Alginate, a naturally occurring polysaccharide derived from brown algae, has emerged as a versatile cornerstone in the field of biomedical materials. Its widespread adoption is driven by its exceptional biocompatibility and the unique cation-dependent gelation defined by the “egg-box” model. This review examines the fundamental chemistry of alginate, detailing how its crosslinking mechanisms dictate the physicochemical properties essential for clinical performance. The discussion bridges the gap between polymer structure and diverse biomedical applications, including drug delivery, tissue engineering, and the clinical management of gastrointestinal reflux and wound care. Furthermore, the article evaluates the role of alginate-based systems in the biomedical and metabolic management of obesity and diabetes. By analyzing how alginate influences satiety, glycemic index modulation, and lipid absorption through biophysical mechanisms, this review highlights the transition from fundamental chemical architecture to practical clinical utility. By integrating structural chemistry with physiological impact, this work underscores the evolving potential of alginate-based materials as supportive and functional strategies in modern clinical care.

Hydrogels are hydrated three-dimensional polymeric networks with biomedical potential because they can encapsulate therapeutic agents and provide localised, sustained, or stimulus-responsive drug delivery. Their performance is determined by interacting variables, including polymer composition, synthesis route, crosslinking chemistry, drug loading, swelling, degradation, and the biological microenvironment. This multidimensional design space often makes hydrogel development slow and dependent on trial-and-error experimentation. This review examines the data-driven development of biomedical hydrogels for controlled drug delivery, focusing on clinical applications and emerging machine-learning approaches that support material selection, formulation design, synthesis optimisation, and release prediction. The review first discusses natural and synthetic hydrogels, including alginate, chitosan, gelatin-based systems, hyaluronic acid, and polyethylene glycol, with emphasis on how their physicochemical properties influence biocompatibility, synthesis flexibility, and drug-release behaviour. Key applications are then considered, including wound healing, cancer therapy, glucose-responsive insulin delivery, and inflammatory disease management. Particular attention is given to injectable and stimuli-responsive hydrogels, where formulation conditions and synthesis parameters can be tuned to improve localisation, therapeutic exposure, and release control. The review evaluates machine-learning methods, including random forest, gradient boosting, artificial neural networks, Gaussian process regression, and active learning, for predicting hydrogel properties, modelling release profiles, optimizing synthesis and formulation variables, and prioritizing experimental candidates. Finally, translational challenges are addressed, including small non-standardised datasets, limited external validation, weak in vitro-clinical correlations, material safety, explainability, reproducibility, scalability, and regulatory requirements. By integrating clinical, materials, synthesis, and machine-learning perspectives, this review highlights opportunities for developing safer and clinically relevant hydrogel-based drug-delivery systems.

Cutaneous T-cell lymphoma (CTCL) is a rare type of cancer, and its prevalence has been increasing in recent years. The lack of curative treatments makes this cancer critical in terms of diagnosis and treatment. The two main types of treatment are skin-directed and systemic therapies, with limitations related to adverse effects. Accordingly, there is a need for localized, non-invasive approaches that reduce systemic toxicity. To achieve this, nanotechnology has been explored to deliver safe, appropriate diagnosis and treatment. Gold nanoparticles (AuNPs) and transdermal patches containing AuNPs have shown great promise in the treatment of CTCL with a high safety profile. In this review, the pathophysiology of CTCL and its treatment modalities using AuNPs and transdermal patches are presented from both materials science and clinical perspectives.

Lung cancer arises from mutations in lung cells, disrupting their normal growth cycle and leading to uncontrolled cell division. These rapidly dividing cells lose function and fail to form healthy lung tissue. Several factors contribute to the difficulty of diagnosing and classifying lung nodules, including the high degree of morphological heterogeneity and overlapping characteristics between benign and malignant nodules. Recently, deep learning models have been used in computed tomography (CT)-based lung nodule diagnosis and have demonstrated diagnostic efficiency comparable to that of radiologists. This study introduces LungCNET, a high-performance multi-layer deep convolutional neural network trained on chest CT images to improve lung lesion classification efficiency and accuracy significantly. The data for the Lung Cancer convolutional neural network (LungCNET) were derived from the IQ-OTH/NCCD CT scan dataset (1097 images from 110 cases), split into training (767 images), validation (109) and a held-out test partition (221) that played no role in training or model selection. This dataset encompasses three diagnostic categories: benign, malignant, and normal lung tissues. LungCNET was evaluated against fine-tuned benchmark models that are both established and widely used, spanning architectures introduced between 2014 and 2024, including VGG16, ResNet50, InceptionV3, MobileNetV2, and YOLOv11. On the held-out test partition, LungCNET reached a macro-averaged F1-score of 95.19%, with VGG16 at 94.09% and InceptionV3 at 92.28%; these three models performed comparably, and the separation between them is small relative to the resolution of a test set of this size. LungCNET was, however, the only model to exceed 90% F1-score across all three diagnostic classes simultaneously, and recorded the highest F1-score on the benign class (91.0%), the smallest and most frequently misclassified category, where two of the six models failed entirely. These results support LungCNET as a candidate tool for lung cancer diagnosis, subject to validation on larger and independently sourced datasets.

Diabetes mellitus continues to impose a substantial global health burden, underscoring the need for therapeutic systems capable of achieving precise, adaptive, and patient-friendly glycemic control. Conventional diabetes treatments, including repeated insulin injections and oral hypoglycemic agents, are often constrained by non-physiological drug release, poor adherence, systemic side effects, and the persistent risk of hypoglycemia. In this context, glucose-responsive nanomedicine has emerged as a promising platform for next-generation diabetes therapy by enabling self-regulated and glucose-triggered delivery of insulin and other antidiabetic agents. This review highlights recent advances in glucose-responsive nanomedicine, focusing on the principal sensing mechanisms, including glucose oxidase-based, phenylboronic acid-based, and lectin-mediated systems, as well as the nanoscale carriers engineered to support them, such as polymeric nanoparticles, nanogels, micelles, liposomes, and hybrid nanostructures. These smart platforms offer significant potential to improve drug stability, enhance targeting efficiency, reduce dosing frequency, and more closely mimic endogenous insulin secretion. The review further examines their emerging role in precision diabetes care, particularly in combination with continuous glucose monitoring technologies, wearable devices, and closed-loop therapeutic systems. Despite notable progress at the preclinical level, important barriers to clinical translation remain, including challenges related to biocompatibility, long-term safety, reproducibility, scalable manufacturing, and regulatory approval. Collectively, glucose-responsive nanomedicine represents a rapidly advancing and clinically relevant field with the potential to redefine diabetes management through intelligent and personalized therapeutic strategies. This review provides a focused overview of current developments, key translational challenges, and future directions toward clinical implementation.