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Record W2070356812 · doi:10.1073/pnas.1401998111

Pathophysiology of hantavirus pulmonary syndrome in rhesus macaques

2014· article· en· W2070356812 on OpenAlexaff
David Safronetz, Joseph Prescott, Friederike Feldmann, Elaine Haddock, Rebecca Rosenke, Atsushi Okumura, Douglas Brining, Eric Dahlstrom, Stephen F. Porcella, Hideki Ebihara, Dana Scott, Brian Hjelle, Heinz Feldmann

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2014
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Vectors
Canadian institutionsUniversity of Manitoba
FundersNational Institutes of Health
KeywordsHantavirus pulmonary syndromePathophysiologyHantavirusMedicineVirologyHantavirus InfectionImmunologyPathologyVirus

Abstract

fetched live from OpenAlex

The pathophysiology of hantavirus pulmonary syndrome (HPS) remains unclear because of a lack of surrogate disease models with which to perform pathogenesis studies. Nonhuman primates (NHP) are considered the gold standard model for studying the underlying immune activation/suppression associated with immunopathogenic viruses such as hantaviruses; however, to date an NHP model for HPS has not been described. Here we show that rhesus macaques infected with Sin Nombre virus (SNV), the primary etiological agent of HPS in North America, propagated in deer mice develop HPS, which is characterized by thrombocytopenia, leukocytosis, and rapid onset of respiratory distress caused by severe interstitial pneumonia. Despite establishing a systemic infection, SNV differentially activated host responses exclusively in the pulmonary endothelium, potentially the mechanism leading to acute severe respiratory distress. This study presents a unique chronological characterization of SNV infection and provides mechanistic data into the pathophysiology of HPS in a closely related surrogate animal model. We anticipate this model will advance our understanding of HPS pathogenesis and will greatly facilitate research toward the development of effective therapeutics and vaccines against hantaviral diseases.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.731
Threshold uncertainty score0.215

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.037
GPT teacher head0.319
Teacher spread0.282 · 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 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

Citations85
Published2014
Admission routes1
Has abstractyes

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