Bilateral optic neuritis as the presenting sign of post SARS-CoV-2 acute disseminated encephalomyelitis.

PURPOSE: to report a case of Acute Disseminated EncephaloMyelitis (ADEM) occurring after documented SARS-Cov2 infection and flu-like disease. OBSERVATION: A 59-years-old woman presented with progressive visual loss and right leg paresthesia started 6 days earlier when CT scan excluded abnormalities. Visual acuity was OU hand motion with bilateral slow pupillary response and unremarkable ocular extrinsic motility while visual field testing showed diffuse bilateral sensitivity reduction. The patient had also right leg paresthesia and reported a 2-weeks flu-like syndrome 15 days earlier, with nausea, diarrhea, anosmia, ageusia, cough. Brain Magnetic Resonance Imaging revealed bilateral optic nerve enhancement, multiple brain and spine lesions. SARS-CoV-2 PCR tested negative on nasal swab and positive on cerebrospinal fluid. Patient's serum tested positive for anti-SARS-CoV-2 IgG, negative for anti-aquaporin-4 and anti-myelin oligodendrocyte glycoprotein antibodies. A diagnosis of suspect ADEM post SARS-CoV-2 infection was made and treatment with high dose intravenous methylprednisolone (with subsequent prednisone tapering) and immunoglobulins started. Ten days later vision improved to 20/30 RE and 20/25 LE and 3 months later to 20/20. CONCLUSION AND IMPORTANCE: ADEM may ensue after SARS-CoV-2 virus infection. High suspicious index and prompt aggressive treatment may result in complete vision restauration.

Prediction of 28-day mortality in critically ill patients with COVID-19: Development and internal validation of a clinical prediction model.

BACKGROUND: COVID-19 pandemic has rapidly required a high demand of hospitalization and an increased number of intensive care units (ICUs) admission. Therefore, it became mandatory to develop prognostic models to evaluate critical COVID-19 patients. MATERIALS AND METHODS: We retrospectively evaluate a cohort of consecutive COVID-19 critically ill patients admitted to ICU with a confirmed diagnosis of SARS-CoV-2 pneumonia. A multivariable Cox regression model including demographic, clinical and laboratory findings was developed to assess the predictive value of these variables. Internal validation was performed using the bootstrap resampling technique. The model's discriminatory ability was assessed with Harrell's C-statistic and the goodness-of-fit was evaluated with calibration plot. RESULTS: 242 patients were included [median age, 64 years (56-71 IQR), 196 (81%) males]. Hypertension was the most common comorbidity (46.7%), followed by diabetes (15.3%) and heart disease (14.5%). Eighty-five patients (35.1%) died within 28 days after ICU admission and the median time from ICU admission to death was 11 days (IQR 6-18). In multivariable model after internal validation, age, obesity, procaltitonin, SOFA score and PaO2/FiO2 resulted as independent predictors of 28-day mortality. The C-statistic of the model showed a very good discriminatory capacity (0.82). CONCLUSIONS: We present the results of a multivariable prediction model for mortality of critically ill COVID-19 patients admitted to ICU. After adjustment for other factors, age, obesity, procalcitonin, SOFA and PaO2/FiO2 were independently associated with 28-day mortality in critically ill COVID-19 patients. The calibration plot revealed good agreements between the observed and expected probability of death.

CIR-Based Device-Free People Counting via UWB Signals.

The outbreak of COVID-19 has resulted in many different policies being adopted across the world to reduce the spread of the virus. These policies include wearing surgical masks, hand hygiene practices, increased social distancing and full country-wide lockdown. Specifically, social distancing involves keeping a certain distance from others and avoiding gathering together in large groups. Automatic crowd density estimation is a technological solution that could help in guaranteeing social distancing by reducing the probability that two persons in a public area come in close proximity to each other while moving around. This paper proposes a novel low complexity RF sensing system for automatic people counting based on low cost UWB transceivers. The proposed system is based on an ordinary classifier that exploits features extracted from the channel impulse response of UWB communication signals. Specifically, features are extracted from the sorted list of singular values obtained from the singular value decomposition applied to the matrix of the channel impulse response vector differences. Experimental results achieved in two different environments show that the proposed system is a promising candidate for future automatic crowd density monitoring systems.