The COVID-19 pandemic is caused by the betacoronavirus SARS-CoV-2. In November 2021, the Omicron variant was discovered and immediately classified as a variant of concern (VOC), since it shows ...substantially more mutations in the spike protein than any previous variant, especially in the receptor-binding domain (RBD). We analyzed the binding of the Omicron RBD to the human angiotensin-converting enzyme-2 receptor (ACE2) and the ability of human sera from COVID-19 patients or vaccinees in comparison to Wuhan, Beta, or Delta RBD variants.
All RBDs were produced in insect cells. RBD binding to ACE2 was analyzed by ELISA and microscale thermophoresis (MST). Similarly, sera from 27 COVID-19 patients, 81 vaccinated individuals, and 34 booster recipients were titrated by ELISA on RBDs from the original Wuhan strain, Beta, Delta, and Omicron VOCs. In addition, the neutralization efficacy of authentic SARS-CoV-2 wild type (D614G), Delta, and Omicron by sera from 2× or 3× BNT162b2-vaccinated persons was analyzed.
Surprisingly, the Omicron RBD showed a somewhat weaker binding to ACE2 compared to Beta and Delta, arguing that improved ACE2 binding is not a likely driver of Omicron evolution. Serum antibody titers were significantly lower against Omicron RBD compared to the original Wuhan strain. A 2.6× reduction in Omicron RBD binding was observed for serum of 2× BNT162b2-vaccinated persons. Neutralization of Omicron SARS-CoV-2 was completely diminished in our setup.
These results indicate an immune escape focused on neutralizing antibodies. Nevertheless, a boost vaccination increased the level of anti-RBD antibodies against Omicron, and neutralization of authentic Omicron SARS-CoV-2 was at least partially restored. This study adds evidence that current vaccination protocols may be less efficient against the Omicron variant.
Development of a convolutional neural network that can precisely and quickly identify teeth from x-ray images, without using neighbouring structures as a frame of reference.
Using a database of 11403 ...x-ray images that were precisely annotated by dental professionals we have trained, validated and tested a convolutional neural network (CNN) that can identify teeth according to their position in the oral cavity. Four “levels” were tested, the first one being classification according to the type of the tooth morphologically. This consisted of 4 categories: incisor, canine, premolar and molar. The second “level” added the differentiation between types of incisors, premolars and molars. This “level” had 8 categories, imitating a dental quadrant. The third “level” added maxillary or mandibular origin and a total of 16 categories. Finally, the fourth “level” had 32 categories, meaning every tooth had its own.
The first level offered an 97.83% accuracy on unseen data. The second level offered 92.13%. “Level” three offered 91.14%. The fourth level, while being the most demanding, offered a 91.13%.
The results were the best in the 4 category “level” and the least successful in the 32 category “level”. Interestingly, the difference between the 32 and 16 category level was not significant at all. The developed CNN can identify the morphological type of the tooth with a very high accuracy rate. This opens a door into implementation of artificial intelligence in rapid analysis and cross referencing in (forensic) dental medicine.
This study has been supported as a part of the Croatian Science Foundation under the project IP-2020-02-9423.
The ionospheric D-region affects propagation of electromagnetic waves including ground-based signals and satellite signals during its intensive disturbances. Consequently, the modeling of ...electromagnetic propagation in the D-region is important in many technological domains. One of sources of uncertainty in the modeling of the disturbed D-region is the poor knowledge of its parameters in the quiet state at the considered location and time period. We present the Quiet Ionospheric D-Region (QIonDR) model based on data collected in the ionospheric D-region remote sensing by very low/low frequency (VLF/LF) signals and the Long-Wave Propagation Capability (LWPC) numerical model. The QIonDR model provides both Wait’s parameters and the electron density in the D-region area of interest at a given daytime interval. The proposed model consists of two steps. In the first step, Wait’s parameters are modeled during the quiet midday periods as a function of the daily sunspot number, related to the long-term variations during solar cycle, and the seasonal parameter, providing the seasonal variations. In the second step, the output of the first step is used to model Wait’s parameters during the whole daytime. The proposed model is applied to VLF data acquired in Serbia and related to the DHO and ICV signals emitted in Germany and Italy, respectively. As a result, the proposed methodology provides a numerical tool to model the daytime Wait’s parameters over the middle and low latitudes and an analytical expression valid over a part of Europe for midday parameters.
•There is no standardized method for assessment of port environmental impact.•Using Internet of Things techniques allows the index to be assessed in real time.•PEI integrates main environmental ...aspects into a single metric.•PEI enables interport comparison and yearly progress evaluation for a port.•PEI preliminary results are useful for assessing port environmental impact trends.
In recent years, exchange of goods around the world has mostly been done by the sea, which increased the pollution coming from the port areas. Activities connected with shipping and handling of goods in ports may harm both human health and the environment. These activities include different (mostly diesel-fueled) machinery used in ports, resulting in air emissions including GHG, NOX, SOX, PM, etc. Besides air pollution, port activities affect noise, light, and odor emission, waste accumulation and water pollution. Existing methodologies for estimating environmental impacts of port activities are mostly qualitative and include self-assessment methods which can often lead to biased results. Because of that, there is a need for a quantitative, industry-validated, and cohesive method that would give more accurate results. In this article, the Port Environmental Index (PEI) which has all the attributes described above will be presented. The PEI mission is to integrate all of the main environmental aspects of port such as air emission, waste production, water pollution, noise, light, and odor pollution into one metric that can then be used to assess the port performance and make comparison between ports. The PEI is made as a quantitative composite index based on aggregations of individual indicators for significant aspects of port operations. It includes different indices according to the source of the emission; the Ship Environmental Index (SEI), the Terminal Environmental Index (TEI), and the Port Authority Environmental Index (PAEI). While designing the PEI, correctly choosing the environmental impacts is paramount to properly identify port activities and associated environmental aspects. After their identification, for each significant aspect, a set of representative environmental key performance indicators (eKPIs) is identified. Afterwards, a series of mathematical operations are to be applied: normalization, weighting and aggregation. In this short communication, those methods are outlined yet not definitively chosen. The main idea behind the PEI is to use quantitative, data-based information collected automatically leveraging Internet of Things (IoT) techniques making it possible to assess the environmental impacts of port operations in real-time. The advantages of having such metric in the environmental management plan of a port are numerous. Amongst the most remarkable, it allows inter-port comparison and it can be used for decision making to estimate the impacts using one single metric rather than having many disperse values. Moreover, it can be used by ports for estimating their environmental performance and progress. Since it is based on information collected using IoT technologies provided in real-time, ports can make immediate corrections in their activities.
Coronavirus disease (COVID-19) is an infectious disease caused by SARS-CoV-2. Elderly people, people with immunodeficiency, autoimmune and malignant diseases, as well as people with chronic diseases ...have a higher risk of developing more severe forms of the disease. Pregnant women and children can becomesick, although more often they are only the carriers of the virus. Recent studies have indicated that infants can also be infected by SARS-CoV-2 and develop a severe form of the disease with a fatal outcome. Acute Respiratory Distress Syndrome (ARDS) ina pregnant woman can affect the supply of oxygen to the fetus and initiate the mechanism of metabolic disorders of the fetus and newborn caused by asphyxia. The initial metabolic response of the newborn to the lack of oxygen in the tissues is the activation of anaerobic glycolysis in the tissues and an increase in the concentration of lactate and ketones. Lipid peroxidation, especially in nerve cells, is catalyzed by iron released from hemoglobin, transferrin and ferritin, whose release is induced by tissue acidosis and free oxygen radicals. Ferroptosis-inducing factors can directly or indirectly affect glutathione peroxidase through various pathways, resulting in a decrease in the antioxidant capacity and accumulation of lipid reactive oxygen species (ROS) in the cells, ultimately leading to oxidative cell stress, and finally, death. Conclusion: damage to the mitochondria as a result of lipid peroxidation caused by the COVID-19 disease can cause the death of a newborn and pregnant women as well as short time and long-time sequelae.
Abstract The aim of this study was to characterize the systemic cytokine signature of critically ill COVID-19 patients in a high mortality setting aiming to identify biomarkers of severity, and to ...explore their associations with viral loads and clinical characteristics. We studied two COVID-19 critically ill patient cohorts from a referral centre located in Central Europe. The cohorts were recruited during the pre-alpha/alpha (November 2020 to April 2021) and delta (end of 2021) period respectively. We determined both the serum and bronchoalveolar SARS-CoV-2 viral load and identified the variant of concern (VoC) involved. Using a cytokine multiplex assay, we quantified systemic cytokine concentrations and analyzed their relationship with clinical findings, routine laboratory workup and pulmonary function data obtained during the ICU stay. Patients who did not survive had a significantly higher systemic and pulmonary viral load. Patients infected with the pre-alpha VoC showed a significantly lower viral load in comparison to those infected with the alpha- and delta-variants. Levels of systemic CTACK, M-CSF and IL-18 were significantly higher in non-survivors in comparison to survivors. CTACK correlated directly with APACHE II scores. We observed differences in lung compliance and the association between cytokine levels and pulmonary function, dependent on the VoC identified. An intra-cytokine analysis revealed a loss of correlation in the non-survival group in comparison to survivors in both cohorts. Critically ill COVID-19 patients exhibited a distinct systemic cytokine profile based on their survival outcomes. CTACK, M-CSF and IL-18 were identified as mortality-associated analytes independently of the VoC involved. The Intra-cytokine correlation analysis suggested the potential role of a dysregulated systemic network of inflammatory mediators in severe COVID-19 mortality.
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