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Record W2237873581 · doi:10.5539/jps.v5n1p20

Effect of Cymbopogon Citratus on Oxidative Stress Markers in Erythrocytes from Postmenopausal Woman: A Pilot Study

2015· article· en· W2237873581 on OpenAlexvenueno aff
Gabriela Tassotti Gelatti, Roberta Cattaneo Horn, Natacha Cossettin Mori, Evelise Moraes Berlezi, Ana Caroline Tissiani, Mariana Spanamberg Mayer, Daiana Meggiolaro Gewehr

Bibliographic record

VenueJournal of Plant Studies · 2015
Typearticle
Languageen
FieldMedicine
TopicNatural Antidiabetic Agents Studies
Canadian institutionsnot available
Fundersnot available
KeywordsTBARSCymbopogon citratusOxidative stressThiobarbituric acidAntioxidantChemistryGlutathioneFood scienceAnimal scienceBiochemistryBiologyLipid peroxidationEssential oilEnzyme

Abstract

fetched live from OpenAlex

<strong>Objective:</strong> Analyzing "<em>in vitro</em>" the antioxidant activity of the lemon grass (<em>Cymbopogon citratus Stapf</em>) over markers of oxidative stress in erythrocytes of women on postmenopausal period. <strong>Method:</strong> Total blood with anticoagulant has been collected from 28 women on postmenopausal. The plasma was discarded. The diluted erythrocyte on 5% with saline and divided in 5 groups of treatment: Group 0: erythrocytes without treatment; Group 5, 10, 25 and 50 erythrocytes treated respectively with 5, 10, 25 and 50 g/L of infusion of lemon grass, in <strong>water bath</strong> on 37°C for 1 hour. After this period the erythrocytes were hemolysated in vortex and on the supernatant were evaluated the level of the Thiobarbituric Acid Reactive Substances (TBARS), Carbonylated Proteins (PCs) and of the Reduced Glutathione (GSH). <strong>Results:</strong> There were no significant alterations on the PCs levels of the studied groups. However the TBARS levels got reduced on the group 25 and the GSH levels got increased on the group 50. <strong>Conclusion:</strong> These results indicate that the lemon grass seems to be an effective antioxidant agent when it's used in infusions with concentration of 25 and 50 g/L.

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 score0.214
Threshold uncertainty score0.688

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.042
GPT teacher head0.324
Teacher spread0.282 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

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