Deep Phenotyping of Neurologic Postacute Sequelae of SARS-CoV-2 Infection.

BACKGROUND AND OBJECTIVES: SARS-CoV-2 infection has been associated with a syndrome of long-term neurologic sequelae that is poorly characterized. We aimed to describe and characterize in-depth features of neurologic postacute sequelae of SARS-CoV-2 infection (neuro-PASC). METHODS: Between October 2020 and April 2021, 12 participants were seen at the NIH Clinical Center under an observational study to characterize ongoing neurologic abnormalities after SARS-CoV-2 infection. Autonomic function and CSF immunophenotypic analysis were compared with healthy volunteers (HVs) without prior SARS-CoV-2 infection tested using the same methodology. RESULTS: Participants were mostly female (83%), with a mean age of 45 ± 11 years. The median time of evaluation was 9 months after COVID-19 (range 3-12 months), and most (11/12, 92%) had a history of only a mild infection. The most common neuro-PASC symptoms were cognitive difficulties and fatigue, and there was evidence for mild cognitive impairment in half of the patients (MoCA score <26). The majority (83%) had a very disabling disease, with Karnofsky Performance Status ≤80. Smell testing demonstrated different degrees of microsmia in 8 participants (66%). Brain MRI scans were normal, except 1 patient with bilateral olfactory bulb hypoplasia that was likely congenital. CSF analysis showed evidence of unique intrathecal oligoclonal bands in 3 cases (25%). Immunophenotyping of CSF compared with HVs showed that patients with neuro-PASC had lower frequencies of effector memory phenotype both for CD4+ T cells (p < 0.0001) and for CD8+ T cells (p = 0.002), an increased frequency of antibody-secreting B cells (p = 0.009), and increased frequency of cells expressing immune checkpoint molecules. On autonomic testing, there was evidence for decreased baroreflex-cardiovagal gain (p = 0.009) and an increased peripheral resistance during tilt-table testing (p < 0.0001) compared with HVs, without excessive plasma catecholamine responses. DISCUSSION: CSF immune dysregulation and neurocirculatory abnormalities after SARS-CoV-2 infection in the setting of disabling neuro-PASC call for further evaluation to confirm these changes and explore immunomodulatory treatments in the context of clinical trials.

Longitudinal analyses using 18F-Fluorodeoxyglucose positron emission tomography with computed tomography as a measure of COVID-19 severity in the aged, young, and humanized ACE2 SARS-CoV-2 hamster models.

This study compared disease progression of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) in three different models of golden hamsters: aged (≈60 weeks old) wild-type (WT), young (6 weeks old) WT, and adult (14-22 weeks old) hamsters expressing the human-angiotensin-converting enzyme 2 (hACE2) receptor. After intranasal (IN) exposure to the SARS-CoV-2 Washington isolate (WA01/2020), 2-deoxy-2-[fluorine-18]fluoro-D-glucose positron emission tomography with computed tomography (18F-FDG PET/CT) was used to monitor disease progression in near real time and animals were euthanized at pre-determined time points to directly compare imaging findings with other disease parameters associated with coronavirus disease 2019 (COVID-19). Consistent with histopathology, 18F-FDG-PET/CT demonstrated that aged WT hamsters exposed to 105 plaque forming units (PFU) developed more severe and protracted pneumonia than young WT hamsters exposed to the same (or lower) dose or hACE2 hamsters exposed to a uniformly lethal dose of virus. Specifically, aged WT hamsters presented with a severe interstitial pneumonia through 8 d post-exposure (PE), while pulmonary regeneration was observed in young WT hamsters at that time. hACE2 hamsters exposed to 100 or 10 PFU virus presented with a minimal to mild hemorrhagic pneumonia but succumbed to SARS-CoV-2-related meningoencephalitis by 6 d PE, suggesting that this model might allow assessment of SARS-CoV-2 infection on the central nervous system (CNS). Our group is the first to use (18F-FDG) PET/CT to differentiate respiratory disease severity ranging from mild to severe in three COVID-19 hamster models. The non-invasive, serial measure of disease progression provided by PET/CT makes it a valuable tool for animal model characterization.

Cancer Imaging and Patient Care during the COVID-19 Pandemic.

Patients with cancer have been negatively impacted during the coronavirus disease 2019 (COVID-19) pandemic, as many of these individuals may be immunosuppressed and of older age. Additionally, cancer follow-up or imaging appointments have been delayed in many clinics around the world. Postponement of routine screening exams will result in delays in new cancer diagnoses. Clinics are continuing to monitor and adapt their appointment schedules based on local outbreaks of COVID-19. Studies on COVID-19 in patients with cancer are limited, but consistently indicate that this population is at risk for more severe COVID-19 illness. Data from recent studies also suggest that pediatric patients with cancer have a lower risk of severe COVID-19 illness compared to adults. Certain features of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection detected by lung, brain, and gastrointestinal imaging may confound radiologists' interpretation of cancer diagnosis, staging, and treatment response. Lastly, as clinics begin to re-open for routine appointments, protocols have been put in place to reduce SARS-CoV-2 exposure to patients during their visits. This review details different perspectives on the impact of the COVID-19 pandemic on patients with cancer and on cancer imaging. Keywords: Abdomen/GI, Cardiac, Infection, Nervous-Peripheral.

Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets.

Chest CT is emerging as a valuable diagnostic tool for clinical management of COVID-19 associated lung disease. Artificial intelligence (AI) has the potential to aid in rapid evaluation of CT scans for differentiation of COVID-19 findings from other clinical entities. Here we show that a series of deep learning algorithms, trained in a diverse multinational cohort of 1280 patients to localize parietal pleura/lung parenchyma followed by classification of COVID-19 pneumonia, can achieve up to 90.8% accuracy, with 84% sensitivity and 93% specificity, as evaluated in an independent test set (not included in training and validation) of 1337 patients. Normal controls included chest CTs from oncology, emergency, and pneumonia-related indications. The false positive rate in 140 patients with laboratory confirmed other (non COVID-19) pneumonias was 10%. AI-based algorithms can readily identify CT scans with COVID-19 associated pneumonia, as well as distinguish non-COVID related pneumonias with high specificity in diverse patient populations.