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Record W162516948 · doi:10.1039/9781849737074-00001

Biomimicry and Materials in Medicine

2014· book-chapter· en· W162516948 on OpenAlexaff
Larisa‐Emilia Cheran, Alin Cheran, Michael Thompson

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldNeuroscience
TopicAnesthesia and Neurotoxicity Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNanotechnologyBiocompatibilityBiological materialsMaterials scienceNatural materialsBiomedical engineeringBiomimeticsCeramicEngineeringPolymer scienceMetallurgy

Abstract

fetched live from OpenAlex

This chapter describes the incorporation of man-made materials into a variety of medical devices. There is an emphasis on the properties of materials that “copy” or at least reflect those of natural tissue. This relatively new technology is often called biomimicry and is an important aspect of medical treatment. Following a précis of material physical properties that are potentially applicable to such devices, the chapter systematically, but concisely, reviews particular classes of materials in terms of their use in medicine. Materials such as alloys of nickel and titanium are capable of shape memory transformations, where the mechanism of the effect is based on thermal energy acquired by the alloy through heating provides the energy necessary for the atoms to return to their original positions, so the sample regains its original shape. Such materials are employed in medical devices such as vascular stents, surgical tools, and cardiac catheters. Various ceramics such as zirconia and hydroxyapatite are used widely in implant technology such as hip and joint replacement. A major criterion for this type of material is their apparent biocompatibility in terms of interaction with tissue. In a similar vein, a variety of polymeric materials have been employed not just for tissue replacement but also as scaffolds for growth of cells and as an agent for drug release. There has also been interest in combining polymeric materials with nanoparticles in attempts to take advantage of the properties of these entities. One area that has attracted considerable research with respect to materials in medicine is neuroscience. In particular, quantum dots and other nanoparticle-based optical probes are employed successfully for reporting neurotransmitter concentrations and dynamic molecular processes with respect to neurons and glia cells. Nanotubes and nanowires have found utility for highly local electrical measurements, sensing of neurochemicals, for the delivery of photons to specific locations, and for the local release or collection of chemicals with regard to neural tissue. From a neuroregeneration perspective, carbon nanotubes can perform as a scaffold for the repair of injured nerves. Finally, a significant number of studies have appeared on the use of electronic devices such as field-effect transistors, often incorporating materials such as graphene, for the detection of neurotransmitters and other biochemicals. The chapter finishes with a look at the vexing problem of the material–biological fluid interaction which is crucial as it pertains to implant biocompatibility. There are known deleterious medical effects associated with this issue, such as micro-clot formation, that are thought to be initially instigated by surface protein adsorption. One possibility to ameliorate the problem with dramatic enhancement of biocompatibility through ultra-thin adlayer formation on a polymer substrate is described.

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 categoriesInsufficient 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: Other · Consensus signal: Other
Teacher disagreement score0.358
Threshold uncertainty score0.999

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)0.0020.000

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 head0.084
GPT teacher head0.308
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.

Study designBench or experimental
Domainnot available
GenreOther

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

Citations1
Published2014
Admission routes1
Has abstractyes

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