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Record W1986196732 · doi:10.1586/1744666x.2014.908705

Characterization of host immune responses in Ebola virus infections

2014· review· en· W1986196732 on OpenAlexafffund
Gary Wong, Gary Kobinger, Xiangguo Qiu

Bibliographic record

VenueExpert Review of Clinical Immunology · 2014
Typereview
Languageen
FieldMedicine
TopicViral Infections and Outbreaks Research
Canadian institutionsPublic Health Agency of Canada
FundersCanadian Institutes of Health Research
KeywordsImmune systemImmunologyInnate immune systemEbola virusImmunityAcquired immune systemVirologyInterferonMedicineEpitopeViral replicationBiologyVirusAntigen

Abstract

fetched live from OpenAlex

Ebola causes highly lethal hemorrhagic fever in humans with no licensed countermeasures. Its virulence can be attributed to several immunoevasion mechanisms: an early inhibition of innate immunity started by the downregulation of type I interferon, epitope masking and subversion of the adaptive humoural immunity by secreting a truncated form of the viral glycoprotein. Deficiencies in specific and non-specific antiviral responses result in unrestricted viral replication and dissemination in the host, causing death typically within 10 days after the appearance of symptoms. This review summarizes the host immune response to Ebola infection, and highlights the short- and long-term immune responses crucial for protection, which holds implications for the design of future vaccines and therapeutics.

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)0.003
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.942
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0070.002
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.117
GPT teacher head0.524
Teacher spread0.407 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations92
Published2014
Admission routes2
Has abstractyes

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