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Record W4392973343 · doi:10.48185/jaai.v5i1.974

Integrative Approaches for Advancing Organoid Engineering: From Mechanobiology to Personalized Therapeutics

2024· article· en· W4392973343 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Applied Artificial Intelligence · 2024
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsConcordia University
Fundersnot available
KeywordsOrganoidMechanobiologyPersonalized medicineComputer scienceComputational biologyBiologyNeuroscienceBioinformaticsCell biology

Abstract

fetched live from OpenAlex

This research manuscript aims to explore the integration of cutting-edge technologies in the field of organoid engineering for applications in personalized precision medicine. The research investigative exploration will delve into the multifaceted aspects of organoid research, incorporating mechanobiological modulation, ultrasound stimulation, and acoustofluidics to enhance the engineering of organoids. The focus will extend to the development of organoids-on-a-chip platforms with integrated biosensors, providing real-time monitoring capabilities for improved disease modeling and drug testing. Additionally, the manuscript will address the challenges and opportunities associated with large-scale manufacturing of organoids, emphasizing the scalability of regenerative medicine approaches. The proposed research will contribute to the advancement of 3D tissue models, micro physiological systems, and multi-organoid systems, offering a very comprehensive perspective on the potential of these systematic technologies in reshaping the landscape of personalized medicine.

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.

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)0.001
metaresearch head score (Gemma)0.000
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: Methods · Consensus signal: none
Teacher disagreement score0.901
Threshold uncertainty score0.625

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.072
GPT teacher head0.314
Teacher spread0.241 · 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