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Record W2944079221 · doi:10.3390/v11050434

Treatment of Metastatic Disease through Natural Killer Cell Modulation by Infected Cell Vaccines

2019· review· en· W2944079221 on OpenAlexafffund
Seyedeh Raheleh Niavarani, Christine Lawson, Lee‐Hwa Tai

Bibliographic record

VenueViruses · 2019
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVirus-based gene therapy research
Canadian institutionsCentre Hospitalier Universitaire de SherbrookeUniversité de Sherbrooke
FundersFaculty of Medicine and Health, University of SydneyFonds de Recherche du Québec - SantéCanadian Institutes of Health ResearchUniversité de Sherbrooke
KeywordsOncolytic virusImmune systemImmunotherapyContext (archaeology)ImmunologyCancer immunotherapyAntigenImmunityCancerBiologyCancer researchMedicineVirology

Abstract

fetched live from OpenAlex

Oncolytic viruses (OVs) are a form of immunotherapy that release tumor antigens in the context of highly immunogenic viral signals following tumor-targeted infection and destruction. Emerging preclinical and clinical evidence suggests that this in situ vaccine effect is critical for successful viro-immunotherapy. In this review, we discuss the application of OV as an infected cell vaccine (ICV) as one method of enhancing the potency and breadth of anti-tumoral immunity. We focus on understanding and manipulating the critical role of natural killer (NK) cells and their interactions with other immune cells to promote a clinical outcome. With a synergistic tumor killing and immune activating mechanism, ICVs represent a valuable new addition to the cancer fighting toolbox with the potential to treat malignant disease.

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: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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.

Opus teacher head0.056
GPT teacher head0.367
Teacher spread0.311 · 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
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

Citations4
Published2019
Admission routes2
Has abstractyes

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