PET Imaging of brain muscarinic receptors with 18F-Fluorobenzyl-Dexetimide: A first in human study.

M1 and M4 muscarinic receptor (mAChR) agonists are under development for the treatment of schizophrenia, Alzheimer's and Parkinson's disease. We performed first-in-human PET imaging of mAChR with 18F-Fluorobenzyl-Dexetimide (FDEX) in 10 healthy participants (29.4±4.3yrs). Four underwent dynamic brain scanning for 240 min, and then six underwent static brain scans at 120 and 160-min post injection of 250 MBq of FDEX. Gjedde-Patlak graphical analysis was applied to determine the influx constant (Ki). Regional tissue ratios (SUVR) were calculated using the cerebellar cortex as the reference region. No adverse events were observed. The tracer showed good brain entry (∼4.2% ID at 5 min) but irreversible distribution kinetics over four hours in regions of high mAChR. Binding was consistent with the distribution of mAChR receptors with striatum > cortex > hippocampus >> thalamus >>> cerebellum with low variance in regional binding between subjects. Ki was 0.42±0.04 in the putamen, 0.27±0.01 in frontal cortex, 0.25±0.02 in the hippocampus and 0.10±0.01 in the thalamus. SUVR at 120 and 240 min. were highly correlated with these Ki values with R2 of 0.91 and 0.99 respectively. FDEX yields high quality brain images with uptake in the known distribution of mAChR with remarkably little variance between normal subjects.

Using Artificial Intelligence for COVID-19 Chest X-ray Diagnosis.

BACKGROUND: Coronavirus disease-19 (COVID-19), caused by a novel member of the coronavirus family, is a respiratory disease that rapidly reached pandemic proportions with high morbidity and mortality. In only a few months, it has had a dramatic impact on society and world economies. COVID-19 has presented numerous challenges to all aspects of health care, including reliable methods for diagnosis, treatment, and prevention. Initial efforts to contain the spread of the virus were hampered by the time required to develop reliable diagnostic methods. Artificial intelligence (AI) is a rapidly growing field of computer science with many applications for health care. Machine learning is a subset of AI that uses deep learning with neural network algorithms. It can recognize patterns and achieve complex computational tasks often far quicker and with increased precision than can humans. METHODS: In this article, we explore the potential for the simple and widely available chest X-ray (CXR) to be used with AI to diagnose COVID-19 reliably. Microsoft CustomVision is an automated image classification and object detection system that is a part of Microsoft Azure Cognitive Services. We utilized publicly available CXR images for patients with COVID-19 pneumonia, pneumonia from other etiologies, and normal CXRs as a dataset to train Microsoft CustomVision. RESULTS: Our trained model overall demonstrated 92.9% sensitivity (recall) and positive predictive value (precision), with results for each label showing sensitivity and positive predictive value at 94.8% and 98.9% for COVID-19 pneumonia, 89% and 91.8% for non-COVID-19 pneumonia, 95% and 88.8% for normal lung. We then validated the program using CXRs of patients from our institution with confirmed COVID-19 diagnoses along with non-COVID-19 pneumonia and normal CXRs. Our model performed with 100% sensitivity, 95% specificity, 97% accuracy, 91% positive predictive value, and 100% negative predictive value. CONCLUSIONS: We have used a readily available, commercial platform to demonstrate the potential of AI to assist in the successful diagnosis of COVID-19 pneumonia on CXR images. The findings have implications for screening and triage, initial diagnosis, monitoring disease progression, and identifying patients at increased risk of morbidity and mortality. Based on the data, a website was created to demonstrate how such technologies could be shared and distributed to others to combat entities such as COVID-19 moving forward.

Case Report: 18F-MK6240 Tau Positron Emission Tomography Pattern Resembling Chronic Traumatic Encephalopathy in a Retired Australian Rules Football Player.

Introduction: It remains unclear if tau imaging may assist diagnosis of chronic traumatic encephalopathy (CTE). Flortaucipir PET has shown superior frontal with medial temporal tau binding consistent with the provisional neuropathological criteria for mid-stage CTE in group-level analyses of retired symptomatic NFL players and in one individual with pathologically confirmed CTE. 18F-MK6240 is a new PET ligand that has high affinity for tau. We present the case of a 63-year-old cognitively impaired, former Australian rules football player with distinct superior frontal and medial temporal 18F-MK6240 binding and show it to be significantly different to the pattern seen in prodromal Alzheimer's disease (AD). Findings: The participant was recruited for a study of amyloid-ß and tau several decades after traumatic brain injury. He had multiple concussions during his football career but no cognitive complaints at retirement. A thalamic stroke in his mid 50s left stable mild cognitive deficits but family members reported further short-term memory, behavioral, and personality decline preceding the study. Imaging showed extensive small vessel disease on MRI, a moderate burden of amyloid-ß plaques, and 18F-MK6240 binding in bilateral superior frontal and medial temporal cortices. Voxel-wise analysis demonstrated that the frontally predominant pattern of the participant was significantly different to the posterior temporo-parietal predominant pattern of prodromal AD. Conclusion: Although lacking neuropathological examination to distinguish CTE from a variant of AD, the clear demonstration of a CTE-like tau pattern in a single at-risk individual suggests further research on the potential of 18F-MK6240 PET for identifying CTE is warranted.

C-ME: a 3D community-based, real-time collaboration tool for scientific research and training.

The need for effective collaboration tools is growing as multidisciplinary proteome-wide projects and distributed research teams become more common. The resulting data is often quite disparate, stored in separate locations, and not contextually related. Collaborative Molecular Modeling Environment (C-ME) is an interactive community-based collaboration system that allows researchers to organize information, visualize data on a two-dimensional (2-D) or three-dimensional (3-D) basis, and share and manage that information with collaborators in real time. C-ME stores the information in industry-standard databases that are immediately accessible by appropriate permission within the computer network directory service or anonymously across the internet through the C-ME application or through a web browser. The system addresses two important aspects of collaboration: context and information management. C-ME allows a researcher to use a 3-D atomic structure model or a 2-D image as a contextual basis on which to attach and share annotations to specific atoms or molecules or to specific regions of a 2-D image. These annotations provide additional information about the atomic structure or image data that can then be evaluated, amended or added to by other project members.