In vitro study of gas embolism formation in microfluidic vascular networks
Bibliographic record
Abstract
Vascular gas embolism is a potentially life-threatening medical condition caused by the presence of gas bubbles in the vascular network, leading to reduced blood flow and possible disruptions in the transport of oxygen to tissues and vital organs. Despite its significant implications for patient health, particularly in iatrogenic contexts, the origins and underlying mechanisms remain poorly understood. Since the onset of this condition is physical in nature, the purpose of this study is to investigate the genesis of gas embolism and its varied manifestations by employing a series of abiotic microfluidic devices designed to simulate the human vascular system with different hematological and anatomical configurations, subjected to pressure-related scenarios.The characteristics of bubble formation were first correlated with increasing levels of pressure applied to different geometries of artificial blood vessels, modeled by the first set of microfluidic platforms. Vascular channels with larger widths and ‘intact’ thin walls produced the greatest bubble volumes, especially at high pressures, while smaller channel widths exhibited earlier onset of bubble generation at lower pressure thresholds. The origins of gas embolism were directly related to the presence of excess gases by varying the storage conditions of these vascular systems on a chip and monitoring the impact of pressured gases in the local material network. A second set of microfluidic platforms was then introduced to determine patterns of bubble formation in the presence of local damages and critical areas in the vascular walls. As the integrity of the wall was compromised, the application of pressure quickly generated intravascular bubbles, with greater volumes and increased chaotic behavior appearing in larger channels with wider perforations, respectively. This reveals greater risks associated with gas embolism for patients who have weakened blood vessels, whether due to genetic factors or medical histories.The research also explores the impact of different anatomical and hematological factors on bubble behavior by varying the equivalent hematocrit concentrations of a synthetic blood solution in another designed series of microfluidic devices, comprising two channel widths and three wall thicknesses. These biological factors were correlated with the nucleation density, the generation frequency, the maximum volume, and the mean velocity of generated bubbles. The risks of bubble immobilization and vascular wall damage were determined as well, particularly in smaller channel widths, which were more susceptible to ruptures at lower pressure thresholds.Finally, a novel microscale hyperbaric system was created to model decompression sickness, using compressed air and nitrogen at different pressure thresholds and periods of compression. Rapid decompression from higher pressures and longer compression durations led to the formation of larger and more numerous bubbles, most of which appeared 10 minutes and even 30 minutes post-return to atmospheric conditions. This emphasizes the need for improved safety measures in scenarios involving pressure changes, especially since the symptoms of decompression sickness continue manifesting even 30 minutes post-decompression.These findings reveal the complex interplay of physical, biological, and environmental factors in gas embolism, highlighting the importance of personalized approaches in its management and prevention, as well as the development of stringent safety protocols for medical procedures involving gas introduction
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 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 | 0.001 |
| 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".