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Record W3123806117 · doi:10.1155/2021/8850744

Effects of Various Preextraction Treatments of Crinum asiaticum Leaf on Its Anti-Inflammatory Activity and Chemical Properties

2021· article· en· W3123806117 on OpenAlexaff
Chonthicha Kongkwamcharoen, Arunporn Itharat, Weerachai Pipatrattanaseree, B. Ooraikul

Bibliographic record

VenueEvidence-based Complementary and Alternative Medicine · 2021
Typearticle
Languageen
FieldChemistry
TopicChemical synthesis and alkaloids
Canadian institutionsUniversity of Alberta
FundersRoyal Golden Jubilee (RGJ) Ph.D. ProgrammeThammasat UniversityThailand Research Fund
KeywordsTraditional medicineChemistryAnti-inflammatoryRaw materialFood scienceBiologyMedicineOrganic chemistryPharmacology

Abstract

fetched live from OpenAlex

Crinum asiaticum Linn. has been used in Thai traditional medicine to relieve inflammatory symptoms and treat osteoarthritis. There have been reports on its potent anti-inflammatory property but nothing on the effects of different pretreatments on its chemical properties and anti-inflammatory activity. Pretreatment of herbal raw materials is an important step which affects the overall quality of Thai traditional medicine. The objectives of this study were to investigate different treatments of C. asiaticum leaves prior to ethanolic extraction and to compare the extracts for their anti-inflammatory activity and chemical properties. The treatments included hot air drying in an oven, microwave drying, traditional grilling on a charcoal stove before drying in an oven, and temperature shock in hot and cold water before hot air drying. The anti-inflammatory activity and chemical properties of the extracts were analyzed using the established methods. Results showed that 95% ethanolic extract of hot air oven-dried leaves had the highest anti-inflammatory activity and total phenolic and lycorine contents. We recommend hot air drying as a preextraction treatment for C. asiaticum leaves for its simplicity, best retention of the herbal quality, and suitability for scaling up to an industrial process.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.656

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 head0.058
GPT teacher head0.291
Teacher spread0.233 · 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 designBench or experimental
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

Citations18
Published2021
Admission routes1
Has abstractyes

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