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Understanding the Molecular Mechanisms of Rifaximin in the Treatment of Gastrointestinal Disorders - A Focus on the Modulation of Host Tissue Function

2015· article· en· W2404343343 on OpenAlexafffund
Simon A. Hirota

Bibliographic record

VenueMini-Reviews in Medicinal Chemistry · 2015
Typearticle
Languageen
FieldMedicine
TopicPneumocystis jirovecii pneumonia detection and treatment
Canadian institutionsResearch Canada
FundersCanadian Institutes of Health Research
KeywordsRifaximinPregnane X receptorIrritable bowel syndromeContext (archaeology)Gastrointestinal tractBiologyNuclear receptorAntibioticsPharmacologyMedicineInternal medicineMicrobiologyTranscription factorGeneBiochemistry

Abstract

fetched live from OpenAlex

Rifaximin is a broad-spectrum oral antibiotic, exhibiting limited systemic absorption, that is used clinically to treat a variety of gastrointestinal disorders, including traveller's diarrhea, hepatic encephalopathy, irritable bowel syndrome, and the inflammatory bowel diseases. Rifaximin's antimicrobial properties, in the context of enteric infections, and its effects on the host's intestinal microbiota have been well characterized. More recently, it has been reported that rifaximin can modulate host tissue function through the activation of distinct molecular events. Within the gastrointestinal tract, rifaximin is a selective agonist of the pregnane X receptor (PXR), a nuclear receptor that regulates the expression of genes related to xenobiotic metabolism and drug detoxification. The PXR can also elicit immunomodulatory effects through its interaction with a variety of intracellular signaling cascades, including the nuclear factor kappa B and c-jun N-terminal kinase pathways. In this review, we will summarize the clinical uses of rifaximin and discuss its mechanism of action in relation to the modulation of the intestinal microbiota and the regulation of gastrointestinal host cell function, with a specific focus on PXR-dependent pathways.

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)0.001
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 score0.102
Threshold uncertainty score0.362

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.078
GPT teacher head0.299
Teacher spread0.221 · 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

Citations20
Published2015
Admission routes2
Has abstractyes

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