Displaying publications 1 - 20 of 404 in total

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  1. Ismail AM, Remli MA, Choon YW, Nasarudin NA, Ismail NN, Ismail MA, et al.
    J Integr Bioinform, 2023 Jun 01;20(2).
    PMID: 37341516 DOI: 10.1515/jib-2022-0051
    Analyzing metabolic pathways in systems biology requires accurate kinetic parameters that represent the simulated in vivo processes. Simulation of the fermentation pathway in the Saccharomyces cerevisiae kinetic model help saves much time in the optimization process. Fitting the simulated model into the experimental data is categorized under the parameter estimation problem. Parameter estimation is conducted to obtain the optimal values for parameters related to the fermentation process. This step is essential because insufficient identification of model parameters can cause erroneous conclusions. The kinetic parameters cannot be measured directly. Therefore, they must be estimated from the experimental data either in vitro or in vivo. Parameter estimation is a challenging task in the biological process due to the complexity and nonlinearity of the model. Therefore, we propose the Artificial Bee Colony algorithm (ABC) to estimate the parameters in the fermentation pathway of S. cerevisiae to obtain more accurate values. A metabolite with a total of six parameters is involved in this article. The experimental results show that ABC outperforms other estimation algorithms and gives more accurate kinetic parameter values for the simulated model. Most of the estimated kinetic parameter values obtained from the proposed algorithm are the closest to the experimental data.
    Matched MeSH terms: Models, Biological
  2. Alebraheem J, Abu-Hassan Y
    J Math Biol, 2023 Apr 27;86(5):84.
    PMID: 37103566 DOI: 10.1007/s00285-023-01914-8
    A characteristic of ecosystems is the existence of manifold of independencies which are highly complex. Various mathematical models have made considerable contributions in gaining a better understanding of the predator-prey interactions. The main components of any predator-prey models are, firstly, how the different population classes grow and secondly, how the prey and predator interacts. In this paper, the two populations' growth rates obey the logistic law and the carrying capacity of the predator depends on the available number of prey are considered. Our aim is to clarify the relationship between models and Holling types functional and numerical responses in order to gain insights into predator interferences and to answer an important question how competition is carried out. We consider a predator-prey model and a two-predator one-prey model to explain the idea. The novel approach is explained for the mechanism measurement of predator interference through depending on numerical response. Our approach gives good correspondence between an important real data and computer simulations.
    Matched MeSH terms: Models, Biological*
  3. Kirubakaran R, Uster DW, Hennig S, Carland JE, Day RO, Wicha SG, et al.
    Br J Clin Pharmacol, 2023 Mar;89(3):1162-1175.
    PMID: 36239542 DOI: 10.1111/bcp.15566
    AIM: Existing tacrolimus population pharmacokinetic models are unsuitable for guiding tacrolimus dosing in heart transplant recipients. This study aimed to develop and evaluate a population pharmacokinetic model for tacrolimus in heart transplant recipients that considers the tacrolimus-azole antifungal interaction.

    METHODS: Data from heart transplant recipients (n = 87) administered the oral immediate-release formulation of tacrolimus (Prograf®) were collected. Routine drug monitoring data, principally trough concentrations, were used for model building (n = 1099). A published tacrolimus model was used to inform the estimation of Ka , V2 /F, Q/F and V3 /F. The effect of concomitant azole antifungal use on tacrolimus CL/F was quantified. Fat-free mass was implemented as a covariate on CL/F, V2 /F, V3 /F and Q/F on an allometry scale. Subsequently, stepwise covariate modelling was performed. Significant covariates influencing tacrolimus CL/F were included in the final model. Robustness of the final model was confirmed using prediction-corrected visual predictive check (pcVPC). The final model was externally evaluated for prediction of tacrolimus concentrations of the fourth dosing occasion (n = 87) from one to three prior dosing occasions.

    RESULTS: Concomitant azole antifungal therapy reduced tacrolimus CL/F by 80%. Haematocrit (∆OFV = -44, P 

    Matched MeSH terms: Models, Biological
  4. Mak WY, Ooi QX, Cruz CV, Looi I, Yuen KH, Standing JF
    Br J Clin Pharmacol, 2023 Jan;89(1):330-339.
    PMID: 35976674 DOI: 10.1111/bcp.15496
    AIM: nlmixr offers first-order conditional estimation (FOCE), FOCE with interaction (FOCEi) and stochastic approximation estimation-maximisation (SAEM) to fit nonlinear mixed-effect models (NLMEM). We modelled metformin's pharmacokinetic data using nlmixr and investigated SAEM and FOCEi's performance with respect to bias and precision of parameter estimates, and robustness to initial estimates.

    METHOD: Compartmental models were fitted. The final model was determined based on the objective function value and inspection of goodness-of-fit plots. The bias and precision of parameter estimates were compared between SAEM and FOCEi using stochastic simulations and estimations. For robustness, parameters were re-estimated as the initial estimates were perturbed 100 times and resultant changes evaluated.

    RESULTS: The absorption kinetics of metformin depend significantly on food status. Under the fasted state, the first-order absorption into the central compartment was preceded by zero-order infusion into the depot compartment, whereas for the fed state, the absorption into the depot was instantaneous followed by first-order absorption from depot into the central compartment. The means of relative mean estimation error (rMEE) ( ME E SAEM ME E FOCEi ) and rRMSE ( RMS E SAEM RMS E FOCEi ) were 0.48 and 0.35, respectively. All parameter estimates given by SAEM appeared to be narrowly distributed and were close to the true value used for simulation. In contrast, the distribution of estimates from FOCEi were skewed and more biased. When initial estimates were perturbed, FOCEi estimates were more biased and imprecise.

    DISCUSSION: nlmixr is reliable for NLMEM. SAEM was superior to FOCEi in terms of bias and precision, and more robust against initial estimate perturbations.

    Matched MeSH terms: Models, Biological*
  5. Hamdan N', Kilicman A
    Bull Math Biol, 2022 Oct 26;84(12):138.
    PMID: 36287255 DOI: 10.1007/s11538-022-01096-2
    This paper deals with a deterministic mathematical model of dengue based on a system of fractional-order differential equations (FODEs). In this study, we consider dengue control strategies that are relevant to the current situation in Malaysia. They are the use of adulticides, larvicides, destruction of the breeding sites, and individual protection. The global stability of the disease-free equilibrium and the endemic equilibrium is constructed using the Lyapunov function theory. The relations between the order of the operator and control parameters are briefly analysed. Numerical simulations are performed to verify theoretical results and examine the significance of each intervention strategy in controlling the spread of dengue in the community. The model shows that vector control tools are the most efficient method to combat the spread of the dengue virus, and when combined with individual protection, make it more effective. In fact, the massive use of personal protection alone can significantly reduce the number of dengue cases. Inversely, mechanical control alone cannot suppress the excessive number of infections in the population, although it can reduce the Aedes mosquito population. The result of the real-data fitting revealed that the FODE model slightly outperformed the integer-order model. Thus, we suggest that the FODE approach is worth to be considered in modelling an infectious disease like dengue.
    Matched MeSH terms: Models, Biological
  6. Abu Bakar SA, Syed Mohamed Shahruddin SNS, Ismail N, Wan Md Adnan WAH
    PeerJ, 2022;10:e13694.
    PMID: 35935256 DOI: 10.7717/peerj.13694
    The estimation of biological age (BA) is an important asymptomatic measure that can be used to understand the physical changes and the aging process of a living being. Factors that contribute towards profiling the human biological age can be diverse. Therefore, this study focuses on developing a BA model for patients with Chronic Kidney Disease (CKD). The procedure commences with the selection of significant biomarkers using a correlation test. Appropriate weighting is then assigned to each selected biomarker using the indexing method to produce a BA index. The BA index is matched to the age variation within the sample to acquire additional terms for the chronological age leading ultimately to the estimated BA. From a sample of 190 patients (133 trained data and 57 testing data) obtained from the University of Malaya Medical Centre (UMMC), Malaysia, the intensity of the BA is found to be between three to nine years from the chronological age. Visual observations further validate the high similarities between the training and testing data sets.
    Matched MeSH terms: Models, Biological
  7. Tang BH, Guan Z, Allegaert K, Wu YE, Manolis E, Leroux S, et al.
    Clin Pharmacokinet, 2021 11;60(11):1435-1448.
    PMID: 34041714 DOI: 10.1007/s40262-021-01033-x
    BACKGROUND: Population pharmacokinetic evaluations have been widely used in neonatal pharmacokinetic studies, while machine learning has become a popular approach to solving complex problems in the current era of big data.

    OBJECTIVE: The aim of this proof-of-concept study was to evaluate whether combining population pharmacokinetic and machine learning approaches could provide a more accurate prediction of the clearance of renally eliminated drugs in individual neonates.

    METHODS: Six drugs that are primarily eliminated by the kidneys were selected (vancomycin, latamoxef, cefepime, azlocillin, ceftazidime, and amoxicillin) as 'proof of concept' compounds. Individual estimates of clearance obtained from population pharmacokinetic models were used as reference clearances, and diverse machine learning methods and nested cross-validation were adopted and evaluated against these reference clearances. The predictive performance of these combined methods was compared with the performance of two other predictive methods: a covariate-based maturation model and a postmenstrual age and body weight scaling model. Relative error was used to evaluate the different methods.

    RESULTS: The extra tree regressor was selected as the best-fit machine learning method. Using the combined method, more than 95% of predictions for all six drugs had a relative error of < 50% and the mean relative error was reduced by an average of 44.3% and 71.3% compared with the other two predictive methods.

    CONCLUSION: A combined population pharmacokinetic and machine learning approach provided improved predictions of individual clearances of renally cleared drugs in neonates. For a new patient treated in clinical practice, individual clearance can be predicted a priori using our model code combined with demographic data.

    Matched MeSH terms: Models, Biological*
  8. Mehmood OU, Bibi S, Jamil DF, Uddin S, Roslan R, Akhir MKM
    Sci Rep, 2021 10 14;11(1):20379.
    PMID: 34650140 DOI: 10.1038/s41598-021-99499-z
    The current work analyzes the effects of concentric ballooned catheterization and heat transfer on the hybrid nano blood flow through diseased arterial segment having both stenosis and aneurysm along its boundary. A fractional second-grade fluid model is considered which describes the non-Newtonian characteristics of the blood. Governing equations are linearized under mild stenosis and mild aneurysm assumptions. Precise articulations for various important flow characteristics such as heat transfer, hemodynamic velocity, wall shear stress, and resistance impedance are attained. Graphical portrayals for the impact of the significant parameters on the flow attributes have been devised. The streamlines of blood flow have been examined as well. The present finding is useful for drug conveyance system and biomedicines.
    Matched MeSH terms: Models, Biological
  9. Kalidasan V, Ng WH, Ishola OA, Ravichantar N, Tan JJ, Das KT
    Sci Rep, 2021 Sep 28;11(1):19265.
    PMID: 34584147 DOI: 10.1038/s41598-021-98657-7
    Gene therapy revolves around modifying genetic makeup by inserting foreign nucleic acids into targeted cells via gene delivery methods to treat a particular disease. While the genes targeted play a key role in gene therapy, the gene delivery system used is also of utmost importance as it determines the success of gene therapy. As primary cells and stem cells are often the target cells for gene therapy in clinical trials, the delivery system would need to be robust, and viral-based entries such as lentiviral vectors work best at transporting the transgene into the cells. However, even within lentiviral vectors, several parameters can affect the functionality of the delivery system. Using cardiac-derived c-kit expressing cells (CCs) as a model system, this study aims to optimize lentiviral production by investigating various experimental factors such as the generation of the lentiviral system, concentration method, and type of selection marker. Our findings showed that the 2nd generation system with pCMV-dR8.2 dvpr as the packaging plasmid produced a 7.3-fold higher yield of lentiviral production compared to psPAX2. Concentrating the virus with ultracentrifuge produced a higher viral titer at greater than 5 × 105 infectious unit values/ml (IFU/ml). And lastly, the minimum inhibitory concentration (MIC) of puromycin selection marker was 10 μg/mL and 7 μg/mL for HEK293T and CCs, demonstrating the suitability of antibiotic selection for all cell types. This encouraging data can be extrapolated and applied to other difficult-to-transfect cells, such as different types of stem cells or primary cells.
    Matched MeSH terms: Models, Biological
  10. Chen WH, Chang CM, Mutuku JK, Lam SS, Lee WJ
    J Hazard Mater, 2021 08 15;416:125856.
    PMID: 34492805 DOI: 10.1016/j.jhazmat.2021.125856
    Inhalation of aerosols such as pharmaceutical aerosols or virus aerosol uptake is of great concern to the human population. To elucidate the underlying aerosol dynamics, the deposition fractions (DFs) of aerosols in healthy and asthmatic human airways of generations 13-15 are predicted. The Navier-stokes equations governing the gaseous phase and the discrete phase model for particles' motion are solved using numerical methods. The main forces responsible for deposition are inertial impaction forces and complex secondary flow velocities. The curvatures and sinusoidal folds in the asthmatic geometry lead to the formation of complex secondary flows and hence higher DFs. The intensities of complex secondary flows are strongest at the generations affected by asthma. The DF in the healthy airways is 0%, and it ranges from 1.69% to 52.93% in the asthmatic ones. From this study, the effects of the pharmaceutical aerosol particle diameters in the treatment of asthma patients can be established, which is conducive to inhibiting the inflammation of asthma airways. Furthermore, with the recent development of COVID-19 which causes pneumonia, the predicted physics and effective simulation methods of bioaerosols delivery to asthma patients are vital to prevent the exacerbation of the chronic ailment and the epidemic.
    Matched MeSH terms: Models, Biological
  11. Zeng H, Zhang J, Preising GA, Rubel T, Singh P, Ritz A
    Nucleic Acids Res, 2021 07 02;49(W1):W257-W262.
    PMID: 34037782 DOI: 10.1093/nar/gkab420
    Networks have been an excellent framework for modeling complex biological information, but the methodological details of network-based tools are often described for a technical audience. We have developed Graphery, an interactive tutorial webserver that illustrates foundational graph concepts frequently used in network-based methods. Each tutorial describes a graph concept along with executable Python code that can be interactively run on a graph. Users navigate each tutorial using their choice of real-world biological networks that highlight the diverse applications of network algorithms. Graphery also allows users to modify the code within each tutorial or write new programs, which all can be executed without requiring an account. Graphery accepts ideas for new tutorials and datasets that will be shaped by both computational and biological researchers, growing into a community-contributed learning platform. Graphery is available at https://graphery.reedcompbio.org/.
    Matched MeSH terms: Models, Biological*
  12. Lee JL, Mohd Saffian S, Makmor-Bakry M, Islahudin F, Alias H, Noh LM, et al.
    Br J Clin Pharmacol, 2021 07;87(7):2956-2966.
    PMID: 33377197 DOI: 10.1111/bcp.14712
    AIMS: There is considerable interpatient variability in the pharmacokinetics (PK) of intravenous immunoglobulin G (IVIG), causing difficulty in optimizing individual dosage regimen. This study aims to estimate the population PK parameters of IVIG and to investigate the impact of genetic polymorphism of the FcRn gene and clinical variability on the PK of IVIG in patients with predominantly antibody deficiencies.

    METHODS: Patients were recruited from four hospitals. Clinical data were recorded and blood samples were taken for PK and genetic studies. Population PK parameters were estimated by nonlinear mixed-effects modelling in Monolix®. Models were evaluated using the difference in objective function value, goodness-of-fit plots, visual predictive check and bootstrap analysis. Monte Carlo simulation was conducted to evaluate different dosing regimens for IVIG.

    RESULTS: A total of 30 blood samples were analysed from 10 patients. The immunoglobulin G concentration data were best described by a one-compartment model with linear elimination. The final model included both volume of distribution (Vd) and clearance (CL) based on patient's individual weight. Goodness-of-fit plots indicated that the model fit the data adequately, with minor model mis-specification. Genetic polymorphism of the FcRn gene and the presence of bronchiectasis did not affect the PK of IVIG. Simulation showed that 3-4-weekly dosing intervals were sufficient to maintain IgG levels of 5 g L-1 , with more frequent intervals needed to achieve higher trough levels.

    CONCLUSIONS: Body weight significantly affects the PK parameters of IVIG. Genetic and other clinical factors investigated did not affect the disposition of IVIG.

    Matched MeSH terms: Models, Biological*
  13. Chua P, Lim WK
    Sci Rep, 2021 04 14;11(1):8096.
    PMID: 33854099 DOI: 10.1038/s41598-021-87431-4
    Stroke causes death and disability globally but no neuroprotectant is approved for post-stroke neuronal injury. Neuroprotective compounds can be identified using oxygen glucose deprivation (OGD) of neuronal cells as an in vitro stroke model. Nerve growth factor (NGF)-differentiated PC12 pheochromocytoma cells are frequently used. However, investigators often find their clonal variant undifferentiable and are uncertain of optimal culture conditions. Hence we studied 3 commonly used PC12 variants: PC12 Adh, PC12 from Riken Cell Bank (PC12 Riken) and Neuroscreen-1 (NS-1) cells. We found DMEM the optimal media for PC12 Riken and NS-1 cells. Using a novel serum-free media approach, we identified collagen IV as the preferred adhesive substrate for both cell lines. We found PC12 Adh cells cannot attach without serum and is unable to differentiate using NGF. NS-1 cells differentiated to a maximal 72.7 ± 5.2% %, with substantial basal differentiation. We optimised differentiated NS-1 cells for an in vitro stroke model using 3 h of OGD resulting in ~ 70% viable cells. We screened 5 reported neuroprotectants and provide the first report that serotonin is antiapoptotic in a stroke model and the 5-HT1A agonist 8-hydroxy-2-(di-n-propylamino) tetralin (8-OH-DPAT) is neuroprotective in PC12 cells. Thus we demonstrate the optimisation and validation for a PC12 cell-based in vitro stroke model.
    Matched MeSH terms: Models, Biological*
  14. Wills C, Wang B, Fang S, Wang Y, Jin Y, Lutz J, et al.
    PLoS Comput Biol, 2021 Apr;17(4):e1008853.
    PMID: 33914731 DOI: 10.1371/journal.pcbi.1008853
    When Darwin visited the Galapagos archipelago, he observed that, in spite of the islands' physical similarity, members of species that had dispersed to them recently were beginning to diverge from each other. He postulated that these divergences must have resulted primarily from interactions with sets of other species that had also diverged across these otherwise similar islands. By extrapolation, if Darwin is correct, such complex interactions must be driving species divergences across all ecosystems. However, many current general ecological theories that predict observed distributions of species in ecosystems do not take the details of between-species interactions into account. Here we quantify, in sixteen forest diversity plots (FDPs) worldwide, highly significant negative density-dependent (NDD) components of both conspecific and heterospecific between-tree interactions that affect the trees' distributions, growth, recruitment, and mortality. These interactions decline smoothly in significance with increasing physical distance between trees. They also tend to decline in significance with increasing phylogenetic distance between the trees, but each FDP exhibits its own unique pattern of exceptions to this overall decline. Unique patterns of between-species interactions in ecosystems, of the general type that Darwin postulated, are likely to have contributed to the exceptions. We test the power of our null-model method by using a deliberately modified data set, and show that the method easily identifies the modifications. We examine how some of the exceptions, at the Wind River (USA) FDP, reveal new details of a known allelopathic effect of one of the Wind River gymnosperm species. Finally, we explore how similar analyses can be used to investigate details of many types of interactions in these complex ecosystems, and can provide clues to the evolution of these interactions.
    Matched MeSH terms: Models, Biological
  15. Her Choong F, Keat Yap B
    Chemphyschem, 2021 03 03;22(5):493-498.
    PMID: 33377300 DOI: 10.1002/cphc.202000873
    Cell-penetrating peptides are used in the delivery of peptides and biologics, with some cell-penetrating peptides found to be more efficient than others. The exact mechanism of how they interact with the cell membrane and penetrate it, however, remains unclear. This study attempts to investigate the difference in free energy profiles of three cell-penetrating peptides (TAT, CPP1 and CPP9) with a model lipid bilayer (DOPC) using molecular dynamics pulling simulations with umbrella sampling. Potential mean force (PMF) and free energy barrier between the peptides and DOPC are determined using WHAM analysis and MM-PBSA analysis, respectively. CPP9 is found to have the smallest PMF value, followed by CPP1 and TAT, consistent with the experimental data. YDEGE peptide, however, does not give the highest PMF value, although it is a non-cell-permeable peptide. YDEGE is also found to form water pores, alongside with TAT and CPP9, suggesting that it is difficult to distinguish true water pore formation from artefacts arising from pulling simulations. On the contrary, free energy analysis of the peptide-DOPC complex at the lipid-water interface with MM-PBSA provides results consistent with experimental data with CPP9 having the least interaction with DOPC and lowest free energy barrier, followed by CPP1, TAT and YDEGE. These findings suggest that peptide-lipid interaction at the lipid-water interface has a direct correlation with the penetration efficiency of peptides across the lipid bilayer.
    Matched MeSH terms: Models, Biological
  16. Asif M, Ahmadian A, Azeem M, Pansera BA
    PLoS One, 2021;16(11):e0259423.
    PMID: 34748588 DOI: 10.1371/journal.pone.0259423
    In this paper, the Duckworth-Lewis-Stern (DLS) and Duckworth-Lewis-McHale-Asif (DLMA) methods of revising targets for a team batting in second innings in an interrupted Limited Overs International Cricket (LOI), are examined for fairness. The work discusses four significant points: flexibility, intuition, simplicity, and goodness-of-fit of the two mentioned methods. The research findings have shown that the DLMA method is better in every aspect than the DLS method. Further, the data of 1764 ODI matches played during 2004-2021 to investigate the compatibility of the DLMA for high run-scoring One-Day International matches. The results show that DLMA is compatible to the situation of the well-above run-scoring situation.
    Matched MeSH terms: Models, Biological
  17. Muhamad MAH, Che Hasan R, Md Said N, Ooi JL
    PLoS One, 2021;16(9):e0257761.
    PMID: 34555110 DOI: 10.1371/journal.pone.0257761
    Integrating Multibeam Echosounder (MBES) data (bathymetry and backscatter) and underwater video technology allows scientists to study marine habitats. However, use of such data in modeling suitable seagrass habitats in Malaysian coastal waters is still limited. This study tested multiple spatial resolutions (1 and 50 m) and analysis window sizes (3 × 3, 9 × 9, and 21 × 21 cells) probably suitable for seagrass-habitat relationships in Redang Marine Park, Terengganu, Malaysia. A maximum entropy algorithm was applied, using 12 bathymetric and backscatter predictors to develop a total of 6 seagrass habitat suitability models. The results indicated that both fine and coarse spatial resolution datasets could produce models with high accuracy (>90%). However, the models derived from the coarser resolution dataset displayed inconsistent habitat suitability maps for different analysis window sizes. In contrast, habitat models derived from the fine resolution dataset exhibited similar habitat distribution patterns for three different analysis window sizes. Bathymetry was found to be the most influential predictor in all the models. The backscatter predictors, such as angular range analysis inversion parameters (characterization and grain size), gray-level co-occurrence texture predictors, and backscatter intensity levels, were more important for coarse resolution models. Areas of highest habitat suitability for seagrass were predicted to be in shallower (<20 m) waters and scattered between fringing reefs (east to south). Some fragmented, highly suitable habitats were also identified in the shallower (<20 m) areas in the northwest of the prediction models and scattered between fringing reefs. This study highlighted the importance of investigating the suitable spatial resolution and analysis window size of predictors from MBES for modeling suitable seagrass habitats. The findings provide important insight on the use of remote acoustic sonar data to study and map seagrass distribution in Malaysia coastal water.
    Matched MeSH terms: Models, Biological
  18. Dass SC, Kwok WM, Gibson GJ, Gill BS, Sundram BM, Singh S
    PLoS One, 2021;16(5):e0252136.
    PMID: 34043676 DOI: 10.1371/journal.pone.0252136
    The second wave of COVID-19 in Malaysia is largely attributed to a four-day mass gathering held in Sri Petaling from February 27, 2020, which contributed to an exponential rise of COVID-19 cases in the country. Starting from March 18, 2020, the Malaysian government introduced four consecutive phases of a Movement Control Order (MCO) to stem the spread of COVID-19. The MCO was implemented through various non-pharmaceutical interventions (NPIs). The reported number of cases reached its peak by the first week of April and then started to reduce, hence proving the effectiveness of the MCO. To gain a quantitative understanding of the effect of MCO on the dynamics of COVID-19, this paper develops a class of mathematical models to capture the disease spread before and after MCO implementation in Malaysia. A heterogeneous variant of the Susceptible-Exposed-Infected-Recovered (SEIR) model is developed with additional compartments for asymptomatic transmission. Further, a change-point is incorporated to model disease dynamics before and after intervention which is inferred based on data. Related statistical analyses for inference are developed in a Bayesian framework and are able to provide quantitative assessments of (1) the impact of the Sri Petaling gathering, and (2) the extent of decreasing transmission during the MCO period. The analysis here also quantitatively demonstrates how quickly transmission rates fall under effective NPI implementation within a short time period. The models and methodology used provided important insights into the nature of local transmissions to decision makers in the Ministry of Health, Malaysia.
    Matched MeSH terms: Models, Biological*
  19. Norlia M, Jinap S, Nor-Khaizura MAR, Radu S, John JM, Rahman MAH, et al.
    Int J Food Microbiol, 2020 Dec 16;335:108836.
    PMID: 33065380 DOI: 10.1016/j.ijfoodmicro.2020.108836
    Aspergillus flavus is the predominant species that produce aflatoxins in stored peanuts under favourable conditions. This study aimed to describe the growth and aflatoxin production by two A. flavus strains isolated from imported raw peanuts and to model the effects of temperature and aw on their colony growth rate as a function of temperature and aw in Peanut Meal Extract Agar (PMEA). A full factorial design with seven aw levels (0.85-0.98 aw) and five temperature levels (20-40 °C) was used to investigate the growth and aflatoxin production. Colony diameter was measured daily for 28 days while AFB1 and total aflatoxin were determined on day 3, 7, 14, and 21. The maximum colony growth rate, μmax (mm/day) was estimated by using the primary model of Baranyi, and the μmax was then fitted to the secondary model; second-order polynomial and linear Arrhenius-Davey to describe the colony growth rate as a function of temperature and aw. The results indicated that both strains failed to grow at temperature of 20 °C with aw <0.94 and aw of 0.85 for all temperatures except 30 °C. The highest growth rate was observed at 30 °C, with 0.98 aw for both strains. The analysis of variance showed a significant effect of strain, temperature, and aw on the fungal growth and aflatoxin production (p 
    Matched MeSH terms: Models, Biological
  20. Law KB, Peariasamy KM, Gill BS, Singh S, Sundram BM, Rajendran K, et al.
    Sci Rep, 2020 12 10;10(1):21721.
    PMID: 33303925 DOI: 10.1038/s41598-020-78739-8
    The susceptible-infectious-removed (SIR) model offers the simplest framework to study transmission dynamics of COVID-19, however, it does not factor in its early depleting trend observed during a lockdown. We modified the SIR model to specifically simulate the early depleting transmission dynamics of COVID-19 to better predict its temporal trend in Malaysia. The classical SIR model was fitted to observed total (I total), active (I) and removed (R) cases of COVID-19 before lockdown to estimate the basic reproduction number. Next, the model was modified with a partial time-varying force of infection, given by a proportionally depleting transmission coefficient, [Formula: see text] and a fractional term, z. The modified SIR model was then fitted to observed data over 6 weeks during the lockdown. Model fitting and projection were validated using the mean absolute percent error (MAPE). The transmission dynamics of COVID-19 was interrupted immediately by the lockdown. The modified SIR model projected the depleting temporal trends with lowest MAPE for I total, followed by I, I daily and R. During lockdown, the dynamics of COVID-19 depleted at a rate of 4.7% each day with a decreased capacity of 40%. For 7-day and 14-day projections, the modified SIR model accurately predicted I total, I and R. The depleting transmission dynamics for COVID-19 during lockdown can be accurately captured by time-varying SIR model. Projection generated based on observed data is useful for future planning and control of COVID-19.
    Matched MeSH terms: Models, Biological*
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