Author

P. Seeberger

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Aug 2026

Myeloperoxidase/ASGPR Dual-Targeting Self-Amplifying Nanoplatform for Synergistic Anti-Metastatic Therapy of Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) remains highly lethal due to a complex tumor microenvironment (TME) that limits therapeutic efficacy. Herein, a dual-targeted nanotherapeutic platform (SC@GRT-COF-366) based on a covalent organic framework (COF-366) is reported for synergistic HCC treatment. This multifunctional system integrates chemotherapy, photodynamic therapy, autophagy regulation, and TME remodeling to synergistically inhibit tumor metastasis. Gal-D5HT-modified nanoparticles achieve enhanced HCC targeting via myeloperoxidase (MPO)-responsive aggregation in the inflammatory microenvironment and ASGPR-mediated uptake. Upon light irradiation, COF-366 generates singlet oxygen to induce tumor cell apoptosis and simultaneously enhances MPO expression and neutrophil infiltration, further amplifying MPO-triggered nanoparticle aggregation and establishing a self-amplifying retention-therapy cascade. Meanwhile, co-loaded sorafenib and chloroquine enable combined chemotherapy and autophagy inhibition, effectively overcoming drug resistance. In vivo results demonstrate significantly enhanced and prolonged tumor accumulation compared with single-targeted systems, resulting in a tumor growth inhibition rate of 93.5 ± 1.02% in subcutaneous models and effective suppression of lung metastasis in orthotopic HCC models. Notably, treatment markedly reduces neutrophil extracellular traps (NETs) formation, indicating favorable remodeling of the tumor immune microenvironment. Collectively, this multifunctional COF-based nanoplatform integrates dual-targeted delivery, amplified tumor retention, and synergistic multimodal therapy, offering a promising strategy for advanced HCC treatment.

Le Wang, Xiang Wang, Hengrui Li et al. · Small · 0 citations

Preprint Aug 2026

PINN-Phase: A physics-informed neural network for curvature-driven multiphase-field evolution

Phase-field simulation of polycrystalline microstructures becomes costly when many related cases must be evolved over long times. We introduce PINN-Phase, a physics-informed neural time integrator that advances the full multiphase field from its initial condition and enforces phase bounds and unit sum at every step; on the reported explicit multiphase-field benchmarks, post-initial-condition reference states serve only for evaluation. Without case-specific tuning, a single trained 25-grain model meets all predefined criteria in seven of eight unseen microstructures fixed before evaluation and in both stress cases, with 0.94-3.71% terminal grain-label disagreement across the ten cases. Each 12,000-step rollout takes about 5.2 min on a single GPU and reaches nearly three times the temporal horizon represented during training. A pre-registered 64-grain model reaches 6.09% disagreement, retaining all 21 reference survivors plus one additional grain; a post-evaluation continuation with a doubled training horizon and 25 additional epochs reaches 3.97% and the exact survivor set. In three dimensions, one trained 16-grain 96^3 model recovers the exact terminal active set and all three extinction identities in six of six unseen microstructures, five of which meet the complete predefined qualification. These results demonstrate structurally admissible long-horizon prediction and prospective initial-condition transfer within fixed benchmark families.

Seifallah Elfetni, P. Seeberger, Theodore Tyrikos-Ergas · 0 citations