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Record W4229023112 · doi:10.1038/s41598-022-11123-w

Impact of experimental colitis on mitochondrial bioenergetics in intestinal epithelial cells

2022· article· en· W4229023112 on OpenAlexafffund
Luke Goudie, Nicole L. Mancini, Timothy E. Shutt, Graham P. Holloway, Chunlong Mu, Arthur Wang, Derek M. McKay, Jane Shearer

Bibliographic record

VenueScientific Reports · 2022
Typearticle
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsAlberta Children's HospitalUniversity of GuelphUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBioenergeticsColitisMitochondrionBiologyCell biologyMicrobiologyMedicineImmunology

Abstract

fetched live from OpenAlex

Abstract Intestinal homeostasis is highly dependent on optimal epithelial barrier function and permeability. Intestinal epithelial cells (IEC) regulate these properties acting as cellular gatekeepers by selectively absorbing nutrients and controlling the passage of luminal bacteria. These functions are energy demanding processes that are presumably met through mitochondrial-based processes. Routine methods for examining IEC mitochondrial function remain sparse, hence, our objective is to present standardized methods for quantifying mitochondrial energetics in an immortalized IEC line. Employing the murine IEC4.1 cell line, we present adapted methods and protocols to examine mitochondrial function using two well-known platforms: the Seahorse Extracellular Flux Analyzer and Oxygraph-2 k. To demonstrate the applicability of these protocols and instruments, IEC were treated with and without the murine colitogenic agent, dextran sulfate sodium (DSS, 2% w/v). Profound impairments with DSS treatment were found with both platforms, however, the Oxygraph-2 k allowed greater resolution of affected pathways including short-chain fatty acid metabolism. Mitochondrial functional analysis is a novel tool to explore the relationship between IEC energetics and functional consequences within the contexts of health and disease. The outlined methods offer an introductory starting point for such assessment and provide the investigator with insights into platform-specific capabilities.

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 score0.002
Threshold uncertainty score0.008

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.019
GPT teacher head0.300
Teacher spread0.280 · 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

Citations6
Published2022
Admission routes2
Has abstractyes

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