Usage of Conventional PCR Technology for the Detection of HLA-B27 Allele: A Significant Molecular Marker of Ankylosing Spondylitis

Ankylosing spondylitis is a chronic inflammatory disease that has been linked to the human leukocyte antigen class I allele HLA-B27. More than 90 % of patients with ankylosing spondylitis possess the HLA-B27 allele, but only 1 % of people with HLA-B27 develop the disease. Ankylosing spondylitis predominately affects young males. The present study was planned to find out the involvement of HLA-B27 specific allele in relation to age and sex in symptomatic suspected patients of ankylosing spondylitis using conventional PCR technology. Forty symptomatic patients of ankylosing spondylitis were included in the present study. SSP–PCR technique was used to detect the HLA-B27 specific allele. This study showed (out of 40 symptomatic suspected cases of ankylosing spondylitis only 12 patients were detected positive with HLA-B27 allele, while remaining 28 were negative) that the positivity rate for ankylosing spondylitis with HLA-B27 allele is low. Furthermore, it was observed that the males above 50 years are more prone to develop AS with HLA-B27 specific allele. It could be concluded that the conventional PCR technology is a rapid, sensitive, and confirmatory method for the diagnosis of ankylosing spondylitis.     

An undergraduate student flow model: Australian higher education

The probability of completing and the time to complete an undergraduate course are likely to vary by the age and sex of the student, and the field of study undertaken. In this paper a Markov chain is used to model the movement of undergraduates through the higher education system in Australia. Given the age of the student when they commence a course, this model provides estimates of the probability of them completing the course. It also provides estimates for the mean time a student takes to complete the course, and mean time they spend in the higher education system.     

A Pilot Study on COVID-19 Positive Subjects: An Excerpt of Post-Infection-Pro-Diabetic Disposition & Related Consequences in Correlation to Hepato-Pancreatic Bio-Markers,Pro-Inflammatory Cytokines and Other Risk Factors

COVID-19, a global pandemic that led to increased morbidity and mortality worldwide since its outcome at the end of the year 2019. A newly discovered variant of severe acute respiratory distress syndrome coronavirus-2 (SARS-CoV-2) was the arbitrator for spreading the syndrome by droplet transmission causing multi-organ failure in many occasions. A post-infection-pro-diabetic disposition was found evident in this study with the persistence of hepato-pancreatic aberrations in respect of reference range of tissue specific bio-markers in hospital admitted COVID-19 cases. The results of this study show that hyperglycemia is a risk factor in precipitating disease oriented complications to the patients with COVID-19 disease. A post-infection follow- up on glycemic-index and related complexities is a vital need to the COVID-19 infected convalescent subjects. Implementation of guidelines on social measure and awareness of anti-viral interventions may be the only way to prevent COVID-19 transmission.     

A Pilot Study on Blood Components in COVID-19 Affected Subjects: A Correlation to UPR Signalling and ER-Stress

AbstractThe endoplasmic reticulum (ER) is the site for protein synthesis, its folding and secretion. An intricate set of signalling pathways, called UPR pathways, have been evolved by ER in mammalian cells, to allow the cell to respond the presence of misfolded proteins within the ER. Breaching of these signalling systems by disease oriented accumulation of unfolded proteins may develop cellular stress. The aim of this study is to explore whether COVID-19 infection is responsible for developing this kind of endoplasmic reticulum related stress (ER-stress). ER-stress was evaluated by checking the expression of ER-stress markers e.g. PERK (adapting) and TRAF2 (alarming). ER-stress was correlated to several blood parameters viz. IgG, pro- and anti-inflammatory cytokines, leukocytes, lymphocytes, RBC, haemoglobin and PaO₂/FiO₂ ratio (ratio of arterial oxygen partial pressure to fractional inspired oxygen) in COVID-19 affected subjects. COVID-19 infection was found to be a state of protein homeostasis (proteostasis) collapse. Changes in IgG levels showed very poor immune response by the infected subjects. At the initial phase of the disease, pro-inflammatory cytokine levels were high and anti-inflammatory cytokines levels were low; though they were partly compromised at later phase of the disease. Total leukocyte concentration increased over the period of time; while percentage of lymphocytes were dropped. No significant changes were observed in cases of RBC counts and haemoglobin (Hb) levels. Both RBC and Hb were maintained at their normal range. In mildly stressed group, PaO₂/FiO₂ ratio (oxygenation status) was in the higher side of normal range; whereas in other two groups the ratio was in respiratory distress syndrome mode. Virus could induce mild to severe ER-stress, which could be the cause of cellular death and systemic dysfunction introducing fatal consequences.Graphical AbstractSchematic representation of SARS-CoV-2 infection and related consequences.     

Improved Breast Cancer Classification Through Combining Graph Convolutional Network and Convolutional Neural Network

AimIn a pilot study to improve detection of malignant lesions in breast mammograms, we aimed to develop a new method called BDR-CNN-GCN, combining two advanced neural networks: (i) graph convolutional network (GCN); and (ii) convolutional neural network (CNN).MethodWe utilised a standard 8-layer CNN, then integrated two improvement techniques: (i) batch normalization (BN) and (ii) dropout (DO). Finally, we utilized rank-based stochastic pooling (RSP) to substitute the traditional max pooling. This resulted in BDR-CNN, which is a combination of CNN, BN, DO, and RSP. This BDR-CNN was hybridized with a two-layer GCN, and yielded our BDR-CNN-GCN model which was then utilized for analysis of breast mammograms as a 14-way data augmentation method.ResultsAs proof of concept, we ran our BDR-CNN-GCN algorithm 10 times on the breast mini-MIAS dataset (containing 322 mammographic images), achieving a sensitivity of 96.20±2.90%, a specificity of 96.00±2.31% and an accuracy of 96.10±1.60%.ConclusionOur BDR-CNN-GCN showed improved performance compared to five proposed neural network models and 15 state-of-the-art breast cancer detection approaches, proving to be an effective method for data augmentation and improved detection of malignant breast masses.     

Evaluation of a Training Program in Aging Research for Social Work Faculty

Since 2004, we have offered a postgraduate training program in aging research for social work faculty from across the country. The overarching goal of the program is to expand the pool of social work faculty engaged in aging research. This, in turn, will reinvigorate participants' teaching; prepare them to update aging-related content in the curriculum, with special emphasis on cultural diversity and methodological issues; and enhance their ability to motivate new generations of students to pursue the study of aging. Program design includes an initial 8-day institute in Summer I; ongoing consultation, update meetings at several national conventions, and a 4-day midyear meeting during the intervening year; and a follow-up institute during Summer II. Between 2004 and 2009, seven cohorts of social work faculty (with 14–15 members in each cohort) participated in the program. More than 40 percent of the participants were from communities of color including African American, Hispanic American, Native American, and Asian American. This article reports the results of initial evaluation from two most recent cohorts (Cohort 6 and Cohort 7). In addition, it reports the results of a follow-up study we conducted in June 2010 on cohorts 1–5. Key lessons learned in offering the program are also shared.     

Investigating the Alignment of Bhutanese Mathematics Teachers’ Planned Approaches Within the Context of a Reformed Curriculum

Reform of mathematics education has been in focus in many countries including those in major economic transition. This paper reports a segment of a study which was conducted in Bhutan, where a reformed elementary mathematics curriculum has been recently introduced. The reformed curriculum is based on social constructivism and its design has been influenced by the USA’s National Council of Teachers of Mathematics (NCTM) standards. This paper reports upon the planned teaching of a sample of elementary (primary) teachers in their implementation of the reformed curriculum. The data for this part of the investigation were aggregated from a qualitative survey of randomly selected elementary school teachers (N = 72) from 40 schools across the country. A set of 3 analytical rubrics, based on intentions embedded in the curriculum, was developed to analyse the alignment of teachers’ planned sample learning activities with the intentions of the curriculum. Our conclusions are that while the curriculum’s intentions might be meaningfully stated in the curriculum documents, implementation remains problematic because there appears to be a mis-alignment between curriculum intentions and classroom practices. For instance, teachers by not adopting evidence-based approaches recommended in the curriculum document and are continuing to deny students opportunities to achieve deep understanding in achieving national priorities. This paper also argues that the proposed analytical rubrics may be of value for Bhutanese mathematics educators and, with minor modification, educators in other contexts, as they critique planned and actual practices.