Comprehensive review on COVID-19: etiology, pathogenicity, and treatment
Bibliographic record
Abstract
With the unprecedented surge of severe COVID-19 cases in early 2020, researchers and medical professionals worked actively to identify effective viral infection treatments based on a scientific understanding of viruses. Over the past few years, an enormous amount of research has investigated the viral infection and replication processes following the first SARS-CoV-2 case. With this knowledge, many drugs have been explicitly created to inhibit viral replication or decrease the severity of the immune response. Additionally, scientists have utilized decades of research and techniques to expedite SARS-CoV-2 vaccine development. SARS-CoV-2, a positive-strand RNA virus, belongs to the Sarbecovirus subgroup of Betacoronaviruses. Its emergence is not unique; previous outbreaks like SARS and MERS have shaped our understanding of coronavirus-related diseases. Molecular clock analysis suggests that the ancestor of all current coronaviruses existed over 10,000 years ago, with subsequent evolution occurring around 3300-2400 BC. Researchers have explored synthetic and natural treatments alongside other antiviral therapies, corticosteroids, and immunotherapies. Additionally, using artificial intelligence and nano-based technologies enriched SARS-CoV-2 diagnosis and management. In this comprehensive review, we provide recent literature on COVID-19, exploring its evolving etiology, pathogenicity, and pathophysiology, alongside developments in synthetic and natural therapeutic strategies, vaccines, artificial intelligence in diagnosis, and nano-based technologies.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".