Cell Biology of Human Cytomegalovirus Latency: Implications for Pathogenesis and Treatment
Bibliographic record
Abstract
Human cytomegalovirus (HCMV), like all herpes viruses, can establish lifelong infections of the host. This is due to the capacity to establish latency-a defining characteristic of herpes virus infection. In healthy individuals, pathology associated with HCMV infection is rare due, in part, to a robust immune response that controls replication. Consequently, in patients with impaired immune responses substantial pathogenesis is observed due to a failure of immunological control. In this review, I discuss the biology of latency and reactivation with an emphasis on aspects important for our understanding of pathogenesis and treatment. In particular, I will represent how fundamental understanding of the cellular and molecular details of viral latency have, and will continue to be, pivotal for attempts to therapeutically target latent HCMV with a view to reducing the burden of disease. This will include pharmacological and immunological therapies that utilise the modulation of both host and viral functions important for latency and reactivation as well as strategies to harness the very well characterised and prodigious immune response directed against replicating HCMV to target latent infections as well.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".