Case report: Immunological characteristics of de novo ulcerative colitis in a child post COVID-19.

The pathological mechanisms of de novo inflammatory bowel disease (IBD) following SARS-CoV-2 infection are unknown. However, cases of coexisting IBD and multisystem inflammatory syndrome in children (MIS-C), which occurs 2-6 weeks after SARS-CoV-2 infection, have been reported, suggesting a shared underlying dysfunction of immune responses. Herein, we conducted the immunological analyses of a Japanese patient with de novo ulcerative colitis following SARS-CoV-2 infection based on the pathological hypothesis of MIS-C. Her serum level of lipopolysaccharide-binding protein, a microbial translocation marker, was elevated with T cell activation and skewed T cell receptor repertoire. The dynamics of activated CD8+ T cells, including T cells expressing the gut-homing marker α4ß7, and serum anti-SARS-CoV-2 spike IgG antibody titer reflected her clinical symptoms. These findings suggest that SARS-CoV-2 infection may trigger the de novo occurrence of ulcerative colitis by impairing intestinal barrier function, T cell activation with a skewed T cell receptor repertoire, and increasing levels of anti-SARS-CoV-2 spike IgG antibodies. Further research is needed to clarify the association between the functional role of the SARS-CoV-2 spike protein as a superantigen and ulcerative colitis.

System for coronavirus classification based on a new textural descriptor

The combination of Chest X-Ray imaging and Artificial Intelligence (AI) has proven its efficiency in coronavirus disease (COVID-19) detection [1]. The present paper proposes an efficient COVID-19 detection system based on a new textural features descriptor: Monogenic Local Binary Pattern Variance (MLBPV). An Artificial Neural Network (ANN) model is used for Regions Of Interest (ROIs) classification. Evaluating MLBPV, it outperforms other tested models by achieving an Area Under Curve (A-{z}) of 0.96263 and an accuracy of 99.9805%. Comparing our method with previous ones proves that ours provides the best performance. This model may be implemented in digital X-Ray machine for radiography and help radiologists. © 2022 IEEE.

Prevalence of low back pain and osteoporosis in health care workers after the COVID 19 pandemic

Aim: Prevalence of low back pain and osteoporosis in health care workers after the COVID 19 pandemic. Material(s) and Method(s): The present prospective study was conducted among 300 apparently healthy adults who are working as a health care individual in the institute. A questionnaire addressing known risk factors for osteoporosis was made. The severity of the LBP was graded using a visual analogue scale for pain (VAS). The VAS is a reliable scale used to register the intensity of chronic pain where 0 signifies no pain and 10 signifies the worst pain imaginable. Those who had chronic LBP were also questioned on whether the onset of LBP preceded the Covid-19 pandemic, and whether the severity of the LBP had increased during the pandemic. Result(s): Light, moderate, sedentary and vigorous physical activity was revealed in 50.1%, 33.6%, 11.1% and 5.2% of the subjects respectively. >1 hour sun exposure in a day was reported among 15.4% of the subjects.In this study, low back was found among 42.7% of the subjects. Mean BMD level was -0.49+/-2.40. Mean BMD level was lower in subjects having back pain, sedentary/vigorous physical activity and no sun exposure as compared to counterparts. Conclusion(s): The confinement decreed due to the COVID-19 pandemic led to a significant increase in LBPintensity among health care workers. Copyright © 2022 Ubiquity Press. All rights reserved.

Musculoskeletal health complaints: A growing concern that should be investigated elaborately in Bangladesh.

Evidence from the past few decades suggests that the most increases in disability-related musculoskeletal health complaints (MHC) have occurred in low-income and middle-income countries (LMICs). Past studies identified long sitting, higher commute time to the office, and traffic congestion predictors of MHC in Bangladesh. Additionally, post-acute COVID-19 patients reported MHC at a higher rate in Bangladesh. Further studies are needed to recommend exclusive initiatives from authorities to tackle the upcoming tsunami of MHC in LMICs, for example, in Bangladesh.

Local Binary Pattern Based COVID-19 Detection Method Using Chest X-Ray Images

COVID-19 is a global pandemic disease that is rapidly spreading around the world. Automatic early diagnosis of COVID-19 with computer-aided tools is crucial for disease treatment and control. In this context, X-ray imaging is an easily accessible and alternative tool in the early diagnosis of COVID-19. However, various lung diseases such as COVID-19, viral pneumonia, bacterial pneumonia are similar to each other and these images may not be distinguished from each other. Thus, the similarity of COVID-19 symptoms to viral pneumonia can lead to misdiagnosis. In this study, the local binary pattern (LBP) based COVID-19 detection method is studied. The textural features are extracted with LBP and supervised learning methods are performed with these features. Different classifiers such as kNN, Naive Bayes, Neural Network, and SVM are used in the training stage and experimental studies are conducted on an open-access dataset. Performance evaluations of classifiers are made with various performance metrics. As a result of experimental studies conducted in different types and dimensions, over 99% accuracy is achieved with the LBP+SVM method.

INFLUENCE OF ANTIBIOTIC THERAPY ON INDICATORS OF ENDOTOXINEMIA AND SYSTEMIC INFLAMMATION IN ACUTE SARS-COV-2 LUNG DAMAGE

Background. Prescribing antibacterial drugs for the treatmentofa new coronavirus infection at the outpatient stage is often unreasonable and can also lead to an aggravation of the patient's condition due to the effect of this group of drugs on the intestinal microflora and lead to other undesirable effects. The aim: to assess the level of lipopolysaccharide-binding protein and indicators of systemic inflammation in patients with moderate viral SARS-CoV-2 lung disease on the background of antibiotic therapy. Materials and methods. 60 patients hospitalized in the infectious diseases departmentwitha positive PCRresultfor SARS-CoV-2in the age group 44-70 years oldwere examined. The patients were divided into 2 groups: group 1 (n = 26) - patients who didnotreceive antibacterialdrugs atthe outpatientstage, group 2 (n= 34)- patients who receivedantibiotic therapy. The control group was also selected(n= 20). Patients underwent a study of the level of lipopolysaccharide-binding protein (LBP), ferritin and C-reactive protein in the peripheral blood. Results. Inthe groupofpatientswithnewcoronavirusinfectionwhowereadmittedto the inpatientstage oftreatmentandreceivedantibacterialtherapy atthe outpatient stage, a significantly higher levels of LBP - 37.3 [13.8;50.4] µg/ml (p < 0.05) and ferritin- 276.00 [184.00;463.00] µg/ml (p < 0.05)were revealed, comparedwith group 1 and the control group. Conclusions. In the group of patients who received antibiotic therapy at the outpatient stage, a significantly higher level of LBP was revealed compared to the group in which this group of drugs was not used. These results indicate the possible impact of uncontrolled and early intake of antibacterial drugs on the gut microbiome andintestinal permeability, andalso prove the needfor a more responsible approach to the choice of starting therapy for new coronavirus infection. © 2022 by the authors.

A novel LT-LBP based prediction model for COVID-CT images with Machine Learning

Logarithmic Transformation of Local Binary Pattern (LT-LBP) is introduced with machine learning algorithms for the classification of covid CT images. Preprocessing the input information plays a key role in machine learning as well as deep learning models. CT images and Chest X-rays are significant in diagnosing the disease. Most of the medical images are greyscale images. Texture analysis is one of the ways to obtain information from medical images and Local binary pattern is an efficient texture operator. With the texture pattern LBP, a novel method as Logarithmic Transformation of Local Binary Pattern (LT-LBP) is proposed in this paper. We applied 2695 CT images and 115 Italian COVID Positive CT images and 5 COVID positive CT images from the Chennai region. The CT-Scans and Chest X-ray images have endured for preprocessing and texture analysis with Local Binary Pattern (LBP) and trained with Support Vector Machine (SVM), K-nearest neighbors (KNN), Random Forest (RF), Logistic Regression (LR) as machine learning algorithms. The LT-LBP gives a better result when compared with normal LBP when combined with SVM and RF. The retrospective study gives the result as accuracy percentage of 95.7 with LT-LBP combined with SVM and also 91.4 percent of accuracy results for LT-LBP with RF © 2021 IEEE.

SVM Based COVID-19 Detection from CT Scan Image using Local Feature

During the outbreak time of pandemic COVID-19, we are so much worried and scared about our life. Going to the hospital is a very risky job to test COVID-19 because ordinary people can be easily infected by the COVID-19 patient. and also it is very time-consuming to get the test result of the COVID-19. In order to focus on these issues, a machine learning model is designed to detect COVID-19 using the local feature of CT-scan images and Support Vector Machine (SVM). A publicly available SARS-CoV-2 (severe Acute Respiratory Syndrome Coronavirus 2) dataset is used in the proposed system which contains 2481 CT scan images in total where 1252 samples are COVID and the rest of the samples are Non-COVID. Local Binary Pattern (LBP) algorithm is applied to extract local features from images. We also compare our proposed model with some state-of-the-art algorithms such as Decision Tree (DT), K-Nearest Neighbor (KNN), and AdaBoost based on some evaluation matrices i.e. precision, recall, and F1 score. According to the result, the proposed model provides 97.09% F1 score which is better to detect COVID-19 for clinical usage. © 2021 IEEE.

Multi-Channel Based Image Processing Scheme for Pneumonia Identification.

Pneumonia is a prevalent severe respiratory infection that affects the distal and alveoli airways. Across the globe, it is a serious public health issue that has caused high mortality rate of children below five years old and the aged citizens who must have had previous chronic-related ailment. Pneumonia can be caused by a wide range of microorganisms, including virus, fungus, bacteria, which varies greatly across the globe. The spread of the ailment has gained computer-aided diagnosis (CAD) attention. This paper presents a multi-channel-based image processing scheme to automatically extract features and identify pneumonia from chest X-ray images. The proposed approach intends to address the problem of low quality and identify pneumonia in CXR images. Three channels of CXR images, namely, the Local Binary Pattern (LBP), Contrast Enhanced Canny Edge Detection (CECED), and Contrast Limited Adaptive Histogram Equalization (CLAHE) CXR images are processed by deep neural networks. CXR-related features of LBP images are extracted using shallow CNN, features of the CLAHE CXR images are extracted by pre-trained inception-V3, whereas the features of CECED CXR images are extracted using pre-trained MobileNet-V3. The final feature weights of the three channels are concatenated and softmax classification is utilized to determine the final identification result. The proposed network can accurately classify pneumonia according to the experimental result. The proposed method tested on publicly available dataset reports accuracy of 98.3%, sensitivity of 98.9%, and specificity of 99.2%. Compared with the single models and the state-of-the-art models, our proposed network achieves comparable performance.

Local binary pattern and deep learning feature extraction fusion for COVID-19 detection on computed tomography images.

The deadly coronavirus virus (COVID-19) was confirmed as a pandemic by the World Health Organization (WHO) in December 2019. It is important to identify suspected patients as early as possible in order to control the spread of the virus, improve the efficacy of medical treatment, and, as a result, lower the mortality rate. The adopted method of detecting COVID-19 is the reverse-transcription polymerase chain reaction (RT-PCR), the process is affected by a scarcity of RT-PCR kits as well as its complexities. Medical imaging using machine learning and deep learning has proved to be one of the most efficient methods of detecting respiratory diseases, but to train machine learning features needs to be extracted manually, and in deep learning, efficiency is affected by deep learning architecture and low data. In this study, handcrafted local binary pattern (LBP) and automatic seven deep learning models extracted features were used to train support vector machines (SVM) and K-nearest neighbour (KNN) classifiers, to improve the performance of the classifier, a concatenated LBP and deep learning feature was proposed to train the KNN and SVM, based on the performance criteria, the models VGG-19 + LBP achieved the highest accuracy of 99.4%. The SVM and KNN classifiers trained on the hybrid feature outperform the state of the art model. This shows that the proposed feature can improve the performance of the classifiers in detecting COVID-19.

Deep Learning-Based Approaches to Improve Classification Parameters for Diagnosing COVID-19 from CT Images.

Patients infected with the COVID-19 virus develop severe pneumonia, which generally leads to death. Radiological evidence has demonstrated that the disease causes interstitial involvement in the lungs and lung opacities, as well as bilateral ground-glass opacities and patchy opacities. In this study, new pipeline suggestions are presented, and their performance is tested to decrease the number of false-negative (FN), false-positive (FP), and total misclassified images (FN + FP) in the diagnosis of COVID-19 (COVID-19/non-COVID-19 and COVID-19 pneumonia/other pneumonia) from CT lung images. A total of 4320 CT lung images, of which 2554 were related to COVID-19 and 1766 to non-COVID-19, were used for the test procedures in COVID-19 and non-COVID-19 classifications. Similarly, a total of 3801 CT lung images, of which 2554 were related to COVID-19 pneumonia and 1247 to other pneumonia, were used for the test procedures in COVID-19 pneumonia and other pneumonia classifications. A 24-layer convolutional neural network (CNN) architecture was used for the classification processes. Within the scope of this study, the results of two experiments were obtained by using CT lung images with and without local binary pattern (LBP) application, and sub-band images were obtained by applying dual-tree complex wavelet transform (DT-CWT) to these images. Next, new classification results were calculated from these two results by using the five pipeline approaches presented in this study. For COVID-19 and non-COVID-19 classification, the highest sensitivity, specificity, accuracy, F-1, and AUC values obtained without using pipeline approaches were 0.9676, 0.9181, 0.9456, 0.9545, and 0.9890, respectively; using pipeline approaches, the values were 0.9832, 0.9622, 0.9577, 0.9642, and 0.9923, respectively. For COVID-19 pneumonia/other pneumonia classification, the highest sensitivity, specificity, accuracy, F-1, and AUC values obtained without using pipeline approaches were 0.9615, 0.7270, 0.8846, 0.9180, and 0.9370, respectively; using pipeline approaches, the values were 0.9915, 0.8140, 0.9071, 0.9327, and 0.9615, respectively. The results of this study show that classification success can be increased by reducing the time to obtain per-image results through using the proposed pipeline approaches.

A new deep learning pipeline to detect Covid-19 on chest X-ray images using local binary pattern, dual tree complex wavelet transform and convolutional neural networks.

In this study, which aims at early diagnosis of Covid-19 disease using X-ray images, the deep-learning approach, a state-of-the-art artificial intelligence method, was used, and automatic classification of images was performed using convolutional neural networks (CNN). In the first training-test data set used in the study, there were 230 X-ray images, of which 150 were Covid-19 and 80 were non-Covid-19, while in the second training-test data set there were 476 X-ray images, of which 150 were Covid-19 and 326 were non-Covid-19. Thus, classification results have been provided for two data sets, containing predominantly Covid-19 images and predominantly non-Covid-19 images, respectively. In the study, a 23-layer CNN architecture and a 54-layer CNN architecture were developed. Within the scope of the study, the results were obtained using chest X-ray images directly in the training-test procedures and the sub-band images obtained by applying dual tree complex wavelet transform (DT-CWT) to the above-mentioned images. The same experiments were repeated using images obtained by applying local binary pattern (LBP) to the chest X-ray images. Within the scope of the study, four new result generation pipeline algorithms having been put forward additionally, it was ensured that the experimental results were combined and the success of the study was improved. In the experiments carried out in this study, the training sessions were carried out using the k-fold cross validation method. Here the k value was chosen as 23 for the first and second training-test data sets. Considering the average highest results of the experiments performed within the scope of the study, the values of sensitivity, specificity, accuracy, F-1 score, and area under the receiver operating characteristic curve (AUC) for the first training-test data set were 0,9947, 0,9800, 0,9843, 0,9881 and 0,9990 respectively; while for the second training-test data set, they were 0,9920, 0,9939, 0,9891, 0,9828 and 0,9991; respectively. Within the scope of the study, finally, all the images were combined and the training and testing processes were repeated for a total of 556 X-ray images comprising 150 Covid-19 images and 406 non-Covid-19 images, by applying 2-fold cross. In this context, the average highest values of sensitivity, specificity, accuracy, F-1 score, and AUC for this last training-test data set were found to be 0,9760, 1,0000, 0,9906, 0,9823 and 0,9997; respectively.

Elevated markers of gut leakage and inflammasome activation in COVID-19 patients with cardiac involvement.

BACKGROUND: A high proportion of COVID-19 patients have cardiac involvement, even those without known cardiac disease. Downregulation of angiotensin converting enzyme 2 (ACE2), a receptor for SARS-CoV-2 and the renin-angiotensin system, as well as inflammatory mechanisms have been suggested to play a role. ACE2 is abundant in the gut and associated with gut microbiota composition. We hypothesized that gut leakage of microbial products, and subsequent inflammasome activation could contribute to cardiac involvement in COVID-19 patients. METHODS: Plasma levels of a gut leakage marker (LPS-binding protein, LBP), a marker of enterocyte damage (intestinal fatty acid binding protein, IFABP), a gut homing marker (CCL25, ligand for chemokine receptor CCR9) and markers of inflammasome activation (IL-1ß, IL-18 and their regulatory proteins) were measured at three time points (day 1, 3-5 and 7-10) in 39 hospitalized COVID-19 patients and related to cardiac involvement. RESULTS: Compared to controls, COVID-19 patients had elevated plasma levels of LBP and CCL25 but not IFABP, suggesting impaired gut barrier function and accentuated gut homing of T cells without excessive enterocyte damage. Levels of LBP were twice as high at baseline in patients with elevated cardiac markers compared with those without and remained elevated during hospitalization. Also, markers of inflammasome activation were moderately elevated in patients with cardiac involvement. LBP was associated with higher NT-pro-BNP levels, whereas IL-18, IL-18BP and IL-1Ra were associated with higher troponin levels. CONCLUSION: Patients with cardiac involvement had elevated markers of gut leakage and inflammasome activation, suggestive of a potential gut-heart axis in COVID-19.