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Record W4303579033 · doi:10.3390/applmicrobiol2040058

The Growth Medium Affects the Viability of IPEC-J2 Animal Cell Line in the Presence of Probiotic Bacteria

2022· article· en· W4303579033 on OpenAlexafffund
Marie-Josée Lemay, Yves Raymond, Claude P. Champagne, Julie Brassard

Bibliographic record

VenueApplied Microbiology · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProbiotics and Fermented Foods
Canadian institutionsAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food Canada
KeywordsViability assayProbioticLactic acidBacteriaIncubationFood scienceMicrobiologyFlow cytometryBiologyIn vitroChemistryBiochemistryImmunology

Abstract

fetched live from OpenAlex

Background: The IPEC-J2 cell line is frequently used as an in vitro model to study the bioactivity of live probiotics. However, lactic acid bacteria (LB) acidify the medium, and the impact of pH and lactic acid accumulation on cell viability seem to be underestimated. Methods: IPEC-J2 viability was assessed by neutral red and flow cytometry in the presence of eight probiotics at concentrations between 106 and 109 bacteria/mL in maintenance and buffered media. Results: It was shown that a high inoculation level led to higher cytotoxic effects on IPEC-J2 cells after 22 h of incubation and that viability losses were more related to a combination of low pH and lactic acid than to the probiotics themselves. Furthermore, with LB at 106 and 107 bacteria/mL, the addition of phosphates to the media significantly reduced the drop in the pH and preserved the IPEC-J2 viability between 100% and 69%, compared to a highly variable viability between 100% and 17.5% in the unbuffered media. Conclusions: Under certain in vitro conditions, probiotics can lead to the deterioration of animal cells, and pH neutralization is an essential parameter in the cell–probiotic system in order to preserve cell viability and to better evaluate the bioactive properties of live probiotics.

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 score0.144
Threshold uncertainty score0.217

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 science0.0010.000
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 head0.008
GPT teacher head0.188
Teacher spread0.180 · 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

Citations3
Published2022
Admission routes2
Has abstractyes

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