Search results for: drug chart

Authors: Asfa Hussain, Chee Tang, Mien Nguyen

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Introduction: The safe usage of medications is dependent on clear, well-documented prescribing. Medical drug charts should be regularly checked to ensure that they are fit for purpose. Aims: The purpose of this study was to evaluate whether the Isabel Hospice drug charts were effective or prone to medical errors. The aim was to create a comprehensive palliative care drug chart in line with medico-legal guidelines and to minimise drug administration and prescription errors. Methodology: 50 medical drug charts were audited from March to April 2020, to assess whether they complied with medico-legal guidelines, in a hospice within East of England. Meetings were held with the larger multi-disciplinary team (MDT), including the pharmacists, nursing staff and doctors, to raise awareness of the issue. A preliminary drug chart was created, using the input from the wider MDT. The chart was revised and trialled over 15 times, and each time feedback from the MDT was incorporated into the subsequent template. In the midst of the COVID-19 pandemic in September 2020, the finalised drug chart was trialled. 50 new palliative drug charts were re-audited, to evaluate the changes made. Results: Prescribing and administration errors were high prior to the implementation of the new chart. This improved significantly after introducing the new drug charts, therefore improving patient safety and care. The percentage of inadequately documented allergies went down from 66% to 20% and incorrect oxygen prescription from 40% to 16%. The prescription drug-drug interactions decreased by 30%. Conclusion: It is vital to have clear standardised drug charts, in line with medico-legal standards, to allow ease of prescription and administration of medications and ensure optimum patient-centred care. This closed loop audit demonstrated significant improvement in documentation and prevention of possible fatal drug errors and interactions.

Keywords: palliative care, drug chart, medication errors, drug-drug interactions, COVID-19, patient safety

Procedia PDF Downloads 135

Authors: Michael B. C. Khoo, J. L. Khoo, W. C. Yeong, W. L. Teoh

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The Shewhart Xbar control chart is a useful process monitoring tool in manufacturing industries to detect the presence of assignable causes. However, it is insensitive in detecting small process shifts. To circumvent this problem, adaptive control charts are suggested. An adaptive chart enables at least one of the chart’s parameters to be adjusted to increase the chart’s sensitivity. Two common adaptive charts that exist in the literature are the double sampling (DS) Xbar and variable sampling interval (VSI) Xbar charts. This paper compares the performances of the DS and VSI Xbar charts, based on the average time to signal (ATS) criterion. The ATS profiles of the DS Xbar and VSI Xbar charts are obtained using the Mathematica and Statistical Analysis System (SAS) programs, respectively. The results show that the VSI Xbar chart is generally superior to the DS Xbar chart.

Keywords: adaptive charts, average time to signal, double sampling, charts, variable sampling interval

Procedia PDF Downloads 257

Authors: Saowanit Sukparungsee

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Generally, the traditional Shewhart p chart has been developed by for charting the binomial data. This chart has been developed using the normal approximation with condition as low defect level and the small to moderate sample size. In real applications, however, are away from these assumptions due to skewness in the exact distribution. In this paper, a modified Exponentially Weighted Moving Average (EWMA) control chat for detecting a change in binomial data by improving square root transformations, namely ISRT p EWMA control chart. The numerical results show that ISRT p EWMA chart is superior to ISRT p chart for small to moderate shifts, otherwise, the latter is better for large shifts.

Keywords: number of defects, exponentially weighted moving average, average run length, square root transformations

Procedia PDF Downloads 401

Authors: Ali Akbar Heydari

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Control charts are an important tool of statistical quality control. Thesecharts are used to detect and eliminate unwanted special causes of variation that occurred during aperiod of time. The design and operation of control charts require the determination of three design parameters: the sample size (n), the sampling interval (h), and the width coefficient of control limits (k). Thevariable parameters (VP) x-bar controlchart is the x-barchart in which all the design parameters vary between twovalues. These values are a function of the most recent process information. In fact, in the VP x-bar chart, the position of each sample point on the chart establishes the size of the next sample and the timeof its sampling. The synthetic x-barcontrol chartwhich integrates the x-bar chart and the conforming run length (CRL) chart, provides significant improvement in terms of detection power over the basic x-bar chart for all levels of mean shifts. In this paper, we introduce the syntheticVP x-bar control chart for monitoring changes in the process mean. To determine the design parameters, we used a statistical design based on the minimum out of control average run length (ARL) criteria. The optimal chart parameters of the proposed chart are obtained using the Markov chain approach. A numerical example is also done to show the performance of the proposed chart and comparing it with the other control charts. The results show that our proposed syntheticVP x-bar controlchart perform better than the synthetic x-bar controlchart for all shift parameter values. Also, the syntheticVP x-bar controlchart perform better than the VP x-bar control chart for the moderate or large shift parameter values.

Keywords: control chart, markov chain approach, statistical design, synthetic, variable parameter

Procedia PDF Downloads 129

Authors: Khaliun Nyambayar, Nomindari Azzaya, Batkhuyag Purevjav

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Pharmacovigilance was officially introduced in Mongolia in 2003, in accordance with the Health Minister Order 183 for the registry of adverse drug reactions (ADR), approved in 2006 and was reviewed in 2010. This study was designed to evaluate the incidence and common types of adverse drug reactions among hospitalized children, the frequency of adverse drug reaction reported by health care providers, and the follow-up processes resulting from adverse drug reactions. A retrospective study of paediatric patients who experienced an adverse drug reaction from 2015 to 2019, extracted from the “yellow” card at the State Research Center for Maternal and Child Health, (city). A total of 417 adverse drug reactions were reported with an overall incidence was 80 (21.5%). Adverse reactions resulting from the use of antibiotics (particularly gentamycin, cephalosporins, and vancomycin) were usually mild. ADR’s were reported by physicians and nurses (93.8%), pharmacists (6.25%). Although documentation of physician notification occurred for 93% of adverse drug reactions, only 29% of cases were documented in the patient's medical chart, 13% included follow-up education for individuals involved, and 10% were updated in the allergy profile of the hospital computer system. Measures to improve the detection and reporting of adverse drug reactions by all health care professionals should be improved, to enhance our understanding of the nature and impact of these reactions in children.

Keywords: adverse drug reaction, pediatric, yellow card, Mongolia

Procedia PDF Downloads 82

Authors: Yupaporn Areepong

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The main goal of this paper is to study Statistical Process Control (SPC) with Exponentially Weighted Moving Average (EWMA) control chart when observations are serially-correlated. The characteristic of control chart is Average Run Length (ARL) which is the average number of samples taken before an action signal is given. Ideally, an acceptable ARL of in-control process should be enough large, so-called (ARL0). Otherwise it should be small when the process is out-of-control, so-called Average of Delay Time (ARL1) or a mean of true alarm. We find explicit formulas of ARL for EWMA control chart for Seasonal Autoregressive and Moving Average processes (SARMA) with Exponential white noise. The results of ARL obtained from explicit formula and Integral equation are in good agreement. In particular, this formulas for evaluating (ARL0) and (ARL1) be able to get a set of optimal parameters which depend on smoothing parameter (λ) and width of control limit (H) for designing EWMA chart with minimum of (ARL1).

Keywords: average run length, optimal parameters, exponentially weighted moving average (EWMA), control chart

Procedia PDF Downloads 526

Authors: Shourav Ahmed, M. Gulam Kibria, Kais Zaman

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Control charts graphically verify variation in quality parameters. Attribute type control charts deal with quality parameters that can only hold two states, e.g., good or bad, yes or no, etc. At present, p-control chart is most commonly used to deal with attribute type data. In construction of p-control chart using binomial distribution, the value of proportion non-conforming must be known or estimated from limited sample information. As the probability distribution of fraction non-conforming (p) is considered in hyperbinomial distribution unlike a constant value in case of binomial distribution, it reduces the risk of false detection. In this study, a statistical control chart is proposed based on hyperbinomial distribution when prior estimate of proportion non-conforming is unavailable and is estimated from limited sample information. We developed the control limits of the proposed modified p-chart using the mean and variance of hyperbinomial distribution. The proposed modified p-chart can also utilize additional sample information when they are available. The study also validates the use of modified p-chart by comparing with the result obtained using cumulative distribution function of hyperbinomial distribution. The study clearly indicates that the use of hyperbinomial distribution in construction of p-control chart yields much accurate estimate of quality parameters than using binomial distribution.

Keywords: binomial distribution, control charts, cumulative distribution function, hyper binomial distribution

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Authors: V. Makis, L. Jafari

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In this paper, we develop an optimal Bayesian chart to control the expected number of defects per inspection unit in production processes with long production runs. We formulate this control problem in the optimal stopping framework. The objective is to determine the optimal stopping rule minimizing the long-run expected average cost per unit time considering partial information obtained from the process sampling at regular epochs. We prove the optimality of the control limit policy, i.e., the process is stopped and the search for assignable causes is initiated when the posterior probability that the process is out of control exceeds a control limit. An algorithm in the semi-Markov decision process framework is developed to calculate the optimal control limit and the corresponding average cost. Numerical examples are presented to illustrate the developed optimal control chart and to compare it with the traditional u-chart.

Keywords: Bayesian u-chart, economic design, optimal stopping, semi-Markov decision process, statistical process control

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Authors: Hoda Abugharsa

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In this paper we propose to design a sorter circuit using insertion sorting algorithm. The circuit will be designed using Algorithmic State Machines (ASM) method. That means converting the insertion sorting flowchart into an ASM chart. Then the ASM chart will be used to design the sorter circuit and the control unit.

Keywords: insert sorting algorithm, ASM chart, sorter circuit, state machine, control unit

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Authors: Young Jae Choung, Seoung Bum Kim

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Statistical Process Control (SPC) is a popular technique for process monitoring. A widely used tool in SPC is a control chart, which is used to detect the abnormal status of a process and maintain the controlled status of the process. Traditional control charts, such as Hotelling’s T2 control chart, are effective techniques to detect abnormal observations and monitor processes. However, many complicated manufacturing systems exhibit nonlinearity because of the different demands of the market. In this case, the unregulated use of a traditional linear modeling approach may not be effective. In reality, many industrial processes contain the nonlinear and time-varying properties because of the fluctuation of process raw materials, slowing shift of the set points, aging of the main process components, seasoning effects, and catalyst deactivation. The use of traditional SPC techniques with time-varying data will degrade the performance of the monitoring scheme. To address these issues, in the present study, we propose a parameterless self-organizing map (PLSOM)-based control chart. The PLSOM-based control chart not only can manage a situation where the distribution or parameter of the target observations changes, but also address the nonlinearity of modern manufacturing systems. The control limits of the proposed PLSOM chart are established by estimating the empirical level of significance on the percentile using a bootstrap method. Experimental results with simulated data and actual process data from a thin-film transistor-liquid crystal display process demonstrated the effectiveness and usefulness of the proposed chart.

Keywords: control chart, parameter-less self-organizing map, self-organizing map, time-varying property

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Authors: Viliam Makis, Farnoosh Naderkhani, Leila Jafari

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In this paper, we present a model and an algorithm for the calculation of the optimal control limit, average cost, sample size, and the sampling interval for an optimal Bayesian chart to control the proportion of defective items produced using a semi-Markov decision process approach. Traditional p-chart has been widely used for controlling the proportion of defectives in various kinds of production processes for many years. It is well known that traditional non-Bayesian charts are not optimal, but very few optimal Bayesian control charts have been developed in the literature, mostly considering finite horizon. The objective of this paper is to develop a fast computational algorithm to obtain the optimal parameters of a Bayesian p-chart. The decision problem is formulated in the partially observable framework and the developed algorithm is illustrated by a numerical example.

Keywords: Bayesian control chart, semi-Markov decision process, quality control, partially observable process

Procedia PDF Downloads 289

Authors: L. A. Toro, N. Prieto, J. J. Vargas

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There are many control charts for monitoring mean and variance. Among these, the X y R, X y S, S2 Hotteling and Shewhart control charts, for mentioning some, are widely used for monitoring mean a variance in industrial processes. In particular, the Shewhart charts are based on the information about the process contained in the current observation only and ignore any information given by the entire sequence of points. Moreover, that the Shewhart chart is a control chart without memory. Consequently, Shewhart control charts are found to be less sensitive in detecting smaller shifts, particularly smaller than 1.5 times of the standard deviation. These kind of small shifts are important in many industrial applications. In this study and effective alternative to Shewhart control chart was implemented. In case of univariate process an Exponentially Moving Average (EWMA) control chart was developed and Multivariate Exponentially Moving Average (MEWMA) control chart in case of multivariate process. Both of these charts were based on memory and perform better that Shewhart chart while detecting smaller shifts. In these charts, information the past sample is cumulated up the current sample and then the decision about the process control is taken. The mentioned characteristic of EWMA and MEWMA charts, are of the paramount importance when it is necessary to control industrial process, because it is possible to correct or predict problems in the processes before they come to a dangerous limit.

Keywords: control charts, multivariate exponentially moving average (MEWMA), exponentially moving average (EWMA), industrial control process

Procedia PDF Downloads 323

Authors: Encarnación Álvarez-Verdejo, Raúl Amor-Pulido, Pablo J. Moya-Fernández, Juan F. Muñoz-Rosas, Francisco J. Blanco-Encomienda

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Many companies use the statistical tool named as statistical quality control, and which can have a high cost for the companies interested on these statistical tools. The evaluation of the quality of products and services is an important topic, but the reduction of the cost of the implantation of the statistical quality control also has important benefits for the companies. For this reason, it is important to implement a economic design for the various steps included into the statistical quality control. In this paper, we describe some relevant aspects related to the economic design of a quality control chart for the proportion of defective items. They are very important because the suggested issues can reduce the cost of implementing a quality control chart for the proportion of defective items. Note that the main purpose of this chart is to evaluate and control the proportion of defective items of a production process.

Keywords: proportion, type I error, economic plan, distribution function

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Authors: Umar Farouk Abbas, Danjuma Mustapha, Hamisu Idi

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It is now a known fact that quality is an important issue in manufacturing industries. A control chart is an integrated and powerful tool in statistical process control (SPC). The mean µ and standard deviation σ parameters are estimated. In general, the multivariate exponentially weighted moving average (MEWMA) and multivariate cumulative sum (MCUSUM) are used in the detection of small shifts in joint monitoring of several correlated variables; the charts used information from past data which makes them sensitive to small shifts. The aim of the paper is to compare the performance of Shewhart xbar, MEWMA, and MCUSUM control charts in terms of their false rates when parameters are estimated with autocorrelation. A simulation was conducted in R software to generate the average run length (ARL) values of each of the charts. After the analysis, the results show that a comparison of the false alarm rates of the charts shows that MEWMA chart has lower false alarm rates than the MCUSUM chart at various levels of parameter estimated to the number of ARL0 (in control) values. Also noticed was that the sample size has an advert effect on the false alarm of the control charts.

Keywords: average run length, MCUSUM chart, MEWMA chart, false alarm rate, parameter estimation, simulation

Procedia PDF Downloads 175

Authors: Deepika Bhaskar, Neena Wadehra, Megha Gulati, Aruna Narula, R. Vishnu, Gunjan Katyal

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With drug resistance becoming widespread in Plasmodium falciparum infections, development of the alternative drugs is the desired strategy for prevention and cure of malaria. Three drug targets were selected to screen promising drug molecules from the GSK library of around 14000 molecules. Using an in silico structure-based drug designing approach, the differences in binding energies of the substrate and inhibitor were exploited between target sites of parasite and human to design a drug molecule against Plasmodium. The docking studies have shown several promising molecules from GSK library with more effective binding as compared to the already known inhibitors for the drug targets. Though stronger interaction has been shown by several molecules as compare to reference, few molecules have shown the potential as drug candidates though in vitro studies are required to validate the results.

Keywords: plasmodium, malaria, drug targets, in silico studies

Procedia PDF Downloads 415

Authors: O. Ikpotokin

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In any production process, every product is aimed to attain a certain standard, but the presence of assignable cause of variability affects our process, thereby leading to low quality of product. The ability to identify and remove this type of variability reduces its overall effect, thereby improving the quality of the product. In case of a univariate control chart signal, it is easy to detect the problem and give a solution since it is related to a single quality characteristic. However, the problems involved in the use of multivariate control chart are the violation of multivariate normal assumption and the difficulty in identifying the quality characteristic(s) that resulted in the out of control signals. The purpose of this paper is to examine the use of non-parametric control chart (the bootstrap approach) for obtaining control limit to overcome the problem of multivariate distributional assumption and the p-value method for detecting out of control signals. Results from a performance study show that the proposed bootstrap method enables the setting of control limit that can enhance the detection of out of control signals when compared, while the p-value method also enhanced in identifying out of control variables.

Keywords: bootstrap control limit, p-value method, out-of-control signals, p-value, quality characteristics

Procedia PDF Downloads 316

Authors: Sara Ataei, Molouk Hadjibabaie, Shirinsadat Badri, Amirhossein Moslehi, Iman Karimzadeh, Ardeshir Ghavamzadeh

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Purpose: To assess the pattern and probable risk factors for moderate and major drug–drug interactions in a referral hematology-oncology ward in Iran. Methods: All patients admitted to hematology–oncology ward of Dr. Shariati Hospital during a 6-month period and received at least two anti-cancer or non-anti-cancer medications simultaneously were included. All being scheduled anti-cancer and non-anti-cancer medications both prescribed and administered during ward stay were considered for drug–drug interaction screening by Lexi-Interact On- Desktop software. Results: One hundred and eighty-five drug–drug interactions with moderate or major severity were detected from 83 patients. Most of drug–drug interactions (69.73 %) were classified as pharmacokinetics. Fluconazole (25.95 %) was the most commonly offending medication in drug–drug interactions. Interaction of sulfamethoxazole-trimethoprim with fluconazole was the most common drug–drug interaction (27.27 %). Vincristine with imatinib was the only identified interaction between two anti-cancer agents. The number of administered medications during ward stay was considered as an independent risk factor for developing a drug–drug interaction. Conclusions: Potential moderate or major drug–drug interactions occur frequently in patients with hematological malignancies or related diseases. Performing larger standard studies are required to assess the real clinical and economical effects of drug–drug interactions on patients with hematological and non-hematological malignancies.

Keywords: drug–drug interactions, hematology–oncology ward, hematological malignancies

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Authors: Rominkumar Busa, Shahira K. C., Lijiya A.

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Text recognition is an important area in computer vision which deals with detecting and recognising text from an image. The Optical Character Recognition (OCR) is a saturated area these days and with very good text recognition accuracy. However the same OCR methods when applied on text with small font sizes like the text data of chart images, the recognition rate is less than 30%. In this work, aims to extract small text in images using the deep learning model, CRNN with CTC loss. The text recognition accuracy is found to improve by applying image enhancement by super resolution prior to CRNN model. We also observe the text recognition rate further increases by 18% by applying the proposed method, which involves super resolution and character segmentation followed by CRNN with CTC loss. The efficiency of the proposed method shows that further pre-processing on chart image text and other small text images will improve the accuracy further, thereby helping text extraction from chart images.

Keywords: small text extraction, OCR, scene text recognition, CRNN

Procedia PDF Downloads 93

Authors: Pimporn Thongmuang

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The import value of antimicrobial drugs reached approximately fifteen million Baht in 2010, considered as the highest import value of all modern drugs, and this value is rising every year. Antimicrobials are considered the hazardous drugs by the Ministry of Public Health. This research was conducted in order to investigate the past knowledge of drug use and Antimicrobial drug use behavior. A total of 757 students were selected as the samples out of a population of 1,800 students. This selected students had the experience of Antimicrobial drugs use a year ago. A questionnaire was utilized in this research. The findings put on the view that knowledge gained by the students about proper use of antimicrobial drugs was not brought into practice. This suggests that the education procedure regarding drug use needs adjustment. And therefore the findings of this research are expected to be utilized as guidelines for educating people about the proper use of antimicrobial drugs. At a broader perspective, correct drug use behavior of the public may potentially reduce drug cost of the Ministry of Public Health of Thailand.

Keywords: drug use knowledge, antimicrobial drugs, drug use behavior, drug

Procedia PDF Downloads 245

Authors: Seulki Lee, Seoung Bum Kim

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Statistical process control (SPC) is a practical and effective method for quality control. The most important and widely used technique in SPC is a control chart. The main goal of a control chart is to detect any assignable changes that affect the quality output. Most conventional control charts, such as Hotelling’s T2 charts, are commonly based on the assumption that the quality characteristics follow a multivariate normal distribution. However, in modern complicated manufacturing systems, appropriate control chart techniques that can efficiently handle the nonnormal processes are required. To overcome the shortcomings of conventional control charts for nonnormal processes, several methods have been proposed to combine statistical learning algorithms and multivariate control charts. Statistical learning-based control charts, such as support vector data description (SVDD)-based charts, k-nearest neighbors-based charts, have proven their improved performance in nonnormal situations compared to that of the T2 chart. Beside the nonnormal property, time-varying operations are also quite common in real manufacturing fields because of various factors such as product and set-point changes, seasonal variations, catalyst degradation, and sensor drifting. However, traditional control charts cannot accommodate future condition changes of the process because they are formulated based on the data information recorded in the early stage of the process. In the present paper, we propose a SVDD algorithm-based control chart, which is capable of adaptively monitoring time-varying and nonnormal processes. We reformulated the SVDD algorithm into a time-adaptive SVDD algorithm by adding a weighting factor that reflects time-varying situations. Moreover, we defined the updating region for the efficient model-updating structure of the control chart. The proposed control chart simultaneously allows efficient model updates and timely detection of out-of-control signals. The effectiveness and applicability of the proposed chart were demonstrated through experiments with the simulated data and the real data from the metal frame process in mobile device manufacturing.

Keywords: multivariate control chart, nonparametric method, support vector data description, time-varying process

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Authors: Devatha Kalyan Kumar, R. Poovarasan

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In this paper, we have taken certain important factors and health parameters of diabetes patients especially among children by birth (pediatric congenital) where using the above three metrics methods we are going to assess the importance of each attributes in the dataset and thereby determining the most highly responsible and co-related attribute causing diabetics among young patients. We use cost optimization, control chart and Spearmen methodologies for the real-time application of finding the data efficiency in this diabetes dataset. The Spearmen methodology is the correlation methodologies used in software development process to identify the complexity between the various modules of the software. Identifying the complexity is important because if the complexity is higher, then there is a higher chance of occurrence of the risk in the software. With the use of control; chart mean, variance and standard deviation of data are calculated. With the use of Cost optimization model, we find to optimize the variables. Hence we choose the Spearmen, control chart and cost optimization methods to assess the data efficiency in diabetes datasets.

Keywords: correlation, congenital diabetics, linear relationship, monotonic function, ranking samples, pediatric

Procedia PDF Downloads 227

Authors: Rashini Maduka, C. R. Wijesinghe, A. R. Weerasinghe

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Drug-drug interactions (DDIs) can happen when two or more drugs are taken together. Today DDIs have become a serious health issue due to adverse drug effects. In vivo and in vitro methods for identifying DDIs are time-consuming and costly. Therefore, in-silico-based approaches are preferred in DDI identification. Most machine learning models for DDI prediction are used chemical and biological drug properties as features. However, some drug features are not available and costly to extract. Therefore, it is better to make automatic feature engineering. Furthermore, people who have diabetes already suffer from other diseases and take more than one medicine together. Then adverse drug effects may happen to diabetic patients and cause unpleasant reactions in the body. In this study, we present a model with a graph convolutional autoencoder and a graph decoder using a dataset from DrugBank version 5.1.3. The main objective of the model is to identify unknown interactions between antidiabetic drugs and the drugs taken by diabetic patients for other diseases. We considered automatic feature engineering and used Known DDIs only as the input for the model. Our model has achieved 0.86 in AUC and 0.86 in AP.

Keywords: drug-drug interaction prediction, graph embedding, graph convolutional networks, adverse drug effects

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Authors: Farzad Jalilian, Mehdi Mirzaei Alavijeh, Fazel Zinat Motlagh

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Social support is an important benchmark of health for people in avoidance conditions. The main goal of this study was to determine the three kinds of social support (family, friend and other significant) to drug cessation among male addicts, in Kermanshah, the west of Iran. This cross-sectional study was conducted among 132 addicts, randomly selected to participate voluntarily in the study. Data were collected from conduct interviews based on standard questionnaire and analyzed by using SPSS-18 at 95% significance level. The majority of addicts were young (Mean: 30.4 years), and with little education. Opium (36.4%), Crack (21.2%), and Methamphetamine (12.9%) were the predominant drugs. Inabilities to reject the offer and having addict friends are the most often reasons for drug usage. Almost, 18.9% reported history of drug injection. 43.2% of the participants already did drug cessation at least once. Logistic regression showed the family support (OR = 1.110), age (OR = 1.106) and drug use initiation age (OR = 0.918) was predicting drug cessation. Our result showed; family support is a more important effect among types of social support in drug cessation. It seems that providing educational program to addict’s families for more support of patients at drug cessation can be beneficial.

Keywords: drug cessation, family support, drug use, initiation age

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Authors: Temesgen Geremew

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Functionalized nanoparticles have emerged as a revolutionary platform for drug delivery, offering significant advantages over traditional methods. By strategically modifying their surface properties, these nanoparticles can be designed to target specific tissues and cells, significantly reducing off-target effects and enhancing therapeutic efficacy. This targeted approach allows for lower drug doses, minimizing systemic exposure and potential side effects. Additionally, functionalization enables controlled release of the encapsulated drug, improving drug stability and reducing the frequency of administration, leading to improved patient compliance. This work explores the immense potential of functionalized nanoparticles in revolutionizing drug delivery, addressing limitations associated with conventional therapies and paving the way for personalized medicine with precise and targeted treatment strategies.

Keywords: nanoparticles, drug, nanomaterials, applications

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Authors: Adegoke Yinka Adebayo

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An understanding of the critical product attributes that impact on in vivo performance is key to the production of safe and effective medicines. Thus, a key driver for our research is the development of new basic science and technology underpinning the development of new pharmaceutical products. Research includes the structure and properties of drugs and excipients, biopharmaceutical characterisation, pharmaceutical processing and technology and formulation and analysis.

Keywords: drug discovery, drug development, drug delivery

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Authors: Liaquat Ahmad, Muhammad Aslam, Muhammad Azam

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In this article, we propose a control chart based on repetitive group sampling scheme for the location parameter. This charting scheme is based on the regression estimator; an estimator that capitalize the relationship between the variables of interest to provide more sensitive control than the commonly used individual variables. The control limit coefficients have been estimated for different sample sizes for less and highly correlated variables. The monitoring of the production process is constructed by adopting the procedure of the Shewhart’s x-bar control chart. Its performance is verified by the average run length calculations when the shift occurs in the average value of the estimator. It has been observed that the less correlated variables have rapid false alarm rate.

Keywords: average run length, control charts, process shift, regression estimators, repetitive group sampling

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Authors: Chia-Hau Liu, Tai-Yue Wang

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Traditional multivariate control charts assume that measurement from manufacturing processes follows a multivariate normal distribution. However, this assumption may not hold or may be difficult to verify because not all the measurement from manufacturing processes are normal distributed in practice. This study develops a new multivariate control chart for monitoring the processes with non-normal data. We propose a mechanism based on integrating the one-class classification method and the adaptive technique. The adaptive technique is used to improve the sensitivity to small shift on one-class classification in statistical process control. In addition, this design provides an easy way to allocate the value of type I error so it is easier to be implemented. Finally, the simulation study and the real data from industry are used to demonstrate the effectiveness of the propose control charts.

Keywords: multivariate control chart, statistical process control, one-class classification method, non-normal data

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Authors: S. Niamkaeo, O. Robert, O. Chaowalit

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In this investigation, we focus on discovering spatial relationship of drug smuggling along the northern border of Thailand. Thailand is no longer a drug production site, but Thailand is still one of the major drug trafficking hubs due to its topographic characteristics facilitating drug smuggling from neighboring countries. Our study areas cover three districts (Mae-jan, Mae-fahluang, and Mae-sai) in Chiangrai city and four districts (Chiangdao, Mae-eye, Chaiprakarn, and Wienghang) in Chiangmai city where drug smuggling of methamphetamine crystal and amphetamine occurs mostly. The data on drug smuggling incidents from 2011 to 2017 was collected from several national and local published news. Geo-spatial drug smuggling database was prepared. Decision tree algorithm was applied in order to discover the spatial relationship of factors related to drug smuggling, which was converted into rules using rule-based system. The factors including land use type, smuggling route, season and distance within 500 meters from check points were found that they were related to drug smuggling in terms of rules-based relationship. It was illustrated that drug smuggling was occurred mostly in forest area in winter. Drug smuggling exhibited was discovered mainly along topographic road where check points were not reachable. This spatial relationship of drug smuggling could support the Thai Office of Narcotics Control Board in surveillance drug smuggling.

Keywords: decision tree, drug smuggling, Geographic Information System, GIS knowledge discovery, rule-based system

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Authors: Afsaneh Ghorbanzadeh, Afshin Farahbakhsh, Zakieh Bayat

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The drug capsulation was used for release and targeted delivery in determined time, place and temperature or pH. The DOX nanocapsules were used to reduce and to minimize the unwanted side effects of drug. In this paper, the encapsulation methods of doxorubicin (DOX) and the labeling it by the magnetic core of iron (Fe3O4) has been studied. The Fe3O4 was conjugated with DOX via hydrazine bond. The solution was capsuled by the sensitive polymer of heat or pH such as chitosan-g-poly (N-isopropylacrylamide-co-N,N-dimethylacrylamide), dextran-g-poly(N-isopropylacrylamide-co-N,N-dimethylacrylamide) and mPEG-G2.5 PAMAM by hydrazine bond. The drug release was very slow at temperatures lower than 380°C. There was a rapid and controlled drug release at temperatures higher than 380°C. According to experiments, the use mPEG-G2.5PAMAM is the best method of DOX nanocapsules synthesis, because in this method, the drug delivery time to certain place is lower than other methods and the percentage of released drug is higher. The synthesized magnetic carrier system has potential applications in magnetic drug-targeting delivery and magnetic resonance imaging.

Keywords: drug carrier, drug release, doxorubicin, iron oxide NPs

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Authors: Farzad Jalilian, Mehdi Mirzaei Alavijeh

Abstract:

Background: Substance addiction is one of the major worldwide problems that destroys economy, familial relationships, and the abuser’s career and has several side effects; in the meantime drug injection due to the possibility of shared use of syringes among drug users could have multiple complications to be followed. The purpose of this study was to determine the prevalence of drug injection among male prisoners in Kermanshah city, the west of Iran. Methods: In this cross-sectional study 615 male prisoners were randomly selected to participate voluntarily in the study. Participants filled out a writing self-report questionnaire. Data were analyzed by the SPSS software (ver. 21.0) at 95% significant level. Results: The mean age of respondents was 31.13 years [SD: 7.76]. Mean initiation age for drug use was 14.36 years (range, 9-34 years). Almost, 39.4 % reported a history of drug use before prison. Opium (33.2%) and crystal (27.1%) was the most used drug among prisoners. Furthermore, 9.3 % had a history of injection addiction. There was a significant correlation between age, crime type, marital status, economic status, unprotected sex and drug injection (P < 0.05). Conclusion: The low age of drug abuse and the prevalence of drug injection among offenders can be as a warning for responsible; in this regard, implementation of prevention programs to risky behavior and harm reduction among high-risk groups can follow useful results.

Keywords: substance abuse, drug injection, prison, Iran

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