Stem cells, cell therapies, and bioengineering in lung biology and diseases 2023
Bibliographic record
Abstract
Repair and regeneration of a diseased lung using stem cells or bioengineered tissues is an exciting therapeutic approach for a variety of lung diseases and critical illnesses. Over the past decade, increasing evidence from preclinical models suggests that mesenchymal stromal cells, which are not normally resident in the lung, can be used to modulate immune responses after injury, but there have been challenges in translating these promising findings to the clinic. In parallel, there has been a surge in bioengineering studies investigating the use of artificial and acellular lung matrices as scaffolds for three-dimensional lung or airway regeneration, with some recent attempts of transplantation in large animal models. The combination of these studies with those involving stem cells, induced pluripotent stem cell derivatives, and/or cell therapies is a promising and rapidly developing research area. These studies have been further paralleled by significant increases in our understanding of the molecular and cellular events by which endogenous lung stem and/or progenitor cells arise during lung development and participate in normal and pathological remodeling after lung injury. For the 2023 Stem Cells, Cell Therapies, and Bioengineering in Lung Biology and Diseases Conference, scientific symposia were chosen to reflect the most cutting-edge advances in these fields. Sessions focused on the integration of "omics" technologies with function, the influence of immune cells on regeneration, and the role of the extracellular matrix in regeneration. The necessity for basic science studies to enhance fundamental understanding of lung regeneration and to design innovative translational studies was reinforced throughout the conference.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".