Ultrasound treatment inhibits SARS-CoV-2 in vitro infectivity

BackgroundCOVID-19 (coronavirus disease 2019) is a disease caused by infection with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), affecting millions of people worldwide, with a high rate of deaths. The present study aims to evaluate ultrasound (US) as a physical method for virus inactivation. Materials and methodsThe US-transductor was exposed to the SARS-CoV-2 viral solution for 30 minutes. Vero-E6 cells were infected with medium exposure or not with the US, using 3-12, 5-10, or 6-18MHz as frequencies applied. We performed confocal microscopy to determine virus infection and replicative process. Moreover, we detected the virus particles with a titration assay. ResultsWe observed an effective infection of SARS-CoV-2 Wuhan, Delta, and Gamma strains in comparison with mock, an uninfected experimental group. The US treatment was able to inhibit the Wuhan strain in all applied frequencies. Interestingly, 3-12 and 6-18MHz did not inhibit SARS-CoV-2 delta and gamma variants infection, on the other hand, 5-10MHz was able to abrogate infection and replication in all experimental conditions. ConclusionsThese results show that SARS-CoV-2 is susceptible to US exposure at a specific frequency 5-10MHz and could be a novel tool for reducing the incidence of SARS-CoV-2 infection.

Targeting C5aR1 signaling reduced neutrophil extracellular traps and ameliorates COVID-19 pathology

Patients with severe COVID-19 develop acute respiratory distress syndrome (ARDS) that may progress to cytokine storm syndrome, organ dysfunction, and death. Considering that complement component 5a (C5a), through its cellular receptor C5aR1, has potent proinflammatory actions, and plays immunopathological roles in inflammatory diseases, we investigated whether C5a/C5aR1 pathway could be involved in COVID-19 pathophysiology. C5a/C5aR1 signaling increased locally in the lung, especially in neutrophils of critically ill COVID-19 patients compared to patients with influenza infection, as well as in the lung tissue of K18-hACE2 Tg mice (Tg mice) infected with SARS-CoV-2. Genetic and pharmacological inhibition of C5aR1 signaling ameliorated lung immunopathology in Tg-infected mice. Mechanistically, we found that C5aR1 signaling drives neutrophil extracellular trap (NET)s-dependent immunopathology. These data confirm the immunopathological role of C5a/C5aR1 signaling in COVID-19 and indicate that antagonist of C5aR1 could be useful for COVID-19 treatment.

CASP4/11 contributes to pulmonary inflammation and disease exacerbation in COVID-19

Infection with SARS-CoV-2 induces COVID-19, an inflammatory disease that is usually self-limited, but depending on patient conditions may culminate with critical illness and patient death. The virus triggers activation of intracellular sensors, such as the NLRP3 inflammasome, which promotes inflammation and aggravates the disease. Thus, identification of host components associated with NLRP3 inflammasome is key for understanding the physiopathology of the disease. Here, we reported that SARS-CoV-2 induces upregulation and activation of human Caspase-4/CASP4 (mouse Caspase-11/CASP11) and this process contributes to inflammasome activation in response to SARS-CoV-2. CASP4 was expressed in lung autopsy of lethal cases of COVID-19 and CASP4 expression correlates with expression of inflammasome components and inflammatory mediators such as CASP1, IL1B, IL18 and IL6. In vivo infections performed in transgenic hACE2 humanized mouse, deficient or sufficient for Casp11, indicate that hACE2 Casp11-/- mice were protected from disease development, with reduced body weight loss, reduced temperature variation, increased pulmonary parenchymal area, reduced clinical score of the disease and reduced mortality. Collectively, our data establishes that CASP4/11 contributes to disease pathology and contributes for future immunomodulatory therapeutic interventions to COVID-19.

Artificial intelligence applied to support medical decisions for the automatic analysis of echocardiogram images: A systematic review.

The echocardiogram is a test that is widely used in Heart Disease Diagnoses. However, its analysis is largely dependent on the physician's experience. In this regard, artificial intelligence has become an essential technology to assist physicians. This study is a Systematic Literature Review (SLR) of primary state-of-the-art studies that used Artificial Intelligence (AI) techniques to automate echocardiogram analyses. Searches on the leading scientific article indexing platforms using a search string returned approximately 1400 articles. After applying the inclusion and exclusion criteria, 118 articles were selected to compose the detailed SLR. This SLR presents a thorough investigation of AI applied to support medical decisions for the main types of echocardiogram (Transthoracic, Transesophageal, Doppler, Stress, and Fetal). The article's data extraction indicated that the primary research interest of the studies comprised four groups: 1) Improvement of image quality; 2) identification of the cardiac window vision plane; 3) quantification and analysis of cardiac functions, and; 4) detection and classification of cardiac diseases. The articles were categorized and grouped to show the main contributions of the literature to each type of ECHO. The results indicate that the Deep Learning (DL) methods presented the best results for the detection and segmentation of the heart walls, right and left atrium and ventricles, and classification of heart diseases using images/videos obtained by echocardiography. The models that used Convolutional Neural Network (CNN) and its variations showed the best results for all groups. The evidence produced by the results presented in the tabulation of the studies indicates that the DL contributed significantly to advances in echocardiogram automated analysis processes. Although several solutions were presented regarding the automated analysis of ECHO, this area of research still has great potential for further studies to improve the accuracy of results already known in the literature.

COVID-19 Bimodal Clinical and Pathological Phenotypes

BackgroundPatients with coronavirus disease-2019 (COVID-19) present varying clinical complications. Different viral load and host response related to genetic and immune background are probably the reasons for these differences. We aimed to sought clinical and pathological correlation that justifies the different clinical outcomes among COVID-19 autopsies cases. MethodsMinimally invasive autopsy was performed on forty-seven confirmed COVID-19 patients from May-July, 2020. Electronic medical record of all patients was collected and a comprehensive histopathological evaluation was performed. Immunohistochemistry, immunofluorescence, special stain, western blotting and post-mortem real-time reverse transcriptase polymerase chain reaction on fresh lung tissue were performed. ResultsWe show that 5/47 (10,6%) patients present a progressive decline in oxygenation index for acute respiratory distress syndrome (PaO2/FiO2 ratio), low compliance levels, interstitial fibrosis, high -SMA+ cells/protein expression, high collagens I/III deposition and NETs(P<0.05), named as fibrotic phenotype (N=5). Conversely, 10/47 (21,2%) patients demonstrated progressive increase in PaO2/FiO2 ratio, high pulmonary compliance levels, preserved elastic framework, increase thrombus formation and high platelets and D-dimer levels at admission (P<0.05), named as thrombotic phenotype. While 32/47 (68,1%) had a mixed phenotypes between both ones. ConclusionsWe believe that categorization of patients based on these two phenotypes can be used to develop prognostic tools and potential therapies since the PaO2/FiO2 ratio variation and D-dimer levels correlate with the underlying fibrotic or thrombotic pathologic process, respectively; which may indicate possible clinical outcome of the patient.

Pupillary light reflex as a diagnostic aid from computational viewpoint: A systematic literature review.

This work presents a detailed and complete review of publications on pupillary light reflex (PLR) used to aid diagnoses. These are computational techniques used in the evaluation of pupillometry, as well as their application in computer-aided diagnoses (CAD) of pathologies or physiological conditions that can be studied by observing the movements of miosis and mydriasis of the human pupil. A careful survey was carried out of all studies published over the last 10 years which investigated, electronic devices, recording protocols, image treatment, computational algorithms and the pathologies related to PLR. We present the frontier of existing knowledge regarding methods and techniques used in this field of knowledge, which has been expanding due to the possibility of performing diagnoses with high precision, at a low cost and with a non-invasive method.

SARS-CoV-2 infects brain astrocytes of COVID-19 patients and impairs neuronal viability

Although increasing evidence confirms neuropsychiatric manifestations associated mainly with severe COVID-19 infection, the long-term neuropsychiatric dysfunction has been frequently observed after mild infection. Here we show the spectrum of the cerebral impact of SARS-CoV-2 infection ranging from long-term alterations in mildly infected individuals (orbitofrontal cortical atrophy, neurocognitive impairment, excessive fatigue and anxiety symptoms) to severe acute damage confirmed in brain tissue samples extracted from the orbitofrontal region (via endonasal trans-ethmoidal approach) from individuals who died of COVID-19. We used surface-based analyses of 3T MRI and identified orbitofrontal cortical atrophy in a group of 81 mildly infected patients (77% referred anosmia or dysgeusia during acute stage) compared to 145 healthy volunteers; this atrophy correlated with symptoms of anxiety and cognitive dysfunction. In an independent cohort of 26 individuals who died of COVID-19, we used histopathological signs of brain damage as a guide for possible SARS-CoV-2 brain infection, and found that among the 5 individuals who exhibited those signs, all of them had genetic material of the virus in the brain. Brain tissue samples from these 5 patients also exhibited foci of SARS-CoV-2 infection and replication, particularly in astrocytes. Supporting the hypothesis of astrocyte infection, neural stem cell-derived human astrocytes in vitro are susceptible to SARS-CoV-2 infection through a non-canonical mechanism that involves spike-NRP1 interaction. SARS-CoV-2-infected astrocytes manifested changes in energy metabolism and in key proteins and metabolites used to fuel neurons, as well as in the biogenesis of neurotransmitters. Moreover, human astrocyte infection elicits a secretory phenotype that reduces neuronal viability. Our data support the model in which SARS-CoV-2 reaches the brain, infects astrocytes and consequently leads to neuronal death or dysfunction. These deregulated processes are also likely to contribute to the structural and functional alterations seen in the brains of COVID-19 patients.

SARS-CoV-2 triggered neutrophil extracellular traps (NETs) mediate COVID-19 pathology

Severe COVID-19 patients develop acute respiratory distress syndrome that may progress to respiratory failure. These patients also develop cytokine storm syndrome, and organ dysfunctions, which is a clinical picture that resembles sepsis. Considering that neutrophil extracellular traps (NETs) have been described as an important factors of tissue damage in sepsis, we investigated whether NETs would be produced in COVID-19 patients and participate in the lung tissue damage. A cohort of 32 hospitalized patients with a confirmed diagnosis of COVID-19 and respective healthy controls were enrolled. NETs concentration was assessed by MPO-DNA PicoGreen assay or by confocal immunofluorescence. The cytotoxic effect of SARS-CoV-2-induced NETs was analyzed in human epithelial lung cells (A549 cells). The concentration of NETs was augmented in plasma and tracheal aspirate from COVID-19 patients and their neutrophils spontaneously released higher levels of NETs. NETs were also found in the lung tissue specimens from autopsies of COVID-19 patients. Notably, viable SARS-CoV-2 can directly induce in vitro release of NETs by healthy neutrophils in a PAD-4-dependent manner. Finally, NETs released by SARS-CoV-2-activated neutrophils promote lung epithelial cell death in vitro. These results unravel a possible detrimental role of NETs in the pathophysiology of COVID-19. Therefore, the inhibition of NETs represent a potential therapeutic target for COVID-19.

Automated Evaluation System for Human Pupillary Behavior.

Analyzing human pupillary behavior is a non-invasive method for evaluating neurological activity. This method contributes to the medical field because changes in pupillary behavior can be correlated with several health conditions such as Parkinson, Alzheimer, autism and diabetes. Analyzing human pupillary behavior is simple and low-cost, and may be used as a complementary diagnosis. Therefore, this work aims to develop an automated system to evaluate human pupillary behavior. The solution consists of a portable recording device, a pupillometer; integrated with a recording and evaluation software based on computer vision. The system is able to stimulate, record, measure and extract relevant features of human pupillary behavior. The results show that the proposed system is fast and accurate, and can be used as an assessment tool for real and extensive clinical practice and research.