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In Situ 3D Label-Free Contactless Bioprinting of Cells through Diamagnetophoresis

2016· article· en· W2546526105 on OpenAlexafffund
Abdel Rahman Abdel Fattah, Elvira Meleca, Sarah Mishriki, Alina Lelic, Fei Geng, Rakesh P. Sahu, Suvojit Ghosh, Ishwar K. Puri

Bibliographic record

VenueACS Biomaterials Science & Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsMcMaster University
FundersFraunhofer-Institut für Zelltherapie und ImmunologieNatural Sciences and Engineering Research Council of CanadaCanada Foundation for InnovationMcMaster University
KeywordsIn situMaterials scienceSpheroidSuspension (topology)NozzleDiamagnetismMicrofluidicsNanotechnologyBiomedical engineeringCell cultureChemistryBiologyMagnetic field

Abstract

fetched live from OpenAlex

Using whole blood, we demonstrate the first realization of a novel macroscale, contactless, label-free method to print in situ three-dimensional (3D) cell assemblies of different morphologies and sizes. This novel bioprinting method does not use nozzles that can contaminate the cell suspension, or to which cells can adhere. Instead, we utilize the intrinsic diamagnetic properties of whole blood cells to magnetically manipulate them in situ in a nontoxic paramagnetic medium, creating (a) rectangular bar, (b) three-pointed star, and (c) spheroids of varying sizes. We envision the technique to be transferable to other cell lines, with potential applications in tissue engineering and drug screening.

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 score0.003

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.010
GPT teacher head0.207
Teacher spread0.197 · 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

Citations40
Published2016
Admission routes2
Has abstractyes

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