COVID-19 Detection on Chest X-ray and CT Scan: A Review of the Top-100 Most Cited Papers.

Since the beginning of the COVID-19 pandemic, many works have been published proposing solutions to the problems that arose in this scenario. In this vein, one of the topics that attracted the most attention is the development of computer-based strategies to detect COVID-19 from thoracic medical imaging, such as chest X-ray (CXR) and computerized tomography scan (CT scan). By searching for works already published on this theme, we can easily find thousands of them. This is partly explained by the fact that the most severe worldwide pandemic emerged amid the technological advances recently achieved, and also considering the technical facilities to deal with the large amount of data produced in this context. Even though several of these works describe important advances, we cannot overlook the fact that others only use well-known methods and techniques without a more relevant and critical contribution. Hence, differentiating the works with the most relevant contributions is not a trivial task. The number of citations obtained by a paper is probably the most straightforward and intuitive way to verify its impact on the research community. Aiming to help researchers in this scenario, we present a review of the top-100 most cited papers in this field of investigation according to the Google Scholar search engine. We evaluate the distribution of the top-100 papers taking into account some important aspects, such as the type of medical imaging explored, learning settings, segmentation strategy, explainable artificial intelligence (XAI), and finally, the dataset and code availability.

Impact of Lung Segmentation on the Diagnosis and Explanation of COVID-19 in Chest X-ray Images.

COVID-19 frequently provokes pneumonia, which can be diagnosed using imaging exams. Chest X-ray (CXR) is often useful because it is cheap, fast, widespread, and uses less radiation. Here, we demonstrate the impact of lung segmentation in COVID-19 identification using CXR images and evaluate which contents of the image influenced the most. Semantic segmentation was performed using a U-Net CNN architecture, and the classification using three CNN architectures (VGG, ResNet, and Inception). Explainable Artificial Intelligence techniques were employed to estimate the impact of segmentation. A three-classes database was composed: lung opacity (pneumonia), COVID-19, and normal. We assessed the impact of creating a CXR image database from different sources, and the COVID-19 generalization from one source to another. The segmentation achieved a Jaccard distance of 0.034 and a Dice coefficient of 0.982. The classification using segmented images achieved an F1-Score of 0.88 for the multi-class setup, and 0.83 for COVID-19 identification. In the cross-dataset scenario, we obtained an F1-Score of 0.74 and an area under the ROC curve of 0.9 for COVID-19 identification using segmented images. Experiments support the conclusion that even after segmentation, there is a strong bias introduced by underlying factors from different sources.

Microbial bioconversion of feathers into antioxidant peptides and pigments and their liposome encapsulation.

OBJECTIVES: The co-encapsulation of bioactive peptides obtained from degradation of chicken feathers and flexirubin-type pigment produced by Chryseobacterium sp. kr6 into phosphatidylcholine liposomes was investigated. RESULTS: Control empty liposomes showed mean diameter of 168.5 nm, varying to 185.4, 102.0 and 98.5 nm after the encapsulation of peptides, pigment and their co-encapsulation, respectively. Control liposomes presented zeta potential of - 20.9 mV, while the formulations containing the bioactive compounds showed values of - 30 mV or higher in magnitude. Infrared analysis revealed typical spectra for phosphatidylcholine, suggesting that no new chemical bonds were formed after encapsulation. ABTS radical scavenging assay showed that the antioxidant activity of the compounds was maintained after encapsulation. CONCLUSIONS: Feather waste can be a valuable substrate for simultaneous production of antioxidant peptides and pigment by Chryseobacterium sp. kr6, and their encapsulation into liposomes may be a suitable alternative for delivery of these natural antioxidants.

COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios.

BACKGROUND AND OBJECTIVE: The COVID-19 can cause severe pneumonia and is estimated to have a high impact on the healthcare system. Early diagnosis is crucial for correct treatment in order to possibly reduce the stress in the healthcare system. The standard image diagnosis tests for pneumonia are chest X-ray (CXR) and computed tomography (CT) scan. Although CT scan is the gold standard, CXR are still useful because it is cheaper, faster and more widespread. This study aims to identify pneumonia caused by COVID-19 from other types and also healthy lungs using only CXR images. METHODS: In order to achieve the objectives, we have proposed a classification schema considering the following perspectives: i) a multi-class classification; ii) hierarchical classification, since pneumonia can be structured as a hierarchy. Given the natural data imbalance in this domain, we also proposed the use of resampling algorithms in the schema in order to re-balance the classes distribution. We observed that, texture is one of the main visual attributes of CXR images, our classification schema extract features using some well-known texture descriptors and also using a pre-trained CNN model. We also explored early and late fusion techniques in the schema in order to leverage the strength of multiple texture descriptors and base classifiers at once. To evaluate the approach, we composed a database, named RYDLS-20, containing CXR images of pneumonia caused by different pathogens as well as CXR images of healthy lungs. The classes distribution follows a real-world scenario in which some pathogens are more common than others. RESULTS: The proposed approach tested in RYDLS-20 achieved a macro-avg F1-Score of 0.65 using a multi-class approach and a F1-Score of 0.89 for the COVID-19 identification in the hierarchical classification scenario. CONCLUSIONS: As far as we know, the top identification rate obtained in this paper is the best nominal rate obtained for COVID-19 identification in an unbalanced environment with more than three classes. We must also highlight the novel proposed hierarchical classification approach for this task, which considers the types of pneumonia caused by the different pathogens and lead us to the best COVID-19 recognition rate obtained here.

Neonatal handling impairs intradimensional shift and alters plasticity markers in the medial prefrontal cortex of adult rats.

Stress response can be modulated by neonatal/childhood events. Neonatal handling (NH) is an animal model in which the animals are subjected to brief separations from the dam during the first days of life, and it leads to lower emotionality and behavioral changes in adulthood. The aim of this study was to observe if early events, such as (NH), may program associative learning and behavioral flexibility in adult male rats and if these changes could be related to altered neurochemistry in the medial prefrontal cortex (mPFC). We evaluated proteins related to synaptic plasticity (brain-derived neurotrophic factor [BDNF] and synaptophysin [SYP]) as well as Na+/K+-ATPase activity. Additionally, we evaluated proteins related to the dopaminergic system (tyrosine hydroxylase [TH] and phosphorylated TH [pTH]), since this system appears to be affected in some neonatal interventions. Neonatally handled animals exhibited impairment in simple discrimination and intradimensional shift but not in reversal or compound discrimination; in addition, no alteration in switching from an egocentric spatial to a cued strategy was observed. These effects were accompanied by a decrease in SYP levels and Na+/K+-ATPase activity, suggesting reduced synaptic function. These results indicate that NH increases attention to irrelevant stimuli and/or impairs associative learning, and this is accompanied by neurochemical alterations in the (mPFC).

Emergence of Acinetobacter baumannii ST730 carrying the blaOXA-72 gene in Brazil.

Over the last decade, Acinetobacter baumannii resistant to carbapenems has emerged in many medical centres and has been commonly associated with high morbimortality. In Brazil, this resistance is mainly attributed to the spread of OXA-23-producing clones and, to a lesser extent, to OXA-143-producing clones. Here, we describe, for the first time, two OXA-72-producing A. baumannii isolates in southern Brazil to a broad spectrum of antibiotics, except polymyxin B and tigecycline. Molecular typing by multilocus sequence typing (MLST) demonstrated that both OXA-72-producing isolates belong to a new sequence type (ST), ST730, which was recently identified in OXA-23-producing A. baumannii isolates in São Paulo, Brazil. We demonstrate that the two A. baumannii ST730 isolates carrying blaOXA-72share a common ancestral origin with the blaOXA-23producers in Brazil. This observation reinforces the importance of strain-typing methods in order to clarify the dynamics of the emergence of new clones in a geographic region.

Emergence of Acinetobacter baumannii ST730 carrying the blaOXA-72 gene in Brazil

Over the last decade, Acinetobacter baumannii resistant to carbapenems has emerged in many medical centres and has been commonly associated with high morbimortality. In Brazil, this resistance is mainly attributed to the spread of OXA-23-producing clones and, to a lesser extent, to OXA-143-producing clones. Here, we describe, for the first time, two OXA-72-producing A. baumannii isolates in southern Brazil to a broad spectrum of antibiotics, except polymyxin B and tigecycline. Molecular typing by multilocus sequence typing (MLST) demonstrated that both OXA-72-producing isolates belong to a new sequence type (ST), ST730, which was recently identified in OXA-23-producing A. baumannii isolates in São Paulo, Brazil. We demonstrate that the two A. baumannii ST730 isolates carrying blaOXA-72share a common ancestral origin with the blaOXA-23producers in Brazil. This observation reinforces the importance of strain-typing methods in order to clarify the dynamics of the emergence of new clones in a geographic region.