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Record W4406871448 · doi:10.1590/0001-3765202520231380

Specific immunohistochemical expression of Mmp-26 in prostatic adenocarcinoma

2025· article· en· W4406871448 on OpenAlexaff
ROMILDO LUCIANO DA SILVA, Francisco Luís Almeida Paes, Sandra Maria Souza da Silva, F.G.A. Santos, Eduarda Santos de Santana, Jacinto da Costa Silva Neto

Bibliographic record

VenueAnais da Academia Brasileira de Ciências · 2025
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsCentre for Global Health Research
Fundersnot available
KeywordsImmunohistochemistryProstatic adenocarcinomaMatrix metalloproteinaseAdenocarcinomaExpression (computer science)MedicineOncologyPathologyCancer researchInternal medicineCancerComputer science

Abstract

fetched live from OpenAlex

Matrix metalloproteinases (MMP) have been identified as biomarkers for several diseases, including cancer. The increase in the expression of these enzymes has been related to greater tumor aggressiveness. MMP-26 is expressed constitutively in the endometrium and some cancer cells of epithelial origin. However, there is a lack of studies on its expression on prostatic carcinoma. In this study, the evaluation of MMP-26 reactivity by immunohistochemistry (IHC) was carried out in 150 paraffinized samples representative of benign and malignant prostatic lesions. 70 of the 150 samples showed IHC immunopositivity, being more prevalent in carcinoma cases (44 out of 70 cases) with moderate and strong intensity. The expression and intensity of the MMP-26 reaction showed a significant association with total PSA values. As expected, serum PSA levels were higher in cases of carcinoma than in prostatic hyperplasia or atrophy. Studies have demonstrated the potential of MMP-26 as a tumor marker, and our results have shown that its immunoexpression was useful to differentiate a group of benign and malignant samples in prostate tumors. This characteristic can assist in the predictive assessment and, consequently, in the development of new strategies for the diagnosis, prognosis, and treatment of prostate cancer.

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 score0.016
Threshold uncertainty score0.806

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.029
GPT teacher head0.336
Teacher spread0.307 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

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