MétaCan
Menu
Back to cohort
Record W2144145035 · doi:10.1139/cjm-2015-0350

Cellular and viral determinants of retroviral nuclear entry

2015· review· en· W2144145035 on OpenAlexvenueno aff
Faysal Bin Hamid, Jinsun Kim, Cha‐Gyun Shin

Bibliographic record

VenueCanadian Journal of Microbiology · 2015
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVirus-based gene therapy research
Canadian institutionsnot available
FundersNational Research Foundation of KoreaNational Research Foundation
KeywordsBiologyNucleoporinNuclear transportNuclear poreGenomeViral entryCell nucleusDNACell biologyViral structural proteinViral proteinGeneticsRNA interferenceRNACytoplasmGeneViral replicationVirus

Abstract

fetched live from OpenAlex

Retroviruses must integrate their cDNA into the host genome to generate proviruses. Viral DNA-protein complexes interact with cellular proteins and produce pre-integration complexes, which carry the viral genome and cross the nuclear pore channel to enter the nucleus and integrate viral DNA into host chromosomal DNA. If the reverse transcripts fail to integrate, linear or circular DNA species such as 1- and 2-long terminal repeats are generated. Such complexes encounter numerous cellular proteins in the cytoplasm, which restrict viral infection and protect the nucleus. To overcome host cell defenses, the pathogens have evolved several evasion strategies. Viral proteins often contain nuclear localization signals, allowing entry into the nucleus. Among more than 1000 proteins identified as required for HIV infection by RNA interference screening, karyopherins, cleavage and polyadenylation specific factor 6, and nucleoporins have been predominantly studied. This review discusses current opinions about the synergistic relationship between the viral and cellular factors involved in nuclear import, with focus on the unveiled mysteries of the host-pathogen interaction, and highlights novel approaches to pinpoint therapeutic targets.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.971
Threshold uncertainty score0.942

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.025
GPT teacher head0.295
Teacher spread0.269 · 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 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

Citations44
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of MicrobiologySame topicVirus-based gene therapy researchFrench-language works237,207