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Record W2591152221 · doi:10.1159/000455871

HER2 Overexpression in Retinoblastoma: A Potential Therapeutic Target

2017· article· en· W2591152221 on OpenAlexaff
David Cordeiro Sousa, Pablo Zoroquiaín, María Eugenia Orellana, Ana Beatriz Toledo Dias, Evangelina Espósito, Miguel N. Burnier

Bibliographic record

VenueOcular Oncology and Pathology · 2017
Typearticle
Languageen
FieldMedicine
TopicOcular Oncology and Treatments
Canadian institutionsMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsRetinoblastomaMedicineMalignancyImmunohistochemistryEnucleationCancer researchPathologyStainingBiologyGeneSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Retinoblastoma (RB) is the most common primary intraocular malignancy. Current therapies are associated with high morbidity in the short- and long-term. Human epidermal growth factor receptor 2 (HER2) is a transmembrane protein detected in 15-30% of breast cancers, but it has also been described in other malignancies. Recently, it has been claimed that a truncated version of this protein is expressed in RB, responsive to directed therapies in vitro. We scored HER2 overexpression in RB tissue samples and discussed its potential clinical utility. METHODS: HER2 overexpression was investigated using immunohistochemistry; the overexpression was evaluated with a score ranging from 0 to 3+ according to the membranous staining pattern in archival formalin-fixed, paraffin-embedded RBs. RESULTS: A total of 60 RB cases and a RB cell line (Y79) were considered. The mean age at enucleation was 31.6 ± 31.5 months. The mean time from diagnosis to enucleation was 11.8 ± 11.2 months (range 1-44). Five (8%) cases were multifocal. HER2 overexpression was negative in all RB cases (49 cases scored 0 and 11 scored 1+) and in the Y79 cell line. CONCLUSIONS: Overall, we were not able to demonstrate the overexpression of HER2. Further studies should clarify and better elucidate the potential role of HER2-targeted therapies in RB.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score0.600

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.016
GPT teacher head0.320
Teacher spread0.304 · 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 designObservational
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

Citations3
Published2017
Admission routes1
Has abstractyes

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