High-throughput and label-free screening of red blood cell stiffness: A study of sickle cell disease
Bibliographic record
Abstract
Understanding the morphological and mechanical changes in cells are important for diagnostic and treatment methods in various diseases. In sickle cell disease (SCD), the mutated hemoglobin (HbS) aggregates inside the red blood cells (RBCs), making them rigid and, in extreme cases, sickle-shaped, resulting in anemia, episodes of pain, and multiple organ damage. Existing techniques are too costly and insensitive since the effect of the HbS gene (heterozygous and homozygous) is variable both in prevalence and clinical manifestations. In this work, we present a label-free, cost-effective, high-throughput electro-fluidic technique to study changes in the mechanical and morphological characteristics of RBCs. We validate our device by quantitatively comparing the mechanical properties of RBCs as a function of stiffness-altering drug (Latrunculin-A) with measurements using AFM. We demonstrate the on-site application of our system by screening SCD patients based on their RBC stiffness changes. The signatures of patient-specific heterogeneity in the RBC mechanical properties may help in monitoring clinical variability and identification of high-risk patients along with targeted therapies. The versatility of our measurements opens the whole cell stiffness as a preliminary screening biomarker in other haematological conditions, tumor cell identification, in veterinary sciences as well as in evaluating hydrogel technologies. • Sickle Cell Disease caused by protein aggregation lead RBC stiffening causing pain and multiple organ damage. • Population-wide screening is vital for monitoring clinical variability and identification of high risk SCD patients. • An electro-fluidic device is presented as a label-free, high-throughput, & resolution tool for cellular stiffness detection. • Whole cell stiffness (WCS) is quantified as preliminary screening parameter for detecting hematological conditions like SCD. • The device is tested in-hospital conditions for screening SCD patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".