Convolutional Sparse Support Estimator Based Covid-19 Recognition from X-ray Images

Coronavirus disease (Covid-19) has been the main agenda of the whole world since it came in sight in December 2019. It has already caused thousands of causalities and infected several millions worldwide. Any technological tool that can be provided to healthcare practitioners to save time, effort, and possibly lives has crucial importance. The main tools practitioners currently use to diagnose Covid-19 are Reverse Transcription-Polymerase Chain reaction (RT-PCR) and Computed Tomography (CT), which require significant time, resources and acknowledged experts. X-ray imaging is a common and easily accessible tool that has great potential for Covid-19 diagnosis. In this study, we propose a novel approach for Covid-19 recognition from chest X-ray images. Despite the importance of the problem, recent studies in this domain produced not so satisfactory results due to the limited datasets available for training. Recall that Deep Learning techniques can generally provide state-of-the-art performance in many classification tasks when trained properly over large datasets, such data scarcity can be a crucial obstacle when using them for Covid-19 detection. Alternative approaches such as representation-based classification (collaborative or sparse representation) might provide satisfactory performance with limited size datasets, but they generally fall short in performance or speed compared to Machine Learning methods. To address this deficiency, Convolution Support Estimation Network (CSEN) has recently been proposed as a bridge between model-based and Deep Learning approaches by providing a non-iterative real-time mapping from query sample to ideally sparse representation coefficient' support, which is critical information for class decision in representation based techniques.Comment: 10 page

COVID-19 Infection Map Generation and Detection from Chest X-Ray Images

Computer-aided diagnosis has become a necessity for accurate and immediate coronavirus disease 2019 (COVID-19) detection to aid treatment and prevent the spread of the virus. Numerous studies have proposed to use Deep Learning techniques for COVID-19 diagnosis. However, they have used very limited chest X-ray (CXR) image repositories for evaluation with a small number, a few hundreds, of COVID-19 samples. Moreover, these methods can neither localize nor grade the severity of COVID-19 infection. For this purpose, recent studies proposed to explore the activation maps of deep networks. However, they remain inaccurate for localizing the actual infestation making them unreliable for clinical use. This study proposes a novel method for the joint localization, severity grading, and detection of COVID-19 from CXR images by generating the so-called infection maps. To accomplish this, we have compiled the largest dataset with 119,316 CXR images including 2951 COVID-19 samples, where the annotation of the ground-truth segmentation masks is performed on CXRs by a novel collaborative human-machine approach. Furthermore, we publicly release the first CXR dataset with the ground-truth segmentation masks of the COVID-19 infected regions. A detailed set of experiments show that state-of-the-art segmentation networks can learn to localize COVID-19 infection with an F1-score of 83.20%, which is significantly superior to the activation maps created by the previous methods. Finally, the proposed approach achieved a COVID-19 detection performance with 94.96% sensitivity and 99.88% specificity

Advance Warning Methodologies for COVID-19 using Chest X-Ray Images

Coronavirus disease 2019 (COVID-19) has rapidly become a global health concern after its first known detection in December 2019. As a result, accurate and reliable advance warning system for the early diagnosis of COVID-19 has now become a priority. The detection of COVID-19 in early stages is not a straightforward task from chest X-ray images according to expert medical doctors because the traces of the infection are visible only when the disease has progressed to a moderate or severe stage. In this study, our first aim is to evaluate the ability of recent \textit{state-of-the-art} Machine Learning techniques for the early detection of COVID-19 from chest X-ray images. Both compact classifiers and deep learning approaches are considered in this study. Furthermore, we propose a recent compact classifier, Convolutional Support Estimator Network (CSEN) approach for this purpose since it is well-suited for a scarce-data classification task. Finally, this study introduces a new benchmark dataset called Early-QaTa-COV19, which consists of 1065 early-stage COVID-19 pneumonia samples (very limited or no infection signs) labelled by the medical doctors and 12 544 samples for control (normal) class. A detailed set of experiments shows that the CSEN achieves the top (over 97%) sensitivity with over 95.5% specificity. Moreover, DenseNet-121 network produces the leading performance among other deep networks with 95% sensitivity and 99.74% specificity.Comment: 12 page

Reliable Covid-19 Detection using Chest X-Ray Images

Coronavirus disease 2019 (COVID-19) has emerged the need for computer-aided diagnosis with automatic, accurate, and fast algorithms. Recent studies have applied Machine Learning algorithms for COVID-19 diagnosis over chest X-ray (CXR) images. However, the data scarcity in these studies prevents a reliable evaluation with the potential of overfitting and limits the performance of deep networks. Moreover, these networks can discriminate COVID-19 pneumonia usually from healthy subjects only or occasionally, from limited pneumonia types. Thus, there is a need for a robust and accurate COVID-19 detector evaluated over a large CXR dataset. To address this need, in this study, we propose a reliable COVID-19 detection network: ReCovNet, which can discriminate COVID-19 pneumonia from 14 different thoracic diseases and healthy subjects. To accomplish this, we have compiled the largest COVID-19 CXR dataset: QaTa-COV19 with 124,616 images including 4603 COVID-19 samples. The proposed ReCovNet achieved a detection performance with 98.57% sensitivity and 99.77% specificity.acceptedVersionPeer reviewe

EVOTECH® endoscope cleaner and reprocessor (ECR) simulated-use and clinical-use evaluation of cleaning efficacy

<p>Abstract</p> <p>Background</p> <p>The objective of this study was to perform simulated-use testing as well as a clinical study to assess the efficacy of the EVOTECH^®Endoscope Cleaner and Reprocessor (ECR) cleaning for flexible colonoscopes, duodenoscopes, gastroscopes and bronchoscopes. The main aim was to determine if the cleaning achieved using the ECR was at least equivalent to that achieved using optimal manual cleaning.</p> <p>Methods</p> <p>Simulated-use testing consisted of inoculating all scope channels and two surface sites with Artificial Test Soil (ATS) containing 10⁸cfu/mL of <it>Enterococcus faecalis, Pseudomonas aeruginosa </it>and <it>Candida albicans</it>. Duodenoscopes, colonoscopes, and bronchoscopes (all Olympus endoscopes) were included in the simulated use testing. Each endoscope type was tested in triplicate and all channels and two surface sites were sampled for each scope. The clinical study evaluated patient-used duodenoscopes, bronchoscopes, colonoscopes, and gastroscopes (scopes used for emergency procedures were excluded) that had only a bedside flush prior to being processed in the ECR (i.e. no manual cleaning). There were 10 to 15 endoscopes evaluated post-cleaning and to ensure the entire ECR cycle was effective, 5 endoscopes were evaluated post-cleaning and post-high level disinfection. All channels and two external surface locations were sampled to evaluate the residual organic and microbial load. Effective cleaning of endoscope surfaces and channels was deemed to have been achieved if there was < 6.4 μg/cm²of residual protein, < 1.8 μg/cm²of residual hemoglobin and < 4 Log₁₀viable bacteria/cm². Published data indicate that routine manual cleaning can achieve these endpoints so the ECR cleaning efficacy must meet or exceed these to establish that the ECR cleaning cycle could replace manual cleaning</p> <p>Results</p> <p>In the clinical study 75 patient-used scopes were evaluated post cleaning and 98.8% of surfaces and 99.7% of lumens met or surpassed the cleaning endpoints set for protein, hemoglobin and bioburden residuals. In the simulated-use study 100% of the Olympus colonoscopes, duodenoscopes and bronchoscopes evaluated met or surpassed the cleaning endpoints set for protein, and bioburden residuals (hemoglobin was not evaluated).</p> <p>Conclusions</p> <p>The ECR cleaning cycle provides an effective automated approach that ensures surfaces and channels of flexible endoscopes are adequately cleaned after having only a bedside flush but no manual cleaning. It is crucial to note that endoscopes used for emergency procedures or where reprocessing is delayed for more than one hour MUST still be manually cleaned prior to placing them in the ECR.</p

Participation of CD45, NKR-P1A and ANK61 antigen in rat hepatic NK cell (pit cell)mediated target cell cytotoxicity.

AIM:Several triggering receptors have been described to be involved in natural killer (NK) cell-mediated target cytotoxicity. In these studies, NK cells deriv ed from blood or spleen were used. Pit cells are liver-specific NK cells that possess a higher level of natural cytotoxicity and a different morphology when compared to blood NK cells. The aim of this study was to characterize the role of the NK triggering molecules NKR P1A, ANK61 antigen, and CD45 in pit cell medi ated killing of target cells.METHODS:( 51) Crrelease and DNA fragmentation were used to quantify target cell lysis and apoptosis, respectively.RESULTS:Flow cytometric analysis showed that pit cells expressed CD45, NK R P1A, and ANK61 antigen. Treatment of pit cells with monoclonal antibody (mAb)to CD45 (ANK74) not only inhibited CC531s or YAC 1 target lysis but also apopt osis induced by pit cells. The mAbs to NKR P1A (3.2.3) and ANK61 antigen (ANK61 )had no effect on pit cell mediated CC531s or YAC 1 target cytolysis or apopto sis, while they did increase the Fcgamma receptor positive (FcgammaR(+)) P815 cytolysi s and apoptosis. This enhanced cytotoxicity could be inhibited by 3,4 dichloroi socoumarin, an inhibitor of granzymes.CONCLUSION:These results indicate that CD45 participates in pit cell med iated CC531s and YAC-1 target cytolysis and apoptosis. NKR-P1A and ANK61 antigen on pit cells function as activation structures against Fc gammaR( +) P 815 cells, which was mediated by the perforin/granzyme pathway.JOURNAL ARTICLEinfo:eu-repo/semantics/publishe

On the cell biology of pit cells, the liver-specific NK cells.

JOURNAL ARTICLEinfo:eu-repo/semantics/publishe