Kyoto, Japan -- Synucleinopathies are a group of neurodegenerative disorders that includes serious conditions such as Parkinson's disease and dementia with Lewy bodies. There are currently no cures for these disorders and treatment is limited to mitigating symptoms. Recently, antibody-based therapies have attracted considerable attention, but alternative approaches are still necessary.

Therapy development for synucleinopathies tends to target the alpha-synuclein protein, αSyn, the abnormal aggregation of which is a hallmark of these diseases. However, targeting this protein using conventional strategies for drug discovery is stymied by the molecule's lack of a stable three-dimensional structure, which cultivates aggregation.

Interested in understanding how abnormal protein aggregation drives neurodegeneration, a team of researchers at Kyoto University had an idea that was both scientifically intriguing and therapeutically promising: could RNA aptamers -- often described as "chemical antibodies" -- directly recognize αSyn's disordered regions and suppress pathological aggregation?

FSP1 (fibroblastspecific protein 1) was originally considered a marker of fibroblasts, but recent studies have confirmed that it is primarily expressed by a proinflammatory macrophage subset in liver injury and participates in the regulation of inflammation and fibrosis. This study systematically explores the interplay among these three elements.

Polymer-based flexible wireless sensors have demonstrated significant potential in health monitoring. However, existing studies have predominantly focused on material design, energy harvesting, and wireless communication, while research addressing signal attenuation, noise accumulation, and data distortion within wireless links remains relatively limited. Physiological signals are highly susceptible to multi-source interference during acquisition and transmission; without effective data processing and intelligent analysis strategies, monitoring outcomes may fail to meet the accuracy and stability requirements of clinical applications. To address this research gap, this review constructs a comprehensive framework encompassing signal sensing, link transmission, and intelligent processing, building on advances in flexible materials and wireless energy supply modes. Special attention is given to the critical roles of channel compensation, noise suppression, feature extraction, lightweight artificial intelligence models, and edge computing in enhancing data quality and reducing energy consumption. Furthermore, the review systematically analyzes the impact of wireless link stability and energy management on monitoring performance. By reinforcing the synergistic mechanisms between data processing and communication, this work aims to provide design principles and development directions for the next generation of high-reliability, low-power, and intelligent wireless flexible health monitoring systems.