Activation of cellular signalling pathways and apoptosis by the aldehyde acrolein – A major environmental hazard
Bibliographic record
Abstract
Interest in the molecular actions of acrolein has increased in light of growing knowledge that implicates this reactive aldehyde in a wide range of pathophysiologies including neurodegenerative diseases, various lung disorders including chronic obstructive pulmonary disease, atherosclerosis, and certain cancers. This is rendered complex because acrolein exists in mixtures of environmental pollutants. Reactive α,β-unsaturated aldehydes like acrolein are major components of common environmental pollutants such as cigarette, automobile exhaust, and smoke from wood, coal, forest and house fires. It is a natural constituent of several foods and is generated in the human body during inflammation or oxidation of unsaturated lipids. Acrolein is also a toxic metabolic product of the widely used anticancer drug cyclophosphamide and is generated from the enzymatic oxidation of polyamines. It is a toxic by-product of lipid peroxidation and has been implicated as a mediator of oxidative damage in cells and tissues. The purpose of this review is to assess the literature about the activation of cell signalling pathways and transcription factors, and cell survival and cell death pathways by acrolein. Several reports show that anti-apoptosis processes dominate at lower dose exposures to acrolein, whereas pro-apoptotic processes and necrosis dominate at higher dose exposures. There has been improved understanding about the deleterious molecular and cellular mechanisms that are triggered in cells in response to acrolein injury. However, more progress is required to define the contributions of acrolein to human diseases and to design efficient therapeutic strategies based on the biochemical modulation of acrolein activity.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".