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Record W7117111892 · doi:10.1134/s0003683825600034

Response Mechanism of Lactobacillus plantarum under Simulated Digestion Based on Proteomics and Metabolomics Analysis

2025· article· en· W7117111892 on OpenAlexaff
Leyi Zhao, Ziqing Cheng, Yuxi Ling, Y. Wu, M. Zhang, H. Yang, Zuming Li

Bibliographic record

VenueApplied Biochemistry and Microbiology · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProbiotics and Fermented Foods
Canadian institutionsUniversity of ManitobaSinai Health System
Fundersnot available
KeywordsLactobacillus plantarumMetabolomicsProteomicsProbioticDigestion (alchemy)MetabolismBacteriaLactobacillusMechanism (biology)

Abstract

fetched live from OpenAlex

Abstract Proteomics and a metabolomics strategy were used to investigate the probiotic properties of Lactobacillus plantarum BW2013 under simulated in vitro digestion. Thirty seven significant differential proteins including 6-phosphogluconate dehydrogenase, pyruvate kinase, F0F1 ATP synthase, ABC-type transporters, ribosomal and universal stress proteins and 40 significant differential metabolites, such as indole-3-lactic acid, Pro, trans-cinnamic acid, betaine, citric acid, D-fructose, and γ-aminobutyric acid were identified and characterized. The association analysis indicated that most upregulated proteins and metabolites are involved primarily in stress response, regulation of intestinal flora and accumulation of beneficial substances relating to carbohydrate and energy metabolic pathways, amino acid metabolism pathways, nucleotide metabolism pathways, overall/universal stress response. The results provide new insights into the response mechanism of L. plantarum BW2013 under the simulated digestion conditions, facilitating the exploitation of its potential application in probiotic products.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.006
GPT teacher head0.195
Teacher spread0.189 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2025
Admission routes1
Has abstractyes

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