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Record W3046094702 · doi:10.7554/elife.56235

The identification of dual protective agents against cisplatin-induced oto- and nephrotoxicity using the zebrafish model

2020· article· en· W3046094702 on OpenAlexafffund
Jaime Wertman, Nicole Melong, Matthew R. Stoyek, Olivia Piccolo, Stewart Langley, Benno Orr, Shelby L. Steele, Babak Razaghi, Jason N. Berman

Bibliographic record

VenueeLife · 2020
Typearticle
Languageen
FieldNeuroscience
TopicHearing, Cochlea, Tinnitus, Genetics
Canadian institutionsAppili Therapeutics (Canada)Dalhousie UniversityAgricultural Research Institute of OntarioUniversity of TorontoMcMaster UniversityIzaak Walton Killam Health Centre
FundersIWK Health CentreDalhousie University
KeywordsOtotoxicityCisplatinNephrotoxicityZebrafishPharmacologyDopamineChemistryBiologyMedicineKidneyBiochemistryInternal medicineChemotherapyEndocrinology

Abstract

fetched live from OpenAlex

Dose-limiting toxicities for cisplatin administration, including ototoxicity and nephrotoxicity, impact the clinical utility of this effective chemotherapy agent and lead to lifelong complications, particularly in pediatric cancer survivors. Using a two-pronged drug screen employing the zebrafish lateral line as an in vivo readout for ototoxicity and kidney cell-based nephrotoxicity assay, we screened 1280 compounds and identified 22 that were both oto- and nephroprotective. Of these, dopamine and L-mimosine, a plant-based amino acid active in the dopamine pathway, were further investigated. Dopamine and L-mimosine protected the hair cells in the zebrafish otic vesicle from cisplatin-induced damage and preserved zebrafish larval glomerular filtration. Importantly, these compounds did not abrogate the cytotoxic effects of cisplatin on human cancer cells. This study provides insights into the mechanisms underlying cisplatin-induced oto- and nephrotoxicity and compelling preclinical evidence for the potential utility of dopamine and L-mimosine in the safer administration of cisplatin.

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)0.000
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation 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 score0.396
Threshold uncertainty score0.378

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
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 integrity0.0000.000
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 head0.127
GPT teacher head0.318
Teacher spread0.192 · 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 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

Citations24
Published2020
Admission routes2
Has abstractyes

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