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Record W4414749562 · doi:10.2147/btt.s547672

Migrasomes: A New Role in Disease Diagnosis and Treatment

2025· review· en· W4414749562 on OpenAlexaff
Xiaolin Zhang, Shang Wang, Yuyun Jiang, Yanwei Yang, Liyue Huo, Yuepeng Zhou, Zhe Yang, Xuefeng Wang

Bibliographic record

VenueBiologics · 2025
Typereview
Languageen
FieldNeuroscience
TopicNeuroscience of respiration and sleep
Canadian institutionsMagna International (Canada)
Fundersnot available
KeywordsDiseasePathogenesisNeuroinflammationOrganelleCellPathologicalSignal transductionCell type

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.999
Threshold uncertainty score0.696

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.138
GPT teacher head0.368
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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