Bacterial Oncotraits Rather than Spatial Organization Are Associated with Dysplasia in Ulcerative Colitis
Bibliographic record
Abstract
BACKGROUND AND AIMS: Colonic bacterial biofilms are frequently present in ulcerative colitis [UC] and may increase dysplasia risk through pathogens expressing oncotraits. This prospective cohort study aimed to determine [1] the association of oncotraits and longitudinal biofilm presence with dysplasia risk in UC, and [2] the relation of bacterial composition with biofilms and dysplasia risk. METHODS: Faeces and left- and right-sided colonic biopsies were collected from 80 UC patients and 35 controls. Oncotraits [FadA of Fusobacterium, BFT of Bacteroides fragilis, colibactin [ClbB] and Intimin [Eae] of Escherichia coli] were assessed in faecal DNA with multiplex quantitative polymerase chain reaction [qPCR]. Biopsies were screened for biofilms [n = 873] with 16S rRNA fluorescent in situ hybridiation. Shotgun metagenomic sequencing [n = 265], and ki67-immunohistochemistry were performed. Associations were determined with a mixed-effects regression model. RESULTS: Biofilms were highly prevalent in UC patients [90.8%] with a median persistence of 3 years (interquartile range [IQR] 2-5 years). Biofilm-positive biopsies showed increased epithelial hypertrophy [p = 0.025] and a reduced Shannon diversity independent of disease status [p = 0.015], but were not significantly associated with dysplasia in UC: adjusted odds ratio [aOR] 1.45, 95% confidence interval [CI] 0.63-3.40. In contrast, ClbB independently associated with dysplasia [aOR 7.16, 95% CI 1.75-29.28], and FadA and Fusobacteriales were associated with a decreased dysplasia risk in UC [aOR 0.23, 95% CI 0.06-0.83, p <0.01]. CONCLUSIONS: Biofilms are a hallmark of UC; however, because of their high prevalence are a poor biomarker for dysplasia. In contrast, colibactin presence and FadA absence independently associate with dysplasia in UC and might therefore be valuable biomarkers for future risk stratification and intervention strategies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".