COVID-19 infection and cancer regression: a review of current evidence, potential mechanisms, and clinical perspectives on a Paradoxical phenomenon
Bibliographic record
Abstract
Since its emergence, the coronavirus (SARS-CoV-2) outbreak has been a pandemic responsible for about 7 million deaths worldwide. Numerous studies have been conducted to determine the virus's multiorgan system involvement, particularly its relation to cancer biology. Spontaneous regression of cancer has been observed in some patients with the coronavirus, which may be attributed to the virus's ability to trigger specific immune responses that can be oncolytic and help reduce and eliminate oncogenic cells. This study aims to explore the paradoxical effects of COVID-19 in inducing cancer regression. The paradoxical effect of SARS-CoV-2 infection has been attributed to the possibility of a heightened immune activation possibly triggered by the virus, and some of these include increased levels of cytokines such as interferon and tumor necrosis factor-alpha (TNF-α), as well as the activation of T cells and natural killer (NK) cells. COVID-19-induced cancer regression presents new perspectives on the relationship between viral infections and the immune system's antitumor capabilities. This would help foster future research investigating specific immune pathways activated during SARS-CoV-2 and discover how these can be therapeutically harnessed to aid cancer regression.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".