Background: Diabetes is a chronic global health issue that requires effective management to improve patient outcomes and quality of life. Nursing education plays a critical role in empowering diabetic patients with self-management skills. Aim This systematic review evaluates the impact of diabetes-focused nursing education on patient outcomes and quality of life. Methods: This study uses PRISMA guidelines and a systematic approach to identify and evaluate relevant literature. Results and Discussion: Among the 14 studies reviewed, eight emphasized self-management education, while four incorporated multidisciplinary approaches. Findings consistently demonstrated that structured nursing education programs significantly improved self-management behaviors, glycemic control, and patient knowledge. For instance, nurse-led self-management programs resulted in substantial enhancements in self-care skills and diabetes-related knowledge. Moreover, interventions that combined health education with psychological support were particularly effective, leading to better blood glucose control and increased adherence to treatment. Studies that examined quality of life reported reductions in anxiety, improved lifestyle habits, and better overall self-management. These findings highlight the multifaceted benefits of nursing education, suggesting that structured, supportive programs positively impact both clinical and psychological aspects of diabetes care. Conclusion: The review emphasizes the value of comprehensive nursing education that integrates both clinical guidance and psychological support for holistic diabetes management. Implications: Ongoing professional development and culturally sensitive education programs are recommended to address the diverse needs of diabetic patients. Future research should investigate the long-term effects of nursing education and explore innovative strategies to enhance diabetes management outcomes.

The Gambia uses the Primary Health Care model with no trauma response system. Trauma patients are transferred through multiple levels of health care facilities before definitive care hospitals. This study was conducted to identify predictors of injury factors associated with transfer. In this study, we examined characteristics of transferred patients compared to those directly admitted in definitive care hospitals. The study was conducted in two major trauma hospitals in The Gambia. 251 road traffic injury (RTI) patients were either transferred (84%) from lower-level health centers or directly admitted (16%) to one of the study hospitals. Transferred patients were more likely to have been pedestrian/bicyclists (aOR = 1.81; 95% CI = 0.86 – 3.80). Administration of antibiotics was significantly associated with direct admit than transferred patients (aOR = 6.84; 95% CI = 2.38 – 19.68). Transferred patients were more likely to receive intravenous fluid compared to direct admits (aOR = 0.03; 95% CI = 0.01 – 0.08). The study results have implications for policies and planning in the healthcare setting in The Gambia and other LMICs with similar settings. Based on the findings of this study, it is essential that hospital management teams adapt to increasing reliance of RTI patients on lower-level healthcare facilities. The study results suggest increased burden on lower-level health care facilities. Efforts and resources should focus more on supporting lower-level facilities.

1) Background: Culture shock is a common experience by internationally educated nurses (IENs) working in foreign countries, characterized by disorientation and discomfort due to distinct norms, values, and rituals. 2) Aim: this study explores culture shock in IENs and explores mitigation techniques to enhance understanding of challenges faced in foreign cultural contexts. 3) Method: Using Concept Analysis by Walker and Avant (2019). 4) Results: A total of 20 articles were reviewed. Four major attributes were identified: psychological and emotional impact, communication barriers, acculturation and quality of life, and organizational challenges. 5) Conclusion: This paper explores the challenges faced by nurses from foreign countries due to cultural adjustment and proposes solutions to minimize its effects. It is beneficial for nurses, healthcare organizations, and policymakers, aiming to improve patient care and health outcomes. 6) Implication for Practice: Addressing culture shock can promote a smooth transition, enhance nurses' experience, and improve their cultural competence. Providing tailored orientation and mentorship programs can help IENs feel supported and empowered, leading to increased job satisfaction, retention rates, and better patient outcomes.

Objective: This systematic review aimed to identify, synthesize, and critically analyze the available evidence on clinical protocols used for the prevention and treatment of prosthetic joint infection (PJI) in total hip arthroplasty (THA), based on studies published between 2000 and 2025. Methods: The review was conducted according to PRISMA guidelines. Electronic searches were performed in PubMed (MEDLINE), Scopus, Web of Science, and Embase between January and April 2025. Eligible studies included clinical trials, cohort studies, case-control studies, systematic reviews, and meta-analyses published in English that addressed either preventive or therapeutic strategies for PJI in THA. Study selection, data extraction, and quality assessment were carried out independently by two reviewers. Due to the heterogeneity of the included studies, a qualitative synthesis was performed. Results: A total of 32 studies were included. Preventive measures identified in the literature comprised combined antibiotic prophylaxis (cefazolin and gentamicin), multimodal perioperative protocols such as ACERTO, nasal decolonization for Staphylococcus aureus, silver-impregnated dressings, and structured post-discharge surveillance. Treatment strategies included DAIR (Debridement, Antibiotics, and Implant Retention), the DAPRI technique, one-stage and two-stage revision surgeries, muscle flap reconstructions, and protocols without spacers. These interventions were associated with significantly reduced infection rates and improved clinical outcomes when applied appropriately and in accordance with patient-specific factors. Conclusion: Effective prevention and treatment of PJI in total hip arthroplasty require a systematic and evidence-based approach. Integrated protocols—spanning preoperative optimization, meticulous intraoperative techniques, and rigorous postoperative monitoring—have proven effective in reducing infection incidence. In cases of established infection, surgical management must be tailored to the timing of infection, microbial profile, and host conditions. Two-stage revision remains the gold standard for complex infections, while one-stage revision and emerging techniques like DAPRI offer promising results in selected cases. This review contributes to the standardization of clinical practice and supports improved patient outcomes.

Recent advancements in the screening, diagnosis, and management of obstructive sleep apnea (OSA) have significantly improved patient outcomes. For screening, the use of home sleep apnea testing (HSAT) has become more prevalent, offering a convenient and cost-effective alternative to traditional in-lab polysomnography. HSAT devices have shown good specificity and sensitivity, particularly in patients with a high pre-test probability of OSA. In terms of diagnosis, advancements in wearable technology and mobile health applications have enabled continuous monitoring of sleep patterns and respiratory parameters. These tools provide valuable data that can be used to identify OSA more accurately and promptly. Additionally, machine learning algorithms are being integrated into diagnostic processes to enhance the accuracy of OSA detection by analyzing large datasets and identifying patterns indicative of the condition. Management of OSA has also seen significant progress. Continuous positive airway pressure (CPAP) therapy remains the gold standard, but new developments include auto-adjusting CPAP devices that optimize pressure settings based on real-time feedback. Mandibular advancement devices and hypoglossal nerve stimulation are emerging as effective alternatives for patients who are CPAP-intolerant. Furthermore, lifestyle interventions such as weight management, positional therapy, and exercise have been shown to complement medical treatments, leading to better overall outcomes. This review article highlights these advancements that collectively contribute to improved patient adherence, reduced symptoms, and enhanced quality of life for individuals with OSA.

Digital therapeutics (DTx) play a transformative role in diabetes management by leveraging technology to provide personalized, data-driven medical interventions. These tools enhance self-management by offering continuous monitoring and real-time feedback on glucose levels, diet, and physical activity. This personalized approach helps patients adhere to treatment plans and make informed lifestyle changes, leading to improved clinical outcomes such as reduced HbA1c levels and better overall diabetes control. The importance of DTx lies in their ability to make diabetes care more accessible and convenient. Mobile apps and telemedicine platforms enable patients to receive support and guidance from anywhere, reducing the need for frequent in-person visits. Additionally, DTx often include behavioral support features like reminders, educational content, and motivational tools, which are crucial for maintaining healthy habits and managing stress. Currently, the dynamics of DTx in diabetes are rapidly evolving, with increasing integration of artificial intelligence and machine learning to further personalize and optimize care. As the adoption of these technologies grows, they hold the potential to significantly improve patient outcomes and revolutionize diabetes management on a global scale. This article will focus on the benefits of novel digital therapeutics for prevention and management of type II diabetes that are currently available in the market.

Delirium is a major complication most commonly observed in patients with advanced cancer. However, despite its prevalence, the early diagnosis, management, and prevention of this condition have not seen significant progress. Aim of this research is to provide insights into the prevalence of delirium, the optimization of interventions for managing delirium symptoms, their effectiveness and the impact of underlying factors on the reversibility of delirium in advanced cancer patients receiving palliative care. The review involved systematic searches of relevant databases including MEDLINE, CINAHL, ProQuest Nursing and Allied Health, and PsychInfo using refined search terms. Eight publications out of 614 studies originally searched were selected and critically reviewed. Their quality was assessed using Joanna Briggs Institute's Critical Appraisal Tool for Case Series. Data abstraction and content analysis were performed to synthesize the findings. Delirium is prevalent among advanced cancer patients in palliative care, with rates ranging from 10.3% to 24.1%. Pharmacotherapy and non-pharmacological interventions showed effectiveness in reducing delirium symptoms. Delirium was found to be reversible through palliative care interventions, antipsychotic medications, and exercise therapy. Effective delirium management is crucial in improving the quality of life of cancer patients. This review emphasizes the importance of subtype-specific treatments, standardized guidelines, and long-term follow-up studies. Implementing evidence-based individualized approaches to delirium management can optimize treatment efficacy and clinical outcomes in patients as well as improve the quality of care. Tailored interventions, standardized protocols, and further research are hereby recommended.

Background: Over the past decade, there has been a rapid advancement in technology and virtual reality applications, leading to the emergence of the metaverse - a virtual universe where users interact with each other and their surroundings through immersive experiences. In the nursing profession, the metaverse presents unique opportunities to enhance patient care, education, and collaboration. Aim: To analyze and identify the attributes of metaverse in nursing, exploring its dimensions, benefits, challenges, and implications. By examining relevant literature, this study will contribute to a better understanding of the metaverse in nursing. Method/Design: Concept analysis by Walker and Avant (2019). Results: Metaverse in nursing involves three defining attributes: use of advanced technologies, better access to education and healthcare, and collaboration and community building. Antecedents of metaverse in nursing require technological advancements, increased digital literacy, demand for innovative education, globalization of healthcare education Conclusion: Metaverse in Nursing suggests that it is a promising technology that has the potential to enhance nursing practice and improve patient outcomes, but further research is needed to fully explore the impact of its integration.

Background: There are currently numerous risk instruments available to aid in predicting the present or future chance of getting a cancer diagnosis. It aids in determining a person's likelihood of developing certain cancers by looking at various risk factors, including environmental, behavioral, and genetic. Aim: To analyze the effectiveness of cancer risk assessment techniques utilized in primary care settings. Methods: An integrative review of literature Results: Five (5) studies were met the criteria based on the inclusion and exclusion criteria. These tools demonstrated effectiveness in improving patient outcomes and serving as useful therapeutic tools in the primary care setting. Conclusion: Advantages that may aid clinicians in the primary care setting in validating the diagnosis and assisting patients in determining the early signs and symptoms in the diagnosis of cancer. The role of assessment tools can enhance the reliability and caliber of clinical judgment, which can enhance patient outcomes. Implications: The role of healthcare professionals, such as oncologists, nurses, and the healthcare team, on cancer risk assessment in the primary care setting across the lifespan is crucial to ensure a care plan tailored to each patient’s needs.

Introduction: The city of Wuhan in China reported the first case of coronavirus, termed as SARS-CoV-2, in December 2019. To date, 187,827,660 cases have been reported to the WHO (3). With current research focusing on potential therapeutic agents for the coronavirus disease and vaccines, there remain major gaps in our understanding of the pathophysiology and clinical course of this viral pneumonia. Secondary infections are one of them. In this systematic review, we analyze the outcomes of two fungal infections in patients of COVID-19, viz. Mucormycosis and candida. Methodology: A systematic review has been done on secondary infections with mucor and candida fungi in patients of COVID-19. Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines were used. Twenty-three studies were included in the final analysis. Our review included studies from various countries across the globe. The risk of bias was analyzed using the NIH Quality Assessment Tool for Case Series Studies. This study did not require ethical approval as data was obtained from already available databases, and patients were not directly involved. Results: A total of 23 articles were included in the final review and the total number of patients included was 79 Male: female ratio was calculated to be 1.6 and the average age of patients was 52 years (ranging from 24-86 years). Various types of comorbidities were seen in the included patients, the most common being diabetes mellitus. Among the 18 patients in the cohort of mucormycosis, 7 patients died and four studies did not report patient outcomes. Among the 61 patients, 13 patients died and one patient was still ventilated at the time of publication. Conclusion: Secondary infections after COVID-19 are a cause of major concerns. Further studies and case reports are needed to better understand the various other types of secondary infections and also to formulate strategies to prevent these.

Artificial intelligence systems have been previously used to predict post-operative complications in small studies and single institutions. Here we developed a robust artificial intelligence model that predicts the risk of having cardiac, pulmonary, thromboembolic, or septic complications after elective, non-cardiac, non-ambulatory surgery. We combined structured and unstructured electronic health record data from 3.5 million surgical encounters from 25 medical centers between 2009 and 2017. Our neural network model predicted postoperative comorbidities 15 to 80 times faster than classical models. As such, our model can be used to assess the risk of having a specific complication postoperatively in a fraction of a second. With our model, we believe clinicians will be able to identify high-risk surgical patients and use their good judgment to mitigate upcoming risks, ultimately improving patient outcomes [1].

Advances of wearables, sensors, smart devices, and electronic health records have generated patient-oriented longitudinal data sources that are analyzed with advanced analytical tools to generate enormous opportunities to understand patient health conditions and needs, transforming healthcare significantly from conventional paradigms to more patient-specific and preventive approaches. Artificial intelligence (AI) with a machine learning methodology is prominently considered as it is uniquely suitable to derive predictions and recommendations from complex patient datasets. Recent studies have shown that precise data aggregation methods exhibit an important role in the precision and reliability of clinical outcome distribution models. There is an essential need to develop an effective and powerful multifunctional machine learning platform to enable healthcare professionals to comprehend challenging biomedical multifactorial datasets to understand patient-specific scenarios and to make better clinical decisions, potentially leading to the optimist patient outcomes. There is a substantial drive to develop the networking and interoperability of clinical systems, the laboratory, and public health. These steps are delivered in concert with efforts at enabling usefully analytic tools and technologies for making sense of the eruption of overall patient’s information from various sources. However, the full efficiency of this technology can only be eliminated when ethical, legal, and social challenges related to reducing the privacy of healthcare information are successfully absorbed. Public and media are to be informed about the capabilities and limitations of the technologies and the paramount to be balanced is juvenile public healthcare data privacy debate. While this is ongoing, the measures have been progressed from patient data protection abuses for progress to realize the full potential of AI technology for hosting the health system, with benefits for all stakeholders. Any protection program should be based on fairness, transparency, and a full commitment to data privacy. On-going innovative systems that use AI to manage clinical data and analyzes are proposed. These tools can be used by healthcare providers, especially in defining specific scenarios related to biomedical data management and analysis. These platforms ensure that the significant and potentially predictive parameters associated with the diagnosis, treatment, and progression of the disease have been recognized. With the systematic use of these solutions, this work can contribute to the realization of noticeable improvements in the provision of real-time, personalized, and efficient medicine at a reduced cost [1].

Healthcare is increasingly recognized as a data-intensive industry. Multi-hospital networks, among other organizations, face mounting operational and governance challenges because of rigid data-integration pipelines that support all data sources and destinations in the network. These pipelines have become difficult to modify, causing them to lag behind the changing needs of the clinical operation. Scalable data-pipeline architectures better support clinical decision making, optimize hospital operations, ease data quality and compliance concerns, and contribute to improved patient outcomes. Meeting scalability goals requires breaking up monolithic data-integration pipelines into smaller decoupled components and aligning service-level agreements of pipeline components and source systems. Parallelization and adoption of distributed data-warehouse technology mitigate the burden of ingesting data into a multi-hospital network. However, latency requirements still warrant the construction of separate pipelines for data ingress from clinical devices, electronic health records, and external laboratory-information systems. Healthcare associations recommend near real-time data availability for a growing list of clinical and operational applications. Mishandling the real-time ingestion of data from clinical devices, in particular, compromises availability and performance. Scalable architectural patterns for real-time streaming Ingestion from heterogeneous data sources, transport processes, and back-end processing structures are detailed.