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Record W4386929899 · doi:10.3390/nu15184077

Thymus atlanticus: A Source of Nutrients with Numerous Health Benefits and Important Therapeutic Potential for Age-Related Diseases

2023· review· en· W4386929899 on OpenAlexaff
Adil El Midaoui, Farid Khallouki, Réjean Couture, Florina Moldovan, Mahmoud Ali Ismael, Brice Ongali, Marie Yvonne Akoume, Chakib Alem, Ali Ait Boughrous, Wafa Zennouhi, Mhammed Chaoui Roqai, Lhoussain Hajji, Imen Ghzaiel, Anne Véjux, Gérard Lizard

Bibliographic record

VenueNutrients · 2023
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicPhytochemistry and Biological Activities
Canadian institutionsCentre Hospitalier Universitaire Sainte-JustineUniversité de Montréal
FundersUniversité de Bourgogne
KeywordsSeasoningDyslipidemiaBiologyMediterranean dietEssential nutrientMediterranean BasinTraditional medicineDiabetes mellitusNutrientMedicineMediterranean climateEcologyInternal medicineEndocrinology

Abstract

fetched live from OpenAlex

Thymus atlanticus (Lamiaceae) is a plant endemic to the Mediterranean basin that is found in significant quantities in the arid regions of Morocco. Thymus atlanticus is used in traditional medicine to treat infectious and non-infectious diseases. It is also used for the isolation of essential oils and for the seasoning of many dishes in the Mediterranean diet. The major constituents of Thymus atlanticus are saponins, flavonoids, tannins, alkaloids, various simple and hydroxycinnamic phenolic compounds, and terpene compounds. Several of these compounds act on signaling pathways of oxidative stress, inflammation, and blood sugar, which are parameters often dysregulated during aging. Due to its physiochemical characteristics and biological activities, Thymus atlanticus could be used for the prevention and/or treatment of age-related diseases. These different aspects are treated in the present review, and we focused on phytochemistry and major age-related diseases: dyslipidemia, cardiovascular diseases, and type 2 diabetes.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.993
Threshold uncertainty score0.575

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.045
GPT teacher head0.275
Teacher spread0.230 · 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 designOther design
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

Citations5
Published2023
Admission routes1
Has abstractyes

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