Journal of investigative medicine : the official publication of the American Federation for Clinical Research
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This study intended to delineate the mechanism and functional role of integrin α2 (ITGA2) in non-small-cell lung cancer (NSCLC) cell immune escape. Bioinformatics analysis was utilized to analyze ITGA2 expression in NSCLC tissues, and correlations between ITGA2 expression and patient survival time, ITGA2 expression and programmed cell death ligand 1 (PD-L1; CD274) expression, and ITGA2 expression and CD8+ T-cell infiltration. Quantitative real-time polymerase chain reaction detected ITGA2 expression. ⋯ Clinical sample testing unveiled that ITGA2 was upregulated in NSCLC tissues. PD-L1 upregulation was seen in exosomes separated from patient blood, and correlation analysis showed a positive correlation of exosomal PD-L1 expression in blood with ITGA2 expression in tissues. This study displays a novel mechanism and role of ITGA2 in NSCLC immune escape, providing directions for the clinical therapy of NSCLC patients.
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Metabolic dysfunction-associated fatty liver disease (MAFLD) is strongly associated with disturbances in the intestinal microbiota. Herein, the biological effects and mechanism of Bifidobacterium bifidum BGN4 fractions in regulating hepatocyte ferroptosis during MAFLD progression were investigated. To establish an in vitro model of MAFLD, LO2 cells were subjected to palmitic acid (PA). ⋯ Moreover, we observed that BGN4 fractions inhibited CYP2E1 transcription by suppressing SREBP1 nuclear translocation. In addition, CYP2E1 overexpression eliminated the inhibitory effect of BGN4 fractions on PA-induced hepatocyte oxidative stress and ferroptosis. These findings collectively indicated that BGN4 fractions reduced CYP2E1 expression by inhibiting SREBP1 nuclear translocation, thereby suppressing hepatocyte oxidative stress and ferroptosis during the development of MAFLD.
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The coronavirus disease 2019 (COVID-19) pandemic, which emerged in late 2019, has caused millions of infections and fatalities globally, disrupting various aspects of human society, including socioeconomic, political, and educational systems. One of the key challenges during the COVID-19 pandemic is accurately predicting the clinical development and outcome of the infected patients. In response, scientists and medical professionals globally have mobilized to develop prognostic strategies such as risk scores, biomarkers, and machine learning models to predict the clinical course and outcomes of COVID-19 patients. ⋯ Our model outperforms the clinical predictive models regarding patient mortality risk and classification in the literature. Therefore, we conclude that our robust model can help healthcare professionals to manage COVID-19 patients more effectively. We expect that early prediction of COVID-19 patients and preventive interventions can reduce the mortality risk of patients.
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Although reports of familial clustering of hematologic malignancies have appeared for decades, the cause(s) of this uncommon occurrence is still not completely understood. Most modern investigations, however, support a genetic rather than an environmental exposure as a cause of this observation. Most pedigrees of families with familial hematologic malignancies demonstrate age of onset anticipation, with the disease diagnosed at an earlier age in successive generations. ⋯ In preparation for molecular studies of familial clustering of hematologic malignancies, we have collected pedigrees on 738 families and have previously demonstrated anticipation in those with familial plasma cell myeloma, chronic lymphocytic leukemia, Hodgkin lymphoma or non-Hodgkin lymphoma (NHL). Here we present data on 36 families with both plasma cell myeloma and NHL in their pedigrees and demonstrate strong evidence for anticipation in these families. We encourage all health care personnel to ask patients multiple times about family medical history and carefully take note of family histories from individuals with uncommon illnesses and to refer families with clustering of such illnesses for further investigation.
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The immune microenvironment plays an important role in the regulation of diseases. The characterization of the cellular composition of immune cell infiltrates in diseases and respective models is a major task in pathogenesis research and diagnostics. ⋯ Accordingly, we developed a robust, rapid RT-qPCR-based approach to determine and quantify the abundance of prominent immune cell populations such as T cells, helper T (Th) cells, cytotoxic T cells, Th1 cells, B cells, and macrophages in mouse tissues. The results were independently validated by the gold standards IHC and FACS in corresponding tissues and showed high concordance.