An automated method for dynamic red blood cell aggregate detection in microfluidic flow
Bibliographic record
Abstract
OBJECTIVE: Red blood cell (RBC) aggregation is a unique phenomenon that occurs when red blood cells are subjected to low shear rates. Little is known about the sizes, shapes and behaviour of aggregates flowing in healthy humans. However, excessive aggregation has been shown to be an indication of pathological conditions. Therefore, characterizing RBC aggregates is important to medical research. The objective of this study was to develop a reliable technique based on image processing to assess and characterize human RBC aggregation subjected to controlled and measurable shear rates in a two-fluid flow microfluidic shearing system. APPROACH: Images of RBC suspensions at [Formula: see text], [Formula: see text] and [Formula: see text] entrained by a phosphate buffered saline solution in a PDMS microchannel were captured with a high speed camera. An algorithm for processing the RBC aggregate images is presented and validated (1) on a sample of known diameter hollow glass microspheres and (2) by comparing RBC aggregate size results with those of an ImageJ image processing technique and those obtained by manual detection by two independent researchers. MAIN RESULTS: The proposed image processing algorithm provides a very good agreement with the manufacturer data for the glass microspheres. It also performs well on the RBC suspension images, with errors of 2-4 [Formula: see text] with respect to the manual results. SIGNIFICANCE: The proposed automated method for RBC aggregate detection is found to be reliable and fairly accurate and will serve researchers and, perhaps in the future, clinicians to assess healthy and pathological RBC aggregation under flowing conditions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".