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Record W2032313288 · doi:10.1080/02652040701776620

Microencapsulation of a recombinant aminopeptidase (PepN) from<b><i>Lactobacillus rhamnosus</i></b>S93 in chitosan-coated alginate beads

2008· article· en· W2032313288 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Microencapsulation · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEnzyme Catalysis and Immobilization
Canadian institutionsAgriculture and Agri-Food CanadaMcGill UniversitySte. Anne's Hospital
Fundersnot available
KeywordsChitosanLactobacillus rhamnosusNuclear chemistryChemistryDissolutionPolymerBeadMaterials scienceControlled releaseChromatographyLactobacillusFood scienceBiochemistryOrganic chemistryNanotechnologyComposite material

Abstract

fetched live from OpenAlex

A recombinant aminopeptidase (90 kDa) of Lactobacillus rhamnosus S93 produced by E. coli was encapsulated in alginate or chitosan-treated alginate beads prepared by an extrusion method. This study investigated the effects of alginate, CaCl2, chitosan concentrations, hardening time, pH and alginate/enzyme ratios on the encapsulation efficiency (EE) and the enzyme release (ER). Chitosan in the gelling solution significantly increased the EE from 30.2% (control) to 88.6% (coated). This polycationic polymer retarded the ER from beads during their dissolution in release buffer. An increase in alginate and chitosan concentrations led to greater EE and lesser ER from the beads. The greatest EE was observed in a pH 5.4 solution (chitosan-CaCl2) during bead formation. Increasing the CaCl2 concentration over 0.1 M neither affected the EE nor the ER. Increasing hardening time beyond 10 min led to a decrease in EE and the alginate:enzyme ratio (3 : 1) was optimal to prevent the ER.

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.

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 score0.142
Threshold uncertainty score0.810

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 head0.011
GPT teacher head0.225
Teacher spread0.214 · 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