In recent years, the proliferation of wearable healthcare devices has marked a revolutionary shift in the personal health monitoring and management paradigm. These devices, ranging from fitness trackers to advanced biosensors, have not only made healthcare more accessible, but have also transformed the way individuals engage with their health data. By continuously monitoring health signs, from physical-based to biochemical-based such as heart rate and blood glucose levels, wearable technology offers insights into human health, enabling a proactive rather than a reactive approach to healthcare. This shift towards personalized health monitoring empowers individuals with the knowledge and tools to make informed decisions about their lifestyle and medical care, potentially leading to the earlier detection of health issues and more tailored treatment plans. This review presents the fabrication methods of flexible wearable healthcare devices and their applications in medical care. The potential challenges and future prospectives are also discussed.

Fucoxanthin (FN), a carotenoid derived from brown seaweed and algae, offers significant health benefits. However, its unique structure leads to challenges in stability and bioavailability. To overcome these issues, we successfully encapsulated fucoxanthin in solid lipid nanoparticles (SLNs) utilizing health-safe materials, achieving remarkable results. SLNs exhibited a nanoscale size of 248.98 ± 4.0 nm, along with an impressive encapsulation efficiency of 98.30% ± 0.26% and a loading capacity of 5.48% ± 0.82% in lipid. The polydispersity index (PDI) was measured at 0.161 ± 0.03, indicating a narrow size distribution, while the high negative zeta potential of −32.93 ± 1.2 mV suggests excellent stability. Pharmacokinetic studies conducted in Sprague–Dawley rats revealed an exceptional oral bioavailability of 2723.16% compared to fucoxanthin crystals, likely attributed to the enhanced stability and improved cellular uptake of the SLNs. To further improve bioavailability, we creatively applied enteric coatings to the freeze-dried SLNs, effectively protecting fucoxanthin from gastric degradation, which is supported by in vitro digestion results. These findings underscore the potential of SLNs as a superior delivery system for fucoxanthin, significantly enhancing its therapeutic efficacy and broadening its application in the food and pharmaceutical industries.

Artificial intelligence (AI) has become a transformative tool in the field of molecular biosensing, enabling data-driven optimization in sensor design, signal processing, and real-time monitoring. AI promotes the discovery of biomarkers, the design of high-affinity receptors, and the rational engineering of sensing materials, thereby enhancing sensitivity, specificity, and detection accuracy. In the development of biosensors, AI-assisted strategies have accelerated the identification of novel molecular targets, guided the design of proteins and aptamers with enhanced binding performance, and optimized plasmonic and nanophotonic structures through forward prediction and inverse design frameworks. The integration of artificial intelligence has significantly enhanced the performance of various biosensing platforms, including optical, electrochemical, and microfluidic biosensors. It also enabled automatic feature extraction, noise reduction, dimensionality reduction, and multimodal data fusion, overcoming the challenges posed by complex signals, environmental interference, and device variations. These capabilities are particularly crucial for wearable molecular biosensors, as low signal strength, motion artifacts, and fluctuations in physiological conditions impose strict requirements on robustness and real-time reliability. This review systematically summarizes the latest advancements in AI-assisted molecular biosensors, highlighting representative sensing strategies and algorithms for wearable and real-time monitoring, and discusses the current challenges and future development opportunities of intelligent biosensing technologies.

Obesity and associated metabolic disorders pose significant health challenges. Fucoxanthin, a lipophilic compound, has shown promising anti-obesity potential, but its poor solubility and bioavailability limit therapeutic efficacy. The successful formulation of solid lipid nanoparticles (SLNs) amplified fucoxanthin’s efficacy in mitigating obesity and the associated metabolic dysregulation. High-fat diet (HFD)-induced obese mice were treated with free fucoxanthin, lyophilized SLNs (L-SLN), and dispersed SLNs (D-SLN) loaded with fucoxanthin. The intervention with D-SLN demonstrated the most significant reduction in body weight gain (29.94%) and fat mass gain (61.80%) compared to the HFD group (p < 0.05), alongside notable improvements in metabolic indicators including fasting blood glucose, liver enzymes, lipid profile, and inflammatory markers such as leptin and monocyte chemoattractant protein 1 (MCP-1) levels. Histopathological evaluation corroborated these findings, showing highly reduced hepatic lipid droplet accumulation and improved adipocyte and testicular morphology. This advancement paved the way for translating fucoxanthin into a clinically effective anti-obesity agent.

Topological phononic materials have been widely used in many fields, such as topological antennas, asymmetric waveguides, and noise insulation. However, due to the limitations of the manufacturing process, topological protection is vulnerable to some severe defects that may affect the application effect. Therefore, the quality inspection of topological materials is essential to ensure reliable results. Due to the low contrast and irregularity of defects and the similarity of topological phononics, they are difficult to recognize by traditional image processing algorithms, so manual detection is still mainstream at present. But manual detection requires experienced inspectors, which is expensive and time-consuming. In addition, topological materials are expensive to produce, and there is no large publicly available dataset, but deep learning usually relies on large datasets for training. To solve the above problems, we propose an automatic deep learning topology structure defect detection method (ADLTSDM) in this work, which could classify not only the structure of topological materials but also detect the defects of topological phononics based on a small dataset. ADLTSDM exploits the prior knowledge of the topological material structure and achieves an augmentation factor of more than 100 times through the random and fixed interval screenshot algorithm, thus enabling the training of deep neural networks with only two raw data. For defect detection, ADLTSDM has an accuracy of more than 97% and improves detection speed by more than 38% compared with manual detection. For structure classification, ADLTSDM can achieve an accuracy of over 99% and seven times faster speed compared with manual classification. Besides, the detection standard of ADLTSDM is unified, so the accuracy will not be affected by the experience of the inspectors, which has more potential in high-throughput industrial applications.