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Record W4237865178 · doi:10.1186/1746-6148-5-24

Trace metals and over-expression of metallothioneins in bladder tumoral lesions: a case-control study

2009· article· en· W4237865178 on OpenAlexfundno aff
André F.S. Amaral, Teresa Cymbron, Fátima Gärtner, Armindo Rodrigues

Bibliographic record

VenueBMC Veterinary Research · 2009
Typearticle
Languageen
FieldNursing
TopicTrace Elements in Health
Canadian institutionsnot available
FundersInterregCanadian Immunization Research Network
KeywordsMetallothioneinCadmiumBladder cancerImmunohistochemistryPathologyChromiumBiomarkerCarcinogenChemistryBladder NeoplasmMedicineCancerInternal medicineBiochemistry

Abstract

fetched live from OpenAlex

Previous studies have provided some evidence of a possible association between cancer and metallothioneins. Whether this relates to an exposure to carcinogenic metals remains unclear. In order to examine the association between the expression of metallothioneins and bladder tumors, and to compare the levels of arsenic, cadmium, chromium, lead and nickel in animals with bladder tumors and animals without bladder tumors, 37 cases of bovine bladder tumors and 17 controls were collected. The detection and quantification of metallothioneins in bladder tissue of both cases and controls was performed by immunohistochemistry. And the quantification of metals in tissue and hair was assessed by inductively coupled plasma – mass spectrometry. Increased expression of metallothioneins was associated with bladder tumors when compared with non-tumoral bladder tissue (OR = 9.3, 95% CI: 1.0 – 480). The concentrations of cadmium, chromium, lead and nickel in hair of cases were significantly higher than those of controls. However, as for the concentration of metals in bladder tissue, the differences were not significant. Though the sample size was small, the present study shows an association between bladder tumors and metallothioneins. Moreover, it shows that concentrations of metals such as cadmium, chromium, lead and nickel in hair may be used as a biomarker of exposure.

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)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.819
Threshold uncertainty score0.740

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.208
GPT teacher head0.471
Teacher spread0.263 · 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

Citations12
Published2009
Admission routes1
Has abstractyes

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