Simultaneous Detection of L‐Lactate and D‐Glucose Using DNA Aptamers in Human Blood Serum**
Bibliographic record
Abstract
Abstract L‐lactate is a key metabolite indicative of physiological states, glycolysis pathways, and various diseases such as sepsis, heart attack, lactate acidosis, and cancer. Detection of lactate has been relying on a few enzymes that need additional oxidants. In this work, DNA aptamers for L‐lactate were obtained using a library‐immobilization selection method and the highest affinity aptamer reached a K d of 0.43 mM as determined using isothermal titration calorimetry. The aptamers showed up to 50‐fold selectivity for L‐lactate over D‐lactate and had little responses to other closely related analogs such as pyruvate or 3‐hydroxybutyrate. A fluorescent biosensor based on the strand displacement method showed a limit of detection of 0.55 mM L‐lactate, and the sensor worked in 90 % serum. Simultaneous detection of L‐lactate and D‐glucose in the same solution was achieved. This work has broadened the scope of aptamers to simple metabolites and provided a useful probe for continuous and multiplexed monitoring.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".