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Record W2966776527 · doi:10.1684/vir.2018.0737

Les protéines antisens des virus HTLV

2018· article· fr· W2966776527 on OpenAlexaff
Clément Caté, Émilie Larocque, Jean‐Marie Péloponèse, Jean-Michel Mesnard, Éric Rassart, Benoı̂t Barbeau

Bibliographic record

VenueVirologie · 2018
Typearticle
Languagefr
FieldImmunology and Microbiology
TopicT-cell and Retrovirus Studies
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsMolecular biologyBiologyVirusVirology

Abstract

fetched live from OpenAlex

Les virus T-lymphotropiques humains (HTLV) sont composés de quatre membres : HTLV-1, 2, 3 et 4. Issus d'une transmission de virus simiens, les virus HTLV sont capables d'infecter plusieurs types de cellules du système immunitaire.HTLV-1 est le premier rétrovirus humain isolé responsable de pathologies chez l'individu infecté.Bien que l'expression des gènes des rétrovirus dépende généralement du promoteur situé dans leur LTR 5 , il a été démontré que le LTR 3'des HTLV possédait aussi une activité promotrice responsable de la production d'un transcrit antisens in vivo.Ces transcrits sont capables de produire des protéines dites antisens, nommées HBZ, APH-2, APH-3 et APH-4, respectivement pour HTLV-1, 2, 3 et 4. La transcription antisens chez HTLV-1 a été analysée en détail et la protéine HBZ est la plus étudiée des quatre protéines antisens.Il est aujourd'hui avéré qu'elle possède des rôles importants pour la réplication virale et le développement de la leucémie.Peu d'études ont cependant été réalisées sur la transcription antisens chez HTLV-2 et encore moins chez HTLV-3/4, bien qu'il semble très probable que la transcription antisens joue également un rôle crucial dans leur processus infectieux.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

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)0.0020.001

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 head0.063
GPT teacher head0.299
Teacher spread0.236 · 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 designNot applicable
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

Citations1
Published2018
Admission routes1
Has abstractyes

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