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Record W2765129303 · doi:10.1016/j.jalz.2017.06.706

[P2–057]: INVESTIGATING THE EFFECTS OF LEVOTHYROXINE AND LIOTHYRONINE ON BETA‐AMYLOID AGGREGATION

2017· article· en· W2765129303 on OpenAlexaff
Jonathan Kenneth Lynn Sutley, Praveen P. N. Rao

Bibliographic record

VenueAlzheimer s & Dementia · 2017
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsThyroidAmyloid (mycology)HormoneLevothyroxineInternal medicineEndocrinologyIn vitroChemistryThyroid hormonesMedicineBiochemistry

Abstract

fetched live from OpenAlex

Based on a long-standing association between Alzheimer's disease, dementia, and thyroid dysfunction, various thyroid hormones were tested for their effects on the aggregation kinetics of Aβ40 and Aβ42. Levothyroxine (T4), liothyronine (T3), and their precursors were the primary compounds explored. In vitro assays involving Aβ40 and Aβ42 were conducted with the application of various thyroid hormone compounds including T4 and T3. The morphology of select samples were studied and micrographed using transmission electron microscopy. Computational chemistry was also conducted by assessing and ranking potential binding interactions between select thyroid compounds and beta-amyloid peptide dimers. Preliminary in vitro data indicates significant inhibition of beta-amyloid aggregation by thyroid hormones. Further studies are in progress. These results suggest that thyroid hormones have the potential to act as direct inhibitors of amyloid aggregation.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.030
GPT teacher head0.311
Teacher spread0.281 · 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 designBench or experimental
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

Citations1
Published2017
Admission routes1
Has abstractyes

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