An Overview of the Involvement of D-Serine in Cognitive Impairment in Normal Aging and Dementia
Bibliographic record
Abstract
Dementia, of which Alzheimer's disease (AD) is the most common form, is characterized by progressive cognitive deterioration, including profound memory loss, which affects functioning in many aspects of life. Although cognitive deterioration is relatively common in aging and aging is a risk factor for AD, the condition is not necessarily a part of the aging process. The N-methyl-D-aspartate glutamate receptor (NMDAR) and its co-agonist D-serine are currently of great interest as potential important contributors to cognitive function in normal aging and dementia. D-Serine is necessary for activation of the NMDAR and in maintenance of long-term potentiation (LTP) and is involved in brain development, neuronal connectivity, synaptic plasticity and regulation of learning and memory. In this paper, we review evidence, from both preclinical and human studies, on the involvement of D-serine (and the enzymes involved in its metabolism) in regulation of cognition. Potential mechanisms of action of D-serine are discussed in the context of normal aging and in dementia, as is the potential for using D-serine as a potential biomarker and/or therapeutic agent in dementia. Although there is some controversy in the literature, it has been proposed that in normal aging there is decreased expression of serine racemase and decreased levels of D-serine and down-regulation of NMDARs, resulting in impaired synaptic plasticity and deficits in learning and memory. In contrast, in AD there appears to be activation of serine racemase, increased levels of D-serine and overstimulation of NMDARs, resulting in cytotoxicity, synaptic deficits, and dementia.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".