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Multimodal medical image fusion combining saliency perception and generative adversarial network
A novel approach to prepare a composite of hydroxyapatite with cellulose nanocomposites by novel methods including theoretical studies
Fasting for weight loss is all the rage: what are the health benefits?
Static training improves insulin resistance in skeletal muscle of type 2 diabetic mice via the IGF-2/IGF-1R pathway
Development and evaluation of curcumin nano-niosomes for glioma-targeted therapy
Abstract Glioma remains a significant global health challenge, and is characterized by a persistently high mortality rate. Chemotherapy is a common treatment for glioma, but many anticancer drugs exhibit poor permeability across the blood–brain barrier (BBB) and fail to reach tumor tissues adequately, while also exerting toxic effects on normal cells. To address these issues, this study investigated the use of niosomes (Nio), which are biocompatible, biodegradable, and non-immunogenic, to encapsulate curcumin (Cur) and enhance its delivery to glioma tissues. Niosomes were prepared using the non-ionic surfactant sorbitan monostearate (Span 60) and cholesterol as carrier materials, and subsequently modified with transferrin (TF) to facilitate receptor-mediated transport across the BBB. The resulting TF-modified curcumin niosomes (TF-Cur-Nio) demonstrated enhanced targeting of brain tumors, improved anti-glioma efficacy, and favorable in vivo safety. These findings suggest that the TF-Cur-Nio delivery system has significant potential for advancing glioma treatment by overcoming the limitations of conventional chemotherapy and improving drug delivery to the brain.
The magnitude and associated factors of childhood cancer treatment abandonment at the university of Gondar comprehensive specialized hospital, Ethiopia
Why is my cello howling?
Physics-informed neural networks with hybrid Kolmogorov-Arnold network and augmented Lagrangian function for solving partial differential equations
Reviving the biodiversity around an ancient palace
Protein waste turned into antibiotics as a defence strategy of human cells
Socio-demographic environmental and clinical factors influencing asthma control in community pharmacies of Lahore Pakistan
Feature-aware domain invariant representation learning for EEG motor imagery decoding
Artificial intelligence accelerates the identification of nature-derived potent LOXL2 inhibitors
Abstract The role of LOXL2 in cancer has been widely demonstrated, but current therapies targeting LOXL2 are not yet fully developed. We believe that selective nature-derived inhibition of LOXL2 may provide a better therapeutic approach for the treatment of cancer. Therefore, we adopted a comprehensive approach combining deep learning and traditional computer-aided drug design methods to screen LOXL2 selective inhibitors. Bioactivity and affinity of the potential LOXL2 inhibitors were determined by molecular docking and virtual screening. At the same time, we experimentally tested the effect of potential LOXL2 inhibitors on cancer cells. Validation showed that it could inhibit proliferation and migration, promote apoptosis of CT26 cells, and reduce the expression level of LOXL2 protein. As a result, we identified a potent LOXL2 inhibitor: the natural product Forsythoside A, and demonstrated that Forsythoside A has an inhibitory effect on tumors.