Migrasomes: A New Role in Disease Diagnosis and Treatment
Bibliographic record
Abstract
Migrasomes, vesicle-like organelles observed during cell migration, have emerged as a significant focus in cell biology. These organelles play a pivotal role in intercellular communication, signal transduction, and tissue development through the release of signalling molecules. Evidence indicates that the pathogenesis and progression of various diseases are closely associated with aberrant cell migration, impaired intercellular communication, and disrupted signalling pathways. Notably, migrasomes can facilitate the invasion and metastasis of tumor cells: they carry metastasis-promoting signals and help form an immunosuppressive microenvironment. Additionally, migrasomes mediate viral spread. Migrasomes derived from macrophages can accelerate the progression of cardiovascular and cerebrovascular diseases by promoting neuroinflammation and neuronal damage. Meanwhile, migrasomes derived from podocytes serve as biomarkers for early kidney injury. Thus, elucidating the role of migrasomes in pathological processes and defining their specific functions holds great promise for developing novel therapeutic strategies for diseases. This review synthesizes current advances in migrasome biology, highlighting their potential as diagnostic biomarkers and therapeutic targets for conditions such as cancer, viral infections, and renal disorders.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".