Successful pregnancy following trans-myometrial embryo transfer using oocyte aspiration needle

The climax of the process of in-vitro fertilization (IVF) is the stage of embryo transfer. Difficulty in embryo transfer is recognised as one of the factors militating against successful outcome. The client was a 38 year old business woman who required in-vitro fertilization on account of tubal factor and male factor infertility (poor motility). Routine mock embryo transfer indicated severe cervical stenosis which was confirmed at hysteroscopy. She subsequently had trans-myometrial embryo transfer. Blood pregnancy test at 2 weeks was positive and interval transvaginal ultrasound confirmed 2 viable intrauterine foetal poles

Effect of delayed sterilization on the production of intravenous fluids (parenterals)

The effect of delayed sterilization on the production of intravenous fluids using 5% (w/v) dextrose solution was studied. The 5% (w/v) dextrose was prepared with distilled water. The solution was inoculated with a  laboratory isolate of Escherichia coli and thereafter divided into two: Solutions A and B. Sample A was  sterilized within 1 h of innoculation, while B was incubated at 37‹C for 48 h and sterilized after. The microbial  load was determined before sterilization. Both samples were then tested for pyrogenicity using rabbit test of  pyrogen. The results revealed a significant (P < 0.05) increase in E. coli count in solution B from 8 } 2 to 99 } 5 cfu/ml after 48 h. Sample A became pyrogen-free after sterilization (sum of response (SR) of 3 rabbits,  0.75‹C), while sample B contained 40 EU/ml (limit: . 0.5 EU/ml for large volume parenteral). A 10 times dilution of sample B with pyrogen-free 0.9% w/v sodium chloride produced a response of 2.80‹C and SR of 3 rabbits (failed limit . 2.65‹C). From this study, a delay of up to 48 h before sterilizing solutions intended for parenteral use could produce high pyrogenicity.Key words: Parenterals, pyrogen, Escherichia coli, sterilization, microbial load

Genetic linkage map of cowpea (Vigna unguiculata (L.) Walp) using SNP markers

Genetic linkage maps provide a genomic framework for quantitative trait loci identification applied in marker assisted selection breeding in crops with limited resources. It serves as a powerful tool to breeders for analysing the mode of inheritance of genes of interest and monitoring of the transmission of target genes from parents to progeny. Single nucleotide polymorphism (SNP) markers have a great influence on mapping research studies: for faster development of new varieties of crops due to its abundance and high number of polymorphism. Thus, this research work aimed at constructing genetic linkage map of cowpea using SNP markers. This will facilitate development of new varieties of cowpea. Single seed descent method of breeding was used to generate mapping population between IT-95K-193- 12 and Ife-Brown. Fresh leaves from both parental lines and F1 hybrids were genotyped with 135 SNP markers. Data from125 polymorphic SNP markers were used to construct linkage map. Results revealed 12 linkage maps with an average of 10 markers to a chromosome and average marker distance of 3.02 cM between the markers. Constructed map provides basic information that could assist in genetic improvement of cowpea, most especially in developing brown blotch resistant cowpea varieties through detection of quantitative trait loci (QTL) responsible for brown blotch resistance in cowpea when utilizing marker assisted selection method.Key words: Cowpea, genetic linkage map, single nucleotide polymorphism (SNP) markers, brown blotch resistance

A Novel Multimodal Biometric Authentication System Using Machine Learning and Blockchain

Secure user authentication has become an important issue in modern society as in many consumer applications, especially financial transactions, it is extremely important to prove the identity of the user. In this context, biometric authentication methods that rely on physical and behavioural characteristics have been proposed as an alternative for convolutional systems that rely on simple passwords, Personal Identification Number or tokens. However, in real-world applications, authentication systems that involve a single biometric faced many issues, especially lack accuracy and noisy data, which boost the research community to create multibiometric systems that involve a variety of biometrics. Those systems provide better performance and higher accuracy compared to other authentication methods. However, most of them are inconvenient and requires complex interactions from the user. Thus, in this paper, we present a multimodal authentication system that relies on machine learning and blockchain, intending to provide a more reliable, transparent, and convenient authentication mechanism. The proposed system combines tow important biometrics: fingerprint and face with age, and gender features. The supervised learning algorithm Decision Tree has been used to combine the results of the biometrics verification process and produce a confidence level related to the user. The initial experimental results show the efficiency and robustness of the proposed systems