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Record W4386127613 · doi:10.11159/icbb23.001

Biocompatible surfaces for biomedical devices base on the intermediate water concept

2023· article· en· W4386127613 on OpenAlexvenueno aff
Masaru Tanaka

Bibliographic record

VenueProceedings of the World Congress on New Technologies · 2023
Typearticle
Languageen
FieldEngineering
TopicModular Robots and Swarm Intelligence
Canadian institutionsnot available
Fundersnot available
KeywordsBiocompatible materialBase (topology)Computer scienceMaterials scienceEngineeringBiomedical engineering

Abstract

fetched live from OpenAlex

Water molecules play a crucial role in bio-interfacial interactions, including protein adsorption/desorption and cell adhesion behavior. To understand the role of water in the interaction of proteins and cells at biological interfaces, it is important to compare the states of hydration water with various physicochemical properties of hydrated polymeric biomaterials. Herein, we present the fundamental concepts for determining the interactions of proteins and cells with hydrated polymers along with selected examples corresponding to our recent studies, for example, poly(2-methoxyethyl acrylate) (PMEA), PMEA derivatives, zwitterionic polymers, poly(ethylene glycol), poly(N-vinyl-2-pyrroridone), and poly(2-oxazoline)s, and other polymers including biopolymers (DNA, RNA, proteins, and polysaccharides). The states of water were analyzed by differential scanning calorimetry, in situ attenuated total reflection infrared spectroscopy, soft X-ray emission spectroscopy, surface force measurements, and wide variety of analytical techniques. We found that intermediate water which is loosely bound to a polymer, is a useful indicator of the biocompatibility of polymer surfaces. This finding on intermediate water provides novel insights and helps develop novel experimental models for understanding protein adsorption/cell adhesion in a wide range of polymers, such as those used in biomedical applications.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.824
Threshold uncertainty score0.378

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 science0.0010.000
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 head0.026
GPT teacher head0.251
Teacher spread0.224 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the World Congress on New TechnologiesSame topicModular Robots and Swarm IntelligenceFrench-language works237,207