Miniature Fiber-Spectrophotometer for Real-Time Biomarkers Detection
Bibliographic record
Abstract
This paper presents a miniature and cost-effective cell fiber-spectrophotometer based on fluorescence emission sensing for real-time detection of biomarkers. The prototype comprises a highly sensitive spectrometer embedded in an inexpensive 3D-printed housing and a custom-designed printed circuit board to transmit the data wirelessly. Results show that this portable system can identify micro-volume samples of tubulin protein, a well-known biomarker, to study different types of cancer. Additionally, our ambulatory prototype is small in size (30 mm$\times24$mm$\times43$mm), lightweight (19 g), economical, and low-power (103 mW). Fluorescence spectrophotometry measurement results obtainedin vitrofor stained VERO E6 cells using the presented prototype are reported. A set of samples with varying concentrations of Alexa Fluor 488 were also tested, and their spectral responses were measured within the range of 340–850 nm with a resolution of 15 nm. The measured limits of quantification and detection are 20 and$4~\mu \text{g}$/mL, respectively, while the measured responsivity is 85 mV/nW with a minimum detectable power of 117 fW at 519 nm. Intra- and inter-day relative standard deviations of 1.5% and 4.7%, respectively, were measured during longitudinal sensitivity experiments.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".