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Acute Kidney Injury in Critically Ill Patients with Cutaneous-origin Sepsis: A Systematic Review
No full textAims: This systematic review aims to synthesize current evidence regarding the incidence, clinical progression, and therapeutic management of acute kidney injury (AKI) in adult patients with sepsis originating from severe dermatologic infections. It emphasizes the clinical significance of cutaneous-origin sepsis, the role of risk factors such as hypotension and inflammatory markers, and the importance of a multidisciplinary approach involving nephrology, dermatology, and infectious disease.
Study Design: Systematic literature review. The review followed PRISMA guidelines.
Place and Duration of Study: Databases searched (PubMed, SciELO, LILACS, BVS, MEDLINE) between January 2014 and April 2024.
Methodology: The review followed PRISMA guidelines. Studies published between 2014 and 2024 were included if they addressed AKI in the context of dermatologic-origin sepsis. Eligible study designs included observational studies, case series, and clinical reports in English, Portuguese, or Spanish. Data extraction was performed independently by two reviewers using a standardized form. Quality assessment used the Newcastle-Ottawa Scale (NOS) and JBI checklist, depending on study type.
Results: A total of 582 studies were initially identified, with 14 meeting all eligibility criteria. Included studies involved 2,761 adult ICU patients with infections such as deep cellulitis, necrotizing fasciitis, infected ulcers, and burns. AKI incidence ranged from 21.4% to 64.7%. The presence of AKI was associated with increased need for renal replacement therapy (RRT), longer hospital stays, and higher mortality. Risk factors for poor renal outcomes included sustained hypotension, early vasopressor use, elevated lactate and procalcitonin levels, and persistent oliguria. Common interventions included early antibiotic therapy, volume resuscitation, avoidance of nephrotoxins, and the use of CRRT in unstable patients.
Conclusion: Sepsis of dermatologic origin poses a substantial risk for AKI development, with clinical and mortality impacts comparable to other septic sources. Early identification and integrated multidisciplinary management are crucial for improving renal outcomes. Further prospective and multicenter research is needed to refine diagnostic tools and intervention protocols specific to this high-risk population
A Case Presentation of Babesiosis in A Mongrel Cat
No full textHaemoprotozoan infections are very common and cause devastating losses and pose a major threat to the animals throughout the world. Most of the haemoprotozoan parasites are transmitted by ticks and is of great economic importance in India. A female cat approximately 3 years age was presented to the Small Animal Unit of Veterinary Clinical Complex for treatment. Pale mucous membranes, depression, anorexia, inability to stand and dehydration were major clinical manifestations and clinical examination of patient revealed elevated rectal temperature with normal respiration and pulse rate. Blood was collected aseptically from the cephalic vein for Hemato-biochemical estimation. Blood smears were prepared aseptically from ear tips and were subjected for Giemsa staining to detect any haemoprotozoans present in the animal. Giemsa staining of the blood smears showed positive for presence of Babesia canis in the erythrocytes and was subjected for treatment with Primaquine Phosphate and Doxycycline combination. After few days of treatment, the health of the cat improved with resolution of clinical signs
Democratic Teaching Beliefs and Global Citizenship Competence of Public Elementary School Teachers
No full textThis study aimed to examine the significant relationship between democratic teaching beliefs and global citizenship competence among public elementary school teachers in the Philippines, specifically in the Carmen District, Division of Davao del Norte. A descriptive-correlational research design was employed, involving a sample of 196 teachers from various public elementary schools. Data were gathered using standardized and previously validated questionnaires, adapted from existing literature (with the original sources cited in the full manuscript), and administered through face-to-face surveys. Descriptive and inferential statistics, including mean, standard deviation, Pearson product-moment correlation, and multiple linear regression analyses, were used to analyze the data. The findings revealed that the level of democratic teaching beliefs among teachers was very extensive (M = 4.32), as well as the level of global citizenship competence (M = 4.29). Correlation analysis indicated a significant and moderate positive relationship between democratic teaching beliefs and global citizenship competence (r = 0.60, p = 0.000). Furthermore, regression results confirmed that democratic teaching beliefs significantly influenced global citizenship competence (R = 0.60, p = 0.000). It is recommended that school administrators implement professional development programs focused on strengthening teachers’ democratic beliefs, as these are essential for fostering global citizenship competence. Promoting these values in the classroom may lead to more inclusive, responsible, and globally aware educational practices
Toward a Legislative Framework for Traditional and Complementary Medicine in Africa: A Legal Commentary on Ghana and the Gambia
No full textBackground: Africa’s healthcare systems are inherently pluralistic, with Traditional and Complementary Medicine (TCAM) playing a central role in public health. However, despite constitutional recognition of indigenous healing systems in countries such as Ghana and The Gambia, regulatory frameworks remain narrow, outdated, or entirely absent—failing to reflect the evolving role of Complementary and Alternative Medicine (CAM) modalities such as naturopathy, homeopathy, and Ayurveda.
Aim of the Study: This study aims to examine the constitutional and legal foundations of TCAM in Ghana and The Gambia, identify existing regulatory gaps, and propose a harmonized and inclusive legislative framework that ensures professional recognition and governance autonomy for both traditional and CAM practitioners.
Methodology and Methods: This study employed a doctrinal and comparative legal research approach. It involved critical analysis of key constitutional provisions, including Articles 11(3) and 26(2) of the 1992 Constitution of Ghana and Section 7 of the 1997 Constitution of The Gambia. Relevant statutory instruments and policy documents were also examined, such as Ghana’s Traditional Medicine Practice Act 575 and The Gambia’s Draft Traditional Health Practitioners Bill, 2025. Additionally, international frameworks—particularly the WHO Traditional Medicine Strategy 2014–2023—and national policy drafts like The Gambia’s 2020 Draft Constitution were reviewed to contextualize the legal positioning of traditional medicine within broader global and regional governance structures.
Results and Findings: The study found that both Ghana and The Gambia have constitutional backing for traditional medicine, yet existing laws—such as Ghana’s Traditional Medicine Practice Act (Act 575)—are limited in scope, excluding CAM systems that are increasingly relevant. The Gambia lacks a comprehensive TCAM statute entirely. The findings support the need for a dual regulatory mechanism that separates but equally legitimizes traditional and CAM practitioners. Furthermore, the study advocates for autonomous TCAM governance structures composed exclusively of TCAM stakeholders.
Conclusion and Recommendations: The integration of TCAM through constitutional and legislative reform is both a legal imperative and a public health necessity. The study concludes that renaming existing legislation to explicitly include complementary medicine, developing dual licensure pathways, and institutionalizing TCAM-led governance bodies are critical for regulatory legitimacy. It is recommended that Ghana and The Gambia adopt a unified \u27Traditional and Complementary Medicine Bill\u27 aligned with WHO and indigenous rights frameworks to ensure cultural inclusion, healthcare equity, and legal sustainability across African health systems
Machine Learning Models for Predictive Risk Assessment in Healthcare IT Projects
No full textThis integrative review focuses on the role of machine learning (ML) in improving predictive project risk management in healthcare information technology (HIT) environments. With the ever-increasing complexity of HIT initiatives, e.g. electronic health record (EHR) implementations and tele-medicine systems, the older risk assessment practices are proving inadequate because they are retrospective. ML presents an anticipatory solution, allowing one to analyse and predict various risks of the project in real-time. To investigate these issues, the study employed an integrative review methodology, which enabled the inclusion of diverse scholarly and technical literature spanning empirical studies, theoretical frameworks, and conference proceedings. A systematic search across major academic databases from 2013 to 2025 facilitated the thematic synthesis of ML models (e.g., Gradient Boosting, Random Forest, and SVM) and types of risks covered (technical, operational, strategic, and clinical), risk domains, implementation outcomes, and adoption barriers in healthcare IT project risk management. It was found that ML can be used to increase accuracy in project delay, cost overrun, and compliance prediction, alongside resource allocation optimisation greatly. Nonetheless, its actual use is still restricted by a lack of infrastructure, transparency of algorithms, and moral issues. As the Technology Acceptance Model (TAM) guides the study, the key factors identified that led to adoption are perceived usefulness and ease of use. It suggests multidisciplinary working, transparent model development, and sound ethical constructs as preconditions of effective implementation. The reviewed paper can be added to the developing debate around the use of AI in healthcare by providing practical implications to be used by researchers, system developers, and policymakers interested in implementing ML in risk-resistant HIT project management
Enhancing Program Performance Evaluation through Artificial Intelligence: A Mixed-methods Study Using LLM Models
No full textThe primary aim of this study is to explore how Artificial Intelligence can enhance the effectiveness of program performance evaluations. By leveraging data-driven techniques, the research aims to identify methods that facilitate more accurate assessments of program outcomes using LLM models, thereby enhancing decision-making processes. The study adopts a mixed-methods design, combining qualitative and quantitative approaches to assess the impact of Artificial Intelligence on program performance evaluation. The research was conducted over twelve months, enabling a detailed analysis of both the immediate and long-term impacts of Artificial Intelligence interventions on program management. The methodology employed in this study is structured around a comprehensive approach to data collection and analysis, ensuring robust insights into program performance evaluations. Qualitative research was conducted to identify relevant metrics for assessment. The qualitative component encompasses in-depth interviews with key stakeholders, providing insights into the contextual factors that influence analytics deployment. Concurrently, the quantitative analysis employs statistical methodologies to evaluate performance metrics both before and after the implementation of AI-driven methodologies. The study\u27s findings highlight the critical role of Artificial Intelligence in enhancing program performance evaluation. Detailed data analysis revealed that employing Artificial Intelligence facilitates the extraction of real-time insights, which significantly assist in strategic decision-making. Programs that integrated advanced Artificial Intelligence and data analytics tools showed improved capability in identifying trends, directly impacting their effectiveness and adaptability. The study concludes that Artificial Intelligence and data analytics enhance program performance evaluations. By providing dynamic, real-time insights and risk assessments, the utilization of Artificial Intelligence and data analytics significantly improves decision-making processes and aids in strategic planning. It emphasizes the importance of robust data quality and governance practices that ensure the accuracy and reliability of evaluations
Exploring Tobacco Habits in School Going Adolescents Boys in Sangaria Hanumangarh, District of Rajasthan, India
No full textThe present study was conducted to examine the prevalence, patterns, and influencing factors of tobacco consumption among adolescent school-going boys aged 12 to 18 years in Sangaria city, Hanumangarh district, Rajasthan. A sample of 100 respondents was selected using a purposive sampling method from various schools across the city. Data were collected through structured questionnaires and personal interviews. The findings revealed that a significant proportion of adolescents were engaged in tobacco consumption, with popular products including pan masala, gutkha, bidi, and cigarettes. Peer influence, family habits, curiosity, and media exposure were identified as major contributing factors for initiation. The study also highlighted that peer influence (44%) and personal interest (33%) were the leading causes of tobacco use, while a substantial number (28%) reported being influenced by family members such as fathers or brothers. Despite the high usage rates, 77% of the respondents supported the idea of banning tobacco products, and 89% stated they would not recommend tobacco use to others, indicating a level of awareness about the health hazards associated with tobacco. The study emphasizes the urgent need for targeted health education programs and anti-tobacco campaigns in schools and communities to reduce tobacco use among adolescents
Smart Sericulture Systems Based on Internet of Things (IOT) and Image Processing
No full textSericulture alludes to the raising of silkworms to produce silk. India is the second biggest producer of silk. Temperature, Relative humidity, light and air plays a major role in sound silkworm production and quality cocoon production mainly depends upon the quality of mulberry leaves. Internet of Things is an ecosystem of connected physical objects that are accessible through internet. IoT empowered Arduino based system used to monitor the rearing room conditions and to monitoring the field conditions. In addition to that, Image processing is an analysis and manipulation of a digitized image, tailored to improve image quality and for separate data from images. Silkworm eggs counting system using image processing is a state of art technology to count eggs accurately and reduces the time of manual counting using conventional methods. Cocoon quality assessment using image processing is more reliable, precise, faster and economically feasible. Sex separation using image processing showed high degree of accuracy. This review highlights the potential of smart sericulture systems employing digital technologies. This will further helps the farming and scientific community in precision sericulture
Leveraging on Sustainable Tourism for Economic Development in the COMESA Region: Lessons from Low and Middle-income Countries (LMICs)
No full textTourism has emerged as one of the fastest-growing and most influential economic sectors globally, playing a pivotal role in driving gross domestic product (GDP), generating employment, attracting investment, and facilitating cross-cultural exchange. Globally, the sector contributes an estimated 10.3% to GDP and supports one in every ten jobs. This study explores how sustainable tourism can be harnessed as a catalyst for regional economic integration, resilience, and inclusive development within the Common Market for Eastern and Southern Africa (COMESA). Despite tourism’s significant contribution to GDP and employment across many member states, its role in promoting cross-border cooperation remains underexploited, constrained by policy fragmentation, restrictive visa regimes, underdeveloped infrastructure, and limited coordination. Drawing on a systematic literature review (SLR) and comparative analysis of experiences from low- and middle-income countries (LMICs) such as Rwanda, Costa Rica, and Vietnam, the study critically synthesises evidence on tourism-led integration and sustainable development. The findings reveal persistent structural barriers to regional tourism integration but also highlight pathways for reform, including visa liberalisation, diversification of tourism products, investment in cross-border infrastructure, and embedding sustainability principles in policy frameworks. A conceptual framework is proposed to guide COMESA’s regional tourism integration strategy, aligned with the African Continental Free Trade Area (AfCFTA) and Agenda 2063. Practical recommendations are offered for harmonising tourism policies, facilitating mobility, promoting sustainable tourism practices, and enhancing regional cooperation. This research fills a critical gap by positioning sustainable tourism within the regional integration discourse, providing actionable insights for COMESA policymakers, development practitioners, and researchers. It underscores the potential of tourism not only to drive economic growth but also to foster regional solidarity, resilience, and inclusive development in line with continental aspirations
Study on the Performance of the Steel Slag Base Stabilized by Multiple Solid Waste Geopolymer
No full textWith the deepening of the concept of sustainable development, cement-stabilized macadam—a traditional road base material—faces increasing demand for improvement due to its high resource consumption, significant environmental pollution, and poor shrinkage performance. Steel slag, an important solid waste product of the steel industry, accumulates in large quantities, resulting in resource waste and environmental pollution. Conventional cement-stabilized steel slag base materials suffer from drawbacks such as high carbon emissions and poor volumetric stability. In this context, geopolymers emerge as an environmentally friendly alternative starting from the raw material level. This study proposes the use of three solid waste materials—fly ash, ground granulated blast furnace slag (GGBS), and red mud—in combination with an alkali activator to enhance the performance of geopolymer-stabilized steel slag as a road base material. These industrial solid wastes serve as cementitious materials replacing traditional cement, while steel slag is introduced as a substitute for natural aggregates. A series of laboratory tests, including compressive strength tests, splitting tensile tests, drying shrinkage tests, and freeze-thaw cycle tests, was conducted to comprehensively evaluate the pavement performance of the geopolymer-stabilized steel slag mixture. The aim is to achieve the dual objectives of solid waste recycling and the development of low-carbon road materials. The results indicate that the geopolymer-stabilized steel slag mixture meets the specifications required for road base materials. Geopolymers show promise as a replacement for cement, with steel slag serving as the base aggregate, forming a new type of green semi-rigid base material