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Background. Progressive multifocal leukoencephalopathy (PML) in natalizumab-treated MS patients is linked to JC virus (JCV) infection. JCV sequence variation and rearrangements influence viral pathogenicity and tropism. To better understand PML development, we analyzed viral DNA sequences in blood, CSF and/or urine of natalizumabtreated PML patients. Methods. Using biofluid samples from 17 natalizumab-treated PML patients, we sequenced multiple isolates of the JCV noncoding control region (NCCR), VP1 capsid coding region, and the entire 5 kb viral genome. Results. Analysis of JCV from multiple biofluids revealed that individuals were infected with a single genotype. Across our patient cohort, multiple PML-associated NCCR rearrangements and VP1 mutations were present in CSF and blood, but absent from urine-derived virus. NCCR rearrangements occurred in CSF of 100% of our cohort. VP1 mutations were observed in blood or CSF in 81% of patients. Sequencing of complete JCV genomes demonstrated that NCCR rearrangements could occur without VPl mutations, but VP1 mutations were not observed without NCCR rearrangement. Conclusions. These data confirm that JCV in natalizumab-PML patients is similar to that observed in other PML patient groups, multiple genotypes are associated with PML, individual patients appear to be infected with a single genotype, and PML-associated mutations arise in patients during PML development.

Over half of adults are seropositive for JC polyomavirus (JCV), but rare individuals develop progressive multifocal leukoencephalopathy (PML), a demyelinating JCV infection of the central nervous system. Previously, PML was primarily seen in immunosuppressed patients with AIDS or certain cancers, but it has recently emerged as a drug safety issue through its association with diverse immunomodulatory therapies. To better understand the relationship between the JCV life cycle and PML pathology, we studied autopsy brain tissue from a 70-year-old psoriasis patient on the integrin alpha-L inhibitor efalizumab following a ~2 month clinical course of PML. Sequence analysis of lesional brain tissue identified PML-associated viral mutations in regulatory (non-coding control region) DNA, capsid protein VP1, and the regulatory agnoprotein, as well as 9 novel mutations in capsid protein VP2, indicating rampant viral evolution. Nine samples, including three gross PML lesions and normal-appearing adjacent tissues, were characterized by histopathology and subject to quantitative genomic, proteomic, and molecular localization analyses. We observed a striking correlation between the spatial extent of demyelination, axonal destruction, and dispersion of JCV along white matter myelin sheath. Our observations in this case, as well as in a case of PML-like disease in an immunocompromised rhesus macaque, suggest that long-range spread of polyomavirus and axonal destruction in PML might involve extracellular association between virus and the white matter myelin sheath.

ObjectiveThis study aimed to create a reliable method for preoperative grading of meningiomas by combining radiomic features and deep learning-based features extracted using a 3D autoencoder. The goal was to utilize the strengths of both handcrafted radiomic features and deep learning features to improve accuracy and reproducibility across different MRI protocols.Materials and methodsThe study included 3,523 patients with histologically confirmed meningiomas, consisting of 1,900 low-grade (Grade I) and 1,623 high-grade (Grades II and III) cases. Radiomic features were extracted from T1-contrast-enhanced and T2-weighted MRI scans using the Standardized Environment for Radiomics Analysis (SERA). Deep learning features were obtained from the bottleneck layer of a 3D autoencoder integrated with attention mechanisms. Feature selection was performed using Principal Component Analysis (PCA) and Analysis of Variance (ANOVA). Classification was done using machine learning models like XGBoost, CatBoost, and stacking ensembles. Reproducibility was evaluated using the Intraclass Correlation Coefficient (ICC), and batch effects were harmonized with the ComBat method. Performance was assessed based on accuracy, sensitivity, and the area under the receiver operating characteristic curve (AUC).ResultsFor T1-contrast-enhanced images, combining radiomic and deep learning features provided the highest AUC of 95.85% and accuracy of 95.18%, outperforming models using either feature type alone. T2-weighted images showed slightly lower performance, with the best AUC of 94.12% and accuracy of 93.14%. Deep learning features performed better than radiomic features alone, demonstrating their strength in capturing complex spatial patterns. The end-to-end 3D autoencoder with T1-contrast images achieved an AUC of 92.15%, accuracy of 91.14%, and sensitivity of 92.48%, surpassing T2-weighted imaging models. Reproducibility analysis showed high reliability (ICC > 0.75) for 127 out of 215 features, ensuring consistent performance across multi-center datasets.ConclusionsThe proposed framework effectively integrates radiomic and deep learning features to provide a robust, non-invasive, and reproducible approach for meningioma grading. Future research should validate this framework in real-world clinical settings and explore adding clinical parameters to enhance its prognostic value.