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Record W2513694291 · doi:10.1038/srep33150

Amphotericin B Inhibits Enterovirus 71 Replication by Impeding Viral Entry

2016· article· en· W2513694291 on OpenAlexafffund
Fengwen Xu, Xiaoxiao Zhao, Siqi Hu, Jian Li, Lijuan Yin, Shan Mei, Tingting Liu, Ying Wang, Lili Ren, Shan Cen, Zhendong Zhao, Jianwei Wang, Qi Jin, Chen Liang, Bin Ai, Fei Guo

Bibliographic record

VenueScientific Reports · 2016
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Immunology Research
Canadian institutionsJewish General Hospital
FundersCanadian Institutes of Health ResearchNational Natural Science Foundation of China
KeywordsReplication (statistics)VirologyAmphotericin BEnterovirusViral replicationEnterovirus 71Enterovirus InfectionsMedicineBiologyMicrobiologyVirusAntifungal

Abstract

fetched live from OpenAlex

Enterovirus 71 (EV71) infection causes hand-foot-and-mouth disease that leads to cardiopulmonary complications and death in young children. There is thus an urgent need to find new treatments to control EV71 infection. In this study, we report potent inhibition of EV71 by a polyene antibiotic Amphotericin B. Amphotericin B profoundly diminished the expression of EV71 RNA and viral proteins in the RD cells and the HEK293 cells. As a result, EV71 production was inhibited by Amphotericin B with an EC50 (50% effective concentration) of 1.75 μM in RD cells and 0.32 μM in 293 cells. In addition to EV71, EV68 was also strongly inhibited by Amphotericin B. Results of mechanistic studies revealed that Amphotericin B targeted the early stage of EV71 infection through impairing the attachment and internalization of EV71 by host cells. As an effective anti-fungi drug, Amphotericin B thus holds the promise of formulating a novel therapeutic to treat EV71 infection.

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 score0.002

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)0.0010.000

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 head0.018
GPT teacher head0.305
Teacher spread0.287 · 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

Citations25
Published2016
Admission routes2
Has abstractyes

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