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Exhaled breath condensate (EBC) is an attractive, non-invasive sample for clinical diagnostics. During EBC collection, its composition is influenced by the collection temperature, a factor that is often not thoroughly monitored and controlled. In this study, we assembled a novel, simple, portable, and inexpensive device for EBC collection, able to maintain a stable temperature at any value between -7 °C and +12 °C. The temperature was controlled using a microcontroller and a thermoelectric cooler that was employed to cool the aluminum block holding the glass tube or the polypropylene syringe. The performance of the novel sampler was compared with the passively cooled RTube™ and a simple EBC sampler, in which the temperature was steadily increasing during sampling. The developed sampler was able to maintain a stable temperature within ±1 °C. To investigate the influence of different sampling temperatures (i.e., +12, -7, -80 °C) on the analyte content in EBC, inorganic ions and organic acids were analyzed by capillary electrophoresis with a capacitively coupled contactless conductivity detector. It was shown that the concentration of metabolites decreased significantly with decreasing temperature. The portability and the ability to keep a stable temperature during EBC sampling makes the developed sampler suitable for point-of-care diagnostics.
KARDIOSPIROX a KONSIL je přístrojové a programové vybavení, které umožňuje sběr a záznam dat v průběhu spiroergometrického vyšetrenia vyhodnocení naměřených dat na konci vyšetřeni, včetně archivace těchto dat na personálním počítači (PC). KARDIOSPIROXje založen na jednočipovém, osmibitovém mikrořadiči MOTOROLA řady HCll. V článkuje popsán princip měřeni jednotlivých parametrů a možnosti programového vybaveni při vyhodnocení vyšetření.
KARDIOSPIROX and KONSIL are hardware and software systems enabling to collect and store data during spiroergometric examinations and to evaluate the results at the end of examination, including the data storage in personal computer, KARDIOSPIROX is based on a one-chip, 8 bit microcontroller MOTOROLA, HCll family. In the paper the principle of specified physiological parameters measuring and the possibilities of implemented software are described.
Photosynthesis research employs several biophysical methods, including the detection of fluorescence. Even though fluorescence is a key method to detect photosynthetic efficiency, it has not been applied/adapted to single-cell confocal microscopy measurements to examine photosynthetic microorganisms. Experiments with photosynthetic cells may require automation to perform a large number of measurements with different parameters, especially concerning light conditions. However, commercial microscopes support custom protocols (through Time Controller offered by Olympus or Experiment Designer offered by Zeiss) that are often unable to provide special set-ups and connection to external devices (e.g., for irradiation). Our new system combining an Arduino microcontroller with the Cell⊕Finder software was developed for controlling Olympus FV1000 and FV1200 confocal microscopes and the attached hardware modules. Our software/hardware solution offers (1) a text file-based macro language to control the imaging functions of the microscope; (2) programmable control of several external hardware devices (light sources, thermal controllers, actuators) during imaging via the Arduino microcontroller; (3) the Cell⊕Finder software with ergonomic user environment, a fast selection method for the biologically important cells and precise positioning feature that reduces unwanted bleaching of the cells by the scanning laser. Cell⊕Finder can be downloaded from http://www.alga.cz/cellfinder. The system was applied to study changes in fluorescence intensity in Synechocystis sp. PCC6803 cells under long-term illumination. Thus, we were able to describe the kinetics of phycobilisome decoupling. Microscopy data showed that phycobilisome decoupling appears slowly after long-term (>1 h) exposure to high light.
Real-time gait event detection (GED) system can be utilized for gait analysis and tracking fitness activities. GED for various types of terrains (e.g., stair-walk, uneven surfaces, etc.) is still an open research problem. This study presents an inertial sensor-based approach for real-time GED system that works for diverse terrains in an uncontrolled environment. The GED system classifies three types of terrains, i.e., flat-walk, stair-ascend and stair-descend, with an average classification accuracy of 99%. It also accurately detects various gait events, including, toe-strike, heel-rise, toe-off, and heel-strike. It is computationally efficient, implemented on a low-cost microcontroller, works in real-time and can be used in portable rehabilitation devices for use in dynamic environments.
- Publikační typ
- časopisecké články MeSH
Blood is carried from the heart to all parts of your body in vessels called arteries. Blood pressure is the force of the blood pushing against the walls of the arteries. Each time the heart beats (about 60 to 70 times a minute at rest), it pumps out blood into the arteries with different value of systolic pressure SP (highest blood pressure when the heart beats) and different value of diastolic pressure DP (lowest blood pressure when the heart relaxes) [3]. Values of SP and DP change during the whole day with dependence on person’s physical and psychical activity. Accuracy of measurement with the modern automatic blood pressure (BP) monitors using oscillometric method is highly depended on condition of cardiovascular system of the monitored person [1]. Especially, with people who suffer from cardiovascular diseases (e.g. arteriosclerosis) the resulting accuracy is much lower when compared to auscultation method. A reasonable solution for improvement of quality of oscillometric method could be an intelligent universal measuring system for evaluation of BP taking into account condition of patient cardiovascular system (CS) of monitored person i.e. the hemodynamics parameters of CS (e.g. heart rate, stroke volume, total peripheral resistance, systemic arterial compliance). Such a system has to be based on an appropriate model of the considered diseases. To create the models, it is very important to establish a database of oscillometric pulsations waveforms (OPW) complemented by the values of “auscultation” blood pressure and information about patients (age, sex, etc.) as well as their diagnosis. This requires a special HW device for measurement of the OPW – we have developed such a device and it has been validated in Czech Certified Metrological Centre, its accuracy is ± 0.5 mmHg in the measuring range 0 to 300 mmHg. We have introduced the concept of oscillometric pulsations waveform (OPW) database that allows testing of oscillometric algorithms for healthy people and mainly for people whose cardiovascular system is not in standard state (arteriosclerosis etc.). The concept is based on oscillometric data retrieving during cuff deflation and on reference BP measurements by auscultation as in [2]. Together with the data, oscillometric pulsations and cuff pressure are saved into the database. For records of OPW we have developed a special HW device that consists of an arm cuff, a pressure sensor, two regulation valves, batteries and electronic circuits. The device can be controlled from PC by a special SW. The connection with the PC is via USB port. The microcontroller controls the pneumatic and the electronic circuits. Cuff pressure is converted into analog voltage by pressure sensor (Piezoresistive Bridge). The analog voltage is amplified by an amplifier TLV2422 and the amplified cuff pressure signal is then separated into 2 channels by a hi-pass filter. Channel 1 is cuff pressure signal (0-300 mm Hg) and channel 2 represents amplified and filtered cuff pulsations (OPW). The 2 signals are digitized by a 12-bit A/D converter in microcontroller ADuC814 with sampling frequency of 200 Hz. The deflation of the cuff is controlled by the regulations valves. The microcontroller communicates with the notebook computer via FTDI chip. Our OPW monitor is connected through the T-pieces and tubes to the cuff, mercury sphygmomanometer and automatic oscillometric blood pressure monitor. Auscultation values are measured by educated staff. Cuff inflation is controlled by microcontroller of the monitor. Then we can directly compare oscillometric and reference (auscultation) method. Moreover, we exactly know the OPW. Nowadays, we have already collected 950 OPW records of 250 people. There are mainly people older than 60 years in the group and we have tried several algorithms (designed in MATLAB ver. 7.00) for evaluation of systolic and diastolic BP [3] and others hemodynamics parameters of the cardiovascular system (mean arterial pressure, heart rate etc.). We have compared values of BP measured by mercury sphygmomanometer and the commercial oscillometric monitor and we have got less than 80% of the measurements results in range of ± 5 mmHg for systolic and diastolic pressure. In more than 20% for both pressures the differences between oscillometric and reference method were greater than ± 5 mmHg. Difference more than -5 mmHg as well as + 5 mmHg was distributed approximately similar. This is a strong motivation for the creation of our database. We have begun apply on measured values some methods of artificial intelligence (AI), especially data mining with system WEKA [5]. We used correlation and searched some association rules. We validated results of standard statistical analysis but we did not find any other strong rules in the data. Nowadays we plan apply these statistical and AI methods directly on measured oscillometric pulsations. This pilot project could be very useful for development of new blood pressure measurement (BPM) methods and also for determination of correct BPM for each group of cardiovascular condition of patients what can be considerable improvement in medical care and patient satisfaction.
Roboty vnímáme nejčastěji jako pomocníky člověka odstraňující namáhavou a monotónní práci. Vzhledem k nemocným a postiženým lidem začíná jejich uplatnění a nasazení v poslední době vzrůstat. Nejvíce se uplatňují v následujících oblastech: Katedra biomedicíncké informatiky (KBI), FBMI ČVUT se ve svých projektech zabývá mj. výzkumem funkčních protéz, asistivních technologií pro hendikepované a experimentálními diagnostickými přístroji. Pro ovládání funkčních protéz, tak pro užití asistivních technologií (např. pohyb kurzoru myši po obrazovce umožňující kvadruplegikovi psát nebo ovládat invalidní vozík) je nezbytné využít dostupných biologických signálů nejčastěji EMG (elektromiogram), EOG (elektrookulogram). Zařízení umožňující postiženému ovládat asistivní pomůcku nebo protézu musí vhodně zpracovat tyto biologické signály a ve vhodném datovém formátu jako řídící veličiny je postoupit řídícímu počítači. Zařízení představující interface mezi člověkem a technologií nazýváme rozhraní člověk – stroj (Human Machine Interface). V rámci pracoviště KBI byli vyvinuté následující rozhraní: Kurzor myši řízený EMG, EOG Prototyp předloketní protézy řízený EMG Kurzor myši řízený EMG signálem představuje rozhraní, které zesílí EMG signál, poté odfiltruje síťový šum a následně provede operaci prahování a transformaci na TTL logiku, kterou zpracuje mikrokontroler a sériovou linkou RS 232 pošle do PC, kde program běžící na pozadí Windows zabezpečí inkrementaci nebo dekrementaci souřadnice kurzoru myši. Kurzor myši řízený EOG signálem představuje zařízení, které signál zesílí a následně odfiltruje všechny mimovolní pohyby oka ze signálu a takto získané napětí A/D převodníkem převede na digitální signál. Tento digitální signál je mikrokontrolérem zprůměrňován klouzavým oknem a poslán přes sériovou linku do PC. Zde opět na pozadí operačního systému běží program, který hodnoty převede na pozici myši na obrazovce. Prototyp předloketní protézy paže řízený pomocí EMG signálů v pozičním a rychlostním módu využívá zařízení obsahující šesti kanálový snímač EMG potenciálů, který zpracuje signál ze šesti nezávislých svalů a mikrokontrolér v něm umístěný zpracuje signály do datového formátu obsahujícího příznak kanálu a amplitudu EMG, který odešle přes sériovou linku RS 232 – USB do řídícího počítače ALIX. ALIX obsahuje řídící logiku protézy a distribuuje povely k řídícím jednotkám pohonů EPOS, které spolu komunikují prostřednictvím CAN sítě.
We consider robots most frequently as assistant for difficult, hard and monotonous work. Importance of robots rise up regarding handicapped people. Department of Biomedical Informatics, Czech Technical University in Prague deals with research and development of active prostheses, assistive technologies for handicapped and experimental diagnostic instruments. The control of active prosthesis or assistive technology is based on available biological signals. The mostly used is EMG or EOG. An example of assistive technology can be a motion of mouse pointer on the screen enabling to quadriplegic to write or operate a wheelchair. The developed device has to correctly process the biosignals and drive the processed biosignals to a control PC as control variables in correct format of data. The device is consisted of interface between a human and the technology so it is called Human Machine Interface (HMI). The following interfaces were developed on the Department of Biomedical Informatics: Mouse pointer controlled by EMG, EOG Prototype of upper limb prosthesis controlled by EMG Mouse pointer controlled by EOG is device which amplifies the signal, filters all nonvolatile motion of the eye out of the signal. The filtered signal is converted by A/D converter to digital signal. The digital signal is smoothen by running average by microcontroller and it is driven via serial bus to PC. The program for operating the device is also written to control the position of the mouse pointer. Prototype of upper limb prosthesis controlled by EMG signals in position and velocity mode is consisted of the devices including six channel EMG sensor, which processes the signal from six independent muscles and an inner microcontroller processes the signal to channel_sign and amplitude format. The signal is afterwards sent via serial bus RS232 – USB to control embedded ALIX PC. ALIX contains control logic of the prosthesis a regulate control commands to control units EPOS. The EPOS units communicate via CAN bus.
We designed a simple, portable, low-cost and low-weight nondispersive infrared (NDIR) spectroscopy-based system for continuous remote sensing of atmospheric methane (CH4) with rapidly pulsed near-infrared light emitting diodes (NIR LED) at 1.65 μm. The use of a microcontroller with a field programmable gate array (μC-FPGA) enables on-the-fly and wireless streaming and processing of large data streams (~2 Gbit/s). The investigated NIR LED detection system offers favourable limits of detection (LOD) of 300 ppm (±5%) CH4,. All the generated raw data were processed automatically on-the-fly in the μC-FPGA and transferred wirelessly via a network connection. The sensing device was deployed for the portable sensing of atmospheric CH4 at a local landfill, resulting in quantified concentrations within the sampling area (ca 400 m2) in the range of 0.5%-3.35% CH4. This NIR LED-based sensor system offers a simple low-cost solution for continuous real-time, quantitative, and direct measurement of CH4 concentrations in indoor and outdoor environments, yet with the flexibility provided by the custom programmable software. It possesses future potential for remote monitoring of gases directly from mobile platforms such as smartphones and unmanned aerial vehicles (UAV).
- Publikační typ
- časopisecké články MeSH
BACKGROUND: Electroporation is an effective technique for genetic manipulation of cells, both in vitro and in vivo. In utero electroporation (IUE) is a special case, which represents a fine application of this technique to genetically modify specific tissues of embryos during prenatal development. Commercially available electroporators are expensive and not fully customizable. We have designed and produced an inexpensive, open-design, and customizable electroporator optimized for safe IUE. We introduce NeuroPorator. METHOD: We used off-the-shelf electrical parts, a single-board microcontroller, and a cheap data logger to build an open-design electroporator. We included a safety circuit to limit the applied electrical current to protect the embryos. We added full documentation, design files, and assembly instructions. RESULT: NeuroPorator output is on par with commercially available devices. Furthermore, the adjustable current limiter protects both the embryos and the uterus from overcurrent damage. A built-in data acquisition module provides real-time visualization and recordings of the actual voltage/current pulses applied to each embryo. Function of NeuroPorator has been demonstrated by inducing focal cortical dysplasia in mice. SIGNIFICANCE AND CONCLUSION: The simple and fully open design enables quick and cheap construction of the device and facilitates further customization. The features of NeuroPorator can accelerate the IUE technique implementation in any laboratory and speed up its learning curve.
- MeSH
- design vybavení MeSH
- elektroporace * metody přístrojové vybavení MeSH
- embryo savčí MeSH
- myši MeSH
- technika přenosu genů * přístrojové vybavení MeSH
- těhotenství MeSH
- uterus MeSH
- zvířata MeSH
- Check Tag
- myši MeSH
- těhotenství MeSH
- ženské pohlaví MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
BACKGROUND AND OBJECTIVE: Diabetes mellitus manifests as prolonged elevated blood glucose levels resulting from impaired insulin production. Such high glucose levels over a long period of time damage multiple internal organs. To mitigate this condition, researchers and engineers have developed the closed loop artificial pancreas consisting of a continuous glucose monitor and an insulin pump connected via a microcontroller or smartphone. A problem, however, is how to accurately predict short term future glucose levels in order to exert efficient glucose-level control. Much work in the literature focuses on least prediction error as a key metric and therefore pursues complex prediction methods such a deep learning. Such an approach neglects other important and significant design issues such as method complexity (impacting interpretability and safety), hardware requirements for low-power devices such as the insulin pump, the required amount of input data for training (potentially rendering the method infeasible for new patients), and the fact that very small improvements in accuracy may not have significant clinical benefit. METHODS: We propose a novel low-complexity, explainable blood glucose prediction method derived from the Intel P6 branch predictor algorithm. We use Meta-Differential Evolution to determine predictor parameters on training data splits of the benchmark datasets we use. A comparison is made between our new algorithm and a state-of-the-art deep-learning method for blood glucose level prediction. RESULTS: To evaluate the new method, the Blood Glucose Level Prediction Challenge benchmark dataset is utilised. On the official test data split after training, the state-of-the-art deep learning method predicted glucose levels 30 min ahead of current time with 96.3% of predicted glucose levels having relative error less than 30% (which is equivalent to the safe zone of the Surveillance Error Grid). Our simpler, interpretable approach prolonged the prediction horizon by another 5 min with 95.8% of predicted glucose levels of all patients having relative error less than 30%. CONCLUSIONS: When considering predictive performance as assessed using the Blood Glucose Level Prediction Challenge benchmark dataset and Surveillance Error Grid metrics, we found that the new algorithm delivered comparable predictive accuracy performance, while operating only on the glucose-level signal with considerably less computational complexity.
- MeSH
- algoritmy MeSH
- diabetes mellitus 1. typu * MeSH
- inzulin MeSH
- krevní glukóza MeSH
- lidé MeSH
- selfmonitoring glykemie * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
