Bibliographic record
Abstract
Development of nanoparticle-based drug delivery systems has been attempted for the treatment of cancer over the past decade. The enhanced permeability and retention (EPR) effect is the major mechanism to passively deliver nanodrugs to tumor tissue. However, a recent systematic review demonstrated limited success of these studies, with the clearance of nanoparticles by the mononuclear phagocytic system (MPS) being a major hurdle. Herein, we propose that nanotechnologists should reconsider their research focuses, aiming for therapeutic targets other than cancer. Treatments for diseases that do not (or less) rely on EPR should be considered, such as active targeting or MPS evasion systems. For example, systemic delivery of drugs through intravenous injection can be used to treat sepsis, multi-organ failure, metabolic disorders, blood diseases, immune and autoimmune diseases, etc. Local delivery of nanodrugs to organs such as the lung, rectum, or bladder may enhance the local drug concentration with less clearance via MPS. In transplant settings, ex vivo organ perfusion provides a new route to repair injury of isolated organs in the absence of MPS. Based on a similar concept, chemotherapy with in vivo lung perfusion techniques and other isolated organ perfusion provides opportunities for cancer therapy.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".