Applications of mesenchymal stem cell technology in bovine species
Bibliographic record
Abstract
Mesenchymal stem cells (MSCs) have received a great deal of attention over the past 20 years mainly because of the results that showed regeneration potential and plasticity that were much stronger than expected in prior decades. Recent findings in this field have contributed to progress in the establishment of cell differentiation methods, which have made stem cell therapy more clinically attractive. In addition, MSCs are easy to isolate and have anti-inflammatory and angiogenic capabilities. The use of stem cell therapy is currently supported by scientific literature in the treatment of several animal health conditions. MSC may be administered for autologous or allogenic therapy following either a fresh isolation or a thawing of a previously frozen culture. Despite the fact that MSCs have been widely used for the treatment of companion and sport animals, little is known about their clinical and biotechnological potential in the economically relevant livestock industry. This review focuses on describing the key characteristics of potential applications of MSC therapy in livestock production and explores the themes such as the concept, culture, and characterization of mesenchymal stem cells; bovine mesenchymal stem cell isolation; applications and perspectives on commercial interests and farm relevance of MSC in bovine species; and applications in translational research.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".