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Record W9009826

Antioxidant therapy in human endocrine disorders.

2010· article· en· W9009826 on OpenAlexaff
Saeid Golbidi, Ismail Laher

Bibliographic record

VenuePubMed · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNutrition, Genetics, and Disease
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsOxidative stressReactive oxygen speciesAntioxidantDiseaseBioinformaticsIntracellularMedicineBiologyPharmacologyCell biologyBiochemistryInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

Reactive oxygen species (ROS) have deleterious or beneficial effects; this dual nature of ROS means that ROS act as intracellular signaling molecules and as defense mechanisms against micro-organisms. An overproduction of ROS results in oxidative stress, a deleterious process that damages cell structures, including lipids, proteins, and DNA. Oxidative stress plays a major role in various human disease states, including endocrine dysfunction. As a safeguard against oxidative stress, several endogenous nonenzymatic and enzymatic antioxidant systems exist. Antioxidants can delay or prevent oxidative stress and are widely used in the hope of maintaining health and preventing diseases. Although early studies suggested that antioxidant supplements promoted health, later clinical trials revealed that it may not be true in all cases. In this article, we provide a brief review of the pathophysiologic aspects of oxidative stress in a number of the most commonly human endocrionopathies (diabetes, male and female infertility and thyroid diseases) and review the therapeutic potentials of existing antioxidant strategies. We focus on human clinical trials and discuss the implications of their results. Based on the data reported so far, we conclude that the results reported challenge us to design better antioxidant trials in future, with a particular emphasis on identifying 1) appropriate doses 2) selecting the right populations 3) treating for optimal durations and 4) specific intracellular targeting mechanisms.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.630
Threshold uncertainty score0.372

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.008
GPT teacher head0.230
Teacher spread0.222 · 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

Citations67
Published2010
Admission routes1
Has abstractyes

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