• Parasites, Hosts and Diseases
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• v.55 no.6
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• pp.601-606
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• 2017

Cystoisospora is responsible for morbidity in immunocompromised patients. PCR is sensitive for diagnosing Cystoisospora; however, it needs reevaluation for differential molecular diagnosis of cystoisosporiasis. We aimed at evaluating melting curve analysis (MCA) after real-time PCR (qPCR) in diagnosis and genotyping of Cystoisospora as an alternative to conventional PCR. We included 293 diarrheic stool samples of patients attending the Department of Clinical Oncology and Nuclear Medicine of Cairo University Hospitals, Egypt. Samples were subjected to microscopy, nested PCR (nPCR), and qPCR targeting the internal transcribed spacer 2 region (ITS2) of the ribosomal RNA (r RNA) gene followed by melting temperatures ($T_ms$) analysis and comparing the results to PCR-RFLP banding patterns. Using microscopy and ITS2-nPCR, 3.1% and 5.8% of cases were Cystoisospora positive, respectively, while 10.9% were positive using qPCR. Genotyping of Cystoisospora by qPCR-MCA revealed 2 genotypes. These genotypes matched with 2 distinct melting peaks with specified $T_ms$ at $85.8^{\circ}C$ and $88.6^{\circ}C$, which indicated genetic variation among Cystoisospora isolates in Egypt. Genotype II proved to be more prevalent (65.6%). HIV-related Kaposi sarcoma and leukemic patients harbored both genotypes with a tendency to genotype II. Genotype I was more prevalent in lymphomas and mammary gland tumors while colorectal and hepatocellular tumors harbored genotype II suggesting that this genotype might be responsible for the development of cystoisosporiasis in immunocompromised patients. Direct reliable identification and differentiation of Cystoisospora species could be established using $qPCR-T_ms$ analysis which is useful for rapid detection and screening of Cystoisospora genotypes principally in high risk groups.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.3
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• pp.247-257
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• 2020

Livestock epidemics, such as foot and mouth disease, are causing enormous economic losses due to their strong infectious power. Early detection of infectious diseases in livestock is very important, but it is difficult to diagnose early in individual farms, and there are frequent cases of transmission through inter-farm movement such as veterinarians and feeding vehicles. In this study, we studied the technology that enables rapid diagnosis without veterinarian farm visits and prevents further spread by automatically monitoring the body temperature of livestock using ubiquitous-based information and communication technology in the early stage of onset and sending it in real time. We have presented a technique for systematically managing livestock epidemics at the farm level, regional level, and national level by using the community mapping technique by using the remote medical treatment system linked to the automatically collected information. In this process, community mapping items for each step and stakeholders were derived for crowd sourcing based spatial information technology.

• Journal of Internet of Things and Convergence
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• v.6 no.4
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• pp.27-32
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• 2020

Along with the aging of the population, the number of dementia patients is increasing, and the social and economic burden is also increasing. Currently, the effective way to manage dementia patients is to identify patients with dementia early. However, in rural and island areas where medical staff are scarce, there is a problem that it is difficult to visit a hospital and get an early examination. Therefore, we propose a remote early detection system for dementia to solve the problems. The remote dementia early diagnosis system is a system that allows a patient to receive examination and treatment from a remote dementia expert using remote medical technology based on real-time image communication. The remote early diagnosis system for dementia consists of a local client system used by medical staff at health centers in the island, an image server that transmits, stores and manages images, and an expert client used by remote dementia experts. The local client subsystem satisfies the current medical law's remote collaboration by allowing the patient to use it with the health center's medical staff. In addition, expert clients are used by dementia experts, and can store/manage patient information, analyze patient history information, and predict the degree of dementia progression in the future.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2004.11a
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• pp.371-374
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• 2004

Induction motors are a critical component of many industrial processes and are frequently integrated in commercially available equipment. Safety, reliability, efficiency, and performance are some of the major concerns of induction motor applications. Preventive maintenance of induction motors has been a topic great interest to industry because of their wide range application of industry. Since the use of mechanical sensors, such as vibration probes, strain gauges, and accelerometers is often impractical, the motor current signature analysis (MACA) techniques have gained murk popularity as diagnostic tool. Fault tolerant control (FTC) strives to make the system stable and retain acceptable performance under the system faults. All present FTC method can be classified into two groups. The first group is based on fault detection and diagnostics (FDD). The second group is independent of FDD and includes methods such as integrity control, reliable stabilization and simultaneous stabilization. This paper presents the fundamental FDD-based FTC methods, which are capable of on-line detection and diagnose of the induction motors. Therefore, our group has developed the embedded distributed fault tolerant and fault diagnosis system for industrial motor. This paper presents its architecture. These mechanisms are based on two 32-bit DSPs and each TMS320F2407 DSP module is checking stator current, voltage, temperatures, vibration and speed of the motor. The DSPs share information from each sensor or DSP through DPRAM with hardware implemented semaphore. And it communicates the motor status through field bus (CAN, RS485). From the designed system, we get primitive sensors data for the case of normal condition and two abnormal conditions of 3 phase induction motor control system is implemented. This paper is the first step to drive multi-motors with serial communication which can satisfy the real time operation using CAN protocol.

• Journal of Genetic Medicine
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• v.12 no.2
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• pp.100-108
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• 2015

Purpose: Conventional methods for the prenatal detection of fetal RhD status involve invasive procedures such as fetal blood sampling and amniocentesis. The identification of cell-free fetal DNA (cffDNA) in maternal plasma creates the possibility of determining fetal RhD status by analyzing maternal plasma DNA. However, some technical problems still exist, especially the lack of a positive control marker for the presence of fetal DNA. Therefore, we assessed the feasibility and accuracy of fetal RHD genotyping incorporating the RASSF1A epigenetic fetal DNA marker from cffDNA in the maternal plasma of RhD-negative pregnant women in Korea. Materials and Methods: We analyzed maternal plasma from 41 pregnant women identified as RhD-negative by serological testing. Multiplex real-time PCR was performed by amplifying RHD exons 5 and 7 and the SRY gene, with RASSF1A being used as a gender-independent fetal epigenetic marker. The results were compared with those obtained by postnatal serological analysis of cord blood and gender identification. Results: Among the 41 fetuses, 37 were RhD-positive and 4 were RhD-negative according to the serological analysis of cord blood. There was 100% concordance between fetal RHD genotyping and serological cord blood results. Detection of the RASSF1A gene verified the presence of cffDNA, and the fetal SRY status was correctly detected in all 41 cases. Conclusion: Noninvasive fetal RHD genotyping with cffDNA incorporating RASSF1A is a feasible, reliable, and accurate method of determining fetal RhD status. It is an alternative to amniocentesis for the management of RhD-negative women and reduces the need for unnecessary RhIG prophylaxis.

• International Journal of Computer Science & Network Security
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• v.23 no.12
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• pp.151-160
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• 2023

Breast cancer is the most dangerous and deadly form of cancer. Initial detection of breast cancer can significantly improve treatment effectiveness. The second most common cancer among Indian women in rural areas. Early detection of symptoms and signs is the most important technique to effectively treat breast cancer, as it enhances the odds of receiving an earlier, more specialist care. As a result, it has the possible to significantly improve survival odds by delaying or entirely eliminating cancer. Mammography is a high-resolution radiography technique that is an important factor in avoiding and diagnosing cancer at an early stage. Automatic segmentation of the breast part using Mammography pictures can help reduce the area available for cancer search while also saving time and effort compared to manual segmentation. Autoencoder-like convolutional and deconvolutional neural networks (CN-DCNN) were utilised in previous studies to automatically segment the breast area in Mammography pictures. We present Automatic SegmenAN, a unique end-to-end adversarial neural network for the job of medical image segmentation, in this paper. Because image segmentation necessitates extensive, pixel-level labelling, a standard GAN's discriminator's single scalar real/fake output may be inefficient in providing steady and appropriate gradient feedback to the networks. Instead of utilising a fully convolutional neural network as the segmentor, we suggested a new adversarial critic network with a multi-scale L1 loss function to force the critic and segmentor to learn both global and local attributes that collect long- and short-range spatial relations among pixels. We demonstrate that an Automatic SegmenAN perspective is more up to date and reliable for segmentation tasks than the state-of-the-art U-net segmentation technique.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.22 no.12
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• pp.1124-1131
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• 2011

In this paper, image reconstruction algorithm for breast cancer detection using MT(Microwave Tomography) was investigated. The breast cancer detection system under development uses 16 transmit/receive antennas. The signal waveform was a sinusoidal wave at 900 MHz. To solve the 2D inverse scattering problem, we used the 2D FDTD (Finite Difference Time Domain) method for forward calculation and LMA(Levenberg-Marquardt Algorithm) for optimization. The result of the image reconstruction using the numerical phantom by MRI(Magnetic Resonance Imaging) obtained from real patient of breast cancer showed that we can detect the position of the tumor accurately.

• Journal of the Korean Society for Nondestructive Testing
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• v.30 no.2
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• pp.121-125
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• 2010

The infrared thermography technology rather than traditional nondestructive methods has benefits with non-contact and non-destructive testings in measuring for the fault diagnosis of the rotating machine. In this work, condition monitoring measurements using this advantage of thermography were proposed. From this study, the novel approach for the damage detection of a rotating machine was conducted based on the spectrum analysis. As results, by adopting the ball bearing used in the rotating machine applied extensively, an spectrum analysis with thermal imaging experiment was performed. Also, as analysing the temperature characteristics obtained from the infrared thermography for in-situ rotating ball bearing under the lubrication condition, it was concluded that infrared thermography for condition monitoring in the rotating machine at real time could be utilized in many industrial fields.

• Nano
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• v.13 no.11
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• pp.1850131.1-1850131.19
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• 2018

In this paper, an electrochemical sensor for epinephrine (EP), a neurotransmitter was developed by anchoring molecularly imprinted polymeric matrix (MIP) on the surface of gold-coated quartz crystal electrode of electrochemical quartz crystal microbalance (EQCM) using starch nanoparticles (Starch NP) - reduced graphene oxide (RGO) nanocomposite as polymeric format for the first time. Use of EP in therapeutic treatment requires proper dose and route of administration. Proper follow-up of neurological disorders and timely diagnosis of them has been found to depend on EP level. The MIP sensor was developed by electrodeposition of starch NP-RGO composite on EQCM electrode in presence of template EP. As the imprinted sites are located on the surface, high specific surface area enables good accessibility and high binding affinity to template molecule. Differential pulse voltammetry (DPV) and piezoelectrogravimmetry were used for monitoring binding/release, rebinding of template to imprinted cavities. MIP-coated EQCM electrode were characterized by contact angle measurements, AFM images, piezoelectric responses including viscoelasticity of imprinted films, and other voltammetric measurements including direct (DPV) and indirect (using a redox probe) measurements. Selectivity was assessed by imprinting factor (IF) as high as 3.26 (DPV) and 3.88 (EQCM). Sensor was rigorously checked for selectivity in presence of other structurally close analogues, real matrix (blood plasma), reproducibility, repeatability, etc. Under optimized conditions, the EQCM-MIP sensor showed linear dynamic ranges ($1-10{\mu}M$). The limit of detection 40 ppb (DPV) and 290 ppb (EQCM) was achieved without any cross reactivity and matrix effect indicating high sensitivity and selectivity for EP. Hence, an eco-friendly MIP-sensor with high sensitivity and good selectivity was fabricated which could be applied in "real" matrices in a facile manner.

• Journal of Radiation Protection and Research
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• v.39 no.3
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• pp.150-158
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• 2014

Radiation-related practitioners and radiation-treated patients at medical institutions are inevitably exposed to radiation for diagnosis and treatment. Although standards for maximum doses are recommended by the International Commission on Radiological Protection (ICPR) and the International Atomic Energy Agency (IAEA), more direct and available measurement and analytical methods are necessary for optimal exposure management for potential exposure subjects such as practitioners and patients. Thus, in this study we developed a system for real-time radiation monitoring at a distance that works with existing portable device. The monitoring system comprises three parts for detection, imaging, and transmission. For miniaturization of the detection part, a scintillation detector was designed based on a silicon photomultiplier (SiPM). The imaging part uses a wireless charge-coupled device (CCD) camera module along with the detection part to transmit a radiation image and measured data through the transmission part using a Bluetooth-enabled portable device. To evaluate the performance of the developed system, diagnostic X-ray generators and sources of $^{137}Cs$, $^{22}Na$, $^{60}Co$, $^{204}Tl$, and $^{90}Sr$ were used. We checked the results for reactivity to gamma, beta, and X-ray radiation and determined that the error range in the response linearity is less than 3% with regard to radiation strength and in the detection accuracy evaluation with regard to measured distance using MCNPX Code. We hope that the results of this study will contribute to cost savings for radiation detection system configuration and to individual exposure management.