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Record W2969997817 · doi:10.1101/746305

Complex symbiont-pathogen interactions inhibit intestinal repair

2019· preprint· en· W2969997817 on OpenAlexaff
David E. Fast, Kristina Petkau, Meghan Ferguson, Min‐Jeong Shin, Anthony Galenza, Benjamin Kostiuk, Stefan Pukatzki, Edan Foley

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2019
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVibrio bacteria research studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsBiologyPathogenProgenitor cellIntestinal epitheliumVibrio choleraeSecretionCell biologyProgenitorMicrobiologyImmune systemEpitheliumMicrobiomeHomeostasisStem cellImmunologyBacteriaGenetics

Abstract

fetched live from OpenAlex

SUMMARY Pathogen-mediated damage to the intestinal epithelium activates compensatory growth and differentiation repair programs in progenitor cells. Accelerated progenitor growth replenishes damaged tissue and maintains barrier integrity. Despite the importance of epithelial renewal to intestinal homeostasis, we know little about the effects of pathogen-commensal interactions on progenitor growth. We found that the enteric pathogen Vibrio cholerae , blocks critical growth and differentiation pathways in Drosophila progenitors despite extensive damage to the epithelial tissue. We showed that inhibition of epithelial repair requires interactions of the Vibrio cholerae type six secretion system with a complex community of symbiotic bacteria, and that elimination of the gut microbiome is sufficient to restore homeostatic growth in infected intestines. Together, this work highlights the importance of pathogen-symbiont interactions on intestinal immune responses and outlines a previously undescribed impact of the type six secretion system on pathogenesis.

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 score0.003
Threshold uncertainty score0.010

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)0.0030.001

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 head0.024
GPT teacher head0.265
Teacher spread0.241 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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