Cellular and viral determinants of retroviral nuclear entry
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".