The post-hematopoietic cell transplantation microbiome: relationships with transplant outcome and potential therapeutic targets
Bibliographic record
Abstract
Microbiota injury occurs in many patients undergoing allogeneic hematopoietic cell transplantation, likely as a consequence of conditioning regimens involving chemo- and radiotherapy, the widespread use of both prophylactic and therapeutic antibiotics, and profound dietary changes during the peri-transplant period. Peri-transplant dysbiosis is characterized by a decrease in bacterial diversity, loss of commensal bacteria and single-taxon domination (e.g., with Enterococcal strains). Clinically, deviation of the post-transplant microbiota from a normal, high-diversity, healthy state has been associated with increased risk of bacteremia, development of graft-versus-host disease and decreases in overall survival. A number of recent clinical trials have attempted to target the microbiota in allogeneic hematopoietic cell transplantation patients via dietary interventions, selection of therapeutic antibiotics, administration of pre- or pro-biotics, or by performing fecal microbiota transplantation. These strategies have yielded promising results but the mechanisms by which these interventions influence transplant-related complications remain largely unknown. In this review we summarize the current approaches to targeting the microbiota, discuss potential underlying mechanisms and highlight the key outstanding areas that require further investigation in order to advance microbiota- targeting therapies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".