Cell‐embedded microgels as emerging miniature <scp>3D</scp> tissue‐mimics toward biochip‐based toxicity screening
Bibliographic record
Abstract
Recent developments in synthetic three-dimensional (3D) gel microenvironments for cell culture have enabled the advancement of bioengineered organ-specific cell niches that resemble the native 3D tissue architecture and mechanics. In particular, the application of 3D cell cultures based on miniaturized hydrogel scaffolds for toxicological analyses is attracting increasing interest because of their facile adaptability to on-chip systems and potential as novel in vitro screening tools. We summarize the current progress in microgel-based 3D cells integrated into biochip platforms and their utilization for the in vitro toxicity evaluation of chemicals and drug candidates. We emphasize the development of tissue-mimicking microgel systems combined with automated gel microarray chips and organ-on-a-chip devices. This review begins with the microscale hydrogel scaffolds that encapsulate mammalian cells and are used for in vitro tissue mimicry purposes. Furthermore, an overview of microgel-based tissue modeling approaches to toxicity testing and screening is provided, along with their technical advantages in drug discovery and alternatives to animal testing.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".