LED Excitation of an Imaging Cytometer for Bead-Based Immunoassay
Bibliographic record
Abstract
We present and demonstrate a light-emitting diode (LED)-excited imaging cytometer for the detection of bead-based immunoassay samples. A broad area green LED illuminates the specimen plane using a set of aspheric lenses and an excitation filter. The imaging module was comprised of an objective lens, filters, tube lens, and camera. To demonstrate the multiplex capability of the presented system, a panel of three sets of beads with varying classification fluorescence intensity was employed. Experimental results revealed that the LED light source provides uniform illumination across the specimen plane, and therefore permits the multiplex detection of three biomarkers. Detection of a sepsis biomarker, procalcitonin, was used to demonstrate the detection sensitivity and measurement range of the system. The imaging cytometer can detect the concentration of procalcitonin as low as 24.4 pg/mL and it holds the potential for being developed for point-of-care testing applications.
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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".