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Regulatory T cells are usually recognized as a specialized subset of CD4+ T cells functioning in establishment and maintenance of immune tolerance. Meanwhile, there is emerging evidence that regulatory T cells (Tregs) are also present in various non-lymphoid tissues, and that they have unique phenotypes credited with activities distinct from regulatory function. Their development and function have been described in plenty of manuscripts in the past two decades. However, with the deepening of research in recent years, emerging evidence revealed some novel mechanisms about how Tregs exert their activities. First, we discuss the expanding family of regulatory lymphocytes briefly and then, try to interpret how fork-head box P3 (Foxp3), a master regulator of the regulatory pathway in the development and function of regulatory T cells, functions. Subsequently, another part of our focus is varieties of tissue Tregs. Next, we primarily discuss recent research on how Tregs work and their faceted functions in terms of soluble mediators, functional proteins, and inhibitory receptors. In particular, unless otherwise noted, the term “Treg” is used here to refer specially to the “CD4+CD25+Foxp3+” regulatory cells.

Trial registrationChiCTR, ChiCTR2000029758. Registered 12 February 2020 - Retrospectively registered

Background Declared as pandemic by WHO, the coronavirus disease 2019 (COVID‐19) pneumonia has brought great damage to human health. The uncontrollable spread and poor progression of COVID‐19 have attracted much attention from all over the world. We designed this study to develop a prognostic nomogram incorporating Prognostic nutritional index (PNI) in COVID‐19 patients. Methods Patients confirmed with COVID‐19 and treated in Renmin Hospital of Wuhan University from January to February 2020 were included in this study. We used logistic regression analysis to find risk factors of mortality in these patients. A prognostic nomogram was constructed and receiver operating characteristics (ROC) curve was drawn to evaluate the predictive value of PNI and this prognostic model. Results Comparison of baseline characteristics showed non‐survivors had higher age (P < .001), male ratio (P = .038), neutrophil‐to‐lymphocyte ratio (NLR) (P < .001), platelet‐to‐lymphocyte ratio (PLR) (P < .001), and PNI (P < .001) than survivors. In the multivariate logistic regression analysis, independent risk factors of mortality in COVID‐19 patients included white blood cell (WBC) (OR 1.285, P = .039), PNI (OR 0.790, P = .029), LDH (OR 1.011, P < .015). These three factors were combined to build the prognostic model. Area under the ROC curve (AUC) of only PNI and the prognostic model was 0.849 (95%Cl 0.811‐0.888) and 0.950 (95%Cl 0.922‐0.978), respectively. And calibration plot showed good stability of the prognostic model. Conclusion This research indicates PNI is independently associated with the mortality of COVID‐19 patients. Prognostic model incorporating PNI is beneficial for clinicians to evaluate progression and strengthen monitoring for COVID‐19 patients.

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