Machine-learning-derived predictive score for early estimation of COVID-19 mortality risk in hospitalized patients.

The clinical course of COVID-19 is highly variable. It is therefore essential to predict as early and accurately as possible the severity level of the disease in a COVID-19 patient who is admitted to the hospital. This means identifying the contributing factors of mortality and developing an easy-to-use score that could enable a fast assessment of the mortality risk using only information recorded at the hospitalization. A large database of adult patients with a confirmed diagnosis of COVID-19 (n = 15,628; with 2,846 deceased) admitted to Spanish hospitals between December 2019 and July 2020 was analyzed. By means of multiple machine learning algorithms, we developed models that could accurately predict their mortality. We used the information about classifiers' performance metrics and about importance and coherence among the predictors to define a mortality score that can be easily calculated using a minimal number of mortality predictors and yielded accurate estimates of the patient severity status. The optimal predictive model encompassed five predictors (age, oxygen saturation, platelets, lactate dehydrogenase, and creatinine) and yielded a satisfactory classification of survived and deceased patients (area under the curve: 0.8454 with validation set). These five predictors were additionally used to define a mortality score for COVID-19 patients at their hospitalization. This score is not only easy to calculate but also to interpret since it ranges from zero to eight, along with a linear increase in the mortality risk from 0% to 80%. A simple risk score based on five commonly available clinical variables of adult COVID-19 patients admitted to hospital is able to accurately discriminate their mortality probability, and its interpretation is straightforward and useful.

Fetal Brain Damage during Maternal COVID-19: Emerging Hypothesis, Mechanism, and Possible Mitigation through Maternal-Targeted Nutritional Supplementation.

The recent outbreak of the novel Coronavirus (SARS-CoV-2 or CoV-2) pandemic in 2019 and the risk of CoV-2 infection during pregnancy led the scientific community to investigate the potential negative effects of Coronavirus infection on pregnancy outcomes and fetal development. In particular, as CoV-2 neurotropism has been demonstrated in adults, recent studies suggested a possible risk of fetal brain damage and fetal brain development impairment, with consequent psychiatric manifestations in offspring of mothers affected by COronaVIrus Disease (COVID) during pregnancy. Through the understanding of CoV-2's pathogenesis and the pathways responsible for cell damage, along with the available data about neurotropic virus attitudes, different strategies have been suggested to lower the risk of neurologic disease in newborns. In this regard, the role of nutrition in mitigating fetal damages related to oxidative stress and the inflammatory environment during viral infection has been investigated, and arginine, n3PUFA, vitamins B1 and B9, choline, and flavonoids were found to be promising in and out of pregnancy. The aim of this review is to provide an overview of the current knowledge on the mechanism of fetal brain damage and the impact of nutrition in reducing inflammation related to worse neurological outcomes in the context of CoV-2 infections during pregnancy.

Lower urinary tract symptoms and mental health during COVID-19 pandemic.

OBJECTIVE: Coronaviruses (CoVs) are a group of RNA viruses involved in several human diseases affecting respiratory, enteric, hepatic, and neurological systems. COVID-19 was identified in 2020 and was named SARS-CoV-2. To limit worldwide contagion, many countries instituted a lockdown, which conducted to disruption of routine life. In fact, pandemic was associated with several stresses among population, such as loss of employment, deaths of family members, friends, or colleagues, financial insecurity, and isolation. This led to long-lasting psychosocial effects as anxiety and depression, increasing the prevalence of stress and traumarelated disorders in the population. The aim of this study was to investigate the correlation between lower urinary tracts symptoms (LUTS) and stress/depressive symptoms during COVID-19 pandemic. MATERIALS AND METHODS: An anonymous cross-sectional webbased survey (comprehending anthropometric data, education level, occupation status, smoking and alcohol habits, current therapies, quarantine and COVID-19 infection status) was conducted from March to May 2020 in Italy. LUTS were examined through National Institute of Health-Chronic Prostatitis Symptom Index (NIH-CPSI) and Genitourinary Pain Index (GUPI). Hamilton Depression Rating Scale (HDRS) was utilized to evaluate depressive and anxiety symptoms. Non-parametric Kruskal-Wallis H Test was used for statistical analysis. RESULTS: A total of 356 out of 461 subjects fully completed the survey, with a response rate of 77.2%. Data showed that subjects involved in economic difficulties, quarantine measures or with increased HDRS reported a significative statistic worsened urinary symptoms (H(3) = 11.731, p = 0.008), quality of life, (H(3) = 10.301, p = 0.016), total NIH-CPSI/GUPI score (H(3) = 42.150, p = 0.000), and quality of life (H(3) = 48.638, p = 0.000). CONCLUSIONS: COVID-19 pandemic provoked several alterations in everyday life. Although general lockdown, quarantine and social distancing have been necessary to prevent virus spreading, this had long term effects on all population in terms of mental and physical health. NIH-CPSI and GUPI scores increased linearly with stress and anxiety levels measured at HDRS, confirming worse LUTS in subjects who suffered anxiety and stress from COVID-19 pandemic.