Towards extracellular matrix normalization for improved treatment of solid tumors
Bibliographic record
Abstract
It is currently challenging to eradicate cancer. In the case of solid tumors, the dense and aberrant extracellular matrix (ECM) is a major contributor to the heterogeneous distribution of small molecule drugs and nano-formulations, which makes certain areas of the tumor difficult to treat. As such, much research is devoted to characterizing this matrix and devising strategies to modify its properties as a means to facilitate the improved penetration of drugs and their nano-formulations. This contribution presents the current state of knowledge on the composition of normal ECM and changes to ECM that occur during the pathological progression of cancer. It also includes discussion of strategies designed to modify the composition/properties of the ECM as a means to enhance the penetration and transport of drugs and nano-formulations within solid tumors. Moreover, a discussion of approaches to image the ECM, as well as ways to monitor changes in the ECM as a function of time are presented, as these are important for the implementation of ECM-modifying strategies within therapeutic interventions. Overall, considering the complexity of the ECM, its variability within different tissues, and the multiple pathways by which homeostasis is maintained (both in normal and malignant tissues), the available literature -while promising -suggests that improved monitoring of ECM remodeling in vivo is needed to harness the described strategies to their full potential, and match them with an appropriate chemotherapy regimen.
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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 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".