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Record W2078658349 · doi:10.12938/bmfh.31.7

Identification of Immunopotentiating Lactic Acid Bacteria that Induce Antibody Production by <i>in vitro</i> Stimulated Human Peripheral Blood Mononuclear Cells

2012· article· en· W2078658349 on OpenAlexaff
Makiko Yamashita, Akira HITAKA, Himiko FUJINO, Takashi Matsumoto, Takanori Hasegawa, Fumiki Morimatsu, Tsukasa Fujiki, Yoshinori Katakura

Bibliographic record

VenueBioscience of Microbiota Food and Health · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProbiotics and Fermented Foods
Canadian institutionsConestoga Meat Packers (Canada)
Fundersnot available
KeywordsPeripheral blood mononuclear cellTLR2AntibodyAntigenImmune systemAdjuvantChemistryIn vitroMolecular biologyBiologyMicrobiologyImmunologyBiochemistryInnate immune system

Abstract

fetched live from OpenAlex

L-leucyl-L-leucine methyl ester (LLME) is known to remove lysosome-rich cells from human peripheral blood mononuclear cells (PBMCs). To evaluate the immunopotentiating ability of lactic acid bacteria (LAB), we adopted the in vitro stimulation protocol of LLME-treated PBMCs as a model assay system and monitored the level of antibody produced by stimulated PBMCs. The results indicated that several LAB strains have immunopotentiating ability against PBMCs, as evidenced by the enhanced antibody production and increased number of antigen-specific B cells. Next, we identified T cells as the direct target cells of the immunopotentiating LAB strain L32, suggesting that L32 induced antibody production by PBMCs through T-cell activation. Finally, we tested the immunopotentiating ability of ligands for Toll-like receptor 2 (TLR2), which is known to mediate the LAB signal, and observed that both L32 and one of the TLR2 ligands, LTA-BS, induced antigen-specific antibody production by in vitro stimulated PBMC. This suggests that L32 and LTA-BS can be used as an adjuvant for stimulating immune reaction in PBMCs.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.110
Threshold uncertainty score0.375

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 head0.022
GPT teacher head0.256
Teacher spread0.233 · 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

Citations1
Published2012
Admission routes1
Has abstractyes

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