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Record W4401380466 · doi:10.47611/jsrhs.v13i1.6274

Circadian and Sleep-Wake Dysfunctions of the Hypothalamus in Alzheimer’s Disease Progression

2024· article· en· W4401380466 on OpenAlexaff
Angelina Kovalchuk, Joanna L Eckhardt

Bibliographic record

VenueJournal of Student Research · 2024
Typearticle
Languageen
FieldNeuroscience
TopicCircadian rhythm and melatonin
Canadian institutionsVanguard College
Fundersnot available
KeywordsCircadian rhythmHypothalamusNeuroscienceHippocampusSleep (system call)Alzheimer's diseaseDiseasePsychologyEndocrinologyMedicineInternal medicine

Abstract

fetched live from OpenAlex

Alzheimer’s disease (AD) is a disorder that causes degeneration of brain cells, cognitive decline, and memory loss. AD is characterized by both the accumulation of tau proteins and the amyloid plaques, which inhibit cell function. Cognitive symptoms are considered manifestations of late-stage AD, though other manifestations such as sleep alterations occur long before these symptoms. Moreover, specific subcortical areas of the brain are affected very early on in AD progression, even before cognitive structures such as the hippocampus. One notable region is the hypothalamus, which regulates circadian rhythms, sleep-wake structure, and other metabolic signals. The hypothalamus is significant in AD progression due to its protective qualities and influence on disease development. This paper investigates the regulation of circadian and sleep-wake cycles within the hypothalamus and how desynchronization may contribute to AD pathogenesis in preclinical and early stages of the disease. Because the hypothalamus is a complex and varied structure, other processes of the hypothalamus are also discussed due to their influence on the sleep-wake cycle. Circadian rhythms also vary among different populations, potentially affecting AD onset. Understanding the influence of circadian and sleep-wake cycles on early AD pathology can provide insight into new interventions and therapeutics preceding later stages of the disease.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.288
Threshold uncertainty score0.320

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.156
GPT teacher head0.425
Teacher spread0.268 · 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 designObservational
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

Citations0
Published2024
Admission routes1
Has abstractyes

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