3D-Printed Exoskeleton Learns From Your Hand ‘…small electric motors at the principal joints worked the prosthetic framework by means of steel cables…’ — Fritz Leiber, 1968.
Smartwatch Powered By Slime Mold ‘Living protoplasm incorporated into the Ampek F-a2 recording system…’ — Philip K. Dick, 1966.
Carpentopod Walking Table ‘Twoflower’s Luggage, which was currently ambling along on its little legs…’
Prosthetic heart valves (PHV) have been studied for around 70 years. They are the best alternative to save the life of patients with cardiac valve diseases. However, current PHVs may still cause significant disadvantages to patients. In general, native heart valves show complex structures and reproducing their functions challenges scientists. Valve repair and replacement are the options to heal heart valve diseases (VHDs), such as stenosis and regurgitation, which show high morbidity and mortality worldwide. Valve repair contributes to the performance of cardiac cycles. However, it fails to restore valve anatomy to its normal condition. On the other hand, replacement is the only alternative to treat valve degeneration. It may do so by mechanical or bioprosthetic valves. Although prostheses may restructure patients’ cardiac cycle, both prostheses may show limitations and potential disadvantages, such as mechanical valves causing thrombogenicity or bioprosthetic valves, calcification. Thus, prostheses require constant improvements to remedy these limitations. Although the design of mechanical valve structures has improved, their raw materials cause great disadvantages, and alternatives for this problem remain scarce. Cardiac valve tissue engineering emerged 30 years ago and has improved over time, e.g., xenografts and fabricated heart valves serving as scaffolds for cell seeding. Thus, this review describes cardiac valve substitutes, starting with the history of valvular prosthesis transplants and ending with some perspectives to alleviate the limitations of artificial valves.
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Researchers have demonstrated that brain cells learn faster and carry out complex networking more effectively than machine learning by comparing how both a Synthetic Biological Intelligence (SBI) system known as “DishBrain” and state-of-the-art RL (reinforcement learning) algorithms react to certain stimuli.
The study, “Dynamic Network Plasticity and Sample Efficiency in Biological Neural Cultures: A Comparative Study with Deep Reinforcement Learning,” published in Cyborg and Bionic Systems, is the first known of its kind.
The research was led by Cortical Labs, the Melbourne-based startup which created the world’s first commercial biological computer, the CL1. The CL1, through which the research was conducted, fuses lab-cultivated neurons from human stem cells with hard silicon to create a more advanced and sustainable form of AI, known as SBI.
Peng, L., Wang, L., Wu, S. et al. Sci Rep 15, 19,938 (2025). https://doi.org/10.1038/s41598-025-04737-3
A review published in Advanced Science highlights the evolution of research related to implantable brain-computer interfaces (iBCIs), which decode brain signals that are then translated into commands for external devices to potentially benefit individuals with impairments such as loss of limb function or speech.
A comprehensive systematic review identified 112 studies, nearly half of which have been published since 2020. Eighty iBCI participants were identified, mostly participating in studies concentrated in the United States, but with growing numbers of studies from Europe, China, and Australia.
The analysis revealed that iBCI technologies are being used to control devices such as robotic prosthetic limbs and consumer digital technologies.
Directly connected to bone, the leg allows wearers to climb stairs, walk at a normal speed, and kick balls.