Hydrogels and microtechnologies for engineering the cellular microenvironment
Bibliographic record
Abstract
Abstract Hydrogels represent a class of materials suitable for numerous biomedical applications such as tissue engineering and drug delivery. Hydrogels are by definition capable of absorbing large amount of fluid, making them adequate for cell seeding and encapsulation as well as for implantation because of their biocompatibility and excellent diffusion properties. They also possess other desirable properties for fundamental research as they have the ability to mimic the basic three‐dimensional (3D) biological, chemical, and mechanical properties of native tissues. Furthermore, their biological interactions with cells can be modified through the numerous side groups of the polymeric chains. Thus, the biological, chemical, and mechanical properties, as well as the degradation kinetics of hydrogels can be tailored depending on the application. In addition, their fabrication process can be combined with microtechnologies to enable precise control of cell‐scale features such as surface topography and the presence of adhesion motifs on the hydrogel material. This ability to control the microscale structure of hydrogels has been used to engineer tissue models and to study cell behavior mechanisms in vitro . New approaches such as bottom‐up and directed assembly of microscale hydrogels (microgels) are currently emerging as powerful methods to enable the fabrication of 3D constructs replicating the microenvironment found in vivo . WIREs Nanomed Nanobiotechnol 2012, 4:235–246. doi: 10.1002/wnan.171 This article is categorized under: Nanotechnology Approaches to Biology > Cells at the Nanoscale Therapeutic Approaches and Drug Discovery > Emerging Technologies Implantable Materials and Surgical Technologies > Nanomaterials and Implants Implantable Materials and Surgical Technologies > Nanotechnology in Tissue Repair and Replacement
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".