Disparities in prevalence and barriers to hypertension control: a systematic review

Controlling hypertension (HTN) remains a challenge, as it is affected by various factors in different settings. This study aimed to describe the disparities in the prevalence and barriers to hypertension control across countries of various income categories. Three scholarly databases—ScienceDirect, PubMed, and Google Scholar—were systematically examined using predefined search terms to identify potentially relevant studies. Original research articles published in English between 2011 and 2022 that reported the prevalence and barriers to HTN control were included. A total of 33 studies were included in this systematic review. Twenty-three studies were conducted in low and middle-income countries (LMIC), and ten studies were from high-income countries (HIC). The prevalence of hypertension control in the LMIC and HIC studies ranged from (3.8% to 50.4%) to (36.3% to 69.6%), respectively. Concerning barriers to hypertension control, patient-related barriers were the most frequently reported (n = 20), followed by medication adherence barriers (n = 10), lifestyle-related barriers (n = 8), barriers related to the affordability and accessibility of care (n = 8), awareness-related barriers (n = 7), and, finally, barriers related to prescribed pharmacotherapy (n = 6). A combination of more than one category of barriers was frequently encountered, with 59 barriers reported overall across the 33 studies. This work reported disparities in hypertension control and barriers across studies conducted in LMIC and HIC. Recognizing the multifactorial nature of the barriers to hypertension control, particularly in LMIC, is crucial in designing and implementing customized interventions

Evaluation of knowledge, experiences, and fear toward prescribing and dispensing corticosteroids among Egyptian healthcare professionals: A cross-sectional study

Background: Corticosteroids (CS) are essential drugs in the treatment of several medical conditions. Assuming different roles, physicians and pharmacists play a primary role in prescribing and dispensing these medications to optimize patients' clinical management. The data on assessing knowledge and experience of healthcare professionals toward CS is scarce. Therefore, this study aimed to assess and compare knowledge, experience, and fears towards CS among Egyptian physicians and pharmacists. Methods: A cross-sectional, self-administrated, validated online questionnaire was used to collect the data from Egyptian healthcare professionals. The questionnaire consisted of four sections with multiple choice questions: sociodemographic (7 questions), knowledge about CS (13 questions), experience with CS prescription/dispensing (5 questions), and fears and preferences toward CS prescription/dispensing (13 questions). Descriptive and inferential statistics were used to analyze the data. Results: A total of 600 responses were analyzed in this study. The study sample was almost two-half of healthcare providers: 303 (50.5%) pharmacists and 297 (49.5%) physicians. Pharmacists had marginally higher knowledge scores as compared to those recorded for physicians (11.29 versus 10.16, respectively; P = 0.047). Physicians had more experience choosing corticosteroids in treatment plans based on their experience (51.8% vs 38.5%) and guideline recommendations (72.8% vs 50.9%) than pharmacists. However, pharmacists had more experience dealing with corticosteroid use based on patients' preferences (19.5% vs 4.9%) and showed a broader scope of experiencing side effects of corticosteroids with their patients. The two professions demonstrated high levels of fear, with pharmacists acknowledging significantly lower concerns about CS than physicians (3.72 versus 4.0, respectively; P = 0.003). Conclusion: Discrepancies exist among healthcare professionals in knowledge and experience, favoring better scientific knowledge of pharmacists related to corticosteroids. Based on these findings, the interprofessional collaborative efforts would provide comprehensive, patient-centered care that maximizes the benefits of CS while minimizing their risks

The intraperitoneal ondansetron for postoperative pain management following laparoscopic cholecystectomy: A proof-of-concept, double-blind, placebo-controlled trial

Background Pain after laparoscopic cholecystectomy remains a major challenge. Ondansetron blocks sodium channels and may have local anesthetic properties. Aims To investigate the effect of intraperitoneal administration of ondansetron for postoperative pain management as an adjuvant to intravenous acetaminophen in patients undergoing laparoscopic cholecystectomy. Methods Patients scheduled for elective laparoscopic cholecystectomy were randomized into two groups (n = 25 each) to receive either intraperitoneal ondansetron or saline injected in the gall bladder bed at the end of the procedure. The primary outcome was the difference in pain from baseline to 24-h post-operative assessed by comparing the area under the curve of visual analog score between the two groups. Results The derived area under response curve of visual analog scores in the ondansetron group (735.8 ± 418.3) was 33.97% lower than (p = 0.005) that calculated for the control group (1114.4 ± 423.9). The need for rescue analgesia was significantly lower in the ondansetron (16%) versus in the control group (54.17%) (p = 0.005), indicating better pain control. The correlation between the time for unassisted mobilization and the area under response curve of visual analog scores signified the positive analgesic influence of ondansetron (rs = 0.315, p = 0.028). The frequency of nausea and vomiting was significantly lower in patients who received ondansetron than that reported in the control group (p = 0.023 (8 h), and 0.016 (24 h) respectively). Conclusions The added positive impact of ondansetron on postoperative pain control alongside its anti-emetic effect made it a unique novel option for patients undergoing laparoscopic cholecystectomy

Electrical power output prediction of combined cycle power plants using a recurrent neural network optimized by waterwheel plant algorithm

It is difficult to analyze and anticipate the power output of Combined Cycle Power Plants (CCPPs) when considering operational thermal variables such as ambient pressure, vacuum, relative humidity, and temperature. Our data visualization study shows strong non-linearity in the experimental data. We observe that CCPP energy production increases linearly with temperature but not pressure. We offer the Waterwheel Plant Algorithm (WWPA), a unique metaheuristic optimization method, to fine-tune Recurrent Neural Network hyperparameters to improve prediction accuracy. A robust mathematical model for energy production prediction is built and validated using anticipated and experimental data residuals. The residuals’ uniformity above and below the regression line suggests acceptable prediction errors. Our mathematical model has an R-squared value of 0.935 and 0.999 during training and testing, demonstrating its outstanding predictive accuracy. This research provides an accurate way to forecast CCPP energy output, which could improve operational efficiency and resource utilization in these power plants

Forecasting wind power based on an improved al-Biruni Earth radius metaheuristic optimization algorithm

Wind power forecasting is pivotal in optimizing renewable energy generation and grid stability. This paper presents a groundbreaking optimization algorithm to enhance wind power forecasting through an improved al-Biruni Earth radius (BER) metaheuristic optimization algorithm. The BER algorithm, based on stochastic fractal search (SFS) principles, has been refined and optimized to achieve superior accuracy in wind power prediction. The proposed algorithm is denoted by BERSFS and is used in an ensemble model’s feature selection and optimization to boost prediction accuracy. In the experiments, the first scenario covers the proposed binary BERSFS algorithm’s feature selection capabilities for the dataset under test, while the second scenario demonstrates the algorithm’s regression capabilities. The BERSFS algorithm is investigated and compared to state-of-the-art algorithms of BER, SFS, particle swarm optimization, gray wolf optimizer, and whale optimization algorithm. The proposed optimizing ensemble BERSFS-based model is also compared to the basic models of long short-term memory, bidirectional long short-term memory, gated recurrent unit, and the k-nearest neighbor ensemble model. The statistical investigation utilized Wilcoxon’s rank-sum and analysis of variance tests to investigate the robustness of the created BERSFS-based model. The achieved results and analysis confirm the effectiveness and superiority of the proposed approach in wind power forecasting

A novel voting classifier for electric vehicles population at different locations using Al-Biruni earth radius optimization algorithm

The rising popularity of electric vehicles (EVs) can be attributed to their positive impact on the environment and their ability to lower operational expenses. Nevertheless, the task of determining the most suitable EV types for a specific site continues to pose difficulties, mostly due to the wide range of consumer preferences and the inherent limits of EVs. This study introduces a new voting classifier model that incorporates the Al-Biruni earth radius optimization algorithm, which is derived from the stochastic fractal search. The model aims to predict the optimal EV type for a given location by considering factors such as user preferences, availability of charging infrastructure, and distance to the destination. The proposed classification methodology entails the utilization of ensemble learning, which can be subdivided into two distinct stages: pre-classification and classification. During the initial stage of classification, the process of data preprocessing involves converting unprocessed data into a refined, systematic, and well-arranged format that is appropriate for subsequent analysis or modeling. During the classification phase, a majority vote ensemble learning method is utilized to categorize unlabeled data properly and efficiently. This method consists of three independent classifiers. The efficacy and efficiency of the suggested method are showcased through simulation experiments. The results indicate that the collaborative classification method performs very well and consistently in classifying EV populations. In comparison to similar classification approaches, the suggested method demonstrates improved performance in terms of assessment metrics such as accuracy, sensitivity, specificity, and F-score. The improvements observed in these metrics are 91.22%, 94.34%, 89.5%, and 88.5%, respectively. These results highlight the overall effectiveness of the proposed method. Hence, the suggested approach is seen more favorable for implementing the voting classifier in the context of the EV population across different geographical areas

Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London