Induced pluripotent stem cells (iPSCs): molecular mechanisms of induction and applications

Signal Transduction and Targeted Therapy, Год журнала: 2024, Номер 9(1)

Опубликована: Апрель 26, 2024

The induced pluripotent stem cell (iPSC) technology has transformed in vitro research and holds great promise to advance regenerative medicine. iPSCs have the capacity for an almost unlimited expansion, are amenable genetic engineering, can be differentiated into most somatic types. been widely applied model human development diseases, perform drug screening, develop therapies. In this review, we outline key developments iPSC field highlight immense versatility of modeling therapeutic applications. We begin by discussing pivotal discoveries that revealed potential a nucleus reprogramming led successful generation iPSCs. consider molecular mechanisms dynamics as well numerous methods available induce pluripotency. Subsequently, discuss various iPSC-based cellular models, from mono-cultures single type complex three-dimensional organoids, how these models elucidate diseases. use examples neurological disorders, coronavirus disease 2019 (COVID-19), cancer diversity disease-specific phenotypes modeled using iPSC-derived cells. also used high-throughput screening toxicity studies. Finally, process developing autologous allogeneic therapies their alleviate

Язык: Английский

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An Organic Optoelectronic Synapse with Multilevel Memory Enabled by Gate Modulation

ACS Applied Materials & Interfaces, Год журнала: 2024, Номер unknown

Опубликована: Апрель 4, 2024

Artificial synaptic devices are emerging as contenders for next-generation computing systems due to their combined advantages of self-adaptive learning mechanisms, high parallel computation capabilities, adjustable memory level, and energy efficiency. Optoelectronic particularly notable responsiveness both voltage inputs light exposure, making them attractive dynamic modulation. However, engineering with reconfigurable plasticity multilevel within a singular configuration present fundamental challenge. Here, we have established an organic transistor-based device that exhibits volatile nonvolatile characteristics, modulated through gate together stimuli. Our demonstrates range behaviors, including short/long-term (STP LTP) well STP–LTP transitions. Further, encoding unit, it delivers exceptional read current levels, achieving program/erase ratio exceeding 105, excellent repeatability. Additionally, prototype 4 × matrix potential in practical neuromorphic systems, showing capabilities the perception, processing, retention image inputs.

Язык: Английский

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Connectome-based reservoir computing with the conn2res toolbox

Nature Communications, Год журнала: 2024, Номер 15(1)

Опубликована: Янв. 22, 2024

Abstract The connection patterns of neural circuits form a complex network. How signaling in these manifests as cognition and adaptive behaviour remains the central question neuroscience. Concomitant advances connectomics artificial intelligence open fundamentally new opportunities to understand how shape computational capacity biological brain networks. Reservoir computing is versatile paradigm that uses high-dimensional, nonlinear dynamical systems perform computations approximate cognitive functions. Here we present : an open-source Python toolbox for implementing networks modular, allowing arbitrary network architecture dynamics be imposed. allows researchers input connectomes reconstructed using multiple techniques, from tract tracing noninvasive diffusion imaging, impose systems, spiking neurons memristive dynamics. versatility us ask questions at confluence neuroscience intelligence. By reconceptualizing function computation, sets stage more mechanistic understanding structure-function relationships

Язык: Английский

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Artificial cognition vs. artificial intelligence for next-generation autonomous robotic agents

Frontiers in Computational Neuroscience, Год журнала: 2024, Номер 18

Опубликована: Март 22, 2024

The trend in industrial/service robotics is to develop robots that can cooperate with people, interacting them an autonomous, safe and purposive way. These are the fundamental elements characterizing fourth fifth industrial revolutions (4IR, 5IR): crucial innovation adoption of intelligent technologies allow development cyber-physical systems , similar if not superior humans. common wisdom intelligence might be provided by AI (Artificial Intelligence), a claim supported more media coverage commercial interests than solid scientific evidence. currently conceived quite broad sense, encompassing LLMs lot other things, without any unifying principle, but self-motivating for success various areas. current view mostly follows purely disembodied approach consistent old-fashioned, Cartesian mind-body dualism, reflected software-hardware distinction inherent von Neumann computing architecture. working hypothesis this position paper road next generation autonomous robotic agents cognitive capabilities requires fully brain-inspired, embodied avoids trap dualism aims at full integration Bodyware Cogniware. We name Artificial Cognition (ACo) ground it Cognitive Neuroscience. It specifically focused on proactive knowledge acquisition based bidirectional human-robot interaction: practical advantage enhance generalization explainability. Moreover, we believe brain-inspired network interactions necessary allowing humans artificial agents, building growing level personal trust reciprocal accountability: clearly missing, although actively sought, AI. ACo work progress take number research threads, some antecedent early attempts define concepts methods. In rest will consider blocks need re-visited unitary framework: principles developmental robotics, methods action representation prospection capabilities, role social interaction.

Язык: Английский

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Sensors and Devices Guided by AI for Personalized Pain Medicine

Cyborg and Bionic Systems, Год журнала: 2024, Номер 5

Опубликована: Янв. 1, 2024

Personalized pain medicine aims to tailor treatment strategies for the specific needs and characteristics of an individual patient, holding potential improving outcomes, reducing side effects, enhancing patient satisfaction. Despite existing markers treatments, challenges remain in understanding, detecting, treating complex conditions. Here, we review recent engineering efforts developing various sensors devices addressing personalized pain. We summarize basics pathology introduce monitoring, assessment, relief. also discuss advancements taking advantage rapidly medical artificial intelligence (AI), such as AI-based analgesia devices, wearable sensors, healthcare systems. believe that these innovative technologies may lead more precise responsive medicine, greatly improved quality life, increased efficiency systems, incidence addiction substance use disorders.

Язык: Английский

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BiœmuS: A new tool for neurological disorders studies through real-time emulation and hybridization using biomimetic Spiking Neural Network

Nature Communications, Год журнала: 2024, Номер 15(1)

Опубликована: Июнь 20, 2024

Abstract Characterization and modeling of biological neural networks has emerged as a field driving significant advancements in our understanding brain function related pathologies. As today, pharmacological treatments for neurological disorders remain limited, pushing the exploration promising alternative approaches such electroceutics. Recent research bioelectronics neuromorphic engineering have fostered development new generation neuroprostheses repair. However, achieving their full potential necessitates deeper biohybrid interaction. In this study, we present novel real-time, biomimetic, cost-effective user-friendly network capable real-time emulation experiments. Our system facilitates investigation replication biophysically detailed dynamics while prioritizing cost-efficiency, flexibility ease use. We showcase feasibility conducting experiments using standard biophysical interfaces variety cells well diverse configurations. envision crucial step towards neuromorphic-based bioelectrical therapeutics, enabling seamless communication with on comparable timescale. Its embedded functionality enhances practicality accessibility, amplifying its real-world applications

Язык: Английский

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Mica/Nylon Composite Nanofiber Film Based Wearable Triboelectric Sensor for Object Recognition

Nano Energy, Год журнала: 2024, Номер 129, С. 110056 - 110056

Опубликована: Июль 27, 2024

Язык: Английский

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Open and remotely accessible Neuroplatform for research in wetware computing

Frontiers in Artificial Intelligence, Год журнала: 2024, Номер 7

Опубликована: Май 2, 2024

Wetware computing and organoid intelligence is an emerging research field at the intersection of electrophysiology artificial intelligence. The core concept involves using living neurons to perform computations, similar how Artificial Neural Networks (ANNs) are used today. However, unlike ANNs, where updating digital tensors (weights) can instantly modify network responses, entirely new methods must be developed for neural networks biological neurons. Discovering these challenging requires a system capable conducting numerous experiments, ideally accessible researchers worldwide. For this reason, we hardware software that allows electrophysiological experiments on unmatched scale. Neuroplatform enables run organoids with lifetime even more than 100 days. To do so, streamlined experimental process quickly produce organoids, monitor action potentials 24/7, provide electrical stimulations. We also designed microfluidic fully automated medium flow change, thus reducing disruptions by physical interventions in incubator ensuring stable environmental conditions. Over past three years, was utilized over 1,000 brain enabling collection 18 terabytes data. A dedicated Application Programming Interface (API) has been conduct remote directly via our Python library or interactive compute such as Jupyter Notebooks. In addition operations, API controls pumps, cameras UV lights molecule uncaging. This execution complex 24/7 including closed-loop strategies processing latest deep learning reinforcement libraries. Furthermore, infrastructure supports use. Currently 2024, freely available purposes, groups have begun it their experiments. article outlines system’s architecture provides specific examples results.

Язык: Английский

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Neuroethics and AI ethics: a proposal for collaboration

BMC Neuroscience, Год журнала: 2024, Номер 25(1)

Опубликована: Авг. 29, 2024

The scientific relationship between neuroscience and artificial intelligence is generally acknowledged, the role that their long history of collaboration has played in advancing both fields often emphasized. Beyond important insights provided by collaborative development, AI raise a number ethical issues are explored neuroethics ethics. Neuroethics ethics have been gaining prominence last few decades, they typically carried out different research communities. However, considering evolving landscape AI-assisted neurotechnologies various conceptual practical intersections neuroscience-such as increasing application neuroscientific research, healthcare neurological mental diseases, use knowledge inspiration for AI-some scholars now calling these two domains. This article seeks to explore how can stimulate theoretical and, ideally, governance efforts. First, we offer some reasons reflection on innovations AI. Next, dimensions think could be enhanced cross-fertilization subfields We believe pace fusion development innovations, broad underspecified calls responsibility do not consider from will only partially successful promoting meaningful changes applications.

Язык: Английский

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Elegans-AI: How the connectome of a living organism could model artificial neural networks

Neurocomputing, Год журнала: 2024, Номер 584, С. 127598 - 127598

Опубликована: Март 24, 2024

This paper introduces Elegans-AI models, a class of neural networks that leverage the connectome topology Caenorhabditis elegans to design deep and reservoir architectures. Utilizing learning models inspired by connectome, this leverages evolutionary selection process consolidate functional arrangement biological neurons within their networks. The initial goal involves conversion natural connectomes into artificial representations. second objective centers on embedding complex circuitry both networks, highlighting neural-dynamic short-term long-term memory capabilities. Lastly, our third aims establish structural explainability examining heterophilic/homophilic properties impact In study, demonstrate superior performance compared similar utilize either randomly rewired or simulated bio-plausible ones. Notably, these achieve top-1 accuracy 99.99% Cifar10 Cifar100, 99.84% MNIST Unsup. They do with significantly fewer parameters, particularly when configurations are used. Our findings indicate clear connection between network patterns, small-world characteristic, outcomes, emphasizing significant role optimization in shaping for improved performance.

Язык: Английский

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