Coronavirus disease (COVID-19) is the pandemic caused by SARS-CoV-2 that has caused more than 2.2 million deaths worldwide. We summarize the reported pathologic findings on biopsy and autopsy in ...patients with severe/fatal COVID-19 and documented the presence and/or effect of SARS-CoV-2 in all organs.
A systematic search of the PubMed, Embase, MedRxiv, Lilacs and Epistemonikos databases from January to August 2020 for all case reports and case series that reported histopathologic findings of COVID-19 infection at autopsy or tissue biopsy was performed. 603 COVID-19 cases from 75 of 451 screened studies met inclusion criteria. The most common pathologic findings were lungs: diffuse alveolar damage (DAD) (92%) and superimposed acute bronchopneumonia (27%); liver: hepatitis (21%), heart: myocarditis (11.4%). Vasculitis was common only in skin biopsies (25%). Microthrombi were described in the placenta (57.9%), lung (38%), kidney (20%), Central Nervous System (CNS) (18%), and gastrointestinal (GI) tract (2%). Injury of endothelial cells was common in the lung (18%) and heart (4%). Hemodynamic changes such as necrosis due to hypoxia/hypoperfusion, edema and congestion were common in kidney (53%), liver (48%), CNS (31%) and GI tract (18%). SARS-CoV-2 viral particles were demonstrated within organ-specific cells in the trachea, lung, liver, large intestine, kidney, CNS either by electron microscopy, immunofluorescence, or immunohistochemistry. Additional tissues were positive by Polymerase Chain Reaction (PCR) tests only. The included studies were from numerous countries, some were not peer reviewed, and some studies were performed by subspecialists, resulting in variable and inconsistent reporting or over statement of the reported findings.
The main pathologic findings of severe/fatal COVID-19 infection are DAD, changes related to coagulopathy and/or hemodynamic compromise. In addition, according to the observed organ damage myocarditis may be associated with sequelae.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Convolutional neural networks (CNNs) have shown a competitive performance in medical imaging applications, such as image segmentation. However, choosing an existing architecture capable of adapting ...to a specific dataset is challenging and requires design expertise. Neural architecture search (NAS) is employed to overcome these limitations. NAS uses techniques to design the Neural Networks architecture. Typically, the models' weights optimization is carried out using a continuous loss function, unlike model topology optimization, which is highly influenced by the specific problem. Genetic programming (GP) is an evolutionary algorithm (EA) capable of adapting to the topology optimization problem of CNNs by considering the attributes of its representation. A tree representation can express complex connectivity and apply variation operations. This article presents a tree-based GP algorithm for evolving CNNs based on the well-known U-Net architecture producing compact and flexible models for medical image segmentation across multiple domains. This proposal is called NAS / GP / U-Net (NASGP-Net). NASGP-Net uses a cell-based encoding and U-Net architecture as a backbone to construct CNNs based on a hierarchical arrangement of primitive operations. Our experiments indicate that our approach can produce remarkable segmentation results with fewer parameters regarding fixed architectures. Moreover, NASGP-Net presents competitive results against NAS methods. Finally, we observed notable performance improvements based on several evaluation metrics, including dice similarity coefficient (DSC), intersection over union (IoU), and Hausdorff distance (HD).
Malignant tumors exhibit rapid growth and high metabolic rates, similar to embryonic stem cells, and depend on aerobic glycolysis, known as the "Warburg effect". This understanding has enabled the ...use of radiolabeled glucose analogs in tumor staging and therapeutic response assessment via PET scans. Traditional treatments like chemotherapy and radiotherapy target rapidly dividing cells, causing significant toxicity. Despite immunotherapy's impact on solid tumor treatment, gaps remain, leading to research on cancer cell evasion of immune response and immune tolerance induction via interactions with the tumor microenvironment (TME). The TME, consisting of immune cells, fibroblasts, vessels, and the extracellular matrix, regulates tumor progression and therapy responses. TME-targeted therapies aim to transform this environment from supporting tumor growth to impeding it and fostering an effective immune response. This review examines the metabolic disparities between immune cells and cancer cells, their impact on immune function and therapeutic targeting, the TME components, and the complex interplay between cancer cells and nontumoral cells. The success of TME-targeted therapies highlights their potential to achieve better cancer control or even a cure.
Drug Reaction with Eosinophilia and Systemic Symptoms (DRESS) syndrome is a rare but severe and sometimes fatal adverse drug reaction that is known to occur with a number of antiepileptic drugs. It ...often follows a prolonged clinical course, which can worsen even after discontinuing the causative drug and administering steroid treatment. Failure to promptly identify the delayed involvement of vital organs, such as the heart and liver, may result in irreversible organ failure and death. We report a case of a presumed sudden death of a young woman who had a documented history of a protracted intermittent hypersensitivity reaction to lamotrigine. Postmortem examination revealed the presence of eosinophilic myocarditis and submassive hepatic necrosis diagnostic of fatal DRESS syndrome that progressed despite early discontinuation of the medication and improvement of dermatologic and hematologic symptoms following steroid therapy.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Summary
Objective
To evaluate the association between body mass index (BMI) and performance of executive functions (EFs) in girls and boys with 9‐ and 10‐year‐old schoolchildren with moderate‐ to ...vigorous‐intensity physical activity (MVPA) and sedentary behaviour.
Methods
A total of 120 schoolchildren (61 girls and 59 boys) were evaluated anthropometrically. The MVPA was evaluated with a self‐report questionnaire. EFs were measured using a neuropsychological battery of Executive Functions and Frontal Lobes‐2 (BANFE‐2).
Results
A high BMI was associated with longer delay in completing inhibitory control tests (p = 0.00, rp = 0.32) and working memory (p = 0.00, rp = 0.26). We observed correlations in time (p = 0.00, rp = −0.43) and hits (p = 0.04, rp = −0.27) of self‐directed signalling test in boys; and girls in alphabetical words order (p = 0.00, rp = −0.39). Active normal weight schoolchildren (ANw) performed better by successfully completed the working memory tasks (H = 26.97, p = 0.00) than sedentary schoolchildren with overweight and obesity. In addition, overweight‐active schoolchildren (AOw) showed better performance on working memory tests in time (p = 0.00) and hits (p = 0.01) than their sedentary peers. Multiple linear regression analysis revealed a significant association between BMI and EFs scores (F = 2.41, df = 98, p = 0.001).
Conclusions
EFs are affected by a high BMI and sedentary behaviour in school children. Boys and girls reflected differences to solve the same challenges. The MVPA has a positive effect on executive control skills mainly in overweight children.
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BFBNIB, FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, UL, UM, UPUK, VSZLJ
Nature-inspired optimization algorithms are meta-heuristics that mimic nature for solving optimization problems. Many optimization problems are constrained and have a bounded search space from which ...some solution vectors leave when the variation operators are applied. Therefore, the use of boundary constraint-handling methods (
BCHM
) is necessary in order to repair the invalid vectors. This paper presents an adaptive scheme to handling boundary constraints in constrained numerical optimization problems. The proposed adaptive scheme operates in two stages: At the first one, when there are still no feasible solutions, a
BCHM
that benefits the exploration of the search space is employed, and in the second stage, one of several BCHMs, according to their associated probabilities, is selected. The methods’ probabilities are updated every learning period so that the methods that generate the best repaired solutions will have a greater chance of being selected. The proposed scheme has been tested within two nature-inspired optimization algorithms: Particle Swarm Optimization and Differential Evolution employing their canonical version as well as one state-of-the-art version specialized in constrained optimization. A set of sixty single-objective constrained real-parameter optimization problems are solved. The results show that this adaptive scheme has a major impact on the algorithm’s performance, and it is able to promote better final results mainly within high-dimensional problems.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Studying chemical components in food of natural origin allows us to understand their nutritional contents. However, nowadays, this analysis is performed using invasive methods that destroy the sample ...under study. These methods are also expensive and time-consuming. Computer vision is a non-invasive alternative to determine the nutritional contents through digital image processing to obtain the colour properties. This work employed a probability mass function (PMF) in colour spaces HSI (hue, saturation, intensity) and CIE L*a*b* (International Commission on Illumination) as inputs for a convolutional neural network (CNN) to estimate the anthocyanin contents in landraces of homogeneous colour. This proposal is called AnthEstNet (Anthocyanins Estimation Net). Before applying the CNN, a methodology was used to take digital images of the bean samples and extract their colourimetric properties represented by PMF. AnthEstNet was compared against regression methods and artificial neural networks (ANN) with different characterisation in the same colour spaces. The performance was measured using precision metrics. Results suggest that AnthEstNet presented a behaviour statistically equivalent to the invasive method results (pH differential method). For probabilistic representation in channels H and S, AnthEstNet obtained a precision value of 87.68% with a standard deviation of 10.95 in the test set of samples. As to root mean square error (RMSE) and R2, this configuration was 0.49 and 0.94, respectively. On the other hand, AnthEstNet, with probabilistic representations on channels a* and b* of the CIE L*a*b* colour model, reached a precision value of 87.49% with a standard deviation of 11.84, an RMSE value of 0.51, and an R2 value of 0.93.
The present study evaluated the effects of blackberry juice that is rich in different concentrations of anthocyanins and polyphenols (2.6 mg/kg anthocyanins, 14.57 mg/kg polyphenols; 5.83 mg/kg ...anthocyanins, 27.10 mg/kg polyphenols; 10.57 mg/kg anthocyanins, 38.40 mg/kg polyphenols) on anxiety-like behaviour in Wistar rats. The rats were treated with blackberry juice for 21 days and then tested in the elevated plus maze, locomotor activity test and forced swim test. The results were compared with a reference anxiolytic drug diazepam (2.0 mg/kg) and vehicle (8.7 ml/kg). The intermediate dose of blackberry juice exerted an anxiolytic-like effect that was similar to diazepam, without affecting locomotive activity. The low and high doses of blackberry juice exerted no significant effects on anxiety-like behaviour compared with vehicle. In the forced swim test, both the high and intermediate doses of blackberry juice reduced total immobility time, suggesting a protective effect against behavioural changes that are induced by acute stress. These findings suggest a potential therapeutic effect of blackberry juice on anxiety that is associated with a stressful event.
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BFBNIB, DOBA, FSPLJ, GIS, IJS, IZUM, KILJ, KISLJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Inflammatory fibroid polyp (IFP), initially considered a reactive process, is now recognized as a benign mesenchymal neoplasm of the gastrointestinal tract. We report a case of a 68-year-old woman ...with medically refractory Crohn disease that presented with intussusception requiring surgical intervention. The resection revealed a jejunal mass consisting of a submucosal proliferation of bland spindle cells in a fibrous stroma infiltrated by numerous eosinophils. By immunohistochemistry, the lesion was positive for vimentin and negative for desmin, smooth muscle actin (SMA), S-100, CD117, DOG1, ALK (D5F3), Melan-A, HMB-45, CD34, and STAT6. Ki-67 proliferative index was low (<1%). The mass was classified as IFP by its characteristic morphology and associated eosinophilia. IFP should be considered in the differential diagnosis of adults with intussusception or bowel obstruction. Definitive treatment typically requires surgical resection of the involved bowel segment.
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FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, UL, UM, UPUK
One of the fields where Artificial Intelligence (AI) must continue to innovate is computer security. The integration of Wireless Sensor Networks (WSN) with the Internet of Things (IoT) creates ...ecosystems of attractive surfaces for security intrusions, being vulnerable to multiple and simultaneous attacks. This research evaluates the performance of supervised ML techniques for detecting intrusions based on network traffic captures. This work presents a new balanced dataset (IDSAI) with intrusions generated in attack environments in a real scenario. This new dataset has been provided in order to contrast model generalization from different datasets. The results show that for the detection of intruders, the best supervised algorithms are XGBoost, Gradient Boosting, Decision Tree, Random Forest, and Extra Trees, which can generate predictions when trained and predicted with ten specific intrusions (such as ARP spoofing, ICMP echo request Flood, TCP Null, and others), both of binary form (intrusion and non-intrusion) with up to 94% of accuracy, as multiclass form (ten different intrusions and non-intrusion) with up to 92% of accuracy. In contrast, up to 90% of accuracy is achieved for prediction on the Bot-IoT dataset using models trained with the IDSAI dataset.
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