Highly sensitive detection of urinary protein variations using tilted fiber grating sensors with plasmonic nanocoatings
Bibliographic record
Abstract
Surface plasmon resonance (SPR) optical fiber biosensors can be used as a cost-effective and relatively simple-to-implement alternative to well established bulky prism configurations for high sensitivity biological sample measurements. The miniaturized size and remote operation ability offer them a multitude of opportunities for single-point sensing in hard-to-reach spaces, even possibly in vivo. The biosensor configuration reported in this work uses a tilted fiber Bragg grating (TFBG) in a commercial single mode fiber coated with a nanometer scale silver film. The key point is that by reducing the silver film thickness to around 20-30 nm (rather than 50 nm for optimal SPR excitation), different modes of the TFBG spectrum present very high but opposite sensitivities to refractive index (RI) changes around the TFBG. Experimental results obtained with the coated TFBG embedded inside a microfluidic channel show an amplitude sensitivity greater than 8000 dB/RIU (Refractive Index Unit) and a limit of detection of 10(-5)RIU. Using this device, the effect of different concentrations of protein in rat urine was clearly differentiated between healthy samples, nephropatic samples and samples from individuals under treatment, with a protein concentration sensitivity of 5.5 dB/(mg/ml) and a limit of detection of 1.5 × 10(-3)mg/ml. Those results show a clear relationship between protein outflow and variations in the RI of the urine samples between 1.3400 and 1.3408, pointing the way to the evaluation and development of new drugs for nephropathy treatments. The integration of TFBGs with microfluidic channels enables precise measurement control over samples with sub-microliter volumes and does not require accurate temperature control because of the elimination of the temperature cross-sensitivity inherent in TFBG devices. Integration of the TFBG with a hypodermic needle on the other hand would allow similar measurements in vivo. The proposed optical fiber/microfluidic plasmonic biosensor represents an appealing solution for rapid, low consumption and highly sensitive detection of analytes at low concentrations in medicine as well as in chemical and environmental monitoring.
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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 | 0.000 | 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 | 0.001 | 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 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".