Portable point-of-care blood analysis system for global health(Conference Presentation)
Bibliographic record
Abstract
In this paper we present a portable blood analysis system based on a disposable cartridge and hand-held reader. The platform can perform all the sample preparation, detection and waste collection required to complete a clinical test. In order to demonstrate the utility of this approach a CD4 T cell enumeration was carried out. A handheld, point-of-care CD4 T cell system was developed based on this system. In particular we will describe a pneumatic, active pumping method to control the on-chip fluidic actuation. Reagents for the CD4 T cell counting assay were dried on a reagent plug to eliminate the need for cold chain storage when used in the field. A micromixer based on the active fluidic actuation was designed to complete sample staining with fluorescent dyes that was dried on the reagent plugs. A novel image detection and analysis algorithm was developed to detect and track the flight of target particles and cells during each analysis. The handheld, point-of-care CD4 testing system was benchmarked against clinical cytometer. The experimental results demonstrated experimental results were closely matched with the flow cytometry. The same platform can be further expanded into a bead-array detection system where other types of biomolecules such as proteins can be detected using the same detection system.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.042 | 0.016 |
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".