Novel Variants in MPV17, PRX, GJB1, and SACS Cause Charcot-Marie-Tooth and Spastic Ataxia of Charlevoix-Saguenay Type Diseases.

Charcot-Marie-Tooth disease (CMT) and autosomal recessive spastic ataxia of Charlevoix-Saguenay type (ARSACS) are large heterogeneous groups of sensory, neurological genetic disorders characterized by sensory neuropathies, muscular atrophies, abnormal sensory conduction velocities, and ataxia. CMT2EE (OMIM: 618400) is caused by mutations in MPV17 (OMIM: 137960), CMT4F (OMIM: 614895) is caused by PRX (OMIM: 605725), CMTX1 (OMIM: 302800) is caused by mutations in GJB1 (OMIM: 304040), and ARSACS (OMIM: 270550) is caused by mutations in SACS (OMIM: 604490). In this study, we enrolled four families: DG-01, BD-06, MR-01, and ICP-RD11, with 16 affected individuals, for clinical and molecular diagnoses. One patient from each family was analyzed for whole exome sequencing and Sanger sequencing was done for the rest of the family members. Affected individuals of families BD-06 and MR-01 show complete CMT phenotypes and family ICP-RD11 shows ARSACS type. Family DG-01 shows complete phenotypes for both CMT and ARSACS types. The affected individuals have walking difficulties, ataxia, distal limb weakness, axonal sensorimotor neuropathies, delayed motor development, pes cavus, and speech articulations with minor variations. The WES analysis in an indexed patient of family DG-01 identified two novel variants: c.83G>T (p.Gly28Val) in MPV17 and c.4934G>C (p.Arg1645Pro) in SACS. In family ICP-RD11, a recurrent mutation that causes ARSACS, c.262C>T (p.Arg88Ter) in SACS, was identified. Another novel variant, c.231C>A (p.Arg77Ter) in PRX, which causes CMT4F, was identified in family BD-06. In family MR-01, a hemizygous missense variant c.61G>C (p.Gly21Arg) in GJB1 was identified in the indexed patient. To the best of our knowledge, there are very few reports on MPV17, SACS, PRX, and GJB1 causing CMT and ARSACS phenotypes in the Pakistani population. Our study cohort suggests that whole exome sequencing can be a useful tool in diagnosing complex multigenic and phenotypically overlapping genetic disorders such as Charcot-Marie-Tooth disease (CMT) and spastic ataxia of Charlevoix-Saguenay type.

Automated COVID-19 Detection from Chest X-Ray Images : A High Resolution Network (HRNet)Approach

The pandemic, originated by novel coronavirus 2019 (COVID-19), continuing its devastating effect on the health, well-being, and economy of the global population. A critical step to restrain this pandemic is the early detection of COVID-19 in the human body, to constraint the exposure and control the spread of the virus. Chest X-Rays are one of the non-invasive tools to detect this disease as the manual PCR diagnosis process is quite tedious and time-consuming. In this work, we propose an automated COVID-19 classifier, utilizing available COVID and non-COVID X-Ray datasets, along with High Resolution Network (HRNet) for feature extraction embedding with the UNet for segmentation purposes. To evaluate the proposed dataset, several baseline experiments have been performed employing numerous deep learning architectures. With extensive experiment, we got 99.26% accuracy, 98.53% sensitivity, and 98.82% specificity with HRNet which surpasses the performances of the existing models. Our proposed methodology ensures unbiased high accuracy, which increases the probability of incorporating X-Ray images into the diagnosis of the disease.