Eliminating Porcine Pathogens: The Role of Genetic Modifications in Enhancing Biosafety of Transplantable Pig Organs
Bibliographic record
Abstract
This study aims to explore genetic and biochemical strategies for eliminating porcine pathogens, thereby enhancing the biosafety of transplantable pig organs. This exploration includes an assessment of current progress in genetic modifications and their effectiveness in reducing or eliminating pathogen-related risks. Key findings in this study highlight important genetic traits and biochemical pathways that contribute to the elimination of porcine pathogens. These advancements include the application of gene editing technologies, such as CRISPR/Cas9, and the development of transgenic pigs that exhibit resistance to specific pathogens. Furthermore, the study discusses the role of antimicrobial peptides and immune system regulation in enhancing pathogen resistance. The results of this study emphasize the crucial role of genetic modifications in ensuring the biosafety of transplantable pig organs. By effectively eliminating or reducing the presence of porcine pathogens, these strategies are expected to advance the field of xenotransplantation and address the organ shortage crisis. The significance of these findings suggests that the future of pig-to-human organ transplantation will move towards a safer and more effective direction.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".