A fuzzy-enhanced deep learning approach for early detection of Covid-19 pneumonia from portable chest X-ray images.

The Covid-19 pandemic is the defining global health crisis of our time. Chest X-Rays (CXR) have been an important imaging modality for assisting in the diagnosis and management of hospitalised Covid-19 patients. However, their interpretation is time intensive for radiologists. Accurate computer aided systems can facilitate early diagnosis of Covid-19 and effective triaging. In this paper, we propose a fuzzy logic based deep learning (DL) approach to differentiate between CXR images of patients with Covid-19 pneumonia and with interstitial pneumonias not related to Covid-19. The developed model here, referred to as CovNNet, is used to extract some relevant features from CXR images, combined with fuzzy images generated by a fuzzy edge detection algorithm. Experimental results show that using a combination of CXR and fuzzy features, within a deep learning approach by developing a deep network inputed to a Multilayer Perceptron (MLP), results in a higher classification performance (accuracy rate up to 81%), compared to benchmark deep learning approaches. The approach has been validated through additional datasets which are continously generated due to the spread of the virus and would help triage patients in acute settings. A permutation analysis is carried out, and a simple occlusion methodology for explaining decisions is also proposed. The proposed pipeline can be easily embedded into present clinical decision support systems.

Fat mass affects nutritional status of ICU COVID-19 patients.

BACKGROUND: Obesity and steatosis are associated with COVID-19 severe pneumonia. Elevated levels of pro-inflammatory cytokines and reduced immune response are typical of these patients. In particular, adipose tissue is the organ playing the crucial role. So, it is necessary to evaluate fat mass and not simpler body mass index (BMI), because BMI leaves a portion of the obese population unrecognized. The aim is to evaluate the relationship between Percentage of Fat Mass (FM%) and immune-inflammatory response, after 10 days in Intensive Care Unit (ICU). METHODS: Prospective observational study of 22 adult patients, affected by COVID-19 pneumonia and admitted to the ICU and classified in two sets: (10) lean and (12) obese, according to FM% and age (De Lorenzo classification). Patients were analyzed at admission in ICU and at 10th day. RESULTS: Obese have steatosis, impaired hepatic function, compromise immune response and higher inflammation. In addition, they have a reduced prognostic nutritional index (PNI), nutritional survival index for ICU patients. CONCLUSION: This is the first study evaluating FM% in COVID-19 patient. We underlined obese characteristic with likely poorly prognosis and an important misclassification of obesity. A not negligible number of patients with normal BMI could actually have an excess of adipose tissue and therefore have an unfavorable outcome such as an obese. Is fundamental personalized patients nutrition basing on disease phases.

Efficacy and Effect of Inhaled Adenosine Treatment in Hospitalized COVID-19 Patients.

Lack of specific antiviral treatment for COVID-19 has resulted in long hospitalizations and high mortality rate. By harnessing the regulatory effects of adenosine on inflammatory mediators, we have instituted a new therapeutic treatment with inhaled adenosine in COVID-19 patients, with the aim of reducing inflammation, the onset of cytokine storm, and therefore to improve prognosis. The use of inhaled adenosine in COVID19 patients has allowed reduction of length of stay, on average 6 days. This result is strengthened by the decrease in SARS-CoV-2 positive days. In treated patients compared to control, a clear improvement in PaO2/FiO2 was observed together with a reduction in inflammation parameters, such as the decrease of CRP level. Furthermore, the efficacy of inhaled exogenous adenosine led to an improvement of the prognosis indices, NLR and PLR. The treatment seems to be safe and modulates the immune system, allowing an effective response against the viral infection progression, reducing length of stay and inflammation parameters.

Can Adenosine Fight COVID-19 Acute Respiratory Distress Syndrome?

Coronavirus disease 2019 (COVID-19) patients can develop interstitial pneumonia, which, in turn, can evolve into acute respiratory distress syndrome (ARDS). This is accompanied by an inflammatory cytokine storm. severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) has proteins capable of promoting the cytokine storm, especially in patients with comorbidities, including obesity. Since currently no resolutive therapy for ARDS has been found and given the scientific literature regarding the use of adenosine, its application has been hypothesized. Through its receptors, adenosine is able to inhibit the acute inflammatory process, increase the protection capacity of the epithelial barrier, and reduce the damage due to an overactivation of the immune system, such as that occurring in cytokine storms. These features are known in ischemia/reperfusion models and could also be exploited in acute lung injury with hypoxia. Considering these hypotheses, a COVID-19 patient with unresponsive respiratory failure was treated with adenosine for compassionate use. The results showed a rapid improvement of clinical conditions, with negativity of SARS-CoV2 detection.