The effects of the NMDAR co-agonist D-serine on the structure and function of the optic tectum
Bibliographic record
Abstract
Abstract The N-methyl-D-aspartate type glutamate receptor (NMDAR) is a molecular coincidence detector which converts correlated patterns of neuronal activity into cues for the structural and functional refinement of developing circuits in the brain. D-serine is an endogenous co-agonist of the NMDAR. In this study, we investigated the effects of potent enhancement of NMDAR-mediated currents by chronic administration of saturating levels of D-serine on the developing Xenopus retinotectal circuit. Chronic exposure to the NMDAR co-agonist D-serine resulted in structural and functional changes to the optic tectum. D-serine administration affected synaptogenesis and dendritic morphology in recently differentiated tectal neurons, resulting in increased arbor compaction, reduced branch dynamics, and higher synapse density. These effects were not observed in more mature neurons. Calcium imaging to examine retinotopic map organization revealed that tectal neurons of animals raised in D-serine had sharper visual receptive fields. These findings suggest that the availability of endogenous NMDAR co-agonists like D-serine at glutamatergic synapses may regulate the refinement of circuits in the developing brain. Significance statement N-methyl-D-aspartate receptors (NMDAR) are implicated in activity-dependent circuit plasticity. We used administration of the NMDAR co-agonist D-serine to further examine the role of the NMDAR in circuit development in vivo . D-serine stabilised dendritic arbors specifically of recently differentiated neurons, promoted synaptogenesis, and led to sharper retinotopic receptive fields in the optic tectum. Together, these results support the idea that signaling in response to synaptic current through NMDARs promotes the maturation of developing brain circuits.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".