Mitochondrial transplantation and its impact on infectious disease progression: a pilot study
Bibliographic record
Abstract
ABSTRACT BACKGROUND Mitochondrial transplantation has recently gained prominence as a novel technique primarily focused on addressing ischemiareperfusion injuries and rare mitochondrial mutation diseases. Platelets, abundant in the bloodstream, play a crucial role in immune function. Upon activation, platelets release mitochondria encapsulated within extracellular vesicles, here referred to as “mitlets”. These mitlets exhibit a preference for being internalized by immune cells circulating in the bloodstream, enhancing their cellular energetics. Herein, we hypothesized that the transplantation of mitlets between young animals and aged animals may exert a significant influence on the progression of infectious diseases. STUDY DESIGN AND METHODS In this study, murine models of Influenza H1N1 infection and sepsis were employed to investigate disease dynamics. Specifically, mitlets isolated from young and healthy mice were transplanted into cohorts of mice of the same age afflicted by H1N1 infection, or into aged mice subjected to polymicrobial infection and sepsis. Survival outcomes and the quantification of cytokine levels were assessed across experimental groups to elucidate the potential therapeutic effects of mitlet transplantation. RESULTS In the matched-age H1N1 infection model, as predicted, mitlet transplantation did not yield a statistically significant improvement in survival, although it did show a trend towards a reduction in the circulating inflammatory cytokine burden. In the young-to-old sepsis model, the transplantation of mitlets was associated with a significant enhancement in survival rates and a substantial reduction in bacterial loads and circulating cytokine levels. DISCUSSION Our findings suggest that mitochondrial transplantation may constitute a safe and promising avenue for enhancing the immune system’s capacity to counter infectious threats. This pilot investigation sets the stage for further exploration. It is plausible that in the future, immune senescence resulting from diminished mitochondrial energy production could be ameliorated through such transplantation interventions. As a consequence, this approach holds substantial potential as a novel immunotherapeutic strategy for the management of infectious diseases.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".