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Record W3200084031 · doi:10.1159/000519415

Neurogenesis in the Maternal Rodent Brain: Impacts of Gestation-Related Hormonal Regulation, Stress, and Obesity

2021· review· en· W3200084031 on OpenAlexafffund
Noshin Noorjahan, Peter A. Cattini

Bibliographic record

VenueNeuroendocrinology · 2021
Typereview
Languageen
FieldNeuroscience
TopicNeurogenesis and neuroplasticity mechanisms
Canadian institutionsUniversity of Manitoba
FundersCanadian Institutes of Health Research
KeywordsNeurogenesisHormoneEndocrinologyPregnancyGestationInternal medicinePsychologyPhysiologyBiologyNeuroscienceMedicine

Abstract

fetched live from OpenAlex

In order to maintain maternal behavior, it is important that the maternal rodent brain promotes neurogenesis. Maternal neurogenesis is altered by the dynamic shifts in reproductive hormone levels during pregnancy. Thus, lifestyle events such as gestational stress and obesity that can affect hormone production will affect neuroendocrine control of maternal neurogenesis. However, there is a lack of information about the regulation of maternal neurogenesis by placental hormones, which are key components of the reproductive hormonal profile during pregnancy. There is also little known about how maternal neurogenesis can be affected by health concerns such as gestational stress and obesity, and its relationship to peripartum mental health disorders. This review summarizes the changing levels of neurogenesis in mice and rats during gestation and postpartum as well as regulation of neurogenesis by pregnancy-related hormones. The influence of neurogenesis on maternal behavior is also discussed while bringing attention to the effect of health-related concerns during gestation, such as stress and obesity on neuroendocrine control of maternal neurogenesis. In doing so, this review identifies the gaps in the literature and specifically emphasizes the importance of further research on maternal brain physiology to address them.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.671
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.046
GPT teacher head0.305
Teacher spread0.259 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations7
Published2021
Admission routes2
Has abstractyes

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