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Record W2142752322 · doi:10.5897/jmpr.9000268

Assaying the variation in secondary metabolites of St. John's wort for its better use as an antibiotic

2010· article· en· W2142752322 on OpenAlexaboutno aff
Goran Nikolić, Saša Zlatković

Bibliographic record

VenueJournal of Medicinal Plants Research · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicNatural Compound Pharmacology Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAntimicrobialHyperforinHypericum perforatumHypericumSecondary metaboliteBiologyChemistryBotanyFood scienceTraditional medicineMicrobiologyMedicineBiochemistry

Abstract

fetched live from OpenAlex

The present study is aimed at investigating the effects of variation in secondary metabolites of St. John’s wort for its better use as an antibiotic. The seasonal dynamics investigation of St. John’s wort secondary metabolites was carried out on annual, biennial and triennial wild-growing plants of the suburban localities, and on the indigenous perennial plants of the mountainous localities. The effects of variation in secondary metabolites of the plant material were monitored using a complex antimicrobial preparation imanin. As plant secondary metabolites, imanin was isolated from flowers and leaves of St. John’s wort by aqueous-alkaline extraction. The quality of imanin contained in St. John’s wort was determined by FTIR and HPLC methods. The imanin extracts were tested for antimicrobial activity against Staphylococcus aureus, Streptococcus agalactiae,Bacillus diphteriae, Bacillus tetani, Clostridium histolyticus, Bacillusmesentericus and Bacillus mycoides. The quantitative effects of temperature and light intensity on imanin accumulations in St. John’s wort were examined depending on the sampling periods and location. The results of antimicrobial activity and quantitative effects of climatic conditions were correlated with vegetation phases of hypericum plants.   Key words: Imanin, St. John’s wort, antimicrobial activity, hyperforin, plant vegetation.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.944
Threshold uncertainty score0.523

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
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 integrity0.0000.001
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 head0.105
GPT teacher head0.393
Teacher spread0.288 · 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 designObservational
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

Citations11
Published2010
Admission routes1
Has abstractyes

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