Microbiome alterations following solid-organ transplantation: consequences, solutions, and prevention
Bibliographic record
Abstract
Abstract: Improved long-term survival following solid-organ transplantation (SOT) has remained elusive over the past several decades, despite significant advances in early survival. The microbiome refers to the genetic material belonging to microbes that live in an ecological balance with the human host, and its importance in human health is increasingly recognized. Extensive research pertaining to the human microbiome has demonstrated that compositional changes in the microbiome can contribute to such diseases as inflammatory bowel disease, metabolic syndrome, and (recently) many of the comorbidities that develop after SOT. It is suggested that the microbiome may be an important environmental variable that could influence health outcomes after SOT. Many factors related to SOT, including end-stage organ disease, surgery, and the use of antimicrobial prophylaxis and immunosuppressive drugs, have been shown to affect microbial composition and function negatively. These alterations could compromise health outcomes after SOT through the dysregulation of important host–microbe interactions, including the modulation of local and systemic host immune function by the gut microbiome, and could contribute to morbidity and even allograft rejection. Such interventions as synbiotic therapy and fecal microbiota transplantation have the potential to prevent or reverse disruption of the microbiome related to SOT and thereby improve the longevity of transplant recipients. Although microbiome research is still a relatively new field, progress is accelerating exponentially. Future research on host–microbiome interactions in the context of SOT will facilitate the development of microbiome-directed treatments to improve patient outcomes on pre- and post-SOT. Keywords: transplantation, immunosuppression, dysbiosis, immune system, microbiome
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".