Flow Cytometry-Assisted Detection of Adenosine in Serum with an Immobilized Aptamer Sensor
Bibliographic record
Abstract
Aptamers are single-stranded nucleic acids that can selectively bind to essentially any molecule of choice. Because of their high stability, low cost, ease of modification, and availability through selection, aptamers hold great promise in addressing key challenges in bioanalytical chemistry. In the past 15 years, many highly sensitive fluorescent aptamer sensors have been reported. However, few such sensors showed high performance in serum samples. Further challenges related to practical applications include detection in a very small sample volume and a low dependence of sensor performance on ionic strength. We report the immobilization of an aptamer sensor on a magnetic microparticle and the use of flow cytometry for detection. Flow cytometry allows the detection of individual particles in a capillary and can effectively reduce the light scattering effect of serum. Since DNA immobilization generated a highly negatively charged surface and caused an enrichment of counterions, the sensor performance showed a lower salt dependence. The detection limits for adenosine are determined to be 178 microM in buffer and 167 microM in 30% serum. Finally, we demonstrated that the detection can be carried out in 10 microL of 90% human blood serum.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".