Introduction: The World Health Organization declared the COVID-19 pandemic in March 2020. COVID-19 still represents a worldwide health emergency, which causesa severe disease that has led to the death of many patients. The pathophysiological mechanism of SARS-CoV-2 determining the tissue damage is not clear and autopsycan be auseful tool to improve the knowledge of this infection and, thus, it can help achieve a timely diagnosis and develop an appropriate therapy. This is an overview of the main post-mortem findings reporting data on the infection effects on several organs.Methods: A systematic literature search was conducted in the PubMed database searching for articles from 1 January to August 31, 2020. Thearticles were selected identifying words/concepts in the titles and/or abstracts that indicated the analysis of the morphological/pathological tissue injuries related to SARS-CoV-2 disease by several investigations.Results: A total of 63 articles were selected. The main investigated tissue was the lung showing a diffuse alveolar damage (DAD) frequently associated with pulmonary thrombotic microangiopathy. Inflammatory findings and vascular damage were observed in other organs such as heart, liver, kidney, brain, spleen, skin and adrenal gland. The immunohistochemical analysis showed tissue inflammatory cells infiltrates. The virus presence was detected by several investigations such as RT-PCR, immunohistochemistry and electron microscope, showing the effect ofSARS-CoV-2not exclusively in the lung.Discussion: The evidence emerging from this review highlighted the importance of autopsy to provide a fundamental base in the process of understanding the consequences ofSARS-CoV-2 infection. COVID-19 is strictly related to a hyper inflammatory state that seems to start with DAD and immuno-thrombotic microangiopathy. Massive activation of the immune system and microvascular damage might also be responsible for indirect damage to other organs, even if the direct effect of the virus on these tissues cannot be excluded.

Pre-clinical evidence largely shows that CBD can produce beneficial effects in AD, PD and MS patients, but its employment for these disorders needs further confirmation from well designed clinical studies. CBD pre-clinical demonstration of antiepileptic activity is supported by recent clinical studies in human epileptic subjects resistant to standard antiepileptic drugs showing its potential use in children and young adults affected by refractory epilepsy. Evidence for use of CBD in PD is still not supported by sufficient data whereas only a few studies including a small number of patients are available.

Objective The aim of this review is to analyze preclinical and clinical studies investigating the anxiety effects of Citrus aurantium or Citrus sinensis essential oils (EOs). Design The bibliographic research was made on the major scientific databases. Analysis included only articles written in English and published on peer-reviewed scientific journals describing preclinical experiments and clinical trials carried out to investigate the antianxiety effects of Citrus aurantium or Citrus sinensis EOs on anxiety disorders. Clinical studies reporting the antianxiety effects of products containing Citrus aurantium or Citrus sinensis EOs in combination with other active substances, including medicinal plants, were excluded. Nine clinical studies fulfilled the criteria adopted for analysis. Results Data show that Citrus aurantium or Citrus sinensis EOs produce anxiolytic effects both in preclinical experiments and in different clinical conditions. Citrus aurantium EO aromatherapy reduced anxiety level in the great part of stress conditions studied (subjects affected by chronic myeloid leukemia and preoperative patients) except for a sample of patients subjected to colonoscopy. Exposition to Citrus sinensis EO in clinical studies shows to be positive in reducing anxiety level in patients waiting for dental treatment as well as in healthy volunteers submitted to an anxiogenic situation. Conclusions Overview of clinical trials conducted with Citrus aurantium or Citrus sinensis on people with anxiety showed that inhalation or oral administration of Citrus aurantium and inhalation of Citrus sinensis can exert beneficial effects on anxiety; however, because of incomplete accuracy in the reporting of methodology, further more complete clinical studies are warranted.

Introduction: Systemic sclerosis (SSc) is a systemic immune-mediated disease, featuring fibrosis of the skin and organs, and has the greatest mortality among rheumatic diseases. The nervous system involvement has recently been demonstrated, although actual lung involvement is considered the leading cause of death in SSc and, therefore, should be diagnosed early. Pulmonary function tests are not sensitive enough to be used for screening purposes, thus they should be flanked by other clinical examinations; however, this would lead to a risk of overtesting, with considerable costs for the health system and an unnecessary burden for the patients. To this extent, Machine Learning (ML) algorithms could represent a useful add-on to the current clinical practice for diagnostic purposes and could help retrieve the most useful exams to be carried out for diagnostic purposes. Method: Here, we retrospectively collected high resolution computed tomography, pulmonary function tests, esophageal pH impedance tests, esophageal manometry and reflux disease questionnaires of 38 patients with SSc, applying, with R, different supervised ML algorithms, including lasso, ridge, elastic net, classification and regression trees (CART) and random forest to estimate the most important predictors for pulmonary involvement from such data. Results: In terms of performance, the random forest algorithm outperformed the other classifiers, with an estimated root-mean-square error (RMSE) of 0.810. However, this algorithm was seen to be computationally intensive, leaving room for the usefulness of other classifiers when a shorter response time is needed. Conclusions: Despite the notably small sample size, that could have prevented obtaining fully reliable data, the powerful tools available for ML can be useful for predicting early lung involvement in SSc patients. The use of predictors coming from spirometry and pH impedentiometry together might perform optimally for predicting early lung involvement in SSc.

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