Comprehensive Flow Cytometry Profiling of the Immune System in COVID-19 Convalescent Individuals
Bibliographic record
Abstract
SARS-CoV-2 has infected more than 200 million people worldwide, with more than 4 million associated deaths. Although more than 80% of infected people develop asymptomatic or mild COVID-19, SARS-CoV-2 can induce a profound dysregulation of the immune system. Therefore, it is important to investigate whether clinically recovered individuals present immune sequelae. The potential presence of a long-term dysregulation of the immune system could constitute a risk factor for re-infection and the development of other pathologies. Here, we performed a deep analysis of the immune system in 35 COVID-19 recovered individuals previously infected with SARS-CoV-2 compared to 16 healthy donors, by flow cytometry. Samples from COVID-19 individuals were analysed from 12 days to 305 days post-infection. We observed that, 10 months post-infection, recovered COVID-19 patients presented alterations in the values of some T-cell, B-cell, and innate cell subsets compared to healthy controls. Moreover, we found in recovered COVID-19 individuals increased levels of circulating follicular helper type 1 (cTfh1), plasmablast/plasma cells, and follicular dendritic cells (foDC), which could indicate that the Tfh-B-foDC axis might be functional to produce specific immunoglobulins 10 months post-infection. The presence of this axis and the immune system alterations could constitute prognosis markers and could play an important role in potential re-infection or the presence of long-term symptoms in some individuals.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".