Oxidized proteins: Mechanisms of removal and consequences of accumulation
Bibliographic record
Abstract
Elevated levels of oxidized proteins are reported in diseased tissue from age-related pathologies such as atherosclerosis, neurodegenerative disorders, and cataract. Unlike the precise mechanisms that exist for the repair of nucleic acids, lipids, and carbohydrates, the primary pathway for the repair of oxidized proteins is complete catabolism to their constitutive amino acids. This process can be inefficient as is evidenced by their accumulation. It is generally considered that damaged proteins are degraded by the proteasome; however, this is only true for mildly oxidized proteins, because substrates must be unfolded to enter the narrow catalytic core. Rather, evidence suggests that moderately or heavily oxidized proteins are endocytosed and enter the endosomal/lysosomal system, indicating co-operation between the proteasomes and the lysosomes. Heavily modified substrates are incompletely degraded and accumulate within the lysosomal compartments resulting in the formation of lipofuscin-like, autofluorescent aggregates. Accumulation eventually results in impaired turnover of large organelles such as proteasomes and mitochondria, lysosomal destablization, leakage of proteases into the cytosol and apoptosis. In this review, we summarize reports published since our last assessments of the field of oxidized protein degradation including a role for modified proteins in the induction of apoptosis.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".