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Record W7055012362

Beyond The Chip: Microphysiological Systems On Multi-well Plates

2024· dissertation· en· W7055012362 on OpenAlexfundno aff

Bibliographic record

VenueMacSphere (McMaster University) · 2024
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicAdvanced Frequency and Time Standards
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsProcess (computing)ScalabilityThroughputScaffoldProcess developmentDrug developmentRelevance (law)
DOInot available

Abstract

fetched live from OpenAlex

The drug development process is lengthy and expensive, with a 90% failure rate among drugs entering clinical trials due to the inadequacy of predictive models in the initial phases of drug development. To overcome these limitations, there is a paradigm shift towards developing micro physiological systems often referred as Organ-on-a-Chip that have be shown to recapitulate organ level functions in vitro. However, despite their promise, these systems often have limited throughput, restricting their widespread use in the drug development process. The work outlined in this thesis aims to bridge this gap by integrating the physiological relevance offered by micro physiological systems with the high throughput capabilities of traditional 2D multi-well plate cultures. The thesis outlines the development of two novel micro physiological systems, engineered in a high throughput multi-well format called the IFlowPlate and AngioPlate. Both the platforms have an open-top design and unlike tradition microphysiological platforms does not need complex pump systems and have built-in connections to achieve perfusion making it more scalable and user-friendly. The IFlowPlate leverages the self-assembly capability of endothelial cells to create a perfusable vascular network. This platform technology was utilized in this work to achieve intravascular perfusion of colon organoids for the first time and demonstrated immune cell circulation and recruitment in response to injury. AngioPlate, the other platform that was developed as a part of this work, utilizes a pre-patterned scaffold completely embedded in native hydrogel matrix to guide cells in forming organ-specific geometries and tubular structures using a novel subtractive manufacturing technique. This platform allowed for fabricating complex and intricate networks to model vascularized terminal lung alveoli and renal proximal tubules. This work demonstrated for the first time that highly complex perfusable tissues embedded in hydrogel can be integrated with multi-well plates to mimic tissue specific structures and interfaces without the use of synthetic membranes or plastic channels. The built-in perfusion channel and the flexible hydrogel matrix allowed for the terminal lung alveoli model to be mechanically actuated to mimic breathing motions. The renal proximal tubule model was used to mimic glucose reabsorption in kidney and model renal inflammation. The latter part of this work focusses on further improving this platform to increase platform robustness and to allow for incorporating supporting cells such as fibroblasts into the hydrogel matrix. This allowed us to model tubular injuries in kidney such as cisplatin induced -nephrotoxicity and TGF- β1 induced- tubulointerstitial fibrosis. Furthermore this work also describes the development of a high-throughput TEER meter that can be integrated with the AngioPlate platform allowing for rapid, non-invasive measurement of renal epithelial barrier integrity. Given that both platforms are designed in a 384-well plate format, they are high throughput and compatible with existing technologies like high-content imaging systems, robotic liquid handling systems, and microplate readers allowing for widespread adoption across diverse research settings. It is anticipated that the contributions described in this work will significantly advance our understanding of disease propagation and accelerate drug development process.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.950
Threshold uncertainty score1.000

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0470.001

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 head0.009
GPT teacher head0.220
Teacher spread0.211 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations0
Published2024
Admission routes1
Has abstractyes

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