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Metal-free long-wavelength light-driven prodrug photoactivation is highly desirable for applications such as neuromodulation, drug delivery, and cancer therapy. Herein, via triplet fusion, we report on the far-red light-driven photo-release of an anti-cancer drug by coupling the boron-dipyrromethene (BODIPY)-based photosensitizer with a photocleavable perylene-based anti-cancer drug. Notably, this metal-free triplet fusion photolysis (TFP) strategy can be further advanced by incorporating an additional functional dopant, i.e. an immunotherapy medicine inhibiting the indoleamine 2,3-dioxygenase (IDO), with the far-red responsive triplet fusion pair in an air-stable nanoparticle. With this IDO inhibitor-assisted TFP system we observed efficient inhibition of primary and distant tumors in a mouse model at record-low excitation power, compared to other photo-assisted immunotherapy approaches. This metal-free TFP strategy will spur advancement in photonics and biophotonics fields.
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http://dx.doi.org/10.1002/anie.202218341 | DOI Listing |
J Am Acad Orthop Surg
August 2025
From the Division of Shoulder and Elbow Surgery, Department of Orthopedic Surgery, Indiana University Health, Muncie, IN (Triplet), the Division of Shoulder and Elbow Surgery, Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN (Sanchez-Sotelo, and Morrey), Division of Orthopedic Oncology,
Substantial bone loss at the time of complex primary and revision shoulder or elbow arthroplasty is challenging. Large bone defects compromise component support and important muscle-tendon units. Megaprosthesis, osteoarticular allografts, vascularized bone transfers, fusions, and allograft prosthetic composites (APCs) have all been described for reconstruction in these difficult situations.
View Article and Find Full Text PDFSci Rep
August 2025
School of Mining Engineering, Heilongjiang University of Science and Technology, Haerbin, 150000, China.
Building segmentation of high-resolution remote sensing images using deep learning effectively reduces labor costs, but still faces the key challenges of effectively modeling cross-scale contextual relationships and preserving fine spatial details. Current Transformer-based approaches demonstrate superior long-range dependency modeling, but still suffer from the problem of progressive information loss during hierarchical feature encoding. Therefore, this study proposed a new semantic segmentation network named SegTDformer to extract buildings in remote sensing images.
View Article and Find Full Text PDFAdv Mater
August 2025
School of Chemical Engineering, Sungkyunkwan University, 2066, Seobu-ro, Jangan-gu, Suwon-si, Gyeonggi, 16419, Republic of Korea.
Blue phosphorescent organic light-emitting diodes (PhOLEDs) face challenges in achieving high efficiency, color purity, and long device lifetime due to exciton quenching and high energy requirements. In this study, two tetradentate Pt(II) complexes, Pt-impy and Pt-Me-impy, are designed and synthesized by incorporating pyridocarbene in their ligands. Pyridocarbene enhances the electrochemical stability, strengthens triplet metal-to-ligand charge transfer characteristics, and improves the spin-orbit coupling, effectively shortening the exciton lifetime and minimizing the quenching effects.
View Article and Find Full Text PDFBrief Bioinform
July 2025
College of Artificial Intelligence, Nanjing Agricultural University, 666 Binjiang Avenue, Jiangbei New District, Nanjing, Jiangsu Province, 211800, China.
Accurately identifying protein functions is essential to understand life mechanisms and thus advance drug discovery. Although biochemical experiments are the gold standard for determining protein functions, they are often time-consuming and labor-intensive. Here, we proposed a novel composite deep-learning method, Multi-source Knowledge Fusion for Gene Ontology prediction (MKFGO), to infer Gene Ontology (GO) attributes through integrating five complementary pipelines built on multi-source biological data.
View Article and Find Full Text PDFIEEE Trans Comput Biol Bioinform
March 2025
The simultaneous use of multiple medications is a common practice in disease treatment, yet the same drug combination can lead to different effects under varying physiological, pharmacological, or genomic conditions-collectively referred to as the 'context'. Accurately predicting the outcomes of drug combinations across diverse contexts, also known as drug relational learning (DRL), is essential for improving therapeutic efficacy and safety. Despite its importance, existing methods face two major challenges: they are often tailored to specific DRL tasks, lacking generalizability, and they fail to explicitly model the influence of context on drug interactions.
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