Hypocatalasemic Fibroblasts Accumulate Hydrogen Peroxide and Display Age‐Associated Pathologies
Bibliographic record
Abstract
Human epidemiological studies point to an association of hypocatalasemia and an increased risk of age-related disease. Unfortunately, the cellular and molecular manifestations of hypocatalasemia are only poorly understood. In this analysis, we have extensively characterized hypocatalasemic human fibroblasts and report that they amass hydrogen peroxide and are oxidatively damaged. Protein and DNA alike are affected, as are functioning and biogenesis of peroxisomes - the subcellular organelles which normally house catalase. Despite these pathologies and their relative inability to grow, the cells do not appear to be intrinsically senescent. With the goal of restoring oxidative balance and perhaps reversing some of the accumulated damage to critical cellular components, we transduced hypocatalasemic fibroblasts with a form of catalase specifically designed to efficiently traffic to peroxisomes. We show the strategy is extremely effective, with dramatic reductions seen in cellular hydrogen peroxide levels. Future longitudinal studies aimed at examining the effects of a more continuous and long-term protein therapy may now commence.
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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".