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Record W4402642014 · doi:10.3390/mps7050074

Co-Culture of Gut Bacteria and Metabolite Extraction Using Fast Vacuum Filtration and Centrifugation

2024· article· en· W4402642014 on OpenAlexfundno aff
Asha Guraka, R.H. Duff, Joe Waldron, Gyanendra Tripathi, Ali Kermanizadeh

Bibliographic record

VenueMethods and Protocols · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsnot available
FundersTrent UniversityUniversity of DerbyNottingham Trent University
KeywordsMetaboliteExtraction (chemistry)BacteriaFiltration (mathematics)CentrifugationChromatographyMicrobiologyChemistryBiologyBiochemistryMathematics

Abstract

fetched live from OpenAlex

This protocol describes a robust method for the extraction of intra and extracellular metabolites of gut bacterial mono and co-cultures. In recent years, the co-culture techniques employed in the field of microbiology have demonstrated significant importance in regard to understanding cell-cell interactions, cross-feeding, and the metabolic interactions between different bacteria, fungi, and microbial consortia which enable the mimicking of complex co-habitant conditions. This protocol highlights a robust reproducible physiologically relevant culture and extraction protocol for the co-culture of gut bacterium. The novel extraction steps are conducted without using quenching and cell disruption through bead-cell methods, freeze-thaw cycles, and sonication, which tend to affect the physical and biochemical properties of intracellular metabolites and secretome. The extraction procedure of inoculated bacterial co-cultures and monocultures use fast vacuum filtration and centrifugation. The extraction methodology is fast, effective, and robust, requiring 4 h to complete.

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: none
Teacher disagreement score0.512
Threshold uncertainty score0.290

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 head0.037
GPT teacher head0.460
Teacher spread0.423 · 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

Citations4
Published2024
Admission routes1
Has abstractyes

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