Comparison of DNA methylation based classification models for precision diagnostics of central nervous system tumors.

As part of the advancement in therapeutic decision-making for brain tumor patients at St. Jude Children's Research Hospital (SJCRH), we developed three robust classifiers, a deep learning neural network (NN), k-nearest neighbor (kNN), and random forest (RF), trained on a reference series DNA-methylation profiles to classify central nervous system (CNS) tumor types. The models' performance was rigorously validated against 2054 samples from two independent cohorts.

Concurrent ependymal and ganglionic differentiation in a subset of supratentorial neuroepithelial tumors with EWSR1-PLAGL1 rearrangement.

Neuroepithelial tumors with fusion of PLAGL1 or amplification of PLAGL1/PLAGL2 have recently been described often with ependymoma-like or embryonal histology respectively. To further evaluate emerging entities with PLAG-family genetic alterations, the histologic, molecular, clinical, and imaging features are described for 8 clinical cases encountered at St. Jude (EWSR1-PLAGL1 fusion n = 6; PLAGL1 amplification n = 1; PLAGL2 amplification n = 1).

High serum prevalence of autoreactive IgG antibodies against peripheral nerve structures in patients with neurological post-COVID-19 vaccination syndrome.

Background: Patients suffering from neurological symptoms after COVID-19 vaccination (post-COVID-19 vaccination syndrome (PCVS)) have imposed an increasing challenge on medical practice, as diagnostic precision and therapeutic options are lacking. Underlying autoimmune dysfunctions, including autoantibodies, have been discussed in neurological disorders after SARS-CoV-2 infection and vaccination. Here, we describe the frequency and targets of autoantibodies against peripheral nervous system tissues in PCVS.

Early monitoring of inlay wear after total knee arthroplasty on plain radiographs using model-based wear measurement.

Wear of the ultra-high molecular-weight polyethylene (UHMWPE) component in total knee arthroplasty contributes to implant failure. It is often detected late, when patients experience pain or instability. Early monitoring could enable timely intervention, preventing implant failure and joint degeneration.