Current Understanding of Novel Coronavirus: Molecular Pathogenesis, Diagnosis, and Treatment Approaches
Bibliographic record
Abstract
An outbreak of “Pneumonia of Unknown Etiology” occurred in Wuhan, China, in late December 2019. Later, the agent factor was identified and coined as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and the disease was named coronavirus disease 2019 (COVID-19). In a shorter period, this newly emergent infection brought the world to a standstill. On 11 March 2020, the WHO declared COVID-19 as a pandemic. Researchers across the globe have joined their hands to investigate SARS-CoV-2 in terms of pathogenicity, transmissibility, and deduce therapeutics to subjugate this infection. The researchers and scholars practicing different arts of medicine are on an extensive quest to come up with safer ways to curb the pathological implications of this viral infection. A huge number of clinical trials are underway from the branch of allopathy and naturopathy. Besides, a paradigm shift on cellular therapy and nano-medicine protocols has to be optimized for better clinical and functional outcomes of COVID-19-affected individuals. This article unveils a comprehensive review of the pathogenesis mode of spread, and various treatment modalities to combat COVID-19 disease.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".