scholarly journals MULTI-DEEP: A novel CAD system for coronavirus (COVID-19) diagnosis from CT images using multiple convolution neural networks

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e10086
Author(s):  
Omneya Attallah ◽  
Dina A. Ragab ◽  
Maha Sharkas
Keyword(s):  
Early Detection ◽  
Principal Components ◽  
Ct Images ◽  
The Other ◽  
Svm Classifier ◽  
Rt Pcr ◽  
Cad System ◽  
Deep Feature ◽  
Number Of Principal Components ◽  
Pcr Test

Coronavirus (COVID-19) was first observed in Wuhan, China, and quickly propagated worldwide. It is considered the supreme crisis of the present era and one of the most crucial hazards threatening worldwide health. Therefore, the early detection of COVID-19 is essential. The common way to detect COVID-19 is the reverse transcription-polymerase chain reaction (RT-PCR) test, although it has several drawbacks. Computed tomography (CT) scans can enable the early detection of suspected patients, however, the overlap between patterns of COVID-19 and other types of pneumonia makes it difficult for radiologists to diagnose COVID-19 accurately. On the other hand, deep learning (DL) techniques and especially the convolutional neural network (CNN) can classify COVID-19 and non-COVID-19 cases. In addition, DL techniques that use CT images can deliver an accurate diagnosis faster than the RT-PCR test, which consequently saves time for disease control and provides an efficient computer-aided diagnosis (CAD) system. The shortage of publicly available datasets of CT images, makes the CAD system’s design a challenging task. The CAD systems in the literature are based on either individual CNN or two-fused CNNs; one used for segmentation and the other for classification and diagnosis. In this article, a novel CAD system is proposed for diagnosing COVID-19 based on the fusion of multiple CNNs. First, an end-to-end classification is performed. Afterward, the deep features are extracted from each network individually and classified using a support vector machine (SVM) classifier. Next, principal component analysis is applied to each deep feature set, extracted from each network. Such feature sets are then used to train an SVM classifier individually. Afterward, a selected number of principal components from each deep feature set are fused and compared with the fusion of the deep features extracted from each CNN. The results show that the proposed system is effective and capable of detecting COVID-19 and distinguishing it from non-COVID-19 cases with an accuracy of 94.7%, AUC of 0.98 (98%), sensitivity 95.6%, and specificity of 93.7%. Moreover, the results show that the system is efficient, as fusing a selected number of principal components has reduced the computational cost of the final model by almost 32%.

scholarly journals First isolation of dengue 4 in the state of São Paulo, Brazil, 2011

2012 ◽  
Vol 54 (1) ◽  
pp. 49-51 ◽  
Author(s):  
Iray Maria Rocco ◽  
Vivian Regina Silveira ◽  
Adriana Yurika Maeda ◽  
Sarai Joaquim dos Santos Silva ◽  
Carine Spenassatto ◽  
...  
Keyword(s):  
Monoclonal Antibodies ◽  
Early Detection ◽  
Sao Paulo ◽  
The State ◽  
The Other ◽  
São Paulo ◽  
Rt Pcr ◽  
Albopictus Cell ◽  
Pcr Assays ◽  
Northwest Region

We report the first isolation of Dengue virus 4 (DENV-4) in the state of São Paulo, from two patients - one living in São José do Rio Preto and the other one in Paulo de Faria, both cities located in the Northwest region of the state. The virus isolations were accomplished in the clone C6/36 Aedes albopictus cell line, followed by indirect immunofluorescence assays, performed with type-specific monoclonal antibodies that showed positive reactions for DENV-4. The results were confirmed by Nested RT-PCR and Real-Time RT-PCR assays. The introduction of DENV-4 in a country that already has to deal with the transmission of three other serotypes increases the possibility of the occurrence of more severe cases of the disease. The importance of early detection of dengue cases, before the virus spreads and major outbreaks occur, should be emphasized.

scholarly journals A Novel Biomarker Test for Sugarcane Yellow Canopy Syndrome

Proceedings ◽  
2020 ◽  
Vol 36 (1) ◽  
pp. 156
Author(s):  
Kate Wathen-Dunn ◽  
Gerard Scalia ◽  
Annelie Marquardt ◽  
Frikkie Botha
Keyword(s):  
Polymerase Chain Reaction ◽  
Early Detection ◽  
Development Process ◽  
Management Practices ◽  
Rt Pcr ◽  
Chain Reaction ◽  
Leaf Yellowing ◽  
Polymerase Chain ◽  
Bioinformatic Approach ◽  
Pcr Test

Yellow canopy syndrome (YCS) is a condition that affects sugarcane crops throughout Queensland, and is most apparent in the warmer summer months when plants are actively growing. Key symptoms of YCS include a yellowing of the mid-canopy leaves, and the accumulation of sucrose and α-glucans in the lamina, midrib and sheath tissue. As no cause for the syndrome has yet been found, a biomarker test for identifying YCS, as distinct from other conditions that cause leaf yellowing, is important to enable early detection before any signs of visual yellowing. This will inform YCS management practices, and drive the research forward. We used an RNAseq and bioinformatic approach to identify six YCS-specific biomarker candidate plant transcripts that were uniquely and consistently up-regulated in YCS. We designed primers against these transcripts, and developed a novel reverse-transcriptase polymerase chain reaction (RT-PCR) test to identify sugarcane plants affected by YCS. The YCS biomarker test is showing early signs of success and is undergoing further validation, with the aim of correctly identifying YCS-affected sugarcane plants before symptoms become apparent. This poster outlines the biomarker candidate discovery and test development process.

scholarly journals Developing a Screening Procedure During the COVID-19 Pandemic: Process and Challenges Faced by a Low-Incidence Area

2021 ◽  
Vol 8 ◽  
Author(s):  
Wei Tang ◽  
Fei Wang ◽  
Jian-Wei Wang ◽  
Yao Huang ◽  
Li Liu ◽  
...  
Keyword(s):  
Ct Images ◽  
Ct Scans ◽  
Screening Procedure ◽  
Rt Pcr ◽  
Interval Time ◽  
Ct Findings ◽  
Low Incidence ◽  
Suspected Pneumonia ◽  
Pcr Test ◽  
Median Interval

Purpose: To summarize the imaging results of COVID-19 pneumonia and develop a computerized tomography (CT) screening procedure for patients at our institution with malignant tumors.Methods: Following epidemiological investigation, 1,429 patients preparing to undergo anti-tumor-treatment underwent CT scans between February 17 and April 16, 2020. When CT findings showed suspected COVID-19 pneumonia after the supervisor radiologist and the thoracic experience radiologist had double-read the initial CT images, radiologists would report the result to our hospital infection control staff. Further necessary examinations, including the RT-PCR test, in the assigned hospital was strongly recommended for patients with positive CT results. The CT examination room would perform sterilization for 30 min to 1 h. If the negative results of any suspected COVID-19 pneumonia CT findings were identified, the radiologists would upload the results to our Hospital Information Systems and inform clinicians within 2 h.Results: Fifty (0.35%, 50/1,429) suspected pneumonia cases, including 29 males and 21 females (median age: 59.5 years old; age range 27–79 years), were identified. A total of 34.0% (17/50) of the patients had a history of lung cancer and 54.0 (27/50) underwent chemotherapy or targeted therapy. Forty-six patients (92.0%) had prior CT scans, and 35 patients (76.1%) with suspected pneumonia were newly seen (median interval time: 62 days). Sub-pleura small patchy or strip-like lesions most likely due to fibrosis or hypostatic pneumonia and cluster of nodular lesions were the two main signs of suspected cases on CT images (34, 68.0%). Twenty-seven patients (54.0%) had, at least once, follow-up CT scan (median interval time: 18.0 days). Only one patient had an increase in size (interval time: 8 days), the immediately RT-PCR test result was negative.Conclusion: CT may be useful as a screening tool for COVID-19 based on imaging features. But the differential diagnosis between COVID-19 and other pulmonary infection and/or non-infectious disease is very difficult due to its overlapping imaging features.The confirmed diagnosis of the COVID-19 infection should be based on the etiologic eventually. The cancer patients at a low-incidence area would continue treatment by screening carefully before admission.

Enteric viruses in drinking water supplies

2002 ◽  
Vol 2 (3) ◽  
pp. 17-22
Author(s):  
A.P. Wyn-Jones ◽  
J. Watkins ◽  
C. Francis ◽  
M. Laverick ◽  
J. Sellwood
Keyword(s):  
Drinking Water ◽  
Heavy Rainfall ◽  
Liquid Culture ◽  
Plaque Assay ◽  
Enteric Viruses ◽  
Enteric Virus ◽  
The Other ◽  
Raw Water ◽  
Rt Pcr ◽  
Water Supplies

Two rural spring drinking water supplies were studied for their enteric virus levels. In one, serving about 30 dwellings, the water was chlorinated before distribution; in the other, which served a dairy and six dwellings the water was not treated. Samples of treated (40 l) and untreated (20 l) water were taken under normal and heavy rainfall conditions over a six weeks period and concentrated by adsorption/elution and organic flocculation. Infectious enterovirus in concentrates was detected in liquid culture and enumerated by plaque assay, both in BGM cells, and concentrates were also analysed by RT-PCR. Viruses were found in both raw water supplies. Rural supplies need to be analysed for viruses as well as bacterial and protozoan pathogens if the full microbial hazard is to be determined.

Breast Cancer Image Classification: A Review

2020 ◽  
Vol 16 ◽  
Author(s):  
Pooja Pathak ◽  
Anand Singh Jalal ◽  
Ritu Rai
Keyword(s):  
Breast Cancer ◽  
Early Detection ◽  
Treatment Options ◽  
Breast Cancer Diagnosis ◽  
Conventional Approach ◽  
Learning Networks ◽  
Cad System ◽  
Ultrasound Computed Tomography ◽  
Core Concepts ◽  
Magnetic Resonance Imaging Mri

Background: Breast cancer represents uncontrolled breast cell growth. Breast cancer is the most diagnosed cancer in women worldwide. Early detection of breast cancer improves the chances of survival and increases treatment options. There are various methods for screening breast cancer such as mammogram, ultrasound, computed tomography, Magnetic Resonance Imaging (MRI). MRI is gaining prominence as an alternative screening tool for early detection and breast cancer diagnosis. Nevertheless, MRI can hardly be examined without the use of a Computer-Aided Diagnosis (CAD) framework, due to the vast amount of data. Objective: This paper aims to cover the approaches used in CAD system for the detection of breast cancer. Method: In this paper, the methods used in CAD systems are categories in two classes: the conventional approach and artificial intelligence (AI) approach. The conventional approach covers the basic steps of image processing such as preprocessing, segmentation, feature extraction and classification. The AI approach covers the various convolutional and deep learning networks used for diagnosis. Conclusion: This review discusses some of the core concepts used in breast cancer and presents a comprehensive review of efforts in the past to address this problem.

scholarly journals Superiority of MALDI-TOF Mass Spectrometry over Real-Time PCR for SARS-CoV-2 RNA Detection

Viruses ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 730
Author(s):  
Magda Rybicka ◽  
Ewa Miłosz ◽  
Krzysztof Piotr Bielawski
Keyword(s):  
Mass Spectrometry ◽  
Gold Standard ◽  
Viral Genome ◽  
False Negative ◽  
Rt Pcr ◽  
Rna Detection ◽  
Maldi Tof ◽  
Negative Results ◽  
False Negative Results ◽  
Pcr Test

At present, the RT-PCR test remains the gold standard for early diagnosis of SARS-CoV-2. Nevertheless, there is growing evidence demonstrating that this technique may generate false-negative results. Here, we aimed to compare the new mass spectrometry-based assay MassARRAY® SARS-CoV-2 Panel with the RT-PCR diagnostic test approved for clinical use. The study group consisted of 168 suspected patients with symptoms of a respiratory infection. After simultaneous analysis by RT-PCR and mass spectrometry methods, we obtained discordant results for 17 samples (10.12%). Within fifteen samples officially reported as presumptive positive, 13 were positive according to the MS-based assay. Moreover, four samples reported by the officially approved RT-PCR as negative were positive in at least one MS assay. We have successfully demonstrated superior sensitivity of the MS-based assay in SARS-CoV-2 detection, showing that MALDI-TOF MS seems to be ideal for the detection as well as discrimination of mutations within the viral genome.

scholarly journals The role of CT imaging for management of COVID-19 in epidemic area: early experience from a University Hospital

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Vikram rao Bollineni ◽  
Koenraad Hans Nieboer ◽  
Seema Döring ◽  
Nico Buls ◽  
Johan de Mey
Keyword(s):  
Ct Scan ◽  
Predictive Value ◽  
Ct Imaging ◽  
Chest Ct ◽  
University Hospital ◽  
Rt Pcr ◽  
Ct Findings ◽  
Epidemic Area ◽  
Chest Ct Scan ◽  
Pcr Test

Abstract Background To evaluate the clinical value of the chest CT scan compared to the reference standard real-time polymerase chain reaction (RT-PCR) in COVID-19 patients. Methods From March 29th to April 15th of 2020, a total of 240 patients with respiratory distress underwent both a low-dose chest CT scan and RT-PCR tests. The performance of chest CT in diagnosing COVID-19 was assessed with reference to the RT-PCR result. Two board-certified radiologists (mean 24 years of experience chest CT), blinded for the RT-PCR result, reviewed all scans and decided positive or negative chest CT findings by consensus. Results Out of 240 patients, 60% (144/240) had positive RT-PCR results and 89% (213/240) had a positive chest CT scans. The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of chest CT in suggesting COVID-19 were 100% (95% CI: 97–100%, 144/240), 28% (95% CI: 19–38%, 27/240), 68% (95% CI: 65–70%) and 100%, respectively. The diagnostic accuracy of the chest CT suggesting COVID-19 was 71% (95% CI: 65–77%). Thirty-three patients with positive chest CT scan and negative RT-PCR test at baseline underwent repeat RT-PCR assay. In this subgroup, 21.2% (7/33) cases became RT-PCR positive. Conclusion Chest CT imaging has high sensitivity and high NPV for diagnosing COVID-19 and can be considered as an alternative primary screening tool for COVID-19 in epidemic areas. In addition, a negative RT-PCR test, but positive CT findings can still be suggestive of COVID-19 infection.

scholarly journals Delayed pulmonary embolism after COVID-19 pneumonia: a case report

2020 ◽  
Author(s):  
Mohamad Kanso ◽  
Thomas Cardi ◽  
Halim Marzak ◽  
Alexandre Schatz ◽  
Loïc Faucher ◽  
...  
Keyword(s):  
Risk Factors ◽  
Intensive Care Unit ◽  
Pulmonary Embolism ◽  
Acute Pulmonary Embolism ◽  
Index Hospitalization ◽  
Rt Pcr ◽  
Moderate Severity ◽  
Respiratory Status ◽  
Case Summary ◽  
Pcr Test

Abstract Background  Since the onset of the COVID-19 pandemic, several cardiovascular manifestations have been described. Among them, venous thromboembolism (VTE) seems to be one of the most frequent, particularly in intensive care unit patients. We report two cases of COVID-19 patients developing acute pulmonary embolism (PE) after discharge from a first hospitalization for pneumonia of moderate severity. Case summary  Two patients with positive RT-PCR test were initially hospitalized for non-severe COVID-19. Both received standard thromboprophylaxis during the index hospitalization and had no strong predisposing risk factors for VTE. Few days after discharge, they were both readmitted for worsening dyspnoea due to PE. One patient was positive for lupus anticoagulant. Discussion  Worsening respiratory status in COVID-19 patients must encourage physicians to search for PE since SARS-CoV-2 infection may act as a precipitant risk factor for VTE. Patients may thus require more aggressive and longer thromboprophylaxis after COVID-19 related hospitalization.

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