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Record W2913856631 · doi:10.1109/nanomed.2018.8641635

Mechanics of Microneedle-Based Fluid Injection into Skin Tissue

2018· article· en· W2913856631 on OpenAlexaff
Pranav Shrestha, Boris Stoeber

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicAdvancements in Transdermal Drug Delivery
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBiomedical engineeringMaterials scienceDrug deliveryOptical coherence tomographyTransdermalHypodermic needleTissue engineeringFluid dynamicsNanotechnologyMedicineMechanicsSyringeRadiology

Abstract

fetched live from OpenAlex

Hollow microneedles are a promising alternative to conventional drug delivery techniques such as oral drug administration and hypodermic injections, and are used for delivering drugs and therapeutics into the skin. Hollow microneedles can improve patient compliance and potentially reduce the required drug dose due to the dose-sparing effect of the skin. However, the mechanics of fluid flow into the skin have not been visualized mainly because the skin is a highly scattering biological material. Here, we report the results from ex-vivo injection experiments into excised porcine skin tissue using hollow microneedles and introduce the use of optical coherence tomography (OCT) for real-time imaging of skin tissue at the micro-scale during intradermal injections. The OCT images are analyzed to measure the distribution of strain in cross-sections of the tissue and to compute correlation maps showing that the skin tissue behaves like a deformable porous medium and absorbs fluid by locally expanding. Our experimental results, relating tissue deformation to fluid flow, can guide theoretical models of fluid flow into biological porous medium and help optimize microneedle-based intradermal drug delivery systems.

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: Empirical · Consensus signal: none
Teacher disagreement score0.531
Threshold uncertainty score0.995

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)0.0060.000

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 head0.061
GPT teacher head0.422
Teacher spread0.360 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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