Aldehyde Sources, Metabolism, Molecular Toxicity Mechanisms, and Possible Effects on Human Health
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it