Sublingual Microcirculation Specificity of Sickle Cell Patients: Morphology of the Microvascular Bed, Blood Rheology, and Local Hemodynamics
Bibliographic record
Abstract
Patients with sickle cell disease (SCD) have poorly deformable red blood cells (RBC) that may impede blood flow into microcirculation. Very few studies have been able to directly visualize microcirculation in humans with SCD. Sublingual video microscopy was performed in eight healthy (HbAA genotype) and four sickle cell individuals (HbSS genotype). Their hematocrit, blood viscosity, red blood cell deformability, and aggregation were individually determined through blood sample collections. Their microcirculation morphology (vessel density and diameter) and microcirculation hemodynamics (local velocity, local viscosity, and local red blood cell deformability) were investigated. The De Backer score was higher (15.9 mm−1) in HbSS individuals compared to HbAA individuals (11.1 mm−1). RBC deformability, derived from their local hemodynamic condition, was lower in HbSS individuals compared to HbAA individuals for vessels < 20 μm. Despite the presence of more rigid RBCs in HbSS individuals, their lower hematocrit caused their viscosity to be lower in microcirculation compared to that of HbAA individuals. The shear stress for all the vessel diameters was not different between HbSS and HbAA individuals. The local velocity and shear rates tended to be higher in HbSS individuals than in HbAA individuals, notably so in the smallest vessels, which could limit RBC entrapment into microcirculation. Our study offered a novel approach to studying the pathophysiological mechanisms of SCD with new biological/physiological markers that could be useful for characterizing the disease activity.
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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 | 0.001 | 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".