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Record W3006731868 · doi:10.1039/c9fo01091b

Effect of the carrier material, drying technology and dissolution media on the viability of<i>Lactobacillus fermentum</i>K73 during simulated gastrointestinal transit

2020· article· en· W3006731868 on OpenAlexafffund
Stephanía Aragón-Rojas, Alan Javier Hernández‐Álvarez, Isabelle Mainville, Yves Arcand, María Ximena Quintanilla‐Carvajal

Bibliographic record

VenueFood & Function · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProbiotics and Fermented Foods
Canadian institutionsCegep de Saint HyacintheAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food CanadaUniversidad de La Sabana
KeywordsLactobacillus fermentumDissolutionGastrointestinal transitDigestion (alchemy)In vitroFood scienceRheologyChemistryMaterials scienceLactic acidChromatographyBiologyBacteriaBiochemistryMedicineComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

The goal of this study was to determine the effect of the carrier material, drying technology and dissolution media during the passage of L. fermentum K73 through a dynamic in vitro digestion system (IViDiS). The carrier materials were (i) culture medium with growing micro-organisms and (ii) culture medium with maltodextrin : sweet whey [0.6 : 0.4]. The carrier materials were dried by spray-drying and freeze-drying to obtain four types of powders. The dissolution media consisted of water and 1% fat milk. The powders were tested using an in vitro dynamic digestion system (IViDiS). The results showed that powders derived from culture medium had the highest protective effect on the viability of L. fermentum K73 in both dissolution media and that survival increased when the powders were tested in milk. The modified Gompertz model was used to model L. fermentum K73 behaviour during the digestion process. The model showed that cells entrapped in culture medium had the longest lag phase and the slowest inactivation rate when evaluated in milk.

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 score0.372
Threshold uncertainty score0.125

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 head0.010
GPT teacher head0.177
Teacher spread0.167 · 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

Citations17
Published2020
Admission routes2
Has abstractyes

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