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Study’s Novelty/Excerpt • This study investigates the prevalence of Enterobacteriaceae in clinical samples from the Federal Teaching Hospital Gombe, providing a comprehensive analysis of 420 non-duplicate isolates over a four-month period. • The research uniquely identifies the distribution of various Enterobacteriaceae species, with Escherichia coli being the most prevalent, and correlates these findings with the types of clinical specimens, revealing urine as the most common source. • This work underscores the necessity for further research into the antimicrobial resistance profiles of these isolates, aiming to inform more effective treatment strategies for bacterial infections in the region. Full Abstract Enterobacteriaceae is a family of Gram-negative, oxidase-negative, and catalase-positive bacteria mostly found in Humans and animals' intestines. Some of these organisms are enteric opportunistic pathogens associated with urinary tract infections, respiratory tract infections, and wound infections, whereas others are regularly pathogenic for humans. This study aimed to determine the prevalence of Enterobacteriaceae in clinical samples from Federal Teaching Hospital Gombe between August 2022 and November 2022. A total of 420 non-duplicate isolates from various clinical samples were analyzed in the study. The isolates were identified based on cultural characteristics, Gram staining, and standard biochemical tests. Out of the 420 isolates identified, Escherichia coli was the most prevalent with 163(38.8%) isolates, followed by Klebsiella pneumoniae with 69(16.4%), Klebsiella oxytoca with 61(14.5%), Proteus spp. 28(6.7%), Citrobacter spp. 27(6.4%), Enterobacter spp. 22(5.2%), Serratia marcescens 13(3.1%), Providencia spp. 12(2.9%), Yersinia enterocolitica 11(2.6%), Morganella morganii 3(0.7%) and Salmonella spp. with 2(0.5%). Based on clinical specimens, urine had the highest percentage of isolates with 53.4%, followed by wound swab (19.1%), stool (10.1%), High vaginal swab (6.8%), Endocervical swab (3.6%), sputum (3.1%), blood (1.4%), Cerebrospinal fluid (1.0%) and semen (1.0%) and then pleural fluid (0.5%). In conclusion, Enterobacteriaceae clinical isolates were highly prevalent in Federal Teaching Hospital Gombe. Further research to assess the antimicrobial resistance profile of these clinical bacterial organisms in the study area is recommended for effective treatment options for bacterial infections.

Background/Objectives: During vaginal delivery, the perineum can be damaged either by episiotomy or by a spontaneous perineal tear, leading to several complications. The wound healing process should proceed as quickly and properly as possible without an infection. Medical grade honey (MGH) may be a potent treatment option due to its antimicrobial and pro-healing activities. This literature study investigated the role of honey in the treatment of vaginal wounds after delivery. Methods: Studies published before 17 July 2024 in the PubMed, Web of Science, Embase, Scopus, EBSCO host/CINAHL, Cochrane Library, and Google Scholar databases about honey, episiotomy wounds, and perineal tears, as well as those investigating wound healing and/or pain, were assessed. Results: Ten studies were included (six RCTs, of which three were double-blind, one was quasi-experimental with a posttest only, and three were observational studies without a control group), with 723 participants in total. Six of the seven controlled studies showed honey significantly improved various outcome measures, such as improved wound healing, and reduced need for pain medication. The three non-controlled studies also had a positive outcome, improving wound healing and decreasing pain intensity and prickling sensation. However, the overall quality of available evidence is limited. Different types of honey concentrations, origins, and additives were used in the included studies. Using a standardized MGH formulation may help to maintain consistent and potent effects. Therefore, additional research is needed to determine the efficacy of MGH in perineal trauma and to establish guidelines for clinical use. Conclusions: Honey potentially has a great effect on wound healing of perineal trauma; however, more research is necessary to substantiate the findings in the current literature.

Electricity power plants are the primary source of carbon dioxide (CO2) production. The combustion of fossil fuels in these power plants releases CO2 and other greenhouse gases (GHGs) into the atmosphere. Emissions of CO2 and other GHGs occur throughout the life cycle of electricity generation technologies, including extraction, processing, transportation, and combustion of fossil fuels. Nowadays, the development of smart cities plays a crucial role in reducing CO2 emissions. By integrating technology and sustainable practices into urban infrastructure, smart cities aim to minimize energy consumption and emissions. This research focuses on creating models that correlate CO2 emissions with the installed capacity of power plants and the energy they produce. Polynomial quadratic regression is applied to establish these correlations, enabling the assessment of both present and future CO2 emission values. The construction of new infrastructure for smart cities aligns with the broader goal of fostering a low-carbon economy. The study also considers the potential contribution of thermal energy to CO2 reduction efforts. Researchers aim to identify strategies for meeting emission reduction goals by analyzing power generation models. This involves implementing policies and technologies aimed at reducing GHG emissions and mitigating climate change. CO2 emissions contribute significantly to global warming, leading to climate change and patterns of extreme weather. The data on CO2 emissions encompasses gases generated from burning fossil fuels, highlighting the importance of reducing reliance on these energy sources.

Energy consumption has always been considered one of the major issues in the six Gulf Cooperation Council (GCC) countries. Therefore, the predicted energy consumption can respond based on several factors and conditions, where which will help in response to plan saving energy for the future. The significant contribution of the present study is the development of software merging the Petri-Nets and ANFIS to become a comprehensive model to find the maximum annual load demand estimation of the GCC countries based on the historical data of the countries. The ANFIS-Petri Nets have expected modeling to represent knowledge and help in rule-based expert systems. The data has several parameters/conditions (factors), where these factors are temperature, humidity, GDP, pressure, and energy consumption. The results lead to a fast and accurate figure have developed an energy estimated system that works by tracking, analyzing, and controlling conditions of the energy system. The Petri-Nets ANFIS developed technique targeting the estimated maximum annual energy consumption believes that obtaining the values is satisfactory for the future of GCC states. In addition, the developed and obtained models have great application for the GCC countries. This might help to develop the models of other countries.

Gohar Azhar,1 Shakshi Sharma,1 Onna Lau,1 Bader Alkharisi,1 Anil Anandam,1 Sakiru Isa,2 Priya Mendiratta,1 Amanda K Pangle,1 Karen Coker,1 Jeanne Wei1 1Department of Geriatrics, Donald W. Reynolds Institute of Aging, University of Arkansas for Medical Sciences, Little Rock, AR, USA; 2Division of Cardiovascular Diseases, University of Tennessee Health Science Center, Memphis, TN, USACorrespondence: Gohar Azhar, Email azhargohar@uams.eduBackground: Delirium is a common medical condition that is highly prevalent in older adults who are at increased risk for its development with any illness, post-surgery or during hospitalization. The purpose of our study was to evaluate the health literacy of older adult patients and their caregivers about delirium, offer a brief educational intervention, and reevaluate their knowledge post intervention.Materials and Methods: We conducted a quality improvement project, focused on delirium health literacy in older adult patients ≥ 60 years and their caregivers. Delirium knowledge of participants was evaluated in a pre-education survey after which they were given a delirium education booklet to read. A post-education delirium survey was conducted within 2– 3 weeks of the educational intervention. Chi-square test was used to analyze the knowledge base of older adults.Results: The study population consisted of a total of 70 older adults who participated in pre-education (n=35) and post-education (n=35) surveys. Older adult patients and their caregivers had significant knowledge gaps about the potential causes or etiologies, risk factors, symptomatology, and prevention of delirium in the pre-education survey. After the educational intervention, in the post-education survey, there were overall improvements in knowledge base of older adults in differentiating delirium with dementia (43% vs 94%, p< 0.01) recognizing signs and symptoms (77% vs 94%, p< 0.05), complications (76% vs 100%, p< 0.01) and identifying the etiological factors associated with delirium.Conclusion: The quality improvement project demonstrated that older adults and caregivers have significant knowledge deficits about the common condition of delirium. This study also demonstrated that older adults were able to improve their health literacy regarding delirium after the intervention. Appropriate education on delirium for patients and caregivers might help in earlier identification, prevention, and better overall management of delirium.Keywords: delirium, education, aging, healthcare

Subgroup analysis may be used to investigate treatment effect heterogeneity among subsets of the study population defined by baseline characteristics. Several methodologies have been proposed in recent years and with these, statistical issues such as multiplicity, complexity, and selection bias have been widely discussed. Some methods adjust for one or more of these issues; however, few of them discuss or consider the stability of the subgroup assignments. We propose exploring the stability of subgroups as a sensitivity analysis step for stratified medicine to assess the robustness of the identified subgroups besides identifying possible factors that may drive this instability. After applying Bayesian credible subgroups, a nonparametric bootstrap can be used to assess stability at subgroup‐level and patient‐level. Our findings illustrate that when the treatment effect is small or not so evident, patients are more likely to switch to different subgroups (jumpers) across bootstrap resamples. In contrast, when the treatment effect is large or extremely convincing, patients generally remain in the same subgroup. While the proposed subgroup stability method is illustrated through Bayesian credible subgroups method on time–to–event data, this general approach can be used with other subgroup identification methods and endpoints. [ABSTRACT FROM AUTHOR]

Gohar Azhar,1 Shakshi Sharma,1 Onna Lau,1 Bader Alkharisi,1 Anil Anandam,1 Sakiru Isa,2 Priya Mendiratta,1 Amanda K Pangle,1 Karen Coker,1 Jeanne Wei1 1Department of Geriatrics, Donald W. Reynolds Institute of Aging, University of Arkansas for Medical Sciences, Little Rock, AR, USA; 2Division of Cardiovascular Diseases, University of Tennessee Health Science Center, Memphis, TN, USACorrespondence: Gohar Azhar, Reynolds Institute on Aging, University of Arkansas for Medical Sciences, 4301 West Markham, Little Rock, AR, 72205, USA, Tel +1 501 526-5935, Email azhargohar@uams.edu

It is important to develop a suitable model to find the suitable design of inverter based on a required specification that needed to calculate the number of units per day requested by the house holders or companies. The present study deals with the required design using a developed Adaptive Neuro-Fuzzy Inference System (ANFIS) method and based on the calculated results the curve fitting is applied to form the suitable formula. The model is formulated as a function of number of units per day for the Gulf Cooperation Council (GCC) region. In general, the GCC region has high-energy consumption influenced by several factors. Therefore, the solar energy is helping to reduce the use of gas or other fuels to generate power. This means that the obtained results will encourage the GCC through the energy field development and setting the future for it to increase the use of solar energy production. The novelty of the present study is to avoid an increase in generation capacity using the gas to produce the electricity. In addition, it will help the GCC countries to avoid load shedding and meet the energy demands in different sectors. Furthermore, the developed model will help the economic development of the GCC countries. These results reduce capital investment, limiting the equipment installed and the expected load needed in the region.

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It is important to develop a suitable model to calculate electricity demand forecasting requested by decision makers. The present study deals with the electrical long-term peak load demand forecasting using a developed Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multiple Linear Regression (MLR) methods. The MLR model is formulated as a function of population and Gross Domestic Product (GDP) for the Gulf Cooperation Council (GCC) region. The Neuro-Fuzzy is thereafter trained using previous sets of data. This training gives a future annual electricity load prediction. The results obtained from the developed models have an acceptable level of mean errors. In general, the GCC region has high-energy consumption influenced by a number of factors, such as population and GDP. The annual variation of both population and GDP growth scenarios is based on development in the country. The obtained results will encourage the GCC through the energy field development and setting the future plans for it. The novelty of the present study is to avoid an increase in generation capacity in mid-term and long-term plans, which will help the GCC countries to avoid load shedding and meet the energy demands in different sectors. The developed models will help the economic development of the GCC countries. It also helps with finding the optimum time for electrical energy trading. The obtained results for the GCC illustrate the average percentage error calculated which was found to be close to 2% and 0.53% in multilinear regression and Neuro-Fuzzy, respectively. These results reduce capital investment, limiting the equipment installed and the expected load needed for better load distribution in the region. In conclusion, the Neuro-Fuzzy is the most accurate technique compared with MLR to estimate future electricity demand and, at the same time, it can be used in power system planning and development.