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Record W4319447912 · doi:10.1016/j.lwt.2023.114498

Effect of exogenous melatonin on the isoflavone content and antioxidant properties of soybean sprouts

2023· article· en· W4319447912 on OpenAlexaff
Siqi Wu, Yanxia Wang, Trust Beta, Suyan Wang, Gerardo Méndez‐Zamora, Pedro Laborda, Daniela D. Herrera‐Balandrano

Bibliographic record

VenueLWT · 2023
Typearticle
Languageen
FieldMedicine
TopicPhytoestrogen effects and research
Canadian institutionsUniversity of Manitoba
FundersNational Natural Science Foundation of China
KeywordsGerminationIsoflavonesAntioxidantMalondialdehydeHydrogen peroxideChemistryMelatoninFood scienceSproutingNitric oxideHorticultureBiologyBiochemistryEndocrinology

Abstract

fetched live from OpenAlex

Sprouting can increase the nutritional quality of seeds, such as soybeans. The current study aimed to evaluate the influence of exogenous melatonin (MLT; 100 and 300 μmol/L) on the isoflavone content and antioxidant properties of two soybean cultivars’ germination process (5, 6, and 7 days). MLT treatments significantly increased the sprout length and germination rate by 53% and 12%, respectively, when compared to water-treated soybean sprouts. Further, seven main isoflavones were quantified, with the highest total isoflavone content (5.101 g/kg) found on day 5 when using 100 μmol/L MLT. Additionally, when applying the same concentration, the highest levels of SOD and CAT were found, with values of 10.05 and 15.10 μkat/g, respectively, therefore resulting in a decrease in malondialdehyde (MDA), hydrogen peroxide (H2O2), and nitric oxide (NO) contents. MLT enhanced the expression levels of several genes related to antioxidant properties, seed germination, and root length. Overall, MLT is a suitable inducer for promoting soybean germination and enhancing the isoflavone content and antioxidant properties of soybean sprouts.

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 score0.010
Threshold uncertainty score0.151

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 head0.059
GPT teacher head0.293
Teacher spread0.234 · 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

Citations22
Published2023
Admission routes1
Has abstractyes

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