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Record W1980453620 · doi:10.1143/jjap.51.11pj04

Amine-Rich Organic Thin Films for Cell Culture: Possible Electrostatic Effects in Cell–Surface Interactions

2012· article· en· W1980453620 on OpenAlexaff
M. R. Wertheimer, Amélie St‐Georges‐Robillard, Sophie Lerouge, Fackson Mwale, Bentsian Elkin, Christian Oehr, Werner Wirges, Reimund Gerhard

Bibliographic record

VenueJapanese Journal of Applied Physics · 2012
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsMcGill UniversityÉcole de Technologie SupérieurePolytechnique Montréal
FundersCentre National de la Recherche Scientifique
KeywordsChinese hamster ovary cellPlasma polymerizationAdhesionParylenePolymerizationPolymerChemistryMaterials scienceThin filmChemical engineeringNanotechnologyBiophysicsOrganic chemistryBiochemistry

Abstract

fetched live from OpenAlex

In recent communications from these laboratories, we observed that amine-rich thin organic layers are very efficient surfaces for the adhesion of mammalian cells. We prepare such deposits by plasma polymerization at low pressure, atmospheric pressure, or by vacuum–ultraviolet photo-polymerization. More recently, we have also investigated a commercially available material, Parylene diX AM. In this article we first briefly introduce literature relating to electrostatic interactions between cells, proteins, and charged surfaces. We then present certain selected cell-response results that pertain to applications in orthopedic and cardiovascular medicine: we discuss the influence of surface properties on the observed behaviors of two particular cell lines, human U937 monocytes, and Chinese hamster ovary cells. Particular emphasis is placed on possible electrostatic attractive forces due to positively charged R-NH 3 + groups and negatively charged proteins and cells, respectively. Experiments carried out with electrets, polymers with high positive or negative surface potentials are added for comparison.

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 score0.009
Threshold uncertainty score0.571

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.009
GPT teacher head0.257
Teacher spread0.249 · 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

Citations6
Published2012
Admission routes1
Has abstractyes

Explore more

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