Convalescent-plasma-transfusion intelligent framework for rescuing COVID-19 patients across centralised/decentralised telemedicine hospitals based on AHP-group TOPSIS and matching component.

As coronavirus disease 2019 (COVID-19) spreads across the world, the transfusion of efficient convalescent plasma (CP) to the most critical patients can be the primary approach to preventing the virus spread and treating the disease, and this strategy is considered as an intelligent computing concern. In providing an automated intelligent computing solution to select the appropriate CP for the most critical patients with COVID-19, two challenges aspects are bound to be faced: (1) distributed hospital management aspects (including scalability and management issues for prioritising COVID-19 patients and donors simultaneously), and (2) technical aspects (including the lack of COVID-19 dataset availability of patients and donors and an accurate matching process amongst them considering all blood types). Based on previous reports, no study has provided a solution for CP-transfusion-rescue intelligent framework during this pandemic that has addressed said challenges and issues. This study aimed to propose a novel CP-transfusion intelligent framework for rescuing COVID-19 patients across centralised/decentralised telemedicine hospitals based on the matching component process to provide an efficient CP from eligible donors to the most critical patients using multicriteria decision-making (MCDM) methods. A dataset, including COVID-19 patients/donors that have met the important criteria in the virology field, must be augmented to improve the developed framework. Four consecutive phases conclude the methodology. In the first phase, a new COVID-19 dataset is generated on the basis of medical-reference ranges by specialised experts in the virology field. The simulation data are classified into 80 patients and 80 donors on the basis of the five biomarker criteria with four blood types (i.e., A, B, AB, and O) and produced for COVID-19 case study. In the second phase, the identification scenario of patient/donor distributions across four centralised/decentralised telemedicine hospitals is identified 'as a proof of concept'. In the third phase, three stages are conducted to develop a CP-transfusion-rescue framework. In the first stage, two decision matrices are adopted and developed on the basis of the five 'serological/protein biomarker' criteria for the prioritisation of patient/donor lists. In the second stage, MCDM techniques are analysed to adopt individual and group decision making based on integrated AHP-TOPSIS as suitable methods. In the third stage, the intelligent matching components amongst patients/donors are developed on the basis of four distinct rules. In the final phase, the guideline of the objective validation steps is reported. The intelligent framework implies the benefits and strength weights of biomarker criteria to the priority configuration results and can obtain efficient CPs for the most critical patients. The execution of matching components possesses the scalability and balancing presentation within centralised/decentralised hospitals. The objective validation results indicate that the ranking is valid.

Role of biological Data Mining and Machine Learning Techniques in Detecting and Diagnosing the Novel Coronavirus (COVID-19): A Systematic Review.

Coronaviruses (CoVs) are a large family of viruses that are common in many animal species, including camels, cattle, cats and bats. Animal CoVs, such as Middle East respiratory syndrome-CoV, severe acute respiratory syndrome (SARS)-CoV, and the new virus named SARS-CoV-2, rarely infect and spread among humans. On January 30, 2020, the International Health Regulations Emergency Committee of the World Health Organisation declared the outbreak of the resulting disease from this new CoV called 'COVID-19', as a 'public health emergency of international concern'. This global pandemic has affected almost the whole planet and caused the death of more than 315,131 patients as of the date of this article. In this context, publishers, journals and researchers are urged to research different domains and stop the spread of this deadly virus. The increasing interest in developing artificial intelligence (AI) applications has addressed several medical problems. However, such applications remain insufficient given the high potential threat posed by this virus to global public health. This systematic review addresses automated AI applications based on data mining and machine learning (ML) algorithms for detecting and diagnosing COVID-19. We aimed to obtain an overview of this critical virus, address the limitations of utilising data mining and ML algorithms, and provide the health sector with the benefits of this technique. We used five databases, namely, IEEE Xplore, Web of Science, PubMed, ScienceDirect and Scopus and performed three sequences of search queries between 2010 and 2020. Accurate exclusion criteria and selection strategy were applied to screen the obtained 1305 articles. Only eight articles were fully evaluated and included in this review, and this number only emphasised the insufficiency of research in this important area. After analysing all included studies, the results were distributed following the year of publication and the commonly used data mining and ML algorithms. The results found in all papers were discussed to find the gaps in all reviewed papers. Characteristics, such as motivations, challenges, limitations, recommendations, case studies, and features and classes used, were analysed in detail. This study reviewed the state-of-the-art techniques for CoV prediction algorithms based on data mining and ML assessment. The reliability and acceptability of extracted information and datasets from implemented technologies in the literature were considered. Findings showed that researchers must proceed with insights they gain, focus on identifying solutions for CoV problems, and introduce new improvements. The growing emphasis on data mining and ML techniques in medical fields can provide the right environment for change and improvement.

Comparison of continuous glucose monitoring in adolescents with type 1 diabetes: Ramadan versus non-Ramadan.

AIM: To assess the impact of fasting on interstitial glucose (IG) in adolescents with type 1 DM (T1DM) by using continuous glucose monitoring (CGM). METHOD: A minimum of 2.5 days CGM was done on adolescents with T1DM during fasting in Ramadan and in the month before or after Ramadan to compare the differences in mean IG, and in the durations of hypoglycemia (<70 mg/dL), hyperglycemia (200-299 mg/dL), and severe hyperglycemia (≥300 mg/dL). RESULTS: Fourteen adolescents were studied, age 15 ±â€¯4 years, duration of diabetes 6 ±â€¯4 years, and HbA1C 8.6 ±â€¯1.1% (70.3 mmol/mol). There was no difference in the mean IG (190 ±â€¯39 and 180 ±â€¯37, p= 0.4), or in the durations of hypoglycemia (5.14 ±â€¯5% and 7.03 ±â€¯4.9%, p=0.3), hyperglycemia (25.35 ±â€¯11.3% and 24.24 ±â€¯10.1% (P=0.7)), and severe hyperglycemia (13.21 ±â€¯13.4% and 10.96 ±â€¯10.6%, P=0.6), between Ramadan and, non-Ramadan, respectively. CONCLUSION: Adolescents with T1DM have the same wide fluctuation in IG during fasting in Ramadan as they do outside Ramadan. Insulin regimen adjustment should be targeting both extremes of glucose abnormality.

A Newly-Discovered Mutation in the RFX6 Gene of the Rare Mitchell-Riley Syndrome.

Mitchell-Riley syndrome is a genetic disorder characterized by neonatal diabetes, pancreatic hypoplasia, intestinal atresia and/or malrotation, biliary atresia, and gallbladder aplasia or hypoplasia. It was considered a variant of the Martinez-Frias syndrome with similar phenotypic characteristics, except for neonatal diabetes and tracheoesophageal fistula. However, the genetic mutation in (regulatory factor X on chromosome 6) RFX6 was only detected in babies who had diabetes, making it different from the previously known mutations for the disease. This is the first reported case of a classical Mitchell-Riley syndrome in the Arab peninsula along with additional features and novel mutations in the RFX6 gene.

Genetic characteristics, clinical spectrum, and incidence of neonatal diabetes in the Emirate of AbuDhabi, United Arab Emirates.

Neonatal diabetes mellitus (NDM) can be transient (TNDM) or permanent (PNDM). Data on NDM from the Gulf region are limited to few studies on PNDM.The objective of this study was to describe the genetic and clinical spectrum of NDM and estimate its incidence in AbuDhabi, capital of the United Arab Emirate (UAE). Patients were identified from the pediatric diabetes clinics and sequencing of known NDM genes was conducted in all families. Twenty-five patients were identified. Incidence during 1985-2013 was 1:29,241 Live births. Twenty-three out of twenty-five had PNDM (incidence 1:31,900) and 2/25 had TNDM (incidence 1:350,903). Eleven out of twenty-five had extra-pancreatic features and three had pancreatic aplasia. The genetic cause was detected in 21/25 (84%). Of the PNDM patients, nine had recessive EIF2AK3 mutations, six had homozygous INS mutations, two with deletion of the PTF1A enhancer, one was heterozygous for KCNJ11 mutation, one harboured a novel ABCC8 variant, and 4/21 without mutations in all known PNDM genes. One TNDM patient had a 6q24 methylation defect and another was homozygous for the INS c-331C>G mutation. This mutation also caused permanent diabetes with variable age of onset from birth to 18 years. The parents of a child with Wolcott-Rallison syndrome had a healthy girl following pre-implantation genetic diagnosis. The child with KCNJ11 mutation was successfully switched from insulin to oral sulphonylurea. The incidence of PNDM in Abu Dhabi is among the highest in the world and its spectrum is different from Europe and USA. In our cohort, genetic testing has significant implications for the clinical management.

Prevalence of autoantibodies in type 1 diabetes patients and its association with the clinical presentation - UAE Eastern Region experience.

BACKGROUND: Positive autoantibodies and its association with the clinical course of type 1 diabetes mellitus (T1DM) have been reported worldwide, however, no such data have been reported in United Arab Emirates population. OBJECTIVES: To study the prevalence of positive autoantibodies in T1DM pediatric patients and its association with the clinical presentation. METHODS: Descriptive retrospective chart review of all new cases of pediatric T1DM at Tawam Hospital. Electronic patient records accessed to obtain data. RESULTS: 61 patients were identified. 88%±8.1 had at least 1 positive antibody and 82% of all patients were positive for anti-glutamic acid decarboxylase (GAD). While comparing the group of any positive antibody (n=54) with the group of all negative antibodies (n=7), a significant difference was found in the mean HbA1C (p=0.02) and nationality (p=0.03). CONCLUSION: The vast majority of our T1DM pediatric patients are autoantibody positive, and anti-GAD antibodies were the most commonly detected antibodies.