MétaCan
Menu
Back to cohort
Record W2030706455 · doi:10.1155/2014/196712

<i>In Vitro</i>Inhibition of Hepatitis C Virus by Antisense Oligonucleotides in PBMC Compared to Hepatoma Cells

2014· article· en· W2030706455 on OpenAlexaff
Samar Samir Youssef, Ahmed M. Fahmy, Moataza H Omran, Amr Saad Mohamed, Mohamed Ali El Desouki, Mostafa K. El Awady

Bibliographic record

VenueBioMed Research International · 2014
Typearticle
Languageen
FieldMedicine
TopicHepatitis C virus research
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsInternal ribosome entry sitePeripheral blood mononuclear cellIn vitroVirologyMolecular biologyHepatitis C virusOligonucleotideBiologyMessenger RNATranslation (biology)VirusMedicineDNAGeneBiochemistry

Abstract

fetched live from OpenAlex

AIM: To assess the efficiency of phosphorothioate antisense oligodeoxynucleotide 1 (S-ODN1) on HCV translation inhibition in PBMC compared to hepatoma cells in vitro for the first time. MATERIALS AND METHODS: The study included 34 treatment naive HCV patients. IRES domain III and IV sequence variations were tested in 45 clones from 9 HCV patients. PBMC of HCV positive patients were subjected to S-ODN in vitro. Concomitantly HepG2 cells infected by the same patient's serum were also treated with S-ODN1 for 24 and 48 hours. Cellular RNA was tested for HCV plus and minus strands by reverse transcription polymerase chain reaction (RT-PCR). RESULTS: Sequence variations were seen in HCV IRES domain III only while domain IV was conserved among all the tested patient's clones. S-ODN1 successfully inhibited HCV translation in HepG2 cells, while in PBMC inhibition was partial. CONCLUSION: HCV IRES domain IV is more conserved than domain IIId in genotype 4 HCV patients. S-ODN against HCV IRES domain IV was not efficient to inhibit HCV translation in PBMC under the study conditions. Further studies testing other S-ODN targeting other HCV IRES domains in PBMC should be done.

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)0.002
metaresearch head score (Gemma)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.042
GPT teacher head0.379
Teacher spread0.337 · 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

Citations3
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueBioMed Research InternationalSame topicHepatitis C virus researchFrench-language works237,207