The Dark Side of "Smart Drugs": Cognitive Enhancement vs. Clinical Concerns. [PDF]
Ingegneri M +6 more
europepmc +1 more source
Cognitive enhancement and authenticity: moving beyond the Impasse. [PDF]
Gordon EC.
europepmc +1 more source
Soft Actuators Integrated with Control and Power Units: Approaching Wireless Autonomous Soft Robots
Soft robots exhibit significant development potential in various applications. However, there are still key technical challenges regarding material improvement, structure design and components integration. This review focuses on the development and challenge of soft actuators, power components, and control components in untethered intelligent soft ...
Renwu Shi, Feifei Pan, Xiaobin Ji
wiley +1 more source
Caffeine in Aging Brains: Cognitive Enhancement, Neurodegeneration, and Emerging Concerns About Addiction. [PDF]
Carbone MG +4 more
europepmc +1 more source
Here, we present a textile, wearable capacitive interface enabling multidirectional remote control by dynamically modulating electrode overlap and spacing via a freely gliding upper electrode. A forearm‐mounted prototype drives robotic and media tasks with 12–15 ms latency, maintains < 0.8% drift after 500 cycles, and remains stably functional at 90 ...
Cagatay Gumus +8 more
wiley +1 more source
Personalized cognitive enhancement for older adults: An aging-friendly closed-loop human-machine interface framework. [PDF]
Zhou S +5 more
europepmc +1 more source
From Lab to Landscape: Environmental Biohybrid Robotics for Ecological Futures
This Perspective explores environmental biohybrid robotics, integrating living tissues, microorganisms, and insects for operation in real‐world ecosystems. It traces the leap from laboratory experiments to forests, wetlands, and urban environments and discusses key challenges, development pathways, and opportunities for ecological monitoring and ...
Miriam Filippi
wiley +1 more source
Curcumin and neuroplasticity: epigenetic mechanisms underlying cognitive enhancement in aging and neurodegenerative disorders. [PDF]
Jiao H, Wang X, Zhang D, Zhou S, Gao F.
europepmc +1 more source
Multi‐Site Transfer Classification of Major Depressive Disorder: An fMRI Study in 3335 Subjects
The study proposes graph convolution network with sparse pooling to learn the hierarchical features of brain graph for MDD classification. Experiment is done on multi‐site fMRI samples (3335 subjects, the largest functional dataset of MDD to date) and transfer learning is applied, achieving an average accuracy of 70.14%.
Jianpo Su +14 more
wiley +1 more source

