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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times24$ </tex-math></inline-formula> mm <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times43$ </tex-math></inline-formula> mm), lightweight (19 g), economical, and low-power (103 mW). Fluorescence spectrophotometry measurement results obtained <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in vitro</i> for 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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4~\mu \text{g}$ </tex-math></inline-formula> /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 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".