Microfluidic bioprinting for the in vitro generation of novel biomimetic human testicular tissues
Bibliographic record
Abstract
Abstract Advances in cancer treatments have greatly improved pediatric cancer survival rates, leading to quality of life considerations and in particular fertility restoration. Accordingly, pre-pubertal patients have the option to cryopreserve testicular tissue for experimental restorative therapies, including in vitro spermatogenesis, wherein testicular tissue is engineered in vitro and spermatozoa are collected for in vitro fertilization (IVF). Current in vitro systems have been unable to reliably support the generation of spermatozoa from human testicular tissues, likely due to the inability for the dissociated testicular cells to recreate the native architecture of testicular tissue found in vivo . Recent advances in 3-D bioprinting can place cells into geometries at fine resolutions comparable to microarchitectures found in native tissues, and therefore hold promise as a tool for the development of a biomimetic in vitro system for human spermatogenesis. This study assessed the utility of bioprinting technology to recreate the precise architecture of testicular tissue and corresponding spermatogenesis for the first time. We printed testicular cell-laden hollow microtubules at similar resolutions to seminiferous tubules, and compared the results to testicular organoids. We show that the human testicular cells retain their viability and functionality post-printing, and illustrate an intrinsic ability to reorganize into their native cytoarchitecture. This study provides a proof of concept for the use of 3-D bioprinting technology as a tool to create biomimetic human testicular tissues.
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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".