Aldehyde Sources, Metabolism, Molecular Toxicity Mechanisms, and Possible Effects on Human Health
Bibliographic record
Abstract
Aldehydes are organic compounds that are widespread in nature. They can be formed endogenously by lipid peroxidation (LPO), carbohydrate or metabolism ascorbate autoxidation, amine oxidases, cytochrome P-450s, or myeloperoxidase-catalyzed metabolic activation. This review compares the reactivity of many aldehydes towards biomolecules particularly macromolecules. Furthermore, it includes not only aldehydes of environmental or occupational concerns but also dietary aldehydes and aldehydes formed endogenously by intermediary metabolism. Drugs that are aldehydes or form reactive aldehyde metabolites that cause side-effect toxicity are also included. The effects of these aldehydes on biological function, their contribution to human diseases, and the role of nucleic acid and protein carbonylation/oxidation in mutagenicity and cytotoxicity mechanisms, respectively, as well as carbonyl signal transduction and gene expression, are reviewed. Aldehyde metabolic activation and detoxication by metabolizing enzymes are also reviewed, as well as the toxicological and anticancer therapeutic effects of metabolizing enzyme inhibitors. The human health risks from clinical and animal research studies are reviewed, including aldehydes as haptens in allergenic hypersensitivity diseases, respiratory allergies, and idiosyncratic drug toxicity; the potential carcinogenic risks of the carbonyl body burden; and the toxic effects of aldehydes in liver disease, embryo toxicity/teratogenicity, diabetes/hypertension, sclerosing peritonitis, cerebral ischemia/neurodegenerative diseases, and other aging-associated diseases.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".