Autophagy and SARS-CoV-2 infection: A possible smart targeting of the autophagy pathway
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.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.015 | 0.018 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".