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Record W2404669572 · doi:10.1017/erm.2015.19

Apoptosis, autophagy and unfolded protein response pathways in Arbovirus replication and pathogenesis

2016· review· en· W2404669572 on OpenAlexafffund
Mahmoud Iranpour, Adel Rezaei Moghadam, Mina Yazdi, Sudharsana Rao Ande, Javad Alizadeh, Emilia Wiecheć, L. Robbin Lindsay, Michael Drebot, Kevin M. Coombs, Saeid Ghavami

Bibliographic record

VenueExpert Reviews in Molecular Medicine · 2016
Typereview
Languageen
FieldMedicine
TopicMosquito-borne diseases and control
Canadian institutionsHealth Sciences CentreManitoba HealthChildren's Hospital Research Institute of ManitobaUniversity of ManitobaPublic Health Agency of Canada
FundersCanadian Institutes of Health ResearchManitoba Medical Service Foundation
KeywordsAutophagyArbovirusPathogenesisApoptosisBiologyViral replicationUnfolded protein responseCell biologyImmunologyVirologyVirusGenetics

Abstract

fetched live from OpenAlex

Arboviruses are pathogens that widely affect the health of people in different communities around the world. Recently, a few successful approaches toward production of effective vaccines against some of these pathogens have been developed, but treatment and prevention of the resulting diseases remain a major health and research concern. The arbovirus infection and replication processes are complex, and many factors are involved in their regulation. Apoptosis, autophagy and the unfolded protein response (UPR) are three mechanisms that are involved in pathogenesis of many viruses. In this review, we focus on the importance of these pathways in the arbovirus replication and infection processes. We provide a brief introduction on how apoptosis, autophagy and the UPR are initiated and regulated, and then discuss the involvement of these pathways in regulation of arbovirus pathogenesis.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.038
GPT teacher head0.357
Teacher spread0.319 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations69
Published2016
Admission routes2
Has abstractyes

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