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Record W4388713293 · doi:10.47413/vidya.v2i2.267

A REVIEW ON STUDY OF ANTHOCYANINS AND THEIR DIFFERENT HEALTH BENEFITS

2023· review· en· W4388713293 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

VenueVIDYA - A JOURNAL OF GUJARAT UNIVERSITY · 2023
Typereview
Languageen
FieldHealth Professions
TopicDiverse Scientific Research Studies
Canadian institutionsImpact
Fundersnot available
KeywordsAnthocyanidinsPetunidinPeonidinMalvidinDelphinidinPelargonidinAnthocyaninAnthocyanidinCyanidinFood scienceChemistryDPPHFlavonoidPigmentBotanyBiochemistryBiologyOrganic chemistryAntioxidant

Abstract

fetched live from OpenAlex

Anthocyanins are natural occurring pigment. Anthocyanins belong to category of flavonoids which are water – soluble found in a variety of fruits and vegetables. In fruits, vegetables, and cereals, anthocyanins are the pigments which give them their red, violet, and blue colours for example purple and red berries, plums, grapes, cabbage, apples, etc., and other food. Because of high level of anthocyanins, it used as natural colorants. Anthocyanins are flavonoid compounds produced by the phenylpropanoid route known as anthocyanidin glucosides. The six most prevalent anthocyanidins are cyanidin, delphinidin, malvidin, peonidin, petunidin, and pelargonidin. Plants are shielded from oxidizers by several phenolic hydroxyl groups present in the structure anthocyanins. This review paper shows the different activity and properties shown by the anthocyanins which are used to cure the diseases. they done many antioxidants activity by using DPPH (2,2-diphenyl-1-picrylhydrazy), antimicrobial activity done by gram-positive bacterial test cultures, TNF-stimulated expression of the endothelial adhesion molecules VCAM-1 and ICAM-1 was used to assess the anti-inflammatory effects of various PASs.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.614
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.002
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 head0.407
GPT teacher head0.510
Teacher spread0.103 · 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