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Record W2462596000 · doi:10.1111/jmi.12437

Utilization of 3D printing for an intravital microscopy platform to study the intestinal microcirculation

2016· article· en· W2462596000 on OpenAlexaff
Ian Burkovskiy, Christine Lehmann, Cindy Jiang, Juan Zhou

Bibliographic record

VenueJournal of Microscopy · 2016
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsDalhousie University
Fundersnot available
KeywordsIntravital microscopyMicroscopyFluorescence microscopeMicrocirculationNanotechnologyProcess (computing)Computer scienceBiomedical engineeringMaterials sciencePathologyMedicineOpticsPhysicsRadiology

Abstract

fetched live from OpenAlex

Intravital microscopy of the intestine is a sophisticated technique that allows qualitative and quantitative in vivo observation of dynamic cellular interactions and blood flow at a high resolution. Physiological conditions of the animal and in particular of the observed organ, such as temperature and moisture are crucial for intravital imaging. Often, the microscopy stage with the animal or the organ of interest imposes limitations on how well the animal can be maintained. In addition, the access for additional oxygen supply or drug administration during the procedure is rather restricted. To address these limitations, we developed a novel intravital microscopy platform, allowing us to have improved access to the animal during the intravital microscopy procedure, as well as improved microenvironmental maintenance. The production process of this prototype platform is based on 3D printing of device parts in a single-step process. The simplicity of production and the advantages of this versatile and customizable design are shown and discussed in this paper. Our design potentially represents a major step forward in facilitating intestinal intravital imaging using fluorescent microscopy.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.174
Threshold uncertainty score0.337

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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)0.0000.000

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 head0.062
GPT teacher head0.373
Teacher spread0.310 · 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

Citations5
Published2016
Admission routes1
Has abstractyes

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