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.