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Myth of Aloe vera phytochemicals and its advancement as medicinal and plant packed cosmetics

2023· article· en· W4366495366 on OpenAlexvenueno aff
Muhammad Awais, Sahar Sadaqat, Sania Naeem, Abdul Qayyum Rao

Bibliographic record

VenueDiscovery Phytomedicine - Journal of Natural Products Research and Ethnopharmacology · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPhytochemistry and biological activity of medicinal plants
Canadian institutionsnot available
Fundersnot available
KeywordsAloe veraCosmeticsContext (archaeology)Traditional medicineBiotechnologyChemical constituentsBiochemical engineeringBiologyChemistryMedicineEngineering

Abstract

fetched live from OpenAlex

A succulent plant Aloe vera has been extensively utilized for various pharmaceutical and biomedical products. Its bioactive components that have different properties of anti-inflammation, anti-oxidation, anti-microbial and immunomodulation have made it more attractive for the scientific community to further explore its hidden facts about its utilization and also in addition to improve its properties through expression of some useful protein which can make it complete plant packed cosmetics. This study briefly summarizes fundamental active components, their biological activities, and significant applications of the constituents found in Aloe vera, and extensively surveyed its research progress in cytology and molecular biology with an emphasis on genetic engineering. Aloe constituents are studied in the context of therapeutics, cosmetics, and hydration, antibacterial and antiviral applications. The information that has been presented here is useful for better comprehending and investigating its possible therapeutic and culinary applications and provides routes for future studies.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.076
GPT teacher head0.367
Teacher spread0.292 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations3
Published2023
Admission routes1
Has abstractyes

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Same venueDiscovery Phytomedicine - Journal of Natural Products Research and EthnopharmacologySame topicPhytochemistry and biological activity of medicinal plantsFrench-language works237,207