Bibliographic record
Abstract
In organ engineering, by combining stem cells, biomaterials and bioreactors, it may be possible for scientists to produce bioengineered organs for transplantation, as well as advancing understanding of organs and diseases. Researchers from the Department of Thoracic Surgery at Tohoku University in Japan and the University of Toronto in Canada are conducting multidisciplinary international research led by thoracic surgeon Assistant Professor Takaya Suzuki (Tohoku University) to create a bioengineering lung using induced lung progenitor cells. The research revolves around designing the respiratory system and involves utilising progenitor-generating cellular reprogramming using transient gene transfection. Ultimately, the researchers are seeking to identify a way to intentionally control the developmental clock of the human body and Suzuki believes that this will be possible if the team can unveil the key mechanisms that govern this clock. Suzuki is working closely with Professor Thomas K Waddell at the University of Toronto and Toronto General Hospital. It was in Waddell's lab that the concept of partial reprogramming was conceived, alongside global leading stem cell scientist Professor Andras Nagy. Alongside his collaborators, Suzuki is seeking to transform the landscape of transplantation medicine. As partial reprogramming can be employed in various organs and cells, any kind of organ bioengineering can benefit from the research.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.002 | 0.001 |
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".