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Record W4405012440 · doi:10.1016/j.celrep.2024.115035

The absence of telomerase leads to immune response and tumor regression in zebrafish melanoma

2024· article· en· W4405012440 on OpenAlexfundno aff
Bruno Lopes-Bastos, Joana Nabais, Tânia Ferreira, Giulia Allavena, Mounir El Maï, Malia Bird, Seniye Targen, Lorenzo Tattini, Da Kang, Jia‐Xing Yue, Gianni Liti, Tânia Carvalho, Miguel Godinho Ferreira

Bibliographic record

VenueCell Reports · 2024
Typearticle
Languageen
FieldMedicine
TopicTelomeres, Telomerase, and Senescence
Canadian institutionsnot available
FundersEuropean Regional Development FundFundação para a Ciência e a TecnologiaCanceropôle Provence-Alpes-Côte d’AzurUniversité Côte d’AzurInstitut National de la Santé et de la Recherche MédicaleInstitut National Du CancerUniversity of OxfordCentre National de la Recherche ScientifiqueFondation ARC pour la Recherche sur le CancerUniversity of TorontoAssociation pour la Recherche sur le CancerUniversity of EdinburghConseil Régional Provence-Alpes-Côte d'AzurInfrastructures en Biologie Santé et AgronomieFondation pour la Recherche MédicaleHoward Hughes Medical Institute
KeywordsZebrafishImmune systemTelomeraseMelanomaBiologyCancer researchRegressionTelomereGeneticsComputational biologyImmunologyDNAGenePsychology

Abstract

fetched live from OpenAlex

tumors exhibit reduced cell proliferation, increased apoptosis, and melanocyte differentiation. Notably, these tumors show enhanced immune cell infiltration and can resume growth when transplanted into immunocompromised hosts. We propose that telomerase is required for melanoma in zebrafish, albeit at later stages of progression, to sustain tumor growth while avoiding immune rejection and regression. Thus, the absence of telomerase restricts melanoma through tumor-autonomous mechanisms (cell-cycle arrest, apoptosis, and melanocyte differentiation) and a non-tumor-autonomous mechanism (immune rejection).

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 score0.004
Threshold uncertainty score0.007

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)0.0020.000

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 head0.010
GPT teacher head0.264
Teacher spread0.254 · 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
Published2024
Admission routes1
Has abstractyes

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