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A digital nervous system aiming toward personalized IoT healthcare.

ABSTRACT: Body area networks (BANs), cloud computing, and machine learning are platforms that can potentially enable advanced healthcare outside the hospital. By applying distributed sensors and drug delivery devices on/in our body and connecting to such communication and decision-making technology, a system for remote diagnostics and therapy is achieved with additional autoregulation capabilities. Challenges with such autarchic on-body healthcare schemes relate to integrity and safety, and interfacing and transduction of electronic signals into biochemical signals, and vice versa. Here, we report a BAN, comprising flexible on-body organic bioelectronic sensors and actuators utilizing two parallel pathways for communication and decision-making. Data, recorded from strain sensors detecting body motion, are both securely transferred to the cloud for machine learning and improved decision-making, and sent through the body using a secure body-coupled communication protocol to auto-actuate delivery of neurotransmitters, all within seconds. We conclude that both highly stable and accurate sensing-from multiple sensors-are needed to enable robust decision making and limit the frequency of retraining. The holistic platform resembles the self-regulatory properties of the nervous system, i.e., the ability to sense, communicate, decide, and react accordingly, thus operating as a digital nervous system.

SUBMITTER: Armgarth A

PROVIDER: S-EPMC8032817 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Viral cellular immune surveillance is a dynamic and fluid system that is driven by finely regulated cellular processes including cytokines and other factors locally in the microenvironment and systemically throughout the body. It is questionable as to what extent the central nervous system (CNS) is an immune-privileged organ protected by the blood-brain barrier (BBB). Recent evidence suggests converging pathways through which viral infection, and its associated immune surveillance processes, may alter the integrity of the blood-brain barrier, and lead to inflammation, swelling of the brain parenchyma and associated neurological syndromes. Here, we expand upon the recent "gateway theory", by which viral infection and other immune activation states may disrupt the specialized tight junctions of the BBB endothelium making it permeable to immune cells and factors. The model we outline here builds upon the proposition that this process may actually be initiated by cytokines of the IL-17 family, and recognizing the intimate balance between TH17 and TH9 cytokine profiles systemically. We argue that immune surveillance events, in response to viruses such as the Human Immunodeficiency Virus (HIV), cause a TH17/TH9 induced gateway through blood brain barrier, and thus lead to characteristic neuroimmune pathology. It is possible and even probable that the novel TH17/TH9 induced gateway, which we describe here, opens as a consequence of any state of immune activation and sustained chronic inflammation, whether associated with viral infection or any other cause of peripheral or central neuroinflammation. This view could lead to new, timely and critical patient-centered therapies for patients with neuroimmune pathologies across a variety of etiologies.BBB - blood brain barrier, BDV - Borna disease virus, CARD - caspase activation and recruitment domains, CD - clusters of differentiation, CNS - central nervous system, DAMP - damage-associated molecular patterns, DENV - Dengue virus, EBOV - Ebola virus, ESCRT - endosomal sorting complex required for transport-I, HepC - Hepatitis C virus, HIV - human immunodeficiency virus, IFN - interferon, ILn - interleukin-n, IRF-n - interferon regulatory factor-n, MAVS - mitochondrial antiviral-signaling, MBGV - Marburg virus, M-CSF - macrophage colony-stimulating factor, MCP-1 - monocyte chemotactic protein 1 (aka CCL2), MHC - major histocompatibility complex, MIP-? ? - macrophage inflammatory protein-1 ? ? (aka CCL3 & CCL4), MIF - macrophage migration inhibitory factor, NVE - Nipah virus encephalitis, NK - natural killer cell, NLR - NLR, NOD - like receptor, NOD - nucleotide oligomerization domain, PAMP - pathogen-associated molecular patterns, PtdIns - phosphoinositides, PV - Poliovirus, RIG-I - retinoic acid-inducible gene I, RIP - Receptor-interacting protein (RIP) kinase, RLR - RIG-I-like receptor, sICAM1 - soluble intracellular adhesion molecule 1, STAT-3 - signal tranducer and activator of transcription-3, sVCAM1 - soluble vascular cell adhesion molecule 1, TANK - TRAF family member-associated NF- . B activator, TBK1 - TANK-binding kinase 1, TLR - Toll-like receptor, TNF - tumor necrosis factor, TNFR - TNF receptor, TNFRSF21 - tumor necrosis factor receptor superfamily member 21, TRADD TNFR-SF1A - associated via death domain, TRAF TNFR - associated factor, Tregs - regulatory T cellsubpopulation (CD4/8+CD25+FoxP3+), VHF - viral hemorrhagic fever.

Project description:BackgroundType 1 diabetes is a chronic condition of blood glucose metabolic disorder caused by a lack of insulin secretion from pancreas cells. In people with type 1 diabetes, hyperglycemia often occurs upon infection incidences. Despite the fact that patients increasingly gather data about themselves, there are no solid findings that uncover the effect of infection incidences on key parameters of blood glucose dynamics to support the effort toward developing a digital infectious disease detection system.ObjectiveThe study aims to retrospectively analyze the effect of infection incidence and pinpoint optimal parameters that can effectively be used as input variables for developing an infection detection algorithm and to provide a general framework regarding how a digital infectious disease detection system can be designed and developed using self-recorded data from people with type 1 diabetes as a secondary source of information.MethodsWe retrospectively analyzed high precision self-recorded data of 10 patient-years captured within the longitudinal records of three people with type 1 diabetes. Obtaining such a rich and large data set from a large number of participants is extremely expensive and difficult to acquire, if not impossible. The data set incorporates blood glucose, insulin, carbohydrate, and self-reported events of infections. We investigated the temporal evolution and probability distribution of the key blood glucose parameters within a specified timeframe (weekly, daily, and hourly).ResultsOur analysis demonstrated that upon infection incidence, there is a dramatic shift in the operating point of the individual blood glucose dynamics in all the timeframes (weekly, daily, and hourly), which clearly violates the usual norm of blood glucose dynamics. During regular or normal situations, higher insulin and reduced carbohydrate intake usually results in lower blood glucose levels. However, in all infection cases as opposed to the regular or normal days, blood glucose levels were elevated for a prolonged period despite higher insulin and reduced carbohydrates intake. For instance, compared with the preinfection and postinfection weeks, on average, blood glucose levels were elevated by 6.1% and 16%, insulin (bolus) was increased by 42% and 39.3%, and carbohydrate consumption was reduced by 19% and 28.1%, respectively.ConclusionsWe presented the effect of infection incidence on key parameters of blood glucose dynamics along with the necessary framework to exploit the information for realizing a digital infectious disease detection system. The results demonstrated that compared with regular or normal days, infection incidence substantially alters the norm of blood glucose dynamics, which are quite significant changes that could possibly be detected through personalized modeling, for example, prediction models and anomaly detection algorithms. Generally, we foresee that these findings can benefit the efforts toward building next generation digital infectious disease detection systems and provoke further thoughts in this challenging field.

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A digital nervous system aiming toward personalized IoT healthcare.

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