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Cells | Free Full-Text | Characterization of T Follicular Helper Cells and T Follicular Regulatory Cells in HIV-Infected and Sero-Negative Individuals

Humoral immune response is important in fighting pathogens by the production of specific antibodies by B cells. In germinal centers, T follicular helper (TFH) cells provide important help to B-cell antibody production but also contribute to HIV persistence. T follicular regulatory (TFR) cells, which inhibit the function of TFH cells, express similar surface markers. Since FOXP3 is the only marker that distinguishes TFR from TFH cells it is unknown whether the increase in TFH cells observed in HIV infection and HIV persistence may be partly due to an increase in TFR cells. Using multicolor flow cytometry to detect TFH and TFR cells in cryopreserved peripheral blood mononuclear cells from HIV-infected and non-infected participants in the UCLA Multicenter AIDS Cohort Study (MACS), we identified CD3+CXCR5+CD4+CD8−BCL6+ peripheral blood TFH (pTFH) cells and CD3+CXCR5+CD4+CD8−FOXP3+ peripheral blood TFR (pTFR) cells. Unlike TFR cells in germinal centers, pTFR cells do not express B cell ....

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Development of diagnostic algorithm using machine learning for distinguishing between active tuberculosis and latent tuberculosis infection | BMC Infectious Diseases

The discrimination between active tuberculosis (ATB) and latent tuberculosis infection (LTBI) remains challenging. The present study aims to investigate the value of diagnostic models established by machine learning based on multiple laboratory data for distinguishing Mycobacterium tuberculosis (Mtb) infection status. T-SPOT, lymphocyte characteristic detection, and routine laboratory tests were performed on participants. Diagnostic models were built according to various algorithms. A total of 892 participants (468 ATB and 424 LTBI) and another 263 participants (125 ATB and 138 LTBI), were respectively enrolled at Tongji Hospital (discovery cohort) and Sino-French New City Hospital (validation cohort). Receiver operating characteristic (ROC) curve analysis showed that the value of individual indicator for differentiating ATB from LTBI was limited (area under the ROC curve (AUC) < 0.8). A total of 28 models were successfully established using machine learning. Among t ....

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