Heterogeneity of Extracellular Vesicles and Particles: Molecular Voxels in the Blood Borne “Hologram” of Organ Function, Dysfunction and Cancer
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
Extracellular vesicles (EVs) and particles (EPs) serve as unique carriers of complex molecular information with increasingly recognized roles in health and disease. Individual EVs/EPs collectively contribute to the molecular fingerprint of their producing cell, reflecting its identity, state, function and phenotype. This property is of particular interest in cancer where enormous heterogeneity of cancer cells is compounded by the presence of altered stromal, vascular and immune cell populations, which is further complicated by systemic responses elicited by the disease in individual patients. These diverse and interacting cellular compartments are dynamically represented by myriads of EVs/EPs released into the circulating biofluids (blood) during cancer progression and treatment. Current approaches of liquid biopsy seek to follow specific elements of the EV/EP cargo that may have diagnostic utility (as biomarkers), such as cancer cell-derived mutant oncoproteins or nucleic acids. However, with emerging technologies enabling high-throughput EV/EP analysis at a single particle level, a more holistic approach may be on the horizon. Indeed, each EV/EP carries multidimensional information (molecular "voxel") that could be integrated across thousands of particles into a larger and unbiased landscape (EV/EP "hologram") reflecting the true cellular complexity of the disease, along with cellular interactions, systemic responses and effects of treatment. Thus, the longitudinal molecular mapping of EV/EP populations may add a new dimension to crucial aspects of cancer biology, personalized diagnostics, and therapy.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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