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Record W2314152749 · doi:10.1021/bm501849p

Release of Anti-inflammatory Peptides from Thermosensitive Nanoparticles with Degradable Cross-Links Suppresses Pro-inflammatory Cytokine Production

2015· article· en· W2314152749 on OpenAlexfundno aff
Scott Poh, Jenny Lin, Alyssa Panitch

Bibliographic record

VenueBiomacromolecules · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Interference and Gene Delivery
Canadian institutionsnot available
FundersNational Institute of General Medical SciencesNational Heart, Lung, and Blood InstituteNational Institutes of HealthNational Institute of Arthritis and Musculoskeletal and Skin DiseasesMcMaster University
KeywordsCystamineTumor necrosis factor alphaChemistryPeptideCytokineNanoparticlePEGylationDrug deliveryInflammationBiophysicsBiochemistryMaterials scienceNanotechnologyImmunologyMedicineOrganic chemistryBiology

Abstract

fetched live from OpenAlex

Pro-inflammatory cytokines tumor necrosis factor α (TNF-α) and interleukin 6 (IL-6) are mediators in the development of many inflammatory diseases. To demonstrate that macrophages take up and respond to thermosensitive nanoparticle drug carriers, we synthesized PEGylated poly(N-isopropylacrylamide-2-acrylamido-2-methyl-1-propanesulfonate) particles cross-linked with degradable disulfide (N,N'-bis(acryloyl)cystamine) (NGPEGSS). An anti-inflammatory peptide (KAFAK) was loaded and released from the thermosensitive nanoparticles and shown to suppress levels of TNF-α and IL-6 production in macrophages. Cellular uptake of fluorescent, thermosensitive, and degradable nanoparticles and therapeutic efficacy of free KAFAK peptide compared to that of KAFAK loaded in PEGylated degradable thermosensitive nanoparticles were examined. The data suggests that the degradable, thermosensitive nanoparticles loaded with KAFAK may be an effective tool to treat inflammatory diseases.

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 score0.001
Threshold uncertainty score0.910

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 head0.017
GPT teacher head0.248
Teacher spread0.231 · 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

Citations36
Published2015
Admission routes1
Has abstractyes

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