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Record W2037851961 · doi:10.1371/journal.pone.0006904

Intracellular Calcium Deficits in Drosophila Cholinergic Neurons Expressing Wild Type or FAD-Mutant Presenilin

2009· article· en· W2037851961 on OpenAlexafffund
Kinga Michno, David Knight, Jorge M. Campussano, Diana van de Hoef, Gabrielle L. Boulianne

Bibliographic record

VenuePLoS ONE · 2009
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsUniversity of TorontoHospital for Sick Children
FundersCanadian Institutes of Health Research
KeywordsPresenilinCalcium in biologyBiologyCalciumCell biologyIntracellularMutantNeuroscienceCalcium signalingGeneticsAlzheimer's diseaseInternal medicineMedicineDiseaseGene

Abstract

fetched live from OpenAlex

Much of our current understanding about neurodegenerative diseases can be attributed to the study of inherited forms of these disorders. For example, mutations in the presenilin 1 and 2 genes have been linked to early onset familial forms of Alzheimer's disease (FAD). Using the Drosophila central nervous system as a model we have investigated the role of presenilin in one of the earliest cellular defects associated with Alzheimer's disease, intracellular calcium deregulation. We show that expression of either wild type or FAD-mutant presenilin in Drosophila CNS neurons has no impact on resting calcium levels but does give rise to deficits in intracellular calcium stores. Furthermore, we show that a loss-of-function mutation in calmodulin, a key regulator of intracellular calcium, can suppress presenilin-induced deficits in calcium stores. Our data support a model whereby presenilin plays a role in regulating intracellular calcium stores and demonstrate that Drosophila can be used to study the link between presenilin and calcium deregulation.

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 score0.003
Threshold uncertainty score0.006

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 science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.093
GPT teacher head0.319
Teacher spread0.226 · 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

Citations29
Published2009
Admission routes2
Has abstractyes

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Same venuePLoS ONE→Same topicAlzheimer's disease research and treatments→French-language works237,207→