Diabetes is one of the top non-communicable illnesses of public health importance. 6.4% of world population is reported to have diabetes mellitus with the figure, feared to double by 2040. Nigeria has estimated 1.6 million cases in 2015, which ranked her the third diabetes endemic country in Africa. Diabetes complication is a known leading cause of the disease high mortality rates and healthcare burden on developing economies. Daily monitoring of blood sugar levels, however, is an effective way to prevent diabetes complications. Glucometer is a simple device used for blood glucose monitoring. The aim of this study was to design and use locally sourced materials to fabricate a cheap digital glucometer that will be able to measure blood glucose levels and sends a distress signal to medics and caregivers via wireless transmission.The method employed includes programming, calibration, assembling, component testing, and the overall device test. The device is equipped with an Atmega32 microcontroller, One-Touch Ultra glucose sensor, a GSM module, emergency button, and internet of things (IoT). Our device and a conventional device (Accuchek) were used to measure the blood sugar level of 200 individuals in groups of 5, the data were analyzed using SPSS2.0 and Microsoft Excel 2010.The p-value was (>0.05) indicating no significant difference between the data generated by our device and the conventional glucometer, while the coefficient of determination (R2) was 0.9838. From the results above, we conclude that our fabricatrd device is effective and reliable.
Telomeres cap ends of eukaryotic chromosomes prevent them from degradation and ensure genomic stability. Cdc13 is an essential telomere recruitment and maintenance protein. A temperature-sensitive point mutation in cdc13 gene leads to telomere impairment, giving rise to cdc13-1 mutants that suffer lethality at enhanced temperatures. Deleting Exo1 gene from these mutants, however, leads to the emergence of temperature-tolerant mutants called survivors. Yeasts are known to exist as either diploids or haploids. These yeast genotypes generate survivors. The frequency of survivorship in the haploid genotype is one cell in 104 cells/generation at 36˚C, however, the frequency at which they emerge in their diploid counterparts at the same temperature is not known. In this study, we investigated the frequency of Survivorship in heterozygous diploids of cdc13-1exo1Δ mutants of S. cerevisiae at 36˚C. Diploids were constructed by mating haploid strains of opposite mating type cdc13-1 exo1:LEU strains with strains of cdc13-1 exo1:HIS. The crosses were 1296 × 3181, 2561 × 3182, 1296 × 3182 and 2561 × 3181. Genetic markers and phenotypic appearance were considered while mating the mutant cells. Using a stick, a smear of one haploid strain was made on each YEPD plate labelled C2, C8, C9, D1, D14, and D15. A smear of another opposite mating type was made on the previous strain. They were mixed and allowed to mate overnight, before culturing on media lacking Luecine and Histidine (-L and -H).
Background: Telomeric DNA is found at the end of eukaryotic chromosomes, where they play a role in protecting the chromosome and the integrity of the genome of the organism through the activity of telomerase. Saccharomyces cerevisiae exists in two genotypes: haploid and diploid. Temperature sensitive point mutation on the cdc13 gene of each genotype and deletion of exo1 gene (cdc13-1Exo1 mutants) give rise to mutant survivors at enhanced temperatures. The mode of inheritance of the thermal tolerance allele in the heterozygous diploid genotype is not known. Materials and Methods: We constructed diploids by mating temperature sensitive haploid strains of opposite mating type cdc13-1 exo1: LEU with temperature resistant strains of cdc13-1 exo1::HIS. The crosses were 1296x3182 (D) and 2561x3181 (C). Using a sterile stick, smear of one haploid strain was made on each YEPD plates labelled C2, C8, C10, D4, D10, and D113. A smear of another opposite mating type was made on the previous strain. They were mixed and allowed to mate for six hours, before culturing on media lacking Luecine and Histidine (–L and –H) to purify and confirm that they are diploids. After confirmation, a loop full aliquot of the diploids were streaked on sterile media lacking leucine and histidine (-L, -H) and on YEPD and cultured at 370C to check thermal tolerance and number of viable colonies from each diploid crosses in (cfu). Result: The heterozygous diploid D thrived at the enhanced temperature of 370C and there is a significant difference in the yield of viable colonies by the D diploids when compared to the yield of the C diploids with P-value of 0.05. Conclusion: The growth of diploid D10 as shown in plate 3.1 establishes that, temperature resistant allele inherited by cdc13-1 exo1heterozygous diploids is a dominant phenotype, and its mode of inheritance is dominant as the heterozygous diploid thrived at the enhanced temperature of 370C.
Machine learning (ML) is a subfield of AI that uses statistical algorithms. Cardiac Arrest or heart failure has been implicated as one of the leading causes of death. The limited accuracy and the inherent invasiveness in diagnosis of this disease call for a revamp of the existing diagnostic protocol. In this study, we developed Machine learning (ML) algorithms for the prediction of cardiac arrest. Our protocol employs different methods for classification of the HD dataset using univariate and Bivariate analysis for prediction of cardiac arrest on input data which contains 11 features such as ChestPainType, age, gender etc and Pair plot to check the distribution of each variable and how it correlated with the target variable (Cardiac Arrest). Our result indicated that the ASY pain type was the highest ChestPainType that had cardiac arrest with 54% while NAP had 22%, ATA had 19% and TA 5%. The male genders were also observed to have the highest rate of cardiac arrest when compared to the female genders. Our protocol was able to predict the occurrence of cardiac arrest and at the same time recommend possible treatments, medication and exercises regime to the patient via the web application interface.
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