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Record W2151751266 · doi:10.1155/s1110724304307011

Microencapsulated Genetically Engineered <i>Lactobacillus plantarum</i> 80 (pCBH1) for Bile Acid Deconjugation and Its Implication in Lowering Cholesterol

2004· article· en· W2151751266 on OpenAlexafffund
Mitchell Jones, Hongmei Chen, Wei Ouyang, Terrence Metz, Satya Prakash

Bibliographic record

VenueBioMed Research International · 2004
Typearticle
Languageen
FieldNursing
TopicInfant Nutrition and Health
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsLactobacillus plantarumCholesterolBile acidCholesterol loweringChemistryBiochemistryLactobacillusInternal medicineMedicineLactic acidPharmacologyBacteriaBiology

Abstract

fetched live from OpenAlex

Cholesterol is known to be a major risk factor for coronary heart disease (CHD). Current treatments for elevated blood cholesterol include dietary management, regular exercise, and drug therapy with fibrates, bile acid sequestrants, and statins. Such therapies, however, are often suboptimal and carry a risk for serious side effects. This study shows that microencapsulated Lactobacillus plantarum 80 (pCBH1) cells can efficiently break down and remove bile acids, and establishes a basis for their use in lowering blood serum cholesterol. Results show that microencapsulated LP80 (pCBH1) is able to effectively break down the conjugated bile acids glycodeoxycholic acid (GDCA) and taurodeoxycholic acid (TDCA) with bile salt hydrolase (BSH) activities of 0.19 and 0.08 $\mu$ mol DCA/mg CDW/h respectively. This article also summarizes the physiological interrelationship between bile acids and cholesterol and predicts the oral doses of microencapsulated Lactobacillus plantarum 80 (pCBH1) cells required for lowering cholesterol.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.047
GPT teacher head0.372
Teacher spread0.325 · 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 source (direct Gemma or distilled Codex), 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

Citations86
Published2004
Admission routes2
Has abstractyes

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