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Record W3008174509 · doi:10.3390/ijms21041375

The Relationships Between Stress, Mental Disorders, and Epigenetic Regulation of BDNF

2020· review· en· W3008174509 on OpenAlexaff
Zhuang Miao, Yan Wang, Zhong Sheng Sun

Bibliographic record

VenueInternational Journal of Molecular Sciences · 2020
Typereview
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsUniversity of Toronto
FundersNational Natural Science Foundation of China
KeywordsEpigeneticsNeurotrophic factorsBrain-derived neurotrophic factorNeuroscienceHistoneDNA methylationPsychologyClinical psychologyMedicineBiologyInternal medicineGeneticsGene expressionGene

Abstract

fetched live from OpenAlex

Brain-derived neurotrophic factor (BDNF), a critical member of the neurotrophic family, plays an important role in multiple stress-related mental disorders. Although alterations in BDNF in multiple brain regions of individuals experiencing stress have been demonstrated in previous studies, it appears that a set of elements are involved in the complex regulation. In this review, we summarize the specific brain regions with altered BDNF expression during stress exposure. How various environmental factors, including both physical and psychological stress, affect the expression of BDNF in specific brain regions are further summarized. Moreover, epigenetic regulation of BDNF, including DNA methylation, histone modification, and noncoding RNA, in response to diverse types of stress, as well as sex differences in the sensitivity of BDNF to the stress response, is also summarized. Clarification of the underlying role of BDNF in the stress process will promote our understanding of the pathology of stress-linked mental disorders and provide a potent target for the future treatment of stress-related illness.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.078
GPT teacher head0.375
Teacher spread0.297 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations161
Published2020
Admission routes1
Has abstractyes

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