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Record W4393186867 · doi:10.1002/marc.202300638

Merging Modular Molecular Design with High Throughput Screening of Cell Adhesion on Antimicrobial Supramolecular Biomaterials

2024· article· en· W4393186867 on OpenAlexaff
Moniek G. J. Schmitz, Jasper G. M. Aarts, Laurence Burroughs, Phanikrishna Sudarsanam, Tim J. M. Kuijpers, Martijn Riool, Leonie de Boer, Xuan Xue, Dragan Bošnački, Sebastian A. J. Zaat, Jan de Boer, Morgan R. Alexander, Patricia Y. W. Dankers

Bibliographic record

VenueMacromolecular Rapid Communications · 2024
Typearticle
Languageen
FieldEngineering
TopicNanofabrication and Lithography Techniques
Canadian institutionsInstitute of Infection and Immunity
FundersDutch Polymer InstituteEuropean CommissionLeids Universitair Medisch CentrumNederlandse Organisatie voor Wetenschappelijk OnderzoekMinisterie van Onderwijs, Cultuur en WetenschapHORIZON EUROPE Widening Participation and Strengthening the European Research AreaUniversiteit Leiden
KeywordsMoietySupramolecular chemistryAdhesionCell adhesionNanotechnologyAntimicrobialModular designChemistryMaterials scienceDNA microarrayCombinatorial chemistryMoleculeBiochemistryStereochemistryComputer scienceOrganic chemistryGene

Abstract

fetched live from OpenAlex

A polymer microarray based on the supramolecular ureido-pyrimidinone (UPy) moiety is fabricated to screen antimicrobial materials for their ability to support cell adhesion. UPy-functionalized additives, either cell-adhesive, antimicrobial or control peptides, are used, and investigated in different combinations at different concentrations, resulting in a library of 194 spots. These are characterized on composition and morphology to evaluate the microarray fabrication. Normal human dermal fibroblasts are cultured on the microarrays and cell adhesion to the spots is systematically analyzed. Results demonstrate enhanced cell adhesion on spots with combinations including the antimicrobial peptides. This study clearly proves the power of the high throughput approach in combination with supramolecular molecules, to screen additive libraries for desired biological response.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.694
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.015
GPT teacher head0.235
Teacher spread0.220 · 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
GenreMethods

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

Citations0
Published2024
Admission routes1
Has abstractyes

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