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Background Serum remnant cholesterol (RC) is associated with the onset of ischemic stroke (IS). However, studies on the correlation between RC levels and recurrent IS are limited, and the predictive value of RC in recurrent IS has not been analyzed. Objective To investigate the correlation between serum RC and the recurrence of IS, and to evaluate the predictive value of RC levels in recurrent IS by detecting serum RC levels in patients with IS. Methods Patients diagnosed as IS and hospitalized in the First Affiliated Hospital of Nanchang University, the Second Affiliated Hospital of Nanchang University, the Second Hospital of Nanchang, and the Third Hospital of Nanchang from March 2019 to March 2021 were included in the study. Relevant clinical information within 48 hours of admission was collected. All patients were followed up for 12 months to record the cases of recurrent IS. Cox regression and Restricted Cubic Spline (RCS) were performed to identify the correlation between RC levels and recurrent IS. Receiver operating characteristic (ROC) curves were plotted to evaluate the predictive value of RC levels in recurrent IS. Results A total of 1 023 eligible patients were included in the study, and 107 (10.46%) of them experienced IS recurrence within 1 year. Multivariable Cox regression analysis showed that high RC was an independent risk factor for recurrent IS (HR=2.709, 95%CI=1.150-6.382; P

Background Stroke is a chronic condition that seriously impairs human health. The correlation between rainfall and onset of stroke remains unclear. Objective To analyze the correlation between rainfall and stroke admissions in Nanchang City, and to provide scientific references for developing a comprehensive prevention and treatment strategy for stroke. Methods Stroke admission data from Nanchang City (2015-2019) from the digital-related group (DRG) system of the Jiangxi Provincial Health Commission Information Center were collected. In addition, atmospheric pollutant data from the national urban air quality real-time release platform and meteorological data from the Nanchang meteorological base station were collected. Basic characteristics of stroke admission patients, air pollutants, and meteorological factors were analyzed. Spearman rank correlation analysis was performed to identify the correlation of case number of stroke admissions with air pollutants and atmospheric factors. Distributional lag nonlinear model was used to explore the linkage between rainfall and stroke admissions. Stratified analysis was conducted based on gender and age (

Objective To explore the effect of cancer related anemia on the abundance of SARS-CoV-2 specific memory CD8+ T cells in peripheral blood. Methods A total of 34 HLA-A*11∶01 positive patients with middle- and late-stage tumors admitted in the Rocket Force Medical Center of PLA from June to July 2023 were enrolled in this study. According to hemoglobin (HGB) level, they were divided into non anemia group (≥120 g/L, n=14), mild anemia group (90~120 g/L, n=14), and moderate to severe anemia group (< 90 g/L, n=6). After their peripheral blood mononuclear cells were isolated, memory CD8+ T cells were stimulated by the epitopes of SARS-CoV-2 and then expanded. Positive epitopes were screened using enzyme-linked immunosorbent spot (ELISPOT) assay, and then MHC-peptide Tetramers were constructed. The proportion and number of Tetramer+ cells and the number of ELISPOT spots were detected in the expanded cells to reflect the abundance of SARS-CoV-2 specific memory CD8+ T cells. The relevance of patients' HGB level with the proportion and number of Tetramer+ cells and the number of ELISPOT spots was analyzed. The differences of the proportion and number of Tetramer+ cells and the number of ELISPOT spots was analyzed in different groups. Results Among the 34 patients, there were 25 males and 9 females, at an age range of 36~78 (median 57) years. The proportion and number of Tetramer+ cells and the number of ELISPOT spots were positively correlated with HGB level (P

Objective To investigate the effect of tumor-infiltrating B cells on the therapeutic efficacy of programmed death ligand-1 [PD-(L)1] inhibitors and elucidate the potential mechanisms. Methods Based on immunotherapy cohorts for melanoma patients in public databases, the relationship of B cells with progression-free survival (PFS) and response to immune checkpoint inhibitors treatment was analyzed. TC-1 and B16-OVA cells were implanted subcutaneously and in the liver in 6-8-week-old female C57BL/6 mice to establish tumor xenograft models. The effect of B cell clearance on PD-(L)1 therapy was compared. Flow cytometry was performed on the 15th day of TC-1 tumor microenvironment (TME) to confirm the number, function and phenotypic changes of T cells. Flow cytometry and quantitative real-time polymerase chain reaction (qPCR) were used to detect B cell surface molecules and cytokines. Results Based on ERP105482 data from the ICBatlas public database, high CD19 expression in the tumors was associated with longer PFS in melanoma patients (753 vs 95 d, HR=0.3, 95%CI: 0.13~0.65, P=0.003). B cells were significantly enriched in immunotherapy-responsive patients (P=0.01). In a mouse TC-1 liver-loaded tumor model, PD-(L)1 antibody treatment reduced tumor mass (P < 0.01), whereas B-cell clearance attenuated the therapeutic efficacy. B cells enhanced PD-(L)1 antibody treatment by promoting T cell infiltration and function, and the treatment resulted in changes in B cell subsets, as evidenced by an increase in PD-1 low-expressing subsets (P < 0.01). Conclusion After PD-(L)1 treatment, a decrease in PD-1 expression on B cell subsets might be one of the potential mechanisms by which B cells enhance the efficacy of PD-(L)1 therapy.

BackgroundAlcohol use disorder （AUD） is a common chronic and relapsing psychiatric disorders. Identifying severe AUD early and intervening promptly is crucial to prevent irreversible harm. Currently， the assessment of AUD severity primarily relies on psychiatric examination by clinicians， and there is limited research on the factors influencing AUD severity and the development of prediction models.ObjectiveTo analyze the factors influencing AUD severity， and construct a risk prediction model to aid in the assessment of disease progression in AUD patients.MethodsA retrospective analysis was conducted on 1 358 first-time hospitalized patients admitted to Nanning Fifth People's Hospital from January 1， 2017 to December 31， 2022. These patients met the Diagnostic and Statistical Manual of Mental Disorders， fifth edition （DSM-5） criteria for AUD. Basic patient data was collected， and the patients were divided into two groups based on disease severity： mild-moderate group （n=330） and severe group （n=1 028）. The patients were randomly divided into training and test sets in a 7∶3 ratio. A Logistic regression model was constructed in the training set， and the predictive ability of the model for disease severity was evaluated using the receiver operating characteristic （ROC） curve in the test set.ResultsCompared with the mild-moderate group， the severe group had a higher proportion of patients living in urban areas （χ2=7.804）， were farmers （χ2=17.991）， had a higher frequency of alcohol consumption （more than 1 to 2 drinks/day） （χ2=35.267）， had a higher age at first drinking （t=-3.858）， had a greater number of comorbid somatic disorders （Z=-22.782）， and had higher proportions of γ-Glutamyl transpeptidase （χ2=259.940） and total bilirubin abnormalities （χ2=148.552） （P

It is a means of injury assessment to detect respiratory rate using an unmanned aerial camera. However, the existing video- based respiratory rate detection algorithms are only applicable to fixed cameras. On the basis of spatial phase based respiratory signal extraction technology, a non-contact measurement method of human respiratory rate with videos recorded by unmanned aerial vehicle (UAV) is proposed. The complex steerable pyramid is used to extract the spatial phase of each frame image, and the phase sequence is obtained in chronological order. Secondly, the empirical mode decomposition (EMD) is used to decompose multiple modal components from the phase sequence, and the frequency variability analysis model is designed to select the components with stable frequencies, or the target respiratory signal. The peak value detection method is used to detect the human respiratory rate. The experimental results show that the average accuracy of the method can reach more than 98%, which is superior to the existing detection methods. [ABSTRACT FROM AUTHOR]