Explore the vast range of titles available.

Background Electromagnetic radiation (EMR) exposure has been linked to oxidative stress and neurochemical imbalances, potentially compromising cellular integrity. By increasing free radical production, EMR disrupts the antioxidant defense system, including glutathione (GSH), glutathione peroxidase (GPx), superoxide dismutase (SOD), and catalase (CAT); which may contribute to neuronal damage and cognitive impairment. Method This study examines the impact of intrauterine mobile phone radiation (MPR) exposure on hippocampal neurotransmitters and oxidative stress markers in albino mice and their offspring. Thirty-five healthy mice, obtained from pregnant females aged 10–14 weeks (weighing 18–20 g), were randomly divided into seven groups (n = 5 per group), Group I (control, no MPR exposure); Groups II–IV (exposed to 2G 0.9 GHz, 3G 1.5 GHz, and 4G 1.95 GHz MPR until parturition, respectively); and Groups V–VII (exposed to the same frequencies until weaning). Results After eight weeks of exposure, hippocampal tissues were analyzed for neurochemical markers (acetylcholinesterase (AChE) and glutamate (GLU)) as well as oxidative stress biomarkers (malondialdehyde (MDA), SOD, and GSH) and their histological structure. MPR exposure resulted in a significant reduction ( p  < 0.05) in AChE levels in groups III–VII, except for group II ( p  > 0.05), while GLU levels significantly increased ( p  < 0.05) in group VII. Oxidative stress analysis revealed significantly elevated MDA and SOD levels ( p  < 0.05) and a marked reduction in GSH levels across all exposed groups ( p  < 0.05). Conclusion Intrauterine exposure to MPR induces oxidative stress and neurochemical imbalances in albino mice, which is characterized by decreased AChE and GSH levels and increased MDA, SOD, and GLU concentrations. These findings suggest that prolonged MPR exposure may disrupt hippocampal function, potentially affecting cognitive and neurodevelopmental processes.

In recent times, the growth of the Internet of Things (IoT), artificial intelligence (AI), and Blockchain technologies have quickly gained pace as a new study niche in numerous collegiate and industrial sectors, notably in the healthcare sector. Recent advancements in healthcare delivery have given many patients access to advanced personalized healthcare, which has improved their well-being. The subsequent phase in healthcare is to seamlessly consolidate these emerging technologies such as IoT-assisted wearable sensor devices, AI, and Blockchain collectively. Surprisingly, owing to the rapid use of smart wearable sensors, IoT and AI-enabled technology are shifting healthcare from a conventional hub-based system to a more personalized healthcare management system (HMS). However, implementing smart sensors, advanced IoT, AI, and Blockchain technologies synchronously in HMS remains a significant challenge. Prominent and reoccurring issues such as scarcity of cost-effective and accurate smart medical sensors, unstandardized IoT system architectures, heterogeneity of connected wearable devices, the multidimensionality of data generated, and high demand for interoperability are vivid problems affecting the advancement of HMS. Hence, this survey paper presents a detailed evaluation of the application of these emerging technologies (Smart Sensor, IoT, AI, Blockchain) in HMS to better understand the progress thus far. Specifically, current studies and findings on the deployment of these emerging technologies in healthcare are investigated, as well as key enabling factors, noteworthy use cases, and successful deployments. This survey also examined essential issues that are frequently encountered by IoT-assisted wearable sensor systems, AI, and Blockchain, as well as the critical concerns that must be addressed to enhance the application of these emerging technologies in the HMS.

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.

Background Carbapenem resistant Enterobacterales (CRE) are important cause of antimicrobial resistance (AMR). AMR is a global problem that disproportionately affects the low and middle income countries (LMIC) more. Appreciating what these risk factors were before the recent COVID-19 epidemic would help us appreciate what changes have happened in the post COVID-19 era. Methods This was a hospital-based cross-sectional study conducted in 2017 in the largest tertiary health facility in northern Nigeria. Epidemiologic risk factors of interest were investigated using an interviewer administered questionnaire, followed by collection of a rectal swab sample. Regression analyses were conducted to appreciate what were the strengths of association between the outcome of interest as rectal carriage of CRE and the investigated risk factors. Results Rectal carriage of CRE was 4.2% ( N  = 168) in this study. Increasing age was independent risk factor (adjusted OR = 0.2, CI:0.04–0.98, p  = 0.047) that increased the risk of rectal carriage of CRE. Education appeared to decrease the risk (unadjusted OR = 0.21, CI:0.06–0.76, p  = 0.018) while length of admission stay in Nigeria increased the risk of rectal carriage of CRE (adjusted OR = 3.9, CI: 1.12–13.49, p  = 0.030) against rectal carriage of CRE. Also the ward a patient was admitted into was an important risk factor that increased the risk (adjusted OR = 3.1, CI:1.14–8.20, p  = 0.030) for rectal carriage of CRE. Conclusions Length of hospital stay and the ward a patient was admitted into were associated with increasing risk for rectal carriage of CRE. Epidemiologic risk factors for rectal carriage of CRE are quite similar within the LMIC context. It will be important to appreciate how these risk factors have changed since the COVID-19 pandemic. This would help make policies that focus on the most efficient counter measures to reduce the burden antimicrobial resistance in the LMIC. Clinical trial number Not applicable.

The demand for sustainable development goals and the absence of systematic development and organised exploration for gold has prompted this study to integrate magnetic and radiometric datasets with lithology to evaluate the gold mineralisation potential in the Ilesha schist belt. This study considers 3168.72 km 2 of the Ilesha schist belt in southwestern Nigeria, a frontier belt for gold deposits. The high-resolution airborne magnetic and radiometric datasets were processed using enhancement techniques, including the analytical signal, lineament density, and K/Th ratio. CET grid analysis, Euler deconvolution, and analytical signal depth estimation methods were used to aid the interpretation. The spatial integration and interpolation were performed using the Analytical Hierarchy Process (AHP) and weighted overlay analytical tools within the ArcGIS environment. The dominant structural controls for potential mineralisation are ENE–WSW and ESE–WNW trends. The depth of the magnetic sources revealed by the analytical signal ranged from 63.17 to 629.47 m, while depths ranging from 47.32 to 457.22 m were obtained from Euler deconvolution. The delineated highly magnetic edge sources, dense lineaments, radiometrically highlighted alteration zones, and lithological hosts for gold mineralisation were integrated to establish the gold mineralisation potential map. The AHP deductions reveal that 10.52% of the study site is within the high mineralisation potential class, a remarkable 60.39% falls within the moderate class, a significant portion (28.86%) falls within the poor class, and 0.23% is considered unfavourable. The result was optimised by validation using known mines, with 94% (i.e., 15 out of 16 mining sites) plotting within the high mineralisation potential class. This assessment provides invaluable insight for stakeholders and policymakers to embark on gold exploration and exploitation and promote sustainable mineral development.

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.

We introduce a novel semiparametric estimator for Mann–Whitney-type causal effects based on the cumulative probability model (CPM). CPMs are rank-based, invariant to monotone transformations of the outcome, and offer flexible outcome regression under confounding. We formalize the estimation under causal consistency, no interference, ignorability, and positivity assumptions, and develop accompanying inference procedures. Through simulations with varying sample sizes and effect magnitudes, the CPM estimator shows reduced variability and improved predictive accuracy relative to mis-specified parametric transformations. We demonstrate its applicability in a large cohort of people with HIV (PWH) in Northern Nigeria by assessing the causal effect of HIV status on albuminuria levels. Overall, our results highlight the value of robust semiparametric methods for causal inference in observational settings beyond average treatment effects. Findings should be interpreted in light of the observational design and the potential for unmeasured confounding.

Background Globally, substance abuse has been identified as a major public health issue. The aim of the study was to determine and compare the prevalence, pattern, and predictors of substance abuse among adolescents in public and private day secondary schools in Katsina State. Methods A cross-sectional comparative study was employed to investigate 1126 adolescents obtained through multistage sampling technique in selected public and private day secondary schools across geopolitical zones spanning both rural and urban LGAs in Katsina State. Data was collected over eight weeks with the aid of pretested interviewer-administered questionnaire and was analysed using IBM SPSS version 25. Ethical approval was obtained from Katsina State Ministry of Health. Results Overall, majority (25.1%) of respondents were 18 years of age (majority, 28% in public and 25.2% in private schools were 17 and 18 years of age respectively. Overall mean age of the study population was 16.98 ± 1.27 years (Public; 16.97 ± 1.237 years and Private;16.99 ± 1.309 years). Overall, most of the respondents were in SS3 (44.1%), (Public; 47.4% and Private; 40.8%). Proportion of adolescents who ever used any substance at least once was 22.02% (7.99% public, 14.03% private). Factors independently associated with substance abuse were being in SS3 class ( p  = 0.022), coming from monogamous family ( p  = 0.014) and peer substance abuse ( p  = 0.017). The logistic regression model reveals that current users in SS3 class, from monogamous setting and whose peers abuse substances are 7 times more likely (aOR = 7.12), 5 times more likely (aOR = 5.4) and 20% more likely (aOR = 0.209) to be in private than in public schools, respectively. Conclusion Prevalence of substance abuse was high. Major predictor was peer substance abuse. Consequently, the state Ministry of Education in collaboration with Ministry of Health and NDLEA should design a substance abuse prevention programme with a view to reducing the menace of substance abuse in the state.

Unstructured supplementary service data also referred to as feature code or quick code has become a monumental part of services and products offered by Telecommunication Operators, and its application to various areas of telecommunication and real-world scenarios. It is a protocol of communication utilized by global system for mobile communications cell phones to communicate with mobile telecommunication network operators and applicable to various areas of trend in modern information technology sectors such as Wireless Application Protocol browsing, mobile-money services, prepaid call back services, menu-based information services, location-based content services, paid content portal, voting surveys, and product promotion. This system was designed by integrating the designed unstructured supplementary service data channel for employee leave management system into the standard global system for mobile communications architecture. The architecture consists of three parts; the front end, the middle end, and the back end. The system is implemented using PHP for the overall programming, MySQL for the database, and Windows 7 operating system as a development environment with Adobe dream weaver CS3 IDE. Apache TOMCAT webserver was used to host the system locally. Two interfaces were developed; one side with the mobile operators, which requires setting up of SS7 stack and the unstructured supplementary service data application over the stack. On the other side, HTTP-basedAPIs were used for the unstructured supplementary service data application. Several types of leaves exist and their usage depends on educational institution policies. An employee may apply for study leave like maternity leave, sick leave, and annual leave. Paper-based work is time consuming, USSD based activities are very simple and effective. This research work was conducted to solve the leave management in an academic institution using the USSD. Using the developed system, 1326 USSD sessions were recorded, 1238 sessions were successful, 59 were incomplete, and 29 failed. Cumulative findings from the 41 respondents reveal that the system is faster, more convenient, and user-friendly than the manual method of applying and managing employee leave, which indicates 94% of the success of the system. Thus, it can be concluded that the system was able to manage the leave management very well and very effectively.

Antimicrobial resistance (AMR) is a major cause of morbidity and mortality. AMR is a global problem that disproportionately affects the low and middle-income countries (LMIC), and Extended Spectrum Beta-lactamases producing Enterobacterales (ESBL-PE) are an important contributor. This study was aimed at understanding what risk factors were associated with the rectal carriage of ESBL-PE before the recent COVID-19 epidemic. This was a cross-sectional study conducted in 2017 at a tertiary health facility in northern Nigeria. Risk factors of interest were investigated using an interviewer-administered questionnaire, and a rectal swab sample was collected for analysis. Multiple logistic regression analyses were done to appreciate what risk factors were associated with rectal ESBL-PE carriage. This study found a rectal ESBL-PE carriage of 87.5% (N = 168). Significant risk factors included length of hospital stay in the past year outside Nigeria (X2 = 14.3, p = 0.003), any form of antibiotic use outside Nigeria (X2 = 8.2, p = 0.004), and beta-lactam antibiotic use outside Nigeria (X2 = 8.2, p = 0.004). Previous hospital admission outside Nigeria was found to be a protective independent risk factor against rectal ESBL-PE carriage (OR = 0.065, CI 0.005-0.806, p = 0.004). Rectal ESBL-PE carriage was high during the pre-COVID era in the LMIC context. As the COVID pandemic era ensures, it will be necessary to appreciate what risk factors for the rectal carriage of CRE look like. This would enable LMIC policymakers to contextualize countermeasures to be effective in mitigating the problem of AMR.