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Record W2886528015 · doi:10.1080/15622975.2018.1500028

Circadian genes in major depressive disorder

2018· review· en· W2886528015 on OpenAlexaff
Lindsay Melhuish Beaupre, Gregory M. Brown, James L. Kennedy

Bibliographic record

VenueThe World Journal of Biological Psychiatry · 2018
Typereview
Languageen
FieldNeuroscience
TopicCircadian rhythm and melatonin
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
Fundersnot available
KeywordsCircadian rhythmMajor depressive disorderPER2PsycINFOSleep (system call)PopulationChronotypePsychologyCLOCKBioinformaticsMedicineNeuroscienceCircadian clockMEDLINEBiologyComputer scienceCognition

Abstract

fetched live from OpenAlex

Background: Sleep disturbances are a common symptom of major depressive disorder (MDD). Sleep is highly regulated by circadian rhythms, controlled by circadian genes, that act through a series of feedback loops to regulate the sleep-wake cycle.Objectives: To the best of our knowledge, a systematic review regarding the core circadian genes and their role in MDD has not been published recently. Also, a review of these genes and their role in sleep disturbances in depressed individuals appears to have never been done. We decided to integrate both concepts into one comprehensive review.Method: The review was done using the appropriate search terms in the following search engines: OVID Medline, Embase, PsycINFO and Pubmed.Results: Based on the data reviewed, none of the circadian genes appear to be associated with MDD, but some are more promising than others. These genes are: CRY1, CRY2, PER2 and NPAS2. When investigating the role of circadian genes in sleep disturbances among individuals with MDD, the most promising candidate gene is TIMELESS. Although the results in this area are limited.Conclusion: Given the promising leads from this review, future studies should investigate circadian genes in sleep disturbances among the depressed population.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.984
Threshold uncertainty score0.826

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.062
GPT teacher head0.333
Teacher spread0.271 · 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 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

Citations23
Published2018
Admission routes1
Has abstractyes

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