In the 21st century, thanks to advances in biotechnology and developing pharmaceutical technology, significant progress is being made in effective drug design. Drug targeting aims to ensure that the drug acts only in the pathological area; it is defined as the ability to accumulate selectively and quantitatively in the target tissue or organ, regardless of the chemical structure of the active drug substance and the method of administration. With drug targeting, conventional, biotechnological and gene-derived drugs target the body’s organs, tissues, and cells that can be selectively transported to specific regions. These systems serve as drug carriers and regulate the timing of release. Despite having many advantageous features, these systems have limitations in thoroughly treating complex diseases such as cancer. Therefore, combining these systems with nanoparticle technologies is imperative to treat cancer at both local and systemic levels effectively. The nanocarrier-based drug delivery method involves encapsulating target-specific drug molecules into polymeric or vesicular systems. Various drug delivery systems (DDS) were investigated and discussed in this review article. The first part discussed active and passive delivery systems, hydrogels, thermoplastics, microdevices and transdermal-based drug delivery systems. The second part discussed drug carrier systems in nanobiotechnology (carbon nanotubes, nanoparticles, coated, pegylated, solid lipid nanoparticles and smart polymeric nanogels). In the third part, drug targeting advantages were discussed, and finally, market research of commercial drugs used in cancer nanotechnological approaches was included.

Background/Objectives: Meniscus injuries represent a critical challenge in orthopedic medicine due to the limited self-healing capacity of the tissue. This study presents the development and characterization of polycaprolactone/graphene oxide (PCL/GO) scaffolds fabricated using 3D bioprinting technology for meniscus cartilage regeneration. Methods: GO was incorporated at varying concentrations (1%, 3%, 5% w/w) to enhance the bioactivity, mechanical, thermal, and rheological properties of PCL scaffolds. Results: Rheological analyses revealed that GO significantly improved the storage modulus (G’) from 36.1 Pa to 97.1 Pa and the yield shear stress from 97.2 Pa to 507.1 Pa, demonstrating enhanced elasticity and flow resistance. Mechanical testing showed that scaffolds with 1% GO achieved an optimal balance, with an elastic modulus of 614 MPa and ultimate tensile strength of 46.3 MPa, closely mimicking the native meniscus’s mechanical behavior. FTIR analysis confirmed the successful integration of GO into the PCL matrix without disrupting its chemical integrity, while DSC analysis indicated improved thermal stability, with increases in melting temperatures. SEM analysis demonstrated a roughened surface morphology conducive to cellular adhesion and proliferation. Fluorescence microscopy using DAPI staining revealed enhanced cell attachment and regular nuclear distribution on PCL/GO scaffolds, particularly at lower GO concentrations. Antibacterial assays exhibited larger inhibition zones against E. coli and S. aureus, while cytotoxicity tests confirmed the biocompatibility of the PCL/GO scaffolds with fibroblast cells. Conclusions: This study highlights the potential of PCL/GO 3D-printed scaffolds as biofunctional platforms for meniscus tissue engineering, combining favorable mechanical, rheological, biological, and antibacterial properties.

In this study, biocomposite membranes were developed by incorporating resveratrol (RSV)-loaded PCL-PEG composites, modified with graphene oxide (GO) and hydroxyapatite (HAP). The aim was to enhance hydrophilicity with GO and improve bioactivity with HAP. The release kinetics of RSV was evaluated by using Franz diffusion cells and compared with various kinetic models, including Korsmeyer-Peppas, Higuchi, and Baker, all of which showed high correlation coefficients ( R²) close to 0.99. Mechanical tests was performed to determine the suitability of these membranes for tissue engineering applications. The composite membrane modified with GO and HAP exhibited tensile strength of 105.2 ± 5.8 MPa, tensile modulus of 3895 ± 159 MPa, elongation at break of 8.4 ± 0.9%, and toughness of 5.88 ± 0.46 MJ/m³. In vitro cell adhesion studies, visualized using DAPI fluorescence staining, demonstrated increased cell adhesion to the composite membranes over periods of 1, 3, 5, 7, and 14 days. These findings highlight the potential of the RSV-loaded PCL-PEG membranes, enhanced with GO and HAP, for applications in bone tissue engineering.

In this study, poly(methyl methacrylate) (PMMA) nanofiber scaffolds reinforced with synthesized nano-hydroxyapatite (n-HA) were fabricated through electrospinning to enhance their potential for applications in bone tissue engineering. Sodium tripolyphosphate (STTP) was utilized as a surfactant to achieve a uniform distribution of particles and improve the structural integrity of the scaffolds. PMMA solutions were prepared at concentrations of the addition of STTP effectively stabilized n-HA dispersion, leading to enhanced fiber morphology, as confirmed by scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), and transmission electron microscopy (TEM). The PMMA_10_HA_S nanofibers demonstrated a homogeneous fiber distribution with an average diameter of 345.40 ± 53.55 nm and a calcium content of 7.1%. Mechanical testing revealed that adding STTP enhanced the mechanical properties, with the n-HA-reinforced 10 wt.% PMMA nanofibers achieving a maximum tensile stress of 4.16 ± 2.13 MPa and an elongation of 7.1 ± 1.95%. Furthermore, cell cytotoxicity assays of different concentrations (25, 50, 75, and 100 mg/mL) using L929 fibroblast cells demonstrated no cytotoxic effect of PMMA_10_HA_S nanofibers. These findings, reinforced by STTP and n-HA, highlight the potential of PMMA_10_HA_S nanofiber scaffolds as promising candidates for bone tissue applications.

Space maintainers (SMs) are essential for preserving dental arch integrity after premature tooth loss. This study aimed to develop a deep learning model to predict the necessity of SMs and identify specific teeth requiring intervention. A dataset of 400 dental X-rays was preprocessed to standardize image dimensions and convert them into numerical representations for machine learning. The dataset was divided into training (80%) and testing (20%) subsets. A Convolutional Neural Network (CNN) was designed with multiple convolutional and pooling layers, followed by fully connected layers for binary classification. The model was trained using 30 epochs and evaluated with accuracy, precision, recall, F1-score, ROC AUC, and MCC. The CNN achieved 94% accuracy, with a precision of 0.93 for Class 0 (no SM needed) and 0.95 for Class 1 (SM needed). The ROC AUC was 0.94, and the MCC was 0.875, indicating strong reliability. When tested on 86 X-ray images, the model successfully identified specific teeth (showing teeth number) requiring SMs, with minimal errors. These results suggest that the proposed AI model provides high-performance predictions for SM necessity, offering a valuable decision-support tool for pediatric dentistry.

Background/Objectives: The first permanent molar (M1) is critical for occlusal development but is highly susceptible to caries and molar–incisor hypomineralization (MIH). When M1 prognosis is poor, extraction may be necessary, requiring accurate evaluation for post-extraction space management. This study aims to develop and validate an automated deep learning framework using YOLOv8n with oriented bounding boxes (OBB) to predict the likelihood of spontaneous space closure following M1 extractions, thereby reducing diagnostic subjectivity and inter-observer variability. Methods: A dataset of 200 pediatric panoramic radiographs was segmented into quadrants and annotated for second permanent molars (M2s) and third molars (M3s). The YOLOv8n-OBB architecture was trained on 640 × 640 pixel images over 100 epochs. The framework integrated M3 presence, M2 Demirjian developmental maturity (proxied by bounding box height), and M2 angulation (via rotation vectors) to map inputs onto an evidence-based clinical decision matrix for prognostic stratification. Results: The model achieved exceptional detection and localization performance with an overall mean average precision (mAP@0.5) of 0.983. Class-specific validation showed high accuracy for M2 (F1-score = 0.978) and M3 (F1-score = 0.904). Quantitative cross-referencing confirmed a seamless mapping of spatial coordinates onto clinical success classes without error propagation. Conclusions: These findings substantiate the YOLOv8n-OBB model as a robust and interpretable decision-support tool. By standardizing prognostic assessments and optimizing treatment planning workflows, the framework serves as an effective aid in pediatric dentistry for managing M1 extractions.