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Record W4200546717 · doi:10.3389/fimmu.2021.793841

Rotavirus Interactions With Host Intestinal Epithelial Cells

2021· review· en· W4200546717 on OpenAlexfundno aff
Joshua O. Amimo, Sergei A. Raev, Juliet Chepngeno, Alfred O. Mainga, Yusheng Guo, Linda J. Saif, Anastasia N. Vlasova

Bibliographic record

VenueFrontiers in Immunology · 2021
Typereview
Languageen
FieldMedicine
TopicViral gastroenteritis research and epidemiology
Canadian institutionsnot available
FundersInternational Development Research Centre
KeywordsBiologyImmunologyIntestinal epitheliumImmune systemRotavirusInnate immune systemChemokineMucusIntestinal mucosaReceptorMicrobiologyCell biologyEpitheliumVirusMedicine

Abstract

fetched live from OpenAlex

Rotavirus (RV) is the foremost enteric pathogen associated with severe diarrheal illness in young children (<5years) and animals worldwide. RV primarily infects mature enterocytes in the intestinal epithelium causing villus atrophy, enhanced epithelial cell turnover and apoptosis. Intestinal epithelial cells (IECs) being the first physical barrier against RV infection employs a range of innate immune strategies to counteract RVs invasion, including mucus production, toll-like receptor signaling and cytokine/chemokine production. Conversely, RVs have evolved numerous mechanisms to escape/subvert host immunity, seizing translation machinery of the host for effective replication and transmission. RV cell entry process involve penetration through the outer mucus layer, interaction with cell surface molecules and intestinal microbiota before reaching the IECs. For successful cell attachment and entry, RVs use sialic acid, histo-blood group antigens, heat shock cognate protein 70 and cell-surface integrins as attachment factors and/or (co)-receptors. In this review, a comprehensive summary of the existing knowledge of mechanisms underlying RV-IECs interactions, including the role of gut microbiota, during RV infection is presented. Understanding these mechanisms is imperative for developing efficacious strategies to control RV infections, including development of antiviral therapies and vaccines that target specific immune system antagonists within IECs.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.972
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.038
GPT teacher head0.346
Teacher spread0.308 · 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

Citations83
Published2021
Admission routes1
Has abstractyes

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