Synaptic Homeostasis and Restructuring across the Sleep-Wake Cycle.

Sleep is critical for hippocampus-dependent memory consolidation. However, the underlying mechanisms of synaptic plasticity are poorly understood. The central controversy is on whether long-term potentiation (LTP) takes a role during sleep and which would be its specific effect on memory. To address this question, we used immunohistochemistry to measure phosphorylation of Ca2+/calmodulin-dependent protein kinase II (pCaMKIIα) in the rat hippocampus immediately after specific sleep-wake states were interrupted. Control animals not exposed to novel objects during waking (WK) showed stable pCaMKIIα levels across the sleep-wake cycle, but animals exposed to novel objects showed a decrease during subsequent slow-wave sleep (SWS) followed by a rebound during rapid-eye-movement sleep (REM). The levels of pCaMKIIα during REM were proportional to cortical spindles near SWS/REM transitions. Based on these results, we modeled sleep-dependent LTP on a network of fully connected excitatory neurons fed with spikes recorded from the rat hippocampus across WK, SWS and REM. Sleep without LTP orderly rescaled synaptic weights to a narrow range of intermediate values. In contrast, LTP triggered near the SWS/REM transition led to marked swaps in synaptic weight ranking. To better understand the interaction between rescaling and restructuring during sleep, we implemented synaptic homeostasis and embossing in a detailed hippocampal-cortical model with both excitatory and inhibitory neurons. Synaptic homeostasis was implemented by weakening potentiation and strengthening depression, while synaptic embossing was simulated by evoking LTP on selected synapses. We observed that synaptic homeostasis facilitates controlled synaptic restructuring. The results imply a mechanism for a cognitive synergy between SWS and REM, and suggest that LTP at the SWS/REM transition critically influences the effect of sleep: Its lack determines synaptic homeostasis, its presence causes synaptic restructuring.

Automatic tracking of cells for video microscopy in patch clamp experiments.

BACKGROUND: Visualisation of neurons labeled with fluorescent proteins or compounds generally require exposure to intense light for a relatively long period of time, often leading to bleaching of the fluorescent probe and photodamage of the tissue. Here we created a technique to drastically shorten light exposure and improve the targeting of fluorescent labeled cells that is specially useful for patch-clamp recordings. We applied image tracking and mask overlay to reduce the time of fluorescence exposure and minimise mistakes when identifying neurons. METHODS: Neurons are first identified according to visual criteria (e.g. fluorescence protein expression, shape, viability etc.) and a transmission microscopy image Differential Interference Contrast (DIC) or Dodt contrast containing the cell used as a reference for the tracking algorithm. A fluorescence image can also be acquired later to be used as a mask (that can be overlaid on the target during live transmission video). As patch-clamp experiments require translating the microscope stage, we used pattern matching to track reference neurons in order to move the fluorescence mask to match the new position of the objective in relation to the sample. For the image processing we used the Open Source Computer Vision (OpenCV) library, including the Speeded-Up Robust Features (SURF) for tracking cells. The dataset of images (n = 720) was analyzed under normal conditions of acquisition and with influence of noise (defocusing and brightness). RESULTS: We validated the method in dissociated neuronal cultures and fresh brain slices expressing Enhanced Yellow Fluorescent Protein (eYFP) or Tandem Dimer Tomato (tdTomato) proteins, which considerably decreased the exposure to fluorescence excitation, thereby minimising photodamage. We also show that the neuron tracking can be used in differential interference contrast or Dodt contrast microscopy. CONCLUSION: The techniques of digital image processing used in this work are an important addition to the set of microscopy tools used in modern electrophysiology, specially in experiments with neuron cultures and brain slices.

Fuzzy method for pre-diagnosis of breast cancer from the Fine Needle Aspirate analysis.

BACKGROUND: Across the globe, breast cancer is one of the leading causes of death among women and, currently, Fine Needle Aspirate (FNA) with visual interpretation is the easiest and fastest biopsy technique for the diagnosis of this deadly disease. Unfortunately, the ability of this method to diagnose cancer correctly when the disease is present varies greatly, from 65% to 98%. This article introduces a method to assist in the diagnosis and second opinion of breast cancer from the analysis of descriptors extracted from smears of breast mass obtained by FNA, with the use of computational intelligence resources--in this case, fuzzy logic. METHODS: For data acquisition of FNA, the Wisconsin Diagnostic Breast Cancer Data (WDBC), from the University of California at Irvine (UCI) Machine Learning Repository, available on the internet through the UCI domain was used. The knowledge acquisition process was carried out by the extraction and analysis of numerical data of the WDBC and by interviews and discussions with medical experts. The PDM-FNA-Fuzzy was developed in four steps: 1) Fuzzification Stage; 2) Rules Base; 3) Inference Stage; and 4) Defuzzification Stage. Performance cross-validation was used in the tests, with three databases with gold pattern clinical cases randomly extracted from the WDBC. The final validation was held by medical specialists in pathology, mastology and general practice, and with gold pattern clinical cases, i.e. with known and clinically confirmed diagnosis. RESULTS: The Fuzzy Method developed provides breast cancer pre-diagnosis with 98.59% sensitivity (correct pre-diagnosis of malignancies); and 85.43% specificity (correct pre-diagnosis of benign cases). Due to the high sensitivity presented, these results are considered satisfactory, both by the opinion of medical specialists in the aforementioned areas and by comparison with other studies involving breast cancer diagnosis using FNA. CONCLUSIONS: This paper presents an intelligent method to assist in the diagnosis and second opinion of breast cancer, using a fuzzy method capable of processing and sorting data extracted from smears of breast mass obtained by FNA, with satisfactory levels of sensitivity and specificity. The main contribution of the proposed method is the reduction of the variation hit of malignant cases when compared to visual interpretation currently applied in the diagnosis by FNA. While the MPD-FNA-Fuzzy features stable sensitivity at 98.59%, visual interpretation diagnosis provides a sensitivity variation from 65% to 98% (this track showing sensitivity levels below those considered satisfactory by medical specialists). Note that this method will be used in an Intelligent Virtual Environment to assist the decision-making (IVEMI), which amplifies its contribution.

A fuzzy model for processing and monitoring vital signs in ICU patients.

BACKGROUND: The area of the hospital automation has been the subject of much research, addressing relevant issues which can be automated, such as: management and control (electronic medical records, scheduling appointments, hospitalization, among others); communication (tracking patients, staff and materials), development of medical, hospital and laboratory equipment; monitoring (patients, staff and materials); and aid to medical diagnosis (according to each speciality). METHODS: In this context, this paper presents a Fuzzy model for helping medical diagnosis of Intensive Care Unit (ICU) patients and their vital signs monitored through a multiparameter heart screen. Intelligent systems techniques were used in the data acquisition and processing (sorting, transforming, among others) it into useful information, conducting pre-diagnosis and providing, when necessary, alert signs to the medical staff. CONCLUSIONS: The use of fuzzy logic turned to the medical area can be very useful if seen as a tool to assist specialists in this area. This paper presented a fuzzy model able to monitor and classify the condition of the vital signs of hospitalized patients, sending alerts according to the pre-diagnosis done helping the medical diagnosis.

S2DIA: a diagnostic system for Diabetes mellitus using SANA platform.

Currently, Diabetes is a very common disease around the world, and with an increase in sedentary lifestyles, obesity and an aging population the number of people with Diabetes worldwide will increase by more than 50%. In this context, the MIT (Massachusetts Institute of Technology) developed the SANA platform, which brings the benefits of information technology to the field of healthcare. It offers healthcare delivery in remote areas, improves patient access to medical specialists for faster, higher quality, and more cost effective diagnosis and intervention. For these reasons, we developed a system for diagnosis of Diabetes using the SANA platform, called S2DIA. It is the first step towards knowing the risks for type 2 Diabetes, and it will be evaluated, especially, in remote/poor areas of Brazil.

Design of an embedded PID controller applied to blood pressure control.

Some diseases, such as hypertension, require a close control of the patient's blood pressure. This is even more critical when that patient is going through--or has just underwent--a surgical procedure In such situations, reducing blood pressure to normal levels is of paramount importance. Usually, this demanding and time consuming monitoring is done manually by clinical personnel and are subject to mistakes and inconsistent practices. In this paper, we propose a solution to the manual monitoring through the design and implementation of an embedded PID controller to handle blood pressure, integrated to an automated monitoring system to assist in detecting anomalies and to optimize the process of patient care.

Angelcare mobile system: homecare patient monitoring using bluetooth and GPRS.

The quick progress in technology has brought new paradigms to the computing area, bringing with them many benefits to society. The paradigm of ubiquitous computing brings innovations applying computing in people's daily life without being noticed. For this, it has used the combination of several existing technologies like wireless communications and sensors. Several of the benefits have reached the medical area, bringing new methods of surgery, appointments and examinations. This work presents telemedicine software that adds the idea of ubiquity to the medical area, innovating the relation between doctor and patient. It also brings security and confidence to a patient being monitored in homecare.

Exploring preprocessing techniques in a three-class brain-machine interface.

In this work, we implemented a brain-machine interface (BMI) based on electroencephalographic (EEG) signals and used it to classify and separate three types of mental tasks: motor imagery with the right and left hands and simple arithmetic sums. In order to reduce dimension of variables and increase classification power, we used both PCA and ICA based algorithms for spectral analysis. Our results show that we were no able to reduce dimension without reducing classification performance.

Mult-I/O - a middleware multi input and output for access devices: a case study applied the biomedical devices.

The great diversity in the architecture of hardware devices allied to many communication protocols, has been hindering the implementation of systems that need to access these devices. Given these differences, it appears the need of providing the access of these devices in a transparent way. In this sense, the present work proposes a middleware, mult input and output for access the devices, as a way of abstracting the writing and reading data mechanisms in hardware devices, contributing this way, for increasing systems productivity, as the developers are just focused in their functional requirements.

Modeling of medical care with stochastic Petri Nets.

Due to the need for management, control, and monitoring of information in an effient way. The hospital automation has been the object of a number of studies owing to constantly evolving technologies. However, many hospital processes are still manual in private and public hospitals. Thus, the aim of this study is to model and simulate of medical care provided to patients in the Intensive Care Unit (ICU), using stochastic Petri Nets and their possible use in a number of automation processes.

Classification of cardiac arrhythmias using competitive networks.

Information generated by sensors that collect a patient's vital signals are continuous and unlimited data sequences. Traditionally, this information requires special equipment and programs to monitor them. These programs process and react to the continuous entry of data from different origins. Thus, the purpose of this study is to analyze the data produced by these biomedical devices, in this case the electrocardiogram (ECG). Processing uses a neural classifier, Kohonen competitive neural networks, detecting if the ECG shows any cardiac arrhythmia. In fact, it is possible to classify an ECG signal and thereby detect if it is exhibiting or not any alteration, according to normality.

Hospital automation system RFID-based: technology embedded in smart devices (cards, tags and bracelets).

RFID is a technology being adopted in many business fields, especially in the medical field. This work has the objective to present a system for automation of a hospital clinical analysis laboratory. This system initially uses contactless smart cards to store patient's data and for authentication of hospital employees in the system. The proposed system also uses RFID tags stuck to containers containing patient's collected samples for the correct identification of the patient who gave away the samples. This work depicts a hospital laboratory workflow, presents the system modeling and deals with security matters related to information stored in the smart cards.

MP-HA: multicycles protocol for hospital automation over multicast addressing.

This paper presents a Multicycles Protocol for Hospital Automation (MP-HA) that works over multicast addressing and uses a Master-Slave architecture. The protocol creates a segmented logical network based on multicast addressing associated to hospital beds. The objective of MP-HA is to ensure the determinism on network through medium access control mechanism increasing the transmission throughput. Thus, it creates a periodical environment making use of parallel cycles which is called multicycles.

An extended model for a spiking neuron class.

This paper proposes an extension to the model of a spiking neuron for information processing in artificial neural networks, developing a new approach for the dynamic threshold of the integrate-and-fire neuron. This new approach invokes characteristics of biological neurons such as the behavior of chemical synapses and the receptor field. We demonstrate how such a digital model of spiking neurons can solve complex nonlinear classification with a single neuron, performing experiments for the classical XOR problem. Compared with rate-coded networks and the classical integrate-and-fire model, the trained network demonstrated faster information processing, requiring fewer neurons and shorter learning periods. The extended model validates all the logic functions of biological neurons when such functions are necessary for the proper flow of binary codes through a neural network.