Sepsis and antibiotic resistance constitute a deadly synergy, causing the loss of millions of lives across the world, with their economic and developmental consequences posing a threat to global prosperity. Their impact is disproportionately felt in resource-limited settings and among vulnerable populations, especially children. A key challenge is prompt diagnosis and timely commencement of appropriate antibiotic therapies. These challenges are compounded in low-income and middle-income countries by a lack of comprehensive epidemiological data, with Nigeria being one such country for which it is lacking. Kaduna is the third largest state in Nigeria, with over 10 million inhabitants, of whom more than half are children under 14 years old. While bacterial sepsis and antimicrobial resistance (AMR) are recognised as a growing problem in the state, there are huge gaps in the current understanding of their aetiology. This project employs a cross-sectional design to investigate the clinical and haematological markers of paediatric sepsis, alongside determining the bacterial cause and prevalence of AMR at four high-turnover hospitals in Kaduna State, Nigeria. Further, whole-genome sequencing of isolated bacterial pathogens will be performed to determine the genetic features of resistance. This project represents the largest surveillance study of paediatric sepsis in Kaduna to date. Additionally, we aim to use the clinical, haematological, microbiological and genomic data to derive predictive models for sepsis causes, treatment strategies and patient outcomes.
This is a hospital-based, cross-sectional study that will recruit up to 461 children with bacterial sepsis who were admitted at the two teaching and two general hospitals in Kaduna State, Nigeria. Children presenting with features of fever, subnormal temperature and body weakness would be recruited into the study and have their blood samples collected. The blood samples will be used for culture, complete blood count, HIV and malaria testing. Accordingly, we will capture clinical presentation, haematological characteristics, causative pathogen from blood culture and patient outcomes. Nutritional status, known congenital immunosuppressive diseases, HIV infection and malaria infection will also be determined and documented. The bacterial isolates will be phenotypically characterised for AMR and genotypically following whole genome sequencing. Known and potential confounders to the outcomes of bacterial sepsis would be assessed in all participants, and adjustment for confounding would be performed using logistic regression and/or stratification±Mantel-Haenszel estimator where applicable.
Ethical approvals were granted by the University of Birmingham (ERN_2115-Jun2024), the Ahmadu Bello University Teaching Hospital (ABUTHZ/HREC/H45/2023), Barau Dikko Teaching Hospital, Kaduna (NHREC/30/11/21A) and the Kaduna State Ministry of Health (MOH/AD M/744/VOL.1/1110018). The study will be conducted using the international guidelines for good clinical practice and based on the principles of the Declaration of Helsinki. The results will be disseminated via oral and poster presentations in scientific conferences and published in peer-reviewed journal articles.
Ebola virus disease remains a significant public health concern. For protection from Ebola virus, the main target populations are epidemiologically identified and often include healthcare workers and refugees. These target populations are also routinely offered vaccines for other vaccine-preventable diseases. However, concomitant use of rVSVG-ZEBOV-GP with other vaccines is not recommended, given the absence of data regarding its reactogenicity and antigen-specific immunogenicity profile when co-administered. The EbolaCov trial aims to inform whether rVSVG-ZEBOV-GP can be administered concurrent to a Pfizer–BioNTech COVID-19 booster dose without an unacceptable increase in reactogenicity and/or loss of humoral immunogenicity to Ebola vaccine antigen.
This is a single-centre, randomised, single-blinded, vaccine safety and immunogenicity study in healthy adults living in Rwanda. Seventy-two participants will be randomised in a 1:1 ratio to two study groups, the first receiving rVSVG-ZEBOV-GP with a placebo, the second group receiving rVSVG-ZEBOV-GP concurrently with a Pfizer–BioNTech COVID-19 booster dose. The primary outcome measures are quantitative serum anti-glycoprotein (GP) antibody responses, as measured by ELISA, 28 days after vaccination, and frequency and severity of adverse events in the 7 days following vaccination. Secondary outcome measures include day 28 and day 180 serum anti-GP and serum SARS-CoV-2 anti-spike protein-specific geometric mean antibody titres.
This trial was approved by the Rwanda National Ethics Committee (reference 442/2024) and the University of Birmingham (reference ERN_2661-Jun2024). All participants were required to provide written informed consent in accordance with good clinical practice. Dissemination of results will be through conference presentations and peer-reviewed publications.
Pan African Clinical Trials Registry (PACTR202407764378004) and ClinicalTrials.gov (NCT06587503)
Artificial Intelligence is revolutionizing healthcare by addressing complex challenges and enhancing patient care. AI technologies, such as machine learning, natural language processing, and predictive analytics, offer significant potential to impact nursing practice and patient outcomes.
This systematic review aims to assess the impact of Artificial Intelligence applications in healthcare on nursing practice and patient outcomes. The goal is to evaluate the effectiveness of these technologies in improving nursing efficiency and patient care and to identify areas requiring further research.
This review, conducted in August 2024, followed PRISMA guidelines. We searched PubMed, GOOGLE SCHOLAR, and Web of Science for studies published up to August 2024. The inclusion criteria were original research on AI in nursing and healthcare practice published in English. A two-stage screening process was used to select relevant studies, which were then analyzed for their impact on nursing practice and patient outcomes.
A total of 5975 studies were surveyed from the previously mentioned databases, which met the inclusion criteria. Findings show that AI applications, including machine learning, robotic process automation, and natural language processing, have improved diagnostic accuracy, patient management, and operational efficiency. Machine learning enhanced disease detection, reduced administrative tasks for nurses, NLP improved documentation accuracy, and physical robots increased patient safety and comfort. Challenges identified include data privacy concerns, integration into existing workflows, and methodological variability.
AI technologies have substantially improved nursing practice and patient outcomes. Addressing challenges related to data privacy and integration, as well as standardizing methodologies, is essential for optimizing AI's potential in healthcare. Further research is needed to explore the long-term impacts, cost-effectiveness, and ethical implications of Artificial Intelligence in this field.
Artificial Intelligence (AI) is revolutionizing healthcare by enhancing nursing practices and improving patient outcomes. Tools such as Clinical Decision Support Systems (CDSS), predictive analytics, robotic process automation (RPA), and remote monitoring empower nurses to make informed decisions, optimize workflows, and monitor patients more effectively. AI enhances decision-making, boosts efficiency, and facilitates personalized care, while aiding in early detection and real-time data analysis. It also contributes to better nurse education and patient safety by minimizing errors and enabling remote consultations. However, for AI to be successfully integrated into healthcare, it is essential to tackle challenges related to training, ethical considerations, and data privacy to guarantee its effective implementation and positive impact on the quality and safety of healthcare.
This study aimed to explore the challenges and opportunities in engaging health development partners in planning healthcare services at a sub-national level in Uganda.
An exploratory qualitative study involving selected health development partner organisations and district local governments.
A study was conducted in Northern Uganda, specifically in 12 districts that comprise the Lango and Acholi sub-regions. The study area has many health development partners compared with the other regions in the country.
A total of 18 participants were enrolled in the study. To be considered for inclusion, a participant had to be working for a district local government in Northern Uganda and involved in planning health services or working for a development partner supporting health services in the region. Most of the participants were men aged between 41 and 50 years.
Factors that affect the involvement of health development partners in planning health services at sub-national levels and opportunities that can facilitate involvement.
The findings show that health development partners serve as a source of information and data, guide the planning and supervision of services, conduct community mobilisation and support infrastructure development. However, differing planning cycles, corruption, power dynamics and budget constraints affect their participation in district health planning. Continuous engagement, even outside budget periods, with respect to the terms agreed upon in the memoranda of understanding (MOU), equitable treatment of all partners and transparency from all parties emerged as opportunities to improve involvement.
The involvement and importance of health development partners in planning district-level health services cannot be overstated. Therefore, addressing the challenges that hinder joint planning through a focus on open communication, mutual respect and adherence to the terms of the MOU can improve working relationships.