Different Types Of Pneumonia Research Articles

Expression and significance of procalcitonin, leukotriene B4, serum amyloid A, and C-reactive protein in children with different types of pneumonia: An observational study.

This study aimed to investigate the expression and significance of serum procalcitonin (PCT), leukotriene B4 (LTB4), Serum amyloid A (SAA), and C-reactive protein (CRP) in children with different types of pneumonia caused by different pathogenic infections. One hundred and one children with pneumonia admitted to The Fifth People Hospital of Zhuhai from July 2019 to June 2020 were enrolled and divided into 38 cases in the bacterial group, 30 cases in the mycoplasma group, and 33 cases in the virus group according to the different types of pathogens. The patients were divided into 42 cases in the noncritical group, 33 cases in the critical group, and 26 cases in the very critical group according to the pediatric clinical illness score (PCIS), and 30 healthy children were selected as the control group during the same period. Comparison of serum PCT, SAA: bacterial group > mycoplasma group > viral group > control group with significant differences (P < .05). Receiver operator characteristic (ROC) analysis showed that the area under the curves (AUCs) of serum PCT, LTB4, SAA, and CRP for the diagnosis of bacterial pneumonia were 1.000, 0.531, 0.969, and 0.833, respectively, and the AUCs for the diagnosis of mycoplasma pneumonia were 0.653, 0.609, 0.547, and 0.652, respectively, and the AUCs for the diagnosis of viral pneumonia were 0.888, 0.570, 0.955, and 1.000, respectively. Comparison of serum PCT, LTB4, SAA: very critical group > critical group > noncritical group > control group, with significant differences (P < .05). Serum PCT, LTB4, and SAA were negatively correlated with PCIS score by Pearson analysis (P < .05). Serum PCT and SAA showed diagnostic value for bacterial pneumonia, and serum SAA and CRP showed diagnostic value for viral pneumonia; serum PCT, LTB4, and SAA correlate with severity of disease and show higher expression with worsening of the condition.

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A novel framework based on deep learning for COVID-19 diagnosis from X-ray images.

The coronavirus infection has endangered human health because of the high speed of the outbreak. A rapid and accurate diagnosis of the infection is essential to avoid further spread. Due to the cost of diagnostic kits and the availability of radiology equipment in most parts of the world, the COVID-19 detection method using X-ray images is still used in underprivileged countries. However, they are challenging due to being prone to human error, time-consuming, and demanding. The success of deep learning (DL) in automatic COVID-19 diagnosis systems has necessitated a detection system using these techniques. The most critical challenge in using deep learning techniques in diagnosing COVID-19 is accuracy because it plays an essential role in controlling the spread of the disease. This article presents a new framework for detecting COVID-19 using X-ray images. The model uses a modified version of DenseNet-121 for the network layer, an image data loader to separate images in batches, a loss function to reduce the prediction error, and a weighted random sampler to balance the training phase. Finally, an optimizer changes the attributes of the neural networks. Extensive experiments using different types of pneumonia expresses satisfactory diagnosis performance with an accuracy of 99.81%. This work aims to design a new deep neural network for highly accurate online recognition of medical images. The evaluation results show that the proposed framework can be considered an auxiliary device to help radiologists accurately confirm initial screening.

Large‐scaled detection of COVID‐19 from X‐ray using transfer learning

AbstractThe pandemic cause as a result of the outbreak of COVID‐19 disease continues to burden the healthcare system despite several interventions using vaccines and other preventive measures. Healthcare settings adopted the use of reverse transcription‐polymerase chain reaction (RT‐PCR) which is hampered by so many challenges such as miss‐diagnosis, false positive results, high cost, especially for those in remote and rural areas, the need for trained medical pathologists, the use of chemicals, and a lack of point‐of‐care detection. The use of radiographic images as an alternative or confirmatory approach has offered medical experts another option, but has some limitations, such as misinterpretation, and can be tedious for analyzing thousands of cases. In order to bridge this gap, we applied two AlexNet models for the classification of different types of pneumonia, including COVID‐19 using X‐ray. Considering the fact that the majority of articles in the literature reported binary classifications of radiographic images. This article utilizes X‐ray images for classification of COVID‐19, non‐COVID‐19 viral pneumonia, bacterial pneumonia, and normal cases using the AlexNet‐SoftMax classifier and the AlexNet‐SVM classifier. The research also evaluates performance based on 5k‐fold and 10k fold cross validation (CV). The results achieved in terms of accuracy, sensitivity, and specificity based on 70:30 partition, 5k, and 10k CV have shown that the models outperformed the majority of the state‐of‐the‐art deep learning architectures.

P-095 Is SARS-CoV-2 really present in human semen? New evidence on this issue

Study questionCan be the SARS-CoV-2 present in human semen samples from patients infected with COVID-19?Summary answerOur research confirms the presence of SARS-CoV-2 in human ejaculate.What is known alreadyIn December 2019 WHO was informed of cases of pneumonia of unknown etiology. This was the beginning of a global pandemic that we are suffering still.COVID-19 disease may damage the male reproductive system. The issue of the presence of SARS-CoV-2 in human semen has been addressed in many studies. Most of them concluded that this virus is not present in human semen. However, until now four groups have reported the presence of SASR-CoV-2.It is quite clear that there is an important controversy about the presence of this virus in semen. Further research is needed to clarify this issue.Study design, size, durationProspective and experimental cohort study to evaluate the presence of SARS-CoV-2 in human semen.Men, COVID-19 disease confirmed, were asked to donate semen sample from July 2020 to March 2021. The patients were recruited with written informed consent.Explicit instructions were given to the correct collection of samples. Participants were interviewed to confirm that collection and transport took place under hygienic conditions. Semen samples were accepted only when instructions were adequately followed.Participants/materials, setting, methodsSemen samples were obtained from 50 men (aged 20-51 years) who were diagnosed with qRT-PCR-COVID19 positive (Ct < 40) in nasopharyngeal swab. Patients were closely followed while in hospital or at home.Each semen sample was divided in three aliquots (1 milliliter each) in order to: 1. Perform a spermiogram, 2. Evaluate SARS-CoV-2 with qRT-PCR-COVID19 and 3. Freeze for later use, if necessary. Patients were asked to provide a new specimen if PCR in semen was positive.Main results and the role of chancePatients included in the study showed different types of pneumonia: mild/moderate (N = 46) and severe (N = 6). Patients with severe symptoms had to be placed in intensive care unit.Average time from admission to hospital to sperm collection was 5,8 days.The study did not find any correlation between fresh semen parameters (volume, pH, count and motility) and severity of illness.SARS-CoV-2 was detected in one of fifty (1/50) fresh semen samples. The patient with SARS-CoV-2 in semen had mild symptoms of pneumonia (fever, headache and muscle pain). The patient was asked about how the semen sample was collected. Standards of hygiene during semen collection were confirmed by patient. Presence of SARS-CoV-2 in patient’s semen sample was verified.Limitations, reasons for cautionIt is possible (though unlikely) that semen sample collection and transportation were incorrect or unhygienic (it was refuted by patient). Besides that, we cannot exclude a false positive in PCR analysis. Although this test has very high specificity with a practical absence of false positives.Wider implications of the findingsAlthough it is evident that the presence of SARS-CoV-2 in human semen is extremely rare, at least four studies confirm this view. In any case the presence of this virus in human semen samples cannot be completely ruled out, on the contrary, further studies are required to confirm this presence.Trial registration number2020-310-1

Multi-branch fusion auxiliary learning for the detection of pneumonia from chest X-ray images

Lung infections caused by bacteria and viruses are infectious and require timely screening and isolation, and different types of pneumonia require different treatment plans. Therefore, finding a rapid and accurate screening method for lung infections is critical. To achieve this goal, we proposed a multi-branch fusion auxiliary learning (MBFAL) method for pneumonia detection from chest X-ray (CXR) images. The MBFAL method was used to perform two tasks through a double-branch network. The first task was to recognize the absence of pneumonia (normal), COVID-19, other viral pneumonia and bacterial pneumonia from CXR images, and the second task was to recognize the three types of pneumonia from CXR images. The latter task was used to assist the learning of the former task to achieve a better recognition effect. In the process of auxiliary parameter updating, the feature maps of different branches were fused after sample screening through label information to enhance the model’s ability to recognize case of pneumonia without impacting its ability to recognize normal cases. Experiments show that an average classification accuracy of 95.61% is achieved using MBFAL. The single class accuracy for normal, COVID-19, other viral pneumonia and bacterial pneumonia was 98.70%, 99.10%, 96.60% and 96.80%, respectively, and the recall was 97.20%, 98.60%, 96.10% and 89.20%, respectively, using the MBFAL method. Compared with the baseline model and the model constructed using the above methods separately, better results for the rapid screening of pneumonia were achieved using MBFAL.

Prevalence of Lung Lesions in Imported Cattle Slaughtered at Abu Simble Abattoir, Egypt

A retrospective study of imported cattle slaughtered at Abu Simble Abattoir, Aswan, Egypt was conducted to determine the main causes of lung condemnation. The economic importance was estimated. During the study, a total number of 81452 slaughtered and investigated male cattle for a period of one full year (December 2018 to December 2019). All the condemned organs were lungs (n=9172; 11.26%) and had pathological lesions. The principal cause of lungs condemnation was pneumonia (47.4%), followed by congestion (28.2%), adhesions (22.1%) and abscesses (2.4%). Lungs histopathological examination recorded different inflammatory reactions with different types of pneumonia, bronchopneumonia, interstitial pneumonia, broncho-interstitial pneumonia, fibrinous pneumonia, hemorrhagic pneumonia, and suppurative pneumonia. The present work concluded that a serious problem may develop due to lung diseases/lesions which have negative effect to the livestock industry, in addition to the public health importance of zoonotic diseases.

Developing a pneumonia diagnosis ontology from multiple knowledge sources.

Background: Pneumonia is difficult to differentiate from other pulmonary diseases because it shares many symptoms with these diseases. Diagnosing pneumonia in clinical practice would benefit from having access to a codified representation of clinical knowledge. An ontology represents a well-established paradigm for such codification. Objectives: The goal of this research is to create Pneumonia Diagnosis Ontology (PNADO) that brings together the medical knowledge dispersed among multiple medical knowledge sources. Material and Methods: We used several clinical practice guidelines (CPGs) describing the pneumonia diagnostic process as a starting point in developing PNADO. Preliminary version of PNADO was subsequently expanded to cover a broader range of the concepts by reusing ontologies from Open Biological and Biomedical Ontology (OBO) Foundry and BioPortal. PNADO was evaluated by examining relevant concepts from the pneumonia-specific systematic reviews, using patient data from the MIMIC-III clinical dataset, and by clinical domain experts. Results: PNADO is a comprehensive ontology and has a rich set of classes and properties that cover different types of pneumonia, pathogens, symptoms, clinical signs, laboratory tests and imaging, clinical findings, complications, and diagnoses. Conclusion: PNADO unifies pneumonia diagnostic concepts from multiple knowledge sources. It is available in the BioPortal repository.

ВОЗМОЖНОСТИ ИСПОЛЬЗОВАНИЯ НЕЙРОННЫХ СЕТЕЙ В ДИАГНОСТИКЕ ПНЕВМОНИИ

In this paper, the possibilities of using neural networks and deep learning in the diagnosis of various diseases are considered. This area is considered on the example of the diagnosis of pneumonia. In the context of the spread of coronovirus infection, the article substantiates the relevance of differentiation of radiographic data to determine different types of pneumonia based on the use of neural networks and deep learning. At the same time, it is emphasized that the algorithm of convolutional neural networks is well suited for a multiclass classification problem or binary classification, which allows solving the problem under consideration.

Comparison of sensitivity of lung ultrasound and chest x-ray imaging for the diagnosis of community-acquired pneumonia in children

Purpose: Lung ultrasound (LUS) has only recently been considered to be a suitable tool for diagnosing community acquired pneumonia (CAP) in children. Little is known about the usefulness of LUS in different types of pneumonia. Therefore, we analyzed and compared the sensitivity of chest X-ray (CXR) and LUS in different etiological types of CAP in children.&#x0D; Methods: We performed a prospective study in 166 children with CAP, who were admitted to the hospital. The participants were stratified into bacterial (n=80), atypical bacterial (n=32) and viral (n=54) pneumonia subgroups, according to the laboratory and microbiological results. LUS and CXR were performed on all patients at admission.&#x0D; Results: Pneumonia was detected with LUS in 161 (97.0%) participants and with CXR in 137 (82.5%) participants (p&lt;0.01). The sensitivity of LUS was calculated as 97.0% (95% CI, 93.1%–99.0%), and the sensitivity of CXR as 82.5% (95% CI, 75.9%– 88.0%), p&lt;0.01. In patients with bacterial CAP, infiltrates were detected with LUS in 79 (98.7%) and with CXR in 67 (83.8%) of cases (p&lt;0.01); in atypical bacterial CAP, with LUS in 30 (93.8%) and with CXR in 28 (87.5%) (p=0.69); and in patients with viral CAP with LUS in 52 (96.3%) and with CXR in 42 (77.8%) (p&lt;0.01).&#x0D; Conclusion: We have determined that LUS is an excellent tool for diagnosing CAP in children. LUS is at least as sensitive as CXR in all types of CAP in children and will probably replace it as the investigation of choice in CAP.

Pathomicrobial and Molecular Investigations of Respiratory System Diseases Affecting Buffaloes

Background: Pathomicrobial and molecular investigations of respiratory system diseases was undertaken on twelve adult buffalo carcasses received for necropsy examination at post mortem facility of Department of Veterinary Pathology, Lala Lajpat Rai University of Veterinary and Animal Sciences, Hisar, Haryana during the period from August, 2018 to February, 2019. Methods: After collection of the samples, laboratory work was undertaken in the laboratories of Department of Veterinary Pathology, Department of Animal Biotechnology and Central Laboratory of the College, LUVAS, Hisar, Haryana (India) in the year 2018-2019 regarding the examination of the clinical history, pathological, microbiological and molecular investigations. Result: The gross pathological changes observed in lungs were variable degree of vascular changes, mild to severe consolidation, fibrin deposition along with adhesions to the thoracic wall. Histopathological findings revealed abnormalities of inflation such as pulmonary emphysema, atelectasis, pulmonary congestion and haemorrhages which was associated with different types of pneumonia viz. fibrinous bronchopneumonia, suppurative giant cell pneumonia, interstitial pneumonia and serous pneumonia. Lung, heart blood and tracheal swab samples collected from the buffalo carcasses revealed eighteen bacterial isolates which were identified by Vitek 2 system. These include E. coli (11 isolates), Salmonella enteric enterica (2 isolates), Acinetobacter ursingii (1 isolate), Staphylococcus haemolyticus (1 isolate), Staphylococcus sciuri (1 isolate), Staphylococcus warneri (1 isolate) and Staphylococcus hominis (1 isolate) mostly belonging to opportunistic pathogen category. E. coli serotypes confirmed from these cases were O83, O149 and O8. The results of in vitro drug sensitivity testing revealed that most of bacterial strains were found sensitive to cefoperazone/sulbactum and co-trimoxazole. Molecular studies confirmed Bovine Herpes Virus-1 (BHV-1) infection in one case with Fibrinous bronchopneumonia through real-time quantitative PCR indicating the prevalence of the infection in the state.

A Cascade-SEME network for COVID-19 detection in chest x-ray images.

PurposeThe worldwide spread of the SARS‐CoV‐2 virus poses unprecedented challenges to medical resources and infection prevention and control measures around the world. In this case, a rapid and effective detection method for COVID‐19 can not only relieve the pressure of the medical system but find and isolate patients in time, to a certain extent, slow down the development of the epidemic. In this paper, we propose a method that can quickly and accurately diagnose whether pneumonia is viral pneumonia, and classify viral pneumonia in a fine‐grained way to diagnose COVID‐19.MethodsWe proposed a Cascade Squeeze‐Excitation and Moment Exchange (Cascade‐SEME) framework that can effectively detect COVID‐19 cases by evaluating the chest x‐ray images, where SE is the structure we designed in the network which has attention mechanism, and ME is a method for image enhancement from feature dimension. The framework integrates a model for a coarse level detection of virus cases among other forms of lung infection, and a model for fine‐grained categorisation of pneumonia types identifying COVID‐19 cases. In addition, a Regional Learning approach is proposed to mitigate the impact of non‐lesion features on network training. The network output is also visualised, highlighting the likely areas of lesion, to assist experts’ assessment and diagnosis of COVID‐19.ResultsThree datasets were used: a set of Chest x‐ray Images for Classification with bacterial pneumonia, viral pneumonia and normal chest x‐rays, a COVID chest x‐ray dataset with COVID‐19, and a Lung Segmentation dataset containing 1000 chest x‐rays with masks in the lung region. We evaluated all the models on the test set. The results shows the proposed SEME structure significantly improves the performance of the models: in the task of pneumonia infection type diagnosis, the sensitivity, specificity, accuracy and F1 score of ResNet50 with SEME structure are significantly improved in each category, and the accuracy and AUC of the whole test set are also enhanced; in the detection task of COVID‐19, the evaluation results shows that when SEME structure was added to the task, the sensitivities, specificities, accuracy and F1 scores of ResNet50 and DenseNet169 are improved. Although the sensitivities and specificities are not significantly promoted, SEME well balanced these two significant indicators. Regional learning also plays an important role. Experiments show that Regional Learning can effectively correct the impact of non‐lesion features on the network, which can be seen in the Grad‐CAM method.ConclusionsExperiments show that after the application of SEME structure in the network, the performance of SEME‐ResNet50 and SEME‐DenseNet169 in both two datasets show a clear enhancement. And the proposed regional learning method effectively directs the network’s attention to focus on relevant pathological regions in the lung radiograph, ensuring the performance of the proposed framework even when a small training set is used. The visual interpretation step using Grad‐CAM finds that the region of attention on radiographs of different types of pneumonia are located in different regions of the lungs.

Typical Imaging Patterns in COVID-19 Infections of the Lung on PlainChest Radiographs to Aid Early Triage.

To evaluate imaging patterns of a COVID-19 infection of the lungs on chest radiographs and their value in discriminating this infection from other viral pneumonias. All 321 patients who presented with respiratory impairment suspicious for COVID-19 infection between February 3 and May 8, 2020 and who received a chest radiograph were included in this analysis. Imaging findings were classified as typical for COVID-19 (bilateral, peripheral opacifications/consolidations), non-typical (findings consistent with lobar pneumonia), indeterminate (all other distribution patterns of opacifications/consolidations), or none (no opacifications/consolidations). The sensitivity, specificity, as well as positive and negative predictive value for the diagnostic value of the category "typical" were determined. Chi² test was used to compare the pattern distribution between the different types of pneumonia. Imaging patterns defined as typical for COVID-19 infections were documented in 35/111 (31.5 %) patients with confirmed COVID-19 infection but only in 4/210 (2 %) patients with any other kind of pneumonia, resulting in a sensitivity of 31.5 %, a specificity of 98.1 %, and a positive and negative predictive value of 89.7 % or 73 %, respectively. The sensitivity could be increased to 45.9 % when defining also unilateral, peripheral opacifications/consolidations with no relevant pathology contralaterally as consistent with a COVID-19 infection, while the specificity decreases slightly to 93.3 %. The pattern distribution between COVID-19 patients and those with other types of pneumonia differed significantly (p < 0.0001). Although the moderate sensitivity does not allow the meaningful use of chest radiographs as part of primary screening, the specific pattern of findings in a relevant proportion of those affected should be communicated quickly as additional information and trigger appropriate protective measures. · COVID-19 infections show specific X-ray image patterns in 1/3 of patients.. · Bilateral, peripheral opacities and/or consolidations are typical imaging patterns.. · Unilateral, peripheral opacities and/or consolidations should also raise suspicion of COVID-19 infection.. · Kasper J, Decker J, Wiesenreiter K et al. Typical Imaging Patterns in COVID-19 Infections of the Lung on Plain Chest Radiographs to Aid Early Triage. Fortschr Röntgenstr 2021; 193: 1189 - 1196.

An Assessment of Clinical Profile &amp; Outcome in Patients with Community Acquired Pneumonia (CAP)

Introduction: Pneumonia is a disease known to mankind from antiquity.&#x0D; Objectives: The study objective is to assess demographic profile of patients and correlate with clinical &amp; radiological findings and to identify various etiological factors &amp; correlate in different types of pneumonia.&#x0D; Methodology: A Prospective clinical observational study was conducted in October 2018 to March 2019 among 50 patients admitted under Department of General Medicine who were diagnosed as community acquired pneumonia during admission at GMERS General Hospital Junagadh, Gujarat.&#x0D; Results: The incidence of CAP was more common in female (56%) than in men (44%). Fever was the commonest (47, 94%) presenting complaint followed by cough 42(84%) and breathlessness 36(72%). Lobar pneumonia was most common (85.7%) in the patients having predisposing lung pathology and Bronchopneumonia was most common (71.4%) in diabetic patients (Table-5). Majority (17, 34%) patients had PSI class I followed by PSI class II (14, 28%).&#x0D; Conclusions: Mean age was 47+1.6. Significant difference noted between types of pneumonia and different etiological factors. In bacterial pneumonia, Pleural effusion (20.8%) was major complication whereas in viral pneumonia, Respiratory failure (47.3%) was major complication.

Pneumonia Detection and Classification Using Deep Learning on Chest X-Ray Images

Pneumonia is a bacterial infection caused people of all ages with mild to severe inflammation of the lung tissue [1]. The best known and most common clinical method for the diagnosis of pneumonia is chest X-ray imaging. But the diagnosis of pneumonia from chest X-ray images is a difficult task, even for specialist radiologists. In developing countries, this lung disease becomes one of the deadliest among children under the age of 5 and causing 15% of deaths recorded annually [2]. Therefore, in this study, firstly the presence of the disease was tried to be determined using chest X-ray dataset. In addition, using the bacterial and viral pneumonia classes which are two different types of pneumonia, multi class classification which consists of viral pneumonia, bacterial pneumonia and healthy has been done. Since the used dataset does not have a balanced distribution among all classes, SMOTE (Synthetic Minority Over-sampling Technique) method has been used to deal with imbalanced dataset. CNN model and models in Ensemble Learning have been created from scratch instead of using pre-trained networks. For each classification problem, two different deep learning methods which are CNN and ensemble learning have been used and 95% average accuracy has been obtained for each model, for binary classification and 78% and 75% average accuracy has been obtained for each model respectively for multi class classification problem.

Pathomorphological studies of respiratory mannheimiosis in goats

The study investigated an outbreak of respiratory mannheimiosis in goat maintained at the Goat Research station, AAU, Burnihat during September- October, 2019. Out of the total 215 nos. of animals at risk, 48 nos. were affected and 16 died with a morbidity and cause specific mortality of 22.32% and 33.3% respectively. The clinical signs exhibited by the affected animals were offfed, high rise of temperature (104°-105°F), thick mucoid nasal discharge and coughing. During necropsy, pneumonia was diagnosed among all the cases and the most frequently affected lobe was right cranial lobe. In most of the cases lung showed fibrinous bronchopneumonia. Consolidated areas of the lungs were swollen and dark red in color. In some cases presence of whitish fine foamy fluid or creamy suppuration could also noticed. Sometimes, the fibrin was adhered in the ribs with or without hydrothorax. The tracheal rings were hemorrhagic and the lumen was filled with the mucus and froth. Microscopically, three different types of pneumonia viz. Fibrinous, suppurative and fibrinopurulant could be seen. Alveolar lumen was filled with fibrinous exudate, erythrocytes, and polymorphonuclear cells. During isolation, the results observed were typical to those exhibited by Mannhemia spp.

CovXNet: A multi-dilation convolutional neural network for automatic COVID-19 and other pneumonia detection from chest X-ray images with transferable multi-receptive feature optimization

With the recent outbreak of COVID-19, fast diagnostic testing has become one of the major challenges due to the critical shortage of test kit. Pneumonia, a major effect of COVID-19, needs to be urgently diagnosed along with its underlying reasons. In this paper, deep learning aided automated COVID-19 and other pneumonia detection schemes are proposed utilizing a small amount of COVID-19 chest X-rays. A deep convolutional neural network (CNN) based architecture, named as CovXNet, is proposed that utilizes depthwise convolution with varying dilation rates for efficiently extracting diversified features from chest X-rays. Since the chest X-ray images corresponding to COVID-19 caused pneumonia and other traditional pneumonias have significant similarities, at first, a large number of chest X-rays corresponding to normal and (viral/bacterial) pneumonia patients are used to train the proposed CovXNet. Learning of this initial training phase is transferred with some additional fine-tuning layers that are further trained with a smaller number of chest X-rays corresponding to COVID-19 and other pneumonia patients. In the proposed method, different forms of CovXNets are designed and trained with X-ray images of various resolutions and for further optimization of their predictions, a stacking algorithm is employed. Finally, a gradient-based discriminative localization is integrated to distinguish the abnormal regions of X-ray images referring to different types of pneumonia. Extensive experimentations using two different datasets provide very satisfactory detection performance with accuracy of 97.4% for COVID/Normal, 96.9% for COVID/Viral pneumonia, 94.7% for COVID/Bacterial pneumonia, and 90.2% for multiclass COVID/normal/Viral/Bacterial pneumonias. Hence, the proposed schemes can serve as an efficient tool in the current state of COVID-19 pandemic. All the architectures are made publicly available at: https://github.com/Perceptron21/CovXNet.

Diagnosis of Coronavirus Disease 2019 (COVID-19) With Structured Latent Multi-View Representation Learning.

Recently, the outbreak of Coronavirus Disease 2019 (COVID-19) has spread rapidly across the world. Due to the large number of infected patients and heavy labor for doctors, computer-aided diagnosis with machine learning algorithm is urgently needed, and could largely reduce the efforts of clinicians and accelerate the diagnosis process. Chest computed tomography (CT) has been recognized as an informative tool for diagnosis of the disease. In this study, we propose to conduct the diagnosis of COVID-19 with a series of features extracted from CT images. To fully explore multiple features describing CT images from different views, a unified latent representation is learned which can completely encode information from different aspects of features and is endowed with promising class structure for separability. Specifically, the completeness is guaranteed with a group of backward neural networks (each for one type of features), while by using class labels the representation is enforced to be compact within COVID-19/community-acquired pneumonia (CAP) and also a large margin is guaranteed between different types of pneumonia. In this way, our model can well avoid overfitting compared to the case of directly projecting high-dimensional features into classes. Extensive experimental results show that the proposed method outperforms all comparison methods, and rather stable performances are observed when varying the number of training data.

A systematic review of chest imaging findings in COVID-19.

Chest computed tomography (CT) is frequently used in diagnosing coronavirus disease 2019 (COVID-19) for detecting abnormal changes in the lungs and monitoring disease progression during the treatment process. Furthermore, CT imaging appearances are correlated with patients presenting with different clinical scenarios, such as early versus advanced stages, asymptomatic versus symptomatic patients, and severe versus nonsevere situations. However, its role as a screening and diagnostic tool in COVID-19 remains to be clarified. This article provides a systematic review and meta-analysis of the current literature on chest CT imaging findings with the aim of highlighting the contribution and judicious use of CT in the diagnosis of COVID-19. A search of PubMed/Medline, Web of Science, ScienceDirect, Google Scholar and Scopus was performed to identify studies reporting chest imaging findings in COVID-19. Chest imaging abnormalities associated with COVID-19 were extracted from the eligible studies and diagnostic value of CT in detecting these abnormal changes was compared between studies consisting of both COVID-19 and non-COVID-19 patients. A random-effects model was used to perform meta-analysis for calculation of pooled mean values and 95% confidence intervals (95% CI) of abnormal imaging findings. Fifty-five studies met the selection criteria and were included in the analysis. Pulmonary lesions more often involved bilateral lungs (78%, 95% CI: 45-100%) and were more likely to have a peripheral (65.35%, 95% CI: 25.93-100%) and peripheral plus central distribution (31.12%, 95% CI: 1.96-74.07%), but less likely to have a central distribution (3.57%, 95% CI: 0.99-9.80%). Ground glass opacities (GGO) (58.05%, 95% CI: 16.67-100%), consolidation (44.18%, 95% CI: 1.61-71.46%) and GGO plus consolidation (52.99%, 95% CI: 19.05-76.79%) were the most common findings reported in 94.5% (52/55) of the studies, followed by air bronchogram (42.50%, 95% CI: 7.78-80.39%), linear opacities (41.29%, 95% CI: 7.44-65.06%), crazy-paving pattern (23.57%, 95% CI: 3.13-91.67%) and interlobular septal thickening (22.91%, 95% CI: 0.90-80.49%). CT has low specificity in differentiating pneumonia-related lung changes due to significant overlap between COVID-19 and non-COVID-19 patients with no significant differences in most of the imaging findings between these two groups (P>0.05). Furthermore, normal CT (13.31%, 95% CI: 0.74-38.36%) was reported in 26 (47.3%) studies. Despite widespread use of CT in the diagnosis of COVID-19 patients based on the current literature, CT findings are not pathognomonic as it lacks specificity in differentiating imaging appearances caused by different types of pneumonia. Further, there is a relatively high percentage of normal CT scans. Use of CT as a first-line diagnostic or screening tool in COVID-19 is not recommended.

Pathological Studies on lung affections in sheep and goat at Kalubia Governorate

The gross and histopathological changes in the lungs of small ruminants at Kalubia Governorate were investigated. A total of 159 lung samples (134 sheep and 25 goats) showed gross lesions were collected from February 2018 till November 2019. Bacterial strains were isolated from 26 cases out of 159 (16.35%) including various types where Staphylococcus aureus was the most predominant isolated strain from the lungs. Based on histopathological examination, different types of pneumonia (28.9%) including; suppurative pneumonia (10.69%), fibrinous pneumonia (7.54%), interstitial pneumonia (6.28%), granulomatous pneumonia (1.26%) and verminous pneumonia (3.14%) were detected. Bronchitis and bronchiolitis (21.38%); pleural fibrosis (4.4%) were recorded. Pulmonary adenomatosis and fibrosarcoma were detected in lungs of sheep with an incidence of 2.52% and 1.26% respectively. The study concluded that pneumonia is an important lesion in the lungs of small ruminant, and suppurative Pneumonia was the most predominant type. Verminous Pneumonia was detected only in goats and not detected in sheep. Pulmonary adenomatosis were recorded in lungs of sheep but not observed in goat.