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Data from Gene Expression Profiling of Childhood Adrenocortical Tumors

2023· preprint· en· W4361262373 on OpenAlexaff
Alina Nico West, Geoffrey Neale, Stanley Pounds, Bonald C. Figueredo, Carlos Rodríguez‐Galindo, Mara Albonei Dudeque Pianovski, Antonio G. Oliveira Filho, David Malkin, Enzo Lalli, Raul C. Ribeiro, Gerard P. Zambetti

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicAdrenal and Paraganglionic Tumors
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersNational Cancer InstituteNational Institutes of HealthAmerican Lebanese Syrian Associated CharitiesAssociation pour la Recherche sur le Cancer
KeywordsAdrenocortical carcinomaGene expression profilingAdrenal cortexGene expressionGeneMicroarrayBiologyCancer researchNorthern blotEpigeneticsMicroarray analysis techniquesPathologyBioinformaticsMedicineEndocrinologyGenetics

Abstract

fetched live from OpenAlex

<div>Abstract<p>Pediatric adrenocortical tumors (ACT) are rare and often fatal malignancies; little is known regarding their etiology and biology. To provide additional insight into the nature of ACT, we determined the gene expression profiles of 24 pediatric tumors (five adenomas, 18 carcinomas, and one undetermined) and seven normal adrenal glands. Distinct patterns of gene expression, validated by quantitative real-time PCR and Western blot analysis, were identified that distinguish normal adrenal cortex from tumor. Differences in gene expression were also identified between adrenocortical adenomas and carcinomas. In addition, pediatric adrenocortical carcinomas were found to share similar patterns of gene expression when compared with those published for adult ACT. This study represents the first microarray analysis of childhood ACT. Our findings lay the groundwork for establishing gene expression profiles that may aid in the diagnosis and prognosis of pediatric ACT, and in the identification of signaling pathways that contribute to this disease. [Cancer Res 2007;67(2):600–8]</p></div>

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)0.000
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 score0.341
Threshold uncertainty score0.950

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.003
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 head0.102
GPT teacher head0.337
Teacher spread0.235 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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