Search results for: diagnostic error

Authors: B. T. Naveen Kumar, Anuj Tyagi, Niraj Kumar Singh, Visanu Boonyawiwat, A. H. Shanthanagouda, Orawan Boodde, K. M. Shankar, Prakash Patil, Shubhkaramjeet Kaur

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Early mortality syndrome (EMS)/Acute Hepatopancreatic Necrosis Disease (AHPND) has emerged as a major obstacle for the shrimp farming around the world. It is caused by a strain of Vibrio parahaemolyticus. The possible preventive and control measure is, early and rapid detection of the pathogen in the broodstock, post-larvae and monitoring the shrimp during the culture period. Polymerase chain reaction (PCR) based early detection methods are good, but they are costly, time taking and requires a sophisticated laboratory. The present study was conducted to develop a simple, sensitive and rapid diagnostic farmer level kit for the reliable detection of AHPND in shrimp. A panel of monoclonal antibodies (MAbs) were raised against the recombinant Pir B protein (rPirB). First, an immunodot was developed by using MAbs G3B8 and Mab G3H2 which showed specific reactivity to purified r-PirB protein with no cross-reactivity to other shrimp bacterial pathogens (AHPND free Vibrio parahaemolyticus (Indian strains), V. anguillarum, WSSV, Aeromonas hydrophila, and Aphanomyces invadans). Immunodot developed using Mab G3B8 is more sensitive than that with the Mab G3H2. However, immunodot takes almost 2.5 hours to complete with several hands-on steps. Therefore, the flow-through assay (FTA) was developed by using a plastic cassette containing the nitrocellulose membrane with absorbing pads below. The sample was dotted in the test zone on the nitrocellulose membrane followed by continuos addition of five solutions in the order of i) blocking buffer (BSA) ii) primary antibody (MAb) iii) washing Solution iv) secondary antibody and v) chromogen substrate (TMB) clear purple dots against a white background were considered as positive reactions. The FTA developed using MAbG3B8 is more sensitive than that with MAb G3H2. In FTA the two MAbs showed specific reactivity to purified r-PirB protein and not to other shrimp bacterial pathogens. The FTA is simple to farmer/field level, sensitive and rapid requiring only 8-10 min for completion. Tests can be developed to kits, which will be ideal for use in biosecurity, for the first line of screening (at the port or pond site) and during monitoring and surveillance programmes overall for the good management practices to reduce the risk of the disease.

Keywords: acute hepatopancreatic necrosis disease, AHPND, flow-through assay, FTA, farmer level, immunodot, pond site, shrimp

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Authors: Win Zaw Hein, Khin Sandar Linn, Su Su Yi Mon, Yoshitaka Goto

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The Earth ionosphere is located at the altitude of about 70 km to several 100 km from the ground, and it is composed of ions and electrons called plasma. In the ionosphere, these plasma makes delay in GPS (Global Positioning System) signals and reflect in radio waves. The delay along the signal path from the satellite to the receiver is directly proportional to the total electron content (TEC) of plasma, and this delay is the largest error factor in satellite positioning and navigation. Sounding observation from the top and bottom of the ionosphere was popular to investigate such ionospheric plasma for a long time. Recently, continuous monitoring of the TEC using networks of GNSS (Global Navigation Satellite System) observation stations, which are basically built for land survey, has been conducted in several countries. However, in these stations, multi-frequency support receivers are installed to estimate the effect of plasma delay using their frequency dependence and the cost of multi-frequency support receivers are much higher than single frequency support GPS receiver. In this research, single frequency GPS receiver was used instead of expensive multi-frequency GNSS receivers to measure the ionospheric plasma variation such as vertical TEC distribution. In this measurement, single-frequency support ublox GPS receiver was used to probe ionospheric TEC. The location of observation was assigned at Mandalay Technological University in Myanmar. In the method, the ionospheric TEC distribution is represented by polynomial functions for latitude and longitude, and parameters of the functions are determined by least-squares fitting on pseudorange data obtained at a known location under an assumption of thin layer ionosphere. The validity of the method was evaluated by measurements obtained by the Japanese GNSS observation network called GEONET. The performance of measurement results using single-frequency of GPS receiver was compared with the results by dual-frequency measurement.

Keywords: ionosphere, global positioning system, GPS, ionospheric delay, total electron content, TEC

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Authors: Tomás Sobrino, Lara García-Varela, Marta Aramburu-Núñez, Mónica Castro, Noemí Gómez-Lado, Mariña Rodríguez-Arrizabalaga, Antía Custodia, Juan Manuel Pías-Peleteiro, José Manuel Aldrey, Daniel Romaus-Sanjurjo, Ángeles Almeida, Pablo Aguiar, Alberto Ouro

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Introduction: Alzheimer’s disease (AD) and other tauopathies are primary causes of dementia, causing progressive cognitive deterioration that entails serious repercussions for the patients' performance of daily tasks. Currently, there is no effective approach for the early diagnosis and treatment of AD and tauopathies. This study suggests a theragnostic approach based on the importance of phosphorylated tau protein (p-Tau) in the early pathophysiological processes of AD. We have developed a novel theragnostic monoclonal antibody (mAb) to provide both diagnostic and therapeutic effects. Methods/Results: We have developed a p-Tau mAb, which was doped with deferoxamine for radiolabeling with Zirconium-89 (89Zr) for PET imaging, as well as fluorescence dies for immunofluorescence assays. The p-Tau mAb was evaluated in vitro for toxicity by MTT assay, LDH activity, propidium iodide/Annexin V assay, caspase-3, and mitochondrial membrane potential (MMP) assay in both mouse endothelial cell line (bEnd.3) and cortical primary neurons cell cultures. Importantly, non-toxic effects (up to concentrations of p-Tau mAb greater than 100 ug/mL) were detected. In vivo experiments in the tauopathy model mice (PS19) show that the 89Zr-pTau-mAb and 89Zr-Fragments-pTau-mAb are stable in circulation for up to 10 days without toxic effects. However, only less than 0.2% reached the brain, so further strategies have to be designed for crossing the Brain-Blood-Barrier (BBB). Moreover, an intraparenchymal treatment strategy was carried out. The PS19 mice were operated to implement osmotic pumps (Alzet 1004) at two different times, at 4 and 7 months, to stimulate the controlled release for one month each of the B6 antibody or the IgG1 control antibody. We demonstrated that B6-treated mice maintained their motor and memory abilities significantly compared with IgG1 treatment. In addition, we observed a significant reduction in p-Tau deposits in the brain. Conclusions /Discussion: A theragnostic pTau-mAb was developed. Moreover, we demonstrated that our p-Tau mAb recognizes very-early pathology forms of p-Tau by non-invasive techniques, such as PET. In addition, p-Tau mAb has non-toxic effects, both in vitro and in vivo. Although the p-Tau mAb is stable in circulation, only 0.2% achieve the brain. However, direct intraventricular treatment significantly reduces cognitive impairment in Alzheimer's animal models, as well as the accumulation of toxic p-Tau species.

Keywords: alzheimer's disease, theragnosis, tau, PET, immunotherapy, tauopathies

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Authors: Joselito Medina-Marin, Maria G. Serna-Diaz, Alejandro Tellez-Jurado, Juan C. Seck-Tuoh-Mora, Eva S. Hernandez-Gress, Norberto Hernandez-Romero, Iaina P. Medina-Serna

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Trichoderma harzianum is a fungus that has been utilized as a low-cost fungicide for biological control of pests, and it is important to determine the optimal conditions to produce the highest amount of conidiospores of Trichoderma harzianum. In this work, the conidiospore production of Trichoderma harzianum is modeled and optimized by using Artificial Neural Networks (AANs). In order to gather data of this process, 30 experiments were carried out taking into account the number of hours of culture (10 distributed values from 48 to 136 hours) and the culture humidity (70, 75 and 80 percent), obtained as a response the number of conidiospores per gram of dry mass. The experimental results were used to develop an iterative algorithm to create 1,110 ANNs, with different configurations, starting from one to three hidden layers, and every hidden layer with a number of neurons from 1 to 10. Each ANN was trained with the Levenberg-Marquardt backpropagation algorithm, which is used to learn the relationship between input and output values. The ANN with the best performance was chosen in order to simulate the process and be able to maximize the conidiospores production. The obtained ANN with the highest performance has 2 inputs and 1 output, three hidden layers with 3, 10 and 10 neurons in each layer, respectively. The ANN performance shows an R2 value of 0.9900, and the Root Mean Squared Error is 1.2020. This ANN predicted that 644175467 conidiospores per gram of dry mass are the maximum amount obtained in 117 hours of culture and 77% of culture humidity. In summary, the ANN approach is suitable to represent the conidiospores production of Trichoderma harzianum because the R2 value denotes a good fitting of experimental results, and the obtained ANN model was used to find the parameters to produce the biggest amount of conidiospores per gram of dry mass.

Keywords: Trichoderma harzianum, modeling, optimization, artificial neural network

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Authors: Keunhong Chae, Seokho Yoon

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Recently, the global navigation satellite system (GNSS) such as Galileo and GPS is employing more satellites to provide a higher degree of accuracy for the location service, thus calling for a more efficient signaling scheme among the satellites used in the overall GNSS network. In that the network throughput is improved, the spatial diversity can be one of the efficient signaling schemes; however, it requires multiple antenna that could cause a significant increase in the complexity of the GNSS. Thus, a diversity scheme called the cooperative signaling was proposed, where the virtual multiple-input multiple-output (MIMO) signaling is realized with using only a single antenna in the transmit satellite of interest and with modeling the neighboring satellites as relay nodes. The main drawback of the cooperative signaling is that the relay nodes receive the transmitted signal at different time instants, i.e., they operate in an asynchronous way, and thus, the overall performance of the GNSS network could degrade severely. To tackle the problem, several modified cooperative signaling schemes were proposed; however, all of them are difficult to implement due to a signal decoding at the relay nodes. Although the implementation at the relay nodes could be simpler to some degree by employing the time-reversal and conjugation operations instead of the signal decoding, it would be more efficient if we could implement the operations of the relay nodes at the source node having more resources than the relay nodes. So, in this paper, we propose a novel cooperative signaling scheme, where the data signals are combined in a unique way at the source node, thus obviating the need of the complex operations such as signal decoding, time-reversal and conjugation at the relay nodes. The numerical results confirm that the proposed scheme provides the same performance in the cooperative diversity and the bit error rate (BER) as the conventional scheme, while reducing the complexity at the relay nodes significantly. Acknowledgment: This work was supported by the National GNSS Research Center program of Defense Acquisition Program Administration and Agency for Defense Development.

Keywords: global navigation satellite network, cooperative signaling, data combining, nodes

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Authors: Adedayo Racheal Agbonna

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The rising level of graduate unemployment in Nigeria has brought about the introduction of entrepreneurship education as a career option for self–reliance and self-employment. Sequel to this, it is important to have an understanding of the determining factors of entrepreneurial intention. Therefore this research empirically investigated the influence of entrepreneurship education on entrepreneurial intention of undergraduate students of selected universities in Ogun State, Nigeria. The study is significant to researchers, university policy makers, and the government. Survey research design was adopted in the study. The population consisted of 17,659 final year undergraduate students universities in Ogun State. The study adopted stratified and random sampling technique. The table of sample size determination was used to determine the sample size for this study at 95% confidence level and 5% margin error to arrive at a sample size of 1877 respondents. The elements of population were 400 level students of the selected universities. A structured questionnaire titled 'Entrepreneurship Education and students’ Entrepreneurial intention' was administered. The result of the reliability test had the following values 0.716, 0.907 and 0.949 for infrastructure, perceived university support, and entrepreneurial intention respectively. In the same vein, from the construct validity test, the following values were obtained 0.711, 0.663 and 0.759 for infrastructure, perceived university support and entrepreneurial intention respectively. Findings of this study revealed that each of the entrepreneurship education variables significantly affected intention University infrastructure B= -1.200, R²=0.679, F (₁,₁₈₇₅) = 3958.345, P < 0.05) Perceived University Support B= -1.027, R²=0.502, F(₁,₁₈₇₅) = 1924.612, P < 0.05). The perception of respondents in public university and private university on entrepreneurship education have a statistically significant difference [F(₁,₁₈₇₅) = 134.614, p < 0.05) α F(₁,₁₈₇₅) = 363.439]. The study concluded that entrepreneurship education positively influenced entrepreneurial intention of undergraduate students in Ogun State, Nigeria. Also, university infrastructure and perceived university support have negative and significant effect on entrepreneurial intention. The study recommended that to promote entrepreneurial intention of university undergraduate students, infrastructures and the university support that can arouse entrepreneurial intention of students should be put in place.

Keywords: entrepreneurship education, entrepreneurial intention, perceived university support, university infrastructure

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Authors: Nia Mair Fry, Matthew Profit, Chenfeng Li

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This work focuses on the development and implementation of a fully implicit-implicit, coupled mechanical deformation and porous flow, finite element software tool. The fully implicit software accurately predicts classical fundamental analytical solutions such as the Terzaghi consolidation problem. Furthermore, it can capture other analytical solutions less well known in the literature, such as Gibson’s sedimentation rate problem and Coussy’s problems investigating wellbore stability for poroelastic rocks. The mechanical volume strains are transferred to the porous flow governing equation in an implicit framework. This will overcome some of the many current industrial issues, which use explicit solvers for the mechanical governing equations and only implicit solvers on the porous flow side. This can potentially lead to instability and non-convergence issues in the coupled system, plus giving results with an accountable degree of error. The specification of a fully monolithic implicit-implicit coupled porous media code sees the solution of both seepage-mechanical equations in one matrix system, under a unified time-stepping scheme, which makes the problem definition much easier. When using an explicit solver, additional input such as the damping coefficient and mass scaling factor is required, which are circumvented with a fully implicit solution. Further, improved accuracy is achieved as the solution is not dependent on predictor-corrector methods for the pore fluid pressure solution, but at the potential cost of reduced stability. In testing of this fully monolithic porous media code, there is the comparison of the fully implicit coupled scheme against an existing staggered explicit-implicit coupled scheme solution across a range of geotechnical problems. These cases include 1) Biot coefficient calculation, 2) consolidation theory with Terzaghi analytical solution, 3) sedimentation theory with Gibson analytical solution, and 4) Coussy well-bore poroelastic analytical solutions.

Keywords: coupled, implicit, monolithic, porous media

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Authors: Yichin Chen

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Taiwan is an island characterized by an active tectonics and high erosion rates. Monitoring the dynamic landscape of Taiwan is an important issue for disaster mitigation, geomorphological research, and watershed management. Long-term and high spatiotemporal landform data is essential for quantifying and simulating the geomorphological processes and developing warning systems. Recently, the advances in unmanned aerial vehicle (UAV) and computational photogrammetry technology have provided an effective way to rebuild and monitor the topography changes in high spatio-temporal resolutions. This study rebuilds the 30-years landform change in the Aiyuzi watershed in 1986-2017 by using the aerial photogrammetry-based techniques. The Aiyuzi watershed, located in central Taiwan and has an area of 3.99 Km², is famous for its frequent landslide and debris flow disasters. This study took the aerial photos by using UAV and collected multi-temporal historical, stereo photographs, taken by the Aerial Survey Office of Taiwan’s Forestry Bureau. To rebuild the orthoimages and digital surface models (DSMs), Pix4DMapper, a photogrammetry software, was used. Furthermore, to control model accuracy, a set of ground control points was surveyed by using eGPS. The results show that the generated DSMs have the ground sampling distance (GSD) of ~10 cm and ~0.3 cm from the UAV’s and historical photographs, respectively, and vertical error of ~1 m. By comparing the DSMs, there are many deep-seated landslides (with depth over 20 m) occurred on the upstream in the Aiyuzi watershed. Even though a large amount of sediment is delivered from the landslides, the steep main channel has sufficient capacity to transport sediment from the channel and to erode the river bed to ~20 m in depth. Most sediments are transported to the outlet of watershed and deposits on the downstream channel. This case study shows that UAV and photogrammetry technology are useful for topography change monitoring effectively.

Keywords: aerial photogrammetry, landslide, landform change, Taiwan

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Authors: Safaa H. Sindi, Michael Roe

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This project divides supply chain (SC) models into seven Eras, according to the evolution of the market’s needs throughout time. The five earliest Eras describe the emergence of supply chains, while the last two Eras are to be created. Research objectives: The aim is to generate the two latest Eras with their respective models that focus on the consumable goods. Era Six contains the Optimal Multidimensional Matrix (OMM) that incorporates most characteristics of the SC and allocates them into four quarters (Agile, Lean, Leagile, and Basic SC). This will help companies, especially (SMEs) plan their optimal SC route. Era Seven creates an Automated Multidimensional Model (AMM) which upgrades the matrix of Era six, as it accounts for all the supply chain factors (i.e. Offshoring, sourcing, risk) into an interactive system with Heuristic Learning that helps larger companies and industries to select the best SC model for their market. Methodologies: The data collection is based on a Fuzzy-Delphi study that analyses statements using Fuzzy Logic. The first round of Delphi study will contain statements (fuzzy rules) about the matrix of Era six. The second round of Delphi contains the feedback given from the first round and so on. Preliminary findings: both models are applicable, Matrix of Era six reduces the complexity of choosing the best SC model for SMEs by helping them identify the best strategy of Basic SC, Lean, Agile and Leagile SC; that’s tailored to their needs. The interactive heuristic learning in the AMM of Era seven will help mitigate error and aid large companies to identify and re-strategize the best SC model and distribution system for their market and commodity, hence increasing efficiency. Potential contributions to the literature: The problematic issue facing many companies is to decide which SC model or strategy to incorporate, due to the many models and definitions developed over the years. This research simplifies this by putting most definition in a template and most models in the Matrix of era six. This research is original as the division of SC into Eras, the Matrix of Era six (OMM) with Fuzzy-Delphi and Heuristic Learning in the AMM of Era seven provides a synergy of tools that were not combined before in the area of SC. Additionally the OMM of Era six is unique as it combines most characteristics of the SC, which is an original concept in itself.

Keywords: Leagile, automation, heuristic learning, supply chain models

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Authors: Mustafa M. Donma, Orkide Donma

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Metabolic syndrome (MetS) is a severe health problem which is common among obese individuals. The components of MetS are rather stable in adults compared to the components discussed for children. Due to the ambiguity in this group of the population, how to diagnose MetS in morbid obese (MO) children still constitutes a matter of discussion. For this purpose, a formula, which facilitates the diagnosis of MetS in MO children, was investigated. The aim of this study was to develop a formula which was capable of discriminating MO children with and without MetS findings. Study population comprised MO children. Age and sex-dependent body mass index (BMI) percentiles of the children were above 99. Metabolic syndrome components were also determined. Elevated systolic and diastolic blood pressures (SBP and DBP), elevated fasting blood glucose (FBG), elevated triglycerides (TRG), and/or depressed high density lipoprotein cholesterol (HDL-C) in addition to central obesity were listed as MetS components for each child. Presence of at least two of these components confirmed that the case was MetS. Two groups were constituted. In the first group, there were forty-two MO children without MetS components. Second group was composed of forty-four MO children with at least two MetS components. Anthropometric measurements, including weight, height, waist, and hip circumferences, were performed following physical examination. Body mass index and homeostatic model assessment of insulin resistance values were calculated. Informed consent forms were obtained from the parents of the children. Institutional Non-Interventional Ethics Committee approved the study design. Blood pressure values were recorded. Routine biochemical analysis, including FBG, insulin (INS), TRG, HDL-C were performed. The performance and the clinical utility of the Diagnostic Obesity Notation Model Assessment Metabolic Syndrome Index (DONMA MetS index) [(INS/FBG)/(HDL-C/TRG)*100] was tested. Appropriate statistical tests were applied to the study data. p value smaller than 0.05 was defined as significant. Metabolic syndrome index values were 41.6±5.1 in MO group and 104.4±12.8 in MetS group. Corresponding values for HDL-C values were 54.5±13.2 mg/dl and 44.2±11.5 mg/dl. There were statistically significant differences between the groups (p<0.001). Upon evaluation of the correlations between MetS index and HDL-C values, a much stronger negative correlation was found in MetS group (r=-0.515; p=0.001) in comparison with the correlation detected in MO group (r=-0.371; p=0.016). From these findings, it was concluded that the statistical significance degree of the difference between MO and MetS groups was highly acceptable for this recently introduced MetS index as expected. This was due to the involvement of all of the biochemically defined MetS components into the index. This is particularly important because each of these four parameters used in the formula is cardiac risk factor. Aside from discriminating MO children with and without MetS findings, MetS index introduced in this study is important from the cardiovascular risk point of view in MetS group of children.

Keywords: children, fasting blood glucose, high density lipoprotein cholesterol, index, insulin, metabolic syndrome, morbid obesity, triglycerides.

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Authors: Luis Amador Jimenez, Md. Shohel Amin

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Pavement performance models are based on projections of observed traffic loads, which makes uncertain to study funding strategies in the long run if history does not repeat. Neural networks can be used to estimate deterioration rates but the learning rate and momentum have not been properly investigated, in addition, economic evolvement could change traffic flows. This study addresses both issues through a case study for roads of Montreal that simulates traffic for a period of 50 years and deals with the measurement error of the pavement deterioration model. Travel demand models are applied to simulate annual average daily traffic (AADT) every 5 years. Accumulated equivalent single axle loads (ESALs) are calculated from the predicted AADT and locally observed truck distributions combined with truck factors. A back propagation Neural Network (BPN) method with a Generalized Delta Rule (GDR) learning algorithm is applied to estimate pavement deterioration models capable of overcoming measurement errors. Linear programming of lifecycle optimization is applied to identify M&R strategies that ensure good pavement condition while minimizing the budget. It was found that CAD 150 million is the minimum annual budget to good condition for arterial and local roads in Montreal. Montreal drivers prefer the use of public transportation for work and education purposes. Vehicle traffic is expected to double within 50 years, ESALS are expected to double the number of ESALs every 15 years. Roads in the island of Montreal need to undergo a stabilization period for about 25 years, a steady state seems to be reached after.

Keywords: pavement management system, traffic simulation, backpropagation neural network, performance modeling, measurement errors, linear programming, lifecycle optimization

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Authors: Cristy Stagnar, Nina Hubig, Diana Ivankovic

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The epigenome is a compelling candidate for mediating long-term responses to environmental effects modifying disease risk. The main goal of this research is to develop a machine learning-based DNA methylation score, which will be valuable in delineating the unique contribution of paternal epigenetic modifications to the germline impacting childhood health outcomes. It will also be a useful tool in validating self-reports of nonsmoking and in adjusting epigenome-wide DNA methylation association studies for this early-life exposure. Using secondary data from two population-based methylation profiling studies, our DNA methylation score is based on CpG DNA methylation measurements from cord blood gathered from children whose fathers smoked pre- and peri-conceptually. Each child’s mother and father fell into one of three class labels in the accompanying questionnaires -never smoker, former smoker, or current smoker. By applying different machine learning algorithms to the accessible resource for integrated epigenomic studies (ARIES) sub-study of the Avon longitudinal study of parents and children (ALSPAC) data set, which we used for training and testing of our model, the best-performing algorithm for classifying the father smoker and mother never smoker was selected based on Cohen’s κ. Error in the model was identified and optimized. The final DNA methylation score was further tested and validated in an independent data set. This resulted in a linear combination of methylation values of selected probes via a logistic link function that accurately classified each group and contributed the most towards classification. The result is a unique, robust DNA methylation score which combines information on DNA methylation and early life exposure of offspring to paternal smoking during pregnancy and which may be used to examine the paternal contribution to offspring health outcomes.

Keywords: epigenome, health outcomes, paternal preconception environmental exposures, supervised machine learning

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Authors: Massimo Miccoli, Luca Marangoni, Alberto Aniello Scaringi, Alessandro Marceddu, Alessandro Amicone

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The susceptibility of deep neural networks (DNNs) to adversarial manipulations is a recognized challenge within the computer vision domain. Adversarial examples, crafted by adding subtle yet malicious alterations to benign images, exploit this vulnerability. Various defense strategies have been proposed to safeguard DNNs against such attacks, stemming from diverse research hypotheses. Building upon prior work, our approach involves the utilization of autoencoder models. Autoencoders, a type of neural network, are trained to learn representations of training data and reconstruct inputs from these representations, typically minimizing reconstruction errors like mean squared error (MSE). Our autoencoder was trained on a dataset of benign examples; learning features specific to them. Consequently, when presented with significantly perturbed adversarial examples, the autoencoder exhibited high reconstruction errors. The architecture of the autoencoder was tailored to the dimensions of the images under evaluation. We considered various image sizes, constructing models differently for 256x256 and 512x512 images. Moreover, the choice of the computer vision model is crucial, as most adversarial attacks are designed with specific AI structures in mind. To mitigate this, we proposed a method to replace image-specific dimensions with a structure independent of both dimensions and neural network models, thereby enhancing robustness. Our multi-modal autoencoder reconstructs the spectral representation of images across the red-green-blue (RGB) color channels. To validate our approach, we conducted experiments using diverse datasets and subjected them to adversarial attacks using models such as ResNet50 and ViT_L_16 from the torch vision library. The autoencoder extracted features used in a classification model, resulting in an MSE (RGB) of 0.014, a classification accuracy of 97.33%, and a precision of 99%.

Keywords: adversarial attacks, malicious images detector, binary classifier, multimodal transformer autoencoder

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Authors: Mehwish Durrani, Rubina Nazli, Muhammad Abubakr, Muhammad Shafiq

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Objectives: To evaluate the high socio-economic status, urbanization, and decrease ambulation can lead to early osteoporosis in women reporting from Peshawar region. Study Design: Descriptive cross-sectional study was done. Sample size was 100 subjects, using 30% proportion of osteoporosis, 95% confidence level, and 9% margin of error under WHO software for sample size determination. Place and Duration of study: This study was carried out in the tertiary referral health care facilities of Peshawar viz PGMI Hayatabad Medical Complex, Peshawar, Khyber Pakhtunkhwa Province, Pakistan. Ethical approval for the study was taken from the Institutional Ethical Research board (IERD) at Post Graduate Medical Institute, Hayatabad Medical Complex, and Peshawar.The study was done in six months time period. Patients and Methods: Levels of CTX1 as a marker of bone degradation in radiographically assessed perimenopausal women was determined. These females were randomly selected and screened for osteoporosis. Hemoglobin in gm/dl, ESR by Westergren method as millimeter in 1 hour, Serum Ca mg/dl, Serum alkaline Phosphatase international units per liter radiographic grade of osteoporosis according to Singh index as 1-6 and CTX 1 level in pg/ml. Results: High levels of CTX1 was observed in perimenopausal osteoporotic women which were radiographically diagnosed as osteoporotic patients. The High socio-economic class also predispose to osteoporosis. Decrease ambulation another risk factor showed significant association with the increased levels of CTX1. Conclusion: The results of this study propose that minimum ambulation and high socioeconomic class both had significance association with the increase levels of serum CTX1, which in turn will lead to osteoporosis and to its complications.

Keywords: osteoporosis, CTX1, perimenopausal women, Hayatabad Medical Complex, Khyberpuktoonkhwa

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Authors: Halimat M. Ajose-Adeogun, Zaynab A. Bello

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Artificial Intelligence (AI) and Natural Language Processing (NLP) have transformed computer language comprehension, allowing computers to comprehend spoken and written language with human-like cognition. NLP, a multidisciplinary area that combines rule-based linguistics, machine learning, and deep learning, enables computers to analyze and comprehend human language. NLP applications in medicine range from tackling issues in electronic health records (EHR) and psychiatry to improving diagnostic precision in orthopedic surgery and optimizing clinical procedures with novel technologies like chatbots. The technology shows promise in a variety of medical sectors, including quicker access to medical records, faster decision-making for healthcare personnel, diagnosing dysplasia in Barrett's esophagus, boosting radiology report quality, and so on. However, successful adoption requires training for healthcare workers, fostering a deep understanding of NLP components, and highlighting the significance of validation before actual application. Despite prevailing challenges, continuous multidisciplinary research and collaboration are critical for overcoming restrictions and paving the way for the revolutionary integration of NLP into medical practice. This integration has the potential to improve patient care, research outcomes, and administrative efficiency. The research methodology includes using NLP techniques for Sentiment Analysis and Emotion Recognition, such as evaluating text or audio data to determine the sentiment and emotional nuances communicated by users, which is essential for designing a responsive and sympathetic chatbot. Furthermore, the project includes the adoption of a Personalized Intervention strategy, in which chatbots are designed to personalize responses by merging NLP algorithms with specific user profiles, treatment history, and emotional states. The synergy between NLP and personalized medicine principles is critical for tailoring chatbot interactions to each user's demands and conditions, hence increasing the efficacy of mental health care. A detailed survey corroborated this synergy, revealing a remarkable 20% increase in patient satisfaction levels and a 30% reduction in workloads for healthcare practitioners. The poll, which focused on health outcomes and was administered to both patients and healthcare professionals, highlights the improved efficiency and favorable influence on the broader healthcare ecosystem.

Keywords: natural language processing, artificial intelligence, healthcare communication, electronic health records, patient care

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Authors: Vishal Pareek, Shubham Gupta, Sushil Chandra Jain

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With increasing high-speed computation demand the power consumption, heat dissipation and chip size issues are posing challenges for logic design with conventional technologies. Recovery of bit loss and bit errors is other issues that require reversibility and fault tolerance in the computation. The reversible computing is emerging as an alternative to conventional technologies to overcome the above problems and helpful in a diverse area such as low-power design, nanotechnology, quantum computing. Bit loss issue can be solved through unique input-output mapping which require reversibility and bit error issue require the capability of fault tolerance in design. In order to incorporate reversibility a number of combinational reversible logic based circuits have been developed. However, very few sequential reversible circuits have been reported in the literature. To make the circuit fault tolerant, a number of fault model and test approaches have been proposed for reversible logic. In this paper, we have attempted to incorporate fault tolerance in sequential reversible building blocks such as D flip-flop, T flip-flop, JK flip-flop, R-S flip-flop, Master-Slave D flip-flop, and double edge triggered D flip-flop by making them parity preserving. The importance of this proposed work lies in the fact that it provides the design of reversible sequential circuits completely testable for any stuck-at fault and single bit fault. In our opinion our design of reversible building blocks is superior to existing designs in term of quantum cost, hardware complexity, constant input, garbage output, number of gates and design of online testable D flip-flop have been proposed for the first time. We hope our work can be extended for building complex reversible sequential circuits.

Keywords: parity preserving gate, quantum computing, fault tolerance, flip-flop, sequential reversible logic

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Authors: Mustafa M. Donma, Orkide Donma

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Obesity is associated with cardiovascular disease risk factors and metabolic syndrome (MetS). In this study, associations between adipokines and adipokine as well as obesity indices were evaluated. Plasma adipokine levels may exhibit variations according to body adipose tissue mass. Besides, upon consideration of obesity as an inflammatory disease, adipokines may play some roles in this process. The ratios of proinflammatory adipokines to adiponectin may act as highly sensitive indicators of body adipokine status. The aim of the study is to present some adipokine indices, which are thought to be helpful for the evaluation of childhood obesity and also to determine the best discriminators in the diagnosis of MetS. 80 prepubertal children (aged between 6-9.5 years) included in the study were divided into three groups; 30 children with normal weight (NW), 25 morbid obese (MO) children and 25 MO children with MetS. Physical examinations were performed. Written informed consent forms were obtained from the parents. The study protocol was approved by Ethics Committee of Namik Kemal University Medical Faculty. Anthropometric measurements, such as weight, height, waist circumference (C), hip C, head C, neck C were recorded. Values for body mass index (BMI), diagnostic obesity notation model assessment Index-II (D2 index) as well as waist-to-hip, head-to-neck ratios were calculated. Adiponectin, resistin, leptin, chemerin, vaspin, progranulin assays were performed by ELISA. Adipokine-to-adiponectin ratios were obtained. SPSS Version 20 was used for the evaluation of data. p values ≤ 0.05 were accepted as statistically significant. Values of BMI and D2 index, waist-to-hip, head-to-neck ratios did not differ between MO and MetS groups (p ≥ 0.05). Except progranulin (p ≤ 0.01), similar patterns were observed for plasma levels of each adipokine. There was not any difference in vaspin as well as resistin levels between NW and MO groups. Significantly increased leptin-to-adiponectin, chemerin-to-adiponectin and vaspin-to-adiponectin values were noted in MO in comparison with those of NW. The most valuable adipokine index was progranulin-to-adiponectin (p ≤ 0.01). This index was strongly correlated with vaspin-to-adiponectin ratio in all groups (p ≤ 0.05). There was no correlation between vaspin-to-adiponectin and chemerin-to--adiponectin in NW group. However, a correlation existed in MO group (r = 0.486; p ≤ 0.05). Much stronger correlation (r = 0.609; p ≤ 0.01) was observed in MetS group between these two adipokine indices. No correlations were detected between vaspin and progranulin as well as vaspin and chemerin levels. Correlation analyses showed a unique profile confined to MetS children. Adiponectin was found to be correlated with waist-to-hip (r = -0.435; p ≤ 0.05) as well as head-to-neck (r = 0.541; p ≤ 0.05) ratios only in MetS children. In this study, it has been investigated if adipokine indices have priority over adipokine levels. In conclusion, vaspin-to-adiponectin, progranulin-to-adiponectin, chemerin-to-adiponectin along with waist-to-hip and head-to-neck ratios were the optimal combinations. Adiponectin, waist-to-hip, head-to-neck, vaspin-to-adiponectin, chemerin-to-adiponectin ratios had appropriate discriminatory capability for MetS children.

Keywords: adipokine indices, metabolic syndrome, obesity indices, pediatric obesity

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Authors: Ghufran Ahmed, Muhammad Osama Siddiqui, Shahbaz Siddiqui, Rauf Ahmad Shams Malick, Faisal Khan, Mubashir Khan

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In this paper, a machine learning-based approach for early prediction of diseases in cows is proposed. Different ML algos are applied to extract useful patterns from the available dataset. Technology has changed today’s world in every aspect of life. Similarly, advanced technologies have been developed in livestock and dairy farming to monitor dairy cows in various aspects. Dairy cattle monitoring is crucial as it plays a significant role in milk production around the globe. Moreover, it has become necessary for farmers to adopt the latest early prediction technologies as the food demand is increasing with population growth. This highlight the importance of state-ofthe-art technologies in analyzing how important technology is in analyzing dairy cows’ activities. It is not easy to predict the activities of a large number of cows on the farm, so, the system has made it very convenient for the farmers., as it provides all the solutions under one roof. The cattle industry’s productivity is boosted as the early diagnosis of any disease on a cattle farm is detected and hence it is treated early. It is done on behalf of the machine learning output received. The learning models are already set which interpret the data collected in a centralized system. Basically, we will run different algorithms on behalf of the data set received to analyze milk quality, and track cows’ health, location, and safety. This deep learning algorithm draws patterns from the data, which makes it easier for farmers to study any animal’s behavioral changes. With the emergence of machine learning algorithms and the Internet of Things, accurate tracking of animals is possible as the rate of error is minimized. As a result, milk productivity is increased. IoT with ML capability has given a new phase to the cattle farming industry by increasing the yield in the most cost-effective and time-saving manner.

Keywords: IoT, machine learning, health care, dairy cows

Procedia PDF Downloads 42

Authors: Magdalena B. Kaziuk, Waldemar Kosiba, Marek J. Kuzniewski

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Introduction: Adipose tissue is a typical place for storage water-insoluble toxins in the body. It's connective tissue, where the intercellular substance consist of fat, which level in people with low physical activity should be 18-25% for women and 13-18% for men. Due to the fat distribution in the body we distinquish two types of obesity: android (visceral, abdominal) and gynoidal (gluteal-femoral, peripheral). Abdominal obesity increases the risk of complications of the cardiovascular system diseases, and impaired renal and liver function. Through the influence on disorders of metabolism, lipid metabolism, diabetes and hypertension, leading to emergence of the metabolic syndrome. So thus, obesity will especially overload kidney function in patients after transplantation. Aim: An attempt was made to estimate the impact of amount fat tissue on transplanted kidney function and excretory function of the liver in patients after Ktx. Material and Methods: The study included 108 patients (50 females, 58 male, age 46.5 +/- 12.9 years) with active kidney transplant after more than 3 months from the transplantation. An analysis of body composition was done by using electrical bioimpedance (BIA) and anthropometric measurements. Estimated basal metabolic rate (BMR), muscle mass, total body water content and the amount of body fat. Information about physical activity were obtained during clinical examination. Nutritional status, and type of obesity were determined by using indicators: Waist to Height Ratio (WHR) and Waist to Hip Ratio (WHR). Excretory functions of the transplanted kidney was rated by calculating the estimated renal glomerular filtration rate (eGFR) using the MDRD formula. Liver function was rated by total bilirubin and alanine aminotransferase levels ALT concentration in serum. In our patients haemolitic uremic syndrome (HUS) was excluded. Results: In 19.44% of patients had underweight, 22.37% of the respondents were with normal weight, 11.11% had overweight, and the rest were with obese (49.08%). People with android stature have a lower eGFR compared with those with the gynoidal stature (p = 0.004). All patients with obesity had higher amount of body fat from a few to several percent. The higher amount of body fat percentage, the lower eGFR had patients (p <0.001). Elevated ALT levels significantly correlated with a high fat content (p <0.02). Conclusion: Increased amount of body fat, particularly in the case of android obesity can be a predictor of kidney and liver damage. Due to that obese patients should have more frequent control of diagnostic functions of these organs and the intensive dietary proceedings, pharmacological and regular physical activity adapted to the current physical condition of patients after transplantation.

Keywords: obesity, body fat, kidney transplantation, glomerular filtration rate, liver function

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Authors: Gayathri Nagarajan, L. D. Dhinesh Babu

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Health care is one of the prominent industries that generate voluminous data thereby finding the need of machine learning techniques with big data solutions for efficient processing and prediction. Missing data, incomplete data, real time streaming data, sensitive data, privacy, heterogeneity are few of the common challenges to be addressed for efficient processing and mining of health care data. In comparison with other applications, accuracy and fast processing are of higher importance for health care applications as they are related to the human life directly. Though there are many machine learning techniques and big data solutions used for efficient processing and prediction in health care data, different techniques and different frameworks are proved to be effective for different applications largely depending on the characteristics of the datasets. In this paper, we present a framework that uses ensemble machine learning technique gradient boosted trees for data classification in health care big data. The framework is built on Spark platform which is fast in comparison with other traditional frameworks. Unlike other works that focus on a single technique, our work presents a comparison of six different machine learning techniques along with gradient boosted trees on datasets of different characteristics. Five benchmark health care datasets are considered for experimentation, and the results of different machine learning techniques are discussed in comparison with gradient boosted trees. The metric chosen for comparison is misclassification error rate and the run time of the algorithms. The goal of this paper is to i) Compare the performance of gradient boosted trees with other machine learning techniques in Spark platform specifically for health care big data and ii) Discuss the results from the experiments conducted on datasets of different characteristics thereby drawing inference and conclusion. The experimental results show that the accuracy is largely dependent on the characteristics of the datasets for other machine learning techniques whereas gradient boosting trees yields reasonably stable results in terms of accuracy without largely depending on the dataset characteristics.

Keywords: big data analytics, ensemble machine learning, gradient boosted trees, Spark platform

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Authors: Michael A. Sprayberry, Vincent C. Paquit

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Process parameter optimization in metal powder bed electron beam melting (MPBEBM) is crucial to ensure the technology's repeatability, control, and industry-continued adoption. Despite continued efforts to address the challenges via the traditional design of experiments and process mapping techniques, there needs to be more successful in an on-the-fly optimization framework that can be adapted to MPBEBM systems. Additionally, data-intensive physics-based modeling and simulation methods are difficult to support by a metal AM alloy or system due to cost restrictions. To mitigate the challenge of resource-intensive experiments and models, this paper introduces a Multi-Objective Reinforcement Learning (MORL) methodology defined as an optimization problem for MPBEBM. An off-policy MORL framework based on policy gradient is proposed to discover optimal sets of beam power (P) – beam velocity (v) combinations to maintain a steady-state melt pool depth and phase transformation. For this, an experimentally validated Eagar-Tsai melt pool model is used to simulate the MPBEBM environment, where the beam acts as the agent across the P – v space to maximize returns for the uncertain powder bed environment producing a melt pool and phase transformation closer to the optimum. The culmination of the training process yields a set of process parameters {power, speed, hatch spacing, layer depth, and preheat} where the state (P,v) with the highest returns corresponds to a refined process parameter mapping. The resultant objects and mapping of returns to the P-v space show convergence with experimental observations. The framework, therefore, provides a model-free multi-objective approach to discovery without the need for trial-and-error experiments.

Keywords: additive manufacturing, metal powder bed fusion, reinforcement learning, process parameter optimization

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Authors: Soowon Park, Min-Ji Kim, Jun-Young Lee

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Motivation: The stigma of mental illness has been studied in many disciplines, including social psychology, counseling psychology, sociology, psychiatry, public health care, and related areas, because individuals labeled as ‘mentally ill’ are often deprived of their rights and their life opportunities. To understand the factors that deepen the stigma of mental illness, it is important to understand the influencing factors of the stigma. Problem statement: Depression is a common disorder in adults, but the incidence of help-seeking is low. Researchers have believed that this poor help-seeking behavior is related to the stigma of mental illness, which results from low mental health literacy. However, it is uncertain that increasing mental health literacy decreases mental health stigmatization. Furthermore, even though decreasing stigmatization is important, the stigma of mental illness is still a stable and long-lasting phenomenon. Thus, factors other than knowledge about mental disorders have the power to maintain the stigma. Investigating the influencing factors that facilitate the stigma of psychiatric disease could help lower the social stigmatization. Approach: Face-to-face interviews were conducted with a multi-clustering sample. A total of 700 Korean participants (38% male), ranging in age from 18 to 78 (M(SD)age= 48.5(15.7)) answered demographical questions, Korean version of Link’s Perceived Devaluation and Discrimination (PDD) scale for the assessment of social stigmatization against depression, and the Korean version of the WHO-Composite International Diagnostic Interview for the assessment of mental disorders. Multiple-regression was conducted to find the predicting factors of social stigmatization against depression. Ages, sex, years of education, income, living location, and experience of mental illness were used as the predictors. Results: Predictors accounted for 14% of the variance in the stigma of depressive disorders (F(6, 693) = 20.27, p < .001). Among those, only age, years of education, and experience of mental illness significantly predicted social stigmatization against depression. The standardized regression coefficient of age had a negative association with stigmatization (β = -.20, p < .001), but years of education (β = .20, p < .001) and experience of mental illness (β = .08, p < .05) positively predicted depression stigmatization. Conclusions: The present study clearly demonstrates the association between personal factors and depressive disorder stigmatization. Younger age, more education, and self-stigma appeared to increase the stigmatization. Young, highly educated, and mentally ill people tend to reject patients with depressive disorder as friends, teachers, or babysitters; they also tend to think that those patients have lower intelligence and abilities. These results suggest the possibility that people from a high social class, or highly educated people, who have the power to make decisions, help maintain the social stigma against mental illness patients. To increase the awareness that people from high social classes have more stigmatization against depressive disorders will help decrease the biased attitudes against mentally ill patients.

Keywords: depressive disorder stigmatization, age, education, self-stigma

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Authors: Anu S. Nair, V. Anantha Subramanian

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This paper presents a surface development and fairing technique combining the features of a modern computer-aided design tool namely the Non-Uniform Rational Basis Spline (NURBS) with an algorithm to obtain a rapidly faired hull form. Some of the older series based designs give sectional area distribution such as in the Wageningen-Lap Series. Others such as the FORMDATA give more comprehensive offset data points. Nevertheless, this basic data still requires fairing to obtain an acceptable faired hull form. This method uses the input of sectional area distribution as an example and arrives at the faired form. Characteristic section shapes define any general ship hull form in the entrance, parallel mid-body and run regions. The method defines a minimum of control points at each section and using the Golden search method or the bisection method; the section shape converges to the one with the prescribed sectional area with a minimized error in the area fit. The section shapes combine into evolving the faired surface by NURBS and typically takes 20 iterations. The advantage of the method is that it is fast, robust and evolves the faired hull form through minimal iterations. The curvature criterion check for the hull lines shows the evolution of the smooth faired surface. The method is applicable to hull form from any parent series and the evolved form can be evaluated for hydrodynamic performance as is done in more modern design practice. The method can handle complex shape such as that of the bulbous bow. Surface patches developed fit together at their common boundaries with curvature continuity and fairness check. The development is coded in MATLAB and the example illustrates the development of the method. The most important advantage is quick time, the rapid iterative fairing of the hull form.

Keywords: computer-aided design, methodical series, NURBS, ship design

Procedia PDF Downloads 152

Authors: Kassaye Balkew Workagegn, Gunnar Klemetsdal, Hans Magnus Gjøen

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In this study, the first objective was to investigate whether non-lethal or non-invasive methods, utilizing body measurements, could be used to efficiently predict fillet weight and fillet yield for a complete diallel cross of three Nile tilapia (Oreochromis niloticus) strains collected from three Ethiopian Rift Valley lakes, Lakes Ziway, Koka and Chamo. The second objective was to estimate heritability of body weight, actual and predicted fillet traits, as well as genetic correlations between these traits. A third goal was to estimate additive, reciprocal, and heterosis effects for body weight and the various fillet traits. As in females, early sexual maturation was widespread, only 958 male fish from 81 full-sib families were used, both for the prediction of fillet traits and in genetic analysis. The prediction equations from body measurements were established by forward regression analysis, choosing models with the least predicted residual error sums of squares (PRESS). The results revealed that body measurements on live Nile tilapia is well suited to predict fillet weight but not fillet yield (R²= 0.945 and 0.209, respectively), but both models were seemingly unbiased. The genetic analyses were carried out with bivariate, multibreed models. Body weight, fillet weight, and predicted fillet weight were all estimated with a heritability ranged from 0.23 to 0.28, and with genetic correlations close to one. Contrary, fillet yield was only to a minor degree heritable (0.05), while predicted fillet yield obtained a heritability of 0.19, being a resultant of two body weight variables known to have high heritability. The latter trait was estimated with genetic correlations to body weight and fillet weight traits larger than 0.82. No significant differences among strains were found for their additive genetic, reciprocal, or heterosis effects, while total heterosis effects were estimated as positive and significant (P < 0.05). As a conclusion, prediction of prediction of fillet weight based on body measurements is possible, but not for fillet yield.

Keywords: additive, fillet traits, genetic correlation, heritability, heterosis, prediction, reciprocal

Procedia PDF Downloads 147

Authors: Benyam Zenebe, Tesfaye Sisay, Gurja Belay, Workabeba Abebe

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Background: The prevalence and antibiogram of pathogenic E. coli strains, which cause diarrhea vary from region to region, and even within countries in the same geographical area. In Ethiopia, diagnostic approaches to E. coli induced diarrhea in children less than five years of age are not standardized. The aim of this study was to determine the involvement of pathogenic E. coli strains in child diarrhea and determine the antibiograms of the isolates in children less than 5 years of age with diarrhea at Addis Ababa University College of Health Sciences TikurAnbessa Specialized Hospital, Addis Ababa, Ethiopia. Methods: A purposive study that included 98 diarrheic children less than five years of age was conducted at Addis Ababa University College of Health Sciences, TikurAnbessa Specialized Hospital, Addis Ababa, Ethiopia to detect pathogenic E. coli biotypes. Stool culture was used to identify presumptive E. coliisolates. Presumptive isolates were confirmed by biochemical tests, and antimicrobial susceptibility tests were performed on confirmed E. coli isolates by the disk diffusion method. DNA was extracted from confirmed isolates by a heating method and subjected to Polymerase Chain Reaction or the presence of virulence genes. Amplified PCR products were analyzed by agarose gel electrophoresis. Data were collected on child demographics and clinical conditions using administered questionnaires. The prevalence of E. coli strains from the total diarrheic children, and the prevalence of pathogenic strains from total E. coli isolates along with their susceptibility profiles; the distribution of pathogenic E.coli biotypes among different age groups and between the sexes were determined by using descriptive statistics. Result: Out of 98 stool specimens collected from diarrheic children less than 5 years of age, 75 presumptive E. coli isolates were identified by culture; further confirmation by biochemical tests showed that only 56 of the isolates were E. coli; 29 of the isolates were found in male children and 27 of them in female children. Out of the 58 isolates of E. coli, 25 pathotypes belonging to different classes of pathogenic strains: STEC, EPEC, EHEC, EAEC were detected by using the PCR technique. Pathogenic E. coli exhibited high rates of antibiotic resistance to many of the antibiotics tested. Moreover, they exhibited multiple drug resistance. Conclusion: This study found that the isolation rate of E. coli and the involvement of antibiotic-resistant pathogenic E. coli in diarrheic children is prominent, and hence focus should be given on the diagnosis and antimicrobial sensitivity testing of pathogenic E. coli at Addis Ababa University College of Health Sciences TikurAnbessa Specialized Hospital. Among antibiotics tested, Cefotitan could be a drug of choice to treat E. coli.

Keywords: antibiotic susceptibility profile, children, diarrhea, E. coli, pathogenic

Procedia PDF Downloads 205

Authors: Masih Nikgou

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ICU nurses or intensive care nurses are highly specialized and trained healthcare personnel. These nurses provide nursing care for patients with life-threatening illnesses or conditions. They provide the experience, knowledge and specialized skills that patients need to survive and recover. Intensive care nurses (ICU) are trained to make momentary decisions and act quickly when the patient's condition changes. Their primary work environment is in the hospital in intensive care units. Typically, ICU patients require a high level of care. ICU nurses work in challenging and complex fields in their nursing profession. They have the primary duty of caring for and saving patients who are fighting for their lives. Intensive care (ICU) nurses are highly trained to provide exceptional care to patients who depend on 24/7 nursing care. A patient in the ICU is often equipped with a ventilator, intubated and connected to several life support machines and medical equipment. Intensive Care Nurses (ICU) have full expertise in considering all aspects of bringing back their patients. Some of the specific responsibilities of ICU nurses include (a) Assessing and monitoring the patient's progress and identifying any sudden changes in the patient's medical condition. (b) Administration of drugs intravenously by injection or through gastric tubes. (c) Provide regular updates on patient progress to physicians, patients, and their families. (d) According to the clinical condition of the patient, perform the approved diagnostic or treatment methods. (e) In case of a health emergency, informing the relevant doctors. (f) To determine the need for emergency interventions, evaluate laboratory data and vital signs of patients. (g) Caring for patient needs during recovery in the ICU. (h) ICU nurses often provide emotional support to patients and their families. (i) Regulating and monitoring medical equipment and devices such as medical ventilators, oxygen delivery devices, transducers, and pressure lines. (j) Assessment of pain level and sedation needs of patients. (k) Maintaining patient reports and records. As the name suggests, critical care nurses work primarily in ICU health care units. ICUs are completely healthy and have proper lighting with strict adherence to health and safety from medical centers. ICU nurses usually move between the intensive care unit, the emergency department, the operating room, and other special departments of the hospital. ICU nurses usually follow a standard shift schedule that includes morning, afternoon, and night schedules. There are also other relocation programs depending on the hospital and region. Nurses who are passionate about data and managing a patient's condition and outcomes typically do well as ICU nurses. An inquisitive mind and attention to processes are equally important. ICU nurses are completely compassionate and are not afraid to advocate for their patients and family members. who are distressed.

Keywords: nursing, intensive care unit, pediatric intensive care unit, mobile intensive care unit, surgical intensive care unite

Procedia PDF Downloads 52

Authors: Ragaa T. Darwish, Maha A. Demellawy, Haidy M. Megahed, Doreen N. Younan, Wael S. Kholeif

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The testing of drug abuse is authentic in order to affirm the misuse of drugs. Several analytical approaches have been developed for the detection of drugs of abuse in pharmaceutical and common biological samples, but few methodologies have been created to identify them from fingerprints. Liquid Chromatography-Mass Spectrometry (LC-MS) plays a major role in this field. The current study aimed at assessing the possibility of detection of some drugs of abuse (tramadol, clonazepam, and phenobarbital) from fingerprints using LC-MS in drug abusers. The aim was extended in order to assess the possibility of detection of the above-mentioned drugs in fingerprints of drug handlers till three days of handling the drugs. The study was conducted on randomly selected adult individuals who were either drug abusers seeking treatment at centers of drug dependence in Alexandria, Egypt or normal volunteers who were asked to handle the different studied drugs (drug handlers). An informed consent was obtained from all individuals. Participants were classified into 3 groups; control group that consisted of 50 normal individuals (neither abusing nor handling drugs), drug abuser group that consisted of 30 individuals who abused tramadol, clonazepam or phenobarbital (10 individuals for each drug) and drug handler group that consisted of 50 individuals who were touching either the powder of drugs of abuse: tramadol, clonazepam or phenobarbital (10 individuals for each drug) or the powder of the control substances which were of similar appearance (white powder) and that might be used in the adulteration of drugs of abuse: acetyl salicylic acid and acetaminophen (10 individuals for each drug). Samples were taken from the handler individuals for three consecutive days for the same individual. The diagnosis of drug abusers was based on the current Diagnostic and Statistical Manual of Mental disorders (DSM-V) and urine screening tests using immunoassay technique. Preliminary drug screening tests of urine samples were also done for drug handlers and the control groups to indicate the presence or absence of the studied drugs of abuse. Fingerprints of all participants were then taken on a filter paper previously soaked with methanol to be analyzed by LC-MS using SCIEX Triple Quad or QTRAP 5500 System. The concentration of drugs in each sample was calculated using the regression equations between concentration in ng/ml and peak area of each reference standard. All fingerprint samples from drug abusers showed positive results with LC-MS for the tested drugs, while all samples from the control individuals showed negative results. A significant difference was noted between the concentration of the drugs and the duration of abuse. Tramadol, clonazepam, and phenobarbital were also successfully detected from fingerprints of drug handlers till 3 days of handling the drugs. The mean concentration of the chosen drugs of abuse among the handlers group decreased when the days of samples intake increased.

Keywords: drugs of abuse, fingerprints, liquid chromatography–mass spectrometry, tramadol

Procedia PDF Downloads 96

Authors: Evgeny A. Ermakov, Lyudmila P. Smirnova, Valentina N. Buneva

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Schizophrenia associated with dysregulation of neurotransmitter processes in the central nervous system and disturbances in the humoral immune system resulting in the formation of antibodies (Abs) to the various components of the nervous tissue. Abs to different neuronal receptors and DNA were detected in the blood of patients with schizophrenia. Abs hydrolyzing DNA were detected in pool of polyclonal autoantibodies in autoimmune and infectious diseases, such catalytic Abs were named abzymes. It is believed that DNA-hydrolyzing abzymes are cytotoxic, cause nuclear DNA fragmentation and induce cell death by apoptosis. Abzymes with DNAase activity are interesting because of the mechanism of formation and the possibility of use as diagnostic markers. Therefore, in our work we have set following goals: to determine the level anti-DNA Abs in the serum of patients with schizophrenia and to study DNA-hydrolyzing activity of IgG of patients with schizophrenia. Materials and methods: In our study there were included 41 patients with a verified diagnosis of paranoid or simple schizophrenia and 24 healthy donors. Electrophoretically and immunologically homogeneous IgGs were obtained by sequential affinity chromatography of the serum proteins on protein G-Sepharose and gel filtration. The levels of anti-DNA Abs were determined using ELISA. DNA-hydrolyzing activity was detected as the level of supercoiled pBluescript DNA transition in circular and linear forms, the hydrolysis products were analyzed by agarose electrophoresis followed by ethidium bromide stain. To correspond the registered catalytic activity directly to the antibodies we carried out a number of strict criteria: electrophoretic homogeneity of the antibodies, gel filtration (acid shock analysis) and in situ activity. Statistical analysis was performed in ‘Statistica 9.0’ using the non-parametric Mann-Whitney test. Results: The sera of approximately 30% of schizophrenia patients displayed a higher level of Abs interacting with single-stranded (ssDNA) and double-stranded DNA (dsDNA) compared with healthy donors. The average level of Abs interacting with ssDNA was only 1.1-fold lower than that for interacting with dsDNA. IgG of patient with schizophrenia were shown to possess DNA hydrolyzing activity. Using affinity chromatography, electrophoretic analysis of isolated IgG homogeneity, gel filtration in acid shock conditions and in situ DNAse activity analysis we proved that the observed activity is intrinsic property of studied antibodies. We have shown that the relative DNAase activity of IgG in patients with schizophrenia averaged 55.4±32.5%, IgG of healthy donors showed much lower activity (average of 9.1±6.5%). It should be noted that DNAase activity of IgG in patients with schizophrenia with a negative symptoms was significantly higher (73.3±23.8%), than in patients with positive symptoms (43.3±33.1%). Conclusion: Anti-DNA Abs of patients with schizophrenia not only bind DNA, but quite efficiently hydrolyze the substrate. The data show a correlation with the level of DNase activity and leading symptoms of patients with schizophrenia.

Keywords: anti-DNA antibodies, abzymes, DNA hydrolysis, schizophrenia

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Authors: Iuri Salukvadze

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Development of Georgian Economy largely depends on its effective use of its transit country potential. The value of Georgia as the part of Europe-Asia corridor has increased; this increases the interest of western and eastern countries to Georgia as to the country that laid on the transit axes that implies transit infrastructure creation and development in Georgia. It is important to use compacted concrete with the additive in modern road construction industry. Even in the 21-century, concrete remains as the main vital constructive building material, therefore innovative, economic and environmentally protected technologies are needed. Georgian construction market requires the use of concrete of new generation, adaptation of nanotechnologies to the local realities that will give the ability to create multifunctional, nano-technological high effective materials. It is highly important to research their physical and mechanical states. The study of compacted concrete with the additives is necessary to use in the road construction in the future and to increase hardness of roads in Georgia. The aim of the research is to study the physical-mechanical properties of the compacted concrete with the additives based on the local materials. Any experimental study needs large number of experiments from one side in order to achieve high accuracy and optimal number of the experiments with minimal charges and in the shortest period of time from the other side. To solve this problem in practice, it is possible to use experiments planning static and mathematical methods. For the materials properties research we will use distribution hypothesis, measurements results by normal law according to which divergence of the obtained results is caused by the error of method and inhomogeneity of the object. As the result of the study, we will get resistible compacted concrete with additives for the motor roads that will improve roads infrastructure and give us saving rate while construction of the roads and their exploitation.

Keywords: construction, seismic protection systems, soil, motor roads, concrete

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Authors: Abdelhalim. N. Mohammed, Mohammed. Y. Esmail

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In the course of recent decades, medical imaging has been dominated by the use of costly film media for review and archival of medical investigation, however due to developments in networks technologies and common acceptance of a standard digital imaging and communication in medicine (DICOM) another approach in light of World Wide Web was produced. Web technologies successfully used in telemedicine applications, the combination of web technologies together with DICOM used to design a web-based and open source DICOM viewer. The Web server allowance to inquiry and recovery of images and the images viewed/manipulated inside a Web browser without need for any preinstalling software. The dynamic site page for medical images visualization and processing created by using JavaScript and HTML5 advancements. The XAMPP ‘apache server’ is used to create a local web server for testing and deployment of the dynamic site. The web-based viewer connected to multiples devices through local area network (LAN) to distribute the images inside healthcare facilities. The system offers a few focal points over ordinary picture archiving and communication systems (PACS): easy to introduce, maintain and independently platforms that allow images to display and manipulated efficiently, the system also user-friendly and easy to integrate with an existing system that have already been making use of web technologies. The wavelet-based image compression technique on which 2-D discrete wavelet transform used to decompose the image then wavelet coefficients are transmitted by entropy encoding after threshold to decrease transmission time, stockpiling cost and capacity. The performance of compression was estimated by using images quality metrics such as mean square error ‘MSE’, peak signal to noise ratio ‘PSNR’ and compression ratio ‘CR’ that achieved (83.86%) when ‘coif3’ wavelet filter is used.

Keywords: DICOM, discrete wavelet transform, PACS, HIS, LAN

Procedia PDF Downloads 143