A PDA‐Functionalized 3D Lung Scaffold Bioplatform to Construct Complicated Breast Tumor Microenvironment for Anticancer Drug Screening and Immunotherapy
Bibliographic record
Abstract
Abstract 2D cell culture occupies an important place in cancer progression and drug discovery research. However, it limitedly models the “true biology” of tumors in vivo. 3D tumor culture systems can better mimic tumor characteristics for anticancer drug discovery but still maintain great challenges. Herein, polydopamine (PDA)‐modified decellularized lung scaffolds are designed and can serve as a functional biosystem to study tumor progression and anticancer drug screening, as well as mimic the tumor microenvironment. PDA‐modified scaffolds with strong hydrophilicity and excellent cell compatibility can promote cell growth and proliferation. After 96 h treatment with 5‐FU, cisplatin, and DOX, higher survival rates in PDA‐modified scaffolds are observed compared to nonmodified scaffolds and 2D systems. The E‐cadhesion formation, HIF‐1α‐mediated senescence decrease, and tumor stemness enhancement can drive drug resistance and antitumor drug screening of breast cancer cells. Moreover, there is a higher survival rate of CD45+/CD3+/CD4+/CD8+ T cells in PDA‐modified scaffolds for potential cancer immunotherapy drug screening. This PDA‐modified tumor bioplatform will supply some promising information for studying tumor progression, overcoming tumor resistance, and screening tumor immunotherapy drugs.
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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) | 0.001 | 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".