Biomaterials-based strategies for <i>in vitro</i> neural models
Bibliographic record
Abstract
technologies have been rapidly gaining the potential to bridge the gap between animal and clinical studies by providing more sophisticated models that recapitulate key aspects of the structure, biochemistry, biomechanics, and functions of human tissues. This was made possible, in large part, by the development of biomaterials that provide cells with physicochemical features that closely mimic the cellular microenvironment of native tissues. Due to the well-known material-driven cellular response and the importance of mimicking the environment of the target tissue, the selection of optimal biomaterials represents an important early step in the design of biomimetic systems to investigate brain structures and functions. This review provides a comprehensive compendium of commonly used biomaterials as well as the different fabrication techniques employed for the design of neural tissue models. Furthermore, the authors discuss the main parameters that need to be considered to develop functional platforms not only for the study of brain physiological functions and pathological processes but also for drug discovery/development and the optimization of biomaterials for neural tissue engineering.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".