Microfluidic‐Based Reconstitution of Functional Lymphatic Microvasculature: Elucidating the Role of Lymphatics in Health and Disease
Bibliographic record
Abstract
The knowledge of the blood microvasculature and its functional role in health and disease has grown significantly attributable to decades of research and numerous advances in cell biology and tissue engineering; however, the lymphatics (the secondary vascular system) has not garnered similar attention, in part due to a lack of relevant in vitro models that mimic its pathophysiological functions. Here, a microfluidic-based approach is adopted to achieve precise control over the biological transport of growth factors and interstitial flow that drive the in vivo growth of lymphatic capillaries (lymphangiogenesis). The engineered on-chip lymphatics with in vivo-like morphology exhibit tissue-scale functionality with drainage rates of interstitial proteins and molecules comparable to in vivo standards. Computational and scaling analyses of the underlying transport phenomena elucidate the critical role of the three-dimensional geometry and lymphatic endothelium in recapitulating physiological drainage. Finally, the engineered on-chip lymphatics enabled studies of lymphatic-immune interactions that revealed inflammation-driven responses by the lymphatics to recruit immune cells via chemotactic signals similar to in vivo, pathological events. This on-chip lymphatics platform permits the interrogation of various lymphatic biological functions, as well as screening of lymphatic-based therapies such as interstitial absorption of protein therapeutics and lymphatic immunomodulation for cancer therapy.
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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) | 0.001 | 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".