Search results for: drug prediction

Authors: Azwin Lubis, Rika Hapsari, Zahrah Hikmah, Anang Endaryanto, Ariyanto Harsono

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Erythema Multiforme Exudativum Major (EMEM) is one of the drug allergy diseases. Drug allergies caused by isoniazid rarely causes EMEM. Cutaneous reactions caused by isoniazid were obtained in 0.98% of patients, but the precise occurrence of Steven Johnson’s Syndrome (SJS) and Toxic Epidermolisis Necrolisis (TEN) due to isoniazid is not known for certain. We present this case to show hypersensitivity of isoniazid in a child. Based on the history of drug intake, physical diagnostic tests, drug elimination and provocation; we established the diagnosis of isoniazid hypersensitivity. The child showed improvement on skin manifestation after stopped isoniazid therapy.

Keywords: erythema multiforme exudativum major, hypersensitivity, elimination test, provocation test

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Authors: Nitin Dwivedi, Jigna Shah

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Aim and objective of study: This article indicates a comparison among various generations of dendrimers, a dendrimer is a bioactive material has repetitively branched molecule and used for delivery of various therapeutic active agents. This debut report compares the effect various generations of PPI dendrimers for brain targeting and management of neurodegenerative disorders potential on single platform. This report involves the study of the various mechanism of synthesis ligand anchored various generations PPI dendrimers deliver the drug directly to the CNS, prove their effectiveness in the management of the various neurodegenerative disease. Material and Methods: The Memantine an anti-Alzheimer drug loaded in different generations (3.0G, 4.0G, and 5.0G) of PPI dendrimers which were synthesized were synthesized. The various studies investigate the effect of PPI dendrimers generation on different characteristic parameters i.e. synthesis procedure, drug loading, release behavior, hemolysis profile at different concentration, MRI study for determine the route drug from olfactory transfer, animal model study in vitro, as well as in vivo performance. The outcomes of the investigation indicate drug delivery benefit as well as superior biocompatibility of 4.0G PPI dendrimer over 3.0G and 5.0G dendrimer, respectively. Results and Conclusion: The above study indicate the superiority of in drug delivery system with maximum drug utilization and minimize the drug dose for neurodegenerative disorder over 5.0G PPI dendrimers. So, 4.0G PPI dendrimers are the safe formulations for the symptomatic treatment of the neurodegenerative disorder. The fifth-generation poly(propyleneimine) (PPI) dendrimers, inherent toxicity due to the presence of many peripheral cationic groups is the major issue that limits their applicability.

Keywords: Alzheimer disease, generation, memantine, PPI

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Authors: Chukwudalu C. Nwazojie, Wole W. Soboyejo, John Obayemi, Ali Salifu Azeko, Sandra M. Jusu, Chinyerem M. Onyekanne

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The conventional methods for the diagnosis and treatment of breast cancer include bulk systematic mammography, ultrasound, dynamic contrast-enhanced fast 3D gradient-echo (GRE) magnetic resonance imaging (MRI), surgery, chemotherapy, and radiotherapy. However, nanoparticles and drug-loaded polymer microspheres for disease (cancer) targeting and treatment have enormous potential to enhance the approaches that are used today. The goal is to produce an implantable biomedical device for localized breast cancer drug delivery within Africa and the world. The main advantage of localized delivery is that it reduces the amount of drug that is needed to have a therapeutic effect. Polymer blends of poly (D,L-lactide-co-glycolide) (PLGA) and polycaprolactone (PCL), which are biodegradable, is used as a drug excipient. This work focuses on the development of PLGA-PCL (poly (D,L-lactide-co-glycolide) (PLGA) blended with based injectable drug microspheres and are loaded with anticancer drugs (prodigiosin (PG), and paclitaxel (PTX) control) and also the conjugated forms of the drug functionalized with LHRH (luteinizing hormone-releasing hormone) (PG-LHRH, and PTX- LHRH control), using a single-emulsion solvent evaporation technique. The encapsulation was done in the presence of PLGA-PCL (as a polymer matrix) and poly-(vinyl alcohol) (PVA) (as an emulsifier). Comparative study of the various drugs release kinetics and degradation mechanisms of the PLGA-PCL with an encapsulated drug is achieved, and the implication of this study is for the potential application of prodigiosin PLGA-PCL loaded microparticles for controlled delivery of cancer drug and treatment to prevent the regrowth or locoregional recurrence, following surgical resection of triple-negative breast tumor.

Keywords: cancer, polymers, drug kinetics, nanoparticles

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Authors: Shilpa Bhilegaonkar, Ram Gaud

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Candesartan cilexetil is a poorly soluble antihypertensive agent with solubility limited bioavailability (15%). To initiate process of solubilisation, it is very much necessary to displace the air at the surface and wet the drug surface with a solvent, with which drug is compatible. Present research adopts the same principle to improve solubility and dissolution of candesartan cilexetil. Solvents used here are surfactant and modified surfactant in different drug: solvent (1:1-1:9) ratio’s for preparation of adsorbates. Adsorbates were then converted into free flowing powders as liquisolid compacts and compressed to form tablets. Liquisolid compacts were evaluated for improvement in saturation solubility and dissolution of candesartan cilexetil. All systems were evaluated for improvement in saturation solubility and dissolution in different medias such as water, 0.1 N HCl, Phosphate buffer pH 6.8 and media given by office of generic drugs along with other physicochemical testing. All systems exhibited a promising advantage in terms of solubility and dissolution without affecting the drug structure as confirmed by IR and XRD. No considerable advantage was seen of increasing solvent ratio with drug.

Keywords: candesartan cilexetil, improved dissolution, solubility, liquisolid

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Authors: P. S. Jagadeesh Kumar, S. Meenakshi Sundaram, Wenli Hu, Yang Yung

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In 3D video games, the dominance of production is unceasingly growing with a protruding level of affordability in terms of budget. Afterward, the automation of speech-lip synchronization technique is customarily onerous and has advanced a critical research subject in virtual reality based 3D video games. This paper presents one of these automatic tools, precisely riveted on the synchronization of the speech and the lip movement of the game characters. A robust and precise speech recognition segment that systematized with Algebraic Code Excited Linear Prediction method is developed which unconventionally delivers lip sync results. The Algebraic Code Excited Linear Prediction algorithm is constructed on that used in code-excited linear prediction, but Algebraic Code Excited Linear Prediction codebooks have an explicit algebraic structure levied upon them. This affords a quicker substitute to the software enactments of lip sync algorithms and thus advances the superiority of service factors abridged production cost.

Keywords: algebraic code excited linear prediction, speech-lip synchronization, video games, virtual reality

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Authors: Patcharakamon Nooeaid, Piyachat Chuysrinuan

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Electrospun natural polymers-based nanofibers are one of the most interesting materials used in tissue engineering and drug delivery applications. Bead-on-string nanofibers have gained considerable interest for sustained drug release. Vancomycin was used as the model drug and sodium alginate (SA)/soy protein isolated (SPI) as the polymer blend to fabricate the bead-on-string nanofibers by aqueous-based electrospinning. The bead-on-string SA/SPI nanofibers were successfully fabricated by the addition of poly(ethylene oxide) (PEO) as a co-blending polymer. SA-PEO with mass ratio of 70/30 showed the best spinnability with continuous nanofibers without the occurrence of beads. Bead structure formed with the addition of SPI and bead number increased with increasing SPI content. The electrospinning of 80/20 SA-PEO/SPI was obtained as a great promising bead-on-string nanofibers for drug loading, while the solution of 50/50 was not able to obtain continuous fibers. In vitro release tests showed that a more sustainable release profile up to 14 days with less initial burst release on day 1 could be obtained from the bead-on-string fibers than from smooth fibers with uniform diameter. In addition, vancomycin-loaded beaded fibers inhibited the growth of Staphylococcus aureus (S. aureus) bacteria. Therefore, the SA-PEO/SPI nanofibers showed the potential to be used as biomaterials for tissue engineering and drug delivery.

Keywords: bead-on-string fibers, electrospinning, drug delivery, tissue engineering

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Authors: Pradeep Singh, Shrish Verma

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Software fault prediction models are created by using the source code, processed metrics from the same or previous version of code and related fault data. Some company do not store and keep track of all artifacts which are required for software fault prediction. To construct fault prediction model for such company, the training data from the other projects can be one potential solution. The earlier we predict the fault the less cost it requires to correct. The training data consists of metrics data and related fault data at function/module level. This paper investigates fault predictions at early stage using the cross-project data focusing on the design metrics. In this study, empirical analysis is carried out to validate design metrics for cross project fault prediction. The machine learning techniques used for evaluation is Naïve Bayes. The design phase metrics of other projects can be used as initial guideline for the projects where no previous fault data is available. We analyze seven data sets from NASA Metrics Data Program which offer design as well as code metrics. Overall, the results of cross project is comparable to the within company data learning.

Keywords: software metrics, fault prediction, cross project, within project.

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Authors: J. Ticona Chambi, E. A. De Almeida, C. A. Andrade Raymundo Gaiotto, A. M. Do Espírito Santo, L. Infantes, S. L. Cuffini

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The solubility of active pharmaceutical ingredients (APIs) is challenging for the pharmaceutical industry. The new multicomponent crystalline forms as cocrystal and solvates present an opportunity to improve the solubility of APIs. Commonly, the procedure to obtain multicomponent crystalline forms of a drug starts by screening the drug molecule with the different coformers/solvents. However, it is necessary to develop methods to obtain multicomponent forms in an efficient way and with the least possible environmental impact. The Hansen Solubility Parameters (HSPs) is considered a tool to obtain theoretical knowledge of the solubility of the target compound in the chosen solvent. H-Bond Propensity (HBP), Molecular Complementarity (MC), Coordination Values (CV) are tools used for statistical prediction of cocrystals developed by the Cambridge Crystallographic Data Center (CCDC). The HSPs and the CCDC tools are based on inter- and intra-molecular interactions. The curcumin (Cur), target molecule, is commonly used as an anti‐inflammatory. The demethoxycurcumin (Demcur) and bisdemethoxycurcumin (Bisdcur) are natural analogues of Cur from turmeric. Those target molecules have differences in their solubilities. In this way, the work aimed to analyze and compare different tools for multicomponent forms prediction (solvates) of Cur, Demcur and Biscur. The HSP values were calculated for Cur, Demcur, and Biscur using the chemical group contribution methods and the statistical optimization from experimental data. The HSPmol software was used. From the HSPs of the target molecules and fifty solvents (listed in the HSP books), the relative energy difference (RED) was determined. The probability of the target molecules would be interacting with the solvent molecule was determined using the CCDC tools. A dataset of fifty molecules of different organic solvents was ranked for each prediction method and by a consensus ranking of different combinations: HSP, CV, HBP and MC values. Based on the prediction, 15 solvents were selected as Dimethyl Sulfoxide (DMSO), Tetrahydrofuran (THF), Acetonitrile (ACN), 1,4-Dioxane (DOX) and others. In a starting analysis, the slow evaporation technique from 50°C at room temperature and 4°C was used to obtain solvates. The single crystals were collected by using a Bruker D8 Venture diffractometer, detector Photon100. The data processing and crystal structure determination were performed using APEX3 and Olex2-1.5 software. According to the results, the HSPs (theoretical and optimized) and the Hansen solubility sphere for Cur, Demcur and Biscur were obtained. With respect to prediction analyses, a way to evaluate the predicting method was through the ranking and the consensus ranking position of solvates already reported in the literature. It was observed that the combination of HSP-CV obtained the best results when compared to the other methods. Furthermore, as a result of solvent selected, six new solvates, Cur-DOX, Cur-DMSO, Bicur-DOX, Bircur-THF, Demcur-DOX, Demcur-ACN and a new Biscur hydrate, were obtained. Crystal structures were determined for Cur-DOX, Biscur-DOX, Demcur-DOX and Bicur-Water. Moreover, the unit-cell parameter information for Cur-DMSO, Biscur-THF and Demcur-ACN were obtained. The preliminary results showed that the prediction method is showing a promising strategy to evaluate the possibility of forming multicomponent. It is currently working on obtaining multicomponent single crystals.

Keywords: curcumin, HSPs, prediction, solvates, solubility

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Authors: Indu Barwal, Shloka Thakur, Subhash C. Yadav

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The plant virus capsid protein based nanoparticles are extensively studied for their application in biomedical research for development of nanomedicines and drug delivery systems. We have developed a chimeric drug delivery system by controlled in vitro assembly of separately bioconjugated fluorescent dye (as reporting molecule), folic acid (as receptor binding biomolecule for targeted delivery) and doxorubicin (as anticancer drug) using modified EDC NHS chemistry on heterologously overexpressed (E. coli) capsid proteins of cowpea chlorotic mottle virus (CCMV). This chimeric vehicle was further encapsidated on gold nanoparticles (20nm) coated with 5≠ thiolated DNA probe to neutralize the positive charge of capsid proteins. This facilitates the in vitro assembly of modified capsid subunits on the gold nanoparticles to develop chimeric GNPs encapsidated targeted drug delivery system. The bioconjugation of functionalities, number of functionality on capsid subunits as well as virus like nanoparticles, structural stability and in vitro assembly were confirmed by SDS PAGE, relative absorbance, MALDI TOF, ESI-MS, Circular dichroism, intrinsic tryptophan fluorescence, zeta particle size analyzer and TEM imaging. This vehicle was stable at pH 4.0 to 8.0 suitable for many organelles targeting. This in vitro assembled chimeric plant virus like particles could be suitable for ideal drug delivery vehicles for subcutaneous cancer treatment and could be further modified for other type of cancer treatment by conjugating other functionalities (targeting, drug) on capsids.

Keywords: chimeric drug delivery vehicles, bioconjugated plant, virus, capsid

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Authors: Mona Mahmoud Abou Samra, Alaa Hamed Salama, Ghada Awad, Soheir Said Mansy

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Proniosomes are well documented for topical drug delivery and preferred over other vesicular systems because they are biodegradable, biocompatible, non-toxic, possess skin penetration ability and prolong the release of drugs by acting as depot in deeper layers of skin. Proniosome drug delivery was preferred due to improved stability of the system than niosomes. The present investigation aimed at formulation development and performance evaluation of proniosomal gel as a vesicular drug carrier system for antifungal drug tolnaftate. Proniosomes was developed using different nonionic surfactants such as span 60 and span 65 with cholesterol in different molar ratios by the Coacervation phase separation method in presence or absence of either lecithin or phospholipon 80 H. Proniosomal gel formulations of tolnaftate were characterized for vesicular shape & size, entrapment efficiency, rheological properties and release study. The effect of surfactants and additives on the entrapment efficiency, particle size and percent of drug released was studied. The selected proniosomal formulations for topical delivery of tolnaftate was subjected to a microbiological study in male rats infected with Trichophyton rubrum; the main cause of Tinea Pedis compared to the free drug and a market product and the results was recorded.

Keywords: fungal infection, proniosome, tolnaftate, trichophyton rubrum

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Authors: Ghaleb A. Husseini, Salma E. Ahmed, Hesham G. Moussa, Ana M. Martins, Mohammad Al-Sayah, Nasser Qaddoumi

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This abstract aims to investigate the use of targeted liposomes as anticancer drug carriers in vitro in combination with ultrasound applied as drug trigger; in order to reduce the side effects caused by traditional chemotherapy. Pegylated liposomes were used to encapsulate calcein and then release this model drug when 20-kHz, 40-kHz, 1-MHz and 3-MHz ultrasound were applied at different acoustic power densities. Fluorescence techniques were then used to measure the percent drug release of calcein from these targeted liposomes. Results showed that as the power density increases, at the four frequencies studied, the release of calcein also increased. Based on these results, we believe that ultrasound can be used to increase the rate and amount of chemotherapeutics release from liposomes.

Keywords: liposomes, calcein release, high frequency ultrasound, low frequency ultrasound, fluorescence techniques

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Authors: Haozhe Xiang

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With the rise of the Internet of Things era, intelligent products are gradually integrating into people's lives. Pedestrian trajectory prediction has become a key issue, which is crucial for the motion path planning of intelligent agents such as autonomous vehicles, robots, and drones. In the current technological context, deep learning technology is becoming increasingly sophisticated and gradually replacing traditional models. The pedestrian trajectory prediction algorithm combining neural networks and attention mechanisms has significantly improved prediction accuracy. Based on in-depth research on deep learning and pedestrian trajectory prediction algorithms, this article focuses on physical environment modeling and learning of historical trajectory time dependence. At the same time, social interaction between pedestrians and scene interaction between pedestrians and the environment were handled. An improved pedestrian trajectory prediction algorithm is proposed by analyzing the existing model architecture. With the help of these improvements, acceptable predicted trajectories were successfully obtained. Experiments on public datasets have demonstrated the algorithm's effectiveness and achieved acceptable results.

Keywords: deep learning, graph convolutional network, attention mechanism, LSTM

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Authors: Somdutt Mujwar, Kamal Raj Pardasani

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Cholera pandemics are caused by facultative pathogenic Vibrio cholera bacteria persisting in the countries having warmer climatic conditions as well as the presence of large water bodies with huge amount of organic matter, it is responsible for the millions of deaths annually. Presently the available therapy for cholera is Oral Rehydration Therapy (ORT) with an antibiotic drug. Excessive utilization of life saving antibiotics drugs leads to the development of resistance by the infectious micro-organism against the antibiotic drugs resulting in loss of effectiveness of these drugs. Also, many side effects are also associated with the use of these antibiotic drugs. This riboswitch is explored as an alternative drug target for Vibrio cholera bacteria to overcome the problem of drug resistance as well as side effects associated with the antibiotics drugs. The bacterial riboswitch is virtually screened with 24407 legends to get possible drug candidates. The 10 ligands showing best binding with the riboswitch are selected to design a pharmacophore, which can be utilized to design lead molecules by using the phenomenon of bioisosterism.

Keywords: cholera, drug design, ligand, riboswitch, pharmacophore

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Authors: Dalin Si, Azizan Aziz, Bertrand Lasternas

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To encourage building owners to purchase electricity at the wholesale market and reduce building peak demand, this study aims to develop models that predict day-ahead hourly electricity consumption and demand using artificial neural network (ANN) and support vector machine (SVM). All prediction models are built in Python, with tool Scikit-learn and Pybrain. The input data for both consumption and demand prediction are time stamp, outdoor dry bulb temperature, relative humidity, air handling unit (AHU), supply air temperature and solar radiation. Solar radiation, which is unavailable a day-ahead, is predicted at first, and then this estimation is used as an input to predict consumption and demand. Models to predict consumption and demand are trained in both SVM and ANN, and depend on cooling or heating, weekdays or weekends. The results show that ANN is the better option for both consumption and demand prediction. It can achieve 15.50% to 20.03% coefficient of variance of root mean square error (CVRMSE) for consumption prediction and 22.89% to 32.42% CVRMSE for demand prediction, respectively. To conclude, the presented models have potential to help building owners to purchase electricity at the wholesale market, but they are not robust when used in demand response control.

Keywords: building energy prediction, data mining, demand response, electricity market

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Authors: Yong-Il Lee, Do-Yeon Hwang, Won-Seog Jeong, Duckshin Park

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Recently, because of the high-speed trains forced ventilation, it is important to control the ventilation. The ventilation is for controlling various contaminants, temperature, and humidity. The high-speed train route is straight to a destination having a high speed. And there are many mountainous areas in Korea. So, tunnel rate is higher then other country. KTX HVAC block off the outdoor air, when entering tunnel. So the high tunnel rate is an effect of ventilation in the KTX cabin. It is important to reduction rate in CO2 concentration prediction. To meet the air quality of the public transport vehicles recommend standards, the KTX cabin of CO2 concentration should be managed. In this study, the concentration change was predicted by CO2 prediction simulation in route to be opened.

Keywords: CO2 prediction, KTX, ventilation, infrastructure and transportation engineering

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Authors: Vano Tsertsvadze, Marina Chavchanidze, Lali Khurtsia

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Problem of drug use is often seen as a combination of psychological and social problems, but this problem can be considered as economically rational decision in the process of buying pleasure (looking after children, reading, harvesting fruits in the fall, sex, eating, etc.). Before the adoption of the decisions people face to a trade-off - when someone chooses a delicious meal, she takes a completely rational decision, that the pleasure of eating has a lot more value than the pleasure which she will experience after two months diet on the summer beach showing off her beautiful body. This argument is also true for alcohol, drugs and cigarettes. Smoking has a negative effect on health, but smokers are not afraid of the threat of a lung cancer after 40 years, more valuable moment is a pleasure from smoking. Our hypothesis - unsatisfied pleasure and frustration, probably determines the risk of dependence on drug abuse. The purpose of research: 1- to determine the relative measure unit of pleasure, which will be used to measure and assess the intensity of various human pleasures. 2- to compare the intensity of the pleasure from different kinds of activity, with pleasures received from drug use. 3- Based on the analysis of data, to identify factors affecting the rational decision making. Research method: Respondents will be asked to recall the greatest pleasure of their life, which will be used as a measure of the other pleasures. The study will use focus groups and structured interviews.

Keywords: drug, drug-user, measurement, satisfaction

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Authors: Katawut Kaewbanjong

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We analyzed a volume of data and found significant user satisfaction in software project factors. A statistical significance analysis (logistic regression) and collinearity analysis determined the significance factors from a group of 71 pre-defined factors from 191 software projects in ISBSG Release 12. The eight prediction models used for testing the prediction potential of these factors were Neural network, k-NN, Naïve Bayes, Random forest, Decision tree, Gradient boosted tree, linear regression and logistic regression prediction model. Fifteen pre-defined factors were truly significant in predicting user satisfaction, and they provided 82.71% prediction accuracy when used with a neural network prediction model. These factors were client-server, personnel changes, total defects delivered, project inactive time, industry sector, application type, development type, how methodology was acquired, development techniques, decision making process, intended market, size estimate approach, size estimate method, cost recording method, and effort estimate method. These findings may benefit software development managers considerably.

Keywords: prediction model, statistical analysis, software project, user satisfaction factor

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Authors: Nagasamy Venkatesh Dhandapani, Amged Awad El-Gied

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Cefixime, a BCS class II drug, is insoluble in water but freely soluble in acetone and in alcohol. The aqueous solubility of cefixime in water is poor and exhibits exceptionally slow and intrinsic dissolution rate. In the present study, cefixime and β-Cyclodextrin (β-CD) solid dispersions were prepared with a view to study the effect and influence of β-CD on the solubility and dissolution rate of this poorly aqueous soluble drug. Phase solubility profile revealed that the solubility of cefixime was increased in the presence of β-CD and was classified as A_L-type. Effect of variable, such as drug:carrier ratio, was studied. Physical characterization of the solid dispersion was characterized by Fourier transform infrared spectroscopy (FT-IR) and Differential scanning calorimetry (DSC). These studies revealed that a distinct loss of drug crystallinity in the solid molecular dispersions is ostensibly accounting for enhancement of dissolution rate in distilled water. The drug release from the prepared solid dispersion exhibited a first order kinetics. Solid dispersions of cefixime showed a 6.77 times fold increase in dissolution rate over the pure drug.

Keywords: β-cyclodextrin, cefixime, dissolution, Kneading method, solid dispersions, release kinetics

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Authors: Desalegn Feyissa Desu

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Drug therapy problem is a significant challenge to provide high quality health care service for the patients. It is associated with morbidity, mortality, increased hospital stay, and reduced quality of life. Moreover, pediatric patients are quite susceptible to drug therapy problems. Thus this study aimed to assess drug therapy problem and its contributing factors among pediatric patients diagnosed with infectious disease admitted to pediatric ward of Jimma university medical center, from April 1 to June 30, 2018. Prospective observational study was conducted among pediatric patients with infectious disease admitted from April 01 to June 30, 2018. Drug therapy problems were identified by using Cipolle’s and strand’s drug related problem classification method. Patient’s written informed consent was obtained after explaining the purpose of the study. Patient’s specific data were collected using structured questionnaire. Data were entered into Epi data version 4.0.2 and then exported to statistical software package version 21.0 for analysis. To identify predictors of drug therapy problems occurrence, multiple stepwise backward logistic regression analysis was done. The 95% CI was used to show the accuracy of data analysis and statistical significance was considered at p-value < 0.05. A total of 304 pediatric patients were included in the study. Of these, 226(74.3%) patients had at least one drug therapy problem during their hospital stay. A total of 356 drug therapy problems were identified among two hundred twenty six patients. Non-compliance (28.65%) and dose too low (27.53%) were the most common type of drug related problems while disease comorbidity [AOR=3.39, 95% CI= (1.89-6.08)], Polypharmacy [AOR=3.16, 95% CI= (1.61-6.20)] and more than six days stay in hospital [AOR=3.37, 95% CI= (1.71-6.64) were independent predictors of drug therapy problem occurrence. Drug therapy problems were common in pediatric patients with infectious disease in the study area. Presence of comorbidity, polypharmacy and prolonged hospital stay were the predictors of drug therapy problem in study area. Therefore, to overcome the significant gaps in pediatric pharmaceutical care, clinical pharmacists, Pediatricians, and other health care professionals have to work in collaboration.

Keywords: drug therapy problem, pediatric, infectious disease, Ethiopia

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Authors: K. Petcharaporn, S. Kumchoo

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The acidity (citric acid) is one of the chemical contents that can refer to the internal quality and the maturity index of tomato. The titratable acidity (%TA) can be predicted by a non-destructive method prediction by using the transmittance short wavelength (SW-NIR). Spectroscopy in the wavelength range between 665-955 nm. The set of 167 tomato samples divided into groups of 117 tomatoes sample for training set and 50 tomatoes sample for test set were used to establish the calibration model to predict and measure %TA by partial least squares regression (PLSR) technique. The spectra were pretreated with MSC pretreatment and it gave the optimal result for calibration model as (R = 0.92, RMSEC = 0.03%) and this model obtained high accuracy result to use for %TA prediction in test set as (R = 0.81, RMSEP = 0.05%). From the result of prediction in test set shown that the transmittance SW-NIR spectroscopy technique can be used for a non-destructive method for %TA prediction of tomatoes.

Keywords: tomato, quality, prediction, transmittance, titratable acidity, citric acid

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Authors: Venkat S. Somayajula

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Ground surface temperature history prediction model plays a vital role in determining standards for international nuclear waste management. International standards for borehole based nuclear waste disposal require paleoclimate cycle predictions on scale of a million forward years for the place of waste disposal. This research focuses on developing a paleoclimate cycle prediction model using Bayesian long-short term memory (LSTM) neural architecture operated on accumulated borehole temperature history data. Bayesian models have been previously used for paleoclimate cycle prediction based on Monte-Carlo weight method, but due to limitations pertaining model coupling with certain other prediction networks, Bayesian models in past couldn’t accommodate prediction cycle’s over 1000 years. LSTM has provided frontier to couple developed models with other prediction networks with ease. Paleoclimate cycle developed using this process will be trained on existing borehole data and then will be coupled to surface temperature history prediction networks which give endpoints for backpropagation of LSTM network and optimize the cycle of prediction for larger prediction time scales. Trained LSTM will be tested on past data for validation and then propagated for forward prediction of temperatures at borehole locations. This research will be beneficial for study pertaining to nuclear waste management, anthropological cycle predictions and geophysical features

Keywords: Bayesian long-short term memory neural network, borehole temperature, ground surface temperature history, paleoclimate cycle

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Authors: Dada Kolawole Segun

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Transdermal drug delivery has gained popularity as a non-invasive method for controlled drug release compared to traditional delivery routes. Dissolvable transdermal patches have emerged as a promising platform for delivering a variety of drugs due to their ease of use. The objective of this research was to create and characterize dissolvable transdermal patches using various compositions and ratios of hyaluronic acid and zinc oxide nanoparticles. A micromolding technique was utilized to fabricate the patches, which were subsequently characterized using scanning electron microscopy, atomic force microscopy, and tensile strength testing. In vitro drug release studies were conducted to evaluate the drug release kinetics of the patches. The study found that the mechanical strength and dissolution properties of the patches were influenced by the hyaluronic acid and zinc oxide nanoparticle ratios used in the fabrication process. Moreover, the patches demonstrated controlled delivery of model drugs through the skin, highlighting their potential for transdermal drug delivery applications. The results suggest that dissolvable transdermal patches can be tailored to meet specific requirements for drug delivery applications using different compositions and ratios of hyaluronic acid and zinc oxide nanoparticles. This development has the potential to improve treatment outcomes and patient compliance in various therapeutic areas.

Keywords: transdermal drug delivery, characterization, skin permeation, biodegradable materials

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Authors: Warda Fatima, Hasnain Javed

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The emerging trend of Drug resistance in childhood Tuberculosis is increasing worldwide and now becoming a priority challenge for National TB Control Programs of the world. Childhood TB accounts for 10-15% of total TB burden across the globe and same proportion is quantified in case of drug resistant TB. One third population suffering from MDR TB dies annually because of non-diagnosis and unavailability of appropriate treatment. However, true Childhood MDR TB cannot be estimated due to non-confirmation. Diagnosis of Pediatric TB by sputum Smear Microscopy and Culture inoculation are limited due to paucibacillary nature and difficulties in obtaining adequate sputum specimens. Diagnosis becomes more difficult when it comes to HIV infected child. New molecular advancements for early case detection of TB and MDR TB in adults have not been endorsed in children. Multi centered trials are needed to design better diagnostic approaches and efficient and safer treatments for DR TB in high burden countries. The aim of the present study is to sketch out the current situation of the childhood Drug resistant TB especially in the developing world and to highlight the classic and novel methods that are to be implemented in high-burden resource-limited locations.

Keywords: drug resistant TB, childhood, diagnosis, novel methods

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Authors: Soha A. Bahanshal, Byung G. Kim

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Identification of patients at high risk for hospital readmission is of crucial importance for quality health care and cost reduction. Predicting hospital readmissions among diabetic patients has been of great interest to many researchers and health decision makers. We build a prediction model to predict hospital readmission for diabetic patients within 30 days of discharge. The core of the prediction model is a modified k Nearest Neighbor called Hybrid Fuzzy Weighted k Nearest Neighbor algorithm. The prediction is performed on a patient dataset which consists of more than 70,000 patients with 50 attributes. We applied data preprocessing using different techniques in order to handle data imbalance and to fuzzify the data to suit the prediction algorithm. The model so far achieved classification accuracy of 80% compared to other models that only use k Nearest Neighbor.

Keywords: machine learning, prediction, classification, hybrid fuzzy weighted k-nearest neighbor, diabetic hospital readmission

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Authors: Stepan Kuchar, Martin Golasowski, Radim Vavrik, Michal Podhoranyi, Boris Sir, Jan Martinovic

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The main goal of this article is to describe the online flood monitoring and prediction system Floreon+ primarily developed for the Moravian-Silesian region in the Czech Republic and the basic process it uses for running automatic rainfall-runoff and hydrodynamic simulations along with their calibration and uncertainty modeling. It takes a long time to execute such process sequentially, which is not acceptable in the online scenario, so the use of high-performance computing environment is proposed for all parts of the process to shorten their duration. Finally, a case study on the Ostravice river catchment is presented that shows actual durations and their gain from the parallel implementation.

Keywords: flood prediction process, high performance computing, online flood prediction system, parallelization

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Authors: Jinfeng Zhang, Chun-Sing Lee

Abstract:

The use of different nanocarriers for delivering hydrophobic pharmaceutical agents to tumor sites has garnered major attention. Despite the merits of these nanocarriers, further studies are needed for improving their drug loading capacities (typically less than 10%) and reducing their potential systemic toxicity. So development of alternative self-carried nanodrug delivery strategies without using any inert carriers is highly desirable. In this study, we developed a self-carried theranostic curcumin (Cur) nanodrug for highly effective cancer therapy in vitro and in vivo with real-time monitoring of drug release. With a biocompatible C18PMH-PEG functionalization, the Cur nanoparticles (NPs) showed excellent dispersibility and outstanding stability in physiological environment, with drug loading capacity higher than 78 wt.%. Both confocal microscopy and flow cytometry confirmed the cellular fluorescent “OFF-ON” activation and real-time monitoring of Cur molecule release, showing its potential for cancer diagnosis. In vitro and in vivo experiments clearly show that therapeutic efficacy of the PEGylated Cur NPs is much better than that of free Cur. This self-carried theranostic strategy with real-time monitoring of drug release may open a new way for simultaneous cancer therapy and diagnosis.

Keywords: drug delivery, in vitro and in vivo cancer therapy, real-time monitoring, self-carried

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Authors: Suriya

Abstract:

Rapid socio-economic development and urbanization have led to an increasingly serious air pollution problem in Ulaanbaatar (UB), the capital of Mongolia. PM₂.₅ pollution has become the most pressing aspect of UB air pollution. Therefore, monitoring and predicting PM₂.₅ concentration in UB is of great significance for the health of the local people and environmental management. As of yet, very few studies have used models to predict PM₂.₅ concentrations in UB. Using data from 0:00 on June 1, 2018, to 23:00 on April 30, 2020, we proposed two deep learning models based on Bayesian-optimized LSTM (Bayes-LSTM) and CNN-LSTM. We utilized hourly observed data, including Himawari8 (H8) aerosol optical depth (AOD), meteorology, and PM₂.₅ concentration, as input for the prediction of PM₂.₅ concentrations. The correlation strengths between meteorology, AOD, and PM₂.₅ were analyzed using the gray correlation analysis method; the comparison of the performance improvement of the model by using the AOD input value was tested, and the performance of these models was evaluated using mean absolute error (MAE) and root mean square error (RMSE). The prediction accuracies of Bayes-LSTM and CNN-LSTM deep learning models were both improved when AOD was included as an input parameter. Improvement of the prediction accuracy of the CNN-LSTM model was particularly enhanced in the non-heating season; in the heating season, the prediction accuracy of the Bayes-LSTM model slightly improved, while the prediction accuracy of the CNN-LSTM model slightly decreased. We propose two novel deep learning models for PM₂.₅ concentration prediction in UB, Bayes-LSTM, and CNN-LSTM deep learning models. Pioneering the use of AOD data from H8 and demonstrating the inclusion of AOD input data improves the performance of our two proposed deep learning models.

Keywords: deep learning, AOD, PM2.5, prediction, Ulaanbaatar

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Authors: Meena Kumari, Ajitha Sharma, Mohan Babu Amberkar, Hasitha Manohar, Joseph Thomas, K. L. Bairy

Abstract:

Aim: To evaluate the pattern of occurrence of adverse drug reactions (ADRs) with platinum compounds in cancer chemotherapy at a tertiary care hospital. Methods: It was a retrospective, descriptive case record study done on patients admitted to the medical oncology ward of Kasturba Hospital, Manipal from July to November 2012. Inclusion criteria comprised of patients of both sexes and all ages diagnosed with cancer and were on platinum compounds, who developed at least one adverse drug reaction during or after the treatment period. CDSCO proforma was used for reporting ADRs. Causality was assessed using Naranjo Algorithm. Results: A total of 65 patients was included in the study. Females comprised of 67.69% and rest males. Around 49.23% of the ADRs were seen in the age group of 41-60 years, followed by 20 % in 21-40 years, 18.46% in patients over 60 years and 12.31% in 1-20 years age group. The anticancer agents which caused adverse drug reactions in our study were carboplatin (41.54%), cisplatin (36.92%) and oxaliplatin (21.54%). Most common adverse drug reactions observed were oral candidiasis (21.53%), vomiting (16.92%), anaemia (12.3%), diarrhoea (12.3%) and febrile neutropenia (0.08%). The results of the causality assessment of most of the cases were probable. Conclusion: The adverse effect of chemotherapeutic agents is a matter of concern in the pharmacological management of cancer as it affects the quality of life of patients. This information would be useful in identifying and minimizing preventable adverse drug reactions while generally enhancing the knowledge of the prescribers to deal with these adverse drug reactions more efficiently.

Keywords: adverse drug reactions, platinum compounds, cancer, chemotherapy

Procedia PDF Downloads 398

Authors: Baljinder Singh, Navneet Sharma

Abstract:

The skin can be used as the site for drug administration for continuous transdermal drug infusion into the systemic circulation. For the continuous diffusion/penetration of the drugs through the intact skin surface membrane-moderated systems, matrix dispersion type systems, adhesive diffusion controlled systems and micro reservoir systems have been developed. Various penetration enhancers are used for the drug diffusion through skin. In matrix dispersion type systems, the drug is dispersed in the solvent along with the polymers and solvent allowed to evaporate forming a homogeneous drug-polymer matrix. Matrix type systems were developed in the present study. In the present work, an attempt has been made to develop a matrix-type transdermal therapeutic system comprising of ondansetron-HCl with different ratios of hydrophilic and hydrophobic polymeric combinations using solvent evaporation technique. The physicochemical compatibility of the drug and the polymers was studied by infrared spectroscopy. The results obtained showed no physical-chemical incompatibility between the drug and the polymers. The patches were further subjected to various physical evaluations along with the in-vitro permeation studies using rat skin. On the basis of results obtained form the in vitro study and physical evaluation, the patches containing hydrophilic polymers i.e. polyvinyl alcohol and poly vinyl pyrrolidone with oleic acid as the penetration enhancer(5%) were considered as suitable for large scale manufacturing with a backing layer and a suitable adhesive membrane.

Keywords: transdermal drug delivery, penetration enhancers, hydrophilic and hydrophobic polymers, ondansetron HCl

Procedia PDF Downloads 296

Authors: Pankaj Prashar, Bimlesh Kumar, Ankita Sood, Anamika Gautam

Abstract:

Pharmacovigilance (PV) is a rapidly growing discipline in pharmaceutical industries as an integral part of clinical research and drug development over the past few decades. PV carries a breadth of scope from drug manufacturing to its regulation with safer utilization. The fundamental steps of PV not only includes data collection and verification, coding of drugs with adverse drug reactions, causality assessment and timely reporting to the authorities but also monitoring drug manufacturing, safety issues, product quality and conduction of due diligence. Standardization of adverse event information, collaboration of multiple departments in different companies, preparation of documents in accordance to both governmental as well as non-governmental organizations (FDA, EMA, GVP, ICH) are the advancements in discipline of PV. De-harmonization, lack of predictive drug safety models, improper funding by government, non-reporting, and non-acceptability of ADRs by developing countries and reports directly from patients to the monitoring centres respectively are the major road backs of PV. Mandatory pharmacovigilance reporting, frequent inspections, funding by government, educating and training medical students, pharmacists and nurses in this segment can bring about empowerment in PV. This area needs to be addressed with a sense of urgency for the safe utilization of pharmaceuticals.

Keywords: pharmacovigilance, regulatory, adverse event, drug safety

Procedia PDF Downloads 83