An updated review on pathogenic coronaviruses (CoVs) amid the emergence of SARS-CoV-2 variants: A look into the repercussions and possible solutions
Bibliographic record
Abstract
SARS-CoV-2, responsible for COVID-19, shares 79% and 50% of its identity with SARS-CoV-1 and MERS-CoV, respectively. It uses the same main cell attachment and entry receptor as SARS-CoV-1, which is the ACE-2 receptor. However, key residues in the receptor-binding domain of its S-protein seem to give it a stronger affinity for the receptor and a better ability to hide from the host immune system. Like SARS-CoV-1 and MERS-CoV, cytokine storms in critically ill COVID-19 patients cause ARDS, neurological pathology, multiorgan failure, and increased death. Though many issues remain, the global research effort and lessons from SARS-CoV-1 and MERS-CoV are hopeful. The emergence of novel SARS-CoV-2 variants and subvariants raised serious concerns among the scientific community amid the emergence of other viral diseases like monkeypox and Marburg virus, which are major concerns for healthcare settings worldwide. Hence, an updated review on the comparative analysis of various coronaviruses (CoVs) has been developed, which highlights the evolution of CoVs and their repercussions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".