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Record W2308276320 · doi:10.1093/ajcp/aqw007

Analysis of HSP90 Expression Is Valuable in the Differential Diagnosis of Ocular Surface Squamous Lesions

2016· article· en· W2308276320 on OpenAlexaff
Pablo Zoroquiaín, D. Faingold, Sarah Algahmdi, Natàlia Vilà, Patrick Logan, Debra-Meghan Sanft, Ana Beatriz Toledo Dias, Sultan Aldrees, Vasco Bravo-Filho, Julia V. Burnier, Miguel N. Burnier

Bibliographic record

VenueAmerican Journal of Clinical Pathology · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHeat shock proteins research
Canadian institutionsMcGill University
Fundersnot available
KeywordsHsp90PathologyMedicineHeat shock proteinStainingDifferential diagnosisMalignant transformationIntraepithelial neoplasiaInternal medicineBiologyCancer

Abstract

fetched live from OpenAlex

OBJECTIVES: The aim of this study was to evaluate heat shock protein 90 (HSP90) expression in squamous lesions (SLs) and to assess its diagnostic value for different lesions within the SL spectrum. METHODS: A total of 70 conjunctival SLs, including 19 papillomas, 22 cases of conjunctival intraepithelial neoplasia (ConINs) I, 11 cases of ConIN II, six cases of ConIN III, and 12 squamous carcinomas (sqCAs), were evaluated using the German immunoreactive score against HSP90. RESULTS: Cytoplasmic HSP90 expression differed between low- and high-grade lesions (P < .001). Among high-grade lesions, the nuclear HSP90 score was higher in the ConIN III-sqCA group than in the ConIN II group (P = .0162). A percentage of total thickness staining of less than 73% differentiated between ConIN III and sqCA. CONCLUSIONS: The expression of HSP90 is particularly useful to differentiate low-grade from high-grade lesions of the conjunctiva. HSP90 may play an important role in the malignant transformation of SLs and could be a new target for therapy.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.113
Threshold uncertainty score0.328

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.045
GPT teacher head0.406
Teacher spread0.360 · 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

Citations10
Published2016
Admission routes1
Has abstractyes

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