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Record W2127679368 · doi:10.5489/cuaj.1810

Endogenous biotin expression in renal and testicular tumors and literature review

2014· article· en· W2127679368 on OpenAlexaffvenueabout
Nader Fahmy, Mark Woo, Mona Alameldin, King Chien Joe Lee, Kyle MacDonald, Lee Goneau, Peter A. Cadieux, Jeremy P. Burton, Stephen E. Pautler

Bibliographic record

VenueCanadian Urological Association Journal · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBiotin and Related Studies
Canadian institutionsWestern University
FundersNortheastern Section of the American Urological Association
KeywordsImmunohistochemistryRenal cell carcinomaPathologyChromophobe cellOncocytomaGerm cell tumorsHistopathologyBiotinMedicineBiologyFrozen section procedureClear cellInternal medicineChemotherapy

Abstract

fetched live from OpenAlex

INTRODUCTION: The aim of this study was to examine endogenous biotin levels in tumour specimens collected from patients with renal and testicular tumours and compare them to the surrounding non-neoplastic surgical margin. METHODS: Frozen samples were obtained from the Ontario Tumour Bank. Renal and testicular tumour tissue were included in this study. Normal tissue from the negative surgical margins of each tumour served as a control. Biotin detection in tissue specimens was determined using immunohistochemistry (IHC). RESULTS: Specimens collected from 56 patients (36 men and 20 women) were included in this study. Histopathology of the 52 renal tumours included 31 (60%) conventional type RCC, 5 (10%) chromophobe RCC, 5 (10%) papillary RCC, 1 (2%) oncocytoma and 10 (19%) upper tract urothelial carcinoma (UC). The 4 testicular tumours included 1 seminomatous (25%) germ cell tumour and 3 (75%) non seminomatous germ cell tumours. CONCLUSION: No biotin signal was perceived in all tested tumour samples. Endogenous biotin expression was detected in the matching non-neoplastic surgical margin of tested renal tissues. This lack of staining may prove to be a valuable tool in future studies.

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)0.001
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 score0.327
Threshold uncertainty score0.263

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.006
GPT teacher head0.193
Teacher spread0.187 · 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

Citations4
Published2014
Admission routes3
Has abstractyes

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