Biomimetic fractal topography enhances podocyte maturation in vitro
Bibliographic record
Abstract
Abstract Cells and tissues in their native environment are organized into intricate fractal structures, which are rarely recapitulated in their culture in vitro . The extent to which fractal patterns that resemble complex topography in vivo influence cell maturation, and the cellular responses to such shape stimulation remain inadequately elucidated. Yet, the application of fractal cues (topographical stimulation via self-similar patterns) as an external input may offer a much-needed solution to the challenge of improving the differentiated cell phenotype in vitro . Here, we established fractality in podocytes, branching highly differentiated kidney cells, and glomerulus structure. Biomimetic fractal patterns derived from glomerular histology were used to generate topographical (2.5-D) substrates for cell culture. Podocytes grown on fractal topography were found to express higher levels of functional markers and exhibit enhanced cell polarity. To track morphological complexities of differentiated podocytes, we employed a fluorescent labelling assay where labelled individual cells are tracked within otherwise optically silent confluent cell monolayer to reveal cell-cell interdigitation. RNAseq analysis suggests enhanced ECM deposition and remodeling in podocytes grown on fractal topography compared to flat surface or non-fractal microcurvature, mediated by YAP signaling. The incorporation of fractal topography into standard tissue culture well plates as demonstrated here may serve as a user-friendly bioengineered platform for high-fidelity cell culture.
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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".