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Melatonin promotes neurogenesis in dentate gyrus in the pinealectomized rat

2009· article· en· W2058041634 on OpenAlexafffund
Kerry Rennie, Maxine De Butte, Bruce A. Pappas

Bibliographic record

VenueJournal of Pineal Research · 2009
Typearticle
Languageen
FieldNeuroscience
TopicCircadian rhythm and melatonin
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDoublecortinNeurogenesisDentate gyrusPinealectomyMelatoninEndocrinologyHippocampal formationInternal medicineBiologyHippocampusNeuroscienceMedicinePineal gland

Abstract

fetched live from OpenAlex

It was previously shown that pinealectomy causes delayed loss of pyramidal neurons in rat hippocampal layers CA1/3 and that this is reversed by melatonin supplementation. Here, we used immunohistologic detection of doublecortin, a protein expressed in newborn neurons, to determine if melatonin supplementation promotes neurogenesis after pinealectomy. It was found that melatonin supplementation significantly increased the number of doublecortin immunoreactive neurons in the dentate gyrus over the postsurgical intervals of 2, 4, 6, 8, 10 and 17 months. The increase was most evident at 6 months postsurgery and thereafter, and was apparent despite a severe decline in doublecortin-labeled cells over the 17 month postsurgical interval in all groups of rats. Doublecortin immunoreactive cells were not observed in the pyramidal layer itself. These results indicate that melatonin promotes neurogenesis in the dentate gyrus of pinealectomized rats. However, it is equivocal that these newborn neurons migrate to the pyramidal layer and account for the reappearance of neurons at this location in these rats. This study provides further evidence for a role of melatonin in promoting neurogenesis, adding another role to its already remarkably pleiotropic profile. The scope and significance of this newly discovered role remains to be determined.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.181
Threshold uncertainty score0.577

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.118
GPT teacher head0.404
Teacher spread0.286 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations73
Published2009
Admission routes2
Has abstractyes

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