area(WA％)of grades 3-8 were compared between groups.Spearman correlations were used to analyze the correlations between quantitative CT parameters and one-year readmission.Results LAA％of the whole lung,right lung,upper lobe of both lungs,middle lobe of the right lung,and lower lobe of the right lung in group A were higher than that in group B(all P＜0.05),which were weakly positively correlated with readmissions within one year(r=0.142-0.187,all P＜0.05).Significant differences of the upper lobe of right lung LAgrade 6,lower lobe of left lung LAgrade 6,lower lobe of left lung LAgrade 8,upper lobe of right lung WA％grade 5,upper lobe of right lung WA％grade 8 and lower lobe of left lung WA％grade 8 were found between groups,which were all correlated with readmissions within one year except for the lower lobe of the left lung LAgrade 6(all P＜0.05).Conclusion One-year readmission in AECOPD patients might be associated with severe airway remodeling and emphysema.

Objective To investigate the expression of ferritinophagy-related gene ELAV-like RNA binding protein 1(ELAVL1)in multiple myeloma(MM)and elucidate its diagnostic and prognostic value for MM.Methods First,we analyzed ELAVL1 expression level in healthy controls and MM patients using data from the GEO and TCGA databases.Subsequently,bone marrow specimens were collected from 28 newly diagnosed MM patients and 20 healthy controls,and qRT-PCR was employed to validate ELAVL1 expression.The diagnostic and prognostic potential of ELAVL1 was assessed using ROC curve analysis and Kaplan-Meier survival curves.Additionally,univariate and multivariate COX regression analyses were performed to identify independent risk factors for MM prognosis.Finally,KEGG and GO enrichment analyses were performed using the DAVID online platform.Results The level of ELAVL1 expression was significantly higher in newly diagnosed MM patients and refractory/relapsed MM patients than in the healthy controls(P＜0.001).Moreover,ELAVL1 expression was positively correlated with the International Staging System(ISS)stage of MM(P＜0.01).Furthermore,qRT-PCR validation confirmed that ELAVL1 expression was elevated in the 28 newly diagnosed MM patients compared to the 20 healthy controls(P＜0.001).ROC curve analysis demonstrated that ELAVL1 could effectively differentiate between newly diagnosed MM patients,healthy controls,and MGUS patients(P＜0.001 and P=0.000 2,respectively).Survival analysis revealed that high ELAVL1 expression was associated with shorter progression-free survival(P=0.0141)and overall survival(P=0.008 0).Univariate and multivariate COX regression analyses identified high ELAVL1 expression as an independent risk factor for poor MM prognosis(P=0.005 0).KEGG analysis suggested that ELAVL1 might be involved in the Hippo and MAPK signaling pathways.Conclusion High ELAVL1 expression in MM may serve as a biomarker for diagnosis and poor prognosis.ELAVL1 may promote MM initiation and progression via the Hippo and MAPK signaling pathways.

Objective To investigate the expression of ferritinophagy-related gene ELAV-like RNA binding protein 1(ELAVL1)in multiple myeloma(MM)and elucidate its diagnostic and prognostic value for MM.Methods First,we analyzed ELAVL1 expression level in healthy controls and MM patients using data from the GEO and TCGA databases.Subsequently,bone marrow specimens were collected from 28 newly diagnosed MM patients and 20 healthy controls,and qRT-PCR was employed to validate ELAVL1 expression.The diagnostic and prognostic potential of ELAVL1 was assessed using ROC curve analysis and Kaplan-Meier survival curves.Additionally,univariate and multivariate COX regression analyses were performed to identify independent risk factors for MM prognosis.Finally,KEGG and GO enrichment analyses were performed using the DAVID online platform.Results The level of ELAVL1 expression was significantly higher in newly diagnosed MM patients and refractory/relapsed MM patients than in the healthy controls(P＜0.001).Moreover,ELAVL1 expression was positively correlated with the International Staging System(ISS)stage of MM(P＜0.01).Furthermore,qRT-PCR validation confirmed that ELAVL1 expression was elevated in the 28 newly diagnosed MM patients compared to the 20 healthy controls(P＜0.001).ROC curve analysis demonstrated that ELAVL1 could effectively differentiate between newly diagnosed MM patients,healthy controls,and MGUS patients(P＜0.001 and P=0.000 2,respectively).Survival analysis revealed that high ELAVL1 expression was associated with shorter progression-free survival(P=0.0141)and overall survival(P=0.008 0).Univariate and multivariate COX regression analyses identified high ELAVL1 expression as an independent risk factor for poor MM prognosis(P=0.005 0).KEGG analysis suggested that ELAVL1 might be involved in the Hippo and MAPK signaling pathways.Conclusion High ELAVL1 expression in MM may serve as a biomarker for diagnosis and poor prognosis.ELAVL1 may promote MM initiation and progression via the Hippo and MAPK signaling pathways.

Objective To observe the correlations of chest CT quantitative parameters and one-year readmission in patients with acute exacerbation of chronic obstructive pulmonary disease(AECOPD).Methods Totally 225 AECOPD patients were included and divided into the readmission group(group A,n=61)and non-readmission group(group B,n=164)according to whether readmitted due to acute exacerbation within one year or not.Quantitative CT parameters,including the percentage of low attenuation area volume to total lung volume(LAA％)and intrapulmonary vascular volume(IPVV)of the whole lung,left/right lung and each lobe,as well as the bronchial lumen area(LA)and percentage of bronchial wall area(WA％)of grades 3-8 were compared between groups.Spearman correlations were used to analyze the correlations between quantitative CT parameters and one-year readmission.Results LAA％of the whole lung,right lung,upper lobe of both lungs,middle lobe of the right lung,and lower lobe of the right lung in group A were higher than that in group B(all P＜0.05),which were weakly positively correlated with readmissions within one year(r=0.142-0.187,all P＜0.05).Significant differences of the upper lobe of right lung LAgrade 6,lower lobe of left lung LAgrade 6,lower lobe of left lung LAgrade 8,upper lobe of right lung WA％grade 5,upper lobe of right lung WA％grade 8 and lower lobe of left lung WA％grade 8 were found between groups,which were all correlated with readmissions within one year except for the lower lobe of the left lung LAgrade 6(all P＜0.05).Conclusion One-year readmission in AECOPD patients might be associated with severe airway remodeling and emphysema.

Objective: To compare the effectiveness of qualitative and quantitative fecal immunochemical tests (FIT) in identifying colorectal cancer, so as to provide insights into perfecting screening strategies for colorectal cancer. Methods: Participants in the Colorectal Cancer Screening Program for Key Populations in Zhejiang Province from May 2020 to December 2021 were recruited, and their demographic information, lifestyle and disease history were collected through a questionnaire survey. Qualitative or quantitative FIT along with a questionnaire-based risk assessment were employed as the initial screening tests. Individuals who were positive in any FIT or had high-risk assessment results were required to attend a subsequent colonoscopy examination. The positive rate, detection rate of colorectal cancer, positive predictive value and number of colonoscopies required were compared between qualitative and quantitative FITs, and stratified analyses by gender and age were conducted. Results: Totally 4 099 769 participants were included. The qualitative FIT group included 3 574 917 individuals, yielding a positive rate of 11.35%, a detection rate of 1.19%, a positive predictive value of 0.48% and 83.84 colonoscopies required to detect one cancer case. The quantitative FIT group involved 524 852 individuals, yielding a positive rate of 6.70%, a detection rate of 2.31%, a positive predictive value of 1.01% and 43.23 colonoscopies required to detect one cancer case. The quantitative FIT group showed significantly higher detection rate of colorectal cancer, higher positive predictive value and less number of colonoscopies required compared to the qualitative FIT group (all P<0.05). The same results were obtained after stratification by gender and age. Conclusion Compared to qualitative FIT, quantitative FIT improves the detection of colorectal cancer and reduces the workload of colonoscopy examinations, making it more suitable for colorectal cancer screening in large-scale populations.

Objective To observe the correlations of chest CT quantitative parameters in patients with acute exacerbation of chronic obstructive pulmonary disease(AECOPD)with blood eosinophil(EOS)level.Methods Chest CT data of 162 AECOPD patients with elevated eosinophils were retrospectively analyzed.The patients were divided into low EOS group(n=105)and high EOS group(n=57)according to the absolute counting of blood EOS.The quantitative CT parameters,including the number of whole lung bronchi and the volume of blood vessels,low-attenuation area percentage(LAA％)of whole lung,of left/right lung and each lobe of lung,as well as the luminal diameter(LD),wall thickness(WT),wall area(WA)and WA percentage of total bronchial cross-section(WA％)of grade 3 to 8 bronchi were compared between groups.Spearman correlations were performed to analyze the correlations of quantitative CT parameters with blood EOS level.Results LAA％of the whole lung,of the left/right lung and each lobe of lung,as well as of the upper lobe of right lung LDgrade4,middle lobe of right lung WTgrade5,upper lobe of right lung WAgrade4,middle lobe of right lung WAgrade5 and lower lobe of left lung WAgrade3 in low EOS group were all higher than those in high EOS group(all P＜0.05).Except for the upper lobe of right lung LDgrade4,the above quantitative CT indexes being significant different between groups were all weakly and negatively correlated with blood EOS level(r=-0.335 to-0.164,all P＜0.05).Conclusion Chest CT quantitative parameters of AECOPD patients were correlated with blood EOS level,among which LAA％,a part of WT and WA were all weakly negatively correlated with blood EOS level.

Triple-negative breast cancer (TNBC) is a nasty disease with extremely high malignancy and poor prognosis. Annexin A3 (ANXA3) is a potential prognosis biomarker, displaying an excellent correlation of ANXA3 overexpression with patients' poor prognosis. Silencing the expression of ANXA3 effectively inhibits the proliferation and metastasis of TNBC, suggesting that ANXA3 can be a promising therapeutic target to treat TNBC. Herein, we report a first-in-class ANXA3-targeted small molecule (R)-SL18, which demonstrated excellent anti-proliferative and anti-invasive activities to TNBC cells. (R)-SL18 directly bound to ANXA3 and increased its ubiquitination, thereby inducing ANXA3 degradation with moderate family selectivity. Importantly, (R)-SL18 showed a safe and effective therapeutic potency in a high ANXA3-expressing TNBC patient-derived xenograft model. Furthermore, (R)-SL18 could reduce the β-catenin level, and accordingly inhibit the Wnt/β-catenin signaling pathway in TNBC cells. Collectively, our data suggested that targeting degradation of ANXA3 by (R)-SL18 possesses the potential to treat TNBC.

Objective:To investigate the feasibility of classifying imaging patterns of dermatomyositis/polymyositis-related interstitial lung disease (DM/PM-ILD) into subtypes based on chest CT radiomics features and a model was constructed by machine learning algorithms.Methods:From November 2011 to November 2020, 107 patients diagnosed with PM/DM-ILD at the First Affiliated Hospital of Xi′an Jiaotong University were retrospectively analyzed. A total of 315 cases with chest CT were collected. Doctors pre-classified image patterns, including 105 cases with non-specific interstitial pneumonia (NSIP), 90 cases with organizing pneumonia (OP), and 66 cases with non-specific interstitial pneumonia combined with organizing pneumonia (NSIP+OP), 35 cases with common interstitial pneumonia (UIP), and 19 cases with diffuse alveolar damage (DAD), ANOVA was used to test the difference of baseline clinical information among the imaging classification groups. All images were divided into the training set and the est set by stratified random sampling at a ratio of 4∶1. In each CT scan, 3D slicer was used to segment each lung lobe, and then reconstructed into 3 mm 3 of voxels, and Pyradiomics library was used to extract the radiomic features of the whole lung and each lobe. The multi-classification goal was achieved by constructing random forest base classifiers for each of the five groups and then voting as the final model. In the process of constructing the base classifier, firstly, the balance between sample groups was achieved by SMOTETomek comprehensive sampling, and the optimal feature set was selected by independent sample t test and L1 regularized least absolute shrinkage and selection operator (LASSO) regression. In this study, the Radiomics model was constructed based on chest CT radiomics features, and the Radiomics + model was constructed by introducing gender and age information. The base classifier and the integration model use the mean accuracy and the area under the receiver operator characteristics analysis curve (AUC) to evaluate the performance, respectively. Results:There was a statistically significant difference ( P<0.05) between the ages of the NSIP, OP, NSIP+OP, UIP, and DAD groups [(57±13),(53±8),(54±10),(44±11), and (46±8)years old, respectively], F=11.82, P<0.001. In the Radiomics model, for each group of NSIP, OP, NSIP+OP, UIP, and DAD, the AUCs of the training set were 0.87, 0.91, 0.91, 0.96, and 0.99, respectively, and the AUC of the test set were 0.81, 0.82, 0.79, 0.93, 0.89. In the final Radiomics + model, for each group of NSIP, OP, NSIP+OP, UIP, and DAD, the AUCs of the training set were 0.89, 0.91, 0.92, 0.97, and 0.99, respectively, and the AUCs of the test set were 0.84, 0.82, 0.78, 0.94, 0.90. Conclusion:Based on chest CT radiomics features and key clinical features (sex, age), the Radiomics + model constructed by machine learning has good classification performance for the imaging patterns of PM/DM-LD.

Objective:To systematically evaluate and analyze the original research of prescription sequence symmetry analysis (PSSA), summarize its research progress and methodological details, and provide a reference for the future use of this method.Methods:The keywords related to PSSA were used for literature retrieval from Chinese databases (CNKI, Wanfang, and VIP) and English databases (PubMed, Embase, and Cochrane). Original articles that were related to PSSA and published before June 30, 2020 were selected. Information form was developed by Excel. Stata was used for the statistics analysis.Results:There were 45 eligible articles included in the research. Since 2013, the number of studies using PSSA has increased rapidly. These studies were mainly conducted in Japan ( n=11, 24.44%), China ( n=10, 22.22%), Denmark ( n=9, 20.00%), and Australia ( n=8, 17.78%). Medical claim database was used most commonly when PSSA was implemented. The included studies involved 16 types of drugs, of which the number of studies of psychotropic drugs and statins was highest ( n=8, 17.78%), and adverse reactions of almost all human systems were involved. In terms of methodology, 35 (77.78%) and 43 (95.56%) studies clearly reported the run-in period and interval period, of which 14 (31.11%) and 9 (20.00%) respectively gave the method or reason for determining the duration. In addition, 16 articles (35.56%) and 18 articles (40.00%) reported sensitivity analysis and subgroup analysis results, respectively. Conclusions:PSSA, one of the effective methods for safety signal detection in healthcare databases, has developed rapidly, but the methodological details and result reporting need to be improved. In China, PSSA research is still in its infancy, and it is necessary to pay attention to the quality of research and promote methodological exploration.

Objective:To systematically summarize and assess risk prediction models for occurrence of cervical cancer and to provide evidence for selecting the most reliable model for practice, and guide cervical cancer screening.Methods:Two groups of keywords related to cervical cancer and risk prediction model were searched on Chinese databases (CNKI, and Wanfang) and English databases (PubMed, Embase, and Cochrane Library). Original articles that developed or validated risk prediction models and published before November 21, 2019, were selected. Information form was created based on the CHARMS checklist. The PROBAST was used to assess the risk of bias.Results:12 eligible articles were identified, describing 15 prediction models, of which five were established in China. The predicted outcomes included multiple stages from cervical precancerous lesions to cancer occurrence, i.e., abnormal Pap smear (1), occurrence or recurrence of CIN (9), and occurrence of cervical cancer (5), etc. The most frequently used predictors were HPV infection (12), age (7), smoking (5), and education (5). There were two models using machine learning to develop models. In terms of model performance, the discrimination ranged from 0.53 to 0.87, while only two models assessed the calibration correctly. Only two models were externally validated in Taiwan of China, using people in different periods. All of the models were at high risk of bias, especially in the analysis domain. The problems were concentrated in the improper handling of missing data (13), preliminary evaluation of model performance (13), improper use of internal validation (12), and insufficient sample size (11). In addition, the problems of inconsistency measurements of predictors and outcomes (8) and the flawed report of the use of blindness for outcome measures (8) were also severe. Compared with the other models, the Rothberg (2018) model had relatively high quality. Conclusions:There are a certain number of cervical cancer risk prediction models, but the quality is poor. It is urgent to improve the measurement of predictors and outcomes, the statistical analysis details such as handling missing data and evaluation of model performance and externally validate existing models to better guide screening.