Starting from the Developmental Origins of Health and Disease (DOHaD) hypotheses proposed by David Barker, namely fetal programming, in the past years, there is a growing evidence of the major role played by epigenetic factors during the intrauterine life and the perinatal period. Furthermore, it has been assessed that these factors can affect the health status in infancy and even in adulthood. In this review, we focus our attention on the fetal programming of the brain, analyzing the most recent literature concerning the epigenetic factors that can influence the development of neuropsychiatric disorders such as bipolar disorders, major depressive disorders, and schizophrenia. The perinatal epigenetic factors have been divided in two main groups: maternal factors and fetal factors. The maternal factors include diet, smoking, alcoholism, hypertension, malnutrition, trace elements, stress, diabetes, substance abuse, and exposure to environmental toxicants, while the fetal factors include hypoxia/asphyxia, placental insufficiency, prematurity, low birth weight, drugs administered to the mother or to the baby, and all factors causing intrauterine growth restriction. A better comprehension of the possible mechanisms underlying the pathogenesis of these diseases may help researchers and clinicians develop new diagnostic tools and treatments to offer these patients a tailored medical treatment strategy to improve their quality of life. Birth Defects Research (Part C) 108:207-223, 2016. © 2016 Wiley Periodicals, Inc.

Artificial intelligence (AI) has penetrated the field of medicine, particularly the field of radiology. Since its emergence, the highly virulent coronavirus disease 2019 (COVID-19) has infected over 10 million people, leading to over 500,000 deaths as of July 1 st , 2020 . Since the outbreak began, almost 28,000 articles about COVID-19 have been published ( https://pubmed.ncbi.nlm.nih.gov ); however, few have explored the role of imaging and artificial intelligence in COVID-19 patients—specifically, those with comorbidities. This paper begins by presenting the four pathways that can lead to heart and brain injuries following a COVID-19 infection. Our survey also offers insights into the role that imaging can play in the treatment of comorbid patients, based on probabilities derived from COVID-19 symptom statistics. Such symptoms include myocardial injury, hypoxia, plaque rupture, arrhythmias, venous thromboembolism, coronary thrombosis, encephalitis, ischemia, inflammation, and lung injury. At its core, this study considers the role of image-based AI, which can be used to characterize the tissues of a COVID-19 patient and classify the severity of their infection. Image-based AI is more important than ever as the pandemic surges and countries worldwide grapple with limited medical resources for detection and diagnosis.

Alzheimer disease (AD) is the most prevalent neurodegenerative disease in the elderly, characterized by accumulation in the brain of misfolded proteins, inflammation, and oxidative damage leading to neuronal cell death. By considering the viewpoint that AD onset and worsening may be influenced by environmental factors causing infection, oxidative stress, and inflammatory reaction, we investigated the changes of the salivary proteome in a population of patients with respect to that in healthy controls (HCs). Indeed, the possible use of saliva as a diagnostic tool has been explored in several oral and systemic diseases. Moreover, the oral cavity continuously established adaptative and protective processes toward exogenous stimuli. In the present study, qualitative/quantitative variations of 56 salivary proteoforms, including post-translationally modified derivatives, have been analyzed by RP-HPLC-ESI-IT-MS and MS/MS analyses, and immunological methods were applied to validate MS results. The salivary protein profile of AD patients was characterized by significantly higher levels of some multifaceted proteins and peptides that were either specific to the oral cavity or also expressed in other body districts: (i) peptides involved in the homeostasis of the oral cavity; (ii) proteins acting as ROS/RNS scavengers and with a neuroprotective role, such as S100A8, S100A9, and their glutathionylated and nitrosylated proteoforms; cystatin B and glutathionylated and dimeric derivatives; (iii) proteins with antimicrobial activity, such as α-defensins, cystatins A and B, histatin 1, statherin, and thymosin β4, this last with a neuroprotective role at the level of microglia. These results suggested that, in response to injured conditions, Alzheimer patients established defensive mechanisms detectable at the oral level. Data are available via ProteomeXchange with identifier PXD021538.

The results of this preliminary study suggest that CT can be used to identify the presence of intraplaque hemorrhage according to the attenuation. A threshold of 25 HU in the volume acquired after the administration of contrast medium is associated with an optimal sensitivity and specificity. Special care should be given to the correct identification of the ROI.

No abstract

In recent years, evidence is growing on the role played by gestational factors in shaping brain development and on the influence of intrauterine experiences on later development of neurodegenerative diseases including Parkinson's (PD) and Alzheimer's disease (AD). The nine months of intrauterine development and the first three years of postnatal life are appearing to be extremely critical for making connections among neurons and among neuronal and glial cells that will shape a lifetime of experience. Here, the multiple epigenetic factors acting during gestation - including maternal diet, malnutrition, stress, hypertension, maternal diabetes, fetal hypoxia, prematurity, low birth weight, prenatal infection, intrauterine growth restriction, drugs administered to the mother or to the baby - are reported, and their ability to modulate brain development, resulting in interindividual variability in the total neuronal and glial burden at birth is discussed. Data from recent literature suggest that prevention of neurodegeneration should be identified as the one method to halt the diffusion of neurodegenerative diseases. The "two hits" hypothesis, first introduced for PD and successfully applied to AD and other neurodegenerative human pathologies, should focus our attention on a peculiar period of our life: the intrauterine and perinatal periods. The first hit to our nervous system occurs early in life, determining a PD or AD imprinting to our brain that will condition our resistance or, alternatively, our susceptibility to develop a neurodegenerative disease later in life. In conclusion, how early life events contribute to late-life development of adult neurodegenerative diseases, including PD and AD, is emerging as a new fascinating research focus. This assumption implies that research on prevention of neurodegenerative diseases should center on events taking place early in life, during gestation and in the perinatal periods, thus presenting a new challenge to perinatologists: the prevention of neurodegenerative human diseases.

Background: COVID-19 lung segmentation using Computed Tomography (CT) scans is important for the diagnosis of lung severity. The process of automated lung segmentation is challenging due to (a) CT radiation dosage and (b) ground-glass opacities caused by COVID-19. The lung segmentation methodologies proposed in 2020 were semi- or automated but not reliable, accurate, and user-friendly. The proposed study presents a COVID Lung Image Analysis System (COVLIAS 1.0, AtheroPoint™, Roseville, CA, USA) consisting of hybrid deep learning (HDL) models for lung segmentation. Methodology: The COVLIAS 1.0 consists of three methods based on solo deep learning (SDL) or hybrid deep learning (HDL). SegNet is proposed in the SDL category while VGG-SegNet and ResNet-SegNet are designed under the HDL paradigm. The three proposed AI approaches were benchmarked against the National Institute of Health (NIH)-based conventional segmentation model using fuzzy-connectedness. A cross-validation protocol with a 40:60 ratio between training and testing was designed, with 10% validation data. The ground truth (GT) was manually traced by a radiologist trained personnel. For performance evaluation, nine different criteria were selected to perform the evaluation of SDL or HDL lung segmentation regions and lungs long axis against GT. Results: Using the database of 5000 chest CT images (from 72 patients), COVLIAS 1.0 yielded AUC of ~0.96, ~0.97, ~0.98, and ~0.96 (p-value < 0.001), respectively within 5% range of GT area, for SegNet, VGG-SegNet, ResNet-SegNet, and NIH. The mean Figure of Merit using four models (left and right lung) was above 94%. On benchmarking against the National Institute of Health (NIH) segmentation method, the proposed model demonstrated a 58% and 44% improvement in ResNet-SegNet, 52% and 36% improvement in VGG-SegNet for lung area, and lung long axis, respectively. The PE statistics performance was in the following order: ResNet-SegNet > VGG-SegNet > NIH > SegNet. The HDL runs in <1 s on test data per image. Conclusions: The COVLIAS 1.0 system can be applied in real-time for radiology-based clinical settings.

SARS-COV 2 is recognized to be responsible for a multi-organ syndrome. In most patients, symptoms are mild. However, in certain subjects, COVID-19 tends to progress more severely. Most of the patients infected with SARS-COV2 fully recovered within some weeks. In a considerable number of patients, like many other viral infections, various long-lasting symptoms have been described, now defined as “long COVID-19 syndrome”. Given the high number of contagious over the world, it is necessary to understand and comprehend this emerging pathology to enable early diagnosis and improve patents outcomes. In this scenario, AI-based models can be applied in long-COVID-19 patients to assist clinicians and at the same time, to reduce the considerable impact on the care and rehabilitation unit. The purpose of this manuscript is to review different aspects of long-COVID-19 syndrome from clinical presentation to diagnosis, highlighting the considerable impact that AI can have.