A Low-Noise Low-Power 0.001Hz-1kHz Neural Recording System-on-Chip With Sample-Level Duty-Cycling.

Advances in brain-machine interfaces and wearable biomedical sensors for healthcare and human-computer interactions call for precision electrophysiology to resolve a variety of biopotential signals across the body that cover a wide range of frequencies, from the mHz-range electrogastrogram (EGG) to the kHz-range electroneurogram (ENG). Existing integrated wearable solutions for minimally invasive biopotential recordings are limited in detection range and accuracy due to trade-offs in bandwidth, noise, input impedance, and power consumption. This article presents a 16-channel wide-band ultra-low-noise neural recording system-on-chip (SoC) fabricated in 65nm CMOS for chronic use in mobile healthcare settings that spans a bandwidth of 0.001 Hz to 1 kHz through a featured sample-level duty-cycling (SLDC) mode. Each recording channel is implemented by a delta-sigma analog-to-digital converter (ADC) achieving 1.0 µ V rms input-referred noise over 1Hz-1kHz bandwidth with a Noise Efficiency Factor (NEF) of 2.93 in continuous operation mode. In SLDC mode, the power supply is duty-cycled while maintaining consistently low input-referred noise levels at ultra-low frequencies (1.1 µV rms over 0.001Hz-1Hz) and 435 M Ω input impedance. The functionalities of the proposed SoC are validated with two human electrophysiology applications: recording low-amplitude electroencephalogram (EEG) through electrodes fixated on the forehead to monitor brain waves, and ultra-slow-wave electrogastrogram (EGG) through electrodes fixated on the abdomen to monitor digestion.

A CMOS Microelectrode Array System With Reconfigurable Sub-Array Multiplexing Architecture Integrating 24,320 Electrodes and 380 Readout Channels.

This article presents a CMOS microelectrode array (MEA) system with a reconfigurable sub-array multiplexing architecture using the time-division multiplexing (TDM) technique. The system consists of 24,320 TiN electrodes with 17.7 µm-pitch pixels and 380 column-parallel readout channels including a low-noise amplifier, a programmable gain amplifier, and a 10-b successive approximation register analog to digital converter. Readout channels are placed outside the pixel for high spatial resolution, and a flexible structure to acquire neural signals from electrodes selected by configuring in-pixel memory is realized. In this structure, a single channel can handle 8 to 32 electrodes, guaranteeing a temporal resolution from 5 kS/s to 20 kS/s for each electrode. A 128 × 190 MEA system was fabricated in a 110-nm CMOS process, and each readout channel consumes 81 µW at 1.5-V supply voltage featuring input-referred noise of 1.48 µVrms without multiplexing and 5.4 µVrms with multiplexing at the action-potential band (300 Hz-10 kHz).

A Real-Time Depth of Anesthesia Monitoring System Based on Deep Neural Network With Large EDO Tolerant EEG Analog Front-End.

In this article, we present a real-time electroencephalogram (EEG) based depth of anesthesia (DoA) monitoring system in conjunction with a deep learning framework, AnesNET. An EEG analog front-end (AFE) that can compensate ±380-mV electrode DC offset using a coarse digital DC servo loop is implemented in the proposed system. The EEG-based MAC, EEGMAC, is introduced as a novel index to accurately predict the DoA, which is designed for applying to patients anesthetized by both volatile and intravenous agents. The proposed deep learning protocol consists of four layers of convolutional neural network and two dense layers. In addition, we optimize the complexity of the deep neural network (DNN) to operate on a microcomputer such as the Raspberry Pi 3, realizing a cost-effective small-size DoA monitoring system. Fabricated in 110-nm CMOS, the prototype AFE consumes 4.33 µW per channel and has the input-referred noise of 0.29 µVrms from 0.5 to 100 Hz with the noise efficiency factor of 2.2. The proposed DNN was evaluated with pre-recorded EEG data from 374 subjects administrated by inhalational anesthetics under surgery, achieving an average squared and absolute errors of 0.048 and 0.05, respectively. The EEGMAC with subjects anesthetized by an intravenous agent also showed a good agreement with the bispectral index value, confirming the proposed DoA index is applicable to both anesthetics. The implemented monitoring system with the Raspberry Pi 3 estimates the EEGMAC within 20 ms, which is about thousand-fold faster than the BIS estimation in literature.

Clinical characteristics of 2009 pandemic influenza A (H1N1) infection in children and the performance of rapid antigen test.

PURPOSE: In autumn 2009, the swine-origin influenza A (H1N1) virus spread throughout South Korea. The aims of this study were to determine the clinical characteristics of children infected by the 2009 H1N1 influenza A virus, and to compare the rapid antigen and real-time polymerase chain reaction (PCR) tests. METHODS: We conducted a retrospective review of patients ≥18 years of age who presented to Soonchunhyang University Hospital in Seoul with respiratory symptoms, including fever, between September 2009 and January 2010. A real-time PCR test was used to definitively diagnose 2009 H1N1 influenza A infection. Medical records of confirmed cases were reviewed for sex, age, and the time of infection. The decision to perform rapid antigen testing was not influenced by clinical conditions, but by individual factors such as economic conditions. Its sensitivity and specificity were evaluated compared to real-time PCR test results. RESULTS: In total, 934 patients tested positive for H1N1 by real-time PCR. The highest number of patients (48.9%) was diagnosed in November. Most patients (48.2%) were aged between 6 and 10 years. Compared with the H1N1 real-time PCR test results, the rapid antigen test showed 22% sensitivity and 83% specificity. Seventy-eight patients were hospitalized for H1N1 influenza A virus infection, and fever was the most common symptom (97.4%). CONCLUSION: For diagnosis of 2009 H1N1 influenza A virus infection, the rapid antigen test was inferior to the real-time PCR test in both sensitivity and specificity. This outcome suggests that the rapid antigen test is inappropriate for screening.

Proteasome function is inhibited by polyglutamine-expanded ataxin-1, the SCA1 gene product.

Spinocerebellar ataxia type 1 (SCA1) is an autosomal-dominant neurodegenerative disorder caused by expansion of the polyglutamine tract in the SCA1 gene product, ataxin-1. Using d2EGFP, a short-lived enhanced green fluorescent protein, we investigated whether polyglutamine-expanded ataxin-1 affects the function of the proteasome, a cellular multicatalytic protease that degrades most misfolded proteins and regulatory proteins. In Western blot analysis and immunofluorescence experiments, d2EGFP was less degraded in HEK 293T cells transfected with ataxin-1(82Q) than in cells transfected with lacZ or empty vector controls. To test whether the stability of the d2EGFP protein was due to aggregation of ataxin-1, we constructed a plasmid carrying ataxin-1-Delta114, lacking the self-association region (SAR), and examined degradation of the d2EGFP. Both the level of ataxin-1-Delta114 aggregates and the amount of d2EGFP were drastically reduced in cells containing ataxin-1-Delta114. Furthermore, d2EGFP localization experiments showed that polyglutamine-expanded ataxin-1 inhibited the general function of the proteasome activity. Taken together, these results demonstrate that polyglutamine-expanded ataxin-1 decreases the activity of the proteasome, implying that a disturbance in the ubiquitin-proteasome pathway is directly involved in the development of spinocerebellar ataxia type1.

p80 coilin, a coiled body-specific protein, interacts with ataxin-1, the SCA1 gene product.

Spinocerebellar ataxia type 1 (SCA1) is an autosomal-dominant neurodegenerative disorder characterized by ataxia and progressive motor deterioration. SCA1 is associated with an elongated polyglutamine tract in ataxin-1, the SCA1 gene product. Using the yeast two-hybrid system and co-immunoprecipitation experiments, we have found that p80 coilin, coiled body-specific protein, binds to ataxin-1. In further experiments with deletion mutants, we found that the C-terminal regions of ataxin-1 and p80 coilin were essential for this interaction. In HeLa cells that have been co-transfected with ataxin-1 and p80 coilin, the p80 coilin protein co-localizes with ataxin-1 aggregates in the nucleoplasm. However, immunohistochemical analysis and immunofluorescence assays showed that mutant ataxin-1 aggregates do not redistribute p80 coilin's dot-like structures in the Purkinje cells of SCA1 transgenic mice. This feature of the interaction between ataxin-1 and p80 coilin suggests that p80 coilin might be implicated in altering the function of ataxin-1.