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Record W3163426718 · doi:10.3791/62519

Fabrication of a Crystalline Nanocellulose Embedded Agarose Biomaterial Ink for Bone Marrow-Derived Mast Cell Culture

2021· article· en· W3163426718 on OpenAlexaff
Leshern Karamchand, Ashley Wagner, Syed Benazir Alam, Marianna Kulka

Bibliographic record

VenueJournal of Visualized Experiments · 2021
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsNational Institute for NanotechnologyUniversity of AlbertaNational Research Council Canada
Fundersnot available
KeywordsBiocompatibilityNanocelluloseMaterials scienceAgaroseViability assayBiomaterial3D bioprintingBiomedical engineeringPropidium iodideTissue engineeringChemistryNanotechnologyCellMedicineBiochemistryApoptosis

Abstract

fetched live from OpenAlex

Three-dimensional (3D) bioprinting utilizes hydrogel-based composites (or biomaterial inks) that are deposited in a pattern, forming a substrate onto which cells are deposited. Because many biomaterial inks can be potentially cytotoxic to primary cells, it is necessary to determine the biocompatibility of these hydrogel composites prior to their utilization in costly 3D tissue engineering processes. Some 3D culture methods, including bioprinting, require that cells be embedded into a 3D matrix, making it difficult to extract and analyze the cells for changes in viability and biomarker expression without eliciting mechanical damage. This protocol describes as proof of concept, a method to assess the biocompatibility of a crystalline nanocellulose (CNC) embedded agarose composite, fabricated into a 24-well culture system, with mouse bone marrow-derived mast cells (BMMCs) using flow cytometric assays for cell viability and biomarker expression. After 18 h of exposure to the CNC/agarose/D-mannitol matrix, BMMC viability was unaltered as measured by propidium iodide (PI) permeability. However, BMMCs cultured on the CNC/agarose/D-mannitol substrate appeared to slightly increase their expression of the high-affinity IgE receptor (FcεRI) and the stem cell factor receptor (Kit; CD117), although this does not appear to be dependent on the amount of CNC in the bioink composite. The viability of BMMCs was also assessed following a time course exposure to hydrogel scaffolds that were fabricated from a commercial biomaterial ink composed of fibrillar nanocellulose (FNC) and sodium alginate using a 3D extrusion bioprinter. Over a period of 6-48 h, the FNC/alginate substrates did not adversely affect the viability of the BMMCs as determined by flow cytometry and microtiter assays (XTT and lactate dehydrogenase). This protocol describes an efficient method to rapidly screen the biochemical compatibility of candidate biomaterial inks for their utility as 3D scaffolds for post-print seeding with mast cells.

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 score0.001
Threshold uncertainty score0.002

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 head0.027
GPT teacher head0.381
Teacher spread0.354 · 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".

Quick stats

Citations2
Published2021
Admission routes1
Has abstractyes

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