Bibliographic record
Abstract
Organ Bioprinting is a new approach in the field of regenerative medicine that try to make a whole intact functionally active organ from tissue specific cells. Providing such organs are very important because every year many patients need to organ transplantation but the number of donors are so limited. Organs made from different tissue with different kinds of cell types and for Organ Bioprinting first we need to provide these cell types. Currently, with regards to advances in stem cell biology specially invention of easy methods for isolation mesenchymal stem cells (MSCs) as one the appropriate cell type for regeneration purpose and also due to their unique properties including lack of immune-rejection in allograft, MSCs have gained attention for using in organ production. MSCs can isolate from many tissues of adults and differentiate in a targeted manner into cells of interest; provide a main material of tissue engineering triangle (i.e. cells, biomaterial and growth factors) for 3D bioprinting of human organs on a substrate. Printers have the ability of designing tissues and organs with fusing living tissue specific cells and extracellular matrix in layers to produce 3D biologically functional new organ. It is conceivable that in the near future, stem cells will play their predicted role; i.e. whole organ production.
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.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".