Role of By-Products of Lipid Oxidation in Alzheimer's Disease Brain: A Focus on Acrolein
Bibliographic record
Abstract
Abundant data consistently support the idea that oxidative stress occurs and is a constant feature of Alzheimer's disease (AD). Some recent evidence indicated that phenomenon is an early event and might be implicated in the pathogenesis of this disease. Lipid peroxidation leads to the formation of a number of aldehydes by-products, including malondialdehyde (MDA), 4-hydroxy-2-nonenal (HNE), and acrolein. The most abundant aldehydes are HNE and MDA while acrolein is the most reactive. Increased levels of specific HNE-histidine and glutathione-HNE Michael adducts in AD brain has been reported. Proteomic analysis demonstrated a large number of protein-bound HNE in AD brain. F2-isoprostanes (F2-IsoPs) levels and neuroprostanes were also significantly increased in mild cognitive impairment (MCI) patients and in late-stage AD. In brain from patients with AD, acrolein has been found to be elevated in hippocampus and temporal cortex where oxidative stress is high. Due to its high reactivity, acrolein is not only a marker of lipid peroxidation but also an initiator of oxidative stress by adducting cellular nucleophilic groups found on proteins, lipids, and nucleic acids. Interestingly, data indicates that lipid peroxidation occurs in the brain of MCI and also in preclinical AD patients suggesting that oxidative damage may play an early role in the pathogenesis of AD. In this review, we will summarize some mechanisms implicated in the toxicity of by-products of lipid peroxidation such as IsoPs, HNE, and acrolein and their implication in AD.
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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 | 0.002 | 0.001 |
| 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) | 0.001 | 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".