Objective To investigate the causal relationship between gut microbiota and idiopathic pulmonary fibrosis (IPF). Methods Genome-wide association studies (GWAS) data of gut microbiota and IPF were obtained from MiBioGen and IEU OpenGWAS, respectively. Instrumental variables were screened by means of significance, linkage disequilibrium, weak instrumental variable screening, and removal of confounding factors (genetics, smoking, host characteristics). Inverse variance weighted (IVW) was used as the main Mendelian randomization (MR) analysis method, and the weighted median, simple mode, MR-Egger, and weighted mode were used to perform MR to reveal the causal effect of gut microbiota and IPF. The Cochrane's Q, leave-one-out, MR-Egger-intercept, and Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) and Steiger tests were used to analyze the heterogeneity, horizontal pleiotropy, outliers, and directionality, respectively. Results IVW analysis results showed that Actinobacteria [OR=1.773, 95%CI (1.323, 2.377), P<0.001], Erysipelatoclostridium [OR=2.077, 95%CI (1.107, 3.896), P=0.023], and Streptococcus [OR=1.35, 95%CI (1.100, 1.657), P=0.004] could increase the risk of IPF. Bifidobacterium [OR=0.668, 95%CI (0.620, 0.720), P<0.001], Ruminococcus [OR=0.434, 95%CI (0.222, 0.848), P=0.015], and Tyzzerella [OR=0.479, 95%CI (0.304, 0.755), P=0.001] could reduce the risk of IPF. No significant heterogeneity, horizontal pleiotropy, outliers, and reverse causality were found. Conclusion Actinobacteria, Erysipelatoclostridium and Streptococcus may increase the risk of IPF, while Bifidobacterium, Ruminococcus and Tyzzerella may reduce the risk of IPF. Regulation of the above gut microbiota may become a new direction in the study of the pathogenesis of IPF.

Objective To analyze the influenza-like illness (ILI) data in Ordos City from 2017 to 2023 and conduct nucleic acid detection of the virus to understand the local influenza epidemic situation, and to provide a reliable basis for influenza prevention and control in the city. Methods Real-time quantitative polymerase chain reaction (qPCR) was used to identify virus subtypes in ILI throat swab samples. Comparisons of positive rates were conducted using the chi-square test, with a significance level of α=0.05. Results From 2017 to 2023, a total of 3,283,434 outpatient and emergency visits were recorded at the Ordos City Central Hospital, including 74,159 ILI cases, with an ILI proportion of 2.26%. The majority of ILI cases (74.43%) occurred in children aged 0~14 years old. The overall positive rate of influenza virus nucleic acid detection was 10.87%, with the highest proportion being subtype A (seasonal H3) at 43.03%. The highest detection rate was observed in the 5~14 years age group, with statistically significant differences in positive rates across age groups (χ²=155.638, P<0.001). Influenza peaks occurred mainly from November to March of the following year. From January to April, three types of influenza were prevalent alternately or mixed, while from October to December, subtype A (seasonal H3) predominated. Positive rates varied significantly across months (χ²=250.923, P<0.001). The temporal trends of ILI proportions and PCR-positive rates were consistent. Conclusion Influenza in Ordos City exhibits distinct seasonal and age distribution characteristics, with alternating or mixed circulation of three virus types. Continued efforts are needed to strengthen influenza surveillance, especially the prevention and control of influenza in infants and adolescents.

ObjectiveTo analyze the epidemiological characteristics of foodborne disease outbreaks in Wuhan from 2006 to 2023, and to provide a scientific basis for the development of foodborne disease prevention and control measures. MethodsDescriptive statistical analyses were performed on foodborne disease outbreaks confirmed by the district and municipal center for disease control and prevention (CDC) in Wuhan from 2006 to 2023, and the attack rate and case fatality rate were calculated as well. ResultsA total of 182 foodborne disease outbreaks were reported in Wuhan from 2006 to 2023, with a cumulative of 2 820 cases. Among which, 3 cases were dead, with an annual average attack rate of 1.22% and a case fatality rate of 0.11%. The highest number of outbreaks occurred in collective canteens (43.96%, 80/182), the highest attack rate was observed in catering facilities (11.03%), and the highest case fatality rate was found in households (1.45%). Among the foodborne disease outbreaks with identified etiologies, microbial factors were the leading causes (36.26%, 66/182), with the main pathogens being norovirus, Bacillus cereus, and other unspecified bacteria. Fungal factors were mainly attributed to poisonous mushrooms, with a relatively high fatality rate of 2.22% (2/90). Outbreaks caused by bacterial factors were more common in the central urban area (30.28%, 33/109), while fungal-related outbreaks were more frequent in the outlying urban areas (24.66%, 18/73). ConclusionCollective canteens are the main venues for foodborne disease outbreaks in Wuhan. Microbial factors are the main pathogenic factors, and poisonous mushrooms are the leading causes to death. It is necessary to strengthen the supervision on collective canteens, carry out various forms of public awareness campaigns on poisonous mushroom poisoning, and, if required, cooperate with the gardening department to eradicate wild poisonous mushrooms in the green belts. A collaborative cooperation involving multiple departments is essential to reduce the occurrence of foodborne disease outbreaks.

Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by persistent inflammation and joint damage, accompanied by the accumulation of plasma cells, which contributes to its pathogenesis. Understanding the genetic alterations occurring during plasma cell differentiation in RA can deepen our comprehension of its pathogenesis and guide the development of targeted therapeutic interventions. Here, our study elucidates the intricate molecular mechanisms underlying plasma cell differentiation by demonstrating that PRDX1 interacts with DOK3 and modulates its degradation by the autophagy-lysosome pathway. This interaction results in the inhibition of plasma cell differentiation, thereby alleviating the progression of collagen-induced arthritis. Additionally, our investigation identifies Salvianolic acid B (SAB) as a potent small molecular glue-like compound that enhances the interaction between PRDX1 and DOK3, consequently impeding the progression of collagen-induced arthritis by inhibiting plasma cell differentiation. Collectively, these findings underscore the therapeutic potential of developing chemical stabilizers for the PRDX1-DOK3 complex in suppressing plasma cell differentiation for RA treatment and establish a theoretical basis for targeting PRDX1-protein interactions as specific therapeutic targets in various diseases.

Objective:To establish a three-dimensional U-shaped residual coordinated attention network (URCA-Net) based on enhanced CT images for small bowel polyp detection and analyze its application effectiveness in intelligent detection of small bowel polyps.Methods:Abdominal CT data of patients with small bowel polyps were collected from the Air Force Medical Center between June 2019 and July 2023. All patients underwent bowel preparation followed by thin-slice spiral CT scanning to obtain enhanced CT arterial phase images. The data were randomly divided into training, validation and test sets in an 8∶1∶1 ratio. The URCA-Net deep learning model was used for small bowel polyp segmentation. The training set was used for model parameter training, the validation set for hyperparameter adjustment and monitoring of model generalization performance and the test set for final unbiased evaluation of the model. An early intelligent detection model for small bowel polyps was constructed, and its performance was evaluated. Evaluation metrics included pixel-level metrics for the segmentation task [Dice Similarity Coefficient (DSC)], as well as sensitivity and precision for polyp detection. A two-stage segmentation strategy was adopted: the first stage segmented the small bowel region to remove external interference, and the second stage performed polyp segmentation within the small bowel region.Results:A total of 78 subjects were included in the study, with an average age of (54±7) years. A total of 23 400 scan images were extracted, including 136 hyperplastic polyps, 298 hamartomatous polyps, 14 adenomatous polyps, and 4 cancerous polyps. On the test set, the average DSC for the first stage (small bowel segmentation) and the second stage (polyp segmentation) was 0.790 and 0.314, respectively. In the second stage task (polyp segmentation based on small bowel region), the polyp segmentation DSC increased to 0.701, with a precision of 0.836 (95% CI: 0.700-0.972) and a sensitivity of 0.759 (95% CI: 0.631-0.888) for polyp detection. Conclusion:The URCA-Net deep learning technique demonstrates good auxiliary diagnostic effectiveness in small bowel polyp detection and can provide a reference for screening and detection of small bowel polyps. The model is capable of generating high-quality segmentation results, which could facilitate evaluating polyp lesion morphology and provide support for downstream tasks such as preoperative navigation and risk prediction.

Objective:To study the cardiac morphology and function of fetuses with different types of complete transposition of the great arteries（cTGA）by using fetal heart quantification（fetal HQ）.Methods:A retrospective study was conducted on 50 fetuses diagnosed with cTGA through fetal echocardiography at Sir Run Run Shaw Hospital，Zhejiang University School of Medicine from July 2020 to December 2024. These cases were categorized into simple cTGA group（ n=31）and complex cTGA group（ n=19）based on the presence of concomitant cardiac anomalies. A control group of 160 normal fetuses with matched gestational ages was selected for comparison. Utilizing fetal HQ technology，the cardiac longitudinal diameter，transverse diameter，area，global sphericity index（GSI），left and right ventricular end-diastolic area（LVEDA，RVEDA），left and right ventricular fractional area change（LVFAC，RVFAC），left and right ventricular global longitudinal strain（LVGLS，RVGLS），and segmental sphericity index of 24 segments for both left and right ventricles（LVSI，RVSI）were measured. The analysis focused on comparing the differences among the simple cTGA group，complex cTGA group，and the control group. Results:Compared to the control group，the simple cTGA group exhibited significantly lower fetal GSI，LVEDA，RVFAC，and RVGLS（all P<0.05）. Statistically significant differences were observed in LVSI segments 1-4 and 10-17，as well as RVSI segments 1-7，9，and 15-23 compared to the control group（all P<0.05）. In comparison with the control group，the complex cTGA group demonstrated significantly reduced fetal GSI，LVFAC，LVGLS，RVFAC，and RVGLS（all P<0.05）. Significant differences were noted in LVSI segments 5-8 and 10-14，along with RVSI segments 1-14 and 24 compared to the control group（all P<0.05）. When compared to the simple cTGA group，the complex cTGA group showed significantly lower LVFAC，LVGLS，RVFAC，and RVGLS（all P<0.05），while GSI and LVEDA were significantly higher（all P<0.05）. Statistically significant differences were observed in LVSI segments 3-4，6-8，and 17，as well as RVSI segments 10-19 between the complex cTGA group and the simple cTGA group（all P<0.05）. Conclusions:The comprehensive parameters provided by Fetal HQ facilitate the assessment of cardiac morphology and function in cTGA fetuses，enabling a deeper understanding of the alterations in cardiac structure and function across different types of cTGA. This advanced analysis offers valuable reference information for clinical guidance during pregnancy.

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.

Purpose To explore the value of the clinical radiomics nomogram based on ultrasound in evaluating the degree of fibrosis in chronic kidney disease(CKD).Materials and Methods This retrospective study included 350 patients with CKD in Nanchong Central Hospital from January 2014 to July 2022 who underwent renal biopsy.The patients were categorized by the tubule atrophy with interstitial fibrosis(TA/IF)and divided into a training cohort(n=245)and test cohort(n=105).The patient demographics were evaluated to establish a clinical prediction model.The XGBoost machine learning model was constructed by extracting the radiomics features from the ultrasound images.The clinical radiomics nomogram prediction model was constructed by combining the radiomics score(Rad score)and important clinical features.The diagnostic performance of the three models was evaluated using receiver operating characteristic curve analysis.Results Among the 350 patients with CKD,226 had TA/IF 0 and 124 had TA/IF 1.Based on the clinical characteristics and Rad score,the clinical radiomics nomogram prediction model had the highest area under the curve in the training and testing cohorts,with the area under the curve of 0.938(95%CI 0.909-0.969)and 0.933(95%CI 0.891-0.980),respectively.Conclusion The ultrasound-based radiomics prediction model has potential value for the noninvasive diagnosis of TA/IF in CKD.Nomogram prediction models based on renal Rad scores and clinic may help clinicians to manage patients.

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 establish a three-dimensional U-shaped residual coordinated attention network (URCA-Net) based on enhanced CT images for small bowel polyp detection and analyze its application effectiveness in intelligent detection of small bowel polyps.Methods:Abdominal CT data of patients with small bowel polyps were collected from the Air Force Medical Center between June 2019 and July 2023. All patients underwent bowel preparation followed by thin-slice spiral CT scanning to obtain enhanced CT arterial phase images. The data were randomly divided into training, validation and test sets in an 8∶1∶1 ratio. The URCA-Net deep learning model was used for small bowel polyp segmentation. The training set was used for model parameter training, the validation set for hyperparameter adjustment and monitoring of model generalization performance and the test set for final unbiased evaluation of the model. An early intelligent detection model for small bowel polyps was constructed, and its performance was evaluated. Evaluation metrics included pixel-level metrics for the segmentation task [Dice Similarity Coefficient (DSC)], as well as sensitivity and precision for polyp detection. A two-stage segmentation strategy was adopted: the first stage segmented the small bowel region to remove external interference, and the second stage performed polyp segmentation within the small bowel region.Results:A total of 78 subjects were included in the study, with an average age of (54±7) years. A total of 23 400 scan images were extracted, including 136 hyperplastic polyps, 298 hamartomatous polyps, 14 adenomatous polyps, and 4 cancerous polyps. On the test set, the average DSC for the first stage (small bowel segmentation) and the second stage (polyp segmentation) was 0.790 and 0.314, respectively. In the second stage task (polyp segmentation based on small bowel region), the polyp segmentation DSC increased to 0.701, with a precision of 0.836 (95% CI: 0.700-0.972) and a sensitivity of 0.759 (95% CI: 0.631-0.888) for polyp detection. Conclusion:The URCA-Net deep learning technique demonstrates good auxiliary diagnostic effectiveness in small bowel polyp detection and can provide a reference for screening and detection of small bowel polyps. The model is capable of generating high-quality segmentation results, which could facilitate evaluating polyp lesion morphology and provide support for downstream tasks such as preoperative navigation and risk prediction.