Determining the SARS-CoV-2 serological immunoassay test performance indices based on the test results frequency distribution

Coronavirus disease 2019 (COVID-19) is known to induce robust antibody response in most of the affected individuals. The objective of the study was to determine if we can harvest the test sensitivity and specificity of a commercial serologic immunoassay merely based on the frequency distribution of the SARS-CoV-2 immunoglobulin (Ig) G concentrations measured in a population-based seroprevalence study. The current study was conducted on a subset of a previously published dataset from the canton of Geneva. Data were taken from two non-consecutive weeks (774 samples from May 4-9, and 658 from June 1-6, 2020). Assuming that the frequency distribution of the measured SARS-CoV-2 IgG is binormal (an educated guess), using a non-linear regression, we decomposed the distribution into its two Gaussian components. Based on the obtained regression coefficients, we calculated the prevalence of SARS-CoV-2 infection, the sensitivity and specificity, and the most appropriate cut-off value for the test. The obtained results were compared with those obtained from a validity study and a seroprevalence population-based study. The model could predict more than 90% of the variance observed in the SARS-CoV-2 IgG distribution. The results derived from our model were in good agreement with the results obtained from the seroprevalence and validity studies. Altogether 138 of 1432 people had SARS-CoV-2 IgG ≥ 0.90, the cut-off value which maximized the Youden’s index. This translates into a true prevalence of 7.0% (95% confidence interval 5.4% to 8.6%), which is in keeping with the estimated prevalence of 7.7% derived from our model. Our model can provide the true prevalence. Having an educated guess about the distribution of test results, the test performance indices can be derived with acceptable accuracy merely based on the test results frequency distribution without the need for conducting a validity study and comparing the test results against a gold-standard test

Illicit methylphenidate use among Iranian medical students: prevalence and knowledge

Afshin Habibzadeh1 Mahasti Alizadeh2 Ayoub Malek3 Leili Maghbooli1 Mohammadali M Shoja4 Kamyar Ghabili41Students' Research Committee, 2Department of Community Medicine, 3Department of Psychiatry, 4Tuberculosis and Lung Disease Research Center, Tabriz University of Medical Sciences, Tabriz, IranBackground: Methylphenidate, a medication prescribed for individuals suffering from attention-deficit/hyperactivity disorder, is increasingly being misused by students.Objective: The aims of this study were to evaluate the frequency of methylphenidate use among a group of Iranian medical students and to assess their knowledge of methylphenidate.Methods: Anonymous, self-administered questionnaires were completed by all medical students entering the university between 2000 and 2007.Results: Methylphenidate users’ mean knowledge score was higher than that of nonusers (15.83 ± 3.14 vs 13.66 ± 3.10, P = 0.008). Age, gender, and school year were positively correlated with knowledge score (P < 0.05). Data analysis demonstrated that 27 participants (8.7%) had taken methylphenidate at least once in their lifetime. The respondents believed that the most common motive for methylphenidate use among youths was that it aided concentration and therefore ability to study.Conclusion: This study indicates a relatively low level of knowledge about methylphenidate among Iranian medical students. More educational programs regarding the use of methylphenidate are required and should be focused on the student suppliers, clinicians, pharmacists, and medical students.Keywords: methylphenidate, medical student, prevalence, Ira

Designing Tuneable Narrowband Bandpass Filter Utilizing Neural Network

Abstract: In this study we aim at adjusting the singleband and dualband bandpass filter designed in a ED02AH technology. The quality factor and center frequency of the filter will change by varactor diodes. Here, we use a neural network to acquire the proper biasing voltages of varactor diodes in order to obtain specific gain and quality factor

Counting of RBCs and WBCs in noisy normal blood smear microscopic images

This work focuses on the segmentation and counting of peripheral blood smear particles which plays a vital role in medical diagnosis. Our approach profits from some powerful processing techniques. Firstly, the method used for denoising a blood smear image is based on the Bivariate wavelet. Secondly, image edge preservation uses the Kuwahara filter. Thirdly, a new binarization technique is introduced by merging the Otsu and Niblack methods. We have also proposed an efficient step-by-step procedure to determine solid binary objects by merging modified binary, edged images and modified Chan-Vese active contours. The separation of White Blood Cells (WBCs) from Red Blood Cells (RBCs) into two sub-images based on the RBC (blood’s dominant particle) size estimation is a critical step. Using Granulometry, we get an approximation of the RBC size. The proposed separation algorithm is an iterative mechanism which is based on morphological theory, saturation amount and RBC size. A primary aim of this work is to introduce an accurate mechanism for counting blood smear particles. This is accomplished by using the Immersion Watershed algorithm which counts red and white blood cells separately. To evaluate the capability of the proposed framework,experiments were conducted on normal blood smear images. This framework was compared to other published approaches and found to have lower complexity and better performance in its constituent steps; hence, it has a better overall performance

A novel mutation in SEPN1 causing rigid spine muscular dystrophy 1: A Case report

Abstract Background Muscular dystrophies are a clinically and genetically heterogeneous group of disorders characterized by variable degrees of progressive muscle degeneration and weakness. There is a wide variability in the age of onset, symptoms and rate of progression in subtypes of these disorders. Herein, we present the results of our study conducted to identify the pathogenic genetic variation involved in our patient affected by rigid spine muscular dystrophy. Case presentation A 14-year-old boy, product of a first-cousin marriage, was enrolled in our study with failure to thrive, fatigue, muscular dystrophy, generalized muscular atrophy, kyphoscoliosis, and flexion contracture of the knees and elbows. Whole-exome sequencing (WES) was carried out on the DNA of the patient to investigate all coding regions and uncovered a novel, homozygous missense mutation in SEPN1 gene (c. 1379 C > T, p.Ser460Phe). This mutation has not been reported before in different public variant databases and also our database (BayanGene), so it is classified as a variation of unknown significance (VUS). Subsequently, it was confirmed that the novel variation was homozygous in our patient and heterozygous in his parents. Different bioinformatics tools showed the damaging effects of the variant on protein. Multiple sequence alignment using BLASTP on ExPASy and WebLogo, revealed the conservation of the mutated residue. Conclusion We reported a novel homozygous mutation in SEPN1 gene that expands our understanding of rigid spine muscular dystrophy. Although bioinformatics analyses of results were in favor of the pathogenicity of the mutation, functional studies are needed to establish the pathogenicity of the variant