Monitoring gait at home with radio waves in Parkinson's disease: A marker of severity, progression, and medication response.

Parkinson's disease (PD) is the fastest-growing neurological disease in the world. A key challenge in PD is tracking disease severity, progression, and medication response. Existing methods are semisubjective and require visiting the clinic. In this work, we demonstrate an effective approach for assessing PD severity, progression, and medication response at home, in an objective manner. We used a radio device located in the background of the home. The device detected and analyzed the radio waves that bounce off people's bodies and inferred their movements and gait speed. We continuously monitored 50 participants, with and without PD, in their homes for up to 1 year. We collected over 200,000 gait speed measurements. Cross-sectional analysis of the data shows that at-home gait speed strongly correlates with gold-standard PD assessments, as evaluated by the Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) part III subscore and total score. At-home gait speed also provides a more sensitive marker for tracking disease progression over time than the widely used MDS-UPDRS. Further, the monitored gait speed was able to capture symptom fluctuations in response to medications and their impact on patients' daily functioning. Our study shows the feasibility of continuous, objective, sensitive, and passive assessment of PD at home and hence has the potential of improving clinical care and drug clinical trials.

Artificial intelligence-enabled detection and assessment of Parkinson's disease using nocturnal breathing signals.

There are currently no effective biomarkers for diagnosing Parkinson's disease (PD) or tracking its progression. Here, we developed an artificial intelligence (AI) model to detect PD and track its progression from nocturnal breathing signals. The model was evaluated on a large dataset comprising 7,671 individuals, using data from several hospitals in the United States, as well as multiple public datasets. The AI model can detect PD with an area-under-the-curve of 0.90 and 0.85 on held-out and external test sets, respectively. The AI model can also estimate PD severity and progression in accordance with the Movement Disorder Society Unified Parkinson's Disease Rating Scale (R = 0.94, P = 3.6 × 10-25). The AI model uses an attention layer that allows for interpreting its predictions with respect to sleep and electroencephalogram. Moreover, the model can assess PD in the home setting in a touchless manner, by extracting breathing from radio waves that bounce off a person's body during sleep. Our study demonstrates the feasibility of objective, noninvasive, at-home assessment of PD, and also provides initial evidence that this AI model may be useful for risk assessment before clinical diagnosis.

Contactless In-Home Monitoring of the Long-Term Respiratory and Behavioral Phenotypes in Older Adults With COVID-19: A Case Series.

Currently, there is a limited understanding of long-term outcomes of COVID-19, and a need for in-home measurements of patients through the whole course of their disease. We study a novel approach for monitoring the long-term trajectories of respiratory and behavioral symptoms of COVID-19 patients at home. We use a sensor that analyzes the radio signals in the room to infer patients' respiration, sleep and activities in a passive and contactless manner. We report the results of continuous monitoring of three residents of an assisted living facility for 3 months, through the course of their disease and subsequent recovery. In total, we collected 4,358 measurements of gait speed, 294 nights of sleep, and 3,056 h of respiration. The data shows differences in the respiration signals between asymptomatic and symptomatic patients. Longitudinally, we note sleep and motor abnormalities that persisted for months after becoming COVID negative. Our study represents a novel phenotyping of the respiratory and behavioral trajectories of COVID recovery, and suggests that the two may be integral components of the COVID-19 syndrome. It further provides a proof-of-concept that contactless passive sensors may uniquely facilitate studying detailed longitudinal outcomes of COVID-19, particularly among older adults.

Electrochemical synthesis of multilayered PEDOT/PEDOT-SH/Au nanocomposites for electrochemical sensing of nitrite.

A multilayered film of poly(3,4-ethylenedioxythiophene)/poly(thiomethyl 3,4- ethylenedioxythiophene)/gold nanoparticle (PEDOT/PEDOT-SH/Au) nanocomposites was successfully synthesized on indium tin oxide (ITO) and glassy carbon electrode (GCE) via an electrochemical technique. The structure and morphology of the composite was characterized by FT-IR, UV-vis, EDS, XPS, and SEM analyses. The prepared multilayered PEDOT/PEDOT-SH/Au nanocomposite was used for the electrochemical catalytic oxidation of nitrite by amperometry. The results showed that the microstructures of PEDOT/PEDOT-SH/Au nanocomposites are not strongly dependent on the substrate. Fibrous PEDOT as hard template absorbed EDOT-SH on it to form porous PEDOT/PEDOT-SH. Porous structure had the advantages of large specific surface area and high porosity for nitrite ion adsorption. The thiol group in PEDOT/PEDOT-SH stabilized Au nanoparticles (NPs) effectively through Au-S bond and allowed Au NPs to have high dispersion and excellent electrocatalytic activity. The PEDOT/PEDOT-SH/Au composite prepared on GCE had a good performance in its electrochemical response to nitrite ions. PEDOT/PEDOT-SH/Au/GCE displayed a low oxidation potential (0.74 V), a fast response time (< 3 s), a low detection limit (0.051 µM), two linear ranges (0.15-1 mM and 1-16 mM), good sensitivity (0.301 µA µM-1 cm-2 and 0.133 µA µM-1 cm-2) with good reproducibility, stability, and selectivity. Graphical abstract Schematic representation of the preparation process of the nitrite ion electrochemical sensor.

Self-assembly of pendant functional groups grafted PEDOT as paracetamol detection material.

In this paper, pendant functional group grafted EDOTs, such as EDOTCH2NH2, EDOTCH2OH and EDOTCH2SH, were selected as monomers for the preparation of their respective polymers via a common chemical oxidative polymerization method in the absence of CTAB by varying the [monomer]/[oxidant] ratios. The self-assembly mechanism of the polymers was systematically studied by discussing the hydrogen bonding effect, acidity and electron-donating ability, as well as the chain initiation and chain growth of the chemically oxidated polymerized monomers. These functional group grafted PEDOTs were applied to the electrochemical determination of paracetamol (PAR) to further investigate the effect of the pendant functional groups (-SH, -OH, -NH2) on the electrochemical sensing behaviour of the polymers. The results indicated that the hydrogen bonding effect of the pendant functional groups was vital to the self-assembly of the polymer chains, and the PEDOTs with -OH and -SH groups had a tendency to self-assemble into a spherical structure, while the PEDOT with an -NH2 group exhibited a fibrous structure. The electrochemical response of PEDOTs with functional groups was better than that that of PEDOT alone, and the highest electrochemical response was observed in PEDOT with an -SH group ([monomer]/[oxidant] = 1 : 8).