To examine the natural growth dynamics of internal plexiform neurofibromas (PNs) in patients with neurofibromatosis 1 (NF1).

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is causing an exponentially increasing number of coronavirus disease 19 (COVID-19) cases globally. Prioritization of medical countermeasures for evaluation in randomized clinical trials is critically hindered by the lack of COVID-19 animal models that enable accurate, quantifiable, and reproducible measurement of COVID-19 pulmonary disease free from observer bias. We first used serial computed tomography (CT) to demonstrate that bilateral intrabronchial instillation of SARS-CoV-2 into crab-eating macaques ( Macaca fascicularis ) results in mild-to-moderate lung abnormalities qualitatively characteristic of subclinical or mild-to-moderate COVID-19 (e.g., ground-glass opacities with or without reticulation, paving, or alveolar consolidation, peri-bronchial thickening, linear opacities) at typical locations (peripheral>central, posterior and dependent, bilateral, multi-lobar). We then used positron emission tomography (PET) analysis to demonstrate increased FDG uptake in the CT-defined lung abnormalities and regional lymph nodes. PET/CT imaging findings appeared in all macaques as early as 2 days post-exposure, variably progressed, and subsequently resolved by 6–12 days post-exposure. Finally, we applied operator-independent, semi-automatic quantification of the volume and radiodensity of CT abnormalities as a possible primary endpoint for immediate and objective efficacy testing of candidate medical countermeasures.

Background: Three aspects of language production are impaired to different degrees in individuals with post-stroke aphasia: ability to repeat words and nonwords, name pictures, and produce sentences. These impairments often persist into the chronic stages, and the neuroanatomical distribution of lesions associated with chronicity of each of these impairments is incompletely understood. Aims: The primary objective of this study was to investigate the lesion correlates of picture naming, sentence production, and nonword repetition deficits in the same participant group because most prior lesion studies have mapped single language impairments. The broader goal of this study was to investigate the extent and degree of overlap and uniqueness among lesions resulting in these deficits in order to advance the current understanding of functional subdivision of neuroanatomical regions involved in language production. Methods & Procedures: In this study, lesion-symptom mapping was used to determine if specific cortical regions are associated with nonword repetition, picture naming, and sentence production scores. Structural brain images and behavioural performance of 31 individuals with post-stroke left hemisphere lesions and a diagnosis of aphasia were used in the lesion analysis. Outcomes & Results: Each impairment was associated with mostly unique, but a few shared lesions. Overall, sentence and repetition deficits were associated with left anterior perisylvian lesions, including the pars opercularis and triangularis of the inferior frontal lobe, anterior superior temporal gyrus, anterior portions of the supramarginal gyrus, the putamen, and anterior portions of the insula. In contrast, impaired picture naming was associated with posterior perisylvian lesions including major portions of the inferior parietal lobe and middle temporal gyrus. The distribution of lesions in the insula was consistent with this antero-posterior perisylvian gradient. Significant voxels in the posterior planum temporale were associated with a combination of all three deficits. Conclusions: These findings emphasise the participation of each perisylvian region in multiple linguistic functions, suggesting a many(functions)-to-many(networks) framework while also identifying functional subdivisions within each region.

The mass production of the graphics processing unit and the coronavirus disease 2019 (COVID-19) pandemic have provided the means and the motivation, respectively, for rapid developments in artificial intelligence (AI) and medical imaging techniques. This has led to new opportunities to improve patient care but also new challenges that must be overcome before these techniques are put into practice. In particular, early AI models reported high performances but failed to perform as well on new data. However, these mistakes motivated further innovation focused on developing models that were not only accurate but also stable and generalizable to new data. The recent developments in AI in response to the COVID-19 pandemic will reap future dividends by facilitating, expediting, and informing other medical AI applications and educating the broad academic audience on the topic. Furthermore, AI research on imaging animal models of infectious diseases offers a unique problem space that can fill in evidence gaps that exist in clinical infectious disease research. Here, we aim to provide a focused assessment of the AI techniques leveraged in the infectious disease imaging research space, highlight the unique challenges, and discuss burgeoning solutions.