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Record W2810569620 · doi:10.1080/21691401.2018.1469026

Effect of artificial cell miniaturization on urea degradation by immobilized<i>E. coli</i>DH5α (pKAU17)

2018· article· en· W2810569620 on OpenAlexaff
Ricardo Duque, Yiming Shan, Monica Joya, Nandini Ravichandran, Berok Asi, Maryam Mobed-Miremadi, Scott B. Mulrooney, Melanie McNeil, Satya Prakash

Bibliographic record

VenueArtificial Cells Nanomedicine and Biotechnology · 2018
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsMcGill University
FundersProgram for Education and Research in Biotechnology, California State University
KeywordsUreaCapsuleMaterials scienceMiniaturizationBiofabricationCatalysisDegradation (telecommunications)Chemical engineeringChromatographyNanotechnologyChemistryBiomedical engineeringComputer scienceOrganic chemistryMedicine

Abstract

fetched live from OpenAlex

Second generation E. coli DH5α (pKAU17) was successfully encapsulated by means of atomization (MA), inkjet printing (MI) and double-encapsulation (DDMI) for the purpose of urea degradation in a simulated uremic medium at 37 °C. Experimentally determined values of the effectiveness factor are 0.83, 0.28 and 0.34 for the MI, MA and DDMI capsules, respectively, suggesting that the catalytic activity of the E. coli DH5α (pKAU17) immobilized in MI capsule (d = 52 μm ± 2.7 μm) is significantly less diffusion-limited than in the case of the MA (d = 1558 μm ± 125 μm) and DDMI (d = 1370 μm ± 60 μm) bio-encapsulation schemes at the 98.3% CI. The proposed novel double encapsulation biofabrication method for alginate-based microspheres, characterized by lower membrane degradation rates due to secondary containment is recommended compared to the standard atomization scheme currently adopted across immobilization-based therapeutic scenarios. A Fickian-based mechanism is proposed with simulations mimicking urea degradation for a single capsule for the atomization and the inkjet schemes.

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 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score0.757

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.006
GPT teacher head0.241
Teacher spread0.235 · 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 teacher head, 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

Citations2
Published2018
Admission routes1
Has abstractyes

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