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Models and Biomarkers for Local Response Prediction in Early-Stage and Oligometastatic Non-small Cell Lung Cancer Patients Treated With Stereotactic Body Radiation Therapy Using Machine Learning.
Cureus
December 2024
Anna and Peter Brojde Lung Cancer Center, Sir Mortimer B. Davis Jewish General Hospital, McGill University, Montreal, CAN.
Background A minority of patients receiving stereotactic body radiation therapy (SBRT) for non-small cell lung cancer (NSCLC) are not good responders. Radiomic features can be used to generate predictive algorithms and biomarkers that can determine treatment outcomes and stratify patients to their therapeutic options. This study investigated and attempted to validate the radiomic and clinical features obtained from early-stage and oligometastatic NSCLC patients who underwent SBRT, to predict local response.
View Article and Find Full Text PDFGMmorph: dynamic spatial matching registration model for 3D medical image based on gated Mamba.
Phys Med Biol
January 2025
School of Software Engineering, Xi'an Jiaotong University, Xi 'an Jiaotong University Innovation Port, Xi 'an, Shaanxi Province, Xi'an, Shaanxi, 710049, CHINA.
Deformable registration aims to achieve nonlinear alignment of image space by estimating a dense displacement field. It is commonly used as a preprocessing step in clinical and image analysis applications, such as surgical planning, diagnostic assistance, and surgical navigation. We aim to overcome these challenges: Deep learning-based registration methods often struggle with complex displacements and lack effective interaction between global and local feature information.
View Article and Find Full Text PDFAlphaFold2's training set powers its predictions of fold-switched conformations.
bioRxiv
October 2024
National Library of Medicine, National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD, 20894, USA.
AlphaFold2 (AF2), a deep-learning based model that predicts protein structures from their amino acid sequences, has recently been used to predict multiple protein conformations. In some cases, AF2 has successfully predicted both dominant and alternative conformations of fold-switching proteins, which remodel their secondary and tertiary structures in response to cellular stimuli. Whether AF2 has learned enough protein folding principles to reliably predict alternative conformations outside of its training set is unclear.
View Article and Find Full Text PDFCombating cisplatin-resistant lung cancer using a coiled-coil lipopeptides modified membrane fused drug delivery system.
J Control Release
January 2025
State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; Institute of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, China. Electronic address:
Drug resistance to chemotherapy in treating cancers becomes an increasingly serious challenge, which leads to treatment failure and poor patient survival. Drug-resistant cancer cells normally reduce intracellular accumulation of drugs by controlling drug uptake and promoting drug efflux, which severely limits the efficacy of chemotherapy. To overcome this problem, a membrane fused drug delivery system (MF-DDS) was constructed to treat cisplatin (DDP)-resistant lung cancer (A549-DDP) by delivering DDP via membrane fusion using a complementary coiled-coil forming peptides (CPK/CPE).
View Article and Find Full Text PDFProteins with alternative folds reveal blind spots in AlphaFold-based protein structure prediction.
Curr Opin Struct Biol
January 2025
National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA; Biochemistry and Biophysics Center, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, 20892, USA. Electronic address:
In recent years, advances in artificial intelligence (AI) have transformed structural biology, particularly protein structure prediction. Though AI-based methods, such as AlphaFold (AF), often predict single conformations of proteins with high accuracy and confidence, predictions of alternative folds are often inaccurate, low-confidence, or simply not predicted at all. Here, we review three blind spots that alternative conformations reveal about AF-based protein structure prediction.
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