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Record W2122295771 · doi:10.1128/jvi.02647-06

Canine Distemper Virus Infection Requires Cholesterol in the Viral Envelope

2007· article· en· W2122295771 on OpenAlexaff
Heidi Imhoff, Veronika von Messling, Georg Herrler, Ludwig Haas

Bibliographic record

VenueJournal of Virology · 2007
Typearticle
Languageen
FieldMedicine
TopicVirology and Viral Diseases
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsBiologyLipid raftInfectivityCanine distemperSyncytiumViral envelopeVirusViral entryCholesterolVirologyCell biologyViral replicationBiochemistry

Abstract

fetched live from OpenAlex

Cholesterol is known to play an important role in stabilizing particular cellular membrane structures, so-called lipid or membrane rafts. For several viruses, a dependence on cholesterol for virus entry and/or morphogenesis has been shown. Using flow cytometry and fluorescence microscopy, we demonstrate that infection of cells by canine distemper virus (CDV) was not impaired after cellular cholesterol had been depleted by the drug methyl-beta-cyclodextrin. This effect was independent of the multiplicity of infection and the cellular receptor used for infection. However, cholesterol depletion of the viral envelope significantly reduced CDV infectivity. Replenishment by addition of exogenous cholesterol restored infectivity up to 80%. Thus, we conclude that CDV entry is dependent on cholesterol in the viral envelope. Furthermore, reduced syncytium formation was observed when the cells were cholesterol depleted during the course of the infection. This may be related to the observation that CDV envelope proteins H and F partitioned into cellular detergent-resistant membranes. Therefore, a role for lipid rafts during virus assembly and release as well is suggested.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 head0.020
GPT teacher head0.327
Teacher spread0.307 · 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 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

Citations45
Published2007
Admission routes1
Has abstractyes

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