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Record W2862485754 · doi:10.1172/jci.insight.121133

Antibody-modified conduits for highly selective cytokine elimination from blood

2018· article· en· W2862485754 on OpenAlexfundno aff
J. Brian McAlvin, Ryan G. Wylie, Krithika Ramchander, Minh-Thuy Nguyen, Charles K. Lok, Morgan K. Moroi, Andre Shomorony, Nikolay V. Vasilyev, P. Armstrong, Jason H. Yang, Alexander M. Lieber, Obiajulu S. Okonkwo, Rohit Karnik, Daniel S. Kohane

Bibliographic record

VenueJCI Insight · 2018
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsnot available
FundersNational Heart, Lung, and Blood InstituteMcMaster UniversityBoston Biomedical Innovation CenterBoston Children's Hospital
KeywordsCytokineLipopolysaccharideTumor necrosis factor alphaIn vivoMedicineImmune systemAntibodyImmunologyIn vitroAcute pancreatitisIschemiaPharmacologyInternal medicineChemistryBiology

Abstract

fetched live from OpenAlex

Cytokines play an important role in dysregulated immune responses to infection, pancreatitis, ischemia/reperfusion injury, burns, hemorrhage, cardiopulmonary bypass, trauma, and many other diseases. Moreover, the imbalance between inflammatory and antiinflammatory cytokines can have deleterious effects. Here, we demonstrated highly selective blood-filtering devices - antibody-modified conduits (AMCs) - that selectively eliminate multiple specific deleterious cytokines in vitro. AMCs functionalized with antibodies against human vascular endothelial growth factor A or tumor necrosis factor α (TNF-α) selectively eliminated the target cytokines from human blood in vitro and maintained them in reduced states even in the face of ongoing infusion at supraphysiologic rates. We characterized the variables that determine AMC performance, using anti-human TNF-α AMCs to eliminate recombinant human TNF-α. Finally, we demonstrated selective cytokine elimination in vivo by filtering interleukin 1 β from rats with lipopolysaccharide-induced hypercytokinemia.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.133
Threshold uncertainty score0.626

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.011
GPT teacher head0.239
Teacher spread0.228 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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
Published2018
Admission routes1
Has abstractyes

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