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Record W2511521044 · doi:10.15252/emmm.201606593

Immunoregulatory functions of immune complexes in vaccine and therapy

2016· review· en· W2511521044 on OpenAlexafffund
Yu‐Mei Wen, Libing Mu, Yan Shi

Bibliographic record

VenueEMBO Molecular Medicine · 2016
Typereview
Languageen
FieldImmunology and Microbiology
TopicImmunotherapy and Immune Responses
Canadian institutionsUniversity of Calgary
FundersCanadian Institutes of Health ResearchNational Institutes of HealthNational Science and Technology Major ProjectNational Institute of Allergy and Infectious DiseasesNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsBeijingChinese academy of sciencesChinaLibrary scienceMedical schoolMedicineBasic researchBiological sciencesMedical educationPolitical scienceBiologyComputer scienceComputational biology

Abstract

fetched live from OpenAlex

Clinical and experimental preparations of IgG/soluble antigen complexes, as well as those formed following antibody therapy in vivo, are multifaceted immune regulators. These immune complexes (ICs) have been tested in humans and animal models, mostly in forms of experimental or clinical vaccination, for at least a century. With intensified research on Fcγ receptor-mediated immune modulation, as well as with immune complex-directed antigen processing, presentation, and inflammatory responses, there are renewed interests of using ICs in vaccines and immunotherapies. Currently, IC-based immune therapy has been broadly experimented in HBV and HIV viral infection control and antitumor treatments. However, mechanistic insights of IC-based treatments are relatively recent subjects of study; strong efforts are needed to establish links to connect laboratory findings with clinical practices. This review covers the history, mechanisms, and in vivo outcomes of this safe and effective therapeutic tool, with a clear aim to bridge laboratory findings with evolving clinical applications.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.977
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.292
Teacher spread0.273 · 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

Citations64
Published2016
Admission routes2
Has abstractyes

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