Persistent attenuation of lymphocyte subsets after mass SARS-CoV-2 infection
Bibliographic record
Abstract
Objectives Growing evidence suggests that lymphocyte subsets are declined in COVID-19 patients, but it is unclear if these alterations persist after widespread exposure to SARS-CoV-2 or how long they last. Methods We analyzed lymphocyte subset data from 40,537 patients across three phases: pre-COVID, mass infection, and post-COVID. The counts of lymphocyte subsets and CD4 + /CD8 + ratios were compared using Mann-Whitney U test or Kruskal-Wallis H test. Monthly post-exposure data were compared with pre-exposure data to assess the persistence of impact on lymphocyte subsets by SARS-CoV-2, and subgroup analyses were performed in patients with cardiovascular disease. Results During mass infection, T cells, CD4 + T cells, CD8 + T cells, NK cells, and B cells dropped significantly. Even 20 months post-infection, CD8 + T cells remained 9.9% below baseline. Baseline lymphocyte subsets differed significantly by sex and age. Immune recovery varied by age and sex, with older adults and males showing prolonged lymphopenia. In cardiovascular disease patients, T lymphocytes remained 72.9% below baseline for 20 months post-infection. Conclusions Our findings redefine SARS-CoV-2 infection as a condition of long-lasting immune compromise. The sustained subnormal lymphocytes—particularly in cardiovascular disease cohorts—highlight a key immunologic feature of long COVID and underscore the need for personalized care.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".