Current Advances in 3D Tissue and Organ Reconstruction
Bibliographic record
Abstract
Bi-dimensional culture systems have represented the most used method to study cell biology outside the body for over a century. Although they convey useful information, such systems may lose tissue-specific architecture, biomechanical effectors, and biochemical cues deriving from the native extracellular matrix, with significant alterations in several cellular functions and processes. Notably, the introduction of three-dimensional (3D) platforms that are able to re-create in vitro the structures of the native tissue, have overcome some of these issues, since they better mimic the in vivo milieu and reduce the gap between the cell culture ambient and the tissue environment. 3D culture systems are currently used in a broad range of studies, from cancer and stem cell biology, to drug testing and discovery. Here, we describe the mechanisms used by cells to perceive and respond to biomechanical cues and the main signaling pathways involved. We provide an overall perspective of the most recent 3D technologies. Given the breadth of the subject, we concentrate on the use of hydrogels, bioreactors, 3D printing and bioprinting, nanofiber-based scaffolds, and preparation of a decellularized bio-matrix. In addition, we report the possibility to combine the use of 3D cultures with functionalized nanoparticles to obtain highly predictive in vitro models for use in the nanomedicine field.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".