Role of senescent CD4+ T cells in breakthrough infection of the new variant strain of SARS-CoV-2 in elderly patients
Bibliographic record
Abstract
In late 2022, Beijing, China saw a large-scale BF.7 Omicron variant breakthrough infection. However, the impact of COVID-19 vaccines on elderly breakthrough-infected patients’ antibodies and immune cells response was unclear. We recruited 329 inpatients over 65 with BF.7 breakthrough infections. We analyzed the link between vaccination and survival in 67 sampled patients, investigating changes in antibody levels, cytokine profiles, as well as immune phenotypes. Experiments revealed that while vaccination could raise antibody levels in the elderly, it showed no significant neutralizing activity against the emerging COVID-19 variant XBB. Flow cytometry showed vaccination increased the proportion of CD4 + senescent T cells. Moreover, we found that elevated frequencies of CD4 + Tsens cells were associated with reduced antigen-specific CD4 + T cell activation, diminished IL-2 production, and lower proportions of Tfh cells, which ultimately leading to impaired neutralizing antibody production, particularly against new emerging variants. We found the immunological efficacy of inactivated vaccines in elderly patients is influenced by the proportion of CD4 + senescent T cells. Future elderly vaccination strategies need optimization in dose number and timing, and future vaccine design should aim to minimize the generation of CD4 + senescent T cells.
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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 | 0.000 | 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".