From Mitochondria to Immunity: The Emerging Roles of Mitochondria‐Derived Vesicles and Small Extracellular Vesicles in Cellular Communication and Disease
Bibliographic record
Abstract
According to the endosymbiotic theory of mitochondrial origin, an α-proteobacterium entered a prokaryotic cell and, through symbiosis, evolved into the mitochondria-the powerhouse of the cell. Like other bacteria, the α-proteobacteria generate their own extracellular vesicles (EVs), a trait that was passed onto the mitochondria, enabling them to generate mitochondria-derived vesicles (MDVs). MDVs, similar to small EVs (sEVs), are vesicles ranging from 30 to 200 nm in diameter and carry cargo for degradation by lysosomes and peroxisomes. MDVs share several features with sEVs, including targeted cargo degradation, biogenesis, packaging into multivesicular bodies, nucleic acid and protein transportation, induction of immune responses, and surface antigen presentation. MDVs may also be released from the cell in a manner similar to sEVs, potentially influencing intercellular communication and immune responses. Furthermore, the presence of MDVs presents opportunities for early disease detection, including neurodegenerative disorders and cancer. In this review, we explore the differences and similarities between MDVs and EVs, including their roles in immunity.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".