Editors’ Choice—A Miniaturized Enzymatic Biosensor for Detection of Sensory-Evoked D-serine Release in the Brain
Bibliographic record
Abstract
D-serine has been implicated as a brain messenger with central roles in neural signaling and plasticity. Disrupted levels of D-serine in the brain have been associated with neurological disorders, including schizophrenia, depression and Alzheimer’s disease. Electrochemical biosensors are attractive tools for measuring real-time in vivo D-serine concentration changes. Current biosensors suffer from relatively large sizes (≥25 μm) making localized cellular measurements challenging, especially for single cell studies. In this work, a robust methodology for the fabrication of a reproducible miniaturized 10 μm D-serine detecting amperometric biosensor was developed. The miniature biosensor incorporated yeast D-amino acid oxidase immobilized on a poly-meta-phenylenediamine modified 10 μm Pt disk microelectrode. The biosensor offered a limit of detection of 0.361 μM (RSD < 10%) with high sensitivity (283 μA cm−2 mM−1, R2 = 0.983). The biosensor was stable for over four hours of continuous use, demonstrated a storage stability of four days and high analyte selectivity. Biosensor selectivity was validated with LC-MS and interferences with yeast D-amino acid oxidase were evaluated using drugs believed to stimulate D-serine release. Ex vivo D-serine measurements were made from Xenopus laevis tadpole brains, demonstrating the utility of the biosensors for measurements on living tissue. We observed that D-serine levels in the brain fluctuate with sensory experience. The biosensors were also used in vivo successfully. Taken together, this study addresses factors for successful and reproducible miniature biosensor fabrication for measuring D-serine in biological samples, for pharmacological evaluation, and for designing point of care devices.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.033 | 0.020 |
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".