Search results for: drug prediction

Authors: André Furtado, Rafael Mineiro, Isabel Gonçalves, Cecília Santos, Telma Quintela

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The development of therapies for central nervous system (CNS) disorders is one of the biggest challenges of current pharmacology, given the unique features of brain barriers, which limit drug delivery. Efflux transporters (ABC transporters) expressed at the blood-cerebrospinal fluid barrier (BCSFB), are the main obstacles for the delivery of therapeutic compounds into the CNS, compromising the effective treatment of brain cancer, brain metastasis from peripheral cancers, or even neurodegenerative disorders. It is thus extremely important to understand the regulation of these transporters for reducing their expression while treating a brain disorder or choosing the most appropriate conditions for drug administration. Based on the fact that the BCSFB have fine-tuned biological rhythms, studying the circadian variation of drug transport processes is critical for choosing the most appropriate time of the day for drug administration. In our study, using an in vitro model of the BCSFB, we characterized the circadian transport profile of methotrexate (MTX) and donepezil (DNPZ), two drugs involved in the treatment of cancer and Alzheimer’s Disease symptoms, respectively. We found that MTX is transported across the basal and apical membranes of the BCSFB in a circadian way. The circadian pattern of an ABC transporter, Abcc4, might be partially responsible for MTX circadian transport. Furthermore, regarding the DNPZ transport study, we observed that the regulation of Abcg2 expression by the circadian rhythm will impact the circadian-dependent transport of DNPZ across the BCSFB. Overall, our results will contribute to the current knowledge on brain pharmacoresistance at the BCSFB by disclosing how circadian rhythms control drug delivery to the brain, setting the grounds for a potential application of chronotherapy to brain diseases to enhance the efficacy of medications and minimize their side effects.

Keywords: blood-cerebrospinal fluid barrier, ABC transporters, drug transport, chronotherapy

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Authors: Fatma M. Benrabha

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The aim of the study was to determine the type and pattern of antibiotic susceptibility of the pathogenic microorganisms causing chronic suppurative otitis media (CSOM), which could lead to better therapeutic decisions and consequently avoidance of appearance of resistance to specific antibiotics. Most frequently isolated agents were Pseudomonas aeruginosa 28.5%; followed by Staphylococcus aureus 18.2%; proteus mirabilis 13.9%; Providencia stuartti 6.7%; Bacteroides melaninogenicus, Aspergillus sp., candida sp., 4.2% each; and other microorganisms were represented in 3-0.2%. Drug sensitivities pattern of Pseudomonas aeruginosa showed that ciprofloxacin was active against the majority of isolates (93.9%) followed by ceftazidime 86.2%, amikacin 76.2% and gentamicin 40.8%. However, Staphylococcus aureus isolates were resistant to penicillin 72.7%, erythromycin 28.6%, cephalothin 18.2%, cloxacillin 8.3% and ciprofloxacin was active against 96.2% of isolates. The resistance pattern of proteus mirabilis were 55.6% to ampicillin, 47.1% to carbencillin, 29.4% to cephalothin, 14.3% to gentamicin and 4.8% to amikacin while 100% were sensitive to ciprofloxacin. We conclude that ciprofloxacin is the best drug of choice in treatment of CSOM caused by the common microorganisms.

Keywords: otitis media, chronic suppurative otitis media (CSOM), microorganism, drug sensitivity

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Authors: Reena K., Mamatha H. G., Somshekarayya, P. Kumar

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Objectives: The annual proficiency testing of intermediate reference laboratories is conducted by the National Reference Laboratory (NRL) to assess the efficiency of the laboratories to correctly identify Mycobacterium tuberculosis and to determine its drug susceptibility pattern. The proficiency testing results from 2013 to 2017 were analyzed to determine laboratories that were consistent in reporting quality results and those that had difficulty in doing so. Methods: A panel of twenty cultures were sent out to each of these laboratories. The laboratories were expected to grow the cultures in their own laboratories, set up drug susceptibly testing by all the methods they were certified for and report the results within the stipulated time period. The turnaround time for reporting results, specificity, sensitivity positive and negative predictive values and efficiency of the laboratory in identifying the cultures were analyzed. Results: Most of the laboratories had reported their results within the stipulated time period. However, there was enormous delay in reporting results from few of the laboratories. This was mainly due to improper functioning of the biosafety level III laboratory. Only 40% of the laboratories had 100% efficiency in solid culture using Lowenstein Jensen medium. This was expected as a solid culture, and drug susceptibility testing is not used for diagnosing drug resistance. Rapid molecular methods such as Line probe assay and Genexpert are used to determine drug resistance. Automated liquid culture system such as the Mycobacterial growth indicator tube is used to determine prognosis of the patient while on treatment. It was observed that 90% of the laboratories had achieved 100% in the liquid culture method. Almost all laboratories had achieved 100% efficiency in the line probe assay method which is the method of choice for determining drug-resistant tuberculosis. Conclusion: Since the liquid culture and line probe assay technologies are routinely used for the detection of drug-resistant tuberculosis the laboratories exhibited higher level of efficiency as compared to solid culture and drug susceptibility testing which are rarely used. The infrastructure of the laboratory should be maintained properly so that samples can be processed safely and results could be declared on time.

Keywords: annual proficiency testing, drug susceptibility testing, intermediate reference laboratory, national reference laboratory

Procedia PDF Downloads 181

Authors: Li Kewen, Su Zhaoxin, Wang Xingmou, Zhu Jian Bing

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Conventional reservoir prediction methods ar not sufficient to explore the implicit relation between seismic attributes, and thus data utilization is low. In order to improve the predictive classification accuracy of reservoir lithology, this paper proposes a deep learning lithology prediction method based on ResNet (Residual Neural Network) and SENet (Squeeze-and-Excitation Neural Network). The neural network model is built and trained by using seismic attribute data and lithology data of Shengli oilfield, and the nonlinear mapping relationship between seismic attribute and lithology marker is established. The experimental results show that this method can significantly improve the classification effect of reservoir lithology, and the classification accuracy is close to 70%. This study can effectively predict the lithology of undrilled area and provide support for exploration and development.

Keywords: convolutional neural network, lithology, prediction of reservoir, seismic attributes

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Authors: Trupti Diwan

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Predicting student failure at school has changed into a difficult challenge due to both the large number of factors that can affect the reduced performance of students and the imbalanced nature of these kinds of data sets. This paper surveys the two elements needed to make prediction on Students’ Academic Performances which are parameters and methods. This paper also proposes a framework for predicting the performance of engineering students. Genetic programming can be used to predict student failure/success. Ranking algorithm is used to rank students according to their credit points. The framework can be used as a basis for the system implementation & prediction of students’ Academic Performance in Higher Learning Institute.

Keywords: classification, educational data mining, student failure, grammar-based genetic programming

Procedia PDF Downloads 422

Authors: Alaa Hamed Salama, Rehab Nabil Shamma

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Introduction: Polymeric micelles that can deliver drug to intended sites of the eye have attracted much scientific attention recently. The aim of this study was to review the aqueous-based formulation of drug-loaded polymeric micelles that hold significant promise for ophthalmic drug delivery. This study investigated the synergistic performance of mixed polymeric micelles made of linear and branched poly (ethylene oxide)-poly (propylene oxide) for the more effective encapsulation of Lornoxicam (LX) as a hydrophobic model drug. Methods: The co-micellization process of 10% binary systems combining different weight ratios of the highly hydrophilic poloxamers; Synperonic® PE/P84, and Synperonic® PE/F127 and the hydrophobic poloxamine counterpart (Tetronic® T701) was investigated by means of photon correlation spectroscopy and cloud point. The drug-loaded micelles were tested for their solubilizing capacity towards LX. Results: Results showed a sharp solubility increase from 0.46 mg/ml up to more than 4.34 mg/ml, representing about 136-fold increase. Optimized formulation was selected to achieve maximum drug solubilizing power and clarity with lowest possible particle size. The optimized formulation was characterized by 1HNMR analysis which revealed complete encapsulation of the drug within the micelles. Further investigations by histopathological and confocal laser studies revealed the non-irritant nature and good corneal penetrating power of the proposed nano-formulation. Conclusion: LX-loaded polymeric nanomicellar formulation was fabricated allowing easy application of the drug in the form of clear eye drops that do not cause blurred vision or discomfort, thus achieving high patient compliance.

Keywords: confocal laser scanning microscopy, Histopathological studies, Lornoxicam, micellar solubilization

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Authors: Smail Tigani, Mohamed Ouzzif

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This work presents a discrete quantitative state prediction algorithm with intelligent behavior making it able to self-improve some performance aspects. The specificity of this algorithm is the capacity of self-rectification of the prediction strategy before the final decision. The auto-rectification mechanism is based on two parallel mathematical models. In one hand, the algorithm predicts the next state based on event transition matrix updated after each observation. In the other hand, the algorithm extracts its residues trend with a linear regression representing historical residues data-points in order to rectify the first decision if needs. For a normal distribution, the interactivity between the two models allows the algorithm to self-optimize its performance and then make better prediction. Designed key performance indicator, computed during a Monte Carlo simulation, shows the advantages of the proposed approach compared with traditional one.

Keywords: discrete state, Markov Chains, linear regression, auto-adaptive systems, decision making, Monte Carlo Simulation

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Authors: Seyed Esmail Seyedi Bariran, Khairul Salleh Mohamed Sahari

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In multi-echelon supply chain networks, optimal supplier selection significantly depends on the accuracy of suppliers’ performance prediction. Different methods of multi criteria decision making such as ANN, GA, Fuzzy, AHP, etc have been previously used to predict the supplier performance but the “black-box” characteristic of these methods is yet a major concern to be resolved. Therefore, the primary objective in this paper is to implement an artificial intelligence-based gene expression programming (GEP) model to compare the prediction accuracy with that of ANN. A full factorial design with %95 confidence interval is initially applied to determine the appropriate set of criteria for supplier performance evaluation. A test-train approach is then utilized for the ANN and GEP exclusively. The training results are used to find the optimal network architecture and the testing data will determine the prediction accuracy of each method based on measures of root mean square error (RMSE) and correlation coefficient (R2). The results of a case study conducted in Supplying Automotive Parts Co. (SAPCO) with more than 100 local and foreign supply chain members revealed that, in comparison with ANN, gene expression programming has a significant preference in predicting supplier performance by referring to the respective RMSE and R-squared values. Moreover, using GEP, a mathematical function was also derived to solve the issue of ANN black-box structure in modeling the performance prediction.

Keywords: Supplier Performance Prediction, ANN, GEP, Automotive, SAPCO

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Authors: Hervice Roméo Fogno Fotsoa, Germaine Djuidje Kenmoe, Claude Vidal Aloyem Kazé

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One of the main applications of machine learning is the prediction of time series. But a more accurate prediction requires a more optimal model of machine learning. Several optimization techniques have been developed, but without considering the input variables disposition of the system. Thus, this work aims to present a new machine learning architecture optimization technique based on their optimal input variables disposition. The validations are done on the prediction of wind time series, using data collected in Cameroon. The number of possible dispositions with four input variables is determined, i.e., twenty-four. Each of the dispositions is used to perform the prediction, with the main criteria being the training and prediction performances. The results obtained from a static architecture and a dynamic architecture of neural networks have shown that these performances are a function of the input variable's disposition, and this is in a different way from the architectures. This analysis revealed that it is necessary to take into account the input variable's disposition for the development of a more optimal neural network model. Thus, a new neural network training algorithm is proposed by introducing the search for the optimal input variables disposition in the traditional back-propagation algorithm. The results of the application of this new optimization approach on the two single neural network architectures are compared with the previously obtained results step by step. Moreover, this proposed approach is validated in a collaborative optimization method with a single objective optimization technique, i.e., genetic algorithm back-propagation neural networks. From these comparisons, it is concluded that each proposed model outperforms its traditional model in terms of training and prediction performance of time series. Thus the proposed optimization approach can be useful in improving the accuracy of time series forecasts. This proves that the proposed optimization approach can be useful in improving the accuracy of time series prediction based on machine learning.

Keywords: input variable disposition, machine learning, optimization, performance, time series prediction

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Authors: Belkacem Selma, Boumediene Selma, Tourkia Guerzou, Abbes Labdelli

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Natural disasters have occurred and will continue to cause human and material damage. Therefore, the idea of "preventing" natural disasters will never be possible. However, their prediction is possible with the advancement of technology. Even if natural disasters are effectively inevitable, their consequences may be partly controlled. The rapid growth and progress of artificial intelligence (AI) had a major impact on the prediction of natural disasters and risk assessment which are necessary for effective disaster reduction. The Earthquakes prediction to prevent the loss of human lives and even property damage is an important factor; that is why it is crucial to develop techniques for predicting this natural disaster. This present study aims to analyze the ability of artificial neural networks (ANNs) to predict earthquakes that occur in a given area. The used data describe the problem of high energy (higher than 10^4J) seismic bumps forecasting in a coal mine using two long walls as an example. For this purpose, seismic bumps data obtained from mines has been analyzed. The results obtained show that the ANN with high accuracy was able to predict earthquake parameters; the classification accuracy through neural networks is more than 94%, and that the models developed are efficient and robust and depend only weakly on the initial database.

Keywords: earthquake prediction, ANN, seismic bumps

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Authors: The Danh Phan

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House price forecasting is a main topic in the real estate market research. Effective house price prediction models could not only allow home buyers and real estate agents to make better data-driven decisions but may also be beneficial for the property policymaking process. This study investigates the housing market by using machine learning techniques to analyze real historical house sale transactions in Australia. It seeks useful models which could be deployed as an application for house buyers and sellers. Data analytics show a high discrepancy between the house price in the most expensive suburbs and the most affordable suburbs in the city of Melbourne. In addition, experiments demonstrate that the combination of Stepwise and Support Vector Machine (SVM), based on the Mean Squared Error (MSE) measurement, consistently outperforms other models in terms of prediction accuracy.

Keywords: house price prediction, regression trees, neural network, support vector machine, stepwise

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Authors: Yong Zhao, Jian He, Cheng Zhang

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Cardiovascular diseases caused by hypertension are extremely threatening to human health, and early diagnosis of hypertension can save a large number of lives. Traditional hypertension detection methods require special equipment and are difficult to detect continuous blood pressure changes. In this regard, this paper first analyzes the principle of heart rate variability (HRV) and introduces sliding window and power spectral density (PSD) to analyze the time domain features and frequency domain features of HRV, and secondly, designs an HRV-based hypertension prediction network by combining Resnet, attention mechanism, and multilayer perceptron, which extracts the frequency domain through the improved ResNet18 features through a modified ResNet18, its fusion with time-domain features through an attention mechanism, and the auxiliary prediction of hypertension through a multilayer perceptron. Finally, the network was trained and tested using the publicly available SHAREE dataset on PhysioNet, and the test results showed that this network achieved 92.06% prediction accuracy for hypertension and outperformed K Near Neighbor(KNN), Bayes, Logistic, and traditional Convolutional Neural Network(CNN) models in prediction performance.

Keywords: feature extraction, heart rate variability, hypertension, residual networks

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Authors: Leonardo Juan Ramírez López, Javier Oswaldo Rodriguez Velasquez

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We have designed a Holter that measures the heart´s activity for over 24 hours, implemented a prediction methodology, and generate alarms as well as indicators to patients and treating physicians. Various diagnostic advances have been developed in clinical cardiology thanks to Holter implementation; however, their interpretation has largely been conditioned to clinical analysis and measurements adjusted to diverse population characteristics, thus turning it into a subjective examination. This, however, requires vast population studies to be validated that, in turn, have not achieved the ultimate goal: mortality prediction. Given this context, our Insight Research Group developed a mathematical methodology that assesses cardiac dynamics through entropy and probability, creating a numerical and geometrical attractor which allows quantifying the normalcy of chronic and acute disease as well as the evolution between such states, and our Tigum Research Group developed a holter device with 12 channels and advanced computer software. This has been shown in different contexts with 100% sensitivity and specificity results.

Keywords: attractor , cardiac, entropy, holter, mathematical , prediction

Procedia PDF Downloads 169

Authors: Aminu Bashir Mohammad, Agwu Ezera, Abdulrazaq G. Habib, Garba Iliyasu

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Background: Drug resistance tuberculosis (DR-TB) continues to be a challenge in developing countries with poor resources. Routine screening for primary DR-TB before commencing treatment is not done in public hospitals in Nigeria, even with the large body of evidence that shows a high prevalence of primary DR-TB. Data on drug resistance and its genetic determinant among follow up TB patients is lacking in Nigeria. Hence the aim of this study was to determine the prevalence and genetic determinant of drug resistance among follow up TB patients in a tertiary hospital in Nigeria. Methods: This was a cross-sectional laboratory-based study conducted on 384 sputum samples collected from consented follow-up tuberculosis patients. Standard microbiology methods (Zeil-Nielsen staining and microscopy) and PCR (Line Probe Assay)] were used to analyze the samples collected. Person’s Chi-square was used to analyze the data generated. Results: Out of three hundred and eighty-four (384) sputum samples analyzed for mycobacterium tuberculosis (MTB) and DR-TB twenty-five 25 (6.5%) were found to be AFB positive. These samples were subjected to PCR (Line Probe Assay) out of which 18(72%) tested positive for DR-TB. Mutations conferring resistance to rifampicin (rpo B) and isoniazid (katG, and or inhA) were detected in 12/18(66.7%) and 6/18(33.3%), respectively. Transmission dynamic of DR-TB was not significantly (p>0.05) dependent on demographic characteristics. Conclusion: There is a need to strengthened the laboratory capacity for diagnosis of TB and drug resistance testing and make these services available, affordable, and accessible to the patients who need them.

Keywords: drug resistance tuberculosis, genetic determinant, intensive phase, Nigeria

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Authors: Zaheer Ahmad, Afzal Shah

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pH responsive block copolymers consist of mPEG and glutamic acid units were syntheiszed in different formulations. The synthesized polymers were structurally investigated. Doxorubicin Hydrocholide (DOX-HCl) as a chemotherapy medication for the treatment of cancer was selected. DOX-HCl was loaded and their drug loading content and drug loading efficiency were determined. The nanocarriers were obtained in small size, well shaped and slightly negative surface charge. The release study was carried out both at pH 7.4 and 5.5 and it was revealed that the release was sustained and in controlled manner and there was no initial burst release. The in vitro release study was further carried out for different formulations with different glutamic acid moieties. Time dependent cell proliferation inhibition of the free drug and drug loaded nanoparticles against human breast cancer cell lines MCF-7 and Zr-75-30 was observed. Cellular uptakes and endocytosis were investigated by confocal laser scanning microscopy (CLSM) and flow cytometery. The biocompatibility, optimum size, shape and surface charge of the developed nanoparticles make the nanoparticles an efficient drug delivery carrier.

Keywords: doxorubicin, glutamic acid, cell proliferation inhibition, breast cancer cell

Procedia PDF Downloads 143

Authors: Junyu Chen, Peng Xu

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In the context of global efforts to enhance building energy efficiency, accurate thermal load forecasting is crucial for both device sizing and predictive control. Variable Refrigerant Flow (VRF) systems are widely used in buildings around the world, yet VRF zonal load prediction has received limited attention. Due to differences between VRF zones in building-level prediction methods, zone-level load forecasting could significantly enhance accuracy. Given that modern VRF systems generate high-quality data, this paper introduces transfer learning to leverage this data and further improve prediction performance. This framework also addresses the challenge of predicting load for building zones with no historical data, offering greater accuracy and usability compared to pure white-box models. The study first establishes an initial variable set of VRF zonal building loads and generates a foundational white-box database using EnergyPlus. Key variables for VRF zonal loads are identified using methods including SRRC, PRCC, and Random Forest. XGBoost and LSTM are employed to generate pre-trained black-box models based on the white-box database. Finally, real-world data is incorporated into the pre-trained model using transfer learning to enhance its performance in operational buildings. In this paper, zone-level load prediction was integrated with transfer learning, and a framework was proposed to improve the accuracy and applicability of VRF zonal load prediction.

Keywords: zonal load prediction, variable refrigerant flow (VRF) system, transfer learning, energyplus

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Authors: Mo-Se Lee, Cheol-Hwi Ahn, Kee-Young Kwahk, Hyunchul Ahn

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Over the past decade, deep learning has been in spotlight among various machine learning algorithms. In particular, CNN (Convolutional Neural Network), which is known as effective solution for recognizing and classifying images, has been popularly applied to classification and prediction problems in various fields. In this study, we try to apply CNN to stock market prediction, one of the most challenging tasks in the machine learning research. In specific, we propose to apply CNN as the binary classifier that predicts stock market direction (up or down) by using a graph as its input. That is, our proposal is to build a machine learning algorithm that mimics a person who looks at the graph and predicts whether the trend will go up or down. Our proposed model consists of four steps. In the first step, it divides the dataset into 5 days, 10 days, 15 days, and 20 days. And then, it creates graphs for each interval in step 2. In the next step, CNN classifiers are trained using the graphs generated in the previous step. In step 4, it optimizes the hyper parameters of the trained model by using the validation dataset. To validate our model, we will apply it to the prediction of KOSPI200 for 1,986 days in eight years (from 2009 to 2016). The experimental dataset will include 14 technical indicators such as CCI, Momentum, ROC and daily closing price of KOSPI200 of Korean stock market.

Keywords: convolutional neural network, deep learning, Korean stock market, stock market prediction

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Authors: Afroze Ibrahim Baqapuri, Ilya Trofimov

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Sponsored search is a multi-billion dollar industry and makes up a major source of revenue for search engines (SE). Click-through-rate (CTR) estimation plays a crucial role for ads selection, and greatly affects the SE revenue, advertiser traffic and user experience. We propose a novel architecture of solving CTR prediction problem by combining artificial neural networks (ANN) with decision trees. First, we compare ANN with respect to other popular machine learning models being used for this task. Then we go on to combine ANN with MatrixNet (proprietary implementation of boosted trees) and evaluate the performance of the system as a whole. The results show that our approach provides a significant improvement over existing models.

Keywords: neural networks, sponsored search, web advertisement, click prediction, click-through rate

Procedia PDF Downloads 572

Authors: Akram Khaleghei, Ghosheh Balagh, Viliam Makis

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In this paper, we investigate the residual life prediction problem for a partially observable system subject to two failure modes, namely a catastrophic failure and a failure due to the system degradation. The system is subject to condition monitoring and the degradation process is described by a hidden Markov model with unknown parameters. The parameter estimation procedure based on an EM algorithm is developed and the formulas for the conditional reliability function and the mean residual life are derived, illustrated by a numerical example.

Keywords: partially observable system, hidden Markov model, competing risks, residual life prediction

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Authors: Rajasekhar Reddy Poonuru, Anusha Parnem

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Purpose: To formulate proliposome powder of agomelatine, an antipsychotic drug, and to evaluate physicochemical, in vitro characters and effect of surface charge on ex vivo intestinal permeation. Methods: Film deposition technique was employed to develop proliposomal powders of agomelatin with varying molar ratios of lipid Hydro Soy PC L-α-phosphatidylcholine (HSPC) and cholesterol with fixed sum of drug. With the aim to derive free flowing and stable proliposome powder, fluid retention potential of various carriers was examined. Liposome formation and number of vesicles formed for per mm3 up on hydration, vesicle size, and entrapment efficiency was assessed to deduce an optimized formulation. Sodium cholate added to optimized formulation to induce surface charge on formed vesicles. Solid-state characterization (FTIR, DSC, and XRD) was performed with the intention to assess native crystalline and chemical behavior of drug. The in vitro dissolution test of optimized formulation along with pure drug was evaluated to estimate dissolution efficiency (DE) and relative dissolution rate (RDR). Effective permeability co-efficient (Peff(rat)) in rat and enhancement ratio (ER) of drug from formulation and pure drug dispersion were calculated from ex vivo permeation studies in rat ileum. Results: Proliposomal powder formulated with equimolar ratio of HSPC and cholesterol ensued in higher no. of vesicles (3.95) with 90% drug entrapment up on hydration. Neusilin UFL2 was elected as carrier because of its high fluid retention potential (4.5) and good flow properties. Proliposome powder exhibited augmentation in DE (60.3 ±3.34) and RDR (21.2±01.02) of agomelation over pure drug. Solid state characterization studies demonstrated the transformation of native crystalline form of drug to amorphous and/or molecular state, which was in correlation with results obtained from in vitro dissolution test. The elevated Peff(rat) of 46.5×10-4 cm/sec and ER of 2.65 of drug from charge induced proliposome formulation with respect to pure drug dispersion was assessed from ex vivo intestinal permeation studies executed in ileum of wistar rats. Conclusion: Improved physicochemical characters and ex vivo intestinal permeation of drug from charge induced proliposome powder with Neusilin UFL2 unravels the potentiality of this system in enhancing oral delivery of agomelatin.

Keywords: agomelatin, proliposome, sodium cholate, neusilin

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Authors: Mohamad Reza Khodabakhsh

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Drug use and addiction are major biological, psychological, and social problems. In drug abuse treatment, it is important to pay attention to personality problems and coping methods of patients. Today, the third-wave treatments in psychotherapy emphasize people's awareness and acceptance of feelings and emotions, cognitions, and behaviors instead of challenging cognitions. For this reason, this research was conducted with the aim of investigating the effectiveness of group therapy based on acceptance and commitment to self-criticism and coping strategies of people with drug use disorder. This research was a quasi-experimental type of research (pre-test-post-test design with an unequal control group), and the statistical population of this research included all men with drug use disorder in Mashhad, 174 of whom among the 75 people eligible for this research, 30 of them were selected by available sampling method and randomly assigned to two experimental and control groups. In this research, Gilbert's self-criticism scale was used to measure self-criticism, and Andler and Barker's coping strategies questionnaire was used to measure coping strategies. Therapeutic intervention (treatment based on acceptance and commitment) was performed on the experimental group for eight sessions of 90 minutes, and then post-tests were taken from both groups, and multivariate analysis of covariance (MANCOVA) was used to analyze the data. The results showed that treatment based on acceptance and commitment significantly reduced self-criticism and improved coping strategies used by patients with drug use disorder (p>0.01). Therefore, treatment based on acceptance and commitment has been effective in reducing self-criticism and improving the coping strategies of patients with drug use disorder due to teaching clients to accept thoughts and conditions.

Keywords: treatment based on acceptance and commitment, self-criticism, coping strategies, addiction

Procedia PDF Downloads 88

Authors: Peter Edwar Mortada Nasif

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The use of CAD (Computer Aided Design) technology is ubiquitous in the architecture, engineering and construction (AEC) industry. This has led to its inclusion in the curriculum of architecture schools in Nigeria as an important part of the training module. This article examines the ethical issues involved in implementing CAD (Computer Aided Design) content into the architectural education curriculum. Using existing literature, this study begins with the benefits of integrating CAD into architectural education and the responsibilities of different stakeholders in the implementation process. It also examines issues related to the negative use of information technology and the perceived negative impact of CAD use on design creativity. Using a survey method, data from the architecture department of Chukwuemeka Odumegwu Ojukwu Uli University was collected to serve as a case study on how the issues raised were being addressed. The article draws conclusions on what ensures successful ethical implementation. Millions of people around the world suffer from hepatitis C, one of the world's deadliest diseases. Interferon (IFN) is treatment options for patients with hepatitis C, but these treatments have their side effects. Our research focused on developing an oral small molecule drug that targets hepatitis C virus (HCV) proteins and has fewer side effects. Our current study aims to develop a drug based on a small molecule antiviral drug specific for the hepatitis C virus (HCV). Drug development using laboratory experiments is not only expensive, but also time-consuming to conduct these experiments. Instead, in this in silicon study, we used computational techniques to propose a specific antiviral drug for the protein domains of found in the hepatitis C virus. This study used homology modeling and abs initio modeling to generate the 3D structure of the proteins, then identifying pockets in the proteins. Acceptable lagans for pocket drugs have been developed using the de novo drug design method. Pocket geometry is taken into account when designing ligands. Among the various lagans generated, a new specific for each of the HCV protein domains has been proposed.

Keywords: drug design, anti-viral drug, in-silicon drug design, hepatitis C virus, computer aided design, CAD education, education improvement, small-size contractor automatic pharmacy, PLC, control system, management system, communication

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Authors: Gayathri Vijayakumar, Preethi Baskaran

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The effluents that are let down by the industries mix with the water bodies and drastically affect the aquatic life, which leads to pollution and bio magnifications. Effluents mostly contain chemicals, heavy metals etc., and cause toxicity to the environment. The pharmaceutical industries too contribute. The by-products and other unwanted waste are discharged without any treatment; these causes DNA damage and affect behavior of aquatic life. The study was conducted on koi carp (Cyprinus carpio) the ornamental variety of common carp. A two week long study was conducted on them using common anti-depressant drug (Diazepam) in various concentrations. These drugs are known to cause behavioral damage and organ malfunctions (muscle twitch). The histopathological study conducted showed permanent muscle twitching and lesions in the fish samples studied. The sociability was also affected in the span of 14 days. Higher concentrations of this drug showed severe damage in the muscle structures. Thus, this drug can cause adverse effects on marine ecosystem and eventually cause bio magnification of drug by running through the food chain.

Keywords: pollution, toxicity, bio-magnifications, koi carp, muscle twitch, diazepam, histopathology

Procedia PDF Downloads 100

Authors: Seun Mayowa Sunday

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Loan prediction is one of the most significant and recognised fields of research in the banking, insurance, and the financial security industries. Some prediction systems on the market include the construction of static software. However, due to the fact that static software only operates with strictly regulated rules, they cannot aid customers beyond these limitations. Application of many machine learning (ML) techniques are required for loan prediction. Four separate machine learning models, random forest (RF), decision tree (DT), k-nearest neighbour (KNN), and logistic regression, are used to create the loan prediction model. Using the anaconda navigator and the required machine learning (ML) libraries, models are created and evaluated using the appropriate measuring metrics. From the finding, the random forest performs with the highest accuracy of 80.17% which was later implemented into the Django framework. For real-time testing, the web application is deployed on the Alibabacloud which is among the top 4 biggest cloud computing provider. Hence, to the best of our knowledge, this research will serve as the first academic paper which combines the model development and the Django framework, with the deployment into the Alibaba cloud computing application.

Keywords: k-nearest neighbor, random forest, logistic regression, decision tree, django, cloud computing, alibaba cloud

Procedia PDF Downloads 135

Authors: Nagat M. Saeed, Mabruka S. Elashheb, Fatma M. Ben Rabaha, Aisha M Edrah

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The aim of the study was to determine the type and pattern of antibiotic susceptibility of the pathogenic microorganisms causing chronic suppurative otitis media (CSOM), which could lead to better therapeutic decisions and consequently avoidance of appearance of resistance to specific antibiotics. Most frequently isolated agents were Pseudomonas aeruginosa 28.5%; followed by Staphylococcus aureus 18.2%; proteus mirabilis 13.9%; Providencia stuartti 6.7%; Bacteroides melaninogenicus, Aspergillus sp., candida sp., 4.2% each; and other microorganisms were represented in 3-0.2%. Drug sensitivities pattern of Pseudomonas aeruginosa showed that ciprofloxacin was active against the majority of isolates (93.9%) followed by ceftazidime 86.2%, amikacin 76.2% and gentamicin 40.8%. However, Staphylococcus aureus isolates were resistant to penicillin 72.7%, erythromycin 28.6%, cephalothin 18.2%, cloxacillin 8.3% and ciprofloxacin was active against 96.2% of isolates. The resistance pattern of proteus mirabilis was 55.6% to ampicillin, 47.1% to carbencillin, 29.4% to cephalothin, 14.3% to gentamicin and 4.8% to amikacin while 100% were sensitive to ciprofloxacin. We conclude that ciprofloxacin is the best drug of choice in the treatment of CSOM caused by the common microorganisms.

Keywords: otitis media, chronic suppurative otitis media (CSOM), microorganisms, drug sensitivity

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Authors: Arianna Zhu, Thomas Bakupog

Abstract:

Taxol (generic name paclitaxel) is an antineoplastic drug used to treat breast, lung, and ovarian cancer. It performs exceptionally well against a wide variety of tumors, including B16 melanoma, L1210 and P388 leukemias, MX-1 mammary tumors, and CX-1 colon tumor xenografts. However, despite taxol’s efficacy in antitumor activity, its aqueous solubility is extremely poor, decreasing its bioavailability and making it difficult for the body to absorb. The objective of this study is to improve the solubility of taxol, thus increasing the bioavailability of the drug in preventing cancer. By modifying the structure of taxol, four novel taxol derivatives were created with improved solubilities. Two of the derivatives were given an additional hydrogen donor and acceptor and thus showed a pronounced positive change in solubility. The results of this work solve the issue of taxol’s inadequate solubility and show potential in increasing the absorption of the drug.

Keywords: Taxol, Solubility, improving bioavailability, logP

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Authors: Paresh Patel, Harshit Patel

Abstract:

Superficial fungal skin infection is among the most common skin disease. The drug administration through skin has received attention due to several advantages: Avoidance of significant pre-systemic metabolism, drug levels within the therapeutic window, drugs with short biological half-lives, decreased side effects, the non-invasive character, and very high acceptance.

Keywords: transdermal spray, ketoconazole, Eudragit® RLPO, therapeutic window

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Authors: Yuan Yang, Mickey Lam

Abstract:

Oral dissolvable films have been considered as a unique alternative approach to conventional oral dosage forms. The films could be administrated via the gastrointestinal tract as conventional dosages or through sublingual/buccal mucosa membranes, which could enhance drug bioavailability by avoiding the first-pass effect and improving permeability due to high blood flow and lymphatic circulation. This work has described a state-of-art technic using nano-emulsion/nano-suspension as a precursor for the film to enhance the bioavailability of BCS class II drugs. The drug molecules are consequentially processed through the emulsification, gelation, and film-casting processes. The gelation process is critical to stabilizing the nano-emulsion for the film-casting as well as controlling the drug release process. Furthermore, the size of the nanoparticle on the film has a strong correlation with the size of the micelles in the precursor and the condition of the gelation process. It has been discovered that nanoparticle from 200 nm to 300 nm has shown the highest permeability for sublingual administration. In one example shown in work, the bioavailability of a low solubilize drug has been increased from 10% to 24% via sublingual administration of the film. The increasing of the bioavailability was thought to be associated with the enhancement of the diffusion process of the drug in the saliva layer above the mucosa membrane and the fact that the presents of the emulsifier help lose the rigid junction of the mucosa cells.

Keywords: oral dissolvable film, nano-suspension, nano-emulsion, bioavailability

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Authors: Nadia A. Mohamed, Zakaria El-Khayat, Wagdy K. B. Khalil, Mehrez E. El-Naggar

Abstract:

Drug nephrotoxicity is still a problem for patients who have taken drugs for elongated periods or permanently. Ultrasound-assisted sol−gel method was used to prepare hollow structured poroussilica nanoemulsion loaded with sodium salicylate as a model drug. The work was extended to achieve the target of the current work via investigating the protective role of this nanoemulsion model as anti-inflammatory drug or ginger for its antioxidant effect against cisplatin-induced nephrotoxicity in male albino rats. The results clarify that the nanoemulsion model was synthesized using ultrasonic assisted with small size and well stabilization as proved by TEM and DLS analysis. Additionally, blood urea nitrogen (BUN), Serum creatinine (SC) and Urinary total protein (UTP) were increased, and the level of creatinine clearance (Crcl) was decreased. All those were met with disorders in oxidative stress and downregulation in the expression of the nephrin gene. Also, histopathological changes of the kidney tissue were observed. These changes back to normal by treatment with silica nanoparticles loaded sodium salicylate (Si-Sc-NPs), ginger or both. Conclusions oil/water nanoemulsion of (Si-Sc NPs) and ginger showed a protective and promising preventive strategy against nephrotoxicity due to their antioxidant and anti-inflammatory effects, and that offers a new approach in attenuating drug induced nephrotoxicity.

Keywords: sodium salicylate nanoencapsulation, nephrin mRNA, drug nephrotoxicity, cisplatin, experimental rats

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Authors: Rui Sang, Fei Deng, Alexander Engel, Ewa M. Goldys, Wei Deng

Abstract:

In this study, we brought together X-ray induced photodynamic therapy (X-PDT) and chemo-drug (5-FU) for the treatment on colorectal cancer cells. This was achieved by developing a lipid-polymer hybrid nanoparticle delivery system (FA-LPNPs-VP-5-FU). It was prepared by incorporating a photosensitizer (verteporfin), chemotherapy drug (5-FU), and a targeting moiety (folic acid) into one platform. The average size of these nanoparticles was around 100 nm with low polydispersity. When exposed to clinical doses of 4 Gy X-ray radiation, FA-LPNPs-VP-5-FU generated sufficient amounts of reactive oxygen species, triggering the apoptosis and necrosis pathway of cancer cells. Our combined X-PDT and chemo-drug strategy was effective in inhibiting cancer cells’ growth and proliferation. Cell cycle analyses revealed that our treatment induced G2/M and S phase arrest in HCT116 cells. Our results indicate that this combined treatment provides better antitumour effect in colorectal cancer cells than each of these modalities alone. This may offer a novel approach for effective colorectal cancer treatment with reduced off-target effect and drug toxicity.

Keywords: pdt, targeted lipid-polymer nanoparticles, verteporfin, colorectal cancer

Procedia PDF Downloads 76