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Record W2620404326 · doi:10.3389/fnagi.2017.00170

Neurodegeneration and the Circadian Clock

2017· review· en· W2620404326 on OpenAlexafffund
Suzanne Hood, Shimon Amir

Bibliographic record

VenueFrontiers in Aging Neuroscience · 2017
Typereview
Languageen
FieldNeuroscience
TopicCircadian rhythm and melatonin
Canadian institutionsConcordia UniversityBishop's University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchHealth Canada
KeywordsCircadian rhythmNeurodegenerationNeuroscienceHuntington's diseaseDiseaseParkinson's diseaseAlzheimer's diseaseCircadian clockMelatoninMedicineBiologyPsychologyInternal medicine

Abstract

fetched live from OpenAlex

Despite varied etiologies and symptoms, several neurodegenerative diseases-specifically, Alzheimer's (AD), Parkinson's (PD), and Huntington's diseases (HDs)-share the common feature of abnormal circadian rhythms, such as those in behavior (e.g., disrupted sleep/wake cycles), physiological processes (e.g., diminished hormone release) and biochemical activities (e.g., antioxidant production). Circadian disturbances are among the earliest symptoms of these diseases, and the molecular mechanisms of the circadian system are suspected to play a pivotal, and possibly causal, role in their natural histories. Here, we review the common circadian abnormalities observed in ADs, PDs and HDs, and summarize the evidence that the molecular circadian clockwork directly influences the course of these disease states. On the basis of this research, we explore several circadian-oriented interventions proposed as treatments for these neurological disorders.

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 studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.077
GPT teacher head0.322
Teacher spread0.244 · 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

Citations151
Published2017
Admission routes2
Has abstractyes

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