Displaying publications 1 - 20 of 390 in total

Abstract:
Sort:
  1. Abdullah N, Goh YX, Othman R, Ismail N, Jalal N, Wan Sallam WAF, et al.
    J Clin Lab Anal, 2023 Apr;37(8):e24898.
    PMID: 37243371 DOI: 10.1002/jcla.24898
    OBJECTIVE: Glycated haemoglobin (HbA1c) is a standard indication for screening type 2 diabetes that also has been widely used in large-scale epidemiological studies. However, its long-term quality (in terms of reproducibility) stored in liquid nitrogen is still unknown. This study is aimed to evaluate the stability and reproducibility of HbA1c measurements from frozen whole blood samples kept at -196°C for more than 7 years.

    METHODS: A total of 401 whole blood samples with a fresh HbA1c measurement were randomly selected from The Malaysian Cohort's (TMC) biobank. The HbA1c measurements of fresh and frozen (stored for 7-8 years) samples were assayed using different high-performance liquid chromatography (HPLC) systems. The HbA1c values of the fresh samples were then calculated and corrected according to the later system. The reproducibility of HbA1c measurements between calculated-fresh and frozen samples was assessed using a Passing-Bablok linear regression model. The Bland-Altman plot was then used to evaluate the concordance of HbA1c values.

    RESULTS: The different HPLC systems highly correlated (r = 0.99) and agreed (ICC = 0.96) with each other. Furthermore, the HbA1c measurements for frozen samples strongly correlate with the corrected HbA1c values of the fresh samples (r = 0.875) with a mean difference of -0.02 (SD: -0.38 to 0.38). Although the mean difference is small, discrepancies were observed within the diabetic and non-diabetic samples.

    CONCLUSION: These data demonstrate that the HbA1c measurements between fresh and frozen samples are highly correlated and reproducible.

    Matched MeSH terms: Linear Models
  2. Ying Ying Tang D, Wayne Chew K, Ting HY, Sia YH, Gentili FG, Park YK, et al.
    Bioresour Technol, 2023 Feb;370:128503.
    PMID: 36535615 DOI: 10.1016/j.biortech.2022.128503
    This study presented a novel methodology to predict microalgae chlorophyll content from colour models using linear regression and artificial neural network. The analysis was performed using SPSS software. Type of extractant solvents and image indexes were used as the input data for the artificial neural network calculation. The findings revealed that the regression model was highly significant, with high R2 of 0.58 and RSME of 3.16, making it a useful tool for predicting the chlorophyll concentration. Simultaneously, artificial neural network model with R2 of 0.66 and low RMSE of 2.36 proved to be more accurate than regression model. The model which fitted to the experimental data indicated that acetone was a suitable extraction solvent. In comparison to the cyan-magenta-yellow-black model in image analysis, the red-greenblue model offered a better correlation. In short, the estimation of chlorophyll concentration using prediction models are rapid, more efficient, and less expensive.
    Matched MeSH terms: Linear Models
  3. Hakimi M, Omar MB, Ibrahim R
    Sensors (Basel), 2023 Jan 16;23(2).
    PMID: 36679816 DOI: 10.3390/s23021020
    The gas sweetening process removes hydrogen sulfide (H2S) in an acid gas removal unit (AGRU) to meet the gas sales' specification, known as sweet gas. Monitoring the concentration of H2S in sweet gas is crucial to avoid operational and environmental issues. This study shows the capability of artificial neural networks (ANN) to predict the concentration of H2S in sweet gas. The concentration of N-methyldiethanolamine (MDEA) and Piperazine (PZ), temperature and pressure as inputs, and the concentration of H2S in sweet gas as outputs have been used to create the ANN network. Two distinct backpropagation techniques with various transfer functions and numbers of neurons were used to train the ANN models. Multiple linear regression (MLR) was used to compare the outcomes of the ANN models. The models' performance was assessed using the mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). The findings demonstrate that ANN trained by the Levenberg-Marquardt technique, equipped with a logistic sigmoid (logsig) transfer function with three neurons achieved the highest R2 (0.966) and the lowest MAE (0.066) and RMSE (0.122) values. The findings suggested that ANN can be a reliable and accurate prediction method in predicting the concentration of H2S in sweet gas.
    Matched MeSH terms: Linear Models
  4. Ammatawiyanon L, Tongkumchum P, Lim A, McNeil D
    Malar J, 2022 Nov 15;21(1):334.
    PMID: 36380322 DOI: 10.1186/s12936-022-04363-8
    BACKGROUND: Malaria remains a serious health problem in the southern border provinces of Thailand. The issue areas can be identified using an appropriate statistical model. This study aimed to investigate malaria for its spatial occurrence and incidence rate in the southernmost provinces of Thailand.

    METHODS: The Thai Office of Disease Prevention and Control, Ministry of Public Health, provided total hospital admissions of malaria cases from 2008 to 2020, which were classified by age, gender, and sub-district of residence. Sixty-two sub-districts were excluded since they had no malaria cases. A logistic model was used to identify spatial occurrence patterns of malaria, and a log-linear regression model was employed to model the incidence rate after eliminating records with zero cases.

    RESULTS: The overall occurrence rate was 9.8% and the overall median incidence rate was 4.3 cases per 1,000 population. Malaria occurence peaked at young adults aged 20-29, and subsequently fell with age for both sexes, whereas incidence rate increased with age for both sexes. Malaria occurrence and incidence rates fluctuated; they appeared to be on the decline. The area with the highest malaria occurrence and incidence rate was remarkably similar to the area with the highest number of malaria cases, which were mostly in Yala province's sub-districts bordering Malaysia.

    CONCLUSIONS: Malaria is a serious problem in forest-covered border areas. The correct policies and strategies should be concentrated in these areas, in order to address this condition.

    Matched MeSH terms: Linear Models
  5. Rosli NS, Ibrahim R, Ismail I, Omar M
    PLoS One, 2022;17(11):e0276142.
    PMID: 36445921 DOI: 10.1371/journal.pone.0276142
    Achieving reliable power efficiency from a high voltage induction motor (HVIM) is a great challenge, as the rigorous control strategy is susceptible to unexpected failure. External cooling is commonly used in an HVIM cooling system, and it is a vital part of the motor that is responsible for keeping the motor at the proper operating temperature. A malfunctioning cooling system component can cause motor overheating, which can destroy the motor and cause the entire plant to shut down. As a result, creating a dynamic model of the motor cooling system for quality performance, failure diagnosis, and prediction is critical. However, the external motor cooling system design in HVIM is limited and separately done in the past. With this issue in mind, this paper proposes a combined modeling approach to the HVIM cooling system which consists of integrating the electrical, thermal, and cooler model using the mathematical model for thermal performance improvement. Firstly, the development of an electrical model using an established mathematical model. Subsequently, the development of a thermal model using combined mathematical and linear regression models to produce motor temperature. Then, a modified cooler model is developed to provide cold air temperature for cooling monitoring. All validated models are integrated into a single model called the HVIM cooling system as the actual setup of the HVIM. Ultimately, the core of this modeling approach is integrating all models to accurately represent the actual signals of the motor cooler temperature. Then, the actual signals are used to validate the whole structure of the model using Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) analysis. The results demonstrate the high accuracy of the HVIM cooling system representation with less than 1% error tolerance based on the industrial plant experts. Thus, it will be helpful for future utilization in quality maintenance, fault identification and prediction study.
    Matched MeSH terms: Linear Models
  6. Keshtegar B, Piri J, Asnida Abdullah R, Hasanipanah M, Muayad Sabri Sabri M, Nguyen Le B
    Front Public Health, 2022;10:1094771.
    PMID: 36817184 DOI: 10.3389/fpubh.2022.1094771
    Ground vibration induced by blasting operations is considered one of the most common environmental effects of mining projects. A strong ground vibration can destroy buildings and structures, hence its prediction and minimization are of high importance. The aim of this study is to estimate the ground vibration through a hybrid soft computing (SC) method, called RSM-SVR, which comprises two main regression techniques: the response surface model (RSM) and support vector regression (SVR). The RSM-SVR model applies an RSM in the first calibrating process and an SVR in the second calibrating process to improve the accuracy of the ground vibration predictions. The predicted results of an RSM, which are obtained using the input data of problems, are used as the input dataset for the regression process of an SVR. The effectiveness and agreement of the RSM-SVR model were compared to those of an SVR optimized with the particle swarm optimization (PSO) and genetic algorithm (GA), RSM, and multivariate linear regression (MLR) based on several statistical factors. The findings confirmed that the RSM-SVR model was considerably superior to other models in terms of accuracy. The amounts of coefficient of determination (R 2) were 0.896, 0.807, 0.782, 0.752, 0.711, and 0.664 obtained from the RSM-SVR, PSO-SVR, GA-SVR, MLR, SVR, and RSM models, respectively.
    Matched MeSH terms: Linear Models
  7. Mohidem NA, Osman M, Muharam FM, Elias SM, Shaharudin R, Hashim Z
    Int J Mycobacteriol, 2021 12 18;10(4):442-456.
    PMID: 34916466 DOI: 10.4103/ijmy.ijmy_182_21
    Background: Early prediction of tuberculosis (TB) cases is very crucial for its prevention and control. This study aims to predict the number of TB cases in Gombak based on sociodemographic and environmental factors.

    Methods: The sociodemographic data of 3325 TB cases from January 2013 to December 2017 in Gombak district were collected from the MyTB web and TB Information System database. Environmental data were obtained from the Department of Environment, Malaysia; Department of Irrigation and Drainage, Malaysia; and Malaysian Metrological Department from July 2012 to December 2017. Multiple linear regression (MLR) and artificial neural network (ANN) were used to develop the prediction model of TB cases. The models that used sociodemographic variables as the input datasets were referred as MLR1 and ANN1, whereas environmental variables were represented as MLR2 and ANN2 and both sociodemographic and environmental variables together were indicated as MLR3 and ANN3.

    Results: The ANN was found to be superior to MLR with higher adjusted coefficient of determination (R2) values in predicting TB cases; the ranges were from 0.35 to 0.47 compared to 0.07 to 0.14, respectively. The best TB prediction model, that is, ANN3 was derived from nationality, residency, income status, CO, NO2, SO2, PM10, rainfall, temperature, and atmospheric pressure, with the highest adjusted R2 value of 0.47, errors below 6, and accuracies above 96%.

    Conclusions: It is envisaged that the application of the ANN algorithm based on both sociodemographic and environmental factors may enable a more accurate modeling for predicting TB cases.

    Matched MeSH terms: Linear Models
  8. Altowayti WAH, Othman N, Al-Gheethi A, Dzahir NHBM, Asharuddin SM, Alshalif AF, et al.
    Molecules, 2021 Oct 13;26(20).
    PMID: 34684757 DOI: 10.3390/molecules26206176
    Sustainable wastewater treatment is one of the biggest issues of the 21st century. Metals such as Zn2+ have been released into the environment due to rapid industrial development. In this study, dried watermelon rind (D-WMR) is used as a low-cost adsorption material to assess natural adsorbents' ability to remove Zn2+ from synthetic wastewater. D-WMR was characterized using scanning electron microscope (SEM) and X-ray fluorescence (XRF). According to the results of the analysis, the D-WMR has two colours, white and black, and a significant concentration of mesoporous silica (83.70%). Moreover, after three hours of contact time in a synthetic solution with 400 mg/L Zn2+ concentration at pH 8 and 30 to 40 °C, the highest adsorption capacity of Zn2+ onto 1.5 g D-WMR adsorbent dose with 150 μm particle size was 25 mg/g. The experimental equilibrium data of Zn2+ onto D-WMR was utilized to compare nonlinear and linear isotherm and kinetics models for parameter determination. The best models for fitting equilibrium data were nonlinear Langmuir and pseudo-second models with lower error functions. Consequently, the potential use of D-WMR as a natural adsorbent for Zn2+ removal was highlighted, and error analysis indicated that nonlinear models best explain the adsorption data.
    Matched MeSH terms: Linear Models
  9. Pak HY, Chuah CJ, Yong EL, Snyder SA
    Sci Total Environ, 2021 Aug 01;780:146661.
    PMID: 34030308 DOI: 10.1016/j.scitotenv.2021.146661
    Land use plays a significant role in determining the spatial patterns of water quality in the Johor River Basin (JRB), Malaysia. In the recent years, there have been several occurrences of pollution in these rivers, which has generated concerns over the long-term sustainability of the water resources in the JRB. Specifically, this water resource is a shared commodity between two states, namely, Johor state of Malaysia and Singapore, a neighbouring country adjacent to Malaysia. Prior to this study, few research on the influence of land use configuration on water quality have been conducted in Johor. In addition, it is also unclear how water quality varies under different seasonality in the presence of point sources. In this study, we investigated the influence of land use and point sources from wastewater treatment plants (WWTPs) on the water quality in the JRB. Two statistical techniques - Multivariate Linear Regression (MLR) and Redundancy Analysis (RA) were undertaken to analyse the relationships between river water quality and land use configuration, as well as point sources from WWTPs under different seasonality. Water samples were collected from 49 sites within the JRB from March to December in 2019. Results showed that influence from WWTPs on water quality was greater during the dry season and less significant during the wet season. In particular, point source was highly positively correlated with ammoniacal‑nitrogen (NH3-N). On the other hand, land use influence was greater than point source influence during the wet season. Residential and urban land use were important predictors for nutrients and organic matter (chemical oxygen demand); and forest land use were important sinks for heavy metals but a significant source of manganese.
    Matched MeSH terms: Linear Models
  10. Shafie AA, Chhabra IK, Wong JHY, Mohammed NS
    Eur J Health Econ, 2021 Jul;22(5):735-747.
    PMID: 33860379 DOI: 10.1007/s10198-021-01287-z
    PURPOSE: To develop a mapping algorithm for generating EQ-5D-3L utility scores from the PedsQL Generic Core Scales (PedsQL GCS) in patients with transfusion-dependent thalassemia (TDT).

    METHODS: The algorithm was developed using data from 345 TDT patients. Spearman's rank correlation was used to evaluate the conceptual overlap between the instruments. Model specifications were chosen using a stepwise regression. Both direct and response mapping methods were attempted. Six mapping estimation methods ordinary least squares (OLS), a log-transformed response using OLS, generalized linear model (GLM), two-part model (TPM), Tobit and multinomial logistic regression (MLOGIT) were tested to determine the root mean squared error (RMSE) and mean absolute error (MAE). Other criterion used were accuracy of the predicted utility score, proportions of absolute differences that was less than 0.03 and intraclass correlation coefficient. An in-sample, leave-one-out cross validation was conducted to test the generalizability of each model.

    RESULTS: The best performing model was specified with three out of the four PedsQL GCS scales-the physical, emotional and social functioning score. The best performing estimation method for direct mapping was a GLM with a RMSE of 0.1273 and MAE of 0.1016, while the best estimation method for response mapping was the MLOGIT with a RMSE of 0.1597 and MAE of 0.0826.

    CONCLUSION: The mapping algorithm developed using the GLM would facilitate the calculation of utility scores to inform economic evaluations for TDT patients when EQ-5D data is not available. However, caution should be exercised when using this algorithm in patients who have poor quality of life.

    Matched MeSH terms: Linear Models
  11. Ahmed A, Saqlain M, Bashir N, Dujaili J, Hashmi F, Mazhar F, et al.
    Qual Life Res, 2021 Jun;30(6):1653-1664.
    PMID: 33582967 DOI: 10.1007/s11136-021-02771-y
    BACKGROUND: Health-related quality of life (HRQoL) is considered to be the fourth 90 of UNAIDS 90-90-90 target to monitor the effects of combination antiretroviral therapy (ART). ART has significantly increased the life expectancy of people living with HIV/AIDS (PLWHA). However, the impact of chronic infection on HRQoL remains unclear, while factors influencing the HRQoL may vary from one country to another. The current study aimed to assess HRQoL and its associated factors among PLWHA receiving ART in Pakistan.

    METHODS: A cross-sectional descriptive study was conducted among PLWHA attending an ART centre of a tertiary care hospital in Islamabad, Pakistan. HRQoL was assessed using a validated Urdu version of EuroQol 5 dimensions 3 level (EQ-5D-3L) and its Visual Analogue Scale (EQ-VAS).

    RESULTS: Of the 602 patients included in the analyses, 59.5% (n = 358) reported no impairment in self-care, while 63.1% (n = 380) were extremely anxious/depressed. The overall mean EQ-5D utility score and visual analogue scale (EQ-VAS) score were 0.388 (SD: 0.41) and 66.20 (SD: 17.22), respectively. Multivariate linear regression analysis revealed that the factors significantly associated with HRQoL were: female gender; age  > 50 years; having primary and secondary education;  > 1 year since HIV diagnosis; HIV serostatus AIDS-converted; higher CD 4 T lymphocytes count; detectable viral load; and increased time to ART.

    CONCLUSIONS: The current findings have shown that PLWHA in Pakistan adherent to ART had a good overall HRQoL, though with significantly higher depression. Some of the factors identified are amenable to institution-based interventions while mitigating depression to enhance the HRQoL of PLWHA in Pakistan. The HRQoL determined in this study could be useful for future economic evaluation studies for ART and in designing future interventions.

    Matched MeSH terms: Linear Models
  12. Gopinath SCB, Ismail ZH, Shapiai MI, Yasin MNM
    PMID: 34009645 DOI: 10.1002/bab.2196
    Current developments in sensors and actuators are heralding a new era to facilitate things to happen effortlessly and efficiently with proper communication. On the other hand, Internet of Things (IoT) has been boomed up with er potential and occupies a wide range of disciplines. This study has choreographed to design of an algorithm and a smart data-processing scheme to implement the obtained data from the sensing system to transmit to the receivers. Technically, it is called "telediagnosis" and "remote digital monitoring," a revolution in the field of medicine and artificial intelligence. For the proof of concept, an algorithmic approach has been implemented for telediagnosis with one of the degenerative diseases, that is, Parkinson's disease. Using the data acquired from an improved interdigitated electrode, sensing surface was evaluated with the attained sensitivity of 100 fM (n = 3), and the limit of detection was calculated with the linear regression value coefficient. By the designed algorithm and data processing with the assistance of IoT, further validation was performed and attested the coordination. This proven concept can be ideally used with all sensing strategies for immediate telemedicine by end-to-end communications.
    Matched MeSH terms: Linear Models
  13. Siavash NK, Ghobadian B, Najafi G, Rohani A, Tavakoli T, Mahmoodi E, et al.
    Environ Res, 2021 05;196:110434.
    PMID: 33166537 DOI: 10.1016/j.envres.2020.110434
    Wind power is one of the most popular sources of renewable energies with an ideal extractable value that is limited to 0.593 known as the Betz-Joukowsky limit. As the generated power of wind machines is proportional to cubic wind speed, therefore it is logical that a small increment in wind speed will result in significant growth in generated power. Shrouding a wind turbine is an ordinary way to exceed the Betz limit, which accelerates the wind flow through the rotor plane. Several layouts of shrouds are developed by researchers. Recently an innovative controllable duct is developed by the authors of this work that can vary the shrouding angle, so its performance is different in each opening angle. As a wind tunnel investigation is heavily time-consuming and has a high cost, therefore just four different opening angles have been assessed. In this work, the performance of the turbine was predicted using multiple linear regression and an artificial neural network in a wide range of duct opening angles. For the turbine power generation and its rotor angular speed in different wind velocities and duct opening angles, regression and an ANN are suggested. The developed neural network model is found to possess better performance than the regression model for both turbine power curve and rotor speed estimation. This work revealed that in higher ranges of wind velocity, the turbine performance intensively will be a function of shrouding angle. This model can be used as a lookup table in controlling the turbines equipped with the proposed mechanism.
    Matched MeSH terms: Linear Models
  14. Veerasamy R, Rajak H
    Turk J Pharm Sci, 2021 04 20;18(2):151-156.
    PMID: 33900700 DOI: 10.4274/tjps.galenos.2020.45556
    Objectives: The present study aimed to establish significant and validated quantitative structure-activity relationship (QSAR) models for neuraminidase inhibitors and correlate their physicochemical, steric, and electrostatic properties with their anti-influenza activity.

    Materials and Methods: We have developed and validated 2D and 3D QSAR models by using multiple linear regression, partial least square regression, and k-nearest neighbor-molecular field analysis methods.

    Results: 2D QSAR models had q2: 0.950 and pred_r2: 0.877 and 3D QSAR models had q2: 0.899 and pred_r2: 0.957. These results showed that the models werere predictive.

    Conclusion: Parameters such as hydrogen count and hydrophilicity were involved in 2D QSAR models. The 3D QSAR study revealed that steric and hydrophobic descriptors were negatively contributed to neuraminidase inhibitory activity. The results of this study could be used as platform for design of better anti-influenza drugs.

    Matched MeSH terms: Linear Models
  15. Chan YC, Binti Mawardi M, Ismail Daud AH
    Malays Fam Physician, 2021 Mar 25;16(1):31-38.
    PMID: 33948140 DOI: 10.51866/oa0001
    Background: Stigmatizing attitudes expressed by health care providers prevent some members of at-risk populations from accessing human immunodeficiency virus (HIV) screening and care. This attitude contributes to the continuity of the infection dissemination within our community, which gives an impact on the healthcare service and the curtailment of the global HIV/acquired immunodeficiency syndrome (AIDS) pandemic.

    Objective: This study was conducted to identify stigmatizing attitudes toward people living with HIV/AIDS (PLWHA) and their determinants among primary health care providers in Kinta District, Perak.

    Methodology: A cross-sectional study was conducted in 36 primary care clinics in Kinta District, Perak. Using stratified random sampling, 365 primary health care providers were recruited into the study. A validated self-administered questionnaire was used to obtain sociodemographic data as well as information on the healthcare experiences of healthcare providers, their knowledge of HIV/AIDS, and attitudes toward PLWHA. Determinants were identified using multiple linear regression.

    Results: More than half of the respondents (54.1%) had never provided care to HIV/AIDS patients. A minority (29.9%) had received training on HIV/AIDS. This study shows that doctors (Coef.= -9.50, 95% CI: -18.93, -0.07, p= 0.048), respondents with HIV-positive relatives, (Coef.= -5.61, 95% CI: -10.57, -0.65, p= 0.027), those who had provided care to HIV/AIDS patients (Coef.= -2.38, 95% CI: -4.31, -0.45, p= 0.016), and those with a higher knowledge score on HIV/AIDS (Coef.= -0.86, 95% CI: -1.59, -0.13, p= 0.021) were less likely to show stigmatizing attitudes toward PLWHA.

    Conclusion: The issue of stigmatizing attitudes toward PLWHA among primary health care providers needs to be addressed. This study finds that knowledge, profession, experiences with caring for PLWHA, gender, and having HIV-positive relatives are significant predictors of stigmatizing attitudes toward PLWHA among primary health care providers in Kinta District, Perak. Interventional programs to improve knowledge and awareness, as well as decrease stigma toward PLWHA, should be implemented among all health care providers, especially those who have no opportunity to provide direct care.

    Matched MeSH terms: Linear Models
  16. Abushagur AAG, Arsad N, Bakar AAA
    Sensors (Basel), 2021 Mar 12;21(6).
    PMID: 33809028 DOI: 10.3390/s21062002
    This work investigates a new interrogation method of a fiber Bragg grating (FBG) sensor based on longer and shorter wavelengths to distinguish between transversal forces and temperature variations. Calibration experiments were carried out to examine the sensor's repeatability in response to the transversal forces and temperature changes. An automated calibration system was developed for the sensor's characterization, calibration, and repeatability testing. Experimental results showed that the FBG sensor can provide sensor repeatability of 13.21 pm and 17.015 pm for longer and shorter wavelengths, respectively. The obtained calibration coefficients expressed in the linear model using the matrix enabled the sensor to provide accurate predictions for both measurements. Analysis of the calibration and experiment results implied improvements for future work. Overall, the new interrogation method demonstrated the potential to employ the FBG sensing technique where discrimination between two/three measurands is needed.
    Matched MeSH terms: Linear Models
  17. Saman SA, Chang KH, Abdullah AFL
    J Forensic Sci, 2021 Mar;66(2):608-618.
    PMID: 33202056 DOI: 10.1111/1556-4029.14625
    Abuse of solvent-based adhesives jeopardizes world population, especially the young generation. Adhesive-related exhibits encountered in forensic cases might need to be determined if they could have come from a particular source or to establish link between cases or persons. This study was aimed to discriminate solvent-based adhesives, especially to aid forensic investigation of glue sniffing activities. In this study, thirteen brands with three samples each, totaling at 39 adhesive samples, were analyzed using attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy followed by chemometric methods. Experimental output showed that adhesive samples utilized in this study were less likely to change in their ATR-FTIR profiles over time, at least up to 2 months. No interference from plastic materials was noticed based on ATR-FTIR profile comparison. Physical examination could differentiate the samples into two groups, namely contact adhesives and cement adhesives. A principal component analysis-score linear discriminative analysis (PC-score LDA) model resulted in 100% and 98.6% correct classification in discriminating the two groups of adhesive samples, forming seven discriminative clusters. Test set with adhesive samples applied glass slide and plastic substrates also demonstrated a 100% correct classification into their respective groups. As a conclusion, the method allowed for discrimination of adhesive samples based on the spectral features, displaying relationship among samples. It is hoped that this comparative information is beneficial to trace the possible source of solvent-based adhesives, whenever they are recovered from a crime scene, for forensic investigation.
    Matched MeSH terms: Linear Models
  18. Ismail MH, Baharuddin KA, Suliman MA, Mohd Shukri MF, Che Has SN, Lo ZZ
    Med J Malaysia, 2021 03;76(2):157-163.
    PMID: 33742622
    INTRODUCTION: Potassium level is measured for patients with high risk of hyperkalemia in the emergency department (ED) using both blood gas analyser (BGA) and biochemistry analyser (BCA). The study was conducted to evaluate the correlation and agreement of potassium measurement between BGA and BCA.

    MATERIALS AND METHODS: This is a prospective cross-sectional study on the data obtained from Hospital Universiti Sains Malaysia (Hospital USM) from Jun 2018 until May 2019. Blood samples were taken via a single prick from venous blood and sent separately using 1ml heparinised syringe and were analysed immediately in ED using BGA (Radiometer, ABL800 FLEX, Denmark) and another sample was sent to the central laboratory of Hospital USM and analysed by BCA (Architect, C8000, USA). Only patients who had potassium levels ≥5.0mmol/L on blood gas results were included. A total of 173 sample pairs were included. The correlation and agreement were evaluated using Passing and Bablok regression, Linear Regression and Bland-Altman test.

    RESULT: Of the 173 sample pairs, the median of potassium level based on BGA and BCA were 5.50mmol/L (IQR: 1.00) and 5.90mmol/L (IQR: 0.95) respectively. There was significant correlation between two measurements (p<0.001, r: 0.36). The agreement between the two measurements showed within acceptable mean difference which was 0.27 mmol/L with 95% limit of agreement were 1.21mmol/L to 1.73mmol/L.

    CONCLUSION: The result of blood gas can be used as a guide for initial treatment of hyperkalaemia in critical cases where time is of the essence. However, BCA result is still the definitive value.

    Matched MeSH terms: Linear Models
  19. Adam H, Gopinath SCB, Arshad MKM, Parmin NA, Hashim U
    Int J Biol Macromol, 2021 Feb 28;171:217-224.
    PMID: 33418041 DOI: 10.1016/j.ijbiomac.2021.01.014
    Misfolding and accumulation of the protein alpha synuclein in the brain cells characterize Parkinson's disease (PD). Electrochemical based aluminum interdigitated electrodes (ALIDEs) was fabricated by using conventional photolithography method and modified the surfaces with zinc oxide and gold nanorod by using spin coating method for the analysis of PD protein biomarker. The device surface modified with gold nanorod of 25 nm diameter was used. The bare devices and the surface modified devices were characterized by Scanning Electron Microscope, 3D-Profilometer, Atomic Force Microscope and high-power microscope. The above measurement was also performed to measure the interaction of antibody with aggregated alpha-synuclein for normal, aggregated and aggregated alpha synuclein in human serum and distinguished against 3 control proteins (PARK1, DJ-1 and Factor IX). The detection limit for normal alpha synuclein was 1 f. with the sensitivity of 1 f. on a linear regression (R2 = 0.9759). The detection limit for aggregated alpha synuclein was 10 aM with the sensitivity of 1 aM on a linear regression (R2 = 0.9797). Also, the detection limit of aggregated alpha synuclein in serum was 10 aM with the sensitivity of 1 aM on a linear regression (R2 = 0.9739). These results however indicate that, serum has only minimal amount of alpha synuclein.
    Matched MeSH terms: Linear Models
  20. Akinpelu AA, Chowdhury ZZ, Shibly SM, Faisal ANM, Badruddin IA, Rahman MM, et al.
    Int J Mol Sci, 2021 Feb 19;22(4).
    PMID: 33669883 DOI: 10.3390/ijms22042090
    This study deals with the preparation of activated carbon (CDSP) from date seed powder (DSP) by chemical activation to eliminate polyaromatic hydrocarbon-PAHs (naphthalene-C10H8) from synthetic wastewater. The chemical activation process was carried out using a weak Lewis acid of zinc acetate dihydrate salt (Zn(CH3CO2)2·2H2O). The equilibrium isotherm and kinetics analysis was carried out using DSP and CDSP samples, and their performances were compared for the removal of a volatile organic compound-naphthalene (C10H8)-from synthetic aqueous effluents or wastewater. The equilibrium isotherm data was analyzed using the linear regression model of the Langmuir, Freundlich and Temkin equations. The R2 values for the Langmuir isotherm were 0.93 and 0.99 for naphthalene (C10H8) adsorption using DSP and CDSP, respectively. CDSP showed a higher equilibrium sorption capacity (qe) of 379.64 µg/g. DSP had an equilibrium sorption capacity of 369.06 µg/g for C10H8. The rate of reaction was estimated for C10H8 adsorption using a pseudo-first order, pseudo-second order and Elovich kinetic equation. The reaction mechanism for both the sorbents (CDSP and DSP) was studied using the intraparticle diffusion model. The equilibrium data was well-fitted with the pseudo-second order kinetics model showing the chemisorption nature of the equilibrium system. CDSP showed a higher sorption performance than DSP due to its higher BET surface area and carbon content. Physiochemical characterizations of the DSP and CDSP samples were carried out using the BET surface area analysis, Fourier-scanning microscopic analysis (FSEM), energy-dispersive X-ray (EDX) analysis and Fourier-transform spectroscopic analysis (FTIR). A thermogravimetric and ultimate analysis was also carried out to determine the carbon content in both the sorbents (DSP and CDSP) here. This study confirms the potential of DSP and CDSP to remove C10H8 from lab-scale synthetic wastewater.
    Matched MeSH terms: Linear Models
Filters
Contact Us

Please provide feedback to Administrator (afdal@afpm.org.my)

External Links