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Record W2922120157 · doi:10.1093/jbcr/irz013.409

519 Development and Use of an Intraoperatively Usable Hand-Held Bio-Printer Delivering Mesenchymal Stem Cells <i>In-situ</i>

2019· article· en· W2922120157 on OpenAlexaff
Gertraud Eylert, Richard Cheng, S He, Jean Gariépy, Alexandra Parousis, Andrea‐Kaye Datu, Axel Güenther, Marc G. Jeschke

Bibliographic record

VenueJournal of Burn Care & Research · 2019
Typearticle
Languageen
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsMesenchymal stem cellMedicineRegeneration (biology)Wound healingTissue engineeringBiomedical engineeringStem cellRegenerative medicineSurgeryPathologyCell biologyBiology

Abstract

fetched live from OpenAlex

Bio-Printing is a promising delivery strategy and is evolving in all disciplines in medicine including printing skin tissue and delivering cells for skin regeneration. Skin regeneration is essential for survival especially in severely burned patients. Mesenchymal stem cells (MSCs) are known for their wound healing and tissue regeneration potential, but the treatment routine and consistent delivery of these MSC represents a challenge. However, most extrusion based bioprinters are designed for in vitro use with dimensions that exceed the ones of the printed tissues, thereby limiting their clinical relevance in large area burns. We have designed and validated in vitro a novel flexible intraoperatively usable light-weight (850g) hand-held bio-printer that overcomes these limitations and shares the form factor of a dermatome. With this hand-held design it is possible to cover large surface areas with different shapes and ankles. Furthermore, this device delivers MSC directly and conformally on the wound, embeeded within an extracellular matrix layer. Our aim is to assess the functionality of this device with this intraoperatively usable system. We conducted in vitro experiments and additional a prospective in vivo experimental large animal study, applying umbilical cord mesenchymal stem cells (UC-MSC) with a hand-held bio-printer in situ in a large full-thickness burn wound healing model, evaluating cell viability after depositioning. The bio-printer deposit fast MSC in an precise pattern embeeded in a 0,12 mm thick stable ECM layer on the wounds. MSC survive >7 days (live/dead staining) with an overall viability of 90% in vitro. We were able to trace viable depositioned stem cell in vivo 2-3 days post-printing microscopically and with flowcytometry. This easy usable new hand-held bio-printer depositions intraoperatively successful MSC onto wounds and hence bears the great potential of being an alternative of covering large burned wounds for wound healing and skin regeneration. The data herein demonstrated that our bio-printer, engineered for the purpose of a intraoperatively cell delivery, has the ability of delivering a fairly high amount of viable cells which we were able to demonstrate in vitro, as well as in vivo considering the fact of a real scenario of cell harvesting in the morning in a laboratory, cell delivery in a syringe to the OR and real-time cell depositioning in-situ with a bio-printer on a living, moving wound healing model which needs regular bandage changes, which bears the high risk of cell-delivery failure. This supports a potential clinical trial as a next step.

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: Methods · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

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)0.0020.001

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 head0.086
GPT teacher head0.365
Teacher spread0.279 · 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
GenreMethods

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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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