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Record W2077683628 · doi:10.1017/s1466252310000022

Immune responses against Marek's disease virus

2010· review· en· W2077683628 on OpenAlexaff
Payvand Parvizi, Mohamed Faizal Abdul-Careem, Kamran Haq, Niroshan Thanthrige-Don, Karel A. Schat, Shayan Sharif

Bibliographic record

VenueAnimal Health Research Reviews · 2010
Typereview
Languageen
FieldMedicine
TopicHerpesvirus Infections and Treatments
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsMarek's diseaseImmune systemVirusBiologyDiseaseInnate immune systemAcquired immune systemEpigeneticsImmunologyVirologyAntibodyFirst lineGeneticsMedicineGene

Abstract

fetched live from OpenAlex

It is more than a century since Marek's disease (MD) was first reported in chickens and since then there have been concerted efforts to better understand this disease, its causative agent and various approaches for control of this disease. Recently, there have been several outbreaks of the disease in various regions, due to the evolving nature of MD virus (MDV), which necessitates the implementation of improved prophylactic approaches. It is therefore essential to better understand the interactions between chickens and the virus. The chicken immune system is directly involved in controlling the entry and the spread of the virus. It employs two distinct but interrelated mechanisms to tackle viral invasion. Innate defense mechanisms comprise secretion of soluble factors as well as cells such as macrophages and natural killer cells as the first line of defense. These innate responses provide the adaptive arm of the immune system including antibody- and cell-mediated immune responses to be tailored more specifically against MDV. In addition to the immune system, genetic and epigenetic mechanisms contribute to the outcome of MDV infection in chickens. This review discusses our current understanding of immune responses elicited against MDV and genetic factors that contribute to the nature of the response.

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)0.009
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.939
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.002
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.004
Insufficient payload (model declined to judge)0.0010.008

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 head0.491
GPT teacher head0.588
Teacher spread0.097 · 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 designNot applicable
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

Citations31
Published2010
Admission routes1
Has abstractyes

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