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Record W7006272714

Tissues-on-a-string: Analyzing transport within tissues via perfusable glass-sheathed hydrogel microtubes

2023· dissertation· en· W7006272714 on OpenAlexaff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2023
Typedissertation
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLepidoptera: Biology and Taxonomy
Canadian institutionsMcGill University
Fundersnot available
KeywordsSelf-healing hydrogelsProcess (computing)BiocompatibilityBiocompatible material
DOInot available

Abstract

fetched live from OpenAlex

Measuring transport of soluble molecules such as nutrients, waste, and therapeutics within tissues is essential to understand the impact of culture dimensionality on tissue function, and to realize improved 3D cell culture discovery platforms.Studying these properties on conventional 3D cell cultures poses great challenges as the delivering or sampling access within these complex 3D tissue structures is either challenging to precisely position within the desired region or simply destructive.In this thesis, we demonstrate proof-of-concept for a simple technique to both deliver and sample soluble factors from a point source within spheroids, using a highly perfusable hollow-core hydrogel microtube and an integrated glass capillary sheathing system, that successfully limits transport to a well-defined region inside the tissue.We successfully demonstrated its applicability by analyzing the transport properties of a model placental trophoblast choriocarcinoma, cultured as a 3D spheroid.Through comparison with finite element modeling of soluble diffusion in our platform, we found that the diffusivity within placental spheroid is heterogeneous as it shows higher diffusivity towards its core and gradually decreases the edge.This platform provides a simple design that could be used to rapidly study diffusion of soluble within a 3D tissue.the heartful energy you brought to the lab.To Karthick, thank you for all the projects we shared and help I got from you.To James, thank you for starting master's with me and sharing common interests even though we are so different (I still can't forgive you for hating poutine).To Ben and Nick W. (no.3), thank you for being fun officemates and losing money in stocks with me and specifically to Ben for saving me countless times in process control and to Nick for trusting me in my meme stocks.To Scott, thank you for the amazing food you made at the chalet.Outside of the lab, I would like to thank all my badminton friends and teammates.The cheerful energy I get from them during competitions, trainings and social events allowed me stay healthy both physically and mentally.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.016
GPT teacher head0.247
Teacher spread0.231 · 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 source (direct Gemma or distilled Codex), 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

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