Self-Organized Nanostructure Modified Microelectrode for Sensitive Electrochemical Glutamate Detection in Stem Cells-Derived Brain Organoids
Bibliographic record
Abstract
Neurons release neurotransmitters such as glutamate to communicate with each other and to coordinate brain functioning. As increased glutamate release is indicative of neuronal maturation and activity, a system that can measure glutamate levels over time within the same tissue and/or culture system is highly advantageous for neurodevelopmental investigation. To address such challenges, we develop for the first time a convenient method to realize functionalized borosilicate glass capillaries with nanostructured texture as an electrochemical biosensor to detect glutamate release from cerebral organoids generated from human embryonic stem cells (hESC) that mimic various brain regions. The biosensor shows a clear catalytic activity toward the oxidation of glutamate with a sensitivity of 93 ± 9.5 nA·µM−1·cm−2. It was found that the enzyme-modified microelectrodes can detect glutamate in a wide linear range from 5 µM to 0.5 mM with a limit of detection (LOD) down to 5.6 ± 0.2 µM. Measurements were performed within the organoids at different time points and consistent results were obtained. This data demonstrates the reliability of the biosensor as well as its usefulness in measuring glutamate levels across time within the same culture system.
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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 | 0.000 |
| 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 | 0.001 | 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".