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Fulltext Optimization of thermostable organic solvent-tolerant lipase production by thermotolerant Rhizopus sp. using solid-state fermentation of palm kernel cake

Riyadi FA, Alam MZ, Salleh MN, Salleh HM

3 Biotech, 2017 Oct;7(5):300.
PMID: 28884067 DOI: 10.1007/s13205-017-0932-1

This study enhanced the production of thermostable organic solvent-tolerant (TS-OST) lipase by locally isolated thermotolerant Rhizopus sp. strain using solid-state fermentation (SSF) of palm kernel cake (PKC). The optimum conditions were achieved using a series of statistical approaches. The cultivation parameters, which include fermentation time, moisture content, temperature, pH, inoculum size, various carbon and nitrogen sources, as well as other supplements, were initially screened by the definitive screening design, and one-factor-at-a-time using PKC as the basal medium. Three significant factors (olive oil concentration, pH, and inoculum size) were further optimized using face-centred central composite design. The results indicated a successful and significant improvement of lipase activity by almost two-fold compared to the initial screening production. The findings showed that the optimal conditions were 2% (v/w) inoculum size, 2% (v/w) olive oil, 0.6% (w/w) peptone, 2% (v/w) ethanol, 70% moisture content at initial pH 10.0 and 45 °C within 72 h of fermentation. Process optimization resulted in maximum lipase activity of 58.63 U/gram dry solids (gds). The analysis of variance showed that the statistical model was significant (p value <0.0001) and reliable with a high value of R2 (0.98) and adjusted R2 (0.96). This indicates a better correlation between the actual and predicted responses of lipase production. By considering this study, the low-cost PKC through SSF appears to be promising in the utilization of agro-industrial waste for TS-OST lipase production. This is because satisfactory enzyme activity could be attained that promises industrial applications.

Matched MeSH terms: Models, Statistical
Fulltext Optimisation of biomass, exopolysaccharide and intracellular polysaccharide production from the mycelium of an identified Ganoderma lucidum strain QRS 5120 using response surface methodology

Supramani S, Ahmad R, Ilham Z, Annuar MSM, Klaus A, Wan-Mohtar WAAQI

AIMS Microbiol, 2019;5(1):19-38.
PMID: 31384700 DOI: 10.3934/microbiol.2019.1.19

Wild-cultivated medicinal mushroom Ganoderma lucidum was morphologically identified and sequenced using phylogenetic software. In submerged-liquid fermentation (SLF), biomass, exopolysaccharide (EPS) and intracellular polysaccharide (IPS) production of the identified G.lucidum was optimised based on initial pH, starting glucose concentration and agitation rate parameters using response surface methodology (RSM). Molecularly, the G. lucidum strain QRS 5120 generated 637 base pairs, which was commensurate with related Ganoderma species. In RSM, by applying central composite design (CCD), a polynomial model was fitted to the experimental data and was found to be significant in all parameters investigated. The strongest effect (p < 0.0001) was observed for initial pH for biomass, EPS and IPS production, while agitation showed a significant value (p < 0.005) for biomass. By applying the optimized conditions, the model was validated and generated 5.12 g/L of biomass (initial pH 4.01, 32.09 g/L of glucose and 102 rpm), 2.49 g/L EPS (initial pH 4, 24.25 g/L of glucose and 110 rpm) and 1.52 g/L of IPS (and initial pH 4, 40.43 g/L of glucose, 103 rpm) in 500 mL shake flask fermentation. The optimized parameters can be upscaled for efficient biomass, EPS and IPS production using G. lucidum.

Matched MeSH terms: Models, Statistical
Fulltext Strategic roles of industrial statistics in modern industry

Djauhari, M.A.

ASM Science Journal, 2011;5(1):53-63.
MyJurnal

Industrial statistics is an important part of the management system in any industry that strives to continuously improve quality and increase productivity and efficiency. That system covers supply chain management, production design and prototyping, production process and marketing. Industrial statisticians, industrial engineers and industrial leaders should work together hand in hand, in the same language, to ensure that the process and products are as expected. The system itself is never complete. Thus, the usefulness, manageability and reliability of all statistical models used in the system are to be considered as first priority, but those skills are not sufficient. Industrial statisticians should also, of course, be able to come and go between the two poles: statistics and industry. This requirement needs a good understanding about the culture of these poles and how to conduct a mutual symbiosis. One of the principal bridges between these cultures is statistical process control (SPC). This paper is to show that modern industry cannot escape from SPC, especially in a multivariate setting. This setting, which characterizes modern industry, consists of two philosophical problems: how to order data and how to measure process variability. Our recent research results sponsored by the Government of Malaysia will be presented to illustrate the challenging statistical problems in modern industry.

Matched MeSH terms: Models, Statistical
Fulltext Application of statistical experimental designs for optimization of medium composition in biodegradation of procion red MX-8B

Khadijah, O., Lee, K.K., Abdullah, M.F.F.

ASM Science Journal, 2010;4(2):103-112.
MyJurnal

Two sequential statistical experimental designs were used to screen and investigate the dependence of the amount of biodegradation of Procion Red MX-8B (PR-MX8B) on the fermentation variables. Fourteen factors were screened using the Plackett-Burman design. Among these factors, the most significant variables which included yeast extract, corn steep solids and starch influencing PR-MX8B decolourisation were statistically elucidated for optimization. The optimum concentrations of 5.00 g/l yeast extract, 2.99 g/l starch and 1.89 g/l corn steep solids were predicted by applying the Box-Behnken design to the second order polynomial model fitted to the results obtained. The best predicted optimal conditions verified experimentally yielded 72.11% while the predicted value from the polynomial model was 79.17%. The experimental values were in good agreement with the predicted values with a 90.81% degree of accuracy.

Matched MeSH terms: Models, Statistical
Exploring the effects of roadway characteristics on the frequency and severity of head-on crashes: case studies from Malaysian federal roads

Hosseinpour M, Yahaya AS, Sadullah AF

Accid Anal Prev, 2014 Jan;62:209-22.
PMID: 24172088 DOI: 10.1016/j.aap.2013.10.001

Head-on crashes are among the most severe collision types and of great concern to road safety authorities. Therefore, it justifies more efforts to reduce both the frequency and severity of this collision type. To this end, it is necessary to first identify factors associating with the crash occurrence. This can be done by developing crash prediction models that relate crash outcomes to a set of contributing factors. This study intends to identify the factors affecting both the frequency and severity of head-on crashes that occurred on 448 segments of five federal roads in Malaysia. Data on road characteristics and crash history were collected on the study segments during a 4-year period between 2007 and 2010. The frequency of head-on crashes were fitted by developing and comparing seven count-data models including Poisson, standard negative binomial (NB), random-effect negative binomial, hurdle Poisson, hurdle negative binomial, zero-inflated Poisson, and zero-inflated negative binomial models. To model crash severity, a random-effect generalized ordered probit model (REGOPM) was used given a head-on crash had occurred. With respect to the crash frequency, the random-effect negative binomial (RENB) model was found to outperform the other models according to goodness of fit measures. Based on the results of the model, the variables horizontal curvature, terrain type, heavy-vehicle traffic, and access points were found to be positively related to the frequency of head-on crashes, while posted speed limit and shoulder width decreased the crash frequency. With regard to the crash severity, the results of REGOPM showed that horizontal curvature, paved shoulder width, terrain type, and side friction were associated with more severe crashes, whereas land use, access points, and presence of median reduced the probability of severe crashes. Based on the results of this study, some potential countermeasures were proposed to minimize the risk of head-on crashes.

Matched MeSH terms: Models, Statistical
The direct and indirect effects of corruption on motor vehicle crash deaths

Hua LT, Noland RB, Evans AW

Accid Anal Prev, 2010 Nov;42(6):1934-42.
PMID: 20728645 DOI: 10.1016/j.aap.2010.05.015

Recent empirical research has found that there is an inverted U-shaped or Kuznets relationship between income and motor vehicle crash (MVC) deaths, such that MVC deaths increase as national income increases and decrease after reaching a critical level. Corruption has been identified as one of the underlying factors that could affect this relationship, primarily by undermining institutional development and effective enforcement schemes. The total effect of corruption can be decomposed into two components, a direct and an indirect effect. The direct effect measures the immediate impact of corruption on MVC deaths by undermining effective enforcement and regulations, while the indirect effect captures the impact of corruption on hindering increases in per capita income and the consequent impact of reduced income on MVC deaths. By influencing economic growth, corruption can lead to an increase or decrease in MVC deaths depending on the income level. Using data from 60 countries between 1982 and 2003, these effects are estimated using linear panel and fixed effects negative binomial models. The estimation results suggest that corruption has different direct effects for less developed and highly developed countries. It has a negative (decreasing) effect on MVC deaths for less developed countries and a positive (increasing) effect on MVC deaths for highly developed countries. For highly developed countries, the total effect is positive at lower per capita income levels, but decreases with per capita income and becomes negative at per capita income levels of about US$ 38,248. For less developed countries, the total effect is negative within the sample range and decreases with increased per capita income. In summary, the results of this study suggest that reduction of corruption is likely a necessary condition to effectively tackle road safety problems.

Matched MeSH terms: Models, Statistical
Modelling of conspicuity-related motorcycle accidents in Seremban and Shah Alam, Malaysia

Radin UR, Mackay MG, Hills BL

Accid Anal Prev, 1996 May;28(3):325-32.
PMID: 8799436

Preliminary analysis of the short-term impact of a running headlights intervention revealed that there has been a significant drop in conspicuity-related motorcycle accidents in the pilot areas, Seremban and Shah Alam, Malaysia. This paper attempts to look in more detail at conspicuity-related accidents involving motorcycles. The aim of the analysis was to establish a statistical model to describe the relationship between the frequency of conspicuity-related motorcycle accidents and a range of explanatory variables so that new insights can be obtained into the effects of introducing a running headlight campaign and regulation. The exogenous variables in this analysis include the influence of time trends, changes in the recording and analysis system, the effect of fasting activities during Ramadhan and the "Balik Kampong" culture, a seasonal cultural-religious holiday activity unique to Malaysia. The model developed revealed that the running headlight intervention reduced the conspicuity-related motorcycle accidents by about 29%. It is concluded that the intervention has been successful in improving conspicuity-related motorcycle accidents in Malaysia.

Matched MeSH terms: Models, Statistical
Predicting crash frequency for multi-vehicle collision types using multivariate Poisson-lognormal spatial model: A comparative analysis

Hosseinpour M, Sahebi S, Zamzuri ZH, Yahaya AS, Ismail N

Accid Anal Prev, 2018 Sep;118:277-288.
PMID: 29861069 DOI: 10.1016/j.aap.2018.05.003

According to crash configuration and pre-crash conditions, traffic crashes are classified into different collision types. Based on the literature, multi-vehicle crashes, such as head-on, rear-end, and angle crashes, are more frequent than single-vehicle crashes, and most often result in serious consequences. From a methodological point of view, the majority of prior studies focused on multivehicle collisions have employed univariate count models to estimate crash counts separately by collision type. However, univariate models fail to account for correlations which may exist between different collision types. Among others, multivariate Poisson lognormal (MVPLN) model with spatial correlation is a promising multivariate specification because it not only allows for unobserved heterogeneity (extra-Poisson variation) and dependencies between collision types, but also spatial correlation between adjacent sites. However, the MVPLN spatial model has rarely been applied in previous research for simultaneously modelling crash counts by collision type. Therefore, this study aims at utilizing a MVPLN spatial model to estimate crash counts for four different multi-vehicle collision types, including head-on, rear-end, angle, and sideswipe collisions. To investigate the performance of the MVPLN spatial model, a two-stage model and a univariate Poisson lognormal model (UNPLN) spatial model were also developed in this study. Detailed information on roadway characteristics, traffic volume, and crash history were collected on 407 homogeneous segments from Malaysian federal roads. The results indicate that the MVPLN spatial model outperforms the other comparing models in terms of goodness-of-fit measures. The results also show that the inclusion of spatial heterogeneity in the multivariate model significantly improves the model fit, as indicated by the Deviance Information Criterion (DIC). The correlation between crash types is high and positive, implying that the occurrence of a specific collision type is highly associated with the occurrence of other crash types on the same road segment. These results support the utilization of the MVPLN spatial model when predicting crash counts by collision manner. In terms of contributing factors, the results show that distinct crash types are attributed to different subsets of explanatory variables.

Matched MeSH terms: Models, Statistical
Applying a random parameters Negative Binomial Lindley model to examine multi-vehicle crashes along rural mountainous highways in Malaysia

Rusli R, Haque MM, Afghari AP, King M

Accid Anal Prev, 2018 Oct;119:80-90.
PMID: 30007211 DOI: 10.1016/j.aap.2018.07.006

Road safety in rural mountainous areas is a major concern as mountainous highways represent a complex road traffic environment due to complex topology and extreme weather conditions and are associated with more severe crashes compared to crashes along roads in flatter areas. The use of crash modelling to identify crash contributing factors along rural mountainous highways suffers from limitations in data availability, particularly in developing countries like Malaysia, and related challenges due to the presence of excess zero observations. To address these challenges, the objective of this study was to develop a safety performance function for multi-vehicle crashes along rural mountainous highways in Malaysia. To overcome the data limitations, an in-depth field survey, in addition to utilization of secondary data sources, was carried out to collect relevant information including roadway geometric factors, traffic characteristics, real-time weather conditions, cross-sectional elements, roadside features, and spatial characteristics. To address heterogeneity resulting from excess zeros, three specialized modelling techniques for excess zeros including Random Parameters Negative Binomial (RPNB), Random Parameters Negative Binomial - Lindley (RPNB-L) and Random Parameters Negative Binomial - Generalized Exponential (RPNB-GE) were employed. Results showed that the RPNB-L model outperformed the other two models in terms of prediction ability and model fit. It was found that heavy rainfall at the time of crash and the presence of minor junctions along mountainous highways increase the likelihood of multi-vehicle crashes, while the presence of horizontal curves along a steep gradient, the presence of a passing lane and presence of road delineation decrease the likelihood of multi-vehicle crashes. Findings of this study have significant implications for road safety along rural mountainous highways, particularly in the context of developing countries.

Matched MeSH terms: Models, Statistical
Fulltext Optimization of culture conditions for flexirubin production by Chryseobacterium artocarpi CECT 8497 using response surface methodology

Venil CK, Zakaria ZA, Ahmad WA

Acta Biochim. Pol., 2015;62(2):185-90.
PMID: 25979288 DOI: 10.18388/abp.2014_870

Flexirubins are the unique type of bacterial pigments produced by the bacteria from the genus Chryseobacterium, which are used in the treatment of chronic skin disease, eczema etc. and may serve as a chemotaxonomic marker. Chryseobacterium artocarpi CECT 8497, an yellowish-orange pigment producing strain was investigated for maximum production of pigment by optimizing medium composition employing response surface methodology (RSM). Culture conditions affecting pigment production were optimized statistically in shake flask experiments. Lactose, l-tryptophan and KH2PO4 were the most significant variables affecting pigment production. Box Behnken design (BBD) and RSM analysis were adopted to investigate the interactions between variables and determine the optimal values for maximum pigment production. Evaluation of the experimental results signified that the optimum conditions for maximum production of pigment (521.64 mg/L) in 50 L bioreactor were lactose 11.25 g/L, l-tryptophan 6 g/L and KH2PO4 650 ppm. Production under optimized conditions increased to 7.23 fold comparing to its production prior to optimization. Results of this study showed that statistical optimization of medium composition and their interaction effects enable short listing of the significant factors influencing maximum pigment production from Chryseobacterium artocarpi CECT 8497. In addition, this is the first report optimizing the process parameters for flexirubin type pigment production from Chryseobacterium artocarpi CECT 8497.

Matched MeSH terms: Models, Statistical*
Developing a dengue prediction model based on climate in Tawau, Malaysia

Jayaraj VJ, Avoi R, Gopalakrishnan N, Raja DB, Umasa Y

Acta Trop, 2019 Sep;197:105055.
PMID: 31185224 DOI: 10.1016/j.actatropica.2019.105055

Dengue is fast becoming the most urgent health issue in Malaysia, recording close to a 10-fold increase in cases over the last decade. With much uncertainty hovering over the recently introduced tetravalent vaccine and no effective antiviral drugs, vector control remains the most important strategy in combating dengue. This study analyses the relationship between weather predictors including its lagged terms, and dengue incidence in the District of Tawau over a period of 12 years, from 2006 to 2017. A forecasting model purposed to predict future outbreaks in Tawau was then developed using this data. Monthly dengue incidence data, mean temperature, maximum temperature, minimum temperature, mean relative humidity and mean rainfall over a period of 12 years from 2006 to 2017 in Tawau were retrieved from Tawau District Health Office and the Malaysian Meteorological Department. Cross-correlation analysis between weather predictors, lagged terms of weather predictors and dengue incidences established statistically significant cross-correlation between lagged periods of weather predictors-namely maximum temperature, mean relative humidity and mean rainfall with dengue incidence at time lags of 4-6 months. These variables were then employed into 3 different methods: a multivariate Poisson regression model, a Seasonal Autoregressive Integrated Moving Average (SARIMA) model and a SARIMA with external regressors for selection. Three models were selected but the SARIMA with external regressors model utilising maximum temperature at a lag of 6 months (p-value:0.001), minimum temperature at a lag of 4 months (p-value:0.01), mean relative humidity at a lag of 2 months (p-value:0.001), and mean rainfall at a lag of 6 months (p-value:0.001) produced an AIC of 841.94, and a log-likelihood score of -413.97 establishing it as the best fitting model of the methodologies utilised. In validating the models, they were utilised to develop forecasts with the model selected with the highest accuracy of predictions being the SARIMA model predicting 1 month in advance (MAE: 7.032, MSE: 83.977). This study establishes the effect of weather on the intensity and magnitude of dengue incidence as has been previously studied. A prediction model remains a novel method of evidence-based forecasting in Tawau, Sabah. The model developed in this study, demonstrated an ability to forecast potential dengue outbreaks 1 to 4 months in advance. These findings are not dissimilar to what has been previously studied in many different countries- with temperature and humidity consistently being established as powerful predictors of dengue incidence magnitude. When used in prognostication, it can enhance- decision making and allow judicious use of resources in public health setting. Nevertheless, the model remains a work in progress- requiring larger and more diverse data.

Matched MeSH terms: Models, Statistical
Performance of univariate forecasting on seasonal diseases: the case of tuberculosis

Permanasari AE, Rambli DR, Dominic PD

Adv Exp Med Biol, 2011;696:171-9.
PMID: 21431557 DOI: 10.1007/978-1-4419-7046-6_17

The annual disease incident worldwide is desirable to be predicted for taking appropriate policy to prevent disease outbreak. This chapter considers the performance of different forecasting method to predict the future number of disease incidence, especially for seasonal disease. Six forecasting methods, namely linear regression, moving average, decomposition, Holt-Winter's, ARIMA, and artificial neural network (ANN), were used for disease forecasting on tuberculosis monthly data. The model derived met the requirement of time series with seasonality pattern and downward trend. The forecasting performance was compared using similar error measure in the base of the last 5 years forecast result. The findings indicate that ARIMA model was the most appropriate model since it obtained the less relatively error than the other model.

Matched MeSH terms: Models, Statistical
Plasma Metabolites as Predictors of Warfarin Outcome in Atrial Fibrillation

Bawadikji AA, Teh CH, Sheikh Abdul Kader MAB, Abdul Wahab MJB, Syed Sulaiman SA, Ibrahim B

Am J Cardiovasc Drugs, 2020 Apr;20(2):169-177.
PMID: 31435902 DOI: 10.1007/s40256-019-00364-2

BACKGROUND: Warfarin is prescribed as an oral anticoagulant to treat/prevent thromboembolism in conditions such as atrial fibrillation. As there is a narrow therapeutic window, treatment with warfarin is challenging. Pharmacometabonomics using nuclear magnetic resonance (NMR) spectroscopy may provide novel techniques for the identification of novel biomarkers of warfarin.
PURPOSE: The aim was to determine the metabolic fingerprint that predicts warfarin response based on the international normalized ratio (INR) in patients who are already receiving warfarin (phase I: identification) and to ascertain the metabolic fingerprint that discriminates stable from unstable INR in patients starting treatment with warfarin (phase II: validation).
EXPERIMENTAL APPROACH: A total of 94 blood samples were collected for phase I: 44 patients with stable INR and 50 with unstable INR. Meanwhile, 23 samples were collected for phase II: nine patients with stable INR and 14 with unstable INR. Data analysis was performed using multivariate analysis including principal component analysis and partial least square-discriminate analysis (PLS-DA), followed by univariate and multivariate logistic regression (MVLR) to develop a model to identify unstable INR biomarkers.
KEY RESULTS: For phase I, the PLS-DA model showed the following results: sensitivity 93.18%, specificity 91.49% and accuracy 92.31%. In the MVLR analysis of phase I, ten regions were associated with unstable INR. For phase II, the PLS-DA model showed the following results: sensitivity 66.67%, specificity 61.54% and accuracy 63.64%.
CONCLUSIONS AND IMPLICATIONS: We have shown that the pharmacometabonomics technique was able to differentiate between unstable and stable INR with good accuracy. NMR-based pharmacometabonomics has the potential to identify novel biomarkers in plasma, which can be useful in individualizing treatment and controlling warfarin side effects, thus, minimizing undesirable effects in the future.

Matched MeSH terms: Models, Statistical
Fulltext Denisova admixture and the first modern human dispersals into Southeast Asia and Oceania

Reich D, Patterson N, Kircher M, Delfin F, Nandineni MR, Pugach I, et al.

Am J Hum Genet, 2011 Oct 07;89(4):516-28.
PMID: 21944045 DOI: 10.1016/j.ajhg.2011.09.005

It has recently been shown that ancestors of New Guineans and Bougainville Islanders have inherited a proportion of their ancestry from Denisovans, an archaic hominin group from Siberia. However, only a sparse sampling of populations from Southeast Asia and Oceania were analyzed. Here, we quantify Denisova admixture in 33 additional populations from Asia and Oceania. Aboriginal Australians, Near Oceanians, Polynesians, Fijians, east Indonesians, and Mamanwa (a "Negrito" group from the Philippines) have all inherited genetic material from Denisovans, but mainland East Asians, western Indonesians, Jehai (a Negrito group from Malaysia), and Onge (a Negrito group from the Andaman Islands) have not. These results indicate that Denisova gene flow occurred into the common ancestors of New Guineans, Australians, and Mamanwa but not into the ancestors of the Jehai and Onge and suggest that relatives of present-day East Asians were not in Southeast Asia when the Denisova gene flow occurred. Our finding that descendants of the earliest inhabitants of Southeast Asia do not all harbor Denisova admixture is inconsistent with a history in which the Denisova interbreeding occurred in mainland Asia and then spread over Southeast Asia, leading to all its earliest modern human inhabitants. Instead, the data can be most parsimoniously explained if the Denisova gene flow occurred in Southeast Asia itself. Thus, archaic Denisovans must have lived over an extraordinarily broad geographic and ecological range, from Siberia to tropical Asia.

Matched MeSH terms: Models, Statistical
Assessment of the likelihood of the introduction of foot-and-mouth disease through importation of live animals into the Malaysia-Thailand-Myanmar peninsula

Wongsathapornchai K, Salman MD, Edwards JR, Morley PS, Keefe TJ, Van Campen H, et al.

Am J Vet Res, 2008 Feb;69(2):252-60.
PMID: 18241023 DOI: 10.2460/ajvr.69.2.252

To assess the likelihood of an introduction of foot-and-mouth disease (FMD) into the Malaysia-Thailand-Myanmar (MTM) peninsula through terrestrial movement of livestock.

Matched MeSH terms: Models, Statistical
Suture Cruroplasty Versus Prosthetic Hiatal Herniorrhaphy for Large Hiatal Hernia: A Meta-analysis and Systematic Review of Randomized Controlled Trials

Memon MA, Memon B, Yunus RM, Khan S

Ann Surg, 2016 Feb;263(2):258-66.
PMID: 26445468 DOI: 10.1097/SLA.0000000000001267

The aim was to conduct a meta-analysis of randomized controlled trials (RCTs) comparing 2 methods of hiatal closure for large hiatal hernia and to evaluate their strengths and flaws.

Matched MeSH terms: Models, Statistical
Objective assessment of psoriasis erythema for PASI scoring

Ahmad Fadzil MH, Ihtatho D, Affandi AM, Hussein SH

Annu Int Conf IEEE Eng Med Biol Soc, 2008;2008:4070-3.
PMID: 19163606 DOI: 10.1109/IEMBS.2008.4650103

Skin colour is vital information in dermatological diagnosis. It reflects pathological condition beneath the skin and commonly being used to indicate the extent of a disease. Psoriasis is a skin disease which is indicated by the appearance of red plaques. Although there is no cure for psoriasis, there are many treatment modalities to help control the disease. To evaluate treatment efficacy, PASI (Psoriasis Area and Severity Index) which is the current gold standard method is used to determine severity of psoriasis lesion. Erythema (redness) is one parameter in PASI. Commonly, the erythema is assessed visually, thus leading to subjective and inconsistent result. In this work, we proposed an objective assessment of psoriasis erythema for PASI scoring. The colour of psoriasis lesion is analyzed by DeltaL, Deltahue, and Deltachroma of CIELAB colour space. References of lesion with different scores are obtained from the selected lesions by two dermatologists. Results based on 38 lesions from 22 patients with various level of skin pigmentation show that PASI erythema score can be determined objectively and consistent with dermatology scoring.

Matched MeSH terms: Models, Statistical
Fulltext Population Pharmacokinetics of Doripenem in Critically Ill Patients with Sepsis in a Malaysian Intensive Care Unit

Abdul-Aziz MH, Abd Rahman AN, Mat-Nor MB, Sulaiman H, Wallis SC, Lipman J, et al.

Antimicrob Agents Chemother, 2016 01;60(1):206-14.
PMID: 26482304 DOI: 10.1128/AAC.01543-15

Doripenem has been recently introduced in Malaysia and is used for severe infections in the intensive care unit. However, limited data currently exist to guide optimal dosing in this scenario. We aimed to describe the population pharmacokinetics of doripenem in Malaysian critically ill patients with sepsis and use Monte Carlo dosing simulations to develop clinically relevant dosing guidelines for these patients. In this pharmacokinetic study, 12 critically ill adult patients with sepsis receiving 500 mg of doripenem every 8 h as a 1-hour infusion were enrolled. Serial blood samples were collected on 2 different days, and population pharmacokinetic analysis was performed using a nonlinear mixed-effects modeling approach. A two-compartment linear model with between-subject and between-occasion variability on clearance was adequate in describing the data. The typical volume of distribution and clearance of doripenem in this cohort were 0.47 liters/kg and 0.14 liters/kg/h, respectively. Doripenem clearance was significantly influenced by patients' creatinine clearance (CL(CR)), such that a 30-ml/min increase in the estimated CL(CR) would increase doripenem CL by 52%. Monte Carlo dosing simulations suggested that, for pathogens with a MIC of 8 mg/liter, a dose of 1,000 mg every 8 h as a 4-h infusion is optimal for patients with a CL(CR) of 30 to 100 ml/min, while a dose of 2,000 mg every 8 h as a 4-h infusion is best for patients manifesting a CL(CR) of >100 ml/min. Findings from this study suggest that, for doripenem usage in Malaysian critically ill patients, an alternative dosing approach may be meritorious, particularly when multidrug resistance pathogens are involved.

Matched MeSH terms: Models, Statistical*
Fulltext Stochasticity of bacterial attachment and its predictability by the extended derjaguin-landau-verwey-overbeek theory

Chia TW, Nguyen VT, McMeekin T, Fegan N, Dykes GA

Appl Environ Microbiol, 2011 Jun;77(11):3757-64.
PMID: 21478319 DOI: 10.1128/AEM.01415-10

Bacterial attachment onto materials has been suggested to be stochastic by some authors but nonstochastic and based on surface properties by others. We investigated this by attaching pairwise combinations of two Salmonella enterica serovar Sofia (S. Sofia) strains (with different physicochemical and attachment properties) with one strain each of S. enterica serovar Typhimurium, S. enterica serovar Infantis, or S. enterica serovar Virchow (all with similar physicochemical and attachment abilities) in ratios of 0.428, 1, and 2.333 onto glass, stainless steel, Teflon, and polysulfone. Attached bacterial cells were recovered and counted. If the ratio of attached cells of each Salmonella serovar pair recovered was the same as the initial inoculum ratio, the attachment process was deemed stochastic. Experimental outcomes from the study were compared to those predicted by the extended Derjaguin-Landau-Verwey-Overbeek (XDLVO) theory. Significant differences (P < 0.05) between the initial and the attached ratios for serovar pairs containing S. Sofia S1296a for all different ratios were apparent for all materials. For S. Sofia S1635-containing pairs, 7 out of 12 combinations of serovar pairs and materials had attachment ratios not significantly different (P > 0.05) from the initial ratio of 0.428. Five out of 12 and 10 out of 12 samples had attachment ratios not significantly different (P > 0.05) from the initial ratios of 1 and 2.333, respectively. These results demonstrate that bacterial attachment to different materials is likely to be nonstochastic only when the key physicochemical properties of the bacteria were significantly different (P < 0.05) from each other. XDLVO theory could successfully predict the attachment of some individual isolates to particular materials but could not be used to predict the likelihood of stochasticity in pairwise attachment experiments.

Matched MeSH terms: Models, Statistical
Describing Functioning in People Living With Spinal Cord Injury Across 22 Countries: A Graphical Modeling Approach

Ehrmann C, Reinhardt JD, Joseph C, Hasnan N, Perrouin-Verbe B, Tederko P, et al.

Arch Phys Med Rehabil, 2020 12;101(12):2112-2143.
PMID: 32980339 DOI: 10.1016/j.apmr.2020.09.374

OBJECTIVE: To provide prevalence estimates for problems in functioning of community-dwelling persons with spinal cord injury (SCI) and to examine associations between various areas of functioning with the purpose of supporting countries in identifying targets for interventions.
DESIGN: Cross-sectional survey.
SETTING: Community, 22 countries including all World Health Organization regions.
PARTICIPANTS: Persons (N=12,591) with traumatic or nontraumatic SCI aged 18 years or older.
INTERVENTIONS: Not applicable.
MAIN OUTCOME MEASURES: We estimated the prevalence of problems in 53 areas of functioning from the Brief International Classification of Functioning, Disability and Health (ICF) core set for SCI, long-term context, or ICF rehabilitation set covering 4 domains: impairments in body functions, impairments in mental functions, independence in performing activities, and restrictions in participation. Associations between areas of functioning were identified and visualized using conditional independence graphs.
RESULTS: Participants had a median age of 52 years, 73% were male, and 63% had paraplegia. Feeling tired, bowel dysfunction, sexual functions, spasticity, pain, carrying out daily routine, doing housework, getting up off the floor from lying on the back, pushing open a heavy door, and standing unsupported had the highest prevalence of problems (>70%). Clustering of associations within the 4 functioning domains was found, with the highest numbers of associations within impairments in mental functions. For the whole International Spinal Cord Injury sample, areas with the highest numbers of associations were circulatory problems, transferring bed-wheelchair, and toileting, while for the World Health Organization European and Western Pacific regions, these were dressing upper body, transferring bed-wheelchair, handling stress, feeling downhearted and depressed, and feeling happy.
CONCLUSIONS: In each domain of functioning, high prevalence of problems and high connectivity of areas of functioning were identified. The understanding of problems and the identification of potential targets for intervention can inform decision makers at all levels of the health system aiming to improve the situation of people living with SCI.

Matched MeSH terms: Models, Statistical

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