Although, coumarins are a group of compounds which are naturally found in some plants, they can be synthetically produced as well. Because of their diverse derivatives, origin and properties most of them can be used for medicinal purposes. For example, they can be used against fungal diseases or in studying structure and biological properties of antifungal agents to discover new compounds with the similar activity. A Structure Property/Activity Relationship (SAR) can be utilized in prediction of biological activity of desired molecules.
In order to represent a relationship between the physicochemical properties of coumarin compounds and their biological activities, 68 coumarins and coumarin derivatives with already reported antifungal activities were selected and eleven attributes were generated. The descriptors were used to perform artificial neural network (ANN) and to build a model for predicting effectiveness of the new ones. The correlation coefficient between the experimental and the predicted MIC values pertaining to all the coumarins was 0.984. This study paves the way for further researches about antifungal activity of coumarins, and offers a powerful tool in modeling and prediction of their bioactivities.
Antifungal activity; Coumarin; Modeling; Neural network
Artificial neural network (ANN) based prediction of the response of a microbend fiber optic sensor is presented. To the best of our knowledge no similar work has been previously reported in the literature. Parallel corrugated plates with three deformation cycles, 6 mm thickness of the spacer material and 16 mm mechanical periodicity between deformations were used in the microbend sensor. Multilayer Perceptron (MLP) with different training algorithms, Radial Basis Function (RBF) network and General Regression Neural Network (GRNN) are used as ANN models in this work. All of these models can predict the sensor responses with considerable errors. RBF has the best performance with the smallest mean square error (MSE) values of training and test results. Among the MLP algorithms and GRNN the Levenberg-Marquardt algorithm has good results. These models successfully predict the sensor responses, hence ANNs can be used as useful tool in the design of more robust fiber optic sensors.
artificial neural networks; fiber optic sensors; microbend sensors; multilayer perceptron; radial basis function; general regression neural network
Mixotrophic metabolism was evaluated as an option to augment the growth and lipid production of marine microalga Tetraselmis sp. FTC 209. In this study, a five-level three-factor central composite design (CCD) was implemented in order to enrich the W-30 algal growth medium. Response surface methodology (RSM) was employed to model the effect of three medium variables, that is, glucose (organic C source), NaNO3 (primary N source), and yeast extract (supplementary N, amino acids, and vitamins) on biomass concentration, Xmax, and lipid yield, Pmax/Xmax. RSM capability was also weighed against an artificial neural network (ANN) approach for predicting a composition that would result in maximum lipid productivity, Prlipid. A quadratic regression from RSM and a Levenberg-Marquardt trained ANN network composed of 10 hidden neurons eventually produced comparable results, albeit ANN formulation was observed to yield higher values of response outputs. Finalized glucose (24.05 g/L), NaNO3 (4.70 g/L), and yeast extract (0.93 g/L) concentration, affected an increase of Xmax to 12.38 g/L and lipid a accumulation of 195.77 mg/g dcw. This contributed to a lipid productivity of 173.11 mg/L per day in the course of two-week cultivation.
In this study, artificial neural network (ANN) analysis of virotherapy in preclinical
breast cancer was investigated.
Materials and Methods:
In this research article, a multilayer feed-forward neural network
trained with an error back-propagation algorithm was incorporated in order to develop a
predictive model. The input parameters of the model were virus dose, week and tamoxifen
citrate, while tumor weight was included in the output parameter. Two different training
algorithms, namely quick propagation (QP) and Levenberg-Marquardt (LM), were used
to train ANN.
The results showed that the LM algorithm, with 3-9-1 arrangement is more efficient
compared to QP. Using LM algorithm, the coefficient of determination (R2) between
the actual and predicted values was determined as 0.897118 for all data.
It can be concluded that this ANN model may provide good ability to predict
the biometry information of tumor in preclinical breast cancer virotherapy. The results
showed that the LM algorithm employed by Neural Power software gave the better performance
compared with the QP and virus dose, and it is more important factor compared to
tamoxifen and time (week).
Neural Network Model; Breast Cancer; Virotherapy
We performed studies on extended series of 79 HEPT ligands (1-[(2-hydroxyethoxy)methyl]-6-(phenylthio)thymine), inhibitors of HIV reverse-transcriptase with anti-HIV biological activity, using quantitative structure–activity relationship (QSAR) methods that imply analysis of correlations and representation of models. A suitable set of molecular descriptors was calculated, and the genetic algorithm was employed to select those descriptors which resulted in the best-fit models. The kernel partial least square and Levenberg–Marquardt artificial neural network were utilized to construct the nonlinear QSAR models. The proposed methods will be of great significance in this research, and would be expected to apply to other similar research fields.
AIDS; Anti-HIV activity; HEPT ligands; QSAR; Genetic algorithm; Levenberg–Marquardt artificial neural network
The purpose of this study was to compare the performance of two methods for gravity inversion of a fault. First method [Particle swarm optimization (PSO)] is a heuristic global optimization method and also an optimization algorithm, which is based on swarm intelligence. It comes from the research on the bird and fish flock movement behavior. Second method [The Levenberg-Marquardt algorithm (LM)] is an approximation to the Newton method used also for training ANNs. In this paper first we discussed the gravity field of a fault, then describes the algorithms of PSO and LM And presents application of Levenberg-Marquardt algorithm, and a particle swarm algorithm in solving inverse problem of a fault. Most importantly the parameters for the algorithms are given for the individual tests. Inverse solution reveals that fault model parameters are agree quite well with the known results. A more agreement has been found between the predicted model anomaly and the observed gravity anomaly in PSO method rather than LM method.
Particle swarm optimization; Levenberg-Marquardt method; Inversion; Gravity data; Fault
Insulin resistance (IR) is one of the most widespread health problems in modern times. The gold standard for quantification of IR is the hyperinsulinemic-euglycemic glucose clamp technique. During the test, a regulated glucose infusion is delivered intravenously to maintain a constant blood glucose concentration. Current control algorithms for regulating this glucose infusion are based on feedback control. These models require frequent sampling of blood, and can only partly capture the complexity associated with regulation of glucose. Here we present an improved clamp control algorithm which is motivated by the stochastic nature of glucose kinetics, while using the minimal need in blood samples required for evaluation of IR. A glucose pump control algorithm, based on artificial neural networks model was developed. The system was trained with a data base collected from 62 rat model experiments, using a back-propagation Levenberg-Marquardt optimization. Genetic algorithm was used to optimize network topology and learning features. The predictive value of the proposed algorithm during the temporal period of interest was significantly improved relative to a feedback control applied at an equivalent low sampling interval. Robustness to noise analysis demonstrates the applicability of the algorithm in realistic situations.
This paper describes artificial neural network (ANN) based prediction of the response of a fiber optic sensor using evanescent field absorption (EFA). The sensing probe of the sensor is made up a bundle of five PCS fibers to maximize the interaction of evanescent field with the absorbing medium. Different backpropagation algorithms are used to train the multilayer perceptron ANN. The Levenberg-Marquardt algorithm, as well as the other algorithms used in this work successfully predicts the sensor responses.
Fiber optic sensor; evanescent field; absorption; artificial neural networks
Complex polarization ratio (CPR) in materials with birefringence and biattenuance is shown as a logarithmic spiral in the complex plane. A multi-state Levenberg-Marquardt nonlinear fitting algorithm using the CPR trajectory collected by polarization sensitive optical coherence tomography (PS-OCT) was developed to determine polarization properties of an anisotropic scattering medium. The Levenberg-Marquardt nonlinear fitting algorithm using the CPR trajectory is verified using simulated PS-OCT data with speckle noise. Birefringence and biattenuance of a birefringent film, ex-vivo rodent tail tendon and in-vivo primate retinal nerve fiber layer were determined using measured CPR trajectories and the Levenberg-Marquardt nonlinear fitting algorithm.
In this work, an acoustic sensor network for a relative localization system is analyzed by reporting the accuracy achieved in the position estimation. The proposed system has been designed for those applications where objects are not restricted to a particular environment and thus one cannot depend on any external infrastructure to compute their positions. The objects are capable of computing spatial relations among themselves using only acoustic emissions as a ranging mechanism. The object positions are computed by a multidimensional scaling (MDS) technique and, afterwards, a least-square algorithm, based on the Levenberg-Marquardt algorithm (LMA), is applied to refine results. Regarding the position estimation, all the parameters involved in the computation of the temporary relations with the proposed ranging mechanism have been considered. The obtained results show that a fine-grained localization can be achieved considering a Gaussian distribution error in the proposed ranging mechanism. Furthermore, since acoustic sensors require a line-of-sight to properly work, the system has been tested by modeling the lost of this line-of-sight as a non-Gaussian error. A suitable position estimation has been achieved even if it is considered a bias of up to 25 of the line-of-sight measurements among a set of nodes.
sensor networks; relative localization; remote sensing
Metal complexes of dichloro-tetramorpholino-cyclophosphazatriene containing divalent cations
such as Ni(II), Co(II), and Mn(II) have been prepared and characterised by standard physico-chemical
procedures (elemental chemical analysis, IR and UV-VIS spectra, conductimetric measurement). The newly
synthesised compounds possessed antifungal activity against Aspergillus and Candida spp., some of them
showing effects comparable to ketoconazole (with minimum inhibitory concentrations in the range of 2- 30
μg/mL) but being generally less active as compared to the azole. Best activity was detected against C.
albicans, and worst activity against A. niger. The mechanism of action of these compounds probably
involves inhibition of ergosterol biosynthesis, and interaction with lanosterol-14-α-demethylase (CYP51A1),
since reduced amounts of ergosterol were evidenced by means of HPLC in cultures of the sensitive strain A. niger treated with some of these inhibitors.
We present an approach based on the improved Levenberg
Marquardt (LM) algorithm of backpropagation (BP) neural network to estimate the light source position in bioluminescent imaging. For solving the forward problem, the table-based random sampling algorithm (TBRS), a fast Monte Carlo simulation method we developed before, is employed here. Result shows that BP is an effective method to position the light source.
Candida albicans is the most common etiologic agent of systemic fungal infections with unacceptably high mortality rates. The existing arsenal of antifungal drugs is very limited and is particularly ineffective against C. albicans biofilms. To address the unmet need for novel antifungals, particularly those active against biofilms, we have screened a small molecule library consisting of 1,200 off-patent drugs already approved by the Food and Drug Administration (FDA), the Prestwick Chemical Library, to identify inhibitors of C. albicans biofilm formation. According to their pharmacological applications that are currently known, we classified these bioactive compounds as antifungal drugs, as antimicrobials/antiseptics, or as miscellaneous drugs, which we considered to be drugs with no previously characterized antifungal activity. Using a 96-well microtiter plate-based high-content screening assay, we identified 38 pharmacologically active agents that inhibit C. albicans biofilm formation. These drugs were subsequently tested for their potency and efficacy against preformed biofilms, and we identified three drugs with novel antifungal activity. Thus, repurposing FDA-approved drugs opens up a valuable new avenue for identification and potentially rapid development of antifungal agents, which are urgently needed.
Naturally occurring antimicrobial peptides hold promise as therapeutic agents against oral pathogens such as Candida albicans, however numerous difficulties have slowed their development. Synthetic, non-peptidic analogs that mimic the properties of these peptides have many advantages and exhibit potent, selective antimicrobial activity. Several series of mimetics (MW <1,000) were developed and screened against oral Candida strains as a proof-of-principle for their antifungal properties. One phenylalkyne and several arylamide compounds with reduced mammalian cytotoxicities were found to be active against C. albicans. These compounds demonstrated rapid fungicidal activity in liquid culture even in the presence of saliva, and demonstrated synergy with standard antifungal agents. When assayed against biofilms grown on denture acrylic, the compounds exhibited potent fungicidal activity as measured by metabolic and fluorescent viability assays. Repeated passages in sub-MIC levels did not lead to resistant Candida in contrast to fluconazole. Our results demonstrate the proof-of principle for the use of these compounds as anti-Candida agents, and their further testing is warranted as novel anti-Candida therapies.
antifungal; denture; fungicide; defensin; resistance
Heterotrophic bacteria associated with two specimens of the marine sponge Erylus discophorus were screened for their capacity to produce bioactive compounds against a panel of human pathogens (Staphylococcus aureus wild type and methicillin-resistant S. aureus (MRSA), Bacillus subtilis, Pseudomonas aeruginosa, Acinetobacter baumanii, Candida albicans and Aspergillus fumigatus), fish pathogen (Aliivibrio fischeri) and environmentally relevant bacteria (Vibrio harveyi). The sponges were collected in Berlengas Islands, Portugal. Of the 212 isolated heterotrophic bacteria belonging to Alpha- and Gammaproteobacteria, Actinobacteria and Firmicutes, 31% produced antimicrobial metabolites. Bioactivity was found against both Gram positive and Gram negative and clinically and environmentally relevant target microorganisms. Bioactivity was found mainly against B. subtilis and some bioactivity against S. aureus MRSA, V. harveyi and A. fisheri. No antifungal activity was detected. The three most bioactive genera were Pseudovibrio (47.0%), Vibrio (22.7%) and Bacillus (7.6%). Other less bioactive genera were Labrenzia, Acinetobacter, Microbulbifer, Pseudomonas, Gordonia, Microbacterium, Micrococcus and Mycobacterium, Paenibacillus and Staphylococcus. The search of polyketide I synthases (PKS-I) and nonribosomal peptide synthetases (NRPSs) genes in 59 of the bioactive bacteria suggested the presence of PKS-I in 12 strains, NRPS in 3 strains and both genes in 3 strains. Our results show the potential of the bacterial community associated with Erylus discophorus sponges as producers of bioactive compounds.
Candida albicans, the most common human pathogenic fungus, can establish a persistent lethal infection in the intestine of the microscopic nematode Caenorhabditis elegans. The C. elegans–C. albicans infection model was previously adapted to screen for antifungal compounds. Modifications to this screen have been made to facilitate a high-throughput assay including co-inoculation of nematodes with C. albicans and instrumentation allowing precise dispensing of worms into assay wells, eliminating two labor-intensive steps. This high-throughput method was utilized to screen a library of 3,228 compounds represented by 1,948 bioactive compounds and 1,280 small molecules derived via diversity-oriented synthesis. Nineteen compounds were identified that conferred an increase in C. elegans survival, including most known antifungal compounds within the chemical library. In addition to seven clinically used antifungal compounds, twelve compounds were identified which are not primarily used as antifungal agents, including three immunosuppressive drugs. This assay also allowed the assessment of the relative minimal inhibitory concentration, the effective concentration in vivo, and the toxicity of the compound in a single assay.
Proteins are dynamic molecules with motions ranging from picoseconds to longer than seconds. Many protein functions, however, appear to occur on the micro to millisecond timescale and therefore there has been intense research of the importance of these motions in catalysis and molecular interactions. Nuclear Magnetic Resonance (NMR) relaxation dispersion experiments are used to measure motion of discrete nuclei within the micro to millisecond timescale. Information about conformational/chemical exchange, populations of exchanging states and chemical shift differences are extracted from these experiments. To ensure these parameters are correctly extracted, accurate and careful analysis of these experiments is necessary.
The software introduced in this article is designed for the automatic analysis of relaxation dispersion data and the extraction of the parameters mentioned above. It is written in Python for multi platform use and highest performance. Experimental data can be fitted to different models using the Levenberg-Marquardt minimization algorithm and different statistical tests can be used to select the best model. To demonstrate the functionality of this program, synthetic data as well as NMR data were analyzed. Analysis of these data including the generation of plots and color coded structures can be performed with minimal user intervention and using standard procedures that are included in the program.
NESSY is easy to use open source software to analyze NMR relaxation data. The robustness and standard procedures are demonstrated in this article.
Protein dynamics; software; cpmg; conformational/chemical exchange; μs-ms motion; van't Hoff; transition state theory
There is an urgent need for the development of new antifungal agents. A facile in vivo model that evaluates libraries of chemical compounds could solve some of the main obstacles in current antifungal discovery. We show that Candida albicans, as well as other Candida species, are ingested by Caenorhabditis elegans and establish a persistent lethal infection in the C. elegans intestinal track. Importantly, key components of Candida pathogenesis in mammals, such as filament formation, are also involved in nematode killing. We devised a Candida-mediated C. elegans assay that allows high-throughput in vivo screening of chemical libraries for antifungal activities, while synchronously screening against toxic compounds. The assay is performed in liquid media using standard 96-well plate technology and allows the study of C. albicans in non-planktonic form. A screen of 1,266 compounds with known pharmaceutical activities identified 15 (∼1.2%) that prolonged survival of C. albicans-infected nematodes and inhibited in vivo filamentation of C. albicans. Two compounds identified in the screen, caffeic acid phenethyl ester, a major active component of honeybee propolis, and the fluoroquinolone agent enoxacin exhibited antifungal activity in a murine model of candidiasis. The whole-animal C. elegans assay may help to study the molecular basis of C. albicans pathogenesis and identify antifungal compounds that most likely would not be identified by in vitro screens that target fungal growth. Compounds identified in the screen that affect the virulence of Candida in vivo can potentially be used as “probe compounds” and may have antifungal activity against other fungi.
Candida spp. are among the most significant causes of nosocomial infections, and disseminated candidiasis continues to have an attributable mortality rate of over 25%. For this reason, we have developed a liquid media assay using the model nematode Caenorhabditis elegans as a model organism for Candida infection. The worms are infected on solid media lawns and then moved to pathogen-free liquid media. Unless antifungal compounds are added to the wells, the majority of worms die within 3–4 d. This model is similar to the infection process in humans, in that Candida cells are able to produce filaments, which are essential for the infection process in humans. We used this pathogen model to create a semi-automated, high-throughput screen using C. elegans to evaluate the antifungal effectiveness of many types of chemical compounds. Through this process, we have identified three compounds that we show have varying degrees of antifungal activity in C. elegans, in vitro, and in mice.
Poly- and mononuclear metal complexes of 2,3,11,12-bis[4-(10-aminodecylcarbonyl)]benzo-18-
crown-6 (L) and Cu(II); Ni(II); Co(II) and Cr(III) have been synthesized and characterized by standard
physico-chemical procedures. In the newly prepared complexes the crown moiety oxygen atoms of the
macrocyclic host did not generally interact with metal ions, whereas the two amino groups of the ligand always did. Several of the newly synthesized compounds act as effective antifungal agents against
Aspergillus and Candida spp., some of them showing activities comparable to
ketoconazole, with minimum inhibitory concentrations
in the range of 0.3−0.5 μg/mL. The mechanism of antifungal action of these
coordination compounds is probably connected to an inhibition of lanosterol-14-α-demethylase, a metallo-enzyme
playing a key role in sterol biosynthesis in fungi, bacteria and eukariotes.
Octanol-water partition coefficient (Kow) is an important thermodynamic property used to characterize the partitioning of solutes between an aqueous and organic phase and has importance in such areas as pharmacology, pharmacokinetics, pharmacodynamics, chemical production and environmental toxicology. We present a non-linear quantitative structure-property relationship model for determining Kow values of new molecules in silico. A total of 823 descriptors were generated for 11,308 molecules whose Kow values are reported in the PhysProp dataset by Syracuse Research. Optimum network architecture and its associated inputs were identified using a wrapper-based feature selection algorithm that combines differential evolution and artificial neural networks. A network architecture of 50-33-35-1 resulted in the least root-mean squared error (RMSE) in the training set. Further, to improve on single-network predictions, a neural network ensemble was developed by combining five networks that have the same architecture and inputs but differ in layer weights. The ensemble predicted the Kow values with RMSE of 0.28 and 0.38 for the training set and internal validation set, respectively. The ensemble performed reasonably well on an external dataset when compared with other popular Kow models in the literature.
mathematical models; neural networks; octanol-water partition coefficient; QSPR; solubility
Candida yeasts are saprophytes naturally present in the environment and forming colonies on human mucous membranes and skin. They are opportunistic fungi that cause severe and even fatal infections in immunocompromised individuals. Several essential oils, including eucalyptus, pine, cinnamon and lemon, have been shown to be effective against Candida strains. This study addresses the chemical composition of some commercial lemon essential oils and their antifungal potential against selected Candida yeast strains. Antifungal potential and minimum inhibitory concentrations were determined for six commercial lemon essential oils against five Candida yeast strains (Candida albicans 31, Candida tropicalis 32, Candida glabrata 33, Candida glabrata 35 and Candida glabrata 38). On the basis of the GCMS analysis, it was found that the tested lemon essential oils had different chemical compositions, but mostly, they contained almost exclusively terpenes and oxygenated terpenes. The tests show that antifungal potential of lemon essential oils against Candida yeast strains was related to the high content of monoterpenoids and the type of Candida strains. From six tested commercial oils, only four (ETJA, Vera-Nord, Avicenna-Oil and Aromatic Art) shows antifungal potential against three Candida species (C. albicans, C.tropicalis and C.glabrata). Vera-Nord and Avicenna-Oil show the best activity and effectively inhibit the growth of the C. albicans strain across the full range of the concentrations used. Our study characterises lemon essential oils, which could be used as very effective natural remedies against candidiasis caused by C. albicans.
Candida albicans; Antifungal; Lemon essential oils; GCMS
Reaction kinetics for complex, highly-interconnected kinetic schemes are modeled using analytical solutions to a system of ordinary differential equations. The algorithm employs standard linear algebra methods that are implemented using MatLab functions in a Visual Basic interface. A graphical user interface for simple entry of reaction schemes facilitates comparison of a variety of reaction schemes. To ensure microscopic balance, graph theory algorithms are used to determine violations of thermodynamic cycle constraints. Analytical solutions based on linear differential equations result in fast comparisons of first order kinetic rates and amplitudes as a function of changing ligand concentrations. For analysis of higher order kinetics, we also implemented a solution using numerical integration. In order to determine rate constants from experimental data, fitting algorithms using the Levenberg-Marquardt algorithm or using Broyden-Fletcher-Goldfarb-Shanno (BFGS) methods were implemented that adjust rate constants to fit the model to imported data. We have included the ability to carry out global fitting of data sets obtained at varying ligand concentrations. These tools are combined in a single package, which we have dubbed VisKin, to guide and analyze kinetic experiments. The software is available online for use on PCs.
Search for naturally occurring compounds with antifungal activity has become quite intense due to the side effects of synthetic fungicides and the development of pathogens against such fungicides. Hence screening of various Siddha drugs for their antifungal activity against various strains of Candida albicans was considered worthwhile. Seven such Siddha drugs were screened for their antifungal activity against fourteen strains of Candida albicans. The results indicate that the drugs Nandhi mezhugu, Vaan mezhugu, Erasa Kenthi mezhugu and Parangi pattai choornam possessed significant antifungal activity against various strains of C.albicans.
Motivation: Recognition of poly(A) signals in mRNA is relatively straightforward due to the presence of easily recognizable polyadenylic acid tail. However, the task of identifying poly(A) motifs in the primary genomic DNA sequence that correspond to poly(A) signals in mRNA is a far more challenging problem. Recognition of poly(A) signals is important for better gene annotation and understanding of the gene regulation mechanisms. In this work, we present one such poly(A) motif prediction method based on properties of human genomic DNA sequence surrounding a poly(A) motif. These properties include thermodynamic, physico-chemical and statistical characteristics. For predictions, we developed Artificial Neural Network and Random Forest models. These models are trained to recognize 12 most common poly(A) motifs in human DNA. Our predictors are available as a free web-based tool accessible at http://cbrc.kaust.edu.sa/dps. Compared with other reported predictors, our models achieve higher sensitivity and specificity and furthermore provide a consistent level of accuracy for 12 poly(A) motif variants.
Supplementary information: Supplementary data are available at Bioinformatics online.
Candida species have been associated with the emergence of resistant strains towards selected antifungal agents. Plant products have been used traditionally as alternative medicine to ease candidal infections. The present study was undertaken to investigate the antifungal susceptibility patterns and growth inhibiting effect of Brucea javanica seeds extract against Candida species.
A total of seven Candida strains that includes Candida albicans ATCC14053, Candida dubliniensis ATCCMYA-2975, Candida glabrata ATCC90030, Candida krusei ATCC14243, Candida lusitaniae ATCC64125, Candida parapsilosis ATCC22019 and Candida tropicalis ATCC13803 were used in this study. The antifungal activity, minimum inhibitory concentration and minimum fungicidal concentration of B. javanica extract were evaluated. Each strain was cultured in Yeast Peptone Dextrose broth under four different growth environments; (i) in the absence and presence of B. javanica extract at respective concentrations of (ii) 1 mg/ml (iii) 3 mg/ml and (iv) 6 mg/ml. The growth inhibitory responses of the candidal cells were determined based on changes in the specific-growth rates (μ) and doubling time (g). The values in the presence of extract were computed as percentage in the optical density relative to that of the total cells suspension in the absence of extract.
B. javanica seeds extract exhibited antifungal properties. C. tropicalis showed the highest growth rate; 0.319 ± 0.002 h-1, while others were in the range of 0.141 ± 0.001 to 0.265 ± 0.005 h-1. In the presence of extract, the lag and log phases were extended and deviated the μ- and g-values. B. javanica extract had significantly reduced the μ-values of C. dubliniensis, C. krusei and C. parapsilosis at more than 80% (ρ < 0.05), while others were reduced within the range of 2.28% to 57.05%. The g-values of most candidal strains were extended and significantly reduced (ρ < 0.05) in relative to the untreated. The candidal population was reduced from an average of 10 x 106 to 6 x 106 CFU/ml.
B. javanica extract exhibited in vitro antifungal activity against seven oral Candida species. The fungistatic and growth inhibiting effects of B. javanica extract have shown that it has potential to be considered as a promising candidate for the development of antifungal agent in oral health products.
Antifungal activity; Brucea javanica; Candida species; Growth inhibitory effect