Biomimetic fractal topography enhances podocyte maturation in vitro
Bibliographic record
Abstract
In their native environment, tissues are organized into intricate fractal structures, rarely recapitulated in their culture in vitro. The extent to which fractal (self-similar) patterns that resemble complex topography in vivo influence cell maturation remain inadequately elucidated. Yet, the application of fractal topographical stimulation may address the challenge of improving the differentiated cell phenotype in vitro. Here, we show fractality in the kidney glomerulus and podocytes, branching highly differentiated cells within the glomerulus. Biomimetic fractal patterns derived from glomerular histology are used to generate topographical (2.5-D) substrates for cell culture. Podocytes grown on fractal topography exhibit higher expression of functional markers and enhanced cell polarity. RNA sequencing suggests podocytes’ enhanced ECM deposition and remodeling on fractal versus flat topography, and enhanced maturation accompanied by stress on fractal versus non-fractal topography. The incorporation of fractal topography into standard well plates may serve as a user-friendly bioengineered platform for high-fidelity cell culture. Liu and colleagues demonstrate that biomimetic fractal patterns derived from glomerular histology can enhance the maturation of podocytes (highly differentiated glomerular cells) that are grown in culture. This work presents a bioengineered platform for improved cell culture fidelity.
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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".