PREVALENCE OF CTX-M-PRODUCING GRAM-NEGATIVE UROPATHOGENS IN SOKOTO, NORTH-WESTERN NIGERIA

Objective: Infections of the urinary tract remains one of the most common bacterial infections with many implicated organisms being Gram-negative, which are increasingly resistant to antimicrobial agents. The aim of the study was to evaluate the resistance of ESBL producing Gram-negative enterobacteriaceae to commonly prescribed antibiotics and the prevalence of CTX-M genes from these isolates using polymerase chain reaction (PCR). Methods: The isolates were collected from urine over a period of 4 mo and studied, and were identified using Microgen Identification Kit (GN-ID). Susceptibility testing was performed by the modified Kirby Bauer disc diffusion method, and results were interpreted according to Clinical and Laboratory Standard Institute (CLSI). Extended-Spectrum Beta-Lactamase (ESBL) production was detected by the double-disc synergy test (DDST). Molecular characterization was based on the isolates that were positive for the phenotypic detection of ESBL. Results: Sixty one (61) isolates of Gram-negative uropathogens were identified. Of these, 19 (31.2%) were E. coli, 15 (24.6%) were Salmonella arizonae, Klebsiella pneumoniae were 7 (11.5%), Klebsiella oxytoca were 3 (4.9%), Enterobacter gergoviae were 6 (9.8%), 4 (6.6%) were Citrobacter freundii, 4 (6.6%) were Serratia marscence, and 1 (1.6%) were Enterobacter aerogenes, Proteus mirabilis, and Edwardsiella tarda each. Analysis of the bacterial susceptibility to antibiotics revealed most of them to be generally resistant to cotrimoxazole (73.3%), nalidixic acid (66.7%), norfloxacin (53.5%), ciprofloxacin (50.5%), gentamicin (48.6%), amoxicillin/clavulanate (45%), and the least resistant was displayed in nitrofurantoin (30%). Of the 15 ESBL producers, 11 (73.3%) were harbouring bla CTX-M genes. Conclusion: The study revealed a high susceptibility to nitrofurantoin, whereas susceptibility to cotrimoxazole was lowest. It further portrays a high prevalence of enterobacteriaceae isolates harbouring bla CTX-M genes in Sokoto metropolis

On the use of electrical resistivity method in mapping potential sources and extent of pollution of groundwater systems in Lapai Town, Niger State, Nigeria

Electrical resistivity method employing the Schlumberger array was used to occupy forty four (44) vertical electrical sounding points in Lapai town with the aim of determining the depth to aquifers, aquifer thicknesses and aquifer protective capacity. The G41 Geotron resistivity meter was used in obtaining the apparent resistivity data which was processed using Interpex 1XD resistivity interpretation software. The results revealed four lithologic sections which include top lateritic soil, sandy clay, fractured basement and fresh basement. Both confined and unconfined aquifers were identified within the area, with four classes of aquifer proactive capacities as high, moderate, weak and poor. While the aquifer at VES 20 was highly protected, twenty other aquifers were moderately protected, eight others had weak protection and fifteen aquifers were poorly protected. The aquifers were generally of good thicknesses and at varying reasonable depths, making them good reservoirs of water in appreciable quantity. The average aquifer thickness was estimated to be 48.36m while the average depth to aquifers was estimated to be 56.68m

Antibacterial activity of Nigerian medicinal plants as panacea for antibiotic resistance: A systematic review

Background & Aim: Antibiotic resistance is one of the global public health threats facing modern health care system. The development of new effective agents has been challenging. Thus, the interest in the use of medicinal plants for the treatment of bacterial infections has increased. Therefore, the aim of this study was to review Nigerian medicinal plants with antibacterial activity. Experimental: This study retrieved data from published articles on Nigerian medicinal plants with antibacterial activity. The Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines were adopted. A systematic search of PUBMED CENTRAL was conducted. The included studies were those published in peer-reviewed English language journals between 1st January 2000 and 31st December 2020 and reported on the key terms; Nigerian medicinal plants with antibacterial activity. Results: The database searches yielded a total of 817 results, and 765 articles were ineligible. After reviewing relevant titles and abstracts, a total of 52 articles on antibacterial were retrieved for full text review. After extensive review of each article, 13 articles were excluded and a total of 39 articles were retained. Furthermore, 4 articles were also removed due to lack of specific compounds stated. Finally, only 35 articles met the inclusion criteria for the assessment of antibacterial activity of Nigerian medicinal plants. The narrative synthesis of the included studies revealed different plants families with broad activities against gram-positive and gram-negative bacteria. Among the bacterial isolates, Staphylococcus aureus was tested more, followed by Escherichia coli and Pseudomonas aeruginosa and the bacteria were subjected to 97 medicinal plants species for antibacterial activity. Recommended applications/industries: The results from this study reveal that many Nigerian medicinal plants contain bioactive compounds with potentials of antibacterial activity and suggest that they could be employed as alternative in the treatment of bacterial infections after safety profiles is appraised

Influence of entrepreneurial competencies on the performance of SMEs in northwest Nigeria

Drawing on the resource-based view (RBV) of the firm and the human capital theory (HCT), this study tested the direct influence of entrepreneurial competencies (i.e., attitudes, skills and knowledge) on the performance of small-scale enterprises (SMEs) operating in Northwest Nigeria. A total of 38 male SME owners (Mean Age = 40.53, SD = 5.94) and 17 female SME owners (Mean Age = 39.35, SD = 4.55) participated in pilot cross-sectional survey. The data collected from the entrepreneurs were analysed using IBM SPSS Statistics 27. The results of the linear regression analysis surprisingly revealed that the relationships between skills (B = 0.18, SE = 0.19, p = 0.36), attitude (B = -0.09, SE = 0.16, p = 0.57), and age (B = 0.03, SE = 0.02, p = 0.11) are not significant. However, the relationship between knowledge and SME performance is significant (B = 0.77, SE = 0.21, p < 0.001), and also accounted for 65% of the variance in firm performance. This is consistent with the assumptions of the RBV and HVT that placed basic entrepreneurial knowledge as a key driver of firm performance. Thus, the study recommends strengthening entrepreneur capabilities to promote better performance among SMEs.Influence of Entrepreneurial Competencie

ANTIBIOTIC SUSCEPTIBILITY TESTING FOR ESCHERICHIA COLI CAUSING URINARY TRACT INFECTIONS IN SOKOTO METROPOLIS

Objective: The study was designed to diffuse awareness on the prevalence of Escherichia coli as a causative agent of urinary tract infection (UTI) in Sokoto metropolis as well as to determine the susceptibility to commonly used antibiotics in Specialist Hospital Sokoto (SHS). This is also to raise awareness of the risk of giving antibiotics and their direct impact on the outcome analysis of UTIs.Methods: This study was conducted at SHS, and ethical approval to carry out the study was obtained from the Ethical Committee of the hospital. Informed consent was obtained from each participant. Early morning, mid-stream clean catch urine samples were collected by patients in sterile disposable containers. The antibiotic susceptibility of the isolates was determined against 10 commonly prescribed antibiotics in SHS using the modified Kirby–Bauer disc agar diffusion.Results: A total of 86 urine samples were analyzed over 2 months, and 34 were culture positive giving an isolation rate of 39.5%, while 48 were culture negative giving a rate of 55.8%, and 4 (4.7%) were undecided. A total of 16 isolates were E. coli (47.1%), while 18 accounts for others (52.9%). The results of antimicrobial susceptibility profile to 10 antibiotics showed that E. coli displayed high susceptibility to vancomycin (91.6%), followed by amikacin (89.2%) and then meropenem (88.0%), while high rate of resistance was found in nalidixic acid (81.2%), followed by co-trimoxazole (73.3%) and then norfloxacin (76.2%).Conclusion: When there is an adequate detection of E. coli and other uropathogens, it will aid in selecting the appropriate antimicrobial therapy and this will also serve as a means of infection control. This will go a long way in reducing the cost of treatment and threat of resistance as witnessed in the management of some uropathogens

Incidence and Antibiotic Susceptibility Profile of Staphylococcus aureus Isolates from Wounds of Patients at Specialist Hospital, Sokoto, Nigeria

Background:   Staphylococcus aureus is an important human pathogen causing varieties of mild to life threatening community and hospital on-set infections. This study was carried out to determine the antibiotic susceptibility profile of Staphylococcus aureus isolated from wounds of patients at a tertiary healthcare facility in Sokoto, Nigeria. Methods:  All wound swabs obtained from patients with wound infections during the study period were cultured on mannitol salt agar media. The isolates were identified using standard microbiological methods. Antibiotic susceptibility test was carried out on the identified isolates using the modified Kirby-Bauer disc diffusion method and methicillin resistant Staphylococcus aureus (MRSA) test was carried out using Oxacillin agar screen test as described by Clinical and Laboratory Standard Institute (CLSI, 2016). Results:     A total of twenty (20) Staphylococcus aureus were isolated from thirty-eight (38) wound specimens investigated. Out of which, five (25.0%) were found to be MRSA. The isolates were resistant to most of the antibiotics tested and susceptible only to Gentamicin (85%), Norfloxacin (80%) and Amoxiclav (50%). Conclusion:    The high incidence of Staphylococcus aureus isolates resistant to the commonly used antibiotics in the hospital calls for urgent need to put in place measures to curtail the spread of MRSA infections in the hospital

Recent Advances in Artificial Intelligence and Wearable Sensors in Healthcare Delivery

Artificial intelligence (AI) and wearable sensors are gradually transforming healthcare service delivery from the traditional hospital-centred model to the personal-portable-device-centred model. Studies have revealed that this transformation can provide an intelligent framework with automated solutions for clinicians to assess patients’ general health. Often, electronic systems are used to record numerous clinical records from patients. Vital sign data, which are critical clinical records are important traditional bioindicators for assessing a patient’s general physical health status and the degree of derangement happening from the baseline of the patient. The vital signs include blood pressure, body temperature, respiratory rate, and heart pulse rate. Knowing vital signs is the first critical step for any clinical evaluation, they also give clues to possible diseases and show progress towards illness recovery or deterioration. Techniques in machine learning (ML), a subfield of artificial intelligence (AI), have recently demonstrated an ability to improve analytical procedures when applied to clinical records and provide better evidence supporting clinical decisions. This literature review focuses on how researchers are exploring several benefits of embracing AI techniques and wearable sensors in tasks related to modernizing and optimizing healthcare data analyses. Likewise, challenges concerning issues associated with the use of ML and sensors in healthcare data analyses are also discussed. This review consequently highlights open research gaps and opportunities found in the literature for future studies

Recent Advances in Artificial Intelligence and Wearable Sensors in Healthcare Delivery

Artificial intelligence (AI) and wearable sensors are gradually transforming healthcare service delivery from the traditional hospital-centred model to the personal-portable-device-centred model. Studies have revealed that this transformation can provide an intelligent framework with automated solutions for clinicians to assess patients&rsquo; general health. Often, electronic systems are used to record numerous clinical records from patients. Vital sign data, which are critical clinical records are important traditional bioindicators for assessing a patient&rsquo;s general physical health status and the degree of derangement happening from the baseline of the patient. The vital signs include blood pressure, body temperature, respiratory rate, and heart pulse rate. Knowing vital signs is the first critical step for any clinical evaluation, they also give clues to possible diseases and show progress towards illness recovery or deterioration. Techniques in machine learning (ML), a subfield of artificial intelligence (AI), have recently demonstrated an ability to improve analytical procedures when applied to clinical records and provide better evidence supporting clinical decisions. This literature review focuses on how researchers are exploring several benefits of embracing AI techniques and wearable sensors in tasks related to modernizing and optimizing healthcare data analyses. Likewise, challenges concerning issues associated with the use of ML and sensors in healthcare data analyses are also discussed. This review consequently highlights open research gaps and opportunities found in the literature for future studies

Artificial Intelligence, Sensors and Vital Health Signs: A Review

Large amounts of patient vital/physiological signs data are usually acquired in hospitals manually via centralized smart devices. The vital signs data are occasionally stored in spreadsheets and may not be part of the clinical cloud record; thus, it is very challenging for doctors to integrate and analyze the data. One possible remedy to overcome these limitations is the interconnection of medical devices through the internet using an intelligent and distributed platform such as the Internet of Things (IoT) or the Internet of Health Things (IoHT) and Artificial Intelligence/Machine Learning (AI/ML). These concepts permit the integration of data from different sources to enhance the diagnosis/prognosis of the patient’s health state. Over the last several decades, the growth of information technology (IT), such as the IoT/IoHT and AI, has grown quickly as a new study topic in many academic and business disciplines, notably in healthcare. Recent advancements in healthcare delivery have allowed more people to have access to high-quality care and improve their overall health. This research reports recent advances in AI and IoT in monitoring vital health signs. It investigates current research on AI and the IoT, as well as key enabling technologies, notably AI and sensors-enabled applications and successful deployments. This study also examines the essential issues that are frequently faced in AI and IoT-assisted vital health signs monitoring, as well as the special concerns that must be addressed to enhance these systems in healthcare, and it proposes potential future research directions