Displaying publications 141 - 160 of 405 in total

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  1. Gavai AK, Supandi F, Hettling H, Murrell P, Leunissen JA, van Beek JH
    PLoS One, 2015;10(3):e0119016.
    PMID: 25806817 DOI: 10.1371/journal.pone.0119016
    Predicting the distribution of metabolic fluxes in biochemical networks is of major interest in systems biology. Several databases provide metabolic reconstructions for different organisms. Software to analyze flux distributions exists, among others for the proprietary MATLAB environment. Given the large user community for the R computing environment, a simple implementation of flux analysis in R appears desirable and will facilitate easy interaction with computational tools to handle gene expression data. We extended the R software package BiGGR, an implementation of metabolic flux analysis in R. BiGGR makes use of public metabolic reconstruction databases, and contains the BiGG database and the reconstruction of human metabolism Recon2 as Systems Biology Markup Language (SBML) objects. Models can be assembled by querying the databases for pathways, genes or reactions of interest. Fluxes can then be estimated by maximization or minimization of an objective function using linear inverse modeling algorithms. Furthermore, BiGGR provides functionality to quantify the uncertainty in flux estimates by sampling the constrained multidimensional flux space. As a result, ensembles of possible flux configurations are constructed that agree with measured data within precision limits. BiGGR also features automatic visualization of selected parts of metabolic networks using hypergraphs, with hyperedge widths proportional to estimated flux values. BiGGR supports import and export of models encoded in SBML and is therefore interoperable with different modeling and analysis tools. As an application example, we calculated the flux distribution in healthy human brain using a model of central carbon metabolism. We introduce a new algorithm termed Least-squares with equalities and inequalities Flux Balance Analysis (Lsei-FBA) to predict flux changes from gene expression changes, for instance during disease. Our estimates of brain metabolic flux pattern with Lsei-FBA for Alzheimer's disease agree with independent measurements of cerebral metabolism in patients. This second version of BiGGR is available from Bioconductor.
    Matched MeSH terms: Models, Biological*
  2. Smith JD
    Math Biosci, 1998 Nov;153(2):151-61.
    PMID: 9825637
    The Gibbs canonical ensemble of statistical mechanics is used to describe the probability distribution of the age classes of mothers of new-borns in an age-structured population. The Malthusian parameter emerges as a Lagrange multiplier corresponding to a generation time constraint, while a new perturbation parameter appears as the Lagrange multiplier corresponding to a maternity constraint. Classical Lotka stability reduces to the unperturbed case of the more general canonical ensemble model. The model is used in a case study of the female (peninsular) Malaysian population of 1970. The Malthusian parameter and perturbation are calculated easily by linear regression. Use of the model identifies an anomaly in the population due to the effects of World War II.
    Matched MeSH terms: Models, Biological*
  3. Mansor AFM, Ibrahim I, Zainuddin AA, Voiculescu I, Nordin AN
    Med Biol Eng Comput, 2018 Jan;56(1):173-181.
    PMID: 29247387 DOI: 10.1007/s11517-017-1756-1
    Electrical cell-substrate impedance sensing (ECIS) is a powerful technique to monitor real-time cell behavior. In this study, an ECIS biosensor formed using two interdigitated electrode structures (IDEs) was used to monitor cell behavior and its response to toxicants. Three different sensors with varied electrode spacing were first modeled using COMSOL Multiphysics and then fabricated and tested. The silver/silver chloride IDEs were fabricated using a screen-printing technique and incorporated with polydimethylsiloxane (PDMS) cell culture wells. To study the effectiveness of the biosensor, A549 lung carcinoma cells were seeded in the culture wells together with collagen as an extracellular matrix (ECM) to promote cell attachment on electrodes. A549 cells were cultured in the chambers and impedance measurements were taken at 12-h intervals for 120 h. Cell index (CI) for both designs were calculated from the impedance measurement and plotted in comparison with the growth profile of the cells in T-flasks. To verify that the ECIS biosensor can also be used to study cell response to toxicants, the A549 cells were also treated with anti-cancer drug, paclitaxel, and its responses were monitored over 5 days. Both simulation and experimental results show better sensitivity for smaller spacing between electrodes. Graphical abstract The fabricated impedance biosensor used screen-printed silver/silver chloride IDEs. Simulation and experimental results show better sensitivity for smaller between electrodes.
    Matched MeSH terms: Models, Biological*
  4. Mohd Yusoff MI
    Comput Math Methods Med, 2020;2020:9328414.
    PMID: 33224268 DOI: 10.1155/2020/9328414
    Researchers used a hybrid model (a combination of health resource demand model and disease transmission model), Bayesian model, and susceptible-exposed-infectious-removed (SEIR) model to predict health service utilization and deaths and mixed-effect nonlinear regression. Further, they used the mixture model to predict the number of confirmed cases and deaths or to predict when the curve would flatten. In this article, we show, through scenarios developed using system dynamics methodology, besides close to real-world results, the detrimental effects of ignoring social distancing guidelines (in terms of the number of people infected, which decreased as the percentage of noncompliance decreased).
    Matched MeSH terms: Models, Biological*
  5. Colin PJ, Allegaert K, Thomson AH, Touw DJ, Dolton M, de Hoog M, et al.
    Clin Pharmacokinet, 2019 06;58(6):767-780.
    PMID: 30656565 DOI: 10.1007/s40262-018-0727-5
    BACKGROUND AND OBJECTIVES: Uncertainty exists regarding the optimal dosing regimen for vancomycin in different patient populations, leading to a plethora of subgroup-specific pharmacokinetic models and derived dosing regimens. We aimed to investigate whether a single model for vancomycin could be developed based on a broad dataset covering the extremes of patient characteristics. Furthermore, as a benchmark for current dosing recommendations, we evaluated and optimised the expected vancomycin exposure throughout life and for specific patient subgroups.

    METHODS: A pooled population-pharmacokinetic model was built in NONMEM based on data from 14 different studies in different patient populations. Steady-state exposure was simulated and compared across patient subgroups for two US Food and Drug Administration/European Medicines Agency-approved drug labels and optimised doses were derived.

    RESULTS: The final model uses postmenstrual age, weight and serum creatinine as covariates. A 35-year-old, 70-kg patient with a serum creatinine level of 0.83 mg dL-1 (73.4 µmol L-1) has a V1, V2, CL and Q2 of 42.9 L, 41.7 L, 4.10 L h-1 and 3.22 L h-1. Clearance matures with age, reaching 50% of the maximal value (5.31 L h-1 70 kg-1) at 46.4 weeks postmenstrual age then declines with age to 50% at 61.6 years. Current dosing guidelines failed to achieve satisfactory steady-state exposure across patient subgroups. After optimisation, increased doses for the Food and Drug Administration label achieve consistent target attainment with minimal (± 20%) risk of under- and over-dosing across patient subgroups.

    CONCLUSIONS: A population model was developed that is useful for further development of age and kidney function-stratified dosing regimens of vancomycin and for individualisation of treatment through therapeutic drug monitoring and Bayesian forecasting.

    Matched MeSH terms: Models, Biological*
  6. Mudali D, Jeevanandam J, Danquah MK
    Crit Rev Biotechnol, 2020 Nov;40(7):951-977.
    PMID: 32633615 DOI: 10.1080/07388551.2020.1789062
    Drug-induced transformations in disease characteristics at the cellular and molecular level offers the opportunity to predict and evaluate the efficacy of pharmaceutical ingredients whilst enabling the optimal design of new and improved drugs with enhanced pharmacokinetics and pharmacodynamics. Machine learning is a promising in-silico tool used to simulate cells with specific disease properties and to determine their response toward drug uptake. Differences in the properties of normal and infected cells, including biophysical, biochemical and physiological characteristics, plays a key role in developing fundamental cellular probing platforms for machine learning applications. Cellular features can be extracted periodically from both the drug treated, infected, and normal cells via image segmentations in order to probe dynamic differences in cell behavior. Cellular segmentation can be evaluated to reflect the levels of drug effect on a distinct cell or group of cells via probability scoring. This article provides an account for the use of machine learning methods to probe differences in the biophysical, biochemical and physiological characteristics of infected cells in response to pharmacokinetics uptake of drug ingredients for application in cancer, diabetes and neurodegenerative disease therapies.
    Matched MeSH terms: Models, Biological*
  7. Gnanasegaran N, Thimiri Govinda Raj DB, Arumugam S
    Methods Mol Biol, 2020;2125:193-196.
    PMID: 31489601 DOI: 10.1007/7651_2019_261
    Several research groups have utilized dental pulp stem cells for numerous studies as treatment modality for Parkinson's disease (PD). However, the roles of dental pulp stem cells in governing the Parkinson's disease inflammatory microenvironment remain to be evaluated. In this article, we elaborate the method where we can investigate the effects of dental pulp stem cells on neurons and microglia in an in vitro inflammatory microenvironment.
    Matched MeSH terms: Models, Biological*
  8. Muniyandi RC, Zin AM, Sanders JW
    Biosystems, 2013 Dec;114(3):219-26.
    PMID: 24120990 DOI: 10.1016/j.biosystems.2013.09.008
    This paper presents a method to convert the deterministic, continuous representation of a biological system by ordinary differential equations into a non-deterministic, discrete membrane computation. The dynamics of the membrane computation is governed by rewrite rules operating at certain rates. That has the advantage of applying accurately to small systems, and to expressing rates of change that are determined locally, by region, but not necessary globally. Such spatial information augments the standard differentiable approach to provide a more realistic model. A biological case study of the ligand-receptor network of protein TGF-β is used to validate the effectiveness of the conversion method. It demonstrates the sense in which the behaviours and properties of the system are better preserved in the membrane computing model, suggesting that the proposed conversion method may prove useful for biological systems in particular.
    Matched MeSH terms: Models, Biological*
  9. Chew YH, Shia YL, Lee CT, Majid FA, Chua LS, Sarmidi MR, et al.
    Mol Cell Endocrinol, 2009 Aug 13;307(1-2):57-67.
    PMID: 19524127 DOI: 10.1016/j.mce.2009.03.005
    A mathematical model to describe the oscillatory bursting activity of pancreatic beta-cells is combined with a model of glucose regulation system in this work to study the bursting pattern under regulated extracellular glucose stimulation. The bursting electrical activity in beta-cells is crucial for the release of insulin, which acts to regulate the blood glucose level. Different types of bursting pattern have been observed experimentally in glucose-stimulated islets both in vivo and in vitro, and the variations in these patterns have been linked to changes in glucose level. The combined model in this study enables us to have a deeper understanding on the regime change of bursting pattern when glucose level changes due to hormonal regulation, especially in the postprandial state. This is especially important as the oscillatory components of electrical activity play significant physiological roles in insulin secretion and some components have been found to be lost in type 2 diabetic patients.
    Matched MeSH terms: Models, Biological*
  10. Ang TK, Safuan HM, Sidhu HS, Jovanoski Z, Towers IN
    Bull Math Biol, 2019 07;81(7):2748-2767.
    PMID: 31201660 DOI: 10.1007/s11538-019-00627-8
    The present paper studies a predator-prey fishery model which incorporates the independent harvesting strategies and nonlinear impact of an anthropogenic toxicant. Both fish populations are harvested with different harvesting efforts, and the cases for the presence and non-presence of harvesting effort are discussed. The prey fish population is assumed to be infected by the toxicant directly which causes indirect infection to predator fish population through the feeding process. Each equilibrium of the proposed system is examined by analyzing the respective local stability properties. Dynamical behavior and bifurcations are studied with the assistance of threshold conditions influencing the persistence and extinction of both predator and prey. Bionomic equilibrium solutions for three possible cases are investigated with certain restrictions. Optimal harvesting policy is explored by utilizing the Pontryagin's Maximum Principle to optimize the profit while maintaining the sustainability of the marine ecosystem. Bifurcation analysis showed that the harvesting parameters are the key elements causing fishery extinction. Numerical simulations of bionomic and optimal equilibrium solutions showed that the presence of toxicant has a detrimental effect on the fish populations.
    Matched MeSH terms: Models, Biological*
  11. Al-Qattan MN, Mordi MN, Mansor SM
    Comput Biol Chem, 2016 10;64:237-249.
    PMID: 27475235 DOI: 10.1016/j.compbiolchem.2016.07.007
    BACKGROUND: Glutathione-s-transferases (GSTs) are enzymes that principally catalyze the conjugation of electrophilic compounds to the endogenous nucleophilic glutathione substrate, besides, they have other non-catalytic functions. The Plasmodium falciparum genome encodes a single isoform of GST (PfGST) which is involved in buffering the toxic heme, thus considered a potential anti-malarial target. In mammals several classes of GSTs are available, each of various isoforms. The human (human GST Pi-1 or hGSTP1) and mouse (murine GST Mu-1 or mGSTM1) GST isoforms control cellular apoptosis by interaction with signaling proteins, thus considered as potential anti-cancer targets. In the course of GSTs inhibitors development, the models of ligands interactions with GSTs are used to guide rational molecular modification. In the absence of X-ray crystallographic data, enzyme kinetics and molecular docking experiments can aid in addressing ligands binding modes to the enzymes.

    METHODS: Kinetic studies were used to investigate the interactions between the three GSTs and each of glutathione, 1-chloro-2,4-dinitrobenzene, cibacron blue, ethacrynic acid, S-hexyl glutathione, hemin and protoporphyrin IX. Since hemin displacement is intended for PfGST inhibitors, the interactions between hemin and other ligands at PfGST binding sites were studied kinetically. Computationally determined binding modes and energies were interlinked with the kinetic results to resolve enzymes-ligands interaction models at atomic level.

    RESULTS: The results showed that hemin and cibacron blue have different binding modes in the three GSTs. Hemin has two binding sites (A and B) with two binding modes at site-A depending on presence of GSH. None of the ligands were able to compete hemin binding to PfGST except ethacrynic acid. Besides bind differently in GSTs, the isolated anthraquinone moiety of cibacron blue is not maintaining sufficient interactions with GSTs to be used as a lead. Similarly, the ethacrynic acid uses water bridges to mediate interactions with GSTs and at least the conjugated form of EA is the true hemin inhibitor, thus EA may not be a suitable lead.

    CONCLUSIONS: Glutathione analogues with bulky substitution at thiol of cysteine moiety or at γ-amino group of γ-glutamine moiety may be the most suitable to provide GST inhibitors with hemin competition.

    Matched MeSH terms: Models, Biological*
  12. Namazi H, Kulish VV, Wong A, Nazeri S
    Biomed Res Int, 2016;2016:8437247.
    PMID: 27376087 DOI: 10.1155/2016/8437247
    Cancer is a class of diseases characterized by out-of-control cells' growth which affect cells and make them damaged. Many treatment options for cancer exist. Chemotherapy as an important treatment option is the use of drugs to treat cancer. The anticancer drug travels to the tumor and then diffuses in it through capillaries. The diffusion of drugs in the solid tumor is limited by penetration depth which is different in case of different drugs and cancers. The computation of this depth is important as it helps physicians to investigate about treatment of infected tissue. Although many efforts have been made on studying and measuring drug penetration depth, less works have been done on computing this length from a mathematical point of view. In this paper, first we propose phase lagging model for diffusion of drug in the tumor. Then, using this model on one side and considering the classic diffusion on the other side, we compute the drug penetration depth in the solid tumor. This computed value of drug penetration depth is corroborated by comparison with the values measured by experiments.
    Matched MeSH terms: Models, Biological*
  13. Raja Nhari RMH, Muhammad Zailani AN, Khairil Mokhtar NF, Hanish I
    PMID: 32027553 DOI: 10.1080/19440049.2020.1717645
    The usage of porcine pepsin or other porcine derivatives in food products is a common practice in European, American and certain Asian countries although it creates issues in religious and personnel health concerns. In this study, porcine pepsin was detected using indirect ELISA that involved the anti-pep80510 polyclonal antibody raised against a specific peptide of porcine pepsin, pep80510. The sensitivity of the assay for standard porcine pepsin was 0.008 µg/g. The immunoassay did not cross-react to other animal rennet and milk proteins except for microbial coagulant from Mucor miehie. The recovery of porcine pepsin in spiked cheese curd within the range of CV < 20% while for porcine pepsin in spiked cheese whey the recovery is also within the range of CV% < 20%.
    Matched MeSH terms: Models, Biological*
  14. Ng SF, Rouse JJ, Sanderson FD, Eccleston GM
    Arch Pharm Res, 2012 Mar;35(4):579-93.
    PMID: 22553050 DOI: 10.1007/s12272-012-0401-7
    Synthetic membranes are composed of thin sheets of polymeric macromolecules that can control the passage of components through them. Generally, synthetic membranes used in drug diffusion studies have one of two functions: skin simulation or quality control. Synthetic membranes for skin simulation, such as the silicone-based membranes polydimethylsiloxane and Carbosil, are generally hydrophobic and rate limiting, imitating the stratum corneum. In contrast, synthetic membranes for quality control, such as cellulose esters and polysulfone, are required to act as a support rather than a barrier. These synthetic membranes also often contain pores; hence, they are called porous membranes. The significance of Franz diffusion studies and synthetic membranes in quality control studies involves an understanding of the fundamentals of synthetic membranes. This article provides a general overview of synthetic membranes, including a brief background of the history and the common applications of synthetic membranes. This review then explores the types of synthetic membranes, the transport mechanisms across them, and their relevance in choosing a synthetic membrane in Franz diffusion cell studies for formulation assessment purposes.
    Matched MeSH terms: Models, Biological*
  15. Thaler L, Reich GM, Zhang X, Wang D, Smith GE, Tao Z, et al.
    PLoS Comput Biol, 2017 Aug;13(8):e1005670.
    PMID: 28859082 DOI: 10.1371/journal.pcbi.1005670
    Echolocation is the ability to use sound-echoes to infer spatial information about the environment. Some blind people have developed extraordinary proficiency in echolocation using mouth-clicks. The first step of human biosonar is the transmission (mouth click) and subsequent reception of the resultant sound through the ear. Existing head-related transfer function (HRTF) data bases provide descriptions of reception of the resultant sound. For the current report, we collected a large database of click emissions with three blind people expertly trained in echolocation, which allowed us to perform unprecedented analyses. Specifically, the current report provides the first ever description of the spatial distribution (i.e. beam pattern) of human expert echolocation transmissions, as well as spectro-temporal descriptions at a level of detail not available before. Our data show that transmission levels are fairly constant within a 60° cone emanating from the mouth, but levels drop gradually at further angles, more than for speech. In terms of spectro-temporal features, our data show that emissions are consistently very brief (~3ms duration) with peak frequencies 2-4kHz, but with energy also at 10kHz. This differs from previous reports of durations 3-15ms and peak frequencies 2-8kHz, which were based on less detailed measurements. Based on our measurements we propose to model transmissions as sum of monotones modulated by a decaying exponential, with angular attenuation by a modified cardioid. We provide model parameters for each echolocator. These results are a step towards developing computational models of human biosonar. For example, in bats, spatial and spectro-temporal features of emissions have been used to derive and test model based hypotheses about behaviour. The data we present here suggest similar research opportunities within the context of human echolocation. Relatedly, the data are a basis to develop synthetic models of human echolocation that could be virtual (i.e. simulated) or real (i.e. loudspeaker, microphones), and which will help understanding the link between physical principles and human behaviour.
    Matched MeSH terms: Models, Biological*
  16. Tan BH, Pan Y, Dong AN, Ong CE
    J Pharm Pharm Sci, 2017;20(1):319-328.
    PMID: 29145931 DOI: 10.18433/J3434R
    In vitro and in silico models of drug metabolism are utilized regularly in the drug research and development as tools for assessing pharmacokinetic variability and drug-drug interaction risk. The use of in vitro and in silico predictive approaches offers advantages including guiding rational design of clinical drug-drug interaction studies, minimization of human risk in the clinical trials, as well as cost and time savings due to lesser attrition during compound development process. This article gives a review of some of the current in vitro and in silico methods used to characterize cytochrome P450(CYP)-mediated drug metabolism for estimating pharmacokinetic variability and the magnitude of drug-drug interactions. Examples demonstrating the predictive applicability of specific in vitro and in silico approaches are described. Commonly encountered confounding factors and sources of bias and error in these approaches are presented. With the advent of technological advancement in high throughput screening and computer power, the in vitro and in silico methods are becoming more efficient and reliable and will continue to contribute to the process of drug discovery, development and ultimately safer and more effective pharmacotherapy. This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.
    Matched MeSH terms: Models, Biological*
  17. Mienda BS, Salihu R, Adamu A, Idris S
    Future Microbiol, 2018 03;13:455-467.
    PMID: 29469596 DOI: 10.2217/fmb-2017-0195
    The growing number of multidrug-resistant pathogenic bacteria is becoming a world leading challenge for the scientific community and for public health. However, advances in high-throughput technologies and whole-genome sequencing of bacterial pathogens make the construction of bacterial genome-scale metabolic models (GEMs) increasingly realistic. The use of GEMs as an alternative platforms will expedite identification of novel unconditionally essential genes and enzymes of target organisms with existing and forthcoming GEMs. This approach will follow the existing protocol for construction of high-quality GEMs, which could ultimately reduce the time, cost and labor-intensive processes involved in identification of novel antimicrobial drug targets in drug discovery pipelines. We discuss the current impact of existing GEMs of selected multidrug-resistant pathogenic bacteria for identification of novel antimicrobial drug targets and the challenges of closing the gap between genome-scale metabolic modeling and conventional experimental trial-and-error approaches in drug discovery pipelines.
    Matched MeSH terms: Models, Biological*
  18. Oroji A, Omar M, Yarahmadian S
    J Theor Biol, 2016 10 21;407:128-137.
    PMID: 27457094 DOI: 10.1016/j.jtbi.2016.07.035
    In this paper, a new mathematical model is proposed for studying the population dynamics of breast cancer cells treated by radiotherapy by using a system of stochastic differential equations. The novelty of the model is essentially in capturing the concept of the cell cycle in the modeling to be able to evaluate the tumor lifespan. According to the cell cycle, each cell belongs to one of three subpopulations G, S, or M, representing gap, synthesis and mitosis subpopulations. Cells in the M subpopulation are highly radio-sensitive, whereas cells in the S subpopulation are highly radio-resistant. Therefore, in the process of radiotherapy, cell death rates of different subpopulations are not equal. In addition, since flow cytometry is unable to detect apoptotic cells accurately, the small changes in cell death rate in each subpopulation during treatment are considered. Subsequently, the proposed model is calibrated using experimental data from previous experiments involving the MCF-7 breast cancer cell line. Consequently, the proposed model is able to predict tumor lifespan based on the number of initial carcinoma cells. The results show the effectiveness of the radiation under the condition of stability, which describes the decreasing trend of the tumor cells population.
    Matched MeSH terms: Models, Biological*
  19. Abdullah A, Deris S, Mohamad MS, Anwar S
    PLoS One, 2013;8(4):e61258.
    PMID: 23593445 DOI: 10.1371/journal.pone.0061258
    One of the key aspects of computational systems biology is the investigation on the dynamic biological processes within cells. Computational models are often required to elucidate the mechanisms and principles driving the processes because of the nonlinearity and complexity. The models usually incorporate a set of parameters that signify the physical properties of the actual biological systems. In most cases, these parameters are estimated by fitting the model outputs with the corresponding experimental data. However, this is a challenging task because the available experimental data are frequently noisy and incomplete. In this paper, a new hybrid optimization method is proposed to estimate these parameters from the noisy and incomplete experimental data. The proposed method, called Swarm-based Chemical Reaction Optimization, integrates the evolutionary searching strategy employed by the Chemical Reaction Optimization, into the neighbouring searching strategy of the Firefly Algorithm method. The effectiveness of the method was evaluated using a simulated nonlinear model and two biological models: synthetic transcriptional oscillators, and extracellular protease production models. The results showed that the accuracy and computational speed of the proposed method were better than the existing Differential Evolution, Firefly Algorithm and Chemical Reaction Optimization methods. The reliability of the estimated parameters was statistically validated, which suggests that the model outputs produced by these parameters were valid even when noisy and incomplete experimental data were used. Additionally, Akaike Information Criterion was employed to evaluate the model selection, which highlighted the capability of the proposed method in choosing a plausible model based on the experimental data. In conclusion, this paper presents the effectiveness of the proposed method for parameter estimation and model selection problems using noisy and incomplete experimental data. This study is hoped to provide a new insight in developing more accurate and reliable biological models based on limited and low quality experimental data.
    Matched MeSH terms: Models, Biological*
  20. Bhat S, Rao G, Murthy KD, Bhat PG
    Indian J Exp Biol, 2007 May;45(5):455-8.
    PMID: 17569288
    The present study was aimed to find out whether a change in the alignment of the pyramid from the north-south axis causes any variation in the effects produced by it on plasma cortisol levels and markers of oxidative stress in erythrocytes of adult-female Wistar rats. Plasma cortisol and erythrocyte TBARS levels were significantly lower whereas erythrocyte GSH was significantly higher in rats kept in pyramid that was aligned on the four cardinal points--north, east, south and west, as compared to normal control rats. Although there was a significant difference in the plasma cortisol level between normal control group and the group of rats kept in randomly aligned pyramid, there was no significant difference between these two groups for the other parameters. Erythrocyte TBARS levels in the group of rats kept in the randomly aligned pyramid was significantly higher than that in the group kept in the magnetically aligned pyramid. The results suggest that the north-south alignment of the pyramid is crucial for its expected effects.
    Matched MeSH terms: Models, Biological*
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