Competitive enzyme linked aptamer based assay for salivary melatonin detection
Bibliographic record
Abstract
Melatonin is a key hormone that regulates the sleep–wake cycle and plays an important role in maintaining circadian rhythm and sleep onset. The daily rise in melatonin secretion is associated with an increased tendency to sleep, occurring approximately 2 h before bedtime. This correlation between melatonin levels and sleep onset makes it a reliable biomarker for circadian rhythm sleep–wake disorders. An accurate assessment of dim light melatonin onset (DLMO) is vital for understanding circadian timing and diagnosing sleep–wake cycle disruptions. However, the traditional methods for detecting melatonin in saliva are either complex or lack the sensitivity required for the accurate assessment of DLMO, especially at low concentrations. Here, we present a novel competitive enzyme-linked aptamer-based assay developed to detect melatonin in saliva. Unlike conventional assays, this technique utilizes chemically synthesized single-stranded DNA or RNA aptamers, which bind to melatonin with high specificity and sensitivity. The assay measures melatonin, attaining a linear dynamic range from 8.62 × 10‒6 M to 3.9 × 10‒11 M, with a detection limit of 2.5 × 10‒12 M (~ 0.57 pg/mL). Additionally, the aptamer showed small binding to its counter targets and acceptable recovery of melatonin when spiked in four times diluted saliva in assay buffer. Overall, the assay portrayed the potential of aptamers to detect low melatonin levels in saliva that could be beneficial in accurately determining DLMO, particularly in individuals with very low melatonin levels, such as the elderly or those with neurodegenerative conditions. Determining precise measurement of DLMO will facilitate the accurate diagnosis of circadian rhythm disruption, enabling healthcare providers to optimize the timing and selection of therapeutic and behavioural interventions tailored to an individual’s unique circadian rhythm.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".