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Record W2063883150 · doi:10.2174/13892037113149990051

Role of Amylin and its Receptors in Neurodegeneration

2013· review· en· W2063883150 on OpenAlexafffund
Wenbin Fu, Jack H. Jhamandas

Bibliographic record

VenueCurrent Protein and Peptide Science · 2013
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
Topic14-3-3 protein interactions
Canadian institutionsUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsAmylinNeurodegenerationReceptorCalcitonin receptorAmyloid (mycology)BiologyCalcitoninSignal transductionG protein-coupled receptorNeuroscienceEndocrinologyCell biologyInternal medicineCalcitonin gene-related peptideBiochemistryNeuropeptideMedicineDiabetes mellitusDiseaseIslet

Abstract

fetched live from OpenAlex

Amylin (islet amyloid polypeptide) and amyloid beta protein (Aβ), identified as proteinaceous deposits within the pancreas of diabetics and the brain of Alzheimer's patients respectively, share many biophysical, physiological and neurotoxic properties. Although no specific β receptor" has been identified, emerging evidence suggests that the amylin receptor serves a putative target receptor for the actions of Aβ in the brain. The amylin receptor consists of a calcitonin receptor dimerized with receptor activity-modifying proteins and is widely distributed within central nervous system. Aβ can directly activate this G protein-coupled receptor and trigger multiple intracellular signal transduction messengers and pathways that include calcium, cAMP, ERK1/2 and Fos. Growing evidence suggests that amylin and amylin receptors are involved in many aspects of neurodegenerative pathophysiology. Developing therapeutic strategies aimed at modulating amylin receptor function may prove useful for treatment of neurodegenerative diseases such as Alzheimer's 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 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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.004

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 head0.038
GPT teacher head0.338
Teacher spread0.300 · 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

Citations3
Published2013
Admission routes2
Has abstractyes

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