Antibody titrations are critical for microflow cytometric analysis of extracellular vesicles
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
Optimization of flow cytometry assays for extracellular vesicles (EVs) often fail to include appropriate reagent titrations - the most critically antibody titration is either not performed or is incomplete. Using nonoptimal antibody concentration is one of the main sources of error leading to a lack of reproducible data. Antibody titration for the analysis of antigens on the surface of EVs is challenging for a variety of technical reasons. Using platelets as surrogates for cells and platelet-derived particles as surrogates for EV populations, we demonstrate our process for antibody titration, highlighting some of the key analysis parameters that may confound and surprise new researchers moving into the field of EV research. Additional care must be exercised to ensure instrument and reagent controls are utilized appropriately. Complete graphical analysis of positive and negative signal intensities, concentration, and separation or stain index data is highly beneficial when paired with visual analysis of the cytometry data. Using analytical flow cytometry procedures optimized for cells for EV analysis can lead to misleading and nonreproducible results.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| 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