On-chip sample preparation for complete blood count from raw blood
Bibliographic record
Abstract
This paper describes a monolithic microfluidic device capable of on-chip sample preparation for both RBC and WBC measurements from whole blood. For the first time, on-chip sample processing (e.g. dilution, lysis, and filtration) and downstream single cell measurement were fully integrated to enable sample preparation and single cell analysis from whole blood on a single device. The device consists of two parallel sub-systems that perform sample processing and electrical measurements for measuring RBC and WBC parameters. The system provides a modular environment capable of handling solutions of various viscosities by adjusting the length of channels and precisely controlling mixing ratios, and features a new 'offset' filter configuration for increased duration of device operation. RBC concentration, mean corpuscular volume (MCV), cell distribution width, WBC concentration and differential are determined by electrical impedance measurement. Experimental characterization of over 100,000 cells from 10 patient blood samples validated the system's capability for performing on-chip raw blood processing and measurement.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".