The tissue engineering applications of coaxial electrospinning are growing due to the potential increased functionality of the fibres compared to basic electrospinning. Previous studies of core and shell scaffolds have placed the active elements in the core, however, the surface response to a biomaterial affects the subsequent behaviour, thus here hydroxyapatite (HA) was added to the shell. Coaxial electrospun polycaprolactone (PCL)-polylactic acid (PLA)/HA (core-shell) scaffolds were produced in 2D sheets using a plate collector, or 3D tubes for bone tissue engineering using a rotating needle collector. The scaffolds include high hydroxyapatite content while retaining their structural and mechanical integrity. The effect of the collector type on fibre diameter, fibre alignment and mechanical properties have been evaluated, and the impact of HA incorporation on bioactivity, BMP-2 release, cell behaviour and mechanical properties for up to 12 weeks degradation were assessed. Fibre uniformity in coaxial electrospinning depends on the relative flow rate of the core and shell solutions. Using a rotating needle collector increased fibre alignment compared to a stationary collector, without affecting fibre diameter significantly, while HA content increased fibre non-uniformity. Coaxial PCL-PLA/HA fibres exhibited significantly higher bioactivity compared to PCL-PLA scaffolds due to the surface exposure of the HA particles. Apatite formation increased with increasing SBF immersion time. Coaxial tubular scaffolds with and without HA incorporation showed gradual reductions in their mechanical properties over 12 weeks in PBS or SBF but still retained their structural integrity. Coaxial scaffolds with and without HA exhibited gradual and sustained BMP-2 release and supported MSCs proliferation and differentiation with no significant difference between the two scaffolds types. These materials therefore show potential applications as bone tissue engineering scaffolds.

Bone is a dynamic, living tissue that exists and renews itself continuously in a 3D manner. Nevertheless, complex clinical conditions require a bone substitute to replace the defective bone and/or accelerate bone healing. Bone tissue engineering aims to treat bone defects that fail to heal on their own. Electrospinning provides an opportunity to create nano- to micro-fibrous scaffolds that mimic the architecture of the natural extracellular matrix (ECM) with high porosity and large specific surface area. Despite these advantages, traditional electrospun meshes can only provide a 2D architecture for cell attachment and proliferation rather than the 3D attachment in native tissue. Fabrication of 3D electrospun scaffolds for bone tissue regeneration is a challenging task, which has attracted significant attention over the past couple of decades. This review highlights recent strategies used to produce 3D electrospun/co-electrospun scaffolds for bone tissue applications describing the materials and procedures. It also considers combining conventional and coaxial electrospinning with other scaffold manufacturing techniques to produce 3D structures which have the potential to engineer missing bone in the human body.

Electrospun nanofiber scaffolds are important in biomedicine, especially skin tissue engineering, in which scaffold porosity and fiber orientation significantly affect cell penetration, nutrition diffusion, and biomechanical integration. Despite progress on image-based, non-destructive methods for SEM analysis, integrating quantitative metrics from multiple morphological descriptors into a unified and automated workflow remains challenging. In this paper, we present a software tool developed using MATLAB App Designer, which facilitates SEM image analysis for non-destructive characterization of electrospun nanofiber scaffolds. This application provides capability to determine porosity, pore size, fiber diameter and fractal dimension as well as estimation of BET surface area and Barrett–Joyner–Halenda (BJH) volume based on geometric models. Eight independently electrospun nanofiber scaffolds, fabricated at identical electrospinning parameters, were analyzed using a combination of automated thresholding, morphological processing, and skeletonization. For each sample, five SEM images were analyzed (total = 40 images). Estimated BET and BJH data were extrapolated from image-based parameters, considering cylindrical-like scaffold geometry to assess internal consistency. High correlations were observed between porosity and surface area, and pore size with BJH volume highlight that the model is appropriate for relative scaffold screening. Statistical comparison detected various methods (Otsu and morphological thresholding) were significantly different from each other ( p < 0.05), indicating importance of method choices on results. A user-friendly GUI allows users to access techniques and view metric outputs easily. Although strong R values were observed, they reflect internal coherence, not external validation. The tool offers a replicable platform for early-stage scaffold assessment in tissue engineering and nanomaterial research.

Medical image segmentation plays a crucial role in the realm of medical imaging. The process involves the division of an image to obtain a comprehensive view and ensure precise diagnostics. There are various methods that are employed, ranging from traditional approaches to the more advanced deep learning techniques. Both play a significant role in enhancing healthcare. With the continuous advancement in technology, there is a growing need for accurate segmentation. While traditional methods such as thresholding and region growing are effective, they may require human intervention for complex cases. Deep learning techniques, particularly Convolutional Neural Networks (CNNs), have significantly improved the process by learning intricate details and accurately segmenting the image. When these methods are combined, healthcare professionals can achieve high-quality, precise results. Furthermore, with the advancements in hardware and technology, real-time segmentation is now possible. Generally, the process of dividing medical images into segments is extremely important for the progress of healthcare with the help of artificial intelligence and the most recent advancements in the industry, such as explainable AI and multimodal learning. However, this meticulously detailed and in-depth review provides an all-encompassing and extensive analysis of the current methods utilized, their multitude of applications across various fields, and the promising emerging advancements that have the potential to pave the way for remarkable future improvements and innovations.

Green-synthesis routes for producing CuO nanoparticles offer a simplified, sustainable, and low-cost replacement for conventional chemical methods, eliminating the need for harsh chemicals and providing an easily scalable process for industrial-level production. Although numerous studies have investigated synthesizing CuO nanoparticles from single plant extracts, comparative assessments of multi-plant-mediated CuO nanoparticles alongside synthetic CuO remain limited. In this work, CuO nanoparticles were green-synthesized from three different plant sources, namely ginger, red onion peels, and garlic, and their physicochemical and biological properties were tested against the synthetic CuO. All plant extracts produced pure-phased monoclinic CuO nanoparticles as confirmed by UV–Vis, XRD, FTIR, and SEM/EDX analyses. SEM showed distinct nanoparticle morphologies, with CuO from ginger extract exhibiting uniform nanocubes, while nanoparticles from red onion and garlic extracts exhibited more aggregated and irregular structures. Their crystallite sizes were 8–9 nm lower than the ~11 nm observed for the synthetic CuO, highlighting the phytochemical role in shaping the nanoparticles’ morphology. The antibacterial efficacy against S. aureus and E. coli showed that ginger-derived and synthetic CuO had the strongest bacterial inhibition and bactericidal potency compared to onion- and garlic-derived CuO samples. However, synthetic CuO had the highest cytotoxicity risk, hindering its suitability for biological uses, while CuO-ginger maintained good cell viability at moderate concentrations. CuO-onion and CuO-garlic gave lower antibacterial cytocompatibility performance due to their thicker capping layers, which led to decreased Cu²⁺ release and ROS production. Ginger-derived CuO achieved an optimal trade-off between antibacterial and cytotoxic efficiency, highlighting its prospects as a candidate for biomedical applications.