Development of a conductive biocomposite combining graphene and amniotic membrane for replacement of the neuronal network of tissue-engineered urinary bladder
Bibliographic record
Abstract
Tissue engineering allows to combine biomaterials and seeded cells to experimentally replace urinary bladder wall. The normal bladder wall however, includes branched neuronal network propagating signals which regulate urine storage and voiding. In this study we introduced a novel biocomposite built from amniotic membrane (Am) and graphene which created interface between cells and external stimuli replacing neuronal network. Graphene layers were transferred without modifying Am surface. Applied method allowed to preserve the unique bioactive characteristic of Am. Tissue engineered constructs composed from biocomposite seeded with smooth muscle cells (SMC) derived from porcine detrusor and porcine urothelial cells (UC) were used to evaluate properties of developed biomaterial. The presence of graphene layer significantly increased electrical conductivity of biocomposite. UCs and SMCs showed an organized growth pattern on graphene covered surfaces. Electrical filed stimulation (EFS) applied in vitro led additionally to increased SMCs growth and linear arrangement. 3D printed chamber equipped with 3D printed graphene based electrodes was fabricated to deliver EFS and record pressure changes caused by contracting SMCs seeded biocomposite. Observed contractile response indicated on effective SMCs stimulation mediated by graphene layer which constituted efficient cell to biomaterial interface.
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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) | 0.000 | 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) | 0.000 | 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".