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Record W4389914501 · doi:10.3390/ijms242417578

Copper in Gynecological Diseases

2023· article· en· W4389914501 on OpenAlexafffund
Rocío Ayelem Conforti, María Belén Delsouc, Edith Zorychta, Carlos M. Telleria, Marilina Casais

Bibliographic record

VenueInternational Journal of Molecular Sciences · 2023
Typearticle
Languageen
FieldNursing
TopicTrace Elements in Health
Canadian institutionsMcGill University Health CentreMcGill University
FundersAgencia Nacional de Promoción Científica y TecnológicaConsejo Nacional de Investigaciones Científicas y TécnicasGovernment of CanadaHealth CanadaOvarian Cancer CanadaUniversidad Nacional de San Luis
KeywordsMetastasisAngiogenesisCrosstalkMicronutrientCancer researchReactive oxygen speciesDiabetes mellitusCell growthMedicineCopper deficiencyEnzymeDual roleBioinformaticsCancerBiologyChemistryCell biologyCopperBiochemistryInternal medicineEndocrinologyPathology

Abstract

fetched live from OpenAlex

Copper (Cu) is an essential micronutrient for the correct development of eukaryotic organisms. This metal plays a key role in many cellular and physiological activities, including enzymatic activity, oxygen transport, and cell signaling. Although the redox activity of Cu is crucial for enzymatic reactions, this property also makes it potentially toxic when found at high levels. Due to this dual action of Cu, highly regulated mechanisms are necessary to prevent both the deficiency and the accumulation of this metal since its dyshomeostasis may favor the development of multiple diseases, such as Menkes' and Wilson's diseases, neurodegenerative diseases, diabetes mellitus, and cancer. As the relationship between Cu and cancer has been the most studied, we analyze how this metal can affect three fundamental processes for tumor progression: cell proliferation, angiogenesis, and metastasis. Gynecological diseases are characterized by high prevalence, morbidity, and mortality, depending on the case, and mainly include benign and malignant tumors. The cellular processes that promote their progression are affected by Cu, and the mechanisms that occur may be similar. We analyze the crosstalk between Cu deregulation and gynecological diseases, focusing on therapeutic strategies derived from this metal.

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)0.001
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 score0.106
Threshold uncertainty score0.174

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
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 head0.034
GPT teacher head0.383
Teacher spread0.349 · 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

Citations25
Published2023
Admission routes2
Has abstractyes

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