Biomaterials-based strategies for <i>in vitro</i> neural models
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 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 it