Characterization and antibiogram study of pneumonia causing bacteria isolated from blood culture of children in northern Bangladesh

Children are usually affected by pneumonia, which is a common ailment caused by Pathogenic Streptococcus pneumoniae. This study's objective was to isolate and identify S. pneumoniae, which was recovered from blood samples of suspected paediatric pneumonia patients using conventional techniques, such as antibiotic sensitivity profiles and molecular approaches. In this study, forty (40) samples from three major hospitals in the Dinajpur region of Bangladesh were collected and assessed using various bacteriological, biochemical, antibiotic susceptibility test, and molecular techniques. 37.5% of the 40 samples tested positive for pneumonia, and 15 isolates were discovered. In terms of age, pneumonia was more common in children aged 3-5 years (50%) than in those aged 6 to 8 (33.33%), 9 to 11 (25%) and 12 to 15 (20%). According to the results of the current study, the study area had no statistically significant impact (P > 0.05), while age and socioeconomic status had a significant impact on the prevalence of pneumonia in patients with pneumonia (P 0.05). The age group for which pneumonia was most prevalent (at 50%) was that for children between the ages of 3-5. Poor socioeconomic status was associated with the highest prevalence of pneumonia (54.54%). By sequencing the 16S rRNA gene, S. pneumoniae was identified as S. pneumoniae NBRC102642. In the antibiotic investigation, S. pneumoniae was found to be extremely resistant to ciprofloxacin, amikacin, vancomycin, and cefexime, but responsive to erythromycin and azithromycin, as well as neomycin, kanamycin, streptomycin, and bacitracin. S. pneumoniae causes serious complications in paediatric patients, and this scenario requires prevention through vaccination and the development of new, efficient antibiotic therapies for pneumonia. If specific laboratory features of paediatric patients with pneumonia are understood, sepsis will be easier to detect early, treat, and reduce mortality.

Lung abscess due to Streptococcus intermedius associated with SARS CoV-2 infection in pregnancy: Unusual presentation and complication of a commensal bacteria during pregnancy.

Streptococcus intermedius is a commensal bacterium reported in a few cases as the causative agent of brain and lung abscesses, pneumonia, and endocarditis. Lung abscesses due to Streptococcus intermedius are rare, especially in pregnancy. We describe the first case of lung abscess due to Streptococcus intermedius in a pregnant woman.

COV-ELM classifier: An extreme learning machine based identification of COVID-19 using chest X-ray images

Coronaviruses constitute a family of viruses that gives rise to respiratory diseases. COVID-19 is an infectious disease caused by a newly discovered coronavirus also termed Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). As COVID-19 is highly contagious, early diagnosis of COVID-19 is crucial for an effective treatment strategy. However, the reverse transcription-polymerase chain reaction (RT-PCR) test which is considered to be a gold standard in the diagnosis of COVID-19 suffers from a high false-negative rate. Therefore, the research community is exploring alternative diagnostic mechanisms. Chest X-ray (CXR) image analysis has emerged as a feasible and effective diagnostic technique towards this objective. In this work, we propose the COVID-19 classification problem as a three-class classification problem to distinguish between COVID-19, normal, and pneumonia classes. We propose a three-stage framework, named COV-ELM based on extreme learning machine (ELM). Our dataset comprises CXR images in a frontal view, namely Posteroanterior (PA) and Erect anteroposterior (AP). Stage one deals with preprocessing and transformation while stage two deals with feature extraction. These extracted features are passed as an input to the ELM at the third stage, resulting in the identification of COVID-19. The choice of ELM in this work has been motivated by its faster convergence, better generalization capability, and shorter training time in comparison to the conventional gradient-based learning algorithms. As bigger and diverse datasets become available, ELM can be quickly retrained as compared to its gradient-based competitor models. We use 10-fold cross-validation to evaluate the results of COV-ELM. The proposed model achieved a macro average F1-score of 0.95 and the overall sensitivity of 0.94 +/- 0.02 at a 95% confidence interval. When compared to state-of-the-art machine learning algorithms, the COV-ELM is found to outperform its competitors in this three-class classification scenario. Further, LIME has been integrated with the proposed COV-ELM model to generate annotated CXR images. The annotations are based on the superpixels that have contributed to distinguish between the different classes. It was observed that the superpixels correspond to the regions of the human lungs that are clinically observed in COVID-19 and Pneumonia cases.

Comparison of deep vein thrombosis risks in acute respiratory distress syndrome caused by COVID-19 and bacterial pneumonia: a retrospective cohort study.

BACKGROUND: High incidence of deep vein thrombosis (DVT) has been observed in patients with acute respiratory distress syndrome (ARDS) caused by COVID-19 and those by bacterial pneumonia. However, the differences of incidence and risk factors of DVT in these two groups of ARDS had not been reported before. STUDY DESIGN AND METHODS: We performed a retrospective cohort study to investigate the difference of DVT in incidence and risk factors between the two independent cohorts of ARDS and eventually enrolled 240 patients, 105 of whom with ARDS caused by COVID-19 and 135 caused by bacterial pneumonia. Lower extremity venous compression ultrasound scanning was performed whenever possible regardless of clinical symptoms in the lower limbs. Clinical characteristics, including demographic information, clinical history, vital signs, laboratory findings, treatments, complications, and outcomes, were analyzed for patients with and without DVT in these two cohorts. RESULTS: The 28-days incidence of DVT was higher in patients with COVID-19 than in those with bacterial pneumonia (57.1% vs 41.5%, P = 0.016). Taking death as a competitive risk, the Fine-Gray test showed no significant difference in the 28-day cumulative incidence of DVT between these two groups (P = 0.220). Fine-Gray competing risk analysis also showed an association between increased CK (creatine kinase isoenzyme)-MB levels (P = 0.003), decreased PaO2 (partial pressure of arterial oxygen)/FiO2 (fraction of inspired oxygen) ratios (P = 0.081), increased D-dimer levels (P = 0.064) and increased incidence of DVT in COVID-19 cohort, and an association between invasive mechanical ventilation (IMV; P = 0.001) and higher incidence of DVT and an association between VTE prophylaxis (P = 0.007) and lower incidence of DVT in bacterial pneumonia cohort. The sensitivity and specificity of the corresponding receiver operating characteristic curve originating from the combination of CK-MB levels, PaO2/FiO2 ratios, and D-dimer levels ≥0.5 µg/mL were higher than that of the DVT Wells score (P = 0.020) and were not inferior to that of the Padua prediction score (P = 0.363) for assessing the risk of DVT in COVID-19 cohort. CONCLUSIONS: The incidence of DVT in patients with ARDS caused by COVID-19 is higher than those caused by bacterial pneumonia. Furthermore, the risk factors for DVT are completely different between these two ARDS cohorts. It is suggested that COVID-19 is probably an additional risk factor for DVT in ARDS patients.

Quantitative Assessment of Chest CT Patterns in COVID-19 and Bacterial Pneumonia Patients: a Deep Learning Perspective.

BACKGROUND: It is difficult to distinguish subtle differences shown in computed tomography (CT) images of coronavirus disease 2019 (COVID-19) and bacterial pneumonia patients, which often leads to an inaccurate diagnosis. It is desirable to design and evaluate interpretable feature extraction techniques to describe the patient's condition. METHODS: This is a retrospective cohort study of 170 confirmed patients with COVID-19 or bacterial pneumonia acquired at Yeungnam University Hospital in Daegu, Korea. The Lung and lesion regions were segmented to crop the lesion into 2D patches to train a classifier model that could differentiate between COVID-19 and bacterial pneumonia. The K-means algorithm was used to cluster deep features extracted by the trained model into 20 groups. Each lesion patch cluster was described by a characteristic imaging term for comparison. For each CT image containing multiple lesions, a histogram of lesion types was constructed using the cluster information. Finally, a Support Vector Machine classifier was trained with the histogram and radiomics features to distinguish diseases and severity. RESULTS: The 20 clusters constructed from 170 patients were reviewed based on common radiographic appearance types. Two clusters showed typical findings of COVID-19, with two other clusters showing typical findings related to bacterial pneumonia. Notably, there is one cluster that showed bilateral diffuse ground-glass opacities (GGOs) in the central and peripheral lungs and was considered to be a key factor for severity classification. The proposed method achieved an accuracy of 91.2% for classifying COVID-19 and bacterial pneumonia patients with 95% reported for severity classification. The CT quantitative parameters represented by the values of cluster 8 were correlated with existing laboratory data and clinical parameters. CONCLUSION: Deep chest CT analysis with constructed lesion clusters revealed well-known COVID-19 CT manifestations comparable to manual CT analysis. The constructed histogram features improved accuracy for both diseases and severity classification, and showed correlations with laboratory data and clinical parameters. The constructed histogram features can provide guidance for improved analysis and treatment of COVID-19.

COVIDetection-Net: A tailored COVID-19 detection from chest radiography images using deep learning.

In this study, a medical system based on Deep Learning (DL) which we called "COVIDetection-Net" is proposed for automatic detection of new corona virus disease 2019 (COVID-19) infection from chest radiography images (CRIs). The proposed system is based on ShuffleNet and SqueezeNet architecture to extract deep learned features and Multiclass Support Vector Machines (MSVM) for detection and classification. Our dataset contains 1200 CRIs that collected from two different publicly available databases. Extensive experiments were carried out using the proposed model. The highest detection accuracy of 100 % for COVID/NonCOVID, 99.72 % for COVID/Normal/pneumonia and 94.44 % for COVID/Normal/Bacterial pneumonia/Viral pneumonia have been obtained. The proposed system superior all published methods in recall, specificity, precision, F1-Score and accuracy. Confusion Matrix (CM) and Receiver Operation Characteristics (ROC) analysis are also used to depict the performance of the proposed model. Hence the proposed COVIDetection-Net can serve as an efficient system in the current state of COVID-19 pandemic and can be used in everywhere that are facing shortage of test kits.

CoroDet: A deep learning based classification for COVID-19 detection using chest X-ray images.

BACKGROUND AND OBJECTIVE: The Coronavirus 2019, or shortly COVID-19, is a viral disease that causes serious pneumonia and impacts our different body parts from mild to severe depending on patient's immune system. This infection was first reported in Wuhan city of China in December 2019, and afterward, it became a global pandemic spreading rapidly around the world. As the virus spreads through human to human contact, it has affected our lives in a devastating way, including the vigorous pressure on the public health system, the world economy, education sector, workplaces, and shopping malls. Preventing viral spreading requires early detection of positive cases and to treat infected patients as quickly as possible. The need for COVID-19 testing kits has increased, and many of the developing countries in the world are facing a shortage of testing kits as new cases are increasing day by day. In this situation, the recent research using radiology imaging (such as X-ray and CT scan) techniques can be proven helpful to detect COVID-19 as X-ray and CT scan images provide important information about the disease caused by COVID-19 virus. The latest data mining and machine learning techniques such as Convolutional Neural Network (CNN) can be applied along with X-ray and CT scan images of the lungs for the accurate and rapid detection of the disease, assisting in mitigating the problem of scarcity of testing kits. METHODS: Hence a novel CNN model called CoroDet for automatic detection of COVID-19 by using raw chest X-ray and CT scan images have been proposed in this study. CoroDet is developed to serve as an accurate diagnostics for 2 class classification (COVID and Normal), 3 class classification (COVID, Normal, and non-COVID pneumonia), and 4 class classification (COVID, Normal, non-COVID viral pneumonia, and non-COVID bacterial pneumonia). RESULTS: The performance of our proposed model was compared with ten existing techniques for COVID detection in terms of accuracy. A classification accuracy of 99.1% for 2 class classification, 94.2% for 3 class classification, and 91.2% for 4 class classification was produced by our proposed model, which is obviously better than the state-of-the-art-methods used for COVID-19 detection to the best of our knowledge. Moreover, the dataset with x-ray images that we prepared for the evaluation of our method is the largest datasets for COVID detection as far as our knowledge goes. CONCLUSION: The experimental results of our proposed method CoroDet indicate the superiority of CoroDet over the existing state-of-the-art-methods. CoroDet may assist clinicians in making appropriate decisions for COVID-19 detection and may also mitigate the problem of scarcity of testing kits.

CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images.

BACKGROUND AND OBJECTIVE: The novel Coronavirus also called COVID-19 originated in Wuhan, China in December 2019 and has now spread across the world. It has so far infected around 1.8 million people and claimed approximately 114,698 lives overall. As the number of cases are rapidly increasing, most of the countries are facing shortage of testing kits and resources. The limited quantity of testing kits and increasing number of daily cases encouraged us to come up with a Deep Learning model that can aid radiologists and clinicians in detecting COVID-19 cases using chest X-rays. METHODS: In this study, we propose CoroNet, a Deep Convolutional Neural Network model to automatically detect COVID-19 infection from chest X-ray images. The proposed model is based on Xception architecture pre-trained on ImageNet dataset and trained end-to-end on a dataset prepared by collecting COVID-19 and other chest pneumonia X-ray images from two different publically available databases. RESULTS: CoroNet has been trained and tested on the prepared dataset and the experimental results show that our proposed model achieved an overall accuracy of 89.6%, and more importantly the precision and recall rate for COVID-19 cases are 93% and 98.2% for 4-class cases (COVID vs Pneumonia bacterial vs pneumonia viral vs normal). For 3-class classification (COVID vs Pneumonia vs normal), the proposed model produced a classification accuracy of 95%. The preliminary results of this study look promising which can be further improved as more training data becomes available. CONCLUSION: CoroNet achieved promising results on a small prepared dataset which indicates that given more data, the proposed model can achieve better results with minimum pre-processing of data. Overall, the proposed model substantially advances the current radiology based methodology and during COVID-19 pandemic, it can be very helpful tool for clinical practitioners and radiologists to aid them in diagnosis, quantification and follow-up of COVID-19 cases.