A variety of treatment modalities currently exist for epilepsy, a debilitating disorder. With the emergence of drug-resistant epilepsy, however, new options are being explored. Deep brain stimulation is a neuromodulation technique that can prove to be a ground-breaking treatment option for pediatric epilepsy. It employs a neurosurgical method in which electrodes are implanted within the brain that send impulses to control abnormal brain activity. Significant gaps exist in literature, thereby emphasizing the importance of further research in this promising approach.
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http://dx.doi.org/10.1007/s10143-024-02930-y | DOI Listing |
Sci Adv
January 2025
New Cornerstone Science Laboratory, CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety & CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology of China, Beijing 100190, China.
Deep brain stimulation technology enables the neural modulation with precise spatial control but requires permanent implantation of conduits. Here, we describe a photothermal wireless deep brain stimulation nanosystem capable of eliminating α-synuclein aggregates and restoring degenerated dopamine neurons in the substantia nigra to treat Parkinson's disease. This nanosystem (ATB NPs) consists of gold nanoshell, an antibody against the heat-sensitive transient receptor potential vanilloid family member 1 (TRPV1), and β-synuclein (β-syn) peptides with a near infrared-responsive linker.
View Article and Find Full Text PDFNeurogenetics
January 2025
Department of Neuroscience and Behavioural Sciences, School of Medicine at Ribeirão Preto, University of São Paulo, Bandeirantes Av. 3900, Ribeirão Preto, São Paulo, 14040-900, Brazil.
Neuronal Ceroid Lipofuscinosis 11 (CLN11) is an ultra-rare subtype of adult-onset Neuronal Ceroid Lipofuscinosis. Its phenotype is variable and not fully known. A 21-year-old man was evaluated in our neurogenetic outpatient clinic for early onset complex phenotype, including learning difficulties, cerebellar ataxia, cone-rod dystrophy, epilepsy, and dystonia.
View Article and Find Full Text PDFOsteoporos Int
January 2025
Academy for Engineering and Technology, Fudan University, Shanghai, China.
Unlabelled: This study utilized deep learning for bone mineral density (BMD) prediction and classification using biplanar X-ray radiography (BPX) images from Huashan Hospital Medical Checkup Center. Results showed high accuracy and strong correlation with quantitative computed tomography (QCT) results. The proposed models offer potential for screening patients at a high risk of osteoporosis and reducing unnecessary radiation and costs.
View Article and Find Full Text PDFRadiol Artif Intell
January 2025
From the Department of Radiation Oncology (A.S.G., V.H., H.S.) and Department of Radiology and Imaging Sciences (B.D.W.), Emory University School of Medicine, 1701 Uppergate Dr, C5008 Winship Cancer Institute, Atlanta, GA 30322; Department of Radiology, University of Miami {School of Medicine?}, Miami, Fla (S.S., A.A.M.); Department of {Radiology?} Northwestern University {Feinberg School of Medicine?}, Chicago, Ill (L.A.D.C.); Department of Biostatistics and Bioinformatics, Emory University Rollins School of Public Health, Atlanta, Ga (Y.L.); Department of Psychology, Emory University, Atlanta, Ga (M.T.); and Department of Radiology, Duke University Medical Center, Durham, NC (B.J.S.).
Purpose To develop and evaluate the performance of NNFit, a self-supervised deep-learning method for quantification of high-resolution short echo-time (TE) echo-planar spectroscopic imaging (EPSI) datasets, with the goal of addressing the computational bottleneck of conventional spectral quantification methods in the clinical workflow. Materials and Methods This retrospective study included 89 short-TE whole-brain EPSI/GRAPPA scans from clinical trials for glioblastoma (Trial 1, May 2014-October 2018) and major-depressive-disorder (Trial 2, 2022- 2023). The training dataset included 685k spectra from 20 participants (60 scans) in Trial 1.
View Article and Find Full Text PDFiScience
January 2025
Division of Optometry, Health Sciences, City University of London, London EC1V 0HB, UK.
A key property of our environment is the mirror symmetry of many objects, although symmetry is an abstract global property with no definable shape template, making symmetry identification a challenge for standard template-matching algorithms. We therefore ask whether Deep Neural Networks (DNNs) trained on typical natural environmental images develop a selectivity for symmetry similar to that of the human brain. We tested a DNN trained on such typical natural images with object-free random-dot images of 1, 2, and 4 symmetry axes.
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