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Record W4410877422 · doi:10.1016/j.fochx.2025.102607

Nettle (Urtica dioica) leaves as a novel food: Nutritional, phytochemical profiles, and bioactivities

2025· article· en· W4410877422 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFood Chemistry X · 2025
Typearticle
Languageen
FieldMedicine
TopicMedicinal plant effects and applications
Canadian institutionsLethbridge College
FundersKing Saud UniversityDepartment of Science and Technology, Ministry of Science and Technology, India
KeywordsUrtica dioicaPhytochemicalBiologyTraditional medicineBotanyUrticaceaeMedicine

Abstract

fetched live from OpenAlex

Urtica dioica is widely distributed across temperate regions of the world and is highly beneficial and packed with nutritional value including a rich profile of phytochemicals, amino acids, and essential minerals. Due to abundance of these attributes, the nutritional, antioxidant, and antimicrobial properties of Urtica dioica were assessed systematically. Additionally, proximate, functional, antinutrient, mineral, polyphenolic compounds, FRAP, and DPPH assays were also examined. The findings revealed that Urtica dioica contains high Mg, Ca, Fe, and Zn. The examined values of DPPH and FRAP showed high antioxidant potential of Urtica dioica. UHPLC and GC–MS analysis confirmed Urtica dioica as an excellent source of phenolic and bioactive compounds. In view of these experimental findings, Urtica dioica can be incorporated into edible coatings and functional foods. Its excellent antimicrobial properties against Gram +ve and Gram -ve bacteria underscore its viability as an excellent source of functional ingredients in edible coatings.

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.

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 score0.035
Threshold uncertainty score0.484

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 head0.014
GPT teacher head0.264
Teacher spread0.250 · 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