Molecular Monitoring of SARS-CoV-2 in Different Sewage Plants in Venice and the Implications for Genetic Surveillance

Wastewater-based epidemiology is now widely used as an indirect tool to monitor the spread of SARS-CoV-2. In this study, five different sample matrices representing diverse phases of the wastewater treatment process were collected during the second wave of SARS-CoV-2 from two wastewater treatment plants (WWTPs) serving the Civil Hospital and Sacca Fisola island in Venice, Italy. Positive SARS-CoV-2 detections occurred at both WWTPs, and data on viral genome detection rate and quantification suggest that the pellet (i.e., the particulate resulting from the influent) is a sensitive matrix that permits reliable assessment of infection prevalence while reducing time to results. On the contrary, analysis of post-treatment matrices provides evidence of the decontamination efficacy of both WWTPs. Finally, direct sequencing of wastewater samples enabled us to identify B.1.177 and B.1.160 as the prevalent SARS-CoV-2 lineages circulating in Venice at the time of sampling. This study confirmed the suitability of wastewater testing for studying SARS-CoV-2 circulation and established a simplified workflow for the prompt detection and characterization of the virus.

PERCEPTION OF THE BENEFITS AND LIMITATIONS OF TELEVISIT IN A GROUP OF PATIENTS WITH CHRONIC HEART FAILURE

Telemedicine (T) has now become an indispensable operational tool for the management of patients with chronic conditions, in particular for patients with chronic heart failure (HF). The current COVID 19 pandemic has accelerated the development and application oh this method in daily clinical practice. Up to now, the evaluation of he advantage and disadvantage of televisit by cardiological patients have been very limited. In order to assess the satisfaction and criticalities of this method, we administered a satisfaction questionnaire to 74 patients (49 males and 25 females with an average of 75) followed in our clinic. Of the 74 questionnaire administered, 70 were received. When asked whether the inclusion of T had improved the quality of service 43 patients (61%) answered a lot, 24 patients (34%) sufficiently and 3 patients (4%) not at all. When asked if the organization of the T had been adequate, 50 patients (71%) responded adequately, 19 patients (27%) sufficiently adequate and 1 patient (1%) not at all adequate. When asked about the modalities of connection to the T for 54 patients (77%) they were found to be sìmple, for 13 patients (18%) a little complicated and for 3 patients (4%) difficult, very complicated. When asked what would be proposed to improve the service 7 patients (10%) answered the possible extension of hours also in the afternoon, 3 patients (4%) the provision by the ASL of electromedical devices, 14 patients (20%) the ability to directly request an appointment in T and 38 patients (54%) all options. In detail, 1 patient (1%) requested both the extension of the afternoon schedule and the provision of electromedicals, 4 patients (6%) requested both the extension of the afternoon schedule and the direct request for an appointment, 2 patients (2%) required both electromedical equipment and direct request for an appointment, 1 patient (1%) did not indicate any option. Finally, as regards the degree of satisfaction, 45 patients (64%) were considered totally satisfied, 22 patients (31%) partially satisfied and 3 patients (4%) dissatisfied. Conclusions: The televisit in the setting of patients with chronic HF, despite the limitations of a limited series, was generally well accepted, easy to access and with some useful operational proposal for public administration.

Functional immune mapping with deep-learning enabled phenomics applied to immunomodulatory and COVID-19 drug discovery (preprint)

Development of accurate disease models and discovery of immune-modulating drugs is challenged by the immune systems highly interconnected and context-dependent nature. Here we apply deep-learning-driven analysis of cellular morphology to develop a scalable "phenomics" platform and demonstrate its ability to identify dose-dependent, high-dimensional relationships among and between immunomodulators, toxins, pathogens, genetic perturbations, and small and large molecules at scale. High-throughput screening on this platform demonstrates rapid identification and triage of hits for TGF-{beta}- and TNF--driven phenotypes. We deploy the platform to develop phenotypic models of active SARS-CoV-2 infection and of COVID-19-associated cytokine storm, surfacing compounds with demonstrated clinical benefit and identifying several new candidates for drug repurposing. The presented library of images, deep learning features, and compound screening data from immune profiling and COVID-19 screens serves as a deep resource for immune biology and cellular-model drug discovery with immediate impact on the COVID-19 pandemic.

Identification of potential treatments for COVID-19 through artificial intelligence-enabled phenomic analysis of human cells infected with SARS-CoV-2 (preprint)

To identify potential therapeutic stop-gaps for SARS-CoV-2, we evaluated a library of 1,670 approved and reference compounds in an unbiased, cellular image-based screen for their ability to suppress the broad impacts of the SARS-CoV-2 virus on phenomic profiles of human renal cortical epithelial cells using deep learning. In our assay, remdesivir is the only antiviral tested with strong efficacy, neither chloroquine nor hydroxychloroquine have any beneficial effect in this human cell model, and a small number of compounds not currently being pursued clinically for SARS-CoV-2 have efficacy. We observed weak but beneficial class effects of {beta}-blockers, mTOR/PI3K inhibitors and Vitamin D analogues and a mild amplification of the viral phenotype with {beta}-agonists.