Investigating the Mechanism of Intravascular Bubble Formation in Designed Arrays of Vascularized Systems on a Chip
Bibliographic record
Abstract
Vascular gas embolism is a rare medical condition, resulting from the existence of air or gas in the venous or arterial system. Gas embolism is associated with a wide range of circulatory, cardiovascular, and neurological complications that can lead to sudden and unexplained death. Despite the recent increase in related studies, gas embolism remains under-reported with a poor understanding of its genesis and pathophysiology. In this work, intravascular bubble formation is investigated in an array of biomimetic microscale systems, where the endogenous generation of gas bubbles is induced by variations in the surrounding pressure. Microfluidic devices, based on polydimethylsiloxane, are designed and fabricated as vascularized systems on a chip with one main channel at two different diameters (30 µm, and 40 µm), surrounded by a pressure chamber (200 µm) on each side, at a separation of 50 µm. Two blood-equivalent solutions, at 20% and 46% hematocrit concentrations were prepared from a glycerin and xanthan gum mixture to mimic the physicochemical characteristics of the blood. As the volume of injected air increased, the events related to gas embolism were occurring at shorter timespans with more significant characteristics, i.e., length and number of bubbles. Additionally, correlations were established between the input parameters, i.e., the vascular diameter and equivalent hematocrit concentration, and the output parameters, i.e., the bubble size, velocity, frequency, and nucleation sites.Clinical Relevance- The reported results constitute a reproducible observation and quantification of intravascular bubble formation induced by global pressure variations, where the emergence of bubbles exhibits different patterns depending on biological characteristics related to gender and medical history.
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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".