Rapid detection of tear lactoferrin for diagnosis of dry eyes by using fluorescence polarization-based aptasensor
Bibliographic record
Abstract
Differentiating dry eye disease (DED) from allergic or viral conjunctivitis rapidly and accurately is important to ensure prompt diagnosis and treatment. Tear lactoferrin (LF), a multi-functional glycoprotein found in tears, decreases significantly in patients with DED, and has been considered as a DED diagnostic biomarker. Measuring tear LF level, however, takes time and requires the use of bulky instruments. Herein, a homogeneous carbon nanostructure-based aptasensor with high sensitivity and selectivity has been developed by applying fluorescence polarization (FP) technology. The FP of carbon dots (CDs) bioconjugated with LF aptamers (CDs-aptamer) is 21.2% higher than that of CDs, which can be further amplified (1.81 times) once interacting with graphene oxide nanosheets (GONS). In the presence of LF, GONS separates from CDs-aptamer because of the stronger binding affinity between CDs-aptamer to LF, resulting in the decrease of FP value. A linear relationship is observed between FP value and LF concentration in spiked tear samples from 0.66 to 3.32 mg/mL. The selectivity of the aptasensor has been investigated by measuring other proteins. The results indicate that the FP-based aptasensor is a cost-effective method with high sensitivity and selectivity in detection of tear LF.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".