The process of embryo implantation is complex yet highly coordinated, involving differentiation of multiple cell types and dynamic mechanistic or compensatory shifts in metabolic patterns. Metabolic regulation plays a critical role in embryo implantation by modulating embryo competence and endometrial receptivity. Accordingly, metabolites function not only as sources of energy but also as signaling molecules that orchestrate communication between the endometrium and blastocyst. Here, we review the regulatory roles of key metabolites, including carbohydrates, lipids, amino acids, and their derivatives in various reproductive processes, such as blastocyst formation and activation, establishment of uterine receptivity, and embryo implantation. Interpretation of metabolic signaling may help to elucidate the intricate process of embryo implantation and provide new insights into potential clinical interventions and improvements in assisted reproductive technology.

This study aimed to determine the association of excessive gestational weight gain (GWG) and the glycated hemoglobin (HbA1c) level with adverse pregnancy outcomes in patients with gestational diabetes mellitus (GDM).

Researchers have developed a super-hydrated supramolecular hydrogel that combines high mechanical strength with persistent low friction. Inspired by natural articular cartilage, the material leverages zwitterionic units to form a stable hydration layer and microphase-separated structure, achieving a friction coefficient as low as 0.057 and maintaining lubrication over 100,000 sliding cycles.

Harnessing the power of generative AI, researchers at Tsinghua University have developed AIGP—a diffusion-based generative framework that enables instant translation of optical properties into fabrication-ready metasurfaces. By using transmission, phase, and polarization as “prompts,” AIGP directly maps optical properties to subwavelength, fabricable structures, generating high-fidelity metasurface designs in seconds. This breakthrough overcomes critical bottlenecks in photonic inverse design and paves the way for large-scale, AI-driven generative optical devices.

A 72-year-old man developed aggressive skin cancer on his left thumb 30 years after a burn injury, illustrating the decades-long latency of Marjolin ulcer. This rare but deadly cancer behaves far more aggressively than common skin cancers, underscoring the critical need for lifelong monitoring of old scars and immediate biopsy of any suspicious changes.

Researchers have demonstrated the world’s first simultaneous generation of 3,070 optical vortices at megawatt-class peak power (58 MW). By reformulating Hermite–Gaussian to Laguerre–Gaussian mode conversion and implementing it through a compact multibeam interference architecture, the team achieved more than three orders of magnitude improvement in both vortex number and peak power. The work establishes a scalable framework for high-intensity structured-light generation.

A new review shows that AI, teledermatology, and mobile follow-up tools can help family doctors in China manage chronic skin diseases and related mental health and metabolic issues—shifting care from one-off visits to continuous, whole-life support.

Natural biomass hydrogels are emerging as promising building blocks for intelligent sensors because they combine softness, water-rich structures, tunable networks, and abundant functional groups. A new review brings together recent progress in turning materials such as cellulose, chitosan, sodium alginate, gelatin, starch, hemicellulose, proteins, and lignin into responsive sensing platforms. By focusing on the correlation between crosslinking networks and sensor behavior, the review connects material design with practical performance, including sensitivity, response speed, durability, and environmental stability. The work offers a clearer roadmap for developing sustainable sensors for wearable electronics, healthcare monitoring, environmental sensing, and smart human–machine interfaces.

A comprehensive review published in Skin outlines the emergence of “Dermatology AI 2.0”, a fundamental transition from pattern recognition to cognitive and actionable intelligence. Built on four core pillars—causal inference, skin digital twins, predictive intervention, and distributed autonomous networks—this new paradigm enables AI to diagnose rare diseases 30% more accurately, predict disease flares with >90% accuracy, and deliver full-lifecycle skin health management. The review emphasizes that AI will not replace clinicians but will automate routine tasks, allowing physicians to focus on complex cases and patient care.

This study establishes a comprehensive three-level data standardization framework for integrating Western medicine (WM) and traditional Chinese medicine (TCM) electronic medical records in psoriasis research. It resolves the “data-rich, information-poor” paradox in dermatology and enables real-world evidence generation and AI-assisted clinical decision support for integrated therapies.