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Record W2564244587 · doi:10.1139/apnm-2016-0515

Protective effects of essential oil of <i>Citrus limon</i> against aspirin-induced toxicity in IEC-6 cells

2016· article· en· W2564244587 on OpenAlexvenueno aff
Hafsia Bouzenna, Najla Hfaïedh, Marie‐Agnès Giroux‐Metges, Abdelfattah Elfeki, Hélène Talarmin

Bibliographic record

VenueApplied Physiology Nutrition and Metabolism · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEssential Oils and Antimicrobial Activity
Canadian institutionsnot available
Fundersnot available
KeywordsAspirinChemistrySuperoxide dismutasePharmacologyToxicityCatalaseCytotoxicityViability assayOxidative stressBiochemistryCellMedicineIn vitro

Abstract

fetched live from OpenAlex

Aspirin, one of the widely used nonsteroidal anti-inflammatory drugs, is the most highly consumed pharmaceutical product in the world. However, it has several side effects in cells. This study was designed to investigate the antioxidative activity and cytoprotective effects of essential oil of Citrus limon (EOC) extracted from leaves against aspirin-induced damages in the rat small intestine epithelial cells (IEC-6). Biochemical indicators were used to assess cytotoxicity and oxidative damages caused by aspirin treatment on IEC-6. Our results showed that the chemical characterization of EOC identified 25 compounds representing 98.19% of the total oil. The major compounds from this oil were z-citral (53.21%), neryl acetate (13.06%), geranyl acetate (10.33%), and limonene (4.23%). Aspirin induced a decrease in cell viability as well as an increase in superoxide dismutase (SOD) and catalase (CAT) activities. Contrariwise, the co-exposure of cells to aspirin and EOC alleviated every above syndrome by an increase in cell survival and decrease in SOD and CAT activities. In conclusion, the essential oil of C. limon has a potent cytoprotective effect against aspirin-induced toxicity in IEC-6 cells.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.241

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 head0.006
GPT teacher head0.194
Teacher spread0.188 · 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 designBench or experimental
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

Citations15
Published2016
Admission routes1
Has abstractyes

Explore more

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