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Record W2899713461 · doi:10.1152/ajplung.00430.2018

Could nanotechnology make vitamin E therapeutically effective?

2018· review· en· W2899713461 on OpenAlexafffund
Tetsuya Tamura, Gail Otulakowski, Brian P. Kavanagh

Bibliographic record

VenueAmerican Journal of Physiology-Lung Cellular and Molecular Physiology · 2018
Typereview
Languageen
FieldMedicine
TopicAntioxidant Activity and Oxidative Stress
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersCanadian Institutes of Health ResearchGovernment of Canada
KeywordsVitamin EAntioxidantReactive oxygen speciesChemistryPharmacologyMedicineBiochemistry

Abstract

fetched live from OpenAlex

Vitamin E (VitE) has important antioxidant and anti-inflammatory effects and is necessary for normal physiological function. α-Tocopherol (α-T), the predominant form of VitE in human tissues, has been extensively studied. Other VitE forms, particularly γ-tocopherol (γ-T), are also potent bioactive molecules. The effects are complex, involving both reactive oxygen and nitrogen species, but trials of VitE have been generally negative. We propose that a nanoparticle approach to delivery of VitE might provide effective delivery and therapeutic effect.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.002

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 head0.011
GPT teacher head0.301
Teacher spread0.291 · 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 source (direct Gemma or distilled Codex), 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

Citations6
Published2018
Admission routes2
Has abstractyes

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