Fluorescent imaging cytometer for the detection of inflammation biomarkers using temporally multiplexed illumination
Bibliographic record
Abstract
In this study, we present a fluorescence imaging cytometer capable of detecting multiple biomarkers using temporally multiplexed illumination. We independently illuminated the classification and quantitative detection channels using multiwavelength LEDs. To quantify the biomarker concentrations, we employed a sandwich immunoassay with Cy5 dye, while the microbeads were internally dyed with three different intensity levels of quantum dots to distinguish among the three cytokines. We conducted separate tests with three cytokines, namely IFN-α, IL-5, and IL-6, at varying concentrations. The detection ranges for these cytokines were determined to be 10 pg/ml-2500 pg/ml, 30 pg/ml-2500 pg/ml, and 30 pg/ml-2500 pg/ml, respectively. The results of the multiplexed experiment, show that our device can independently detect three different biomarkers in a single assay. This validation demonstrates that our device has potential in diagnosing sepsis and other diseases requiring various laboratory results. With further refinement and development, our device holds great promise for enhancing point-of-care medical services.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".