SARS-CoV2 infectivity is potentially modulated by host redox status
Bibliographic record
Abstract
The current coronavirus disease (COVID-19) outbreak caused by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV2) has emerged as a threat to global social and economic systems. Disparity in the infection of SARS-CoV2 among host population and species is an established fact without any clear explanation. To initiate infection, viral S-protein binds to the Angiotensin-Converting Enzyme 2 (ACE2) receptor of the host cell. Our analysis of retrieved amino acid sequences deposited in data bases shows that S-proteins and ACE2 are rich in cysteine (Cys) residues, many of which are conserved in various SARS-related coronaviruses and participate in intra-molecular disulfide bonds. High-resolution protein structures of S-proteins and ACE2 receptors highlighted the probability that two of these disulfide bonds are potentially redox-active, facilitating the primal interaction between the receptor and the spike protein. Presence of redox-active disulfides in the interacting parts of S-protein, ACE2, and a ferredoxin-like fold domain in ACE2, strongly indicate the role of redox in COVID-19 pathogenesis and severity. Resistant animals lack a redox-active disulfide (Cys133-Cys141) in ACE2 sequences, further strengthening the redox hypothesis for infectivity. ACE2 is a known regulator of oxidative stress. Augmentation of cellular oxidation with aging and illness is the most likely explanation of increased vulnerability of the elderly and persons with underlying health conditions to 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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".