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Record W2810843485 · doi:10.1002/9781119070153.ch8

Class‐Related Proinflammatory Effects

2018· other· en· W2810843485 on OpenAlexaff
Rosanne Séguin

Bibliographic record

Venuenot available
Typeother
Languageen
FieldImmunology and Microbiology
TopicImmune Response and Inflammation
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
Fundersnot available
KeywordsProinflammatory cytokineImmune systemImmunologyPharmacologyInnate immune systemDrugMedicineBiologyComputational biologyInflammation

Abstract

fetched live from OpenAlex

This chapter reviews the proinflammatory effects of antisense oligonucleotide (ASO) therapies as a class effect of these drugs. Early in drug development, induction of immune responses such as activation of the alternate complement pathway and induction of cytokines and splenomegaly in animals were observed. Some species were more sensitive for these ASO-induced effects than other species. A key step was the identification of immunostimulatory sequences contained in ASO that recognize innate immune receptors, leading to the induction of proinflammatory responses. ASOs are in development as therapies that leverage this immunostimulatory effect, whereas for other ASO, immune activation effects are undesired side effects. Although elimination of these immunostimulatory sequences from ASO candidates lessened some of the effects, or required higher dose of ASO for the induction of the effects, there remain proinflammatory class effects associated with ASO. Steps aimed at derisking ASO drug candidates during the screening process to optimize safe therapeutics in clinical trials are discussed.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0170.004

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 head0.003
GPT teacher head0.203
Teacher spread0.199 · 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 designBench or experimental
Domainnot available
GenreOther

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

Citations1
Published2018
Admission routes1
Has abstractyes

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