Bioassay studies of metal(II) complexes of 2,2'-(ethane-1,2-diyldiimino)diacetic acid

Ni(II), Cu(II) and Zn(II) coordination compounds with modified diammine 2,2'-(ethane-1,2-diyldiimino)diacetic acid (EDDA) were prepared and characterized. Coordination complexes of the EDDA were characterized by physical measurements including elemental analysis, IR, UV-Visible, magnetic susceptibilities and conductance measurements. The complexes were screened against four pathogenic bacteria like Escherichia coli, Pseudomonas aeruginosa, Klebsiella pneumoniae and Staphylococcus aureus and their concentrations for maximum inhibition zones were obtained. KEY WORDS: EDDA, Coordination complexes, Antibacterial studies Bull. Chem. Soc. Ethiop. 2011, 25(2), 239-245

Synthesis, characterization and antimicrobial studies of transition metal complexes of imidazole derivative

A series of new biologically active complexes of Zn(II), Cu(II), Co(II) and Ni(II) with imidazole derivative have been synthesized. The synthesized chelating agent and metal(II) complexes were screened for antibacterial activities against four pathogenic species of bacteria namely; Eschereschi coli, Pseudomonas aeruginosa, Klesbiella pneumonia and Staphylococcus aureus by agar well diffusion method. The results show that most of the metal complexes were more active than the neat ligand, against these bacterial species as expected. KEY WORDS: 1,3-Di(1H-imidazol-1-yl)-2-propanol, Coordination compounds, Antimicrobial study  Bull. Chem. Soc. Ethiop. 2010, 24(2), 201-207

Spectrum of Bacterial Pathogens Isolated from Burn Wound Patients

Objectives: To find out the spectrum of bacterial pathogens isolated from pus samples of infected burn sites and to come across the sample collection according to post burn day and distribution of Gram positive and negative isolates in samples in relation to time of collection.Methodology: It is a prospective, non-randomized, descriptive study conducted at Microbiology laboratory, Pathology department and burn care center (BCC), Pakistan Institute of Medical Sciences Islamabad for 4 months, from 2nd April to 3rd August 2013. One hundred and ten clinical isolates from 68 patients were collected. Sample collection from referred patients was done at the time of admission and from admitted patients was done during changing their dressings. The sample collected was immediately transferred to the Pathology laboratory and submitted for Culturing. The samples were inoculated on Blood agar and MacConkey agar (Oxoid USA) and incubated aerobically at 35+ 2 0C for 18 -48 hours. After incubation the pathogens were identified with the help of colonial Morphology, gram stain reaction, biochemical tests, and API 20E. Results: Out of 68 samples 110 burn wound pathogens were isolated. 47% of the samples yielded single etiological agent whereas the rest of 53 % had polymicrobial etiology. Gram negatives were in majority 81.82% and Gram positives were18.18 %. The samples in which only one isolate was obtained were 32. Amongst them P. aeruginosa was isolated in (65.6%) of samples. In 31 samples two isolates were obtained. In such samples coexistence of P .aeruginosa and K. pneumoniae was most prevalent i.e. in 10 (32.25%). There were only five samples in which more than two isolates were obtained. Amongst Gram negatives were the predominant bacterial pathogens out of them P. aeruginosa were 53 %. MRSA made the major bulk of Gram positives that is 65 %, the rest 35% were Staph aureus MSSA. Gram positives were isolated more in the first week samples i.e. 30% as compared to the subsequent weeks.Conclusion: It was concluded that more than 80 % of bacterial isolates of burn wound infections were Gram negative and less than 20% were Gram positive i-e Staphylococcus aureus

Feature selection for UK disabled students’ engagement post higher education: a machine learning approach for a predictive employment model

While only 4.2 million people out of a population of 7.9 million disabled people are working, a considerable contribution is still required from universities and industries to increase employability among the disabled, in particular, by providing adequate career guidance post higher education. This study aims to identify the potential predictive features, which will improve the chances of engaging disabled school leavers in employment about 6 months after graduation. MALSEND is an analytical platform that consists of information about UK Destinations Leavers from Higher Education (DLHE) survey results from 2012 to 2017. The dataset of 270,934 student records with a known disability provides anonymised information about students’ age range, year of study, disability type, results of the first degree, among others. Using both qualitative and quantitative approaches, characteristics of disabled candidates during and after school years were investigated to identify their engagement patterns. This article builds on constructing and selecting subsets of features useful to build a good predictor regarding the engagement of disabled students 6 months after graduation using the big data approach with machine learning principles. Features such as age, institution, disability type, among others were found to be essential predictors of the proposed employment model. A pilot was developed, which shows that the Decision Tree Classifier and Logistic Regression models provided the best results for predicting the Standard Occupation Classification (SOC) of a disabled school leaver in the UK with an accuracy of 96%

Impact Of Board Characteristics On Corporate Social Responsibility Disclosure

The purpose of this study is to explore the link between corporate governance characteristics and corporate social responsibility disclosure of listed companies in the Pakistan stock Exchange (PSX), Pakistan. A sample of 179 companies from financial and non-financial sectors are studied from 2009 to 2015. The data is collected from their annual reports and websites. Binary logistic regression analysis is employed to test the models. The results reveal that board size, number of meetings and board independence are significant corporate governance characteristics to establish the link with corporate social responsibility disclosure. This study also explore that the trend of CSR disclosure is increasing in financial as well as non-financial sector. Additionally, the companies disclose their CSR activities lead in financial performance as compare to their counterpart. This study adds in the literature to explore the influence of board characteristics on corporate social responsibility disclosure from a developing country’s perspective

A proposed framework for developing user-centred mobile healthcare applications for the biggest annual mass gathering (Hajj) post COVID-19

The Hajj pilgrimage being the largest annual mass gathering globally with two to three million participants from over 180 counties, will remain a high priority for diseases surveillance for future epidemics or any other international public health emergencies with rapid scalability. This paper highlights the importance of monitoring mass gatherings during a pandemic and how mHealth applications can reduce the burden on health facilities during a mass gathering and tackle future infectious diseases outbreaks. The paper also highlights the importance of developing a user-centred application when designing for a diverse group of users with a shared purpose. As a result, a framework has been proposed to update the current applications or design and develop future mobile health applications. The framework has been developed based on the rationale and evidence found in the literature

An intelligent fuzzy logic-based content and channel aware downlink scheduler for scalable video over OFDMA wireless systems

The recent advancements of wireless technology and applications make downlink scheduling and resource allocations an important research topic. In this paper, we consider the problem of downlink scheduling for multi-user scalable video streaming over OFDMA channels. The video streams are precoded using a scalable video coding (SVC) scheme. We propose a fuzzy logic-based scheduling algorithm, which prioritises the transmission to different users by considering video content, and channel conditions. Furthermore, a novel analytical model and a new performance metric have been developed for the performance analysis of the proposed scheduling algorithm. The obtained results show that the proposed algorithm outperforms the content-blind/channel aware scheduling algorithms with a gain of as much as 19% in terms of the number of supported users. The proposed algorithm allows for a fairer allocation of resources among users across the entire sector coverage, allowing for the enhancement of video quality at edges of the cell while minimising the degradation of users closer to the base station

Using machine learning advances to unravel patterns in subject areas and performances of university students with special educational needs and disabilities (MALSEND): a conceptual approach

Universities and colleges in the UK welcome almost 30,000 disabled students each year. Re-search shows that the dropout from education in the EU for the disabled is at 31.5%, much higher compared to only 12.3% for non-disabled students. Supporting young students who require special educational needs in pursuing higher education is an ambitious and necessary step that needs to be adopted by tertiary education providers worldwide. We propose, MALSEND, a project aiming to develop a platform based on machine and human intelligence to understand learning disability patterns in Higher Education. The platform will analyse da-tasets from universities in the previous years and will help to discover any trends in subject areas and performance among autistic students, dyslexic students or students having attention deficit hyperactive disorder (ADHD), among others. Analysing variables such as students’ courses, modules, performances and other engagement-indices will give new insights on re-search questions, career advice and institutional policy making. This paper describes the activ-ities of the development phases of this concept

Multilayer perceptron neural network-based QoS-aware, content-aware and device-aware QoE prediction model : a proposed prediction model for medical ultrasound streaming over small cell networks

This paper presents a QoS-aware, content-aware and device-aware non-intrusive medical QoE (m-QoE) prediction model over small cell networks. The proposed prediction model utilises a Multilayer Perceptron (MLP) neural network to predict m-QoE. It also acts as a platform to maintain and optimise the acceptable diagnostic quality through a device-aware adaptive video streaming mechanism. The proposed model is trained for an unseen dataset of input variables such as QoS, content features, and display device characteristics, to produce an output value in the form of m-QoE (i.e. MOS). The efficiency of the proposed model is validated through subjective tests carried by medical experts. The prediction accuracy obtained via the correlation coefficient and Root Mean-Square-Error (RMSE) indicates that the proposed model succeeds in measuring m-QoE closer to the visual perception of the medical experts. Furthermore, we have addressed the following two main research questions: (1) How significant is ultrasound video content type in determining m-QoE? and (2) How much of a role does the screen size and device resolution play in medical experts’ diagnostic experience? The former is answered through the content classification of ultrasound video sequences based on their spatio-temporal features, by including these features in the proposed prediction model, and validating their significance through medical experts’ subjective ratings. The latter is answered by conducting a novel subjective experiment of the ultrasound video sequences across multiple devices

Quality of Hypospadias Surgery in a High Volume Hospital: Review of Short to Medium-Term Outcomes after Snodgrass Hypospadias Repair

OBJECTIVES The study aimed to review short-term and medium-term outcomes of Snodgrass repair from one-year to two-year follow-up. METHODOLOGY It’s a retrospective review of 114 patients (secondary data) with distal penile hypospadias without curvature who underwent Snodgrass repair performed by a single surgeon in the department of Urology, Lady Reading Hospital Peshawar from March 2021 to March 2022. Hypospadias objective score Evaluation (HOSE) was used for functional and cosmetic outcomes. RESULTSThe mean age at the time of presentation for surgery was 3 years. The mean documented follow-up was 13 months (2-23 months). 51% (58) of patients presented with coronal hypospadias, followed by sub-coronal, distal and glanular hypospadias. Overall complication rate was 10%. Three patients developed a total breakdown of their wounds that needed further staged procedures. Meatal stenosis was noted in three patients requiring meatal dilatation, and another two came back with non-obliterative strictures. One had gentle dilatation, and the other underwent an optical urethrotomy. The rest of the four patients developed urethrocutanoeus fistulae (UCF), ultimately getting repaired 6 months postoperatively. Good cosmetic and functional results were achieved in 93% of cases (HOSE score of 14 or above). CONCLUSION This study shows that Snodgrass repair is the best option for mainly correcting distal penile and midshaft hypospadias and has an acceptably low complication rate with better short to medium-term outcomes