A prospective cohort-based artificial intelligence evaluation system for the protective efficacy and immune response of SARS-CoV-2 inactivated vaccines.

Background: Novel coronaviruses constitute a significant health threat, prompting the adoption of vaccination as the primary preventive measure. However, current evaluations of immune response and vaccine efficacy are deemed inadequate.

Objectives: The study sought to explore the evolving dynamics of immune response at various vaccination time points and during breakthrough infections.

A prognostic model for SARS-CoV-2 breakthrough infection: Analyzing a prospective cellular immunity cohort.

Background: Following the COVID-19 pandemic, studies have identified several prevalent characteristics, especially related to lymphocyte subsets. However, limited research is available on the focus of this study, namely, the specific memory cell subsets among individuals who received COVID-19 vaccine boosters and subsequently experienced a SARS-CoV-2 breakthrough infection.

Methods: Flow cytometry (FCM) was employed to investigate the early and longitudinal pattern changes of cellular immunity in patients with SARS-CoV-2 breakthrough infections following COVID-19 vaccine boosters.

Early warning signs of thyroid autoantibodies seroconversion: A retrospective cohort study.

Background: Serum anti-thyroid peroxidase antibody (anti-TPO) and anti-thyroglobulin antibody (anti-Tg) levels are key indicators for the diagnosis of autoimmune diseases, especially autoimmune thyroiditis. Before the thyroid autoantibodies turn from negative to positive, it is unknown whether any clinical indicators in the body play a warning role.

Purpose: To establish an early prediction model of seroconversion to positive thyroid autoantibodies.