Search results for: drug prediction

Authors: Mohsen Shati, Seyede Salehe Mortazavi, Seyed Kazem Malakouti, Hamidreza Khanke Fazlollah Ahmadi

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Taking unnecessary or excessive medication or using drugs with no indication (polypharmacy) by people of all ages, especially the elderly, is associated with increased adverse drug reactions (ADR), medical errors, hospitalization and escalating the costs. It may be facilitated or impeded by the healthcare system. In this study, we are going to describe the role of the health system in the practice of polypharmacy in Iranian elderly. In this Inductive qualitative content analysis using Graneheim and Lundman methods, purposeful sample selection until saturation has been made. Participants have been selected from doctors, pharmacists, policy-makers and the elderly. A total of 25 persons (9 men and 16 women) have participated in this study. Data analysis after incorporating codes with similar characteristics revealed 14 subcategories and six main categories of the referral system, physicians’ accessibility, health data management, drug market, laws enforcement, and social protection. Some of the conditions of the healthcare system have given rise to polypharmacy in the elderly. In the absence of a comprehensive specialty and subspecialty referral system, patients may go to any physician office so may well be confused about numerous doctors' prescriptions. Electronic records not being prepared for the patients, failure to comply with laws, lack of robust enforcement for the existing laws and close surveillance are among the contributing factors. Inadequate insurance and supportive services are also evident. Age-specific care providing has not yet been institutionalized, while, inadequate specialist workforce playing a major role. So, one may not ignore the health system as contributing factor in designing effective interventions to fix the problem.

Keywords: elderly, polypharmacy, health system, qualitative study

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Authors: Mohammed N. Al Kindi, Mazin Al Khabouri, Khalsa Al Lamki, Tommasso Pappuci, Giovani Romeo, Nadia Al Wardy

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Hereditary hearing loss is a heterogeneous group of complex disorders with an overall incidence of one in every five hundred newborns presented as syndromic and non-syndromic forms. Cadherin-related 23 (CDH23) is one of the listed deafness causative genes. CDH23 is found to be expressed in the stereocilia of hair cells and the retina photoreceptor cells. Defective CDH23 has been associated mostly with prelingual severe-to-profound sensorineural hearing loss (SNHL) in either syndromic (USH1D) or non-syndromic SNHL (DFNB12). An Omani family diagnosed clinically with severe-profound sensorineural hearing loss was genetically analysed by whole exome sequencing technique. A novel homozygous missense variant, c.A7451C (p.D2484A), in exon 53 of CDH23 was detected. One hundred and thirty control samples were analysed where all were negative for the detected variant. The variant was analysed in silico for pathogenicity verification using several mutation prediction software. The variant proved to be a pathogenic mutation and is reported for the first time in Oman and worldwide. It is concluded that in silico mutation prediction analysis might be used as a useful molecular diagnostics tool benefiting both genetic counseling and mutation verification. The aspartic acid 2484 alanine missense substitution might be the main disease-causing mutation that damages CDH23 function and could be used as a genetic hearing loss marker for this particular Omani family.

Keywords: Cdh23, d2484a, in silico, Oman

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Authors: Yazan Al-Kofahi, Jamal Alqawasmi.

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In this study, a comprehensive literature review (SLR) was conducted, with the main goal of assessing the existing literature about how artificial intelligence (AI), machine learning (ML), deep learning (DL) models are used in sustainable architecture applications and issues including thermal comfort satisfaction, energy efficiency, cost prediction and many others issues. For this reason, the search strategy was initiated by using different databases, including Scopus, Springer and Google Scholar. The inclusion criteria were used by two research strings related to DL, ML and sustainable architecture. Moreover, the timeframe for the inclusion of the papers was open, even though most of the papers were conducted in the previous four years. As a paper filtration strategy, conferences and books were excluded from database search results. Using these inclusion and exclusion criteria, the search was conducted, and a sample of 59 papers was selected as the final included papers in the analysis. The data extraction phase was basically to extract the needed data from these papers, which were analyzed and correlated. The results of this SLR showed that there are many applications of ML and DL in Sustainable buildings, and that this topic is currently trendy. It was found that most of the papers focused their discussions on addressing Environmental Sustainability issues and factors using machine learning predictive models, with a particular emphasis on the use of Decision Tree algorithms. Moreover, it was found that the Random Forest repressor demonstrates strong performance across all feature selection groups in terms of cost prediction of the building as a machine-learning predictive model.

Keywords: machine learning, deep learning, artificial intelligence, sustainable building

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Authors: Omoruyi Gold Idemudia, Alexander P. Sadimenko

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The synthesis of N-heterocycles with novel properties, having broad spectrum biological activities that may become alternative medicinal drugs, have been attracting a lot of research attention due to the emergence of medicinal drug’s limitations such as disease resistance and their toxicity effects among others. Acylpyrazolones have been employed as pharmaceuticals as well as analytical reagent and their application as coordination complexes with transition metal ions have been well established. By way of a condensation reaction with amines acylpyrazolone ketones form a more chelating and superior group of compounds known as azomethines. 4-propyl-3-methyl-1-phenyl-2-pyrazolin-5-one was reacted with phenylhydrazine to get a new phenylhydrazone which was further reacted with aqueous solutions of palladium and platinum salts, in an effort towards the discovery of transition metal based synthetic drugs. The compounds were characterized by means of analytical, spectroscopic, thermogravimetric analysis TGA, as well as x-ray crystallography. 4-propyl-3-methyl-1-phenyl-2-pyrazolin-5-one phenylhydrazone crystallizes in a triclinic crystal system with a P-1 (No. 2) space group based on x-ray crystallography. The bidentate ON ligand formed a square planar geometry on coordinating with metal ions based on FTIR, electronic and NMR spectra as well as magnetic moments. Reported compounds showed antibacterial activities against the nominated bacterial isolates using the disc diffusion technique at 20 mg/ml in triplicates. The metal complexes exhibited a better antibacterial activity with platinum complex having an MIC value of 0.63 mg/ml. Similarly, ligand and complexes also showed antioxidant scavenging properties against 2, 2-diphenyl-1-picrylhydrazyl DPPH radical at 0.5mg/ml relative to ascorbic acid (standard drug).

Keywords: acylpyrazolone, antibacterial studies, metal complexes, phenylhydrazone, spectroscopy

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Authors: Joselito Medina-Marin, Maria G. Serna-Diaz, Juan C. Seck-Tuoh-Mora, Norberto Hernandez-Romero, Irving Barragán-Vite

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Wind speed forecasting is an important issue for planning wind power generation facilities. The accuracy in the wind speed prediction allows a good performance of wind turbines for electricity generation. A model based on artificial neural networks is presented in this work. A dataset with atmospheric information about air temperature, atmospheric pressure, wind direction, and wind speed in Pachuca, Hidalgo, México, was used to train the artificial neural network. The data was downloaded from the web page of the National Meteorological Service of the Mexican government. The records were gathered for three months, with time intervals of ten minutes. This dataset was 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 model with the best performance contains three hidden layers and 9, 6, and 5 neurons, respectively; and the coefficient of determination obtained was r²=0.9414, and the Root Mean Squared Error is 1.0559. In summary, the ANN approach is suitable to predict the wind speed in Pachuca City because the r² value denotes a good fitting of gathered records, and the obtained ANN model can be used in the planning of wind power generation grids.

Keywords: wind power generation, artificial neural networks, wind speed, coefficient of determination

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Authors: Nisa Mohan

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Adverse drug event (ADE) related hospital admissions are common among older people. The first step in prevention is accurately estimating the prevalence of ADE admissions. Clinical coding is an efficient method to estimate the prevalence of ADE admissions. The objective of the study is to estimate the rate of under-coding of ADE admissions in older people in New Zealand and to explore how clinical coders decide whether or not to code an admission as an ADE. There has not been any research in New Zealand to explore these areas. This study is done using a mixed-methods approach. Two common and serious ADEs in older people, namely bleeding and hypoglycaemia were selected for the study. In study 1, eight hundred medical records of people aged 65 years and above who are admitted to hospital due to bleeding and hypoglycemia during the years 2015 – 2016 were selected for quantitative retrospective medical records review. This selection was made to estimate the proportion of ADE-related bleeding and hypoglycemia admissions that are not coded as ADEs. These files were reviewed and recorded as to whether the admission was caused by an ADE. The hospital discharge data were reviewed to check whether all the ADE admissions identified in the records review were coded as ADEs, and the proportion of under-coding of ADE admissions was estimated. In study 2, thirteen clinical coders were selected to conduct qualitative semi-structured interviews using a general inductive approach. Participants were selected purposively based on their experience in clinical coding. Interview questions were designed in a way to investigate the reasons for the under-coding of ADE admissions. The records review study showed that 35% (Cl 28% - 44%) of the ADE-related bleeding admissions and 22% of the ADE-related hypoglycemia admissions were not coded as ADEs. Although the quality of clinical coding is high across New Zealand, a substantial proportion of ADE admissions were under-coded. This shows that clinical coding might under-estimate the actual prevalence of ADE related hospital admissions in New Zealand. The interviews with the clinical coders added that lack of time for searching for information to confirm an ADE admission, inadequate communication with clinicians, along with coders’ belief that an ADE is a small thing might be the potential reasons for the under-coding of the ADE admissions. This study urges the coding policymakers, auditors, and trainers to engage with the unconscious cognitive biases and short-cuts of the clinical coders. These results highlight that further work is needed on interventions to improve the clinical coding of ADE admissions, such as providing education to coders about the importance of ADEs, education to clinicians about the importance of clear and confirmed medical records entries, availing pharmacist service to improve the detection and clear documentation of ADE admissions and including a mandatory field in the discharge summary about external causes of diseases.

Keywords: adverse drug events, bleeding, clinical coders, clinical coding, hypoglycemia

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Authors: Prof. Ammal Mokhtar Metwally, Samia M. Sami, Nihad A. Ibrahim, Fatma A. Shaaban, Iman I. Salama

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Objectives: Reducing neonatal mortality by 2030 is still a challenging goal in developing countries. low birth weight (LBW) is a significant contributor to this, especially where weighing newborns is not possible routinely. The present study aimed to determine a simple, easy, reliable anthropometric measure(s) that can predict LBW) and neonatal mortality. Methods: A prospective cohort study of 570 babies born in districts of El Menia governorate, Egypt (where most deliveries occurred at home) was examined at birth. Newborn weight, length, head, chest, mid-arm, and thigh circumferences were measured. Follow up of the examined neonates took place during their first four weeks of life to report any mortalities. The most predictable anthropometric measures were determined using the statistical package of SPSS, and multiple Logistic regression analysis was performed.: Results: Head and chest circumferences with cut-off points < 33 cm and ≤ 31.5 cm, respectively, were the significant predictors for LBW. They carried the best combination of having the highest sensitivity (89.8 % & 86.4 %) and least false negative predictive value (1.4 % & 1.7 %). Chest circumference with a cut-off point ≤ 31.5 cm was the significant predictor for neonatal mortality with 83.3 % sensitivity and 0.43 % false negative predictive value. Conclusion: Using chest circumference with a cut-off point ≤ 31.5 cm is recommended as a single simple anthropometric measurement for the prediction of both LBW and neonatal mortality. The predicted measure could act as a substitute for weighting newborns in communities where scales to weigh them are not routinely available.

Keywords: low birth weight, neonatal mortality, anthropometric measures, practical screening

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Authors: Haykal. Kay-Anne

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The health and well-being of students is a priority for the Faculty of Medicine at the University of Ottawa. The demands of medical studies are extreme, and many studies confirm that the prevalence of psychological distress is very high among medical students and that it is higher than that of the general population of the same age. The main goal is to identify risk factors for mental health among medical students at the University of Ottawa. The secondary objectives are to determine the variation of these risk factors according to demographic variables, as well as to determine if there is a change in the mental health of students during the 1st and 3rd years of their study. Medical students have a mandatory first and third-year wellness check meeting. This assessment includes a questionnaire on demographic information, mental health, and risk factors such as physical health, sleep, social support, financial stress, education and career, stress and drug use and/or alcohol. Student responses were converted to numerical values and analyzed statistically. The results show that 61% of the variation in the mean of the mental health score is explained by the following risk factors (R2 = 0.61, F (9.396) = 67.197, p < 0.01): lack of sleep and fatigue (β = 0.281, p < 0.001), lack of social support (β = 0.217, p <0.001), poor study or career development (β = 0.195, p < 0.001) and an increase stress and drug and alcohol use (β = -0.239, p < 0.001). No demographic variable has a significant effect on the presence of risk factors. In addition, fixed-effects regression demonstrated significantly lower mental health (p < 0.1) among first-year students (M = 0.587, SD = 0.072) than among third-year students (M = 0.719, SD = 0.071). This preliminary study indicates the need to continue data collection and analysis to increase the significance of the study results. As risk factors are present at the beginning of medical studies, it is important to offer resources to students very early in their medical studies and to have close monitoring and supervision.

Keywords: assessment of mental health, medical students, risk factors for mental health, wellness assessment

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Authors: RuiJin Zhang, HengJiang Cai, JinSong Gui

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It takes Patinopecten yessoensis larvae more than 20 days from spawning to settlement. Due to the natural environmental factors such as current, Patinopecten yessoensis larvae are transported to a distance more than hundreds of kilometers, leading to a high instability of their spatial and temporal distribution and great difficulties in the natural spat collection. Therefore predicting the distribution is of great significance to improve the operating efficiency of the collecting. Hydrodynamic model of Northern China Yellow Sea was established and the motions equations of physical oceanography and verified by the tidal harmonic constants and the measured data velocities of Dalian Bay. According to the passivity drift characteristics of the larvae, combined with the hydrodynamic model and the particle tracking model, the spatial and temporal distribution prediction model was established and the spatial and temporal distribution of the larvae under the influence of flow and wind were simulated. It can be concluded from the model results: ocean currents have greatest impacts on the passive drift path and diffusion of Patinopecten yessoensis larvae; the impact of wind is also important, which changed the direction and speed of the drift. Patinopecten yessoensis larvae were generated in the sea along Zhangzi Island and Guanglu-Dachangshan Island, but after two months, with the impact of wind and currents, the larvae appeared in the west of Dalian and the southern of Lvshun, and even in Bohai Bay. The model results are consistent with the relevant literature on qualitative analysis, and this conclusion explains where the larvae come from in the perspective of numerical simulation.

Keywords: numerical simulation, Patinopecten yessoensis larvae, predicting model, spatial and temporal distribution

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Authors: A. Nicknam, N. Eftekhari, A. Mazarei, M. Ganjvar

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This article presents an alternative collapse capacity intensity measure in the three elements form which is influenced by the spectral ordinates at periods longer than that of the first mode period at near and far source sites. A parameter, denoted by β, is defined by which the spectral ordinate effects, up to the effective period (2T_1), on the intensity measure are taken into account. The methodology permits to meet the hazard-levelled target extreme event in the probabilistic and deterministic forms. A MATLAB code is developed involving OpenSees to calculate the collapse capacities of the 8 archetype RC structures having 2 to 20 stories for regression process. The incremental dynamic analysis (IDA) method is used to calculate the structure’s collapse values accounting for the element stiffness and strength deterioration. The general near field set presented by FEMA is used in a series of performing nonlinear analyses. 8 linear relationships are developed for the 8structutres leading to the correlation coefficient up to 0.93. A collapse capacity near field prediction equation is developed taking into account the results of regression processes obtained from the 8 structures. The proposed prediction equation is validated against a set of actual near field records leading to a good agreement. Implementation of the proposed equation to the four archetype RC structures demonstrated different collapse capacities at near field site compared to those of FEMA. The reasons of differences are believed to be due to accounting for the spectral shape effects.

Keywords: collapse capacity, fragility analysis, spectral shape effects, IDA method

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Authors: Soma Banerjee, Aamen Talukdar, Argha Mandal, Dipankar Chaudhuri

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Japanese Encephalitis is a major infectious disease with nearly half the world’s population living in areas where it is prevalent. Currently, treatment for it involves only supportive care and symptom management through vaccination. Due to the lack of antiviral drugs against Japanese Encephalitis Virus (JEV), the quest for such agents remains a priority. For these reasons, simulation studies of drug targets against JEV are important. Towards this purpose, docking experiments of the kinase inhibitors were done against the chosen target NS3 helicase as it is a nucleoside binding protein. Previous efforts regarding computational drug design against JEV revealed some lead molecules by virtual screening using public domain software. To be more specific and accurate regarding finding leads, in this study a proprietary software Schrödinger-GLIDE has been used. Druggability of the pockets in the NS3 helicase crystal structure was first calculated by SITEMAP. Then the sites were screened according to compatibility with ATP. The site which is most compatible with ATP was selected as target. Virtual screening was performed by acquiring ligands from databases: KinaseSARfari, KinaseKnowledgebase and Published inhibitor Set using GLIDE. The 25 ligands with best docking scores from each database were re-docked in XP mode. Protein structure alignment of NS3 was performed using VAST against MMDB, and similar human proteins were docked to all the best scoring ligands. The low scoring ligands were chosen for further studies and the high scoring ligands were screened. Seventy-three ligands were listed as the best scoring ones after performing HTVS. Protein structure alignment of NS3 revealed 3 human proteins with RMSD values lesser than 2Å. Docking results with these three proteins revealed the inhibitors that can interfere and inhibit human proteins. Those inhibitors were screened. Among the ones left, those with docking scores worse than a threshold value were also removed to get the final hits. Analysis of the docked complexes through 2D interaction diagrams revealed the amino acid residues that are essential for ligand binding within the active site. Interaction analysis will help to find a strongly interacting scaffold among the hits. This experiment yielded 21 hits with the best docking scores which could be investigated further for their drug like properties. Aside from getting suitable leads, specific NS3 helicase-inhibitor interactions were identified. Selection of Target modification strategies complementing docking methodologies which can result in choosing better lead compounds are in progress. Those enhanced leads can lead to better in vitro testing.

Keywords: antivirals, docking, glide, high-throughput virtual screening, Japanese encephalitis, ns3 helicase

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Authors: Husham Bayazed

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Immune responses following surgical trauma play a pivotal role in predicting postoperative outcomes from healing and recovery to postoperative complications. Postoperative complications, including infections and protracted recovery, occur in a significant number of about 300 million surgeries performed annually worldwide. Complications cause personal suffering along with a significant economic burden on the healthcare system in any community. The accurate prediction of postoperative complications and patient-targeted interventions for their prevention remain major clinical provocations. Recent Findings: Recent studies are focusing on immune dysregulation mechanisms that occur in response to surgical trauma as a key determinant of postoperative complications. Antecedent studies mainly were plunging into the detection of inflammatory plasma markers, which facilitate in providing important clues regarding their pathogenesis. However, recent Single-cell technologies, such as mass cytometry or single-cell RNA sequencing, have markedly enhanced our ability to understand the immunological basis of postoperative immunological trauma complications and to identify their prognostic biological signatures. Summary: The advent of proteomic technologies has significantly advanced our ability to predict the risk of postoperative complications. Multiomic modeling of patients' immune states holds promise for the discovery of preoperative predictive biomarkers and providing patients and surgeons with information to improve surgical outcomes. However, more studies are required to accurately predict the risk of postoperative complications in individual patients.

Keywords: immune dysregulation, postoperative complications, surgical trauma, flow cytometry

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Authors: Geng Xiangren, Liu Lei, Gui Ye-Wei, Tang Wei, Wang An-ling

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The malfunction of thermal protection system (TPS) caused by aerodynamic heating is a latent trouble to aircraft structure safety. Accurately predicting the structure temperature field is quite important for the TPS design of hypersonic vehicle. Since Thornton’s work in 1988, the coupled method of aerodynamic heating and heat transfer has developed rapidly. However, little attention has been paid to the influence of structural deformation on aerodynamic heating and structural temperature field. In the flight, especially the long-endurance flight, the structural deformation, caused by the aerodynamic heating and temperature rise, has a direct impact on the aerodynamic heating and structural temperature field. Thus, the coupled interaction cannot be neglected. In this paper, based on the method of static aero-thermo-elasticity, considering the influence of aero-thermo-elasticity deformation, the aerodynamic heating and heat transfer coupled results of hypersonic vehicle wing model were calculated. The results show that, for the low-curvature region, such as fuselage or center-section wing, structure deformation has little effect on temperature field. However, for the stagnation region with high curvature, the coupled effect is not negligible. Thus, it is quite important for the structure temperature prediction to take into account the effect of elastic deformation. This work has laid a solid foundation for improving the prediction accuracy of the temperature distribution of aircraft structures and the evaluation capacity of structural performance.

Keywords: aerothermoelasticity, elastic deformation, structural temperature, multi-field coupling

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Authors: Saravanan Govindaraju, Kyusik Yun

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The purpose of this study is to develop the chitosan doped curcumin gold cluster nanofiber for wound healing and skin cancer drug delivery applications. Chitosan is a typical marine polysaccharide composed of glucosamine and n-acetyl glucosamine biodegradable and biocompatible polymer. Curcumin is a natural bioactive molecule obtained from Curcuma longo, it mostly occurs in some Asian countries like India and China. It has naturally antioxidant, antimicrobial, wound healing and anticancer property. Due to this advantage, we prepared a combination of natural polymer chitosan with Curcumin and gold nanocluster nanofiber (CH-CUR-AuNCs nanofibers). The prepared nanofiber was characterized by using Fourier transform infrared spectroscopy (FT-IR), and scanning electron microscopy (SEM). Antibacterial studies were performed with E.coli and S.aureus. Antioxidant assay, drug release test, and cytotoxicity will be evaluated. Prepared nanofiber emits low intensity of red fluorescent. The FTIR confirm the presence of chitosan and Curcumin in the nanofiber. In vitro study clearly shows the antibacterial activity against the gram negative and gram positive bacteria. Particularly, synthesised nanofibers provide better antibacterial activity against gram negative than gram positive. Cytotoxicity study also provides better killing rate in cancer cell, biocompatible with normal cell. Prepared CH-CUR-AuNCs nanofibers provide the better killing rate to bacterial strains and cancer cells. Finally, prepared nanofiber can be possible to use for wound healing dressing, patch for skin cancer and other biomedical applications.

Keywords: curcumin, chitosan, gold clusters, nanofibers

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Authors: Nadish Anand, Richard D. Gould

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A simplistic model is introduced for determining the thermal characteristics of a Low-rise Residential (LRR) building and then predicts the energy usage by its Heating Ventilation & Air Conditioning (HVAC) system according to changes in weather conditions which are reflected in the Ambient Temperature (Outside Air Temperature). The LRR buildings are treated as a simple lump for solving the heat transfer problem and the model is derived using the lumped capacitance model of transient conduction heat transfer from bodies. Since most contemporary HVAC systems have a thermostat control which will have an offset temperature and user defined set point temperatures which define when the HVAC system will switch on and off. The aim is to predict without any error the Body Temperature (i.e. the Inside Air Temperature) which will estimate the switching on and off of the HVAC system. To validate the mathematical model derived from lumped capacitance we have used EnergyPlus simulation engine, which simulates Buildings with considerable accuracy. We have predicted through the low order model the Inside Air Temperature of a single house kept in three different climate zones (Detroit, Raleigh & Austin) and different orientations for summer and winter seasons. The prediction error from the model for the same day as that of model parameter calculation has showed an error of < 10% in winter for almost all the orientations and climate zones. Whereas the prediction error is only <10% for all the orientations in the summer season for climate zone at higher latitudes (Raleigh & Detroit). Possible factors responsible for the large variations are also noted in the work, paving way for future research.

Keywords: building energy, energy consumption, energy+, HVAC, low order model, lumped capacitance

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Authors: Said Alshukri, Mazhar Hussain Malik

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Green hydrogen is hydrogen produced using renewable energy sources. In the last few years, Oman aimed to reduce its dependency on fossil fuels. Recently, the hydrogen economy has become a global trend, and many countries have started to investigate the feasibility of implementing this sector. Oman created an alliance to establish the policy and rules for this sector. With motivation coming from both global and local interest in green hydrogen, this paper investigates the potential of producing hydrogen from wind and solar energies in three different locations in Oman, namely Duqm, Salalah, and Sohar. By using machine learning-based software “WEKA” and local metrological data, the project was designed to figure out which location has the highest wind and solar energy potential. First, various supervised models were tested to obtain their prediction accuracy, and it was found that the Random Forest (RF) model has the best prediction performance. The RF model was applied to 2021 metrological data for each location, and the results indicated that Duqm has the highest wind and solar energy potential. The system of one wind turbine in Duqm can produce 8335 MWh/year, which could be utilized in the water electrolysis process to produce 88847 kg of hydrogen mass, while a solar system consisting of 2820 solar cells is estimated to produce 1666.223 MWh/ year which is capable of producing 177591 kg of hydrogen mass.

Keywords: green hydrogen, machine learning, wind and solar energies, WEKA, supervised models, random forest

Procedia PDF Downloads 79

Authors: Alex Kiselyov, Suehyun Cho, Darrell Harrington; Florent Cros, Olin Palmer, John Caputo, Michael Kardosh, Eran Oren, William Loudon, Michael Shpigelmacher

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Despite the most aggressive surgical and adjuvant therapeutic strategies, treatment of both pediatric and adult brainstem tumors remains problematic. Novel strategies, including targeted biologics, immunotherapy, and specialized delivery systems such as convection-enhanced delivery (CED), have been proposed. While some of these novel treatments are entering phase I trials, the field is still in need of treatment(s) that exhibits dramatically enhanced potency with optimal therapeutic ratio. Bionaut Labs has developed a modular microrobotic platform for performing localized delivery of diverse therapeutics in vivo. Our biocompatible particles (Bionauts™) are externally propelled and visualized in real-time. Bionauts™ are specifically designed to enhance the effect of radiation therapy via anatomically precise delivery of a radiosensitizing agent, as exemplified by temozolomide (TMZ) and Avastin™ to the brainstem gliomas of diverse origin. The treatment protocol is designed to furnish a better therapeutic outcome due to the localized (vs systemic) delivery of the drug to the neoplastic lesion(s) for use as a synergistic combination of radiation and radiosensitizing agent. In addition, the procedure is minimally invasive and is expected to be appropriate for both adult and pediatric patients. Current progress, including platform optimization, selection of the lead radiosensitizer as well as in vivo safety studies of the Bionauts™ in large animals, specifically the spine and the brain of porcine and ovine models, will be discussed.

Keywords: Bionaut, brainstem, glioma, local delivery, micro-robot, radiosensitizer

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Authors: R. O. Osaseri, A. R. Usiobaifo

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The power transformer which is responsible for the voltage transformation is of great relevance in the power system and oil-immerse transformer is widely used all over the world. A prompt and proper maintenance of the transformer is of utmost importance. The dissolved gasses content in power transformer, oil is of enormous importance in detecting incipient fault of the transformer. There is a need for accurate prediction of the incipient fault in transformer oil in order to facilitate the prompt maintenance and reducing the cost and error minimization. Study on fault prediction and diagnostic has been the center of many researchers and many previous works have been reported on the use of artificial intelligence to predict incipient failure of transformer faults. In this study machine learning technique was employed by using gradient decent algorithms and Support Vector Machine (SVM) in predicting incipient fault diagnosis of transformer. The method focuses on creating a system that improves its performance on previous result and historical data. The system design approach is basically in two phases; training and testing phase. The gradient decent algorithm is trained with a training dataset while the learned algorithm is applied to a set of new data. This two dataset is used to prove the accuracy of the proposed model. In this study a transformer fault diagnostic model based on Support Vector Machine (SVM) and gradient decent algorithms has been presented with a satisfactory diagnostic capability with high percentage in predicting incipient failure of transformer faults than existing diagnostic methods.

Keywords: diagnostic model, gradient decent, machine learning, support vector machine (SVM), transformer fault

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Authors: Mouna Baassi, Mohamed Moussaoui, Hatim Soufi, Sanchaita RajkhowaI, Ashwani Sharma, Subrata Sinha, Said Belaaouad

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Human Immunodeficiency Virus type 1 protease (HIV-1 PR) is one of the most challenging targets of antiretroviral therapy used in the treatment of AIDS-infected people. The performance of protease inhibitors (PIs) is limited by the development of protease mutations that can promote resistance to the treatment. The current study was carried out using statistics and bioinformatics tools. A series of thirty-three compounds with known enzymatic inhibitory activities against HIV-1 protease was used in this paper to build a mathematical model relating the structure to the biological activity. These compounds were designed by software; their descriptors were computed using various tools, such as Gaussian, Chem3D, ChemSketch and MarvinSketch. Computational methods generated the best model based on its statistical parameters. The model’s applicability domain (AD) was elaborated. Furthermore, one compound has been proposed as efficient against HIV-1 protease with comparable biological activity to the existing ones; this drug candidate was evaluated using ADMET properties and Lipinski’s rule. Molecular Docking performed on Wild Type and Mutant Type HIV-1 proteases allowed the investigation of the interaction types displayed between the proteases and the ligands, Darunavir (DRV) and the new drug (ND). Molecular dynamics simulation was also used in order to investigate the complexes’ stability, allowing a comparative study of the performance of both ligands (DRV & ND). Our study suggested that the new molecule showed comparable results to that of Darunavir and may be used for further experimental studies. Our study may also be used as a pipeline to search and design new potential inhibitors of HIV-1 proteases.

Keywords: QSAR, ADMET properties, molecular docking, molecular dynamics simulation.

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Authors: Saba Gachpaz, Hamid Reza Heidari

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The sharp increase in population growth leads to more pressure on agricultural areas to satisfy the food supply. To achieve this, more resources should be consumed and, besides other environmental concerns, highlight sustainable agricultural development. Land-use management is a crucial factor in obtaining optimum productivity. Machine learning is a widely used technique in the agricultural sector, from yield prediction to customer behavior. This method focuses on learning and provides patterns and correlations from our data set. In this study, nine physical control factors, namely, soil classification, electrical conductivity, normalized difference water index (NDWI), groundwater level, elevation, annual precipitation, pH of water, annual mean temperature, and slope in the alluvial plain in Khuzestan (an agricultural hotspot in Iran) are used to decide the best agricultural land use for both rainfed and irrigated agriculture for ten different crops. For this purpose, each variable was imported into Arc GIS, and a raster layer was obtained. In the next level, by using training samples, all layers were imported into the python environment. A random forest model was applied, and the weight of each variable was specified. In the final step, results were visualized using a digital elevation model, and the importance of all factors for each one of the crops was obtained. Our results show that despite 62% of the study area being allocated to agricultural purposes, only 42.9% of these areas can be defined as a suitable class for cultivation purposes.

Keywords: land suitability, machine learning, random forest, sustainable agriculture

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Authors: B. Sadek, N. Khan, Shreesh K. Ojha, Adel Sadeq, D. Lazewska, K. Kiec-Kononowicz

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The involvement of Histamine H3 receptors (H3Rs) in memory and the potential role of H3R antagonists in pharmacological control of neurodegenerative disorders, e.g., Alzheimer disease (AD) is well established. Therefore, the memory-enhancing effects of the H3R antagonist DL77 on MK801-induced cognitive deficits were evaluated in passive avoidance paradigm (PAP) and novel object recognition (NOR) tasks in adult male rats, applying donepezil (DOZ) as a reference drug. Animals pretreated with acute systemic administration of DL77 (2.5, 5, and 10 mg/kg, i.p.) were significantly ameliorated in regard to MK801-induced memory deficits in PAP. The ameliorative effect of most effective dose of DL77 (5 mg/kg, i.p.) was abrogated when animals were pretreated with a co-injection with the H3R agonist R-(α)-methylhistamine (RAMH, 10 mg/kg, i.p.). Moreover, and in the NOR paradigm, DL77 (5 mg/kg, i.p.) reversed MK801-induced deficits long-term memory (LTM), and the DL77-provided procognitive effect was comparable to that of reference drug DOZ, and was reversed when animals were co-injected with RAMH (10 mg/kg, i.p.). However, DL77(5 mg/kg, i.p.) failed to alter short-term memory (STM) impairment in NOR test. Furthermore, DL77 (5 mg/kg) failed to induce any alterations of anxiety and locomotor behaviors of animals naive to elevated-plus maze (EPM), indicating that the ameliorative effects observed in PAP or NOR tests were not associated to alterations in emotions or in natural locomotion of tested animals. These results reveal the potential contribution of H3Rs in modulating CNS neurotransmission systems associated with neurodegenerative disorders, e.g., AD.

Keywords: histamine H3 receptor, antagonist, learning and memory, Alzheimer's disease, neurodegeneration, passive avoidance paradigm, novel object recognition, behavioral research

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Authors: Anindita Sen

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Oral fluid is a promising diagnostic matrix for drugs of abuse compared to urine and serum. Detection of methamphetamine in oral fluid would pave way for the easy evaluation of impairment in drivers during roadside drug testing as well as ensure safe working environments by facilitating evaluation of impairment in employees at workplaces. A membrane-based point-of-care (POC) friendly pre-treatment technique has been developed which aided elimination of interferences caused by salivary proteins and facilitated the demonstration of methamphetamine detection in saliva using a gold nanoparticle based colorimetric aptasensor platform. It was found that the colorimetric response in saliva was always suppressed owing to the matrix effects. By navigating the challenging interfering issues in saliva, we were successfully able to detect methamphetamine at nanomolar levels in saliva offering immense promise for the translation of these platforms for on-site diagnostic systems. This subsequently motivated the development of a handheld portable light meter device that can reliably transduce the aptasensors colorimetric response into absorbance, facilitating quantitative detection of analyte concentrations on-site. This is crucial due to the prevalent unreliability and sensitivity problems of the conventional drug testing kits. The fabricated light meter device response was validated against a standard UV-Vis spectrometer to confirm reliability. The portable and cost-effective handheld detector device features sensitivity comparable to the well-established UV-Vis benchtop instrument and the easy-to-use device could potentially serve as a prototype for a commercial device in the future.

Keywords: aptasensors, colorimetric gold nanoparticle assay, point-of-care, oral fluid

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Authors: Nunila Ferreira de Oliveira, Silvana Martins Mishima

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Diabetes mellitus type 2 (DM2) prevalence, is increasing on the world, in Brazil is considered a public health problem. Treatment focuses on glycemic control depending primarily of lifestyle changes - not drug treatment (NDT), may involve drug therapy (DT) and requires continuous health monitoring. In Brazil this monitoring is performed by the Unified Health System (SUS) through Primary Health Care (PHC), which stimulate people with DM2 empowerment for care management. SUS was approved in 1988 and the PHC operationalization was strengthened with the creation of the Family Health Strategy (FHS) in 1994. Our aim was to analyze the people with DM2 participation in front of the care management health monitoring in the FHS. Qualitative research was carried out through non-participant observation of attendance of 25 people with DM2 in the FHS and interviewed at home. Ethical guidelines were followed. It was found that people with DM2 only follow professionals’ recommendations that make sense according to their own conceptions of health/disease; most of them emphasize the importance of (DT) with little emphasis on the NDT, was found great difficulty in the NDT and lack of knowledge about the disease and care. As regards monitoring the FHS, were observed therapeutic practices based on the bio medical model, although the APS search for another care perspective; NDT is not systematically accompanied by the health team and takes place a few educational activities on the DM2 in the FHS, with low user adoption. The work of the FHS is done by multidisciplinary teams, but we see the need for greater participation of nurses in clinical-care follow-up of this population and may also act in adapting to the NDT. Finally we emphasize the need for professional practices that consider the difficulties to care management by people with DM2, especially because of the NDT. It is noticed that the measures recommended by the FHS professionals are not always developed by people with DM2. We must seek the empowerment of people with DM2 to manage the form of care associated with the FHS team, seeking to reduce the incidence of complications and higher quality of life.

Keywords: diabetes mellitus, primary health care, nursing, management of care

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Authors: Mukul Sharma, Madhusmita Das, Sundeep Chaitanya Vedithi

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Leprosy is a chronic human disease caused by Mycobacterium leprae, that cannot be cultured in vitro. Though treatable with multidrug therapy (MDT), recently, bacteria reported resistance to multiple antibiotics. Targeting DNA replication and repair pathways can serve as the foundation of developing new anti-leprosy drugs. Due to the absence of an axenic culture medium for the propagation of M. leprae, studying cellular processes, especially those belonging to DNA repair pathways, is challenging. Genomic understanding of M. Leprae harbors several protein-coding genes with no previously assigned function known as 'hypothetical proteins'. Here, we report identification and expression of known and hypothetical DNA repair genes from a human skin biopsy and mouse footpads that are involved in base excision repair, direct reversal repair, and SOS response. Initially, a bioinformatics approach was employed based on sequence similarity, identification of known protein domains to screen the hypothetical proteins in the genome of M. leprae, that are potentially related to DNA repair mechanisms. Before testing on clinical samples, pure stocks of bacterial reference DNA of M. leprae (NHDP63 strain) was used to construct standard graphs to validate and identify lower detection limit in the qPCR experiments. Primers were designed to amplify the respective transcripts, and PCR products of the predicted size were obtained. Later, excisional skin biopsies of newly diagnosed untreated, treated, and drug resistance leprosy cases from SIHR & LC hospital, Vellore, India were taken for the extraction of RNA. To determine the presence of the predicted transcripts, cDNA was generated from M. leprae mRNA isolated from clinically confirmed leprosy skin biopsy specimen across all the study groups. Melting curve analysis was performed to determine the integrity of the amplification and to rule out primer‑dimer formation. The Ct values obtained from qPCR were fitted to standard curve to determine transcript copy number. Same procedure was applied for M. leprae extracted after processing a footpad of nude mice of drug sensitive and drug resistant strains. 16S rRNA was used as positive control. Of all the 16 genes involved in BER, DR, and SOS, differential expression pattern of the genes was observed in terms of Ct values when compared to human samples; this was because of the different host and its immune response. However, no drastic variation in gene expression levels was observed in human samples except the nth gene. The higher expression of nth gene could be because of the mutations that may be associated with sequence diversity and drug resistance which suggests an important role in the repair mechanism and remains to be explored. In both human and mouse samples, SOS system – lexA and RecA, and BER genes AlkB and Ogt were expressing efficiently to deal with possible DNA damage. Together, the results of the present study suggest that DNA repair genes are constitutively expressed and may provide a reference for molecular diagnosis, therapeutic target selection, determination of treatment and prognostic judgment in M. leprae pathogenesis.

Keywords: DNA repair, human biopsy, hypothetical proteins, mouse footpads, Mycobacterium leprae, qPCR

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Authors: Rania Hanafi Said, Rasha Mohamed Taha

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Background: By using nanoparticles as drug delivery, it may be possible to avoid the drawbacks of systemic antibiotic dosing, including bacterial antibiotic resistance. The goal of this study was to see how well tetracycline loaded on nanochitosan worked to treat gingival inflammation in albino rats caused by Porphyromonas gingivalis. The study analyzed immunohistochemically the localization of the pro-inflammatory cytokine Interleukin-1beta (IL-1β). Material and methods: In this study, fifty mature male albino rats weighing 150 to 180 grams each were used. They were randomly divided into five groups. We checked for weight changes in rats. Ten male albino rats were included in Group I, which served as a negative control group. Ten rats were included in Group II, where they were exposed once to Porphyromonas. Group III contained ten rats, which were treated the same as Group II plus daily injections of diluted tetracycline powder at the infection sites. Ten rats in Group IV received the same procedure as those in Group II before receiving daily injections of nanochitosan at the injection sites. Finally, Group V, which had ten rats. Following the same protocol as Group II, they received localized injections of tetracycline loaded on nanochitosan once daily. Rats' gingivae were extracted and prepared after they were anesthetized. The biopsies were examined histologically and immunohistochemically by light microscopy. Results: Groups I and V had a nearly normal histological appearance of gingival tissue. In Groups II, III, and IV, degeneration was seen because the epithelial cells were bigger, collagen fibers were pulling away from the lamina propria connective tissue, and the basement membranes had come to an end. There was no discernible difference between groups V and I when they were examined immunohistochemically. Conclusion: The use of nano chitosan as a tetracycline carrier is a novel technique to overcome the drug's rising level of resistance.

Keywords: Immunohistochemistry, Nanochitosan, porphyromonas gingivitis, Tetracycline

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Authors: Ahmed Alghurabi, Mysara Mohyaldinn, Shiferaw Jufar, Obai Younis, Abdullah Abduljabbar

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Erosion modeling equations were typically acquired from regulated experimental trials for solid particles entrained in single-phase or multi-phase flows. Evidently, those equations were later employed to predict the erosion damage caused by the continuous impacts of solid particles entrained in streamflow. It is also well-known that the particle impact angle and velocity do not change drastically in gas-sand flow erosion prediction; hence an accurate prediction of erosion can be projected. On the contrary, high-density fluid flows, such as water flow, through complex geometries, such as sand screens, greatly affect the sand particles’ trajectories/tracks and consequently impact the erosion rate predictions. Particle tracking models and erosion equations are frequently applied simultaneously as a method to improve erosion visualization and estimation. In the present work, computational fluid dynamic (CFD)-based erosion modeling was performed using a commercially available software; ANSYS Fluent. The continuous phase (water flow) behavior was simulated using the realizable K-epsilon model, and the secondary phase (solid particles), having a 5% flow concentration, was tracked with the help of the discrete phase model (DPM). To accomplish a successful erosion modeling, three erosion equations from the literature were utilized and introduced to the ANSYS Fluent software to predict the screen wire-slot velocity surge and estimate the maximum erosion rates on the screen surface. Results of turbulent kinetic energy, turbulence intensity, dissipation rate, the total pressure on the screen, screen wall shear stress, and flow velocity vectors were presented and discussed. Moreover, the particle tracks and path-lines were also demonstrated based on their residence time, velocity magnitude, and flow turbulence. On one hand, results from the utilized erosion equations have shown similarities in screen erosion patterns, locations, and DPM concentrations. On the other hand, the model equations estimated slightly different values of maximum erosion rates of the wire-wrapped screen. This is solely based on the fact that the utilized erosion equations were developed with some assumptions that are controlled by the experimental lab conditions.

Keywords: CFD simulation, erosion rate prediction, material loss due to erosion, water-sand flow

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Authors: Silvia Arrate, Waldo Salud, Eloy París

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The wear of cutting tools is one of the most decisive elements when planning tunneling works, programming the maintenance stops and saving the optimum stock of spare parts during the evolution of the excavation. Being able to predict the behavior of cutting tools can give a very competitive advantage in terms of costs and excavation performance, optimized to the needs of the TBM itself. The incredible evolution of data science in recent years gives the option to implement it at the time of analyzing the key and most critical parameters related to machinery with the purpose of knowing how the cutting head is performing in front of the excavated ground. Taking this as a case study, Metro Line 3 of Guadalajara in Mexico will develop the feasibility of using Specific Energy versus data science applied over parameters of Torque, Penetration, and Contact Force, among others, to predict the behavior and status of cutting tools. The results obtained through both techniques are analyzed and verified in the function of the wear and the field situations observed in the excavation in order to determine its effectiveness regarding its predictive capacity. In conclusion, the possibilities and improvements offered by the application of digital tools and the programming of calculation algorithms for the analysis of wear of cutting head elements compared to purely empirical methods allow early detection of possible damage to cutting tools, which is reflected in optimization of excavation performance and a significant improvement in costs and deadlines.

Keywords: cutting tools, data science, prediction, TBM, wear

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Authors: Umme Hani, H. G. Shivakumar, M. D. Younus Pasha

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The purpose of this work was to design and optimize a novel vaginal drug delivery system for more effective treatment against vaginal candidiasis. To achieve a better therapeutic efficacy and patient compliance in the treatment for vaginal candidiasis, herbal antifungal agent Curcumin which is 2.5 fold more potent than fluconazole at inhibiting the adhesion of candida albicans has been formulated in a bio-adhesive vaginal film. Curcumin was formulated in bio-adhesive film formulations that could be retained in the vagina for prolonged intervals. The polymeric films were prepared by solvent evaporation and optimized for various physicodynamic and aesthetic properties. Curcumin HPβCD (Hydroxypropyl β Cyclodextrin) was first developed to increase the solubility of curcumin. The formation of the Curcumin HPβCD complex was characterized by scanning electron microscopy (SEM), differential scanning calorimetry (DSC), and FT-IR and evaluated for its solubility. Curcumin HPβCD complex was formulated in a bio-adhesive film using hydroxypropyl methyl cellulose (HPMC) and Carbopol 934P and characterized. DSC and FT-IR data of Curcumin HPβCD indicate there was complex formation between the drug and HPβCD. The little moisture content (8.02±0.34% w/w) was present in the film, which helps them to remain stable and kept them from being completely dry and brittle. The mechanical properties, tensile strength, and percentage elongation at break reveal that the formulations were found to be soft and tough. The films showed good peelability, relatively good swelling index, and moderate tensile strength and retained vaginal mucosa up to 8 h. The developed Curcumin vaginal film could be a promising safe herbal medication and can ensure longer residence at the vagina and provide an efficient therapy for vaginal candidiasis.

Keywords: curcumin, curcumin-HPβCD complex, bio-adhesive vaginal film, vaginal candidiasis, 23 factorial design

Procedia PDF Downloads 382

Authors: Sophia Liang Zhou

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For investors, businesses, consumers, and governments, an accurate assessment of future housing prices is crucial to critical decisions in resource allocation, policy formation, and investment strategies. Previous studies are contradictory about macroeconomic determinants of housing price and largely focused on one or two areas using point prediction. This study aims to develop data-driven models to accurately predict future housing market trends in different markets. This work studied five different metropolitan areas representing different market trends and compared three-time lagging situations: no lag, 6-month lag, and 12-month lag. Linear regression (LR), random forest (RF), and artificial neural network (ANN) were employed to model the real estate price using datasets with S&P/Case-Shiller home price index and 12 demographic and macroeconomic features, such as gross domestic product (GDP), resident population, personal income, etc. in five metropolitan areas: Boston, Dallas, New York, Chicago, and San Francisco. The data from March 2005 to December 2018 were collected from the Federal Reserve Bank, FBI, and Freddie Mac. In the original data, some factors are monthly, some quarterly, and some yearly. Thus, two methods to compensate missing values, backfill or interpolation, were compared. The models were evaluated by accuracy, mean absolute error, and root mean square error. The LR and ANN models outperformed the RF model due to RF’s inherent limitations. Both ANN and LR methods generated predictive models with high accuracy ( > 95%). It was found that personal income, GDP, population, and measures of debt consistently appeared as the most important factors. It also showed that technique to compensate missing values in the dataset and implementation of time lag can have a significant influence on the model performance and require further investigation. The best performing models varied for each area, but the backfilled 12-month lag LR models and the interpolated no lag ANN models showed the best stable performance overall, with accuracies > 95% for each city. This study reveals the influence of input variables in different markets. It also provides evidence to support future studies to identify the optimal time lag and data imputing methods for establishing accurate predictive models.

Keywords: linear regression, random forest, artificial neural network, real estate price prediction

Procedia PDF Downloads 103

Authors: Jungkyun Im

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Nonsteroidal anti-inflammatory drugs (NSAIDs) are effective for relieving pain and reducing inflammation. They are nonselective inhibitors of two isoforms of COX, cyclooxygenase-1 (COX-1) and cyclooxygenase-2 (COX-2), and thereby inhibiting the production of hormone-like lipid compounds such as, prostaglandins and thromboxanes which cause inflammation, pain, fever, platelet aggregation, etc. In addition, recently there are many research articles reporting the neuroprotective effect of NSAIDs in neurodegenerative diseases, such as Alzheimer’s disease (AD) and Parkinson’s disease (PD). However, the clinical use of NSAIDs in these diseases is limited by low brain distribution. Therefore, in order to assist the in-depth investigation on the pharmaceutical mechanism of flurbiprofen in neuroprotection and to make flurbiprofen a more potent drug to prevent or alleviate neurodegenerative diseases, delivery of flurbiprofen to brain should be effective and sufficient amount of flurbiprofen must penetrate the BBB thus gaining access into the patient’s brain. We have recently developed several types of guanidine-rich molecular carriers with high molecular weights and good water solubility that readily cross the blood-brain barrier (BBB) and display efficient distributions in the mouse brain. The G8 (having eight guanidine groups) molecular carrier based on D-sorbitol was found to be very effective in delivering anticancer drugs to a mouse brain. In the present study, employing the same molecular carrier, we prepared the flurbiprofen conjugate and studied its BBB permeation by mouse tissue distribution study. Flurbiprofen was attached to a molecular carrier with a fluorescein probe and multiple terminal guanidiniums. The conjugate was found to internalize into live cells and readily cross the BBB to enter the mouse brain. Our novel synthetic flurbiprofen conjugate will hopefully delivery NSAIDs into brain, and is therefore applicable to the neurodegenerative diseases treatment or prevention.

Keywords: flurbiprofen, drug delivery, molecular carrier, organic synthesis

Procedia PDF Downloads 231