Background: and Purpose Live cell-based assays (LCBA) are increasingly used for serological antibody detection due to their ability to preserve antigen conformation, offering moderately higher sensitivity than fixed cell-based assays. However, the clinical necessity of prioritizing LCBA for the detection of aquaporin-4 immunoglobulin G (AQP4-IgG) in neuromyelitis optica spectrum disorder (NMOSD) remains unclear, especially when compared to its established role in diagnosing myelin oligodendrocyte glycoprotein antibody-associated disease. Methods: We compared the performance of live cell-based assays using immunofluorescence (LCBA-IF) and fixed cell-based assays using immunofluorescence (FCBA-IF) in detecting AQP4-IgG in 90 cases of NMOSD meeting 2015 International Panel for Neuromyelitis Optica Diagnosis criteria, alongside 40 controls. Additionally, we further investigated the relationship between AQP4-IgG titers as measured by LCBA-IF and FCBA-IF and clinical parameters in NMOSD patients. Results: Results showed 96.9% agreement between LCBA-IF and FCBA-IF (Cohen’s κ=0.935), with a strong Spearman correlation (0.977, p<0.001). Both methods demonstrated 100% specificity, with LCBA-IF showing slightly higher sensitivity compared to FCBA-IF. Within LCBAIF-tested groups, statistically significant differences in annualized relapse rates were observed across all pairwise comparisons (low-titer vs. moderate-titer, low-titer vs. high-titer, and moderate-titer vs. high-titer; all p<0.050). However, this association reached statistical significance in some FCBA-IF-tested groups. Conclusions Overall, there is a strong concordance between LCBA-IF and FCBA-IF in detecting AQP4-IgG, where LCBA-IF shows slightly higher sensitivity. Furthermore, there is a potential link between elevated AQP4-IgG titers and an increased risk of relapse, and this correlation may appear more pronounced when using LCBA-IF.

Objective To investigate the effects of Ras-related protein Rab-2B(RAB2B)on the biological behaviors of pancreatic cancer cells and elucidate its underlying mechanism.Methods PANC-1 cells,which exhibit relatively high RAB2B expression,and BXPC-3 cells,which display relatively low RAB2B expression,were selected from five pancreatic cancer cell lines.RAB2B-siRNA and pcDNA3.1-RAB2B plasmids were transfected into PANC-1 and BXPC-3 cells using a cell transfection technique.The CCK-8 assay was employed to evaluate the prolif-erative capacity of pancreatic cancer cells following RAB2B intervention.Wound healing and Transwell chamber assays were utilized to assess the migratory and invasive capabilities of pancreatic cancer cells.Additionally,the mRNA and protein expression levels of RAB2B,NF-κB,and Fibronectin 1(FN1)were analyzed by qRT-PCR and Western blot(WB),respectively.Results RAB2B mRNA and protein expression levels were significantly down-regulated in PANC-1 cells following transfection(P<0.05).CCK-8 assay results demonstrated that the proliferative capacity of PANC-1 cells was markedly reduced(P<0.05),and the wound-healing ability was substantially impaired(P<0.01)upon RAB2B knockdown.Transwell assays revealed a significant decrease in cell migration(P<0.01),while Western blot analysis indicated that the expression levels of phosphorylated p65 and FN1 were notably diminished(P<0.01).Conversely,overexpression of RAB2B reversed these aforementioned alterations.Conclusions Knockdown of RAB2B in PANC-1 cells significantly suppresses cell proliferation and migration,whereas overexpression of RAB2B in BXPC-3 cells markedly promotes these processes.This effect is likely mediated through the activation of the NF-κB signaling pathway and the subsequent regulation of FN1 expression.

Objective:To analyze the clinical characteristics of patients with chronic obstructive pulmonary disease (COPD) complicated by fatigue.Methods:COPD patients enrolled in the RealDTC study from June 2023 to March 2024 were included. Demographic data, history of acute exacerbations in the past year, smoking status, biofuel exposure, occupational exposure, modified Medical Research Council (mMRC) dyspnea score, COPD Assessment Test (CAT) score, forced expiratory volume in the first second predicted of percentage (FEV 1%pred), forced expiratory volume in one second (FEV 1)/forced vital capacity (FVC), and comorbidities (bronchial asthma, bronchiectasis, tuberculosis, cardiovascular disease, diabetes mellitus) were collected. Fatigue was evaluated using the Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-F) questionnaire, with a score ≤43 defined as fatigue. Patients were divided into fatigue and non-fatigue groups, and multivariate regression analysis was used to screen factors associated with fatigue in COPD patients. Results:A total of 597 COPD patients were included, of which 280(46.9%) had fatigue symptoms. Compared with non-fatigue patients, fatigue patients had lower FEV 1%pred, FEV 1/FVC, and body mass index (BMI), higher CAT and mMRC scores, and a higher proportion of occupational exposure, bronchiectasis, and treatment with long-acting β 2-agonists (LABA)/long-acting muscarinic antagonists (LAMA)/inhaled corticosteroids (ICS) (all P<0.05). Multivariate regression analysis showed that high CAT score ( OR=2.312, 95% CI: 1.366-3.911), high mMRC score ( OR=1.484, 95% CI: 1.053-2.091), occupational exposure ( OR=1.513, 95% CI: 1.082-2.116), comorbid bronchiectasis ( OR=2.452, 95% CI: 1.102-5.457), low BMI ( OR=0.935, 95% CI: 0.891-0.981), and high CAT-energy score ( OR=1.301, 95% CI: 1.149-1.473) were risk factors for fatigue in COPD patients. The CAT-energy score was highly correlated with the FACIT-F score ( r=0.260, P<0.001), and a CAT-energy score ≥2 could preliminarily screen COPD patients with fatigue. Conclusions:COPD patients with comorbid fatigue have a heavy symptom burden, are more likely to have a history of occupational exposure and bronchiectasis, and the CAT-energy score is of great reference value for screening COPD patients with fatigue.

Objective:To analyze the clinical characteristics of patients with chronic obstructive pulmonary disease (COPD) complicated by anorexia.Methods:This cross-sectional study included patients registered in the RealDTC study from May 2023 to December 2023. Demographic data, COPD Assessment Test (CAT) score, modified Medical Research Council (mMRC) dyspnea questionnaire score, Clinical COPD Questionnaire (CCQ) score, forced expiratory volume in one second (FEV 1), forced expiratory volume in the first second predicted of percentage (FEV 1%pred), FEV 1/forced vital capacity (FVC), Global Initiative for Chronic Obstructive Lung Disease (GOLD) grade, GOLD group, number of acute exacerbations and hospitalizations in the past year, and score of the Functional Assessment of Anorexia Cachexia Therapy-Anorexia/Cachexia Subscale-12 (FAACT-A/CS-12) were collected. Patients with a FAACT-A/CS-12 score ≤30 were diagnosed as having anorexia. Multivariate logistic regression analysis was used to evaluate the influencing factors of anorexia in COPD patients. Results:A total of 617 COPD patients were included, of whom 109(17.7%) had anorexia. Compared with non-anorexia patients, COPD patients with anorexia had higher age, CAT, mMRC and CCQ scores, and more acute exacerbations and hospitalizations in the past year, while body mass index, FEV 1, FEV 1%pred and FEV 1/FVC were lower (all P<0.05). The proportions of patients with primary education or below, GOLD 3-4 grade and GOLD E group were higher in COPD patients with anorexia (all P<0.05). Logistic regression analysis showed that a CAT score of 10-<20 [odds ratio ( OR)=4.017, 95% confidence interval ( CI): 1.673-59.645], a CAT score of 20-<30 ( OR=9.686, 95% CI: 3.777-24.842), a CAT score of ≥30 ( OR=78.286, 95% CI: 7.654-800.689) and ≥1 hospitalization in the past year ( OR=2.050, 95% CI: 1.292-3.254) were independent risk factors for anorexia in COPD patients (all P<0.05). Conclusions:COPD patients with anorexia have poor lung function, high symptom burden and high risk of acute exacerbation. Clinicians should pay attention to the management of COPD patients with anorexia and take corresponding intervention measures.

Objective:To analyze the clinical characteristics of frailty in elderly patients with chronic obstructive pulmonary disease (COPD).Methods:COPD patients aged ≥65 years registered in the RealDTC study from June 2023 to March 2024 were included. Demographic data, history of exacerbations in the past year, exposure to risk factors (smoking, biomass fuel exposure, occupational exposure), modified Medical Research Council (mMRC) dyspnea score, COPD Assessment Test (CAT) score, forced expiratory volume in the first second predicted of percentage (FEV 1%pred), forced expiratory volume in one second (FEV 1) to forced vital capacity (FVC), and comorbidities (bronchial asthma, bronchiectasis, pulmonary tuberculosis, cardiovascular disease, diabetes mellitus) were collected. According to Fried′s frailty phenotype, patients meeting any 3 of the 5 criteria were defined as frail and divided into a frailty group and a non-frailty group. Multivariate regression analysis was used to screen the related factors of frailty in elderly COPD patients, and the receiver operating characteristic (ROC) curve was used to calculate the area under the curve (AUC) of related factors for frailty assessment. Results:A total of 496 elderly COPD patients were included, of which 144(29.0%) had comorbid frailty. The frailty group had lower mass body index (BMI), FEV 1%pred, and FEV 1/FVC, higher mMRC and CAT scores, more exacerbations and hospitalizations in the past year (all P<0.001), and higher proportions of patients with junior high school education or below, Global Initiative for Chronic Obstructive Lung Disease (GOLD) group E, and GOLD grades 3 and 4 (all P<0.05). Multivariate regression analysis showed that low education level ( OR=2.117, 95% CI: 1.119-4.003), low BMI ( OR=0.927, 95% CI: 0.867-0.991), GOLD grade 4 ( OR=4.251, 95% CI: 1.477-12.235), high CAT score ( OR=1.174, 95% CI: 1.127-1.224), and high mMRC score ( OR=4.578, 95% CI: 3.364-6.231) were independent risk factors for frailty in elderly COPD patients (all P<0.05). The ROC curve showed that CAT score (AUC=0.78) and mMRC score (AUC=0.81) had the highest AUC for assessing frailty in elderly COPD patients. Conclusions:Elderly COPD patients with frailty have lower BMI, worse lung function, and more severe symptom burden. The results provide clinical reference for the management of frail elderly COPD patients.

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

Objective:To explore the diagnostic value of a clinical-radiomics model based on the T 1 mapping and apparent diffusion coefficient (ADC)-based radiomics, and the clinical indicator for renal fibrosis (RF) caused by chronic kidney disease (CKD). Methods:This cross-sectional study prospectively and consecutively enrolled 122 patients with CKD at the Second Affiliated Hospital of Soochow University from September 2021 to December 2023 who were randomly allocated to a training set ( n=85) or a validation set ( n=37) in an approximate 7∶3 ratio using simple random sampling. Patients underwent T 1 mapping and diffusion-weighted imaging scans. Renal biopsy was performed within 3 days after the MRI scans. Patients were categorized into three groups based on the degree of RF: no RF ( n=25), mild RF ( n=55), and moderate to severe RF ( n=42). To differentiate the presence of RF (no RF vs. any RF) and the severity of RF (mild RF vs. moderate to severe RF), univariate and multivariate logistic regression were used to optimize the independent clinical predictor, which constituted the clinical model. Radiomics features were extracted from regions of interest delineated within the renal parenchyma of the right kidney on T 1 mapping and ADC maps. Features were selected using least absolute shrinkage and selection operator regression to build the radiomics model. A clinical-radiomics model was subsequently constructed by integrating the independent clinical predictors with the selected radiomics features. Model diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUC). Calibration curve was plotted to assess model calibration, and decision curve analysis was performed to evaluate clinical net benefit. Results:Univariate logistic regression analysis revealed that estimated glomerular filtration rate (eGFR), serum creatinine, and blood urea nitrogen exhibited statistically significant differences ( P0.05) in distinguishing both the presence and severity of RF. Multivariate analysis identified eGFR as an independent clinical predictor for both the presence of RF ( OR=0.939, 95% CI 0.898-0.982, P=0.006) and RF severity ( OR=0.956, 95% CI 0.917-0.997, P=0.037). From the MRI images, 7 radiomics features were selected to build the radiomics model for distinguishing the presence of RF, and 8 features were selected for the model assessing RF severity. These radiomics models were then combined with eGFR to construct the clinical-radiomics models. The clinical-radiomics models demonstrated the highest diagnostic performance, with an AUC of 0.935 (95% CI 0.859-0.977) for RF presence and 0.967 (95% CI 0.891-0.995) for RF severity in the training set, and 0.914 (95% CI 0.774-0.981) and 0.908 (95% CI 0.748-0.981) in the validation set. Calibration curves and decision curve analysis confirmed that the clinical-radiomics models exhibited excellent calibration and provided the highest clinical net benefit for assessing RF in CKD patients. Conclusion:The clinical-radiomics model integrating T 1 mapping and ADC-based radiomics and eGFR can effectively improve the diagnostic performance for RF in CKD patients.

Objective:To investigate the miRNA-mRNA regulatory network in a rat model of Tourette syndrome(TS)using transcriptomic technology and to screen key signaling pathways and potential traditional Chinese medicine(TCM)candidates for intervention.Methods:A TS rat model was established using iminodipropionitrile(IDPN).RNA sequencing was performed to identify differentially expressed miRNAs and mRNAs in the brain tissues of TS rats.Bioinformatics analysis was applied to construct interaction networks,and network pharmacology was further employed to screen potential TCM compounds.Results:After 7 days of IDPN modeling,the model group exhibited motor and stereotypical behavioral changes,with behavioral scores greater than 3 points.Hema toxylin-eosin(HE)staining revealed irregular neuronal nuclear morphology,uneven chromatin distribution,nuclear pyknosis,and increased glial cell density.KEGG enrichment analysis identified key pathways:calcium signaling pathway,neuroactive ligand-receptor interaction,p53 signaling pathway,ECM-receptor interaction,and TGF-β signaling pathway.miR-125a-3p,miR-106-3p,and miR-760-3p were identified as pivotal miRNAs.Potential TCM candidates included Ajuga decumbens,Acanthopanax bark,Codonopsis pilosula,Stephania japonica,Os Draconis,Notopterygium root,Siraitia grosvenorii,Zanthoxylum nitidum root,Morinda officinalis,and Corydalis yanhusuo.Conclusion:The miRNAs miR-106-3p,miR-125a-3p,and miR-760-3p may mediate TS pathogenesis by altering critical signaling networks,including the calcium signaling pathway,neuroactive ligand-receptor interaction,and ECM-receptor interaction pathways,leading to neuroimmune inflammation and dopaminergic system dysregulation.TCM compounds such as Corydalis yanhusuo and Ajuga decumbens may exert therapeutic effects through multi-component synergistic regulation of these miRNAs and downstream pathways.

Aim To explore the possible regulatory effect of leptin on acyl-CoA synthetase long chain fami-ly member ACSL5 and their effect on migration and in-vasion of breast cancer cell,and to explore the underly-ing mechanism.Methods The expression of leptin receptor was detected by immunofluorescence assay.The migration and invasion ability of MCF-7 cells were detected by wound healing assay and Transwell assay respectively.The downstream target gene of leptin was analyzed by PCR microarray data.The expression of ACSL5 in breast cancer and its correlation with the staging and prognosis of breast cancer patients were as-sessed uing bioinformatics methods.The expression of ACSL5 in MCF-7 cells treated with different concentra-tions of leptin was detected using real time fluorescence quantitative polymerase chain reaction(RT-qPCR).Overexpressing ACSL5 was constructed by lentiviral transfection;the expressions of EMT related proteins,AMPK-α and p-AMPK-α were detected by Western blot.Results Leptin promoted breast cancer cell mi-gration and invasion and EMT.ACSL5 was significant-ly low expressed in breast cancer and related to progno-sis.Leptin downregulated the expression of ACSL5 through OBR.Leptin activated AMPK pathway to downregulate ACSL5 and promote migration,invasion and EMT of breast cancer cells.Conclusions Leptin may promote the migration,invasion and EMT of breast cancer by downregulating ACSL5 through activating AMPK pathway.

Objective:To analyze the clinical characteristics of patients with chronic obstructive pulmonary disease (COPD) complicated by fatigue.Methods:COPD patients enrolled in the RealDTC study from June 2023 to March 2024 were included. Demographic data, history of acute exacerbations in the past year, smoking status, biofuel exposure, occupational exposure, modified Medical Research Council (mMRC) dyspnea score, COPD Assessment Test (CAT) score, forced expiratory volume in the first second predicted of percentage (FEV 1%pred), forced expiratory volume in one second (FEV 1)/forced vital capacity (FVC), and comorbidities (bronchial asthma, bronchiectasis, tuberculosis, cardiovascular disease, diabetes mellitus) were collected. Fatigue was evaluated using the Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-F) questionnaire, with a score ≤43 defined as fatigue. Patients were divided into fatigue and non-fatigue groups, and multivariate regression analysis was used to screen factors associated with fatigue in COPD patients. Results:A total of 597 COPD patients were included, of which 280(46.9%) had fatigue symptoms. Compared with non-fatigue patients, fatigue patients had lower FEV 1%pred, FEV 1/FVC, and body mass index (BMI), higher CAT and mMRC scores, and a higher proportion of occupational exposure, bronchiectasis, and treatment with long-acting β 2-agonists (LABA)/long-acting muscarinic antagonists (LAMA)/inhaled corticosteroids (ICS) (all P<0.05). Multivariate regression analysis showed that high CAT score ( OR=2.312, 95% CI: 1.366-3.911), high mMRC score ( OR=1.484, 95% CI: 1.053-2.091), occupational exposure ( OR=1.513, 95% CI: 1.082-2.116), comorbid bronchiectasis ( OR=2.452, 95% CI: 1.102-5.457), low BMI ( OR=0.935, 95% CI: 0.891-0.981), and high CAT-energy score ( OR=1.301, 95% CI: 1.149-1.473) were risk factors for fatigue in COPD patients. The CAT-energy score was highly correlated with the FACIT-F score ( r=0.260, P<0.001), and a CAT-energy score ≥2 could preliminarily screen COPD patients with fatigue. Conclusions:COPD patients with comorbid fatigue have a heavy symptom burden, are more likely to have a history of occupational exposure and bronchiectasis, and the CAT-energy score is of great reference value for screening COPD patients with fatigue.