<scp>SARS‐CoV</scp>‐2 multifaceted interaction with the human host. Part <scp>II</scp>: Innate immunity response, immunopathology, and epigenetics
Bibliographic record
Abstract
The SARS-CoV-2 makes its way into the cell via the ACE2 receptor and the proteolytic action of TMPRSS2. In response to the SARS-CoV-2 infection, the innate immune response is the first line of defense, triggering multiple signaling pathways to produce interferons, pro-inflammatory cytokines and chemokines, and initiating the adaptive immune response against the virus. Unsurprisingly, the virus has developed strategies to evade detection, which can result in delayed, excessive activation of the innate immune system. The response elicited by the host depends on multiple factors, including health status, age, and sex. An overactive innate immune response can lead to a cytokine storm, inflammation, and vascular disruption, leading to the vast array of symptoms exhibited by COVID-19 patients. What is known about the expression and epigenetic regulation of the ACE2 gene and the various players in the host response are explored in this review.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".