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Record W7132892349

Construction of Snthetic Elastomer Lung Scaffolds with Human iPSC-derived Lung Organoids

2023· dissertation· W7132892349 on OpenAlexfundno aff
Bhakti Pandey

Bibliographic record

VenueTSpace · 2023
Typedissertation
Language
FieldMedicine
TopicTissue Engineering and Regenerative Medicine
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOrganoidScaffoldLungInduced pluripotent stem cellHuman lungArtificial lungCell growth
DOInot available

Abstract

fetched live from OpenAlex

Introduction: With the only treatment option for chronic respiratory diseases being lung transplantation, a high-risk procedure, there is an urgent requirement for a curative solution. The synthetic polymer, poly(itaconate-co-citrate co-octanediol) (PICO) with tunable elasticity, tensile strength, and a controllable degradation rate may aid in the growth of lung cells. Methods: 2D and 3D scaffold models were seeded with human induced pluripotent stem cells (iPSC) derived lung organoid (LO) cells and evaluated for cell attachment, viability, proliferation, and morphology. ECM proteins were selected and evaluated for their ability to enhance attachment and proliferation of LO cells. Results: PICO scaffold demonstrates to support the attachment, viability, proliferation, and morphology of LO-dissociated cells in both the 2D and 3D scaffold model. Some of the ECM protein supplements may aid in the attachment and proliferation of LO cells. Conclusions: PICO, as demonstrated in-vitro, can be a useful tissue engineering tool for repairing the lung.

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 score0.001
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 head0.012
GPT teacher head0.322
Teacher spread0.311 · 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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