Mitochondrial abnormalities and disruption of the neuromuscular junction precede the clinical phenotype and motor neuron loss in hFUSWT transgenic mice.

FUS (fused in sarcoma) mislocalization and cytoplasmic aggregation are hallmark pathologies in FUS-related amyotrophic lateral sclerosis and frontotemporal dementia. Many of the mechanistic hypotheses have focused on a loss of nuclear function in the FUS-opathies, implicating dysregulated RNA transcription and splicing in driving neurodegeneration. Recent studies describe an additional somato-dendritic localization for FUS in the cerebral cortex implying a regulatory role in mRNA transport and local translation at the synapse. Here, we report that FUS is also abundant at the pre-synaptic terminal of the neuromuscular junction (NMJ), suggesting an important function for this protein at peripheral synapses. We have previously reported dose and age-dependent motor neuron degeneration in transgenic mice overexpressing human wild-type FUS, resulting in a motor phenotype detected by ∼28 days and death by ∼100 days. Now, we report the earliest structural events using electron microscopy and quantitative immunohistochemistry. Mitochondrial abnormalities in the pre-synaptic motor nerve terminals are detected at postnatal day 6, which are more pronounced at P15 and accompanied by a loss of synaptic vesicles and synaptophysin protein coupled with NMJs of a smaller size at a time when there is no detectable motor neuron loss. These changes occur in the presence of abundant FUS and support a peripheral toxic gain of function. This appearance is typical of a 'dying-back' axonopathy, with the earliest manifestation being mitochondrial disruption. These findings support our hypothesis that FUS has an important function at the NMJ, and challenge the 'loss of nuclear function' hypothesis for disease pathogenesis in the FUS-opathies.

Generative pretrained transformer-4, an artificial intelligence text predictive model, has a high capability for passing novel written radiology exam questions.

PURPOSE: AI-image interpretation, through convolutional neural networks, shows increasing capability within radiology. These models have achieved impressive performance in specific tasks within controlled settings, but possess inherent limitations, such as the inability to consider clinical context. We assess the ability of large language models (LLMs) within the context of radiology specialty exams to determine whether they can evaluate relevant clinical information. METHODS: A database of questions was created with official sample, author written, and textbook questions based on the Royal College of Radiology (United Kingdom) FRCR 2A and American Board of Radiology (ABR) Certifying examinations. The questions were input into the Generative Pretrained Transformer (GPT) versions 3 and 4, with prompting to answer the questions. RESULTS: One thousand seventy-two questions were evaluated by GPT-3 and GPT-4. 495 (46.2%) were for the FRCR 2A and 577 (53.8%) were for the ABR exam. There were 890 single best answers (SBA), and 182 true/false questions. GPT-4 was correct in 629/890 (70.7%) SBA and 151/182 (83.0%) true/false questions. There was no degradation on author written questions. GPT-4 performed significantly better than GPT-3 which selected the correct answer in 282/890 (31.7%) SBA and 111/182 (61.0%) true/false questions. Performance of GPT-4 was similar across both examinations for all categories of question. CONCLUSION: The newest generation of LLMs, GPT-4, demonstrates high capability in answering radiology exam questions. It shows marked improvement from GPT-3, suggesting further improvements in accuracy are possible. Further research is needed to explore the clinical applicability of these AI models in real-world settings.