Chronic wounds such as diabetic foot ulcers, venous leg ulcers, and pressure ulcers often remain trapped in the inflammatory phase due to oxidative stress, protease overactivity, and impaired cellular responses, particularly in diabetic conditions. These wounds require advanced therapeutic strategies beyond conventional care. Regenerative medicine—especially platelet-rich plasma (PRP)-based interventions—has emerged as a promising approach for enhancing wound repair. This review examines recent developments in PRP-loaded scaffolds, focusing on their biological mechanisms, structural advantages, and clinical applications. It synthesizes findings from key studies that integrate PRP with natural and synthetic biomaterials, often combined with bioactive agents like adipose-derived stem cell exosomes. PRP-containing scaffolds promote wound healing through multiple pathways: enhancing cell proliferation, migration, angiogenesis, and extracellular matrix remodeling; reducing inflammation via M2 macrophage polarization; and facilitating collagen deposition. Their antibacterial properties and controlled release of growth factors such as VEGF and TGF-β1 further support tissue regeneration. Additionally, scaffold composition improves mechanical strength, elasticity, and growth factor bioavailability. Innovations such as GelMA/SFMA hydrogels and COL/PRP-ADSC-exos composites have shown superior outcomes in preclinical models. PRP-based scaffolds offer a multifunctional platform for chronic wound treatment by combining biological activity with structural support. Despite existing challenges such as variability in PRP preparation and limited clinical data, ongoing research and emerging technologies hold strong potential to standardize and enhance these therapies for future clinical translation.

Lipid-based Nanoparticles (LBNPs) have emerged as a transformative approach in cancer treatment, offering innovative drug delivery solutions that enhance therapeutic efficacy while minimizing adverse effects. By exploring the characterization, classification, synthesis, targeting strategies, and advantages of LBNPs, this study highlights how LBNPs have been used to overcome the limitations of traditional chemotherapy and improve patient outcomes. As nanotechnology revolutionizes cancer therapy, the emergence of LBNPs as a promising strategy for targeted drug delivery has led to optimism regarding the future of cancer treatment. This review extensively assesses the structure, categories, production methods, targeting strategies, benefits, and recent advancements in LBNPs for treating cancer. It also highlights current challenges and possible future directions. This review is aimed at providing a comprehensive understanding of LBNPs’ potential in cancer therapy. Liposomes, nanostructured lipid carriers, solid lipid nanoparticles, and lipid-polymer hybrid nanoparticles are all types of LBNPs, each with unique features of interest for cancer therapy. These particles can be synthesized through various procedures, such as bulk nanoprecipitation, solvent-based emulsification, or microfluidics. Passive targeting systems, active targeting systems, and responsive delivery platforms direct LBNPs to tumors. Consequently, LBNPs provide an improved drug release pattern that minimizes side effects while enhancing therapeutic efficacy. With the potential for combination therapy, LBNPs offer a hopeful future for cancer treatment. Continued research is expected to improve patient outcomes and overall quality of life in cancer care.

This study explores the practical applications of artificial intelligence (AI) in medical imaging, focusing on machine learning classifiers and deep learning models. The aim is to improve detection processes and diagnose diseases effectively. The study emphasizes the importance of teamwork in harnessing AI’s full potential for image analysis. Collaboration between doctors and AI experts is crucial for developing AI tools that bridge the gap between concepts and practical applications. The study demonstrates the effectiveness of machine learning classifiers, such as forest algorithms and deep learning models, in image analysis. These techniques enhance accuracy and expedite image analysis, aiding in the development of accurate medications. The study evidenced that technologically assisted medical image analysis significantly improves efficiency and accuracy across various imaging modalities, including X-ray, ultrasound, CT scans, MRI, etc. The outcomes were supported by the reduced diagnosis time. The exploration also helps us to understand the ethical considerations related to the privacy and security of data, bias, and fairness in algorithms, as well as the role of medical consultation in ensuring responsible AI use in healthcare.

The recent outbreak of Monkeypox (Mpox), particularly the clade 1b variant, have shifted the epidemiological landscape, making it a Public Health Emergency of International Concern. Africa remains a hotspot with significant ongoing outbreaks, necessitating a focused study on outbreak trends and forecasting to guide health interventions. This study utilizes a comprehensive dataset from the four most affected African countries, covering weekly and cumulative Mpox cases from August 6, 2023, to August 18, 2024. Time series analysis techniques, including ARIMA models and Join Point Regression, were employed to forecast Mpox trends and analyse the annual percentage change in new cases. Descriptive statistics highlighted significant variability in Mpox cases across the studied regions with the mean cases in Africa at 72.55 and a high standard deviation of 60.885. Forecasting models suggest a continued increase in Mpox cases, with cumulative cases expected to reach 6922.95 by the 65th week (95 % CI: 6158.62 to 7687.27) and new cases projected at 45.93 (95 % CI: −88.17 to 180.04). The study underscores the persistent nature of Mpox outbreaks in Africa and the critical need for continuous surveillance and adaptive public health strategies. The forecasts generated offer valuable insights into potential future trends, aiding in the allocation of resources and the implementation of targeted health interventions to curb the spread of the disease.