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P-218 YI Increased Abundance of Colonic Mucosal Faecalibacterium Prausnitzii in Pediatric Treatment-Naive Ulcerative Colitis

2014· article· en· W2318205278 on OpenAlexaboutno aff
Shah Rajesh, Cope Julia, Dorottya Nagy‐Szakal, Dowd Scot, Hollister-Branton Emily, Richárd Kellermayer

Bibliographic record

VenueInflammatory Bowel Diseases · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsnot available
Fundersnot available
KeywordsFaecalibacterium prausnitziiUlcerative colitisMicrobiomeMetagenomicsGastroenterologyMedicineInflammatory bowel diseasePathogenesisDiseaseInternal medicineImmunologyGeneBiologyGut floraBioinformaticsGenetics

Abstract

fetched live from OpenAlex

Ulcerative colitis (UC) pathogenesis likely involves a dysregulated interaction between host genes, intestinal microbes and environment. Twenty percent of UC presents in children, where the disease is usually more aggressive than in adults. In regards to the microbiome, Faecalibacterium prausnitzii has been observed to harbor anti-inflammatory properties, and has been associated both with Crohn's disease (CD) and UC. Lower abundance of F. prausnitzii has been detected in patients with ileal CD. The decreased abundance of this bacterium has been associated with an increased risk of post-surgical relapse in CD patients as well. In UC, conflicting observations have been made about F. prausnitzii abundance. One study connected lower abundance of F. prausnitzii with shorter time in remission and greater risk of UC flare. However, most of these metagenomic observations have limitations, such as restriction to adults, studying stool samples, and including treatment experienced patients. All of these factors may influence microbiome composition, and limited mucosa associated metagenomic data exists in respect to pediatric UC in untreated patients. However, such information is likely to carry important information towards unraveling the pathogenesis of this UC subtype. The aim of our study was to characterize the colonic mucosal microbiome of treatment-naïve pediatric UC patients. Left sided colonic biopsy samples from 9 treatment naive UC patients and 13 controls (no endoscopic or histologic evidence of UC) were studied. The Illumina MiSeq sequencing platform was used to interrogate the V1-V3 regions of the bacterial 16s rRNA gene. Processing of sequence data, assignment of operational taxonomic units (OTUs) and calculation of alpha-diversity were performed in QIIME (Quantitative Insights Into Microbial Ecology). STAMP (Statistical analysis of taxonomic and functional profiles) was used to analyze the high-throughput metagenomic data set. Clinical information, including Montreal classification of disease extent, physician's global assessment of severity and medications were reviewed. Pediatric UC patients had a comparable Shannon diversity index compared to controls. There were no significant differences between the groups when analyzed at the phylum, class, order, family, genus or species levels. There was, however, a trend towards a higher abundance of F. prausnitzii in UC patients compared to controls (P = 0.069). Significantly (Fischer exact test: P = 0.027) more UC patients (7/9 = 77.8%) had >3% F. prausnitzii abundance in the colonic mucosa than controls (3/13 = 23.1%). F. prausnitzii abundance at diagnosis did not predict short term (within 6 months) clinical outcomes in this small cohort. Our study demonstrated limited, but detectable differences between the colonic mucosal microbiome of treatment naive UC patients and controls in spite the small sample sizes. Significantly more UC patients had increased abundance of F. prausnitzii than controls. These results warrant further studies on larger groups to clarify the role of F. prausnitzii in pediatric UC.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

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)0.0020.000

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 head0.006
GPT teacher head0.240
Teacher spread0.234 · 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 designObservational
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".

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Citations0
Published2014
Admission routes1
Has abstractyes

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