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Record W2100884694 · doi:10.1089/biores.2015.0004

Noninvasive Oxygen Monitoring in Three-Dimensional Tissue Cultures Under Static and Dynamic Culture Conditions

2015· article· en· W2100884694 on OpenAlexaff
Birgit Weyand, Mariel Nöhre, Elmar Schmälzlin, M. Stolz, Meir Israelowitz, Christoph Gille, Herb P. von Schroeder, Kerstin Reimers, Peter M. Vogt

Bibliographic record

VenueBioResearch open access · 2015
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsUniversity of Toronto
FundersUniversität für Bodenkultur WienMedizinischen Hochschule Hannover
KeywordsBioreactorLaminar flowOxygenPetri dishLimiting oxygen concentrationMaterials scienceCell cultureChemistryBiophysicsBiomedical engineeringBiological systemAnalytical Chemistry (journal)MechanicsChromatographyPhysicsBiologyEngineeringMicrobiology

Abstract

fetched live from OpenAlex

We present a new method for noninvasive real-time oxygen measurement inside three-dimensional tissue-engineered cell constructs in static and dynamic culture settings in a laminar flow bioreactor. The OPAL system (optical oxygen measurement system) determines the oxygen-dependent phosphorescence lifetime of spherical microprobes and uses a two-frequency phase-modulation technique, which fades out the interference of background fluorescence from the cell carrier and culture medium. Higher cell densities in the centrum of the scaffolds correlated with lower values of oxygen concentration obtained with the OPAL system. When scaffolds were placed in the bioreactor, higher oxygen values were measured compared to statically cultured scaffolds in a Petri dish, which were significantly different at day 1-3 of culture. This technique allows the use of signal-weak microprobes in biological environments and monitors the culture process inside a bioreactor.

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 score0.002

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 communication0.0000.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.108
GPT teacher head0.466
Teacher spread0.358 · 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

Citations25
Published2015
Admission routes1
Has abstractyes

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