Ataxia Telangiectasia Mutated Dysregulation Results in Diabetic Retinopathy
Bibliographic record
Abstract
Ataxia telangiectasia mutated (ATM) acts as a defense against a variety of bone marrow (BM) stressors. We hypothesized that ATM loss in BM-hematopoietic stem cells (HSCs) would be detrimental to both HSC function and microvascular repair while sustained ATM would be beneficial in disease models of diabetes. Chronic diabetes represents a condition associated with HSC depletion and inadequate vascular repair. Gender mismatched chimeras of ATM(-/-) on wild type background were generated and a cohort were made diabetic using streptozotocin (STZ). HSCs from the STZ-ATM(-/-) chimeras showed (a) reduced self-renewal; (b) decreased long-term repopulation; (c) depletion from the primitive endosteal niche; (d) myeloid bias; and (e) accelerated diabetic retinopathy (DR). To further test the significance of ATM in hematopoiesis and diabetes, we performed microarrays on circulating angiogenic cells, CD34(+) cells, obtained from a unique cohort of human subjects with long-standing (>40 years duration) poorly controlled diabetes that were free of DR. Pathway analysis of microarrays in these individuals revealed DNA repair and cell-cycle regulation as the top networks with marked upregulation of ATM mRNA compared with CD34(+) cells from diabetics with DR. In conclusion, our study highlights using rodent models and human subjects, the critical role of ATM in microvascular repair in DR.
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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) | 0.000 | 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".