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Record W4412669604 · doi:10.1002/rmv.70063

Cell Biology of Human Cytomegalovirus Latency: Implications for Pathogenesis and Treatment

2025· review· en· W4412669604 on OpenAlexaff
Matthew B. Reeves

Bibliographic record

VenueReviews in Medical Virology · 2025
Typereview
Languageen
FieldMedicine
TopicCytomegalovirus and herpesvirus research
Canadian institutionsInstitute of Infection and Immunity
FundersMedical Research CouncilRosetrees TrustKidney Research UKWellcome Trust
KeywordsHuman cytomegalovirusPathogenesisLatency (audio)Immune systemImmunologyCytomegalovirusViral replicationVirus latencyBiologyViral pathogenesisDiseaseVirusVirologyHerpesviridaeNeuroscienceMedicineViral diseaseComputer sciencePathology

Abstract

fetched live from OpenAlex

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 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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.989
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.111
GPT teacher head0.454
Teacher spread0.343 · 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.

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

Citations5
Published2025
Admission routes1
Has abstractyes

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