Sharing CD4+ T Cell Loss: When COVID-19 and HIV Collide on Immune System
Bibliographic record
Abstract
COVID-19 is a distinctive infection characterized by elevated inter-human transmission and presenting from absence of symptoms to severe cytokine storm that can lead to dismal prognosis. Like for HIV, lymphopenia and drastic reduction of CD4+ T cell counts in COVID-19 patients have been linked with poor clinical outcome. As CD4+ T cells play a critical role in orchestrating responses against viral infections, important lessons can be drawn by comparing T cell response in COVID-19 and in HIV infection and by studying HIV-infected patients who became infected by SARS-CoV-2. We critically reviewed host characteristics and hyper-inflammatory response in these two viral infections to have a better insight on the large difference in clinical outcome in persons being infected by SARS-CoV-2. The better understanding of mechanism of T cell dysfunction will contribute to the development of targeted therapy against severe COVID-19 and will help to rationally design vaccine involving T cell response for the long-term control of viral infection.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".