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Allergen-immunotherapy (AIT) is an efficacious and disease-modifying treatment option for IgE-mediated diseases. Among these allergic rhinitis, insect venom allergy, food allergy, and allergic asthma are the most common candidates for AIT. AIT gives rise to clinical immunotolerance which may last for years after the treatment cessation. Mechanisms of AIT include suppression of allergic inflammation in target tissues and stimulation of the production of blocking antibodies, especially IgG4 and IgA. These mechanisms are followed by a reduction of underlying allergen-specific Th2 cell-driven responses to the allergens. Tolerance induction takes place through the desensitization of effector cells and stimulation of regulatory T cells that show their effects by mechanisms involving cell-cell cross-talk, but also other mechanisms, e.g., by the production of immunomodulatory cytokines such as, e.g., IL-10 and TGF-beta. From a personalized medical perspective, there is a need for clinical biomarkers of value in selecting responders and optimizing patient care during AIT. Also, a deeper understanding of underlying mechanistic processes will improve AIT's future outcomes. In this paper, the current knowledge of mechanisms in AIT is reviewed with a special focus on biomarkers of this therapy. [ABSTRACT FROM AUTHOR]

Background: COVID-19 pathophysiology and the predictive factors involved are not fully understood, but lymphocytes dysregulation appears to play a role. This paper aims to evaluate lymphocyte subsets in the pathophysiology of COVID-19 and as predictive factors for severe disease. Patient and methods: A prospective cohort study of patients with SARS-CoV-2 bilateral pneumonia recruited at hospital admission. Demographics, medical history, and data regarding SARS-CoV-2 infection were recorded. Patients systematically underwent complete laboratory tests, including parameters related to COVID-19 as well as lymphocyte subsets study at the time of admission. Severe disease criteria were established at admission, and patients were classified on remote follow-up according to disease evolution. Linear regression models were used to assess associations with disease evolution, and Receiver Operating Characteristic (ROC) and the corresponding Area Under the Curve (AUC) were used to evaluate predictive values. Results: Patients with critical COVID-19 showed a decrease in CD3+CD4+ T cells count compared to non-critical (278 (485 IQR) vs. 545 (322 IQR)), a decrease in median CD4+/CD8+ ratio (1.7, (1.7 IQR) vs. 3.1 (2.4 IQR)), and a decrease in median CD4+MFI (21,820 (4491 IQR) vs. 26,259 (3256 IQR)), which persisted after adjustment. CD3+CD8+ T cells count had a high correlation with time to hospital discharge (PC = −0.700 (−0.931, −0.066)). ROC curves for predictive value showed lymphocyte subsets achieving the best performances, specifically CD3+CD4+ T cells (AUC = 0.756), CD4+/CD8+ ratio (AUC = 0.767), and CD4+MFI (AUC = 0.848). Conclusions: A predictive value and treatment considerations for lymphocyte subsets are suggested, especially for CD3CD4+ T cells. Lymphocyte subsets determination at hospital admission is recommended. [ABSTRACT FROM AUTHOR]