Epigenetic alterations in the suprachiasmatic nucleus and hippocampus contribute to age-related cognitive decline
Bibliographic record
Abstract
// Scott H. Deibel 1 , Erin L. Zelinski 1 , Robin J. Keeley 1 , Olga Kovalchuk 2 and Robert J. McDonald 1 1 Canadian Centre for Behavioural Neuroscience, Department of Neuroscience, University of Lethbridge, Lethbridge, AB, Canada 2 Department of Biological Sciences, University of Lethbridge, Lethbridge, AB, Canada Correspondence to: Scott H. Deibel, email: // Olga Kovalchuk, email: // Keywords : aging, brain, memory, circadian rhythms, Gerotarget Received : March 10, 2015 Accepted :April 15, 2015 Published : May 08, 2015 Abstract Circadian rhythm dysfunction and cognitive decline, specifically memory loss, frequently accompany natural aging. Circadian rhythms and memory are intertwined, as circadian rhythms influence memory formation and recall in young and old rodents. Although, the precise relationship between circadian rhythms and memory is still largely unknown, it is hypothesized that circadian rhythm disruption, which occurs during aging, contributes to age-associated cognitive decline, specifically memory loss. While there are a variety of mechanisms that could mediate this effect, changes in the epigenome that occur during aging has been proposed as a potential candidate. Interestingly, epigenetic mechanisms, such as DNA methylation and sirtuin1 (SIRT1) are necessary for both circadian rhythms and memory. During aging, similar alterations of epigenetic mechanisms occur in the suprachiasmatic nucleus (SCN) and hippocampus, which are necessary for circadian rhythm generation and memory, respectively. Recently, circadian rhythms have been linked to epigenetic function in the hippocampus, as some of these epigenetic mechanisms oscillate in the hippocampus and are disrupted by clock gene deletion. The current paper will review how circadian rhythms and memory change with age, and will suggest how epigenetic changes in these processes might contribute to age-related cognitive decline.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".