Accelerated telomere shortening in glycosylphosphatidylinositol (GPI)–negative compared with GPI-positive granulocytes from patients with paroxysmal nocturnal hemoglobinuria (PNH) detected by proaerolysin flow-FISH
Bibliographic record
Abstract
Telomere length has been linked to disease stage and degree of (pan-)cytopenia in patients with bone marrow failure syndromes. The aim of the current study was to analyze the impact of replicative stress on telomere length in residual glycosylphosphatidylinositol-positive (GPI+) versus GPI- hematopoiesis in patients with paroxysmal nocturnal hemoglobinuria (PNH). Peripheral blood granulocytes from 16 patients and 22 healthy individuals were analyzed. For this purpose, we developed proaerolysin flow-FISH, a novel methodology that combines proaerolysin staining (for GPI expression) with flow-FISH (for telomere length measurement). We found significantly shortened telomeres in GPI- granulocytes (mean +/- SE: 6.26 +/- 0.27 telomere fluorescence units [TFU]), both compared with their GPI+ counterparts (6.88 +/- 0.38 TFU; P = .03) as well as with age-matched healthy individuals (7.73 +/- 0.23 TFU; P < .001). Our findings are in support of a selective growth advantage model of PNH assuming that damage to the GPI+ hematopoietic stem-cell (HSC) compartment leads to compensatory hyperproliferation of residual GPI- HSCs.
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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".