Methanol Induces a Discrete Transcriptional Dysregulation that Leads to Cytokine Overproduction in Activated Lymphocytes
Bibliographic record
Abstract
Methanol is an important cause of acute alcohol intoxication; it is ubiquitously present at home and in the workplace. Although the existing literature provides a reasonable insight into the immunological impact of ethanol and to a much lesser extent of isopropanol, much less data are available on methanol. We hypothesized on structural grounds that methanol would share the immunosuppressive properties of the two other short-chain alcohols. We report here that methanol increases the proliferative capacity of human T lymphocytes and synergizes with the activating stimuli to augment cytokine production. The cytokine upregulation was observed in vitro at methanol concentrations as low as 0.08% (25mM) as measured by interleukin-2, interferon-γ, and tumor necrosis factor-α release in T cells. Methanol did not affect the antigen receptor-mediated early signaling but promoted a selective and differential activation of the nuclear factor of activated T cells family of transcription factors. These results were further substantiated in a mouse model of acute methanol intoxication in which there was an augmented release of proinflammatory cytokines in the serum in response to the staphylococcal enterotoxin B. Our results suggest that methanol has a discrete immunological footprint of broad significance given the exposure of the general population to this multipurpose solvent.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".