Data and Information Quality at the Canadian Institute for Health Information.
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) are membranous particles released by most cells in our body, which are involved in many cell-to-cell signaling processes. Given the nanometer sizes and heterogeneity of EVs, highly sensitive methods with single-molecule resolution are fundamental to investigating their biophysical properties. Here, we demonstrate the sizing of EVs using a fluorescence-based flow analyzer with single-molecule sensitivity. Using a dye that selectively partitions into the vesicle's membrane, we show that the fluorescence intensity of a vesicle is proportional to its diameter. We discuss the constraints in sample preparation which are inherent to sizing nanoscale vesicles with a fluorescent membrane dye and propose several guidelines to improve data consistency. After optimizing staining conditions, we were able to measure the size of vesicles in the range ∼35-300 nm, covering the spectrum of EV sizes. Lastly, we developed a method to correct the signal intensity from each vesicle based on its traveling speed inside the microfluidic channel, by operating at a high sampling rate (10 kHz) and measuring the time required for the particle to cross the laser beam. Using this correction, we obtained a threefold greater accuracy in EV sizing, with a precision of ±15-25%.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| 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