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Measurement of biochemical oxygen demand of the leachates

Fulazzaky MA

Environ Monit Assess, 2013 Jun;185(6):4721-34.
PMID: 23001555 DOI: 10.1007/s10661-012-2899-z

Biochemical oxygen demand (BOD) of the leachates originally from the different types of landfill sites was studied based on the data measured using the two manometric methods. The measurements of BOD using the dilution method were carried out to assess the typical physicochemical and biological characteristics of the leachates together with some other parameters. The linear regression analysis was used to predict rate constants for biochemical reactions and ultimate BOD values of the different leachates. The rate of a biochemical reaction implicated in microbial biodegradation of pollutants depends on the leachate characteristics, mass of contaminant in the leachate, and nature of the leachate. Character of leachate samples for BOD analysis of using the different methods may differ significantly during the experimental period, resulting in different BOD values. This work intends to verify effect of the different dilutions for the manometric method tests on the BOD concentrations of the leachate samples to contribute to the assessment of reaction rate and microbial consumption of oxygen.

Matched MeSH terms: Linear Models
Transformation of nitrogen dioxide into ozone and prediction of ozone concentrations using multiple linear regression techniques

Ghazali NA, Ramli NA, Yahaya AS, Yusof NF, Sansuddin N, Al Madhoun WA

Environ Monit Assess, 2010 Jun;165(1-4):475-89.
PMID: 19440846 DOI: 10.1007/s10661-009-0960-3

Analysis and forecasting of air quality parameters are important topics of atmospheric and environmental research today due to the health impact caused by air pollution. This study examines transformation of nitrogen dioxide (NO(2)) into ozone (O(3)) at urban environment using time series plot. Data on the concentration of environmental pollutants and meteorological variables were employed to predict the concentration of O(3) in the atmosphere. Possibility of employing multiple linear regression models as a tool for prediction of O(3) concentration was tested. Results indicated that the presence of NO(2) and sunshine influence the concentration of O(3) in Malaysia. The influence of the previous hour ozone on the next hour concentrations was also demonstrated.

Matched MeSH terms: Linear Models*
Diurnal models of traffic-generated CO for Penang, Malaysia

Koh HL, Lim PE

Environ Monit Assess, 1991 Oct;19(1-3):373-82.
PMID: 24233954 DOI: 10.1007/BF00401326

Georgetown of Penang, an old city, is noted for its narrow streets. The existing traffic dispersal system is utterly inadequate to cope with the ever increasing number of cars and motorcycles on the road. The principal objective of this study is to build prediction models of CO to be employed as one of the planning tools in the future design of Penang urban traffic dispersal system. This study involves the monitoring of kerbside CO levels at selected sites and the fitting of hourly-averaged CO data to linear regression models incorporating the residual effect of CO emission due to traffic in the earlier periods and also different categories of vehicles. The best overall regression model appears to be the one based upon the total traffic count of motorcycles. This can be accounted for by the fact that the traffic counts of motorcycles and cars are highly correlated in most cases and that the emissions of CO from motorcycles are more readily detected as they travel closer to the kerb. The inclusion of residual CO in the models significantly improves the correlation coefficient from about 0.4 to about 0.7.

Matched MeSH terms: Linear Models
Comparative study of artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS) and multiple linear regression (MLR) for modeling of Cu (II) adsorption from aqueous solution using biochar derived from rambutan (Nephelium lappaceum) peel

Wong YJ, Arumugasamy SK, Chung CH, Selvarajoo A, Sethu V

Environ Monit Assess, 2020 Jun 17;192(7):439.
PMID: 32556670 DOI: 10.1007/s10661-020-08268-4

Presence of copper within water bodies deteriorates human health and degrades natural environment. This heavy metal in water is treated using a promising biochar derived from rambutan (Nephelium lappaceum) peel through slow pyrolysis. This research compares the efficacies of artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and multiple linear regression (MLR) models and evaluates their capability in estimating the adsorption efficiency of biochar for the removal of Cu (II) ions based on 480 experimental sets obtained in a laboratory batch study. The effects of operational parameters such as contact time, operating temperature, biochar dosage, and initial Cu (II) ion concentration on removing Cu (II) ions were investigated. Eleven different training algorithms in ANN and 8 different membership functions in ANFIS were compared statistically and evaluated in terms of estimation errors, which are root mean squared error (RMSE), mean absolute error (MAE), and accuracy. The effects of number of hidden neuron in ANN model and fuzzy set combination in ANFIS were studied. In this study, ANFIS model with Gaussian membership function and fuzzy set combination of [4 5 2 3] was found to be the best method, with accuracy of 90.24% and 87.06% for training and testing dataset, respectively. Contribution of this study is that ANN, ANFIS, and MLR modeling techniques were used for the first time to study the adsorption of Cu (II) ions from aqueous solutions using rambutan peel biochar.

Matched MeSH terms: Linear Models
Multiple linear regression and regression with time series error models in forecasting PM10 concentrations in Peninsular Malaysia

Ng KY, Awang N

Environ Monit Assess, 2018 Jan 06;190(2):63.
PMID: 29306973 DOI: 10.1007/s10661-017-6419-z

Frequent haze occurrences in Malaysia have made the management of PM10 (particulate matter with aerodynamic less than 10 μm) pollution a critical task. This requires knowledge on factors associating with PM10 variation and good forecast of PM10 concentrations. Hence, this paper demonstrates the prediction of 1-day-ahead daily average PM10 concentrations based on predictor variables including meteorological parameters and gaseous pollutants. Three different models were built. They were multiple linear regression (MLR) model with lagged predictor variables (MLR1), MLR model with lagged predictor variables and PM10 concentrations (MLR2) and regression with time series error (RTSE) model. The findings revealed that humidity, temperature, wind speed, wind direction, carbon monoxide and ozone were the main factors explaining the PM10 variation in Peninsular Malaysia. Comparison among the three models showed that MLR2 model was on a same level with RTSE model in terms of forecasting accuracy, while MLR1 model was the worst.

Matched MeSH terms: Linear Models*
Prediction of power generation and rotor angular speed of a small wind turbine equipped to a controllable duct using artificial neural network and multiple linear regression

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
Fulltext Association of carcinogenic polycyclic aromatic hydrocarbon emissions and smoking with lung cancer mortality rates on a global scale

Motorykin O, Matzke MM, Waters KM, Massey Simonich SL

Environ Sci Technol, 2013 Apr 2;47(7):3410-6.
PMID: 23472838 DOI: 10.1021/es305295d

The objective of this research was to investigate the relationship between lung cancer mortality rates, carcinogenic polycyclic aromatic hydrocarbon (PAH) emissions, and smoking on a global scale, as well as for different socioeconomic country groups. The estimated lung cancer deaths per 100,000 people (ED100000) and age standardized lung cancer death rate per 100,000 people (ASDR100000) in 2004 were regressed on PAH emissions in benzo[a]pyrene equivalence (BaPeq), smoking prevalence, cigarette price, gross domestic product per capita, percentage of people with diabetes, and average body mass index using simple and multiple linear regression for 136 countries. Using stepwise multiple linear regression, a statistically significant positive linear relationship was found between loge(ED100000) and loge(BaPeq) emissions for high (p-value <0.01) and for the combination of upper-middle and high (p-value <0.05) socioeconomic country groups. A similar relationship was found between loge(ASDR100000) and loge(BaPeq) emissions for the combination of upper-middle and high (p-value <0.01) socioeconomic country groups. Conversely, for loge(ED100000) and loge(ASDR100000), smoking prevalence was the only significant independent variable in the low socioeconomic country group (p-value <0.001). These results suggest that reducing BaPeq emissions in the U.S., Canada, Australia, France, Germany, Brazil, South Africa, Poland, Mexico, and Malaysia could reduce ED100000, while reducing smoking prevalence in Democratic People's Republic of Korea, Nepal, Mongolia, Cambodia, and Bangladesh could significantly reduce the ED100000 and ASDR100000.

Matched MeSH terms: Linear Models
Regression and multivariate models for predicting particulate matter concentration level

Nazif A, Mohammed NI, Malakahmad A, Abualqumboz MS

Environ Sci Pollut Res Int, 2018 Jan;25(1):283-289.
PMID: 29032528 DOI: 10.1007/s11356-017-0407-2

The devastating health effects of particulate matter (PM10) exposure by susceptible populace has made it necessary to evaluate PM10 pollution. Meteorological parameters and seasonal variation increases PM10 concentration levels, especially in areas that have multiple anthropogenic activities. Hence, stepwise regression (SR), multiple linear regression (MLR) and principal component regression (PCR) analyses were used to analyse daily average PM10 concentration levels. The analyses were carried out using daily average PM10 concentration, temperature, humidity, wind speed and wind direction data from 2006 to 2010. The data was from an industrial air quality monitoring station in Malaysia. The SR analysis established that meteorological parameters had less influence on PM10 concentration levels having coefficient of determination (R 2) result from 23 to 29% based on seasoned and unseasoned analysis. While, the result of the prediction analysis showed that PCR models had a better R 2 result than MLR methods. The results for the analyses based on both seasoned and unseasoned data established that MLR models had R 2 result from 0.50 to 0.60. While, PCR models had R 2 result from 0.66 to 0.89. In addition, the validation analysis using 2016 data also recognised that the PCR model outperformed the MLR model, with the PCR model for the seasoned analysis having the best result. These analyses will aid in achieving sustainable air quality management strategies.

Matched MeSH terms: Linear Models
The role of digestive factors in determining glycemic response in a multiethnic Asian population

Tan VM, Ooi DS, Kapur J, Wu T, Chan YH, Henry CJ, et al.

Eur J Nutr, 2016 Jun;55(4):1573-81.
PMID: 26160548 DOI: 10.1007/s00394-015-0976-0

PURPOSE: There are wide inter-individual differences in glycemic response (GR). We aimed to examine key digestive parameters that influence inter-individual and ethnic differences in GR in healthy Asian individuals.
METHODS: Seventy-five healthy male subjects (25 Chinese, 25 Malays, and 25 Asian-Indians) were served equivalent available carbohydrate amounts (50 g) of jasmine rice (JR) and basmati rice (BR) on separate occasions. Postprandial blood glucose concentrations were measured at fasting (-5 and 0 min) and at 15- to 30-min interval over 180 min. Mastication parameters (number of chews per mouth and chewing time per mouthful), saliva α-amylase activity, AMY1 gene copy numbers and gastric emptying rate were measured to investigate their relationships with GR.
RESULTS: The GR for jasmine rice was significantly higher than for basmati rice (P 0.05).
CONCLUSION: Mastication parameters contribute significantly to GR. Eating slowly and having larger food boluses before swallowing (less chewing), both potentially modifiable, may be beneficial in glycemic control.

Matched MeSH terms: Linear Models
Fulltext Dietary and lifestyle determinants of acrylamide and glycidamide hemoglobin adducts in non-smoking postmenopausal women from the EPIC cohort

Obón-Santacana M, Lujan-Barroso L, Freisling H, Cadeau C, Fagherazzi G, Boutron-Ruault MC, et al.

Eur J Nutr, 2017 Apr;56(3):1157-1168.
PMID: 26850269 DOI: 10.1007/s00394-016-1165-5

PURPOSE: Acrylamide was classified as 'probably carcinogenic' to humans in 1994 by the International Agency for Research on Cancer. In 2002, public health concern increased when acrylamide was identified in starchy, plant-based foods, processed at high temperatures. The purpose of this study was to identify which food groups and lifestyle variables were determinants of hemoglobin adduct concentrations of acrylamide (HbAA) and glycidamide (HbGA) in 801 non-smoking postmenopausal women from eight countries in the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort.
METHODS: Biomarkers of internal exposure were measured in red blood cells (collected at baseline) by high-performance liquid chromatography/tandem mass spectrometry (HPLC/MS/MS) . In this cross-sectional analysis, four dependent variables were evaluated: HbAA, HbGA, sum of total adducts (HbAA + HbGA), and their ratio (HbGA/HbAA). Simple and multiple regression analyses were used to identify determinants of the four outcome variables. All dependent variables (except HbGA/HbAA) and all independent variables were log-transformed (log2) to improve normality. Median (25th-75th percentile) HbAA and HbGA adduct levels were 41.3 (32.8-53.1) pmol/g Hb and 34.2 (25.4-46.9) pmol/g Hb, respectively.
RESULTS: The main food group determinants of HbAA, HbGA, and HbAA + HbGA were biscuits, crackers, and dry cakes. Alcohol intake and body mass index were identified as the principal determinants of HbGA/HbAA. The total percent variation in HbAA, HbGA, HbAA + HbGA, and HbGA/HbAA explained in this study was 30, 26, 29, and 13 %, respectively.
CONCLUSIONS: Dietary and lifestyle factors explain a moderate proportion of acrylamide adduct variation in non-smoking postmenopausal women from the EPIC cohort.

Matched MeSH terms: Linear Models
Fulltext Main nutrient patterns are associated with prospective weight change in adults from 10 European countries

Freisling H, Pisa PT, Ferrari P, Byrnes G, Moskal A, Dahm CC, et al.

Eur J Nutr, 2016 Sep;55(6):2093-104.
PMID: 26303194 DOI: 10.1007/s00394-015-1023-x

PURPOSE: Various food patterns have been associated with weight change in adults, but it is unknown which combinations of nutrients may account for such observations. We investigated associations between main nutrient patterns and prospective weight change in adults.
METHODS: This study includes 235,880 participants, 25-70 years old, recruited between 1992 and 2000 in 10 European countries. Intakes of 23 nutrients were estimated from country-specific validated dietary questionnaires using the harmonized EPIC Nutrient DataBase. Four nutrient patterns, explaining 67 % of the total variance of nutrient intakes, were previously identified from principal component analysis. Body weight was measured at recruitment and self-reported 5 years later. The relationship between nutrient patterns and annual weight change was examined separately for men and women using linear mixed models with random effect according to center controlling for confounders.
RESULTS: Mean weight gain was 460 g/year (SD 950) and 420 g/year (SD 940) for men and women, respectively. The annual differences in weight gain per one SD increase in the pattern scores were as follows: principal component (PC) 1, characterized by nutrients from plant food sources, was inversely associated with weight gain in men (-22 g/year; 95 % CI -33 to -10) and women (-18 g/year; 95 % CI -26 to -11). In contrast, PC4, characterized by protein, vitamin B2, phosphorus, and calcium, was associated with a weight gain of +41 g/year (95 % CI +2 to +80) and +88 g/year (95 % CI +36 to +140) in men and women, respectively. Associations with PC2, a pattern driven by many micro-nutrients, and with PC3, a pattern driven by vitamin D, were less consistent and/or non-significant.
CONCLUSIONS: We identified two main nutrient patterns that are associated with moderate but significant long-term differences in weight gain in adults.

Matched MeSH terms: Linear Models
Lipid-lowering treatment in hypercholesterolaemic patients: the CEPHEUS Pan-Asian survey

Park JE, Chiang CE, Munawar M, Pham GK, Sukonthasarn A, Aquino AR, et al.

Eur J Prev Cardiol, 2012 Aug;19(4):781-94.
PMID: 21450606 DOI: 10.1177/1741826710397100

BACKGROUND: Treatment of hypercholesterolaemia in Asia is rarely evaluated on a large scale, and data on treatment outcome are scarce. The Pan-Asian CEPHEUS study aimed to assess low-density lipoprotein cholesterol (LDL-C) goal attainment among patients on lipid-lowering therapy.
METHODS: This survey was conducted in eight Asian countries. Hypercholesterolaemic patients aged ≥18 years who had been on lipid-lowering treatment for ≥3 months (stable medication for ≥6 weeks) were recruited, and lipid concentrations were measured. Demographic and other clinically relevant information were collected, and the cardiovascular risk of each patient was determined. Definitions and criteria set by the updated 2004 National Cholesterol Education Program guidelines were applied.
RESULTS: In this survey, 501 physicians enrolled 8064 patients, of whom 7281 were included in the final analysis. The mean age was 61.0 years, 44.4% were female, and 85.1% were on statin monotherapy. LDL-C goal attainment was reported in 49.1% of patients overall, including 51.2% of primary and 48.7% of secondary prevention patients, and 36.6% of patients with familial hypercholesterolaemia. The LDL-C goal was attained in 75.4% of moderate risk, 55.4% of high risk, and only 34.9% of very high-risk patients. Goal attainment was directly related to age and inversely related to cardiovascular risk and baseline LDL-C.
CONCLUSION: A large proportion of Asian hypercholesterolaemic patients on lipid-lowering drugs are not at recommended LDL-C levels and remain at risk for cardiovascular disease. Given the proven efficacy of lipid-lowering drugs in the reduction of LDL-C, there is room for further optimization of treatments to maximize benefits and improve outcomes.

Matched MeSH terms: Linear Models
Fulltext Psychosocial mechanisms of change in symptoms of Persistent Complex Bereavement Disorder amongst refugees from Myanmar over the course of Integrative Adapt Therapy

Tay AK, Khat Mung H, Badrudduza M, Balasundaram S, Fadil Azim D, Arfah Zaini N, et al.

Eur J Psychotraumatol, 2020 Sep 16;11(1):1807170.
PMID: 33062211 DOI: 10.1080/20008198.2020.1807170

Background: The ability to adapt to the psychosocial disruptions associated with the refugee experience may influence the course of complicated grief reactions. Objective: We examine these relationships amongst Myanmar refugees relocated to Malaysia who participated in a six-week course of Integrative Adapt Therapy (IAT). Method: Participants (n = 170) included Rohingya, Chin, and Kachin refugees relocated to Malaysia. At baseline and six-week post-treatment, we applied culturally adapted measures to assess symptoms of Prolonged Complex Bereavement Disorder (PCBD) and adaptive capacity to psychosocial disruptions, based on the Adaptive Stress Index (ASI). The ASI comprises five sub-scales of safety/security (ASI-1); bonds and networks (ASI-2); injustice (ASI-3); roles and identity (ASI-4); and existential meaning (ASI-5). Results: Multilevel linear models indicated that the relationship between baseline and posttreatment PCBD symptoms was mediated by the ASI scale scores. Further, ASI scale scores assessed posttreatment mediated the relationship between baseline and posttreatment PCBD symptoms. Mediation of PCBD change was greatest for the ASI II scale representing disrupted bonds and networks. Conclusion: Our findings are consistent with the informing model of IAT in demonstrating that changes in adaptive capacity, and especially in dealing with disrupted bonds and networks, may mediate the process of symptom improvement over the course of therapy.

Matched MeSH terms: Linear Models
Age-related rarefaction in retinal vasculature is not linear

Che Azemin MZ, Ab Hamid F, Aminuddin A, Wang JJ, Kawasaki R, Kumar DK

Exp Eye Res, 2013 Nov;116:355-358.
PMID: 24512773 DOI: 10.1016/j.exer.2013.10.010

The fractal dimension is a global measure of complexity and is useful for quantifying anatomical structures, including the retinal vascular network. A previous study found a linear declining trend with aging on the retinal vascular fractal dimension (DF); however, it was limited to the older population (49 years and older). This study aimed to investigate the possible models of the fractal dimension changes from young to old subjects (10-73 years). A total of 215 right-eye retinal samples, including those of 119 (55%) women and 96 (45%) men, were selected. The retinal vessels were segmented using computer-assisted software, and non-vessel fragments were deleted. The fractal dimension was measured based on the log-log plot of the number of grids versus the size. The retinal vascular DF was analyzed to determine changes with increasing age. Finally, the data were fitted to three polynomial models. All three models are statistically significant (Linear: R2 = 0.1270, 213 d.f., p linear regression (p linear and cubic models in a sample with a broader age spectrum.

Matched MeSH terms: Linear Models
Relationship of Anatomy and Function: External Anal Sphincter on Transperineal Ultrasound and Anal Incontinence

Paka C, Kamisan Atan I, Rios R, Dietz HP

Female Pelvic Med Reconstr Surg, 2016 10 27;23(4):238-243.
PMID: 27782978 DOI: 10.1097/SPV.0000000000000350

OBJECTIVE: The aim of this study was to investigate the association of the anatomic integrity of the external anal sphincter (EAS) detected on transperineal ultrasound (TPUS) with symptoms of anal incontinence (AI) as measured by St Mark's Incontinence Score (SMIS) and the visual analog scale (VAS).
METHODS: This is an observational, cross-sectional analysis of 486 women who presented to a tertiary urogynecological center between May 2013 and August 2014. They underwent a standardized interview and an examination that involved 3-dimensional/4-dimensional TPUS. The SMIS and VAS were administered if they answered positively to a question on AI. The association between defects of the EAS and symptoms of AI was evaluated using bivariate tests, as well as adjusting for pertinent covariates using multiple linear regression modeling.
RESULTS: Of the included patients, 17.1% reported AI, and 15.2% had significant EAS defects (≥4 slices) on TPUS imaging. A significant sonographic defect was diagnosed in 23% of women with AI versus 14% of those without (P = 0.033). Women with symptoms of AI were more likely to have a significant defect on TPUS (odds ratio, 1.878; 95% confidence interval, 1.05-3.37). No significant findings were seen when analyzing SMIS, its components, and VAS against sonographic EAS defects.
CONCLUSIONS: The symptom of AI is associated with significant EAS defects detected on TPUS. However, this study failed to show an association between significant EAS defects and the SMIS and VAS.

Matched MeSH terms: Linear Models
High 5-hydroxymethylfurfural concentrations are found in Malaysian honey samples stored for more than one year

Khalil MI, Sulaiman SA, Gan SH

Food Chem Toxicol, 2010 Aug-Sep;48(8-9):2388-92.
PMID: 20595027 DOI: 10.1016/j.fct.2010.05.076

5-Hydroxymethylfurfural (HMF) content is an indicator of the purity of honey. High concentrations of HMF in honey indicate overheating, poor storage conditions and old honey. This study investigated the HMF content of nine Malaysian honey samples, as well as the correlation of HMF formation with physicochemical properties of honey. Based on the recommendation by the International Honey Commission, three methods for the determination of HMF were used: (1) high performance liquid chromatography (HPLC), (2) White spectrophotometry and (3) Winkler spectrophotometry methods. HPLC and White spectrophotometric results yielded almost similar values, whereas the Winkler method showed higher readings. The physicochemical properties of honey (pH, free acids, lactones and total acids) showed significant correlation with HMF content and may provide parameters that could be used to make quick assessments of honey quality. The HMF content of fresh Malaysian honey samples stored for 3-6 months (at 2.80-24.87 mg/kg) was within the internationally recommended value (80 mg/kg for tropical honeys), while honey samples stored for longer periods (12-24 months) contained much higher HMF concentrations (128.19-1131.76 mg/kg). Therefore, it is recommended that honey should generally be consumed within one year, regardless of the type.

Matched MeSH terms: Linear Models
Fulltext Non-Synonymous Single-Nucleotide Polymorphisms and Physical Activity Interactions on Adiposity Parameters in Malaysian Adolescents

Zaharan NL, Muhamad NH, Jalaludin MY, Su TT, Mohamed Z, Mohamed MNA, et al.

Front Endocrinol (Lausanne), 2018;9:209.
PMID: 29755414 DOI: 10.3389/fendo.2018.00209

Background: Several non-synonymous single-nucleotide polymorphisms (nsSNPs) have been shown to be associated with obesity. Little is known about their associations and interactions with physical activity (PA) in relation to adiposity parameters among adolescents in Malaysia.
Methods: We examined whether (a) PA and (b) selected nsSNPs are associated with adiposity parameters and whether PA interacts with these nsSNPs on these outcomes in adolescents from the Malaysian Health and Adolescents Longitudinal Research Team study (n = 1,151). Body mass indices, waist-hip ratio, and percentage body fat (% BF) were obtained. PA was assessed using Physical Activity Questionnaire for Older Children (PAQ-C). Five nsSNPs were included: beta-3 adrenergic receptor (ADRB3) rs4994, FABP2 rs1799883, GHRL rs696217, MC3R rs3827103, and vitamin D receptor rs2228570, individually and as combined genetic risk score (GRS). Associations and interactions between nsSNPs and PAQ-C scores were examined using generalized linear model.
Results: PAQ-C scores were associated with % BF (β = -0.44 [95% confidence interval -0.72, -0.16], p = 0.002). The CC genotype of ADRB3 rs4994 (β = -0.16 [-0.28, -0.05], corrected p = 0.01) and AA genotype of MC3R rs3827103 (β = -0.06 [-0.12, -0.00], p = 0.02) were significantly associated with % BF compared to TT and GG genotypes, respectively. Significant interactions with PA were found between ADRB3 rs4994 (β = -0.05 [-0.10, -0.01], p = 0.02) and combined GRS (β = -0.03 [-0.04, -0.01], p = 0.01) for % BF.
Conclusion: Higher PA score was associated with reduced % BF in Malaysian adolescents. Of the nsSNPs, ADRB3 rs4994 and MC3R rs3827103 were associated with % BF. Significant interactions with PA were found for ADRB3 rs4994 and combined GRS on % BF but not on measurements of weight or circumferences. Targeting body fat represent prospects for molecular studies and lifestyle intervention in this population.

Matched MeSH terms: Linear Models
Fulltext Differential Patterns of Adherence to Opioid Therapy in Opioid Naïve and Opioid Existing Patients With Different Age Groups

Zin CS, Taufek NH, Ahmad MM

Front Pharmacol, 2019;10:1286.
PMID: 31736760 DOI: 10.3389/fphar.2019.01286

Limited data are available on the adherence to opioid therapy and the influence of different patient groups on adherence. This study examined the patterns of adherence in opioid naïve and opioid existing patients with varying age and gender. This retrospective cohort study was conducted using the prescription databases in tertiary hospital settings in Malaysia from 2010 to 2016. Adult patients aged ≥18 years, receiving at least two opioid prescriptions, were included and stratified into the opioid naïve and existing patient groups. Adherence to opioid therapy was measured using the proportion of days covered (PDC), which was derived by dividing the total number of days covered with any opioids by the number of days in the follow-up period. Generalized linear modeling was used to assess factors associated with PDC. A total of 10,569 patients with 36,650 prescription episodes were included in the study. Of these, 91.7% (n = 9,696) were opioid naïve patients and 8.3% (n = 873) were opioid existing patients. The median PDC was 35.5% (interquartile range (IQR) 10.3-78.7%) and 26.8% (IQR 8.8-69.5%) for opioid naïve and opioid existing patients, respectively. A higher opioid daily dose (coefficient 0.010, confidence interval (CI) 0.009, 0.012 p < 0.0001) and increasing age (coefficient 0.002, CI 0.001, 0.003 p < 0.0001) were associated with higher levels of PDC, while lower PDC values were associated with male subjects (coefficient -0.0041, CI -0.072, -0.010 p = 0.009) and existing opioid patients (coefficient -0.134, CI -0.191, -0.077 p < 0.0001). The suboptimal adherence to opioid medications was commonly observed among patients with non-cancer pain, and the opioid existing patients were less adherent compared to opioid naïve patients. Increasing age and a higher daily opioid dose were factors associated with higher levels of adherence, while male and opioid existing patients were potential determinants for lower levels of adherence to opioid medications.

Matched MeSH terms: Linear Models
Fulltext Intelligent ground vibration prediction in surface mines using an efficient soft computing method based on field data

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
Wide range of body composition measures are associated with cognitive function in community-dwelling older adults

Won H, Abdul Manaf Z, Mat Ludin AF, Shahar S

Geriatr Gerontol Int, 2017 Apr;17(4):554-560.
PMID: 27231255 DOI: 10.1111/ggi.12753

AIM: Studies of the association between body composition, both body fat and body muscle, and cognitive function are rarely reported. The aim of the present study was to determine the association between a wide range of body composition measures with cognitive function in older adults.
METHODS: A total of 2322 Malaysian older adults aged 60 years and older were recruited using multistage random sampling in a population-based cross-sectional study. Out of 2322 older adults recruited, 2309 (48% men) completed assessments on cognitive function and body composition. Cognitive functions were assessed using the Malay version of the Mini-Mental State Examination, the Bahasa Malaysia version of Montreal Cognitive Assessment, Digit Span Test, Digit Symbol Test and Rey Auditory Verbal Learning Test. Body composition included body mass index, mid-upper arm circumference, waist circumference, calf circumference, waist-to-hip ratio, percentage body fat and skeletal muscle mass.
RESULTS: The association between body composition and cognitive functions was analyzed using multiple linear regression. After adjustment for age, education years, hypertension, hypercholesterolemia, diabetes mellitus, depression, smoking status and alcohol consumption, we found that calf circumference appeared as a significant predictor for all cognitive tests among both men and women (P

Matched MeSH terms: Linear Models

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