Clinical Dilemma, Bernard Soulier Syndrome versus Immune Thrombocytopenic Purpura: A Case Report.

BACKGROUND: Bernard Soulier Syndrome (BSS) is a rare autosomal recessive disorder due to deficiency or dysfunction of the glycoprotein GPIb-V-IX complex on the platelet surface. It is also known as hemorrhagiparous thrombocytic dystrophy or congenital hemorrhagiparous thrombocytic dystrophy. The patient usually presents with severe and prolonged bleeding along with characteristics of giant blood platelets and low platelet counts. Manifestations of BSS include epistaxis, gum bleeding, purpuric rashes, menorrhagia, rarely melena, and hematemesis. On the other hand, immune thrombocytopenic purpura (ITP) is an acquired autoimmune disorder in which there is accelerated platelet destruction and reduced platelet production. Isolated thrombocytopenia without fever, lymphadenopathy, and organomegaly usually lead to the diagnosis of immune thrombocytopenia. CASE PRESENTATION: A 20 years old female presented with complaints of recurrent episodes of epistaxis since childhood and menorrhagia during menarche. She was misdiagnosed as ITP elsewhere. Later, based on thorough clinical examination and investigation, the diagnosis was confirmed as BSS. CONCLUSION: BSS should always be taken in the differential diagnosis of ITP, especially when persistent, refractory, and treated unsuccessfully with steroids or splenectomy.

Application of mmWave Radar Sensor for People Identification and Classification.

Device-free indoor identification of people with high accuracy is the key to providing personalized services. Visual methods are the solution but they require a clear view and good lighting conditions. Additionally, the intrusive nature leads to privacy concerns. A robust identification and classification system using the mmWave radar and an improved density-based clustering algorithm along with LSTM are proposed in this paper. The system leverages mmWave radar technology to overcome challenges posed by varying environmental conditions on object detection and recognition. The point cloud data are processed using a refined density-based clustering algorithm to extract ground truth in a 3D space accurately. A bi-directional LSTM network is employed for individual user identification and intruder detection. The system achieved an overall identification accuracy of 93.9% and an intruder detection rate of 82.87% for groups of 10 individuals, demonstrating its effectiveness.

Disseminated Tuberculosis in an Immunocompetent State: A Case Report.

BACKGROUND: Tuberculosis is one of the major infectious diseases of mankind and remains a significant health concern, especially in developing countries. Clinical manifestations of TB are broad and sometimes very challenging for clinicians to diagnose early. Tuberculous psoas abscess was generally secondary to spinal tuberculosis or direct extension from adjacent structures in immunocompromised individuals, but tuberculous psoas abscess in the immunocompetent state is very infrequent. In addition, pancytopenia and new onset neck swelling simultaneously make this presentation a very unusual clinical entity in tuberculosis. CASE PRESENTATION: We now present a case of a 21-years-old, unmarried, otherwise healthy girl presented with fever, lower abdominal pain and weight loss for two months. She also noticed painless neck swelling for 15 days. She later had a tuberculous left sided psoas abscess with pancytopenia and a cold abscess on the left side of the neck with no sign of any other apparent focus, according to the evidence. Diagnosis of disseminated TB without lung involvement was established and ATT was started. The outcome was successful on follow up. CONCLUSION: Among the broad spectrum of atypical manifestations of TB, this case report draws attention to its rarity, diagnostic challenge and awareness of the clinical spectrum, especially in developing countries.

A simultaneous optical and electrical in-vitro neuronal recording system to evaluate microelectrode performance.

OBJECTIVES: In this paper, we aim to detail the setup of a high spatio-temporal resolution, electrical recording system utilising planar microelectrode arrays with simultaneous optical imaging suitable for evaluating microelectrode performance with a proposed 'performance factor' metric. METHODS: Techniques that would facilitate low noise electrical recordings were coupled with voltage sensitive dyes and neuronal activity was recorded both electrically via a customised amplification system and optically via a high speed CMOS camera. This technique was applied to characterise microelectrode recording performance of gold and poly(3,4-ethylenedioxythiophene)/polystyrene sulfonate (PEDOT/PSS) coated electrodes through traditional signal to noise (SNR) calculations as well as the proposed performance factor. RESULTS: Neuronal activity was simultaneously recorded using both electrical and optical techniques and this activity was confirmed via tetrodotoxin application to inhibit action potential firing. PEDOT/PSS outperformed gold using both measurements, however, the performance factor metric estimated a 3 fold improvement in signal transduction when compared to gold, whereas SNR estimated an 8 fold improvement when compared to gold. CONCLUSION: The design and functionality of a system to record from neurons both electrically, through microelectrode arrays, and optically via voltage sensitive dyes was successfully achieved. SIGNIFICANCE: The high spatiotemporal resolution of both electrical and optical methods will allow for an array of applications such as improved detection of subthreshold synaptic events, validation of spike sorting algorithms and a provides a robust evaluation of extracellular microelectrode performance.

Cardiac Electrical Modeling for Closed-Loop Validation of Implantable Devices.

OBJECTIVE: Evaluating and testing cardiac electrical devices in a closed-physiologic-loop can help design safety, but this is rarely practical or comprehensive. Furthermore, in silico closed-loop testing with biophysical computer models cannot meet the requirements of time-critical cardiac device systems, while simplified models meeting time-critical requirements may not have the necessary dynamic features. We propose a new high-level (abstracted) physiologically-based computational heart model that is time-critical and dynamic. METHODS: The model comprises cardiac regional cellular-electrophysiology types connected by a path model along a conduction network. The regional electrophysiology and paths are modeled with hybrid automata that capture non-linear dynamics, such as action potential and conduction velocity restitution and overdrive suppression. The hierarchy of pacemaker functions is incorporated to generate sinus rhythms, while abnormal automaticity can be introduced to form a variety of arrhythmias such as escape ectopic rhythms. Model parameters are calibrated using experimental data and prior model simulations. CONCLUSION: Regional electrophysiology and paths in the model match human action potentials, dynamic behavior, and cardiac activation sequences. Connected in closed loop with a pacing device in DDD mode, the model generates complex arrhythmia such as atrioventricular nodal reentry tachycardia. Such device-induced outcomes have been observed clinically and we can establish the key physiological features of the heart model that influence the device operation. SIGNIFICANCE: These findings demonstrate how an abstract heart model can be used for device validation and to design personalized treatment.

Closing the Loop: Validation of Implantable Cardiac Devices With Computational Heart Models.

OBJECTIVE: Cardiovascular Implantable Electronic Devices (CIEDs) are used extensively for treating life-threatening conditions such as bradycardia, atrioventricular block and heart failure. The complicated heterogeneous physical dynamics of patients provide distinct challenges to device development and validation. We address this problem by proposing a device testing framework within the in-silico closed-loop context of patient physiology. METHODS: We develop an automated framework to validate CIEDs in closed-loop with a high-level physiologically based computational heart model. The framework includes test generation, execution and evaluation, which automatically guides an integrated stochastic optimization algorithm for exploration of physiological conditions. CONCLUSION: The results show that using a closed loop device-heart model framework can achieve high system test coverage, while the heart model provides clinically relevant responses. The simulated findings of pacemaker mediated tachycardia risk evaluation agree well with the clinical observations. Furthermore, we illustrate how device programming parameter selection affects the treatment efficacy for specific physiological conditions. SIGNIFICANCE: This work demonstrates that incorporating model based closed-loop testing of CIEDs into their design provides important indications of safety and efficacy under constrained physiological conditions.

Resonant model-A new paradigm for modeling an action potential of biological cells.

Organ level simulation of bioelectric behavior in the body benefits from flexible and efficient models of cellular membrane potential. These computational organ and cell models can be used to study the impact of pharmaceutical drugs, test hypotheses, assess risk and for closed-loop validation of medical devices. To move closer to the real-time requirements of this modeling a new flexible Fourier based general membrane potential model, called as a Resonant model, is developed that is computationally inexpensive. The new model accurately reproduces non-linear potential morphologies for a variety of cell types. Specifically, the method is used to model human and rabbit sinoatrial node, human ventricular myocyte and squid giant axon electrophysiology. The Resonant models are validated with experimental data and with other published models. Dynamic changes in biological conditions are modeled with changing model coefficients and this approach enables ionic channel alterations to be captured. The Resonant model is used to simulate entrainment between competing sinoatrial node cells. These models can be easily implemented in low-cost digital hardware and an alternative, resource-efficient implementations of sine and cosine functions are presented and it is shown that a Fourier term is produced with two additions and a binary shift.

Development of a Low Cost & Low Noise Amplification System For In Vitro Neuronal Recording through Microelectrode Arrays.

In order to effectively record from electrically active cells through microelectrode arrays a low-noise amplification and data acquisition system is required. Although commercially available, these systems can be expensive and lack the freedom to customise hardware and software. In this work we present a low-cost (US$21 for the first channel + US$11 for each additional channel), low-noise amplifier coupled with an analog to digital converter from National Instruments. The amplifier was designed to (i) operate between 0 and 5 V utilising a DC battery power supply, (ii) operate within a bandwidth of 10 kHz, (iii) remove DC voltage created at the electrode/electrolyte interface with a high-pass cut-off frequency of 0.7 Hz and (iv) have a gain of 2000. Strategies to reduce environment electromagnetic interference at the amplifier front end were employed and involved a customised neural interface board connected between the microelectrode array and amplifier. The constructed amplifier achieved an intrinsic noise amplitude of 0.8 µVrms, which facilitated high quality recordings as exemplified by in vitro recordings from primary hippocampal neurons.

A Parametric Computational Model of the Action Potential of Pacemaker Cells.

OBJECTIVE: A flexible, efficient, and verifiable pacemaker cell model is essential to the design of real-time virtual hearts that can be used for closed-loop validation of cardiac devices. A new parametric model of pacemaker action potential is developed to address this need. METHODS: The action potential phases are modeled using hybrid automaton with one piecewise-linear continuous variable. The model can capture rate-dependent dynamics, such as action potential duration restitution, conduction velocity restitution, and overdrive suppression by incorporating nonlinear update functions. Simulated dynamics of the model compared well with previous models and clinical data. CONCLUSION: The results show that the parametric model can reproduce the electrophysiological dynamics of a variety of pacemaker cells, such as sinoatrial node, atrioventricular node, and the His-Purkinje system, under varying cardiac conditions. SIGNIFICANCE: This is an important contribution toward closed-loop validation of cardiac devices using real-time heart models.

An intracardiac electrogram model to bridge virtual hearts and implantable cardiac devices.

Virtual heart models have been proposed to enhance the safety of implantable cardiac devices through closed loop validation. To communicate with a virtual heart, devices have been driven by cardiac signals at specific sites. As a result, only the action potentials of these sites are sensed. However, the real device implanted in the heart will sense a complex combination of near and far-field extracellular potential signals. Therefore many device functions, such as blanking periods and refractory periods, are designed to handle these unexpected signals. To represent these signals, we develop an intracardiac electrogram (IEGM) model as an interface between the virtual heart and the device. The model can capture not only the local excitation but also far-field signals and pacing afterpotentials. Moreover, the sensing controller can specify unipolar or bipolar electrogram (EGM) sensing configurations and introduce various oversensing and undersensing modes. The simulation results show that the model is able to reproduce clinically observed sensing problems, which significantly extends the capabilities of the virtual heart model in the context of device validation.

Hybrid automata models of cardiac ventricular electrophysiology for real-time computational applications.

Virtual heart models have been proposed for closed loop validation of safety-critical embedded medical devices, such as pacemakers. These models must react in real-time to off-the-shelf medical devices. Real-time performance can be obtained by implementing models in computer hardware, and methods of compiling classes of Hybrid Automata (HA) onto FPGA have been developed. Models of ventricular cardiac cell electrophysiology have been described using HA which capture the complex nonlinear behavior of biological systems. However, many models that have been used for closed-loop validation of pacemakers are highly abstract and do not capture important characteristics of the dynamic rate response. We developed a new HA model of cardiac cells which captures dynamic behavior and we implemented the model in hardware. This potentially enables modeling the heart with over 1 million dynamic cells, making the approach ideal for closed loop testing of medical devices.

Neural network implementation using bit streams.

A new method for the parallel hardware implementation of artificial neural networks (ANNs) using digital techniques is presented. Signals are represented using uniformly weighted single-bit streams. Techniques for generating bit streams from analog or multibit inputs are also presented. This single-bit representation offers significant advantages over multibit representations since they mitigate the fan-in and fan-out issues which are typical to distributed systems. To process these bit streams using ANNs concepts, functional elements which perform summing, scaling, and squashing have been implemented. These elements are modular and have been designed such that they can be easily interconnected. Two new architectures which act as monotonically increasing differentiable nonlinear squashing functions have also been presented. Using these functional elements, a multilayer perceptron (MLP) can be easily constructed. Two examples successfully demonstrate the use of bit streams in the implementation of ANNs. Since every functional element is individually instantiated, the implementation is genuinely parallel. The results clearly show that this bit-stream technique is viable for the hardware implementation of a variety of distributed systems and for ANNs in particular.