MétaCan
Menu
Back to cohort

Identification of Collagen-Binding Proteins in <i>Lactobacillus</i> spp. with Surface-Enhanced Laser Desorption/Ionization–Time of Flight ProteinChip Technology

2000· article· en· W1995167035 on OpenAlexaff
Jeffrey C. Howard, Christine Heinemann, Bradley J. Thatcher, Brian M. Martin, Bing Siang Gan, Gregor Reid

Bibliographic record

VenueApplied and Environmental Microbiology · 2000
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Hydrolysis and Bioactive Peptides
Canadian institutionsWestern UniversityHand and Upper Limb ClinicLawson Health Research Institute
Fundersnot available
KeywordsLactobacillus caseiChemistryLactobacillus rhamnosusMass spectrometryBiochemistryBinding proteinMolecular biologyMicrobiologyChromatographyBiologyLactobacillus

Abstract

fetched live from OpenAlex

Biosurfactants produced by Lactobacillus fermentum RC-14, L. rhamnosus GR-1 and 36, and L. casei Shirota were found to contain proteins that bind to both collagen types III and VI, as determined by surface-enhanced laser desorption/ionization (SELDI)-time of flight mass spectrometry. Both collagen types III and VI immobilized on SELDI preactivated ProteinChip arrays detected several different sizes (2 to 48 kDa) of collagen-binding proteins. Overall, the RC-14-produced biosurfactant contained the greatest number of collagen-binding proteins (RC-14 > GR-1 > 36 > Shirota), including the mature form of a previously cloned 29-kDa collagen-binding protein (referred to in its mature 26-kDa form). Although biosurfactants isolated from L. casei Shirota and L. rhamnosus 36 and GR-1 also contain several collagen-binding proteins, they do not contain the 26-kDa collagen-binding protein. Together, these results demonstrate the utility of the SELDI system as a means of rapidly characterizing clinically important but complex biosurfactant solutions.

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 score0.002
Threshold uncertainty score0.548

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 head0.002
GPT teacher head0.161
Teacher spread0.159 · 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

Citations54
Published2000
Admission routes1
Has abstractyes

Explore more

Same venueApplied and Environmental MicrobiologySame topicProtein Hydrolysis and Bioactive PeptidesFrench-language works237,207