An ultra-thin, all PDMS-based microfluidic lung assist device with high oxygenation capacity
Bibliographic record
Abstract
Preterm neonates with immature lungs require a lung assist device (LAD) to maintain oxygen saturation at normal levels. Over the last decade, microfluidic blood oxygenators have attracted considerable interest due to their ability to incorporate unique biomimetic design and to oxygenate in a physiologically relevant manner. Polydimethylsiloxane (PDMS) has become the main material choice for these kinds of devices due to its high gas permeability. However, fabrication of large area ultrathin microfluidic devices that can oxygenate sufficient blood volumes at clinically relevant flow rates, entirely made of PDMS, have been difficult to achieve primarily due to failure associated with stiction of thin PDMS membranes to each other at undesired locations during assembly. Here, we demonstrate the use of a modified fabrication process to produce large area ultrathin oxygenators entirely made of PDMS and robust enough to withstand the hydraulic conditions that are encountered physiologically. We also demonstrate that a LAD assembled from these ultrathin double-sided microfluidic blood oxygenators can increase the oxygen saturation level by 30% at a flow rate of 30 ml/min and a pressure drop of 21 mm Hg in room air which is adequate for 1 kg preterm neonates. In addition, we demonstrated that our LAD could withstand high blood flow rate of 150 ml/min and increase oxygen saturation by 26.7% in enriched oxygen environment which is the highest gas exchange reported so far by any microfluidic-based blood oxygenators. Such performance makes this LAD suitable to provide support to 1 kg neonate suffering from respiratory distress syndrome.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".