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Record W2946379187 · doi:10.1002/elsc.201900043

Toward enhanced hyperforin production in St. John's wort root cultures

2019· article· en· W2946379187 on OpenAlexaboutno aff
Mariam Gaid, Jan‐Hendrik Grosch, Steve Möller, Ludger Beerhues, Rainer Krull

Bibliographic record

VenueEngineering in Life Sciences · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant tissue culture and regeneration
Canadian institutionsnot available
FundersTechnische Universität Braunschweig
KeywordsHyperforinHypericum perforatumSucroseFood scienceFructoseChemistryFermentationBiomass (ecology)BiochemistryBotanyBiologyPharmacologyAgronomy

Abstract

fetched live from OpenAlex

Abstract During the past decades, several trials targeted a stable, sustainable and economic production of St. John's wort ( Hypericum perforatum ) extract. The value of this extract stems from its use to treat depression and skin irritation due to its hyperforin content. Previously, hyperforin‐forming in vitro root cultures were established. Here, detailed growth and production kinetics have been analyzed over 40 days of cultivation. In the first 10 days, sucrose was completely hydrolyzed to glucose and fructose. The ammonium consumption supported the increase in the biomass and hyperforin production. When sucrose was replaced with glucose/fructose, the linear growth phase started 6 days earlier and resulted in a higher space‐time‐yield. The maximum hyperforin production was 0.82 mg L −1 day −1 , which was 67 % higher than in the sucrose‐supplemented standard cultivation. Buffering the sucrose‐supplemented medium with phosphate caused a 2.7‐fold increase in the product to biomass yield coefficient. However, the combination of monosaccharides and buffering conditions did not cause an appreciable improvements in the production performance of the shake flask approaches. A potential scalability from flask to lab‐scale stirred bioreactors has been demonstrated. The results obtained offer a basis for a scalable production of hyperforin and a sustainable source for a tissue culture‐based phytomedicine.

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 score0.208
Threshold uncertainty score0.241

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 head0.010
GPT teacher head0.233
Teacher spread0.223 · 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

Citations5
Published2019
Admission routes1
Has abstractyes

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