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183. MICROBIOME IN AORTITIS

2019· article· en· W2932666599 on OpenAlex
Ted M. Getz, Gary S. Hoffman, Roshan Padmanabhan, Alexandra Villa‐Forte, Eric E. Roselli, Eugene H. Blackstone, Douglas R. Johnston, Gösta Pettersson, Edward G. Soltesz, Lars G. Svensson, Leonard H. Calabrese, Alison Clifford, Charis Eng

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueLara D. Veeken · 2019
Typearticle
Languageen
FieldMedicine
TopicInfectious Aortic and Vascular Conditions
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAortitisMedicineMicrobiomeInternal medicineBioinformaticsAortaBiology

Abstract

fetched live from OpenAlex

Background: Most non-infectious forms of aortitis are presumed to be autoimmune, resulting from primary systemic large vessel vasculitis (LVV), such as giant cell arteritis (GCA) or Takayasu’s arteritis (TAK) or other rheumatic diseases. Aortitis may also present as “clinically isolated aortitis” (CIA), a non- infectious vasculitis restricted to the aorta.1,2 It is not clear whether common conditions exist within CIA and GCA that contribute to inflammatory aneurysms; or how aortitis specimens compare to those from non-inflammatory thoracic aortic aneurysms. We have utilized sequencing of bacterial-specific 16S ribosomal RNA genes, a sensitive and culture-independent method for both pathogen and commensal detection, to characterize microbiomes of aortas affected by GCA and CIA and compared them to non-inflammatory aorta aneurysm controls. We also compared microbiomes of temporal arteries (TA) from a parallel study to those from aortic aneurysms. Methods: From 220 prospectively enrolled patients undergoing thoracic aorta aneurysm surgery, 49 were selected based on ability to match for age, gender and race (12 CIA, 14 GCA, and 23 non-inflammatory aneurysm controls). Biopsies were collected under surgically aseptic conditions, snap-frozen (-80oC), deidentified and processed at one time in blinded fashion. Taxonomic classification of bacterial sequences was performed to the genus level and relative abundances were calculated. Microbiome differential abundances were analyzed by principal coordinate analysis (PCoA/DESeq2) and predicted functional profiling was performed with PICRUSt. Results: Microbial beta (p = 0.024) and alpha diversity (p = 0.018) differed between aortitis cases and controls. There were no significant differences between microbial communities in CIA and GCA (p > 0.7). The largest differential abundances between aortitis and non-inflammatory control samples included Actinobacteria (P), Actinomycetales (o), Klebsiella (g), Staphylococcus (g), and Propionbacterium (g) [2logfold>2]. Microbiomes of aortas differed significantly from those of TAs, in both the control and GCA groups (p = 0.0002). Conclusion: Thoracic aortic aneurysms are not sterile. GCA and CIA aneurysms share similar microbial communities, but differ from those found in TAs and non-inflammatory aortic aneurysms. Whether these distinctions play a role in the pathogenesis of aortitis or non-inflammatory aneurysms or reflect secondary alterations in tissue substrate is unknown. Disclosures: This work was supported, in part, by the Fasenmyer Clinical Immunology Center (to GSH, LC and CE), and the National Center for Advancing Translational Sciences (NCATS) of the NIH (UL1TR000439 to GSH).

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score0.999

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)0.0020.002

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 head0.008
GPT teacher head0.250
Teacher spread0.242 · 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