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Record W1529570024 · doi:10.1002/9781118843499.ch1

ECM‐Inspired Chemical Cues: Biomimetic Molecules and Techniques of Immobilization

2014· other· en· W1529570024 on OpenAlexaff
Roger Y. Tam, Shawn C. Owen, Molly S. Shoichet

Bibliographic record

Venuenot available
Typeother
Languageen
FieldMaterials Science
TopicSupramolecular Self-Assembly in Materials
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSelf-healing hydrogelsExtracellular matrixNanotechnologyChemistryPeptideComputer scienceMaterials scienceBiochemistry

Abstract

fetched live from OpenAlex

The extracellular matrix (ECM) is a complex environment that provides chemical and physical support to cells. This chapter discusses recent advances in the techniques used to incorporate ECM-inspired chemical signaling molecules into different hydrogels, and their effects on cellular interactions in 3-D. The chapter highlights many examples applicable to neurobiology. The discovery that short peptide sequences showed comparable activity to their respective native ECM proteins from which they were derived has resulted in significant efforts to design peptide-modified biomaterials with which to study cellular interactions. The chapter describes the conjugation of various peptides to different biomaterials using these techniques. A major challenge for incorporating ECM-inspired molecules into hydrogels is to allow user-defined temporal and spatial control over the presentation of biomimetic cues. Current technologies such as immunochemistry and phage display have provided high-throughput methods to discover molecules that are involved in cellular interactions and techniques to develop synthetic peptide analogs.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.495
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0030.000

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 head0.007
GPT teacher head0.243
Teacher spread0.236 · 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 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

Citations2
Published2014
Admission routes1
Has abstractyes

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