Multicellular Quadruple Colorectal Cancer Spheroids as an In Vitro Tool for Antiangiogenic Potential Evaluation of Nanoparticles
Bibliographic record
Abstract
Abstract The encapsulation of bevacizumab (BVZ), angiogenesis inhibitor antibody, into nanocarriers is explored aiming at enhancing its efficacy in colorectal cancer (CRC) treatment while eliminating potential side effects. Still, the translation of such nanomedicines into clinics is not straightforward owing to their preclinical screening in simplistic models, that do not mimic the complexity and heterogeneity of the CRC microenvironment. Herein, the development of a multicellular spheroid of CRC as an advanced preclinical model of CRC capable of screening the antiangiogenic potential of novel nanomedicines is proposed. For that, a quadruple co‐culture is established through the combination of HCT116 cells, human pulmonary microvascular endothelial cell (HPMEC), fibroblasts, and macrophages. It is demonstrated that the developed model displays intrinsic CRC features, such as the organization of cells, expression of tumor microenvironment, extracellular matrix, and the formation of a necrotic core. Moreover, the model is shown to be composed mostly of HCT116 (93%), followed by hypoxia‐inducible factor (3%), HPMEC (3%), and macrophages (1%). Gellan gum/chitosan nanoparticles encapsulating BVZ exhibit superior antiangiogenic properties when compared to free BVZ. Overall, the developed nanoparticles can further be explored as a promising approach in CRC treatment.
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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) | 0.000 | 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 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".