On-chip engineered human lymphatic microvasculature for physio-/pathological transport phenomena studies
Bibliographic record
Abstract
The human vasculature constitutes an integral part of fluid, protein and cellular transport throughout a variety of physiological processes and pathological events. While the blood vascular system has been the topic of numerous studies in connection to its role in physio-/pathological transport phenomena, our secondary vascular system, the lymphatics, has yet to gain similar attention, in part due to a lack of adequate models to study its biological function. Despite their considerable value, animal models limit the ability to perform parametric studies, whereas current in vitro systems are lacking in physiological mimicry. Here, a microfluidic-based approach is developed that allows for precise control over the transport of growth factors and interstitial fluid flow, which we leverage to recapitulate the in vivo growth of lymphatic capillaries. Using this approach, physiological tissue functionality is validated by characterizing the drainage rate of extracellular solutes and proteins. Finally, lymphatic-immune interactions are studied to affirm inflammation-driven responses by the lymphatics, which recruit immune cells via chemotactic signals, similarly to in vivo , pathological events. Results demonstrate the utility of this platform to study lymphatic biology and disease, as well as use as a screening assay to predict lymphatic absorption of therapeutic biologics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 teacher head, 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".