Covid-19 Detection Based on Chest X-Ray Images Using DCT Compression and NN

Covid-19 is a highly contagious virus spreading all over the world. It is caused by SARS-CoV-2. virus. Some of the most common symptoms are fever, cough, sore throat, tiredness, and loss of smell or taste. There are two types of tests for COVID-19: the PCR test and the antigen test. Automatic detection of Covid-19 from publicly available resources is essential. This paper employs the commonly available chest x-ray (CXR) images in the classification of Covid-19, normal and viral pneumonia cases. The proposed method divides the CXR images into subblocks and computes the Discrete Cosine Transform (DCT) for every subblock. The DCT energy compaction capability is employed to produce a compressed version for each CXR image. Few spectral DCT components are incorporated as features for each image. The compressed images are scanned by average pooling windows to reduce the dimension of the final feature vectors. A multilayer artificial neural network is employed in the 3-set classification. The proposed method achieved an average accuracy of 95 %. While the proposed method achieves comparable accuracy relative to recent state-of-the-art techniques, its computational burden and implementation time is much less

Fast COVID-19 Detection from Chest X-Ray Images Using DCT Compression

Novel coronavirus (COVID-19) is a new strain of coronavirus, first identified in a cluster with pneumonia symptoms caused by SARS-CoV-2 virus. It is fast spreading all over the world. Most infected people will develop mild to moderate illness and recover without hospitalization. Currently, real-time quantitative reverse transcription-PCR (rqRT-PCR) is popular for coronavirus detection due to its high specificity, simple quantitative analysis, and higher sensitivity than conventional RT-PCR. Antigen tests are also commonly used. It is very essential for the automatic detection of COVID-19 from publicly available resources. Chest X-ray (CXR) images are used for the classification of COVID-19, normal, and viral pneumonia cases. The CXR images are divided into sub-blocks for finding out the discrete cosine transform (DCT) for every sub-block in this proposed method. In order to produce a compressed version for each CXR image, the DCT energy compaction capability is used. For each image, hardly few spectral DCT components are included as features. The dimension of the final feature vectors is reduced by scanning the compressed images using average pooling windows. In the 3-set classification, a multilayer artificial neural network is used. It is essential to triage non-COVID-19 patients with pneumonia to give out hospital resources efficiently. Higher size feature vectors are used for designing binary classification for COVID-19 and pneumonia. The proposed method achieved an average accuracy of 95% and 94% for the 3-set classification and binary classification, respectively. The proposed method achieves better accuracy than that of the recent state-of-the-art techniques. Also, the time required for the implementation is less

Not All Neonatal Encephalopathies Are due to Perinatal Hypoxia

A late preterm female neonate, born to a consanguineously married couple by normal vaginal delivery and unremarkable family history, was admitted to our NICU soon after birth for management of respiratory distress secondary to meconium aspiration syndrome and persistent pulmonary hypertension of newborn. She required intensive ventilatory and hemodynamic support and was encephalopathic since birth but did not fulfill the criteria for therapeutic hypothermia. Extensive metabolic workup revealed no diagnosis, but neuroimaging showed characteristic findings consistent with the diagnosis of leukoencephalopathy with brainstem and spinal cord involvement and lactate elevation. This was supported by the result of whole exome sequence that identified a novel homozygous mutation, c.1191 + 11A>C, for DARS2 gene confirming the diagnosis

The AGE-RAGE axis in an Arab population: The United Arab Emirates Healthy Futures (UAEHFS) pilot study

© 2017 The Authors Aims The transformation of the United Arab Emirates (UAE) from a semi-nomadic to a high income society has been accompanied by increasing rates of obesity and Type 2 diabetes mellitus. We examined if the AGE-RAGE (receptor for advanced glycation endproducts) axis is associated with obesity and diabetes mellitus in the pilot phase of the UAE Healthy Futures Study (UAEHFS). Methods 517 Emirati subjects were enrolled and plasma/serum levels of AGE, carboxy methyl lysine (CML)-AGE, soluble (s)RAGE and endogenous secretory (es)RAGE were measured along with weight, height, waist and hip circumference (WC/HC), blood pressure, HbA1c, Vitamin D levels and routine chemistries. The relationship between the AGE-RAGE axis and obesity and diabetes mellitus was tested using proportional odds models and linear regression. Results After covariate adjustment, AGE levels were significantly associated with diabetes status. Levels of sRAGE and esRAGE were associated with BMI and levels of sRAGE were associated with WC/HC. Conclusions The AGE-RAGE axis is associated with diabetes status and obesity in this Arab population. Prospective serial analysis of this axis may identify predictive biomarkers of obesity and cardiometabolic dysfunction in the UAEHFS

Characterization of greater middle eastern genetic variation for enhanced disease gene discovery

The Greater Middle East (GME) has been a central hub of human migration and population admixture. The tradition of consanguinity, variably practiced in the Persian Gulf region, North Africa, and Central Asia1-3, has resulted in an elevated burden of recessive disease4. Here we generated a whole-exome GME variome from 1,111 unrelated subjects. We detected substantial diversity and admixture in continental and subregional populations, corresponding to several ancient founder populations with little evidence of bottlenecks. Measured consanguinity rates were an order of magnitude above those in other sampled populations, and the GME population exhibited an increased burden of runs of homozygosity (ROHs) but showed no evidence for reduced burden of deleterious variation due to classically theorized ‘genetic purging’. Applying this database to unsolved recessive conditions in the GME population reduced the number of potential disease-causing variants by four- to sevenfold. These results show variegated genetic architecture in GME populations and support future human genetic discoveries in Mendelian and population genetics

Novel PDE6A mutation in an Emirati patient with retinitis pigmentosa

Mutations in the PDE6A gene are known to cause a form of retinitis pigmentosa (RP43), characterized by progressive retinal degeneration. We describe an Emirati patient with RP caused by a novel mutation in PDE6A. Clinical diagnosis of RP was made based on clinical evaluation and electroretinograms. The molecular analysis involved performing whole-exome sequencing, which enabled the identification of a homozygous 2-bp deletion (c.1358_1359delAT) in PDE6A, which was predicted to result in a frameshift and premature termination (p.Ile452Serfs*7). The mutation completely removed the catalytic PDEase domain in the protein. The parents were found to be heterozygous carriers of the variant. We thus report the first known case of a pathological variant in the PDE6A gene from the Arabian Peninsula

Identification of a novel CTCF mutation responsible for syndromic intellectual disability – a case report

Abstract Background Autosomal dominant mental retardation 21 (MRD21) is a very rare condition, characterized by short stature, microcephaly, mild facial dysmorphisms and intellectual disability that ranged from mild to severe. MRD21 is caused by mutations in CCCTC-binding factor (CTCF) and this was established through only four unrelated cases, two of which had frameshift mutations. CTCF is a master transcriptional regulator that controls chromatin structure and may serve as insulator and transcriptional activator and repressor. Case presentation This study presents, clinically and molecularly, an Emirati patient with de novo frameshift mutation in CTCF. This novel mutation was uncovered using whole exome sequencing and was confirmed by Sanger sequencing in the trio. In silico analysis, using SIFT Indel, indicates that this frameshift; p.Lys206Profs*13 is functionally damaging with the likely involvement of nonsense-mediated mRNA decay. Conclusions Upon comparing the clinical picture of the herewith-reported individual with previously reported cases of MRD21, there seems to be many common symptoms, and few new ones that were not observed before. This helps to further define this rare condition and its molecular underpinnings

Additional file 1: of A novel, putatively null, FGD1 variant leading to Aarskog-Scott syndrome in a family from UAE

Methods. (DOCX 14 kb