Protein misfolding thermodynamics

It is known that protein misfolding is governed by the hydrophobic effect of solutes at hydrophobic amino acid side chains. The hydrophobic force of nonaqueous solutes acts as a driving force for the spatial rearrangement of protein side chains, whose structural transitions need to be regulated in both time and space. Smaller hydrophobic solutes exert more effect at protein side chains, which involves the clustering of proteins into misfolded shapes. The consequences of misfolding are loss of protein function, gain of toxic function, or both. This is a physical process, whose result has been directly linked to a large number of human diseases

A wideband low-distortion CMOS current driver for tissue impedance analysis

Bioimpedance measurements are performed in a variety of medical applications including cancer detection in tissue. Such applications require wideband (typically 1 MHz) accurate ac current drivers with high output impedance and low distortion. This paper presents an integrated current driver that fulfills these requirements. The circuit uses negative feedback to accurately set the output current amplitude into the load. It was fabricated in a 0.35- μm complementary metal–oxide–semiconductor (CMOS) process technology, occupies a core area of 0.4 mm, and operates from ±2.5-V power supplies. For a maximum output current of 1mA p-p, the measured total harmonic distortion is below 0.1%, and the variability of the output current with respect to the load is below 0.5% up to 800 kHz increasing to 0.86% at 1 MHz. The current driver was tested for the detection of cancer sites from postoperative human colon specimens. The circuit is intended for use in active electrode applications

A framework for adapting deep brain stimulation using Parkinsonian state estimates

The mechanisms underlying the beneficial effects of deep brain stimulation (DBS) for Parkinson's disease (PD) remain poorly understood and are still under debate. This has hindered the development of adaptive DBS (aDBS). For further progress in aDBS, more insight into the dynamics of PD is needed, which can be obtained using machine learning models. This study presents an approach that uses generative and discriminative machine learning models to more accurately estimate the symptom severity of patients and adjust therapy accordingly. A support vector machine is used as the representative algorithm for discriminative machine learning models, and the Gaussian mixture model is used for the generative models. Therapy is effected using the state estimates obtained from the machine learning models together with a fuzzy controller in a critic-actor control approach. Both machine learning model configurations achieve PD suppression to desired state in 7 out of 9 cases; most of which settle in under 2 s

Absorption and optimal plasmonic resonances for small ellipsoidal particles in lossy media

A new simplified formula is derived for the absorption cross section of small dielectric ellipsoidal particles embedded in lossy media. The new expression leads directly to a closed form solution for the optimal conjugate match with respect to the surrounding medium, i.e. the optimal permittivity of the ellipsoidal particle that maximizes the absorption at any given frequency. This defines the optimal plasmonic resonance for the ellipsoid. The optimal conjugate match represents a metamaterial in the sense that the corresponding optimal permittivity function may have negative real part (inductive properties), and can not in general be implemented as a passive material over a given bandwidth. A necessary and sufficient condition is derived for the feasibility of tuning the Drude model to the optimal conjugate match at a single frequency, and it is found that all the prolate spheroids and some of the (not too flat) oblate spheroids can be tuned into optimal plasmonic resonance at any desired center frequency. Numerical examples are given to illustrate the analysis. Except for the general understanding of plasmonic resonances in lossy media, it is also anticipated that the new results can be useful for feasibility studies with e.g. the radiotherapeutic hyperthermia based methods to treat cancer based on electrophoretic heating in gold nanoparticle suspensions using microwave radiation

Toward on-demand deep brain stimulation using online Parkinson’s disease prediction driven by dynamic detection

In Parkinson’s disease (PD), on-demand deep brain stimulation (DBS) is required so that stimulation is regulated to reduce side effects resulting from continuous stimulation and PD exacerbation due to untimely stimulation. Also, the progressive nature of PD necessitates the use of dynamic detection schemes that can track the nonlinearities in PD. This paper proposes the use of dynamic feature extraction feature extraction and dynamic pattern classification to achieve dynamic PD detection taking into account the demand for high accuracy, low computation and real-time detection. The dynamic feature extraction and dynamic pattern classification are selected by evaluating a subset of feature extraction, dimensionality reduction and classification algorithms that have been used in brain machine interfaces. A novel dimensionality reduction technique, the maximum ratio method (MRM) is proposed, which provides the most efficient performance. In terms of accuracy and complexity for hardware implementation, a combination having discrete wavelet transform for feature extraction, MRM for dimensionality reduction and dynamic k-nearest neighbor for classification was chosen as the most efficient. It achieves mean accuracy measures of classification accuracy 99.29%, F1-score of 97.90% and a choice probability of 99.86%

On the application of frequency selective common mode feedback for multifrequency EIT

Common mode voltages are frequently a problem in electrical impedance tomography (EIT) and other bioimpedance applications. To reduce their amplitude common mode feedback is employed. Formalised analyses of both current and voltage feedback is presented in this paper for current drives. Common mode effects due to imbalances caused by the current drives, the electrode connections to the body load and the introduction of the body impedance to ground are considered. Frequency selective narrowband common mode feedback previously proposed to provide feedback stability is examined. As a step towards multifrequency applications the use of narrowband feedback is experimentally demonstrated for two simultaneous current drives. Measured results using standard available components show a reduction of 62dB for current feedback and 31dB for voltage feedback. Frequencies ranged from 50 kHz to 1 MHz

Design of a CMOS active electrode IC for wearable electrical impedance tomography systems

This paper describes the design of an active electrode integrated circuit (IC) for a wearable electrical impedance tomography (EIT) system required for real time monitoring of neonatal lung function. The IC comprises a wideband high power current driver (up to 6 mAp-p output current), a low noise voltage amplifier and two shape sensor buffers. The IC has been designed in a 0.35-μm CMOS technology. It operates from ±9 V power supplies and occupies a total die area of 5 mm2. Post-layout simulations are presented