Development of a Multifunctional Extracorporeal Life Support (ECLS) System for Lung and Kidney Support: The Pneuma-K ECLS System
Bibliographic record
Abstract
Multi-organ failure (MOF), particularly in the coexistence of acute kidney injury (AKI) and acute lung injury (ALI), presents a significant challenge in intensive care units (ICU) and is associated with exceedingly high mortality rates. Respiratory and renal failures are frequently managed by extracorporeal membrane oxygenation (ECMO) and continuous renal replacement therapy (CRRT), respectively. However, employing these therapies using separate devices requires specialized facilities, adds to complexity, and increases the risks of clotting due to the extensive artificial surface areas involved. Therefore, an integrated device capable of providing simultaneous respiratory and renal support is essential. This paper introduces the Pneuma-K ECLS system, which incorporates a multifunctional detoxifying filter (MDF) capable of performing gas exchange and renal replacement in a single cartridge. Ex-vivo blood tests confirmed the ability of the MDF to oxygenate blood, remove carbon dioxide, and eliminate uremic toxins. In addition, animal experiments demonstrated the considerable clinical potential of this novel integrated extracorporeal life support approach. Integrating respiratory and renal support into a singular device could mitigate risks, conserve resources, and enhance the survival rates of critically ill patients suffering from concurrent lung and kidney failure.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".