Engineered microtissues for disease modelling and drug screening (Conference Presentation)
Bibliographic record
Abstract
Pre-clinical research is often conducted in two-dimensional (2D) cancer cell cultures or in animal models to identify the molecular pathways that underlie the onset and course of a disease or to assess the effectiveness of experimental therapies. However, the reductionist 2D method may distort interactions between cells and integrins and does not recreate 3D cell morphology or match the natural in situ environment of tissues. Despite the fact that animal models offer the natural 3D milieu in which cells reside, interspecies differences, cost, and ethical concerns continue to be important barriers to the development of these models. Therefore, there is a critical need for creating bioengineered in vitro models that can use biomaterials and cells obtained from humans to replicate the 3D cytostructure and microenvironment of diseases. In this talk, I will give an overview of our work on microphysiological tissue models for drug screening in this lecture. To this end, I will discuss the c
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".