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Record W3113934619 · doi:10.13005/bpj/2084

Consumption of Food Sources of Antioxidant Associated with Cognitive Function and Oxidative Stress Markers 4-HNE

2020· article· en· W3113934619 on OpenAlexaboutno aff
Faradila Faradila, Ratna Deliana Siregar, NurIndrawaty Liputo

Bibliographic record

VenueBiomedical & Pharmacology Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicNutritional Studies and Diet
Canadian institutionsnot available
Fundersnot available
KeywordsCognitionAntioxidantOxidative stressFood scienceMontreal Cognitive AssessmentMedicineTraditional medicineConsumption (sociology)Environmental healthCognitive impairmentBiologyInternal medicineBiochemistryPsychiatry

Abstract

fetched live from OpenAlex

Background and Aim : The contain of antioxidant in vegetables, fruits, spices, and tea has a protective effect from oxidative stress which can cause impaired cognitive function.This study aimed todetermine the relationship between the consumption of antioxidant-rich foods such as vegetables, fruits, spices, and tea with 4-HNE plasma levels and cognitive function of elderly. Material and method : The study design was cross-sectional, and was conducted in the Lima Puluh Kota district, West Sumatra in 2018.Interviewing antioxidant food intake was carried out using the Food Frequency Questioner (FFQ), cognitive function was assessed by the Indonesian version of the Montreal Cognitive Assessment (MoCA-Ina), plasma 4-HNE was measured by the ELISA method. Finally, the data was analyzed by Mann-Whitney and Chi-square statistical tests. Result :The result showed that 83 elderly (57.2%) experience impaired cognitive function. There was no significant relationship between consumption of antioxidant foods and plasma levels of HNE. However, consumption of vegetables, fruits, spices, and tea has a significant relationship with cognitive function. Conclusion:This study concluded that consumption of vegetables, fruits, spices, and tea can protect the elderly from impaired cognitive function.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.384
Threshold uncertainty score0.454

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 studies0.0000.001
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 head0.029
GPT teacher head0.300
Teacher spread0.272 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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