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Record W3107143805 · doi:10.3390/molecules25225477

Cyst Reduction by Melatonin in a Novel Drosophila Model of Polycystic Kidney Disease

2020· article· en· W3107143805 on OpenAlexafffund
Cassandra Millet-Boureima, Roman Rozencwaig, Felix Polyak, Chiara Gamberi

Bibliographic record

VenueMolecules · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and Kidney Cyst Diseases
Canadian institutionsConcordia University
FundersMitacs
KeywordsAutosomal dominant polycystic kidney diseasePolycystic kidney diseaseMelatoninCystMedicineKidneyRenal functionInternal medicineCaspaseCancer researchEndocrinologyPathologyBiologyApoptosisProgrammed cell deathGenetics

Abstract

fetched live from OpenAlex

Autosomal dominant polycystic kidney disease (ADPKD) causes progressive cystic degeneration of the renal tubules, the nephrons, eventually severely compromising kidney function. ADPKD is incurable, with half of the patients eventually needing renal replacement. Treatments for ADPKD patients are limited and new effective therapeutics are needed. Melatonin, a central metabolic regulator conserved across all life kingdoms, exhibits oncostatic and oncoprotective activity and no detected toxicity. Here, we used the Bicaudal C (BicC) Drosophila model of polycystic kidney disease to test the cyst-reducing potential of melatonin. Significant cyst reduction was found in the renal (Malpighian) tubules upon melatonin administration and suggest mechanistic sophistication. Similar to vertebrate PKD, the BicC fly PKD model responds to the antiproliferative drugs rapamycin and mimics of the second mitochondria-derived activator of caspases (Smac). Melatonin appears to be a new cyst-reducing molecule with attractive properties as a potential candidate for PKD treatment.

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 score0.005

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 head0.012
GPT teacher head0.221
Teacher spread0.209 · 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

Citations14
Published2020
Admission routes2
Has abstractyes

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