Assembling an Anti-COVID-19 Artillery in the Battle against the New Coronavirus
Bibliographic record
Abstract
The panic and confusion surrounding the pandemic caused by the novel coronavirus requires a systematic study of the disease (COVID-19) and the arsenal of weapons available to the biochemist in the fight against infection. When developing a particularly bad flu in January 2020 while in India after the visit of a friend, who had just travelled back from Wuhan (China), it gave me an early opportunity to study the tricky diagnosis of this dreaded disease first-hand. The somewhat unusual symptoms and a lingering weakness and malaise for months suggested that it was no ordinary influenza virus. Since that time, a baffling number of disparate symptoms have been ascribed to COVID-19 infection including respiratory, gastrointestinal, circulatory, urinary tract and nerve dysfunction that have even resulted in multi-organ failure in some cases. Naturally, an array of risk factors have also been identified ranging from age, sex, obesity, diabetes, and hypertension to cigarette smoking that can increase mortality rate dramatically. In the intervening period, much research has appeared on biochemical compounds that may help to prevent this infection and, possibly, aid in patient recovery. Among these bioactive molecules are certain anti-inflammatory substances such as vitamin D, zinc, chloroquine, soy isoflavones like genistein, and glycyrrhizic acid, some of which may be successful in attacking different biochemical processes of the new coronavirus and disarming its deadly artillery against the human host. In a few instances, the viral processes that are inhibited by these chemicals are essential for the replication and reproduction of this RNA virus thereby striking a lethal blow to its machinery. Thus, taken together, these compounds may form a worthy arsenal against a formidable foe in the absence of an effective vaccine, and, especially, if relapse or re-infection proves to be a common occurrence in recovered COVID-19 patients.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".