Altered Adhesion Molecule Expression of Leukocytes of SCD Patients in Crisis and Steady State
Bibliographic record
Abstract
Background: Sickle cell disease (SCD) patients experience ischemic events resulting from vaso-occlusion of both macro-and microcirculation.A risk factor associated with increased morbidity and mortality is leukocytosis in the absence of infection.This study investigated the role of leukocytes in the pathogenesis of acute sickle cell crisis.Procedure: Children were enrolled at two tertiary care centers in three groups: hemoglobin SS (Hb SS) patients admitted to hospital with an acute crisis, non-crisis or steady state SS patients, and sickle cell screen negative, racematched controls.Flow cytometry measured cell surface expression of adhesion molecules.Results: 28 Hb SS and 10 control patients were enrolled.Elevated white blood and platelet counts were observed for the crisis children (P<0.01)compared to healthy control children.There was a significant increase in the expression of adhesion molecules on neutrophils and monocytes (CD11, CD 18 and CD-62L) in children in steady state (P<0.05)compared to crisis, and healthy children.71% of crisis children were receiving non-steroidal anti-inflammatory drugs (NSAIDs) or hydroxycarbamide, whereas none of the patients in steady state were receiving these drugs.Conclusion: Further investigation is needed to explain these findings, but is likely due to the use of NSAIDs and hydroxycarbamide in our crisis patients.
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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) | 0.002 | 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".