Effect of removing Kupffer cells on nanoparticle tumor delivery
Bibliographic record
Abstract
A recent metaanalysis shows that 0.7% of nanoparticles are delivered to solid tumors. This low delivery efficiency has major implications in the translation of cancer nanomedicines, as most of the nanomedicines are sequestered by nontumor cells. To improve the delivery efficiency, there is a need to investigate the quantitative contribution of each organ in blocking the transport of nanoparticles to solid tumors. Here, we hypothesize that the removal of the liver macrophages, cells that have been reported to take up the largest amount of circulating nanoparticles, would lead to a significant increase in the nanoparticle delivery efficiency to solid tumors. We were surprised to discover that the maximum achievable delivery efficiency was only 2%. In our analysis, there was a clear correlation between particle design, chemical composition, macrophage depletion, tumor pathophysiology, and tumor delivery efficiency. In many cases, we observed an 18-150 times greater delivery efficiency, but we were not able to achieve a delivery efficiency higher than 2%. The results suggest the need to look deeper at other organs such as the spleen, lymph nodes, and tumor in mediating the delivery process. Systematically mapping the contribution of each organ quantitatively will allow us to pinpoint the cause of the low tumor delivery efficiency. This, in effect, enables the generation of a rational strategy to improve the delivery efficiency of nanoparticles to solid tumors either through the engineering of multifunctional nanosystems or through manipulation of biological barriers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".