Modifying the tumor microenvironment using nanoparticle therapeutics
Bibliographic record
Abstract
Treatment of cancer has come a long way from the initial 'radical surgeries' to the multimodality treatments. For the major part of the last century, cancer was considered as a monocellular disorder, and treatment strategies were designed according to that hypothesis. However, the mortality rate from cancer continued to be high and a comprehensive treatment remained elusive. Recent progress in research has demonstrated that tumors are a complex network of neoplastic and non-neoplastic cells. The non-neoplastic cells, which are collectively called stroma, assist in tumor survival and progression. It has been shown that disrupting the tumor-stromal balance leads to significant effects on the tumor survival, and effective treatment can be achieved by targeting one or more of the stromal components. In this review, we summarize the roles of various stromal components in promoting tumor progression, and discuss innovative nanoparticle-mediated drug targeting strategies for stromal depletion and the subsequent effects on the tumors. Perspectives and the future directions are also provided. WIREs Nanomed Nanobiotechnol 2016, 8:891-908. doi: 10.1002/wnan.1406 For further resources related to this article, please visit the WIREs website.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".