Stimulated Suicidal Erythrocyte Death in Arteritis
Bibliographic record
Abstract
BACKGROUND/AIMS: Arteritis is an inflammatory disease of the vascular wall leading to ischemia and vascular occlusion. Complications of arteritis include anemia, which could, at least in theory, result from suicidal erythrocyte death or eryptosis, which is characterized by erythrocyte shrinkage and phosphatidylserine (PS) exposure at the erythrocyte surface. Cellular mechanisms involved in the stimulation of eryptosis include increased cytosolic Ca2+-concentration ([Ca2+]i), oxidative stress and ceramide formation. The present study explored whether and how arteritis influences eryptosis. METHODS: Blood was drawn from patients suffering from arteritis (n=17) and from healthy volunteers (n=21). PS exposure was estimated from annexin V-binding, erythrocyte volume from forward scatter, [Ca2+]i from Fluo3-fluorescence, reactive oxygen species (ROS) from DCFDA fluorescence and ceramide abundance from FITC-conjugated antibody binding in flow cytometry. The patients suffered from anemia despite 2.8±0.4% reticulocytes. RESULTS: The percentage of PS-exposing erythrocytes was significantly higher in patients (1.1±0.1%) than in healthy volunteers (0.3±0.1%). The increase in PS exposure was paralleled by increase in oxidative stress and [Ca2+]i but not by significant changes of ceramide abundance. Erythrocyte PS exposure and ROS production were significantly enhanced in erythrocytes exposed to patient plasma as compared to exposure to plasma from healthy volunteers. CONCLUSION: Arteritis is associated with enhanced eryptosis due to increased [Ca2+]i and oxidative stress. The eryptosis contributes to or even accounts for the anemia in those patients. As eryptotic erythrocytes adhere to endothelial cells of the vascular wall, they could impede microcirculation and thus contribute to vascular occlusion.
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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 | 0.001 |
| 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) | 0.002 | 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".