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Record W1966227373 · doi:10.1196/annals.1404.030

Zinc, Metallothioneins, and Longevity:

2007· review· en· W1966227373 on OpenAlexaff
Eugenio Mocchegiani, Robertina Giacconi, Catia Cipriano, Laura Costarelli, Elisa Muti, Silvia Tesei, Roberta Papa, Fiorella Marcellini, Erminia Mariani, Lothar Rink, George Herbein, Audrey Varin, Tamàs Fülöp, Daniela Monti, Jolanta Jajte, George Dedoussis, Efstathios S. Gonos, Ioannis P. Trougakos, Marco Malavolta

Bibliographic record

VenueAnnals of the New York Academy of Sciences · 2007
Typereview
Languageen
FieldNursing
TopicTrace Elements in Health
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsLongevityZincMetallothioneinBiologyChemistryGenetics

Abstract

fetched live from OpenAlex

Aging is an inevitable biological process that is associated with gradual and spontaneous biochemical and physiological changes and increased susceptibility to diseases. Because nutritional factors are involved in improving immune functions, metabolic harmony, and antioxidant defense, some nutritional factors, such as zinc, may modify susceptibility to disease and promote healthy aging. In vitro (human lymphocytes exposed to endotoxins) and in vivo (old or young mice fed with low zinc dietary intake) studies revealed that zinc is important for immune efficiency (innate and adaptive), antioxidant activity (supeoxide dismutase), and cell differentiation via clusterin/apolipoprotein J. Intracellular zinc homeostasis is regulated by metallothioneins (MT) via ion release through the reduction of thiol groups in the MT molecule. This process is crucial in aging because high MT levels are not able to release zinc, resulting in low intracellular free ion availability for biological functions. Improvement in these functions occurs in the elderly after physiological zinc supplementation. In this study, the selection of elderly subjects for zinc supplementation is discussed in relation to the genetic background of MT and pro-inflammatory cytokines, such as interleukin-6, because the latter is involved both in MT-gene expression and in intracellular zinc homeostasis.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.982
Threshold uncertainty score0.837

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.326
GPT teacher head0.477
Teacher spread0.151 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations51
Published2007
Admission routes1
Has abstractyes

Explore more

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