An Optofluidic System for Monitoring Fluorescently Activated Protein Biomarkers
Bibliographic record
Abstract
Abstract The ability to monitor protein biomarkers at clinically relevant concentrations is a powerful approach that provides insights into health status. The synergistic combination of microfluidics and photonics (optofluidic) has emerged as a new means to achieve this capability for detection and manipulation of biological samples. Here in this work, we demonstrate an optofluidic device which integrates the detection and the particle‐focusing components to facilitate on‐chip measurement of protein levels. With this design, we use a laser‐induced fluorescence technique for the direct measurement of protein levels on magnetic bead complexes tagged with fluorescein isothiocyanate (FITC) fluorophore. The developed system was employed for the detection of human immunoglobulin G (IgG) as a model protein within a range of 0–20 μg/mL, showing an ability to resolve the fluorescence signal from different IgG concentrations down to 5.4 nM (0.81 μg/mL). The selectivity in detecting specific IgG target was tested by comparing the fluorescence signal levels from other interfering proteins such as bovine serum albumin (BSA) and human IgM. Although our system was designed for detecting IgG protein, this optofluidic device can be a powerful platform for the rapid detection of a diverse range of biomarkers, including viral pathogens retaining adequate signal sensitivity and selectivity.
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 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 | 0.001 | 0.002 |
| 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".