Advancing Organ‐on‐Chip Models With a Sacrificial Granular Hydrogel Strategy for Enhanced Permeability and Biomimicry
Bibliographic record
Abstract
Infectious diseases such as malaria, leishmaniasis, and human immunodeficiency virus (HIV) involve pathogens with complex life cycles that span multiple organs, including the bone marrow (BM), a niche for latent or cryptic infections. Studying these hidden stages in patients presents significant technical and ethical challenges, underscoring the need for advanced in vitro models such as organ-on-chip (OoC) platforms. While cell-laden hydrogels can replicate tissue-like 3D-microenvironments, their small mesh size may restrict pathogen migration and cell-pathogen interactions, both critical for establishing infection on-chip. To overcome this limitation, this work develops a "reversed" granular hydrogel strategy that creates interconnected microporosity in hydrogels incorporated into organ-on-chip compartments. Sacrificial alginate (ALG) µgels are embedded as porogens in a fibrin-collagen (FIB-COL) precursor inside a custom BM-on-chip and, after crosslinking, are selectively removed by in situ enzymatic/chemical leaching to yield highly porous hydrogels (pFIB-COL). The pFIB-COL supports 3D-cultures of mesenchymal stromal cells, endothelial cells, and erythroblasts. Physical and cellular analyses show reduced flow resistance, enhanced particle and cell permeation, more uniform cell distribution and improved endothelial network formation compared with native FIB-COL. This versatile strategy is readily adaptable to other hydrogel systems, providing a valuable tool for the faithful modeling of infection processes in biomimetic 3D-microenvironments within OoC devices.
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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".