Bee products as a source of promising therapeutic and chemoprophylaxis strategies against <scp>COVID</scp>‐19 (<scp>SARS‐CoV</scp>‐2)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The emergence of novel coronavirus (SARS-CoV-2) in 2019 in China marked the third outbreak of a highly pathogenic coronavirus infecting humans. The novel coronavirus disease (COVID-19) spread worldwide, becoming an emergency of major international concern. However, even after a decade of coronavirus research, there are still no licensed vaccines or therapeutic agents to treat the coronavirus infection. In this context, apitherapy presents as a promising source of pharmacological and nutraceutical agents for the treatment and/or prophylaxis of COVID-19. For instance, several honeybee products, such as honey, pollen, propolis, royal jelly, beeswax, and bee venom, have shown potent antiviral activity against pathogens that cause severe respiratory syndromes, including those caused by human coronaviruses. In addition, the benefits of these natural products to the immune system are remarkable, and many of them are involved in the induction of antibody production, maturation of immune cells, and stimulation of the innate and adaptive immune responses. Thus, in the absence of specific antivirals against SARS-CoV-2, apitherapy could offer one hope toward mitigating some of the risks associated with COVID-19.
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.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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 it