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Record W2340057693 · doi:10.5539/cco.v5n1p79

Prostatic Tissue Levels of 43 Trace Elements in Patients with Prostate Adenocarcinoma

2016· article· en· W2340057693 on OpenAlexvenueno aff
Vladimir Zaichick, Sofia Zaichick

Bibliographic record

VenueCancer and Clinical Oncology · 2016
Typearticle
Languageen
FieldNursing
TopicTrace Elements in Health
Canadian institutionsnot available
Fundersnot available
KeywordsProstateProstate cancerTrace elementUrologyAdenocarcinomaNeutron activation analysisChemistryInductively coupled plasma mass spectrometryMedicinePathologyCancerInternal medicineRadiochemistryMass spectrometryChromatography

Abstract

fetched live from OpenAlex

Adenocarcinoma of prostate gland is an internationally important health problem in men, particularly in developed countries. The aim of this exploratory study was to evaluate whether significant changes in the prostatic tissue levels of trace elements exist in the malignantly transformed prostate. Prostatic tissue levels of 43 trace elements were prospectively evaluated in 36 patients with prostate adenocarcinoma and 37 healthy males. Measurements were performed using a combination of non-destructive and destructive methods: instrumental neutron activation analysis and inductively coupled plasma mass spectrometry, respectively. Tissue samples were divided into two portions. One was used for morphological study while the other was intended for trace element analysis. The reliability of difference in the results between normal and cancerous prostate tissues was evaluated by Student’s t-test. It was found that the contents of Ag, Al, Au, B, Be, Br, Ce, Cr, Dy, Er, Fe, Gd, Hg, Li, Mn, Nd, Ni, Pr, Sb, Sn, Th, Tl, Y, and Zr were significantly higher while those of Cd, Co, Rb, Sc, Se, and Zn were significantly lower in cancerous tissues than in normal tissues. Moreover, it was shown that malignant transformation significantly changed the relationships of trace elements in prostate. Thus, in adenocarcinoma transformed prostate tissue the trace element metabolism is significantly disturbed.

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)0.001
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 score0.402
Threshold uncertainty score0.349

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.059
GPT teacher head0.430
Teacher spread0.371 · 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

Citations33
Published2016
Admission routes1
Has abstractyes

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