Effect of Tocilizumab vs Standard Care on Clinical Worsening in Patients Hospitalized With COVID-19 Pneumonia: A Randomized Clinical Trial

The coronavirus disease 2019 (COVID-19) pandemic is threatening billions of people worldwide. Tocilizumab has shown promising results in retrospective studies in patients with COVID-19 pneumonia with a good safety profile

Intravenous methylprednisolone pulses in hospitalised patients with severe COVID-19 pneumonia, A double-blind, randomised, placebo-controlled trial

Rationale: Pulse glucocorticoid therapy is used in hyperinflammation related to coronavirus 2019 (COVID-19). We evaluated the efficacy and safety of pulse intravenous methylprednisolone in addition to standard treatment in COVID-19 pneumonia. Methods: In this multicenter, randomised, double-blind, placebo-controlled trial, 304 hospitalised patients with Covid-19 pneumonia were randomised to receive 1 g of methylprednisolone intravenously for 3 consecutive days or placebo in addition to standard dexamethasone. The primary outcome was the duration of the patient hospitalisation, calculated as the time interval between randomisation and hospital discharge without the need of supplementary oxygen. The key secondary outcomes were survival free from invasive ventilation with orotracheal intubation and overall survival. Results: Overall, 112 of 151 (75.4%) patients in the pulse methylprednisolone arm and 111 of 150 (75.2%) in the placebo arm were discharged from hospital without oxygen within 30 days from randomisation. Median time to discharge was similar in both groups [15 days (95% confidence interval (CI), 13.0 to 17.0) and 16 days (95%CI, 13.8 to 18.2); hazard ratio (HR), 0.92; 95% CI 0.71-1.20; p=0.528]. No significant differences between pulse methylprednisolone and placebo arms were observed in terms of admission to Intensive Care Unit with orotracheal intubation or death (20.0% versus 16.1%; HR, 1.26; 95%CI, 0.74-2.16; p=0.176), or overall mortality (10.0% versus 12.2%; HR, 0.83; 95%CI, 0.42-1.64; p=0.584). Serious adverse events occurred with similar frequency in the two groups. Conclusions: Methylprenisolone pulse therapy added to dexamethasone was not of benefit in patients with COVID-19 pneumonia. Message of the study: Pulse glucocorticoid therapy is used for severe and/or life threatening immuno-inflammatory diseases. The addition of pulse glucocorticoid therapy to the standard low dose of dexamethasone scheme was not of benefit in patients with COVID-19 pneumonia

Communication and visiting policies in Italian intensive care units during the first COVID-19 pandemic wave and lockdown: a nationwide survey

Background: During the first coronavirus disease 2019 (COVID-19) pandemic wave, an unprecedented number of patients with respiratory failure due to a new, highly contagious virus needed hospitalization and intensive care unit (ICU) admission. The aim of the present study was to describe the communication and visiting policies of Italian intensive care units (ICUs) during the first COVID-19 pandemic wave and national lockdown and compare these data with prepandemic conditions. Methods: A national web-based survey was conducted among 290 Italian hospitals. Each ICU (active between February 24 and May 31, 2020) was encouraged to complete an individual questionnaire inquiring the hospital/ICU structure/organization, communication/visiting habits and the role of clinical psychology prior to, and during the first COVID-19 pandemic wave. Results: Two hundred and nine ICUs from 154 hospitals (53% of the contacted hospitals) completed the survey (202 adult and 7 pediatric ICUs). Among adult ICUs, 60% were dedicated to COVID-19 patients, 21% were dedicated to patients without COVID-19 and 19% were dedicated to both categories (Mixed). A total of 11,102 adult patients were admitted to the participating ICUs during the study period and only approximately 6% of patients received at least one visit. Communication with family members was guaranteed daily through an increased use of electronic devices and was preferentially addressed to the same family member. Compared to the prepandemic period, clinical psychologists supported physicians more often regarding communication with family members. Fewer patients received at least one visit from family members in COVID and mixed-ICUs than in non-COVID ICUs, l (0 [0–6]%, 0 [0–4]% and 11 [2–25]%, respectively, p < 0.001). Habits of pediatric ICUs were less affected by the pandemic. Conclusions: Visiting policies of Italian ICUs dedicated to adult patients were markedly altered during the first COVID-19 wave. Remote communication was widely adopted as a surrogate for family meetings. New strategies to favor a family-centered approach during the current and future pandemics are warranted

Studio di interazione hardware/software tra Raspberry e nanoVNA

L’obiettivo della tesi è quello di realizzare un progetto basato sull’interazione tra Raspberry Pi e NanoVNA, allo scopo di rendere automatico il processo di acquisizione dei dati di questo strumento di misura. A tal fine è stato sviluppato un programma in linguaggio Python, eseguibile dal terminale del Raspberry Pi, che permette all’utente di inserire agevolmente i parametri essenziali, come l’orario di inizio e termine delle misurazioni e l'intervallo di tempo tra una rilevazione e l’altra

Forecasting Air Temperature on Edge Devices with Embedded AI

With the advent of the Smart Agriculture, the joint utilization of Internet of Things (IoT) and Machine Learning (ML) holds the promise to significantly improve agricultural production and sustainability. In this paper, the design of a Neural Network (NN)-based prediction model of a greenhouse’s internal air temperature, to be deployed and run on an edge device with constrained capabilities, is investigated. The model relies on a time series-oriented approach, taking as input variables the past and present values of the air temperature to forecast the future ones. In detail, we evaluate three different NN architecture types—namely, Long Short-Term Memory (LSTM) networks, Recurrent NNs (RNNs) and Artificial NNs (ANNs)—with various values of the sliding window associated with input data. Experimental results show that the three best-performing models have a Root Mean Squared Error (RMSE) value in the range 0.289÷0.402°C, a Mean Absolute Percentage Error (MAPE) in the range of 0.87÷1.04%, and a coefficient of determination (R2) not smaller than 0.997. The overall best performing model, based on an ANN, has a good prediction performance together with low computational and architectural complexities (evaluated on the basis of the NetScore metric), making its deployment on an edge device feasible

VegIoT Garden: a modular IoT Management Platform for Urban Vegetable Gardens

Nowadays, the agricultural sector is facing challenges especially because of an extensive range of grueling trends. In this context, new highly technological applications—such as Internet of Things (IoT), Precision Agriculture (PA), and blockchain—are enabling Smart Agriculture (SA), which holds the promise to support future needs. In this extended abstract, a low-cost, modular, and energy-efficient IoT platform for SA, denoted as VegIoT Garden, based on Commercial-Off-The-Shelf (COTS) devices, adopting short- and long-range communication protocols (IEEE 802.11 and LoRa), and aiming at enhancing the management of vegetable gardens through the collection, monitoring, and analysis of sensor data, related to relevant parameters of growing plants (i.e., air and soil humidity and temperature), is presented. The infrastructure is completed with an Internet-enabled Home Node (HN) and an iOS-based mobile App, developed in order to simplify data visualization and plants’ status monitoring. The proposed IoT system has been validated in a real scenario (a vegetable garden) for more than a week: the collected data highlighted possible causes for a disease contracted by vegetables (namely, tomato’s blossom-end root), thus validating VegIoT Garden

False positive absent somatosensory evoked potentials in cardiac arrest with therapeutic hypothermia.

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AI at the Edge: a Smart Gateway for Greenhouse Air Temperature Forecasting

Controlling and forecasting environmental variables (e.g., air temperature) is usually a key and complex part in a greenhouse management architecture. Indeed, a greenhouse inner micro-climate, which is the result of an extensive set of inter-related environmental variables influenced by external weather conditions, has to be tightly monitored, regulated, and, some-times, forecast. Nowadays, Wireless Sensor Networks (WSNs) and Machine Learning (ML) are two of the most successful technologies to deal with this challenge. In this paper, we discuss how a Smart Gateway (GW), acting as a collector for sensor data coming from a WSN installed in a greenhouse, could be enriched with a Neural Network (NN)-based prediction model allowing to forecast a greenhouse’s inner air temperature. In the case of missing sensor data coming from the WSN, the proposed prediction algorithm, fed with meteorological open data (gathered from the DarkSky repository), is run on the GW in order to predict the missing values. Despite the model is especially designed to be lightweight and executable by a device with constrained capabilities, it can be adopted either at Cloud or at GW level to forecast future air temperature’s values, in order to support the management of a greenhouse. Experimental results show that the NN-based prediction algorithm can forecast greenhouse air temperature with a Root Mean Square Error (RMSE) of 1.50°C, a Mean Absolute Percentage Error (MAPE) of 4.91%, and a R2 score of 0.965