Mesenchymal stromal cells 2.0: thinking outside the box
Bibliographic record
Abstract
Mesenchymal stromal cells (MSCs) are non-hematopoietic progenitor cells that can be derived from a variety of sources including bone marrow and adipose tissues among others. MSCs are plastic adherent and easy to culture ex vivo, making them attractive platforms for cell-based technologies. They have an impressive immunoplasticity and can express a suppressive or inflammatory phenotype depending on their stimuli. While MSCs are mainly used in tissue regeneration or as a tool to suppress unwanted inflammation, their pro-inflammatory phenotype includes their ability to act as antigen presenting cells (APCs). This property, along with their ease of expansion and manipulation, make them excellent candidates as alternatives to dendritic cell-based technologies, especially for cancer vaccination. To generate stable MSCs with an APC-like phenotype, two main venues have been explored: genetic and pharmacological reprogramming. Routes to generating MSC-APCs have shown great promise in therapeutic and prophylactic settings in vivo, demonstrating effective tumor control in multiple murine models. Mechanistically, MSC-APCs appear to be generated in response to reactive oxygen species and endoplasmic reticulum stress. While much remains to be uncovered with respect to their phenotype, these reprogrammed cells show great promise as the next generation of cancer vaccine platforms. Herein, we describe the state-of-the-art in routes to reprogramming MSCs and discuss their future in the immune-oncology space as potent cancer vaccines.
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 | 0.001 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".