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Record W3043288378 · doi:10.3390/v12070771

ZIKV Infection Induces DNA Damage Response and Alters the Proteome of Gastrointestinal Cells

2020· article· en· W3043288378 on OpenAlexafffund
Kathleen K. M. Glover, Kevin M. Coombs

Bibliographic record

VenueViruses · 2020
Typearticle
Languageen
FieldMedicine
TopicMosquito-borne diseases and control
Canadian institutionsChildren's Hospital Research Institute of ManitobaUniversity of Manitoba
FundersChildren's Hospital Research Institute of Manitoba
KeywordsZika virusBiologyProteomeProteomicsProgenitor cellVirologyImmunologyMicrocephalyVirusBioinformaticsCell biologyStem cellGenetics

Abstract

fetched live from OpenAlex

The zika virus (ZIKV) is a neurotropic virus that causes congenital abnormalities in babies when they are infected in utero. Some studies have reported these congenital abnormalities result from ZIKV attacking neural progenitor cells within the brain which differentiate into neurons, oligodendrocytes, and astrocytes. Each of these glial cells play important roles during development of the fetal brain. In addition to ZIKV-induced congenital abnormalities, infected patients experience gastrointestinal complications. There are presently no reports investigating the role of this virus at the proteomic level in gastrointestinal associated cells, so we conducted an in vitro proteomic study of ZIKV-induced changes in Caco-2, a colon-derived human cell line which is known to be permissive to ZIKV infection. We used SomaScan, a new aptamer-based proteomic tool to identify host proteins that are dysregulated during ZIKV infection at 12, 24, and 48 h post-infection. Bioinformatic analyses predicted that dysregulation of differentially-regulated host proteins results in various gastrointestinal diseases. Validation of the clinical relevance of these promising protein targets will add to the existing knowledge of ZIKV biology. These potential proteins may be useful targets towards the development of therapeutic interventions.

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)0.000
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 score0.628
Threshold uncertainty score0.214

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)0.0000.000

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 head0.031
GPT teacher head0.276
Teacher spread0.244 · 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

Citations9
Published2020
Admission routes2
Has abstractyes

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