Search results for: disease prediction

Authors: Sina Mahdavi

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Background and Objective: Multiple sclerosis (MS) is an inflammatory autoimmune disease of the CNS that affects the myelination process in the central nervous system (CNS). Complex interactions of various "environmental or infectious" factors may act as triggers in autoimmunity and disease progression. The association between viral infections, especially Human Herpesvirus 6 (HHV-6), and MS is one potential cause that is not well understood. In this study, we aim to summarize the available data on HHV-6 infection in MS disease progression. Materials and Methods: For this study, the keywords "Multiple sclerosis", " Human Herpesvirus 6 ", and "central nervous system" in the databases PubMed and Google Scholar between 2017 and 2022 were searched, and 12 articles were chosen, studied, and analyzed. Results: HHV 6 tends towards TCD 4+ lymphocytes and enters the CNS due to the weakening of the blood-brain barrier due to inflammatory damage. Following the observation that the HHV-6 U24 protein has a seven amino acid sequence with myelin basic protein, which is one of the main components of the myelin sheath, it could cause a molecular mimicry mechanism followed by cross-reactivity. Reactivation of HHV-6 in the CNS can cause the release of proinflammatory cytokines, including TNF-α, leading to immune-mediated demyelination in patients with MS. Conclusion: There is a high expression of endogenous retroviruses during the course of MS, which indicates the relationship between HHV-6 and MS, and that this virus can play a role in the development of MS by creating an inflammatory state. Therefore, measures to modulate the expression of HHV-6 may be effective in reducing inflammatory processes in demyelinated areas of MS patients.

Keywords: multiple sclerosis, human herpesvirus 6, central nervous system, autoimmunity

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Authors: Amna Shoaib, Sidrah Hanif, Rashid Mehmood

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Current research work was carried out to check influence of farmyard manure (FYM) in Lycopersicon esculentum L. against Fusarium oxysporum f. sp. lycopersici (FO) in copper polluted soil. Silt-loam soil naturally enriched with 70 ppm of Cu was inoculated with 1 x 106 spore suspensions of FO and incorporated with 0%, 1%, 1.5% or 2% FYM. The multilateral interaction of host-pathogen-metal-organic amendment was assessed in terms of morphology, growth, yield, physiology, biochemistry and metal uptake in tomato plant after 30 and 60 days of sowing. When soil was inoculated with FO, plant growth and biomass were significantly increased during vegetative stage, while declining during flowering stage with substantial increase in productivity over control. Infected plants exhibited late wilting and disease severity was found on 26-50% of plant during reproductive stage. Incorporation of up to 1% FYM suppressed disease severity, improved plant growth and biomass, while it decreased yield. Rest of manure doses was found ineffective in suppressing disease. Content of total chlorophyll, sugar and protein were significantly declined in FO inoculated plants and incorporation of FYM caused significant reduction or no influence on sugar and chlorophyll content, and no pronounced difference among different FYM doses were observed. On the other hand, proline, peroxidase, catalase and nitrate reductase activity were found to be increased in infected plants and incorporation of 1-2% FYM further enhanced the activity of these enzymes. Tomato plant uptake of 30-40% of copper naturally present in the soil and incorporation of 1-2% FYM markedly decreased plant uptake of metal by 15-30%, while increased Cu retention in soil. Present study concludes that lower dose (1%) of FYM could be used to manage disease, increase growth and biomass, while being ineffective for yield and productivity in Cu-polluted soil. Altered physiology/biochemistry of plant in response to any treatment could be served as basis for resistant against pathogen and metal homeostasis in plants.

Keywords: Lycopersicon esculentum, copper, Fusarium wilt, farm yard manure

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Authors: Mekha Mathew, Varun P Gopi

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Wireless Capsule Endoscopy (WCE) has become a great device in Gastrointestinal (GI) tract diagnosis, which can examine the entire GI tract, especially the small intestine without invasiveness and sedation. Bleeding in the digestive tract is a symptom of a disease rather than a disease itself. Hence the detection of bleeding is important in diagnosing many diseases. In this paper we proposes a novel method for distinguishing bleeding regions from normal regions based on Contourlet transform and Local Binary Pattern (LBP). Experiments show that this method provides a high accuracy rate of 96.38% in CIE XYZ colour space for k-Nearest Neighbour (k-NN) classifier.

Keywords: Wireless Capsule Endoscopy, local binary pattern, k-NN classifier, contourlet transform

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Authors: Ghirmay Teklemicheal, Samsom Mehari, Sara Tesfay

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Objectives: A total of 1074 suspected chikungunya cases were reported in Tesseney Province, Gash Barka region, Eritrea, during an outbreak. This study was aimed to assess the possible association of chikungunya severity among ABO blood groups and other potential determinants. Methods: A sex-matched and age-matched case-control study was conducted during the outbreak. For each case, one control subject had been selected from the mild Chikungunya cases. Along the same line of argument, a second control subject had also been designated through which neighborhood of cases were analyzed, scrutinized, and appeared to the scheme of comparison. Time is always the most sacrosanct element in pursuance of any study. According to the temporal calculation, this study was pursued from October 15, 2018, to November 15, 2018. Coming to the methodological dependability, calculating odds ratios (ORs) and conditional (fixed-effect) logistic regression methods were being applied. As a consequence of this, the data was analyzed and construed on the basis of the aforementioned methodological systems. Results: In this outbreak, 137 severe suspected chikungunya cases and 137 mild chikungunya suspected patients, and 137 controls free of chikungunya from the neighborhood of cases were analyzed. Non-O individuals compared to those with O blood group indicated as significant with a p-value of 0.002. Separate blood group comparison among A and O blood groups reflected as significant with a p-value of 0.002. However, there was no significant difference in the severity of chikungunya among B, AB, and O blood groups with a p-value of 0.113 and 0.708, respectively, and a strong association of chikungunya severity was found with hypertension and diabetes (p-value of < 0.0001); whereas, there was no association between chikungunya severity and asthma with a p-value of 0.695 and also no association with pregnancy (p-value =0.881), ventilator (p-value =0.181), air conditioner (p-value = 0.247), and didn’t use latrine and pit latrine (p-value = 0.318), among individuals using septic and pit latrine (p-value = 0.567) and also among individuals using flush and pit latrine (p-value = 0.194). Conclusions: Non- O blood groups were found to be at risk more than their counterpart O blood group individuals with severe form of chikungunya disease. By the same token, individuals with chronic disease were more prone to severe forms of the disease in comparison with individuals without chronic disease. Prioritization is recommended for patients with chronic diseases and non-O blood group since they are found to be susceptible to severe chikungunya disease. Identification of human cell surface receptor(s) for CHIKV is quite necessary for further understanding of its pathophysiology in humans. Therefore, molecular and functional studies will necessarily be helpful in disclosing the association of blood group antigens and CHIKV infections.

Keywords: Chikungunya, Chikungunya virus, disease outbreaks, case-control studies, Eritrea

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Authors: Jan Henrik Wosnitza

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The logistic regression has developed into a standard approach for estimating conditional probabilities in a wide range of applications including credit risk prediction. The article at hand contributes to the current literature on logistic regression fourfold: First, it is demonstrated that the binary logistic regression automatically meets its model assumptions under very general conditions. This result explains, at least in part, the logistic regression's popularity. Second, the requirement of homoscedasticity in the context of binary logistic regression is theoretically substantiated. The variances among the groups of defaulted and non-defaulted obligors have to be the same across the level of the aggregated default indicators in order to achieve linear logits. Third, this article sheds some light on the question why nonlinear logits might be superior to linear logits in case of a small amount of data. Fourth, an innovative methodology for estimating correlations between obligor-specific log-odds is proposed. In order to crystallize the key ideas, this paper focuses on the example of credit risk prediction. However, the results presented in this paper can easily be transferred to any other field of application.

Keywords: correlation, credit risk estimation, default correlation, homoscedasticity, logistic regression, nonlinear logistic regression

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Authors: Mohammad Reza Dahmardeh Ghaleno, Mohammad Nohtani, Saeedeh Khaledi

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Hydrological models are applied to simulation and prediction floods in watersheds. WetSpa is a distributed, continuous and physically model with daily or hourly time step that explains of precipitation, runoff and evapotranspiration processes for both simple and complex contexts. This model uses a modified rational method for runoff calculation. In this model, runoff is routed along the flow path using Diffusion-Wave Equation which depend on the slope, velocity and flow route characteristics. Garmabrood watershed located in Mazandaran province in Iran and passing over coordinates 53° 10´ 55" to 53° 38´ 20" E and 36° 06´ 45" to 36° 25´ 30"N. The area of the catchment is about 1133 km2 and elevations in the catchment range from 213 to 3136 m at the outlet, with average slope of 25.77 %. Results of the simulations show a good agreement between calculated and measured hydrographs at the outlet of the basin. Drawing upon Nash-Sutcliffe Model Efficiency Coefficient for calibration periodic model estimated daily hydrographs and maximum flow rate with an accuracy up to 61% and 83.17 % respectively.

Keywords: watershed simulation, WetSpa, runoff, flood prediction

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Authors: Muhammad Wahb

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Physical activity is increasingly recognized as a significant factor in maintaining optimal health and preventing chronic diseases. This research scrutinizes the long-term health benefits of sustained physical activity, employing a systematic review of epidemiological studies and randomized control trials conducted over the past decade. The study illuminates the protective effects of regular physical activity against cardiovascular disease, obesity, diabetes, and mental health disorders, with a special focus on the mechanisms involved. Furthermore, the paper provides insights into how public health initiatives can effectively promote physical activity among diverse populations, contributing to improved community health outcomes.

Keywords: physical activity, long-term health benefits, chronic disease prevention, public health

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Authors: Sofia G. Meirinho, Luis G. Dias, António M. Peres, Lígia R. Rodrigues

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The emerging development of electrochemical aptasen sors has enabled the easy and fast detection of protein biomarkers in standard and real samples. Biomarkers are produced by body organs or tumours and provide a measure of antigens on cell surfaces. When detected in high amounts in blood, they can be suggestive of tumour activity. These biomarkers are more often used to evaluate treatment effects or to assess the potential for metastatic disease in patients with established disease. Osteopontin (OPN) is a protein found in all body fluids and constitutes a possible biomarker because its overexpression has been related with breast cancer evolution and metastasis. Currently, biomarkers are commonly used for the development of diagnostic methods, allowing the detection of the disease in its initial stages. A previously described RNA aptamer was used in the current work to develop a simple and sensitive electrochemical aptasensor with high affinity for human OPN. The RNA aptamer was biotinylated and immobilized on a gold electrode by avidin-biotin interaction. The electrochemical signal generated from the aptamer–target molecule interaction was monitored electrochemically using cyclic voltammetry in the presence of [Fe (CN) 6]−3/− as a redox probe. The signal observed showed a current decrease due to the binding of OPN. The preliminary results showed that this aptasensor enables the detection of OPN in standard solutions, showing good selectivity towards the target in the presence of others interfering proteins such as bovine OPN and bovine serum albumin. The results gathered in the current work suggest that the proposed electrochemical aptasensor is a simple and sensitive detection tool for human OPN and so, may have future applications in cancer disease monitoring.

Keywords: osteopontin, aptamer, aptasensor, screen-printed electrode, cyclic voltammetry

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Authors: Nafisa Komilova, Plamena Angelova, Andrey Abramov, Ulugbek Mirkhodjaev

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Parkinson’s disease (PD) is a progressive neurodegenerative disorder which is induced by the loss of dopaminergic neurons in the midbrain. The mechanism of neurodegeneration is associated with the aggregation of misfolded proteins, oxidative stress, and mitochondrial disfunction. Considering this, the process of removal of unwanted organelles or proteins by autophagy is vitally important in neurons, and activation of these processes could be protective in PD. Short-time acidification of cytosol can activate mitophagy and autophagy, and here we used sodium pyruvate and sodium lactate in human fibroblasts with PD mutations (Pink1, Pink1/Park2, α-syn triplication, A53T) to induce changes in intracellular pH. We have found that both lactate and pyruvate in millimolar concentrations can induce short-time acidification of cytosol in these cells. It induced activation of mitophagy and autophagy in control and PD fibroblasts and protected against cell death. Importantly, the application of lactate to acute brain slices of control and Pink1 knockout mice also induced a reduction of pH in neurons and astrocytes that increase the level of mitophagy. Thus, acidification of cytosol by compounds which play important role in cell metabolism also can activate mitophagy and autophagy and protect cells in the familial form of PD.

Keywords: Parkinson's disease, mutations, mitophagy, autophagy

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Authors: Misuk Kim, Seokho Kang, Jehyuk Lee, Hyunchang Cho, Sungzoon Cho

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In semiconductor manufacturing, virtual metrology (VM) refers to methods to predict properties of a wafer based on machine parameters and sensor data of the production equipment, without performing the (costly) physical measurement of the wafer properties (Wikipedia). Additional benefits include avoidance of human bias and identification of important factors affecting the quality of the process which allow improving the process quality in the future. It is however rare to find VM applied to other areas of manufacturing. In this work, we propose to use VM to copper clad laminate (CCL) manufacturing. CCL is a core element of a printed circuit board (PCB) which is used in smartphones, tablets, digital cameras, and laptop computers. The manufacturing of CCL consists of three processes: Treating, lay-up, and pressing. Treating, the most important process among the three, puts resin on glass cloth, heat up in a drying oven, then produces prepreg for lay-up process. In this process, three important quality factors are inspected: Treated weight (T/W), Minimum Viscosity (M/V), and Gel Time (G/T). They are manually inspected, incurring heavy cost in terms of time and money, which makes it a good candidate for VM application. We developed prediction models of the three quality factors T/W, M/V, and G/T, respectively, with process variables, raw material, and environment variables. The actual process data was obtained from a CCL manufacturer. A variety of variable selection methods and learning algorithms were employed to find the best prediction model. We obtained prediction models of M/V and G/T with a high enough accuracy. They also provided us with information on “important” predictor variables, some of which the process engineers had been already aware and the rest of which they had not. They were quite excited to find new insights that the model revealed and set out to do further analysis on them to gain process control implications. T/W did not turn out to be possible to predict with a reasonable accuracy with given factors. The very fact indicates that the factors currently monitored may not affect T/W, thus an effort has to be made to find other factors which are not currently monitored in order to understand the process better and improve the quality of it. In conclusion, VM application to CCL’s treating process was quite successful. The newly built quality prediction model allowed one to reduce the cost associated with actual metrology as well as reveal some insights on the factors affecting the important quality factors and on the level of our less than perfect understanding of the treating process.

Keywords: copper clad laminate, predictive modeling, quality control, virtual metrology

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Authors: Ammar Alali, Mahmoud Abughaban

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Cost reduction and drilling optimization is the goal of many drilling operators. Historically, stuck pipe incidents were a major segment of non-productive time (NPT) associated costs. Traditionally, stuck pipe problems are part of the operations and solved post-sticking. However, the real key to savings and success is in predicting the stuck pipe incidents and avoiding the conditions leading to its occurrences. Previous attempts in stuck-pipe predictions have neglected the local geology of the problem. The proposed predictive tool utilizes geophysical data processing techniques and Machine Learning (ML) algorithms to predict drilling activities events in real-time using surface drilling data with minimum computational power. The method combines two types of analysis: (1) real-time prediction, and (2) cause analysis. Real-time prediction aggregates the input data, including historical drilling surface data, geological formation tops, and petrophysical data, from wells within the same field. The input data are then flattened per the geological formation and stacked per stuck-pipe incidents. The algorithm uses two physical methods (stacking and flattening) to filter any noise in the signature and create a robust pre-determined pilot that adheres to the local geology. Once the drilling operation starts, the Wellsite Information Transfer Standard Markup Language (WITSML) live surface data are fed into a matrix and aggregated in a similar frequency as the pre-determined signature. Then, the matrix is correlated with the pre-determined stuck-pipe signature for this field, in real-time. The correlation used is a machine learning Correlation-based Feature Selection (CFS) algorithm, which selects relevant features from the class and identifying redundant features. The correlation output is interpreted as a probability curve of stuck pipe incidents prediction in real-time. Once this probability passes a fixed-threshold defined by the user, the other component, cause analysis, alerts the user of the expected incident based on set pre-determined signatures. A set of recommendations will be provided to reduce the associated risk. The validation process involved feeding of historical drilling data as live-stream, mimicking actual drilling conditions, of an onshore oil field. Pre-determined signatures were created for three problematic geological formations in this field prior. Three wells were processed as case studies, and the stuck-pipe incidents were predicted successfully, with an accuracy of 76%. This accuracy of detection could have resulted in around 50% reduction in NPT, equivalent to 9% cost saving in comparison with offset wells. The prediction of stuck pipe problem requires a method to capture geological, geophysical and drilling data, and recognize the indicators of this issue at a field and geological formation level. This paper illustrates the efficiency and the robustness of the proposed cross-disciplinary approach in its ability to produce such signatures and predicting this NPT event.

Keywords: drilling optimization, hazard prediction, machine learning, stuck pipe

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Authors: Subhamita Chakraborty, Shubhabrata Datta, Sujay Kumar Mukherjea, Partha Protim Chattopadhyay

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Manufacturing of multiphase high strength steel in hot strip mill have drawn significant attention due to the possibility of forming low temperature transformation product of austenite under continuous cooling condition. In such endeavor, reliable prediction of temperature profile of hot strip coil is essential in order to accesses the evolution of microstructure at different location of hot strip coil, on the basis of corresponding Continuous Cooling Transformation (CCT) diagram. Temperature distribution profile of the hot strip coil has been determined by using finite volume method (FVM) vis-à-vis finite difference method (FDM). It has been demonstrated that FVM offer greater computational reliability in estimation of contact pressure distribution and hence the temperature distribution for curved and irregular profiles, owing to the flexibility in selection of grid geometry and discrete point position, Moreover, use of finite volume concept allows enforcing the conservation of mass, momentum and energy, leading to enhanced accuracy of prediction.

Keywords: simulation, modeling, thermal analysis, coil cooling, contact pressure, finite volume method

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Authors: Rene A. Amadore Jr., Ramar John Bangayan, Therese Eileen L. Natividad

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This is a case of a 59-year-old female who presented with a two-year history of heliotrope rash, Gottron's papules, shawl sign, V-neck sign, and muscle weakness. She was previously managed as a case of systemic lupus erythematosus, who initially responded to unrecalled corticosteroids. She was, however, admitted due to a one-month progressively enlarging sacral mass, which eventually turned out to be an abscess. While the abscess was being treated, her autoimmune condition was worked up, and she was then managed as a case of clinically amyopathic dermatomyositis (CADM) with interstitial lung disease (ILD). She received corticosteroids and underwent the first cycle of cyclophosphamide infusion prior to discharge.

Keywords: clinically amyopathic dermatomyositis, interstitial lung disease, corticosteroids, sacral abscess

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Authors: Nikhil Agrawal, Adam A. Skelton

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Alzheimer’s disease (AD) is one of the most common forms of dementia, which is caused by misfolding and aggregation of amyloid beta (Aβ) peptides into amyloid-β fibrils (Aβ fibrils). To disrupt the remodeling of Aβ fibrils, a number of candidate molecules have been proposed. To study the molecular mechanisms of Aβ fibrils remodeling we performed a series of all-atom molecular dynamics simulations, a total time of 3µs, in explicit solvent. Several previously undiscovered candidate molecule-Aβ fibrils binding modes are unraveled; one of which shows the direct conformational change of the Aβ fibril by understanding the physicochemical factors responsible for binding and subsequent remodeling of Aβ fibrils by the candidate molecule, open avenues into structure-based drug design for AD can be opened.

Keywords: alzheimer’s disease, amyloid, MD simulations, misfolded protein

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Authors: Esa Abiso Godana, Qiya Yang, Kaili Wang, Zhang Hongyin, Xiaoyun Zhang, Lina Zhao

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Blue mold decay caused by Penicillium expansum is among the recent identified diseases of grapes (Vitis vinifera). The increasing concern about use of chemical substance and pesticide in postharvest fruit push the trends of research toward biocontrol strategies which are more sustainable and ecofriendly. In this study, we determined the biocontrol efficacy of Pichia anomala alone and supplemented with 1% chitosan in the grapefruit against blue mold disease caused by P. expansum. The result showed that 1% chitosan better enhances the biocontrol efficacy P. anomala. Chitosan (1% w/v) also improved the number of population of P. anomala in grape wounds, surface and on nutrient yeast dextrose broth (NYDB). P. anomala supplemented with 1% w/v chitosan significantly reduced the disease incidence, lesion diameter and natural decay of grapefruits without affecting the fruit quality as compared to the control. The scanned electron microscope (SEM) concisely illustrates how the high number of yeast cells on the wounds reduced the growth of P. expansum. P. anomala alone or P. anomala supplemented with 1% w/v chitosan are presented as a potential biocontrol alternative against the postharvest blue mold of grapefruit.

Keywords: biocontrol, Pichia anomala, chitosan, Penicillium expansum, grape

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Authors: M.Rüştü Karaman, İsmail İşeri, Kadir Saltalı, A.Reşit Brohi, Ayhan Horuz, Mümin Dizman

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Considerable relations has recently been given to the environmental hazardous caused by agricultural chemicals such as excess fertilizers. In this study, a neural network approach was investigated in the prediction of potential nitrate pollution across different land-use patterns by using a feedforward multilayered computer model of artificial neural network (ANN) with proper training. Periodical concentrations of some anions, especially nitrate (NO3-), and cations were also detected in drainage waters collected from the drain pipes placed in irrigated tomato field, unirrigated wheat field, fallow and pasture lands. The soil samples were collected from the irrigated tomato field and unirrigated wheat field on a grid system with 20 m x 20 m intervals. Site specific nitrate concentrations in the soil samples were measured for ANN based simulation of nitrate leaching potential from the land profiles. In the application of ANN model, a multi layered feedforward was evaluated, and data sets regarding with training, validation and testing containing the measured soil nitrate values were estimated based on spatial variability. As a result of the testing values, while the optimal structures of 2-15-1 was obtained (R2= 0.96, P < 0.01) for unirrigated field, the optimal structures of 2-10-1 was obtained (R2= 0.96, P < 0.01) for irrigated field. The results showed that the ANN model could be successfully used in prediction of the potential leaching levels of nitrate, based on different land use patterns. However, for the most suitable results, the model should be calibrated by training according to different NN structures depending on site specific soil parameters and varied agricultural managements.

Keywords: artificial intelligence, ANN, drainage water, nitrate pollution

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Authors: Ali Kassem

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Background: In the last few years, factors such as insulin resistance (IR) and hepatic steatosis have been linked to progression of hepatic fibrosis.Patients with chronic liver disease, and cirrhosis in particular, are known to be prone to IR. However, chronic HCV (hepatitis C) infection may induce IR, regardless of the presence of liver cirrhosis. Our aims are to study insulin resistance (IR) assessed by HOMA-IR (Homeostatic Model Assessment Insulin Resistance) as a possible risk factor in disease progression in cirrhotic patients and to evaluate the role of IR in hepatic fibrosis progression. The correlations of HOMA-IR values to laboratory, virological and histopathological parameters of chronic HCV are also examined. Methods: The study included 50 people divided into 30 adult chronic hepatitis C patients diagnosed by PCR (polymerase chain reaction) within previous 6 months and 20 healthy controls. The functional and morphological status of the liver were evaluated by ultrasonography and laboratory investigations including liver function tests and by liver biopsy. Fasting blood glucose and fasting insulin levels were measured and body mass index and insulin resistance were calculated. Patients having HOMA-IR >2.5 were labeled as insulin resistant. Results: Chronic hepatitis C patients with IR showed significantly higher mean values of BMI (body mass index) and fasting insulin than those without IR (P < 0.000). Patients with IR were more likely to have steatosis (p = 0.006), higher necroinflammatory activity (p = 0.05). No significant differences were found between the two groups regarding hepatic fibrosis. Conclusion: HOMA-IR measurement could represent a novel marker to identify the cirrhotic patients at greater risk for the progression of liver disease. As IR is a potentially modifiable risk factor, these findings may have important prognostic and therapeutic implications. Assessment of IR by HOMA-IR and improving insulin sensitivity are recommended in patients with HCV and related chronic liver disease.

Keywords: hepatic fibrosis, hepatitis C virus infection, hepatic steatosis, insulin resistance

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Authors: Natalia Espinosa, Arthur Amorim, Rudolf Huebner

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Epilepsy is a chronic neural disease and around 50 million people in the world suffer from this disease, however, in many cases, the individual acquires resistance to the medication, which is known as drug-resistant epilepsy, where a detection system is necessary. This paper showed the development of an automatic system for seizure detection based on artificial neural networks (ANN), which are common techniques of machine learning. Discrete Wavelet Transform (DWT) is used for decomposing electroencephalogram (EEG) signal into main brain waves, with these frequency bands is extracted features for training a feedforward neural network with backpropagation, finally made a pattern classification, seizure or non-seizure. Obtaining 95% accuracy in epileptic EEG and 100% in normal EEG.

Keywords: Artificial Neural Network (ANN), Discrete Wavelet Transform (DWT), Epilepsy Detection , Seizure.

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Authors: Mohsen Shirani, Micaela Demichela

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During the last decades, a number of food crises such as Bovine Spongiform Encephalopathy (BSE), Mad-Cow disease, Dioxin in chicken food, Food-and-Mouth Disease (FMD), have certainly inflicted the reliability of the food industry. Consequently, the trend in applying different scientific methods of risk assessment in food safety has obtained more attentions in the academic and practice. However, lack of practical approach considering entire food supply chain is tangible in the academic literature. In this regard, this paper aims to apply risk assessment tool (FMEA) with integration of Human Factor along the entire supply chain of food production and test the method in a case study of Diary production, and analyze its results.

Keywords: FMEA, food supply chain, risk assessment, human factor

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Authors: Mohua Chatterjee, Rajib De

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Introduction: Bone Marrow Transplant (BMT) is often performed to treat patients with various types of leukemia. A majority of these patients develop complications like chronic musculoskeletal GVHD post-BMT where patients get scleroderma, pain and restricted range of motion of joints of hand. If not treated early, it may cause permanent deformity of hand. This study was done to find the effectiveness of physiotherapy in hand dysfunction caused due to chronic musculoskeletal GVHD of leukemia patients after BMT. Methodology: 23 patients diagnosed with leukemia and having musculoskeletal GVHD were treated with a set of exercises including active exercises and stretching. The outcome was measured by Cochin Hand Function Scale (CHFS) at baseline and after four weeks of intervention. Results: Two patients were not able to carry out exercises beyond two weeks due to relapse of disease and one patient defaulted. It was found that all the patients who received physiotherapy had significant improvement in hand function. Mean CHFS decreased from 63.67 to 27.43 (P value < 0.001) indicating improvement in hand function after four weeks of physiotherapy. Conclusion: Early intervention of physiotherapy is effective in reducing hand dysfunction of leukemia patients with musculoskeletal GVHD post-BMT.

Keywords: bone marrow transplant, hand dysfunction, leukemia, musculoskeletal graft versus host disease, physiotherapy

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Authors: Amin Salighehdar, Ziwen Ye, Mingzhe Liu, Ionut Florescu, Alan F. Blumberg

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Storm surge is an abnormal water level caused by a storm. Accurate prediction of a storm surge is a challenging problem. Researchers developed various ensemble modeling techniques to combine several individual forecasts to produce an overall presumably better forecast. There exist some simple ensemble modeling techniques in literature. For instance, Model Output Statistics (MOS), and running mean-bias removal are widely used techniques in storm surge prediction domain. However, these methods have some drawbacks. For instance, MOS is based on multiple linear regression and it needs a long period of training data. To overcome the shortcomings of these simple methods, researchers propose some advanced methods. For instance, ENSURF (Ensemble SURge Forecast) is a multi-model application for sea level forecast. This application creates a better forecast of sea level using a combination of several instances of the Bayesian Model Averaging (BMA). An ensemble dressing method is based on identifying best member forecast and using it for prediction. Our contribution in this paper can be summarized as follows. First, we investigate whether the ensemble models perform better than any single forecast. Therefore, we need to identify the single best forecast. We present a methodology based on a simple Bayesian selection method to select the best single forecast. Second, we present several new and simple ways to construct ensemble models. We use correlation and standard deviation as weights in combining different forecast models. Third, we use these ensembles and compare with several existing models in literature to forecast storm surge level. We then investigate whether developing a complex ensemble model is indeed needed. To achieve this goal, we use a simple average (one of the simplest and widely used ensemble model) as benchmark. Predicting the peak level of Surge during a storm as well as the precise time at which this peak level takes place is crucial, thus we develop a statistical platform to compare the performance of various ensemble methods. This statistical analysis is based on root mean square error of the ensemble forecast during the testing period and on the magnitude and timing of the forecasted peak surge compared to the actual time and peak. In this work, we analyze four hurricanes: hurricanes Irene and Lee in 2011, hurricane Sandy in 2012, and hurricane Joaquin in 2015. Since hurricane Irene developed at the end of August 2011 and hurricane Lee started just after Irene at the beginning of September 2011, in this study we consider them as a single contiguous hurricane event. The data set used for this study is generated by the New York Harbor Observing and Prediction System (NYHOPS). We find that even the simplest possible way of creating an ensemble produces results superior to any single forecast. We also show that the ensemble models we propose generally have better performance compared to the simple average ensemble technique.

Keywords: Bayesian learning, ensemble model, statistical analysis, storm surge prediction

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Authors: Mahmoud Reza Tahamtan, Mohammad Saleh Bahreini

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Background and Purpose: Cystic echinococcosis, caused by the larval stage of Echinococcus granulosus, is a common parasitic infection of humans and is endemic in many parts of the world, including Iran. So that, one percent of those admitted to surgery departments are hydatid cyst patients, and using the ELISA method, the infection rate has been reported in different regions of Iran from 1.2% to 21.4%. Therefore, the aim of this study was to investigate the seroepidemiology of human hydatid cysts in Fars province, southern Iran, by ELISA method. Methods: In this cross-sectional study, 600 serum samples of persons who were referred to the laboratory of Nemazi Hospital in Shiraz for normal tests were examined for the presence of specific Anti-IgG antibodies to hydatid cysts by ELISA method. During the sampling, a structured questionnaire was used to obtain social data of individuals with determinants of risk factors for Cystic echinococcosis. Finally, the results of the ELISA test, along with demographic information completed by individuals, were analyzed using SPSS software. Results: The average age of the subjects in this study was 40.01 ± 9.166. The prevalence of hydatidosis was reported as 5.66% (34/600). The disease was more in the age group of 21-30, people living in villages, working in rural areas, and people with a history of other parasitic diseases. Statistically, a significant difference was observed between the prevalence of the disease and two risk factors, contact with dogs (OR= 0.042; 95%CI: 0.014-0.12; P= 0.001) and washing vegetables with water (OR= 0.08; 95%CI: 0.011-0.56; P= 0.012). Conclusion: The present study showed that hydatid cyst disease has a significant prevalence in this area. Also, based on the results, contact with dogs and not properly washing vegetables are two important factors of disease transmission.

Keywords: Echinococcus granulosus, Cystic echinococcosis, hydatid cyst, Fars province

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Authors: Jessica Chorostecki, Aashka Shah, Fen Bao, Ginny Bao, Edwin George, Navid Seraji-Bozorgzad, Veronica Gorden, Christina Caon, Elliot Frohman

Abstract:

Background: Parkinson’s Disease (PD) is a neuro degenerative disorder associated with the loss of dopaminergic cells and the presence α-synuclein (AS) aggregation in of Lewy bodies. Both dopaminergic cells and AS are found in the retina. Optical coherence tomography (OCT) allows high-resolution in-vivo examination of retinal structure injury in neuro degenerative disorders including PD. Methods: We performed a cross-section OCT study in patients with definite PD and healthy controls (HC) using Spectral Domain SD-OCT platform to measure the peripapillary retinal nerve fiber layer (pRNFL) thickness and total macular volume (TMV). We performed intra-retinal segmentation with fully automated segmentation software to measure the volume of the RNFL, ganglion cell layer (GCL), inner plexiform layer (IPL), inner nuclear layer (INL), outer plexiform layer (OPL), and the outer nuclear layer (ONL). Segmentation was performed blinded to the clinical status of the study participants. Results: 101 eyes from 52 PD patients (mean age 65.8 years) and 46 eyes from 24 HC subjects (mean age 64.1 years) were included in the study. The mean pRNFL thickness was not significantly different (96.95 μm vs 94.42 μm, p=0.07) but the TMV was significantly lower in PD compared to HC (8.33 mm3 vs 8.58 mm3 p=0.0002). Intra-retinal segmentation showed no significant difference in the RNFL volume between the PD and HC groups (0.95 mm3 vs 0.92 mm3 p=0.454). However, GCL, IPL, INL, and ONL volumes were significantly reduced in PD compared to HC. In contrast, the volume of OPL was significantly increased in PD compared to HC. Conclusions: Our finding of the enlarged OPL corresponds with mRNA expression studies showing localization of AS in the OPL across vertebrate species and autopsy studies demonstrating AS aggregation in the deeper layers of retina in PD. We propose that the enlargement of the OPL may represent a potential biomarker of AS aggregation in PD. Longitudinal studies in larger cohorts are warranted to confirm our observations that may have significant implications in disease monitoring and therapeutic development.

Keywords: Optical Coherence Tomography, biomarker, Parkinson's disease, alpha-synuclein, retina

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Authors: J. DeBoard, R. Dietrich, J. Hughes, K. Yurko, G. Harms

Abstract:

Alzheimer’s disease (AD) is a predominant type of dementia and is likely a major cause of neural network impairment. The pathogenesis of this neurodegenerative disorder has yet to be fully elucidated. There are currently no known cures for the disease, and the best hope is to be able to detect it early enough to impede its progress. Beyond age and genetics, another prevalent risk factor for AD might be traumatic brain injury (TBI), which has similar neurodegenerative hallmarks. Our research focuses on obtaining information and methods to be able to predict when neurodegenerative effects might occur at a clinical level by observation of events at a cellular and molecular level in model mice. First, we wish to introduce our evidence that brain damage can be observed via brain imaging prior to the noticeable loss of neuromuscular control in model mice of AD. We then show our evidence that some blood biomarkers might be able to be early predictors of AD in the same model mice. Thus, we were interested to see if we might be able to predict which mice might show long-term neurodegenerative effects due to differing degrees of TBI and what level of TBI causes further damage and earlier death to the AD model mice. Upon application of TBIs via an apparatus to effectively induce extremely mild to mild TBIs, wild-type (WT) mice and AD mouse models were tested for cognition, neuromuscular control, olfactory ability, blood biomarkers, and brain imaging. Experiments are currently still in process, and more results are therefore forthcoming. Preliminary data suggest that neuromotor control diminishes as well as olfactory function for both AD and WT mice after the administration of five consecutive mild TBIs. Also, seizure activity increases significantly for both AD and WT after the administration of the five TBI treatment. If future data supports these findings, important implications about the effect of TBI on those at risk for AD might be possible.

Keywords: Alzheimer's disease, blood biomarker, neurodegeneration, neuromuscular control, olfaction, traumatic brain injury

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Authors: Sina Mahdavi

Abstract:

Background and Objective: Multiple sclerosis (MS) is an inflammatory autoimmune disease of the CNS that affects the myelination process in the central nervous system (CNS). Complex interactions of various "environmental or infectious" factors may act as triggers in autoimmunity and disease progression. The association between viral infections, especially human endogenous retrovirus (HERV) and MS is one potential cause that is not well understood. This study aims to summarize the available data on HERV infection in MS disease progression. Materials and Methods: For this study, the keywords "Multiple sclerosis", "Human endogenous retrovirus", and "central nervous system" in the databases PubMed, Google Scholar, Sid, and MagIran between 2016 and 2022 were searched and 14 articles chosen, studied, and analyzed. Results: In the leptomeningeal cells of MS patients, a retrovirus-like element associated with reverse transcriptase (RT) activity called multiple sclerosis-associated retroviruses (MSRV) has been identified. HERVs are expressed in the human CNS despite mechanisms to suppress their expression. External factors, especially viral infections such as influenza virus, Epstein-Barr virus, and herpes simplex virus type 1, can activate HERV gene expression. The MSRV coat protein is activated by activating TLR4 at the brain surface, particularly in oligodendroglial progenitor cells and macrophages, leading to immune cascades followed by the downregulation of myelin protein expression. The HERV-K18 envelope gene (env) acts as a superantigen and induces inflammatory responses in patients with MS. Conclusion: There is a high expression of endogenous retroviruses during the course of MS, which indicates the relationship between HERV and MS, that this virus can play a role in the development of MS by creating an inflammatory state. Therefore, measures to modulate the expression of endogenous retroviruses may be effective in reducing inflammatory processes in demyelinated areas of MS patients.

Keywords: multiple sclerosis, human endogenous retrovirus, central nervous system, MSRV

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Authors: A. Venkatakrishnan, M. Juneja, S. Kapoor

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Background: Gastrointestinal dysfunction is an emerging co morbidity seen in autism and may further strengthen the association between autism and celiac disease. This is supported by increased rates (22-70%) of gastrointestinal symptoms like diarrhea, constipation, abdominal discomfort/pain, and gastrointestinal inflammation in children with the etiology of autism is still elusive. In addition to genetic factors, environmental factors such as toxin exposure, intrauterine exposure to certain teratogenic drugs, are being proposed as possible contributing factors in the etiology of Autism Spectrum Disorders (ASD) in cognizance with reports of increased gut permeability and high rates of gastrointestinal symptoms noted in children with ASD, celiac disease has also been proposed as a possible etiological factor. Despite insufficient evidence regarding the benefit of restricted diets in Autism, GFD has been promoted as an alternative treatment for ASD. This study attempts to discern any correlation between ASD and celiac disease. Objective: This cross sectional study aims to determine the proportion of celiac disease in children with ASD. Methods: Study included 155 participants aged 2-12 yrs, diagnosed as ASD as per DSM-5 attending the child development center at a tertiary care hospital in Northern India. Those on gluten free diet or having other autoimmune conditions were excluded. A detailed Performa was filled which included sociodemographic details, history of gastrointestinal symptoms, anthropometry, systemic examination, and pertinent psychological testing was done using was assessed using Developmental Profile-3(DP-3) for Developmental Quotient, Childhood Autism Rating Scale-2 (CARS-2) for severity of ASD, Vineland Adaptive Behavior Scales (VABS) for adaptive behavior, Child Behavior Checklist (CBCL) for behavioral problems and BAMBI (Brief Autism Mealtime Behavior Scales) for feeding problems. Screening for celiac was done by TTG-IgA levels, and total serum IgA levels were measured to exclude IgA deficiency. Those with positive screen were further planned for HLA typing and endoscopic biopsy. Results: A total of 155 cases were included, out of which 5 had low IgA levels and were hence excluded from the study. The rest 150 children had TTG levels below the ULN and normal total serum IgA level. History of Gastrointestinal symptoms was present in 51 (34%) cases abdominal pain was the most frequent complaint (16.6%), followed by constipation (12.6%). Diarrhea was seen in 8 %. Gastrointestinal symptoms were significantly more common in children with ASD above 5 yrs (p-value 0.006) and those who were verbal (p = 0.000). There was no significant association between socio-demographic factors, anthropometric data, or severity of autism with gastrointestinal symptoms. Conclusion: None of the150 patients with ASD had raised TTG levels; hence no association was found between ASD and celiac disease. There is no justification for routine screening for celiac disease in children with ASD. Further studies are warranted to evaluate association of Non Celiac Gluten Sensitivity with ASD and any role of gluten-free diet in such patients.

Keywords: autism, celiac, gastrointestinal, gluten

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Authors: Shalini Tripathi, Pardeep Kumar

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The method of translating observed symptoms into disease names is known as disease diagnosis. The ability to solve clinical problems in a complex manner is critical to a doctor's effectiveness in providing health care. The accuracy of his or her expertise is crucial to the survival and well-being of his or her patients. Artificial Intelligence (AI) has a huge economic influence depending on how well it is applied. In the medical sector, human brain-simulated intellect can help not only with classification accuracy, but also with reducing diagnostic time, cost and pain associated with pathologies tests. In light of AI's present and prospective applications in the biomedical, we will identify them in the paper based on potential benefits and risks, social and ethical consequences and issues that might be contentious but have not been thoroughly discussed in publications and literature. Current apps, personal tracking tools, genetic tests and editing programmes, customizable models, web environments, virtual reality (VR) technologies and surgical robotics will all be investigated in this study. While AI holds a lot of potential in medical diagnostics, it is still a very new method, and many clinicians are uncertain about its reliability, specificity and how it can be integrated into clinical practice without jeopardising clinical expertise. To validate their effectiveness, more systemic refinement of these implementations, as well as training of physicians and healthcare facilities on how to effectively incorporate these strategies into clinical practice, will be needed.

Keywords: Artificial Intelligence, medical diagnosis, virtual reality, healthcare ethical implications 

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Authors: Affiah D. U., Katuri I. P., Emefiene M. E., Amienyo C. A.

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Broccoli (Brassica oleraceae L., var. italica) is one of the most important vegetables that is high in nutrients and bioactive compounds. It easily grown on a wide range of soil types and is adaptable to many different climatic conditions. This study was carried out within Jos North and environs in vitro to evaluate Neem (Azadirachta indica) leaves extract against Albugo candida, the causative agent of white blisters disease of broccoli. Through the survey, prevalence and incidence were accessed and a fluffy white growth symptom on the underside of leaves was also observed on the field. Infected leaves samples were collected from three different farms namely: Farin Gada, Naraguta, and Juth and the organism associated with the disease was isolated. Pathogenicity test carried out revealed the fungal isolate Albugo candida to be responsible for the disease. Antimicrobial susceptibility test was performed using agar well diffusion method to determine the minimum inhibitory concentrations of two extract of Azadirachta indica leaves against the organism. Ethanolic extract had the highest antifungal activities of 3.30±0.21 - 17.61± 0.11 while aqueous extract had the least antifungal activities of 0.00±0.00 - 13.23±0.12. The minimum inhibitory concentration of aqueous was 100 mg/ml while its minimum fungicidal concentration was at 200 mg/ml. For ethanol, the minimum inhibitory concentration was 50 mg/ml while its minimum fungicidal concentration was 100 mg/ml. Plants being less toxic in usage over synthetic or inorganic chemicals makes them easy to handle, easily accessible and renewable. Due to the biosafety of plant extracts and its availability since the plant-based extracts of the two different solvents were found to be effective against the test organism hence, it is recommended for in-depth research to make it readily available for control of other pathogens and pests.

Keywords: antifungal, biocontrol, broccoli, fungi

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Authors: Mansi Chinchanikar

Abstract:

Homoeopathy is the second most widely used medical system in the world, yet the homoeopaths of India and around the world are sick of reading or hearing about how homoeopathy is only a placebo effect and cannot cure or even manage any disease. However, individuals making such unfounded claims should explain to the group how a homoeopathic placebo, particularly one for a neurodevelopmental disease like Attention Deficit Hyperactivity Disorder (ADHD), can be effective in children, with studies to back it up their skeptics. This literary review work exhibits how homoeopathy with a multimodal approach may show a considerable proportion of ADHD patients in India and throughout the world successfully manageable and treatable according to growing study evidence, ruling out the hazardous conventional medicines. Indeed, homeopathy can help cure ADHD symptoms either on its own or in combination with other types of integrative systems.

Keywords: ADHD, adult ADHD, homoeopathy, integrative approach

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Authors: Tea Poklepović, Zdravka Aljinović, Branka Marasović

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Due to the importance of yield curve and its estimation it is inevitable to have valid methods for yield curve forecasting in cases when there are scarce issues of securities and/or week trade on a secondary market. Therefore in this paper, after the estimation of weekly yield curves on Croatian financial market from October 2011 to August 2012 using Nelson-Siegel and Svensson models, yield curves are forecasted using Vector auto-regressive model and Neural networks. In general, it can be concluded that both forecasting methods have good prediction abilities where forecasting of yield curves based on Nelson Siegel estimation model give better results in sense of lower Mean Squared Error than forecasting based on Svensson model Also, in this case Neural networks provide slightly better results. Finally, it can be concluded that most appropriate way of yield curve prediction is neural networks using Nelson-Siegel estimation of yield curves.

Keywords: Nelson-Siegel Model, neural networks, Svensson Model, vector autoregressive model, yield curve

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