3D electron microscopy for analyzing nanoparticles in the tumor endothelium
Bibliographic record
Abstract
Delivering medical agents to diseased tissues has been challenging, leading researchers to study the in vivo transport process in the body for improving delivery. Many imaging techniques exist for mapping the distribution of medical agent-carrying nanoparticles in tissues, but they cannot capture the three-dimensional context of tissues with single nanoparticle resolution. Here, we developed 3DEM-NPD, a three-dimensional electron microscopy (3D EM) machine learning strategy to image and map single nanoparticle distributions (NPD) in tissues. 3DEM-NPD provides unbiased visualization and quantification of individual nanoparticles within organs. We applied this technique to quantify nanoparticle transport through tumor blood vessel endothelial cells. We measured the cell diameter, surface area, and volume and found that traditional 2D EM cannot accurately measure these features. We used machine learning to locate over 550,000 nanoparticles in less than 3 h with an accuracy of over 82%. The 3DEM-NPD method allowed us to establish a metric to quantify nanoparticle transport at the single nanoparticle level and to quantify the morphological features of ~2,800 vesicles. We find that on average there are only 2.4 nanoparticles per vesicle with a theoretical maximum of 158 nanoparticles per vesicle (~66x increase). These surprising results suggest the need to increase vesicle encapsulation efficiency for improved transport and they provide a benchmark for increasing nanoparticle transport and delivery. This technique may provide unique insights into the interactions between medical agents, drug carriers, emerging materials, and cells at the single-nanoparticle level throughout tissues.
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 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".