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Record W1989338833 · doi:10.1139/w00-070

Improved growth and viability of lactobacilli in the presence of<i>Bacillus subtilis</i>(<i>natto</i>), catalase, or subtilisin

2000· article· en· W1989338833 on OpenAlexvenueno aff
Tomohiro Hosoi, Akio Ametani, Kan Kiuchi, Shuichi Kaminogawa

Bibliographic record

VenueCanadian Journal of Microbiology · 2000
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProbiotics and Fermented Foods
Canadian institutionsnot available
Fundersnot available
KeywordsLactobacillus reuteriBacillus subtilisCatalaseLactobacillus acidophilusMicrobiologyBiologyFood scienceSubtilisinProbioticChemistryBacteriaBiochemistryLactobacillusFermentationEnzyme

Abstract

fetched live from OpenAlex

In an effort to demonstrate the potential usefulness of Bacillus subtilis (natto) as a probiotic, we examined the effect of this organism on the growth of three strains of lactobacilli co-cultured aerobically in vitro. Addition of B. subtilis (natto) to the culture medium resulted in an increase in the number of viable cells of all lactobacilli tested. Since B. subtilis (natto) can produce catalase, which has been reported to exhibit a similar growth-promoting effect on lactobacilli, we also examined the effect of bovine catalase on the growth of Lactobacillus reuteri JCM 1112 and L. acidophilus JCM 1132. Both catalase and B. subtilis (natto) enhanced the growth of L. reuteri JCM 1112, whereas B. subtilis (natto) but not catalase enhanced the growth of L. acidophilus JCM 1132. In a medium containing 0.1 mM hydrogen peroxide, its toxic effect on L. reuteri JCM 1112 was abolished by catalase or B. subtilis (natto). In addition, a serine protease from B. licheniformis, subtilisin, improved the growth and viability of L. reuteri JCM 1112 and L. acidophilus JCM 1132 in the absence of hydrogen peroxide. These results indicate that B. subtilis (natto) enhances the growth and (or) viability of lactobacilli, possibly through production of catalase and subtilisin.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.653
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.012
GPT teacher head0.192
Teacher spread0.181 · 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

Citations132
Published2000
Admission routes1
Has abstractyes

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