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Record W2995682824 · doi:10.3390/v11121144

Feline Infectious Peritonitis as a Systemic Inflammatory Disease: Contribution of Liver and Heart to the Pathogenesis

2019· article· en· W2995682824 on OpenAlexaff
Alexandra Malbon, Sonja Fonfara, Marina L. Meli, S. Hahn, Herman Egberink, Anja Kipar

Bibliographic record

VenueViruses · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Virus Infections Studies
Canadian institutionsUniversity of Guelph
FundersUniversität ZürichDeutsche ForschungsgemeinschaftUniversity of GlasgowWellcome Trust
KeywordsFeline infectious peritonitisPathogenesisImmunologyImmune systemMedicineSerositisInflammationCytokineCATSProinflammatory cytokineDiseaseInfectious disease (medical specialty)PathologyArthritisInternal medicine

Abstract

fetched live from OpenAlex

Feline infectious peritonitis (FIP) is a fatal immune-mediated disease of cats, induced by feline coronavirus (FCoV). A combination of as yet poorly understood host and viral factors combine to cause a minority of FCoV-infected cats to develop FIP. Clinicopathological features include fever, vasculitis, and serositis, with or without effusions; all of which indicate a pro-inflammatory state with cytokine release. As a result, primary immune organs, as well as circulating leukocytes, have thus far been of most interest in previous studies to determine the likely sources of these cytokines. Results have suggested that these tissues alone may not be sufficient to induce the observed inflammation. The current study therefore focussed on the liver and heart, organs with a demonstrated ability to produce cytokines and therefore with huge potential to exacerbate inflammatory processes. The IL-12:IL-10 ratio, a marker of the immune system's inflammatory balance, was skewed towards the pro-inflammatory IL-12 in the liver of cats with FIP. Both organs were found to upregulate mRNA expression of the inflammatory triad of cytokines IL-1β, IL-6, and TNF-α in FIP. This amplifying step may be one of the missing links in the pathogenesis of this enigmatic disease.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.525
Threshold uncertainty score0.288

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.013
GPT teacher head0.224
Teacher spread0.211 · 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 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".

Quick stats

Citations40
Published2019
Admission routes1
Has abstractyes

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