A REVIEW ON STUDY OF ANTHOCYANINS AND THEIR DIFFERENT HEALTH BENEFITS
Bibliographic record
Abstract
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".