Role of exosomes in malignant glioma: microRNAs and proteins in pathogenesis and diagnosis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Malignant gliomas are the most common and deadly type of central nervous system tumors. Despite some advances in treatment, the mean survival time remains only about 1.25 years. Even after surgery, radiotherapy and chemotherapy, gliomas still have a poor prognosis. Exosomes are the most common type of extracellular vesicles with a size range of 30 to 100 nm, and can act as carriers of proteins, RNAs, and other bioactive molecules. Exosomes play a key role in tumorigenesis and resistance to chemotherapy or radiation. Recent evidence has shown that exosomal microRNAs (miRNAs) can be detected in the extracellular microenvironment, and can also be transferred from cell to cell via exosome secretion and uptake. Therefore, many recent studies have focused on exosomal miRNAs as important cellular regulators in various physiological and pathological conditions. A variety of exosomal miRNAs have been implicated in the initiation and progression of gliomas, by activating and/or inhibiting different signaling pathways. Exosomal miRNAs could be used as therapeutic agents to modulate different biological processes in gliomas. Exosomal miRNAs derived from mesenchymal stem cells could also be used for glioma treatment. The present review summarizes the exosomal miRNAs that have been implicated in the pathogenesis, diagnosis and treatment of gliomas. Moreover, exosomal proteins could also be involved in glioma pathogenesis. Exosomal miRNAs and proteins could also serve as non-invasive biomarkers for prognosis and disease monitoring. Video Abstract.
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.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it