Mitochondria Targeted Viral Replication and Survival Strategies—Prospective on SARS-CoV-2
Bibliographic record
Abstract
SARS-CoV-2 is a positive sense RNA coronavirus that constitutes a new threat for the global community and economy. While vaccines against SARS-CoV-2 are being developed, the mechanisms through which this virus takes control of an infected cell to replicate remains poorly understood. Upon infection, viruses completely rely on host cell molecular machinery to survive and replicate. To escape from the immune response and proliferate, viruses strategically modulate cellular metabolism and alter subcellular organelle architecture and functions. One way they do this is by modulating the structure and function of mitochondria, a critical cellular metabolic hub but also a key platform for the regulation of cellular immunity. This versatile nature of mitochondria defends host cells from viruses through several mechanisms including cellular apoptosis, ROS signaling, MAVS activation and mitochondrial DNA-dependent immune activation. These events are regulated by mitochondrial dynamics, a process by which mitochondria alter their structure (including their length and connectivity) in response to stress or other cues. It is therefore not surprising that viruses, including coronaviruses hijack these processes for their survival. In this review, we highlight how positive sense RNA viruses modulate mitochondrial dynamics and metabolism to evade mitochondrial mediated immune response in order to proliferate.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".