<scp>SARS‐CoV</scp>‐2 multifaceted interaction with human host. Part I: What we have learnt and done so far, and the still unknown realities
Bibliographic record
Abstract
SARS-CoV-2, the causing agent of the ongoing COVID-19 pandemic, is a beta-coronavirus which has 80% genetic homology with SARS-CoV, but displays increased virulence and transmissibility. Initially, SARS-CoV-2 was considered a respiratory virus generally causing a mild disease, only severe and fatal in the elderly and individuals with underlying conditions. Severe illnesses and fatalities were attributed to a cytokine storm, an excessive response from the host immune system. However, with the number of infections over 10 millions and still soaring, the insidious and stealthy nature of the virus has emerged, as it causes a vast array of diverse unexpected symptoms among infected individuals, including the young and healthy. It has become evident that besides infecting the respiratory tract, SARS-CoV-2 can affect many organs, possibly through the infection of the endothelium. This review presents an overview of our learning curve with the novel virus emergence, transmission, pathology, biological properties and host-interactions. It also briefly describes remedial measures taken until an effective vaccine is available, that is non-pharmaceutical interventions to reduce the viral spread and the repurposing of existing drugs, approved or in development for other conditions to eliminate the virus or mitigate the cytokine storm.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".