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Record W2072699983 · doi:10.1039/c3lc41417e

Lung assist device: development of microfluidic oxygenators for preterm infants with respiratory failure

2013· article· en· W2072699983 on OpenAlexaff
Wen‐I Wu, Niels Rochow, Emily Chan, Gerhard Fusch, Asmaa Manan, Dipen Nagpal, P. Ravi Selvaganapathy, Christoph Fusch

Bibliographic record

VenueLab on a Chip · 2013
Typearticle
Languageen
FieldEngineering
TopicMechanical Circulatory Support Devices
Canadian institutionsMcMaster University
Fundersnot available
KeywordsOxygenatorArtificial lungMembrane oxygenatorMaterials scienceBiomedical engineeringMicrofluidicsPressure dropExtracorporeal membrane oxygenationNanotechnologySurgeryAnesthesiaMedicine

Abstract

fetched live from OpenAlex

This paper reports the development of microfluidic oxygenator (MFO) units designed for a lung assist device (LAD) for newborn infants. This device will be connected to the umbilical vessels like the natural placenta and provide gas exchange. The extracorporeal blood flow is only driven by the pressure difference between the umbilical artery and vein without the use of external pumps. The LAD is designed for use in ambient air (~21% of 760 mmHg). The main focus of this paper is the presentation of the development of the MFO units testing various membrane materials with human blood to enhance gas exchange and in the design of fluidic inlets to lower the pressure drop across the oxygenator. Four different membranes, including thin film PDMS, porous PDMS, and two different pore size porous polycarbonate membranes are compared in this study. Among them, the microfluidic oxygenator with porous PDMS membrane has the highest gas exchange rate of 1.46 μL min(-1) cm(2) for oxygen and 5.27 μL min(-1) cm(2) for carbon dioxide and performs better than a commercial hollow fiber-based oxygenator by 367 and 233%, respectively. A new tapered inlet configuration was designed to reduce the pressure drop across the oxygenator and showed a further 57% improvement over the traditional perpendicular inlet configuration.

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)0.000
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 score0.082
Threshold uncertainty score0.765

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.014
GPT teacher head0.228
Teacher spread0.214 · 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

Citations65
Published2013
Admission routes1
Has abstractyes

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