Autophagy and SARS-CoV-2 infection: A possible smart targeting of the autophagy pathway
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
, 2020, WHO). The molecular mechanism of virus infection and spread in the body is not yet disclosed, but studies on other betacoronaviruses show that, upon cell infection, these viruses inhibit macroautophagy/autophagy flux and cause the accumulation of autophagosomes. No drug has yet been approved for the treatment of SARS-CoV-2 infection; however, preclinical investigations suggested repurposing of several FDA-approved drugs for clinical trials. Half of these drugs are modulators of the autophagy pathway. Unexpectedly, instead of acting by directly antagonizing the effects of viruses, these drugs appear to function by suppressing autophagy flux. Based on the established cross-talk between autophagy and apoptosis, we speculate that over-accumulation of autophagosomes activates an apoptotic pathway that results in apoptotic death of the infected cells and disrupts the virus replication cycle. However, administration of the suggested drugs are associated with severe adverse effects due to their off-target accumulation. Nanoparticle targeting of autophagy at the sites of interest could be a powerful tool to efficiently overcome SARS-CoV-2 infection while avoiding the common adverse effects of these drugs.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 it