An artificial placenta type microfluidic blood oxygenator with double-sided gas transfer microchannels and its integration as a neonatal lung assist device
Bibliographic record
Abstract
Preterm neonates suffering from respiratory distress syndrome require assistive support in the form of mechanical ventilation or extracorporeal membrane oxygenation, which may lead to long-term complications or even death. Here, we describe a high performance artificial placenta type microfluidic oxygenator, termed as a double-sided single oxygenator unit (dsSOU), which combines microwire stainless-steel mesh reinforced gas permeable membranes on both sides of a microchannel network, thereby significantly reducing the diffusional resistance to oxygen uptake as compared to the previous single-sided oxygenator designs. The new oxygenator is designed to be operated in a pumpless manner, perfused solely due to the arterio-venous pressure difference in a neonate and oxygenate blood through exposure directly to ambient atmosphere without any air or oxygen pumping. The best performing dsSOUs showed up to ∼343% improvement in oxygen transfer compared to a single-sided SOU (ssSOU) with the same height. Later, the dsSOUs were optimized and integrated to build a lung assist device (LAD) that could support the oxygenation needs for a 1-2 kg neonate under clinically relevant conditions for the artificial placenta, namely, flow rates ranging from 10 to 60 ml/min and a pressure drop of 10-60 mmHg. The LAD provided an oxygen uptake of 0.78-2.86 ml/min, which corresponded to the increase in oxygen saturation from 57 ± 1% to 93%-100%, under pure oxygen environment. This microfluidic lung assist device combines elegant design with new microfabrication methods to develop a pumpless, microfluidic blood oxygenator that is capable of supporting 30% of the oxygen needs of a pre-term neonate.
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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) | 0.000 | 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".