Displaying publications 61 - 80 of 1459 in total

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  1. Fulltext New regression model for predicting hand-arm vibration (HAV) of Malaysian Army (MA) three-tonne truck steering wheels
    Aziz SA, Nuawi MZ, Nor MJ
    J Occup Health, 2015;57(6):513-20.
    PMID: 26269278 DOI: 10.1539/joh.14-0206-OA
    OBJECTIVE: The objective of this study was to present a new method for determination of hand-arm vibration (HAV) in Malaysian Army (MA) three-tonne truck steering wheels based on changes in vehicle speed using regression model and the statistical analysis method known as Integrated Kurtosis-Based Algorithm for Z-Notch Filter Technique Vibro (I-kaz Vibro).

    METHODOLOGY: The test was conducted for two different road conditions, tarmac and dirt roads. HAV exposure was measured using a Brüel & Kjær Type 3649 vibration analyzer, which is capable of recording HAV exposures from steering wheels. The data was analyzed using I-kaz Vibro to determine the HAV values in relation to varying speeds of a truck and to determine the degree of data scattering for HAV data signals.

    RESULTS: Based on the results obtained, HAV experienced by drivers can be determined using the daily vibration exposure A(8), I-kaz Vibro coefficient (Ƶ(v)(∞)), and the I-kaz Vibro display. The I-kaz Vibro displays also showed greater scatterings, indicating that the values of Ƶ(v)(∞) and A(8) were increasing. Prediction of HAV exposure was done using the developed regression model and graphical representations of Ƶ(v)(∞). The results of the regression model showed that Ƶ(v)(∞) increased when the vehicle speed and HAV exposure increased.

    DISCUSSION: For model validation, predicted and measured noise exposures were compared, and high coefficient of correlation (R(2)) values were obtained, indicating that good agreement was obtained between them. By using the developed regression model, we can easily predict HAV exposure from steering wheels for HAV exposure monitoring.

    Matched MeSH terms: Algorithms
  2. Fulltext An Automated and Intelligent Medical Decision Support System for Brain MRI Scans Classification
    Siddiqui MF, Reza AW, Kanesan J
    PLoS One, 2015;10(8):e0135875.
    PMID: 26280918 DOI: 10.1371/journal.pone.0135875
    A wide interest has been observed in the medical health care applications that interpret neuroimaging scans by machine learning systems. This research proposes an intelligent, automatic, accurate, and robust classification technique to classify the human brain magnetic resonance image (MRI) as normal or abnormal, to cater down the human error during identifying the diseases in brain MRIs. In this study, fast discrete wavelet transform (DWT), principal component analysis (PCA), and least squares support vector machine (LS-SVM) are used as basic components. Firstly, fast DWT is employed to extract the salient features of brain MRI, followed by PCA, which reduces the dimensions of the features. These reduced feature vectors also shrink the memory storage consumption by 99.5%. At last, an advanced classification technique based on LS-SVM is applied to brain MR image classification using reduced features. For improving the efficiency, LS-SVM is used with non-linear radial basis function (RBF) kernel. The proposed algorithm intelligently determines the optimized values of the hyper-parameters of the RBF kernel and also applied k-fold stratified cross validation to enhance the generalization of the system. The method was tested by 340 patients' benchmark datasets of T1-weighted and T2-weighted scans. From the analysis of experimental results and performance comparisons, it is observed that the proposed medical decision support system outperformed all other modern classifiers and achieves 100% accuracy rate (specificity/sensitivity 100%/100%). Furthermore, in terms of computation time, the proposed technique is significantly faster than the recent well-known methods, and it improves the efficiency by 71%, 3%, and 4% on feature extraction stage, feature reduction stage, and classification stage, respectively. These results indicate that the proposed well-trained machine learning system has the potential to make accurate predictions about brain abnormalities from the individual subjects, therefore, it can be used as a significant tool in clinical practice.
    Matched MeSH terms: Algorithms
  3. Alpha shape theory for 3D visualization and volumetric measurement of brain tumor progression using magnetic resonance images
    Hamoud Al-Tamimi MS, Sulong G, Shuaib IL
    Magn Reson Imaging, 2015 Jul;33(6):787-803.
    PMID: 25865822 DOI: 10.1016/j.mri.2015.03.008
    Resection of brain tumors is a tricky task in surgery due to its direct influence on the patients' survival rate. Determining the tumor resection extent for its complete information via-à-vis volume and dimensions in pre- and post-operative Magnetic Resonance Images (MRI) requires accurate estimation and comparison. The active contour segmentation technique is used to segment brain tumors on pre-operative MR images using self-developed software. Tumor volume is acquired from its contours via alpha shape theory. The graphical user interface is developed for rendering, visualizing and estimating the volume of a brain tumor. Internet Brain Segmentation Repository dataset (IBSR) is employed to analyze and determine the repeatability and reproducibility of tumor volume. Accuracy of the method is validated by comparing the estimated volume using the proposed method with that of gold-standard. Segmentation by active contour technique is found to be capable of detecting the brain tumor boundaries. Furthermore, the volume description and visualization enable an interactive examination of tumor tissue and its surrounding. Admirable features of our results demonstrate that alpha shape theory in comparison to other existing standard methods is superior for precise volumetric measurement of tumor.
    Matched MeSH terms: Algorithms
  4. Fulltext Adaptive localization of focus point regions via random patch probabilistic density from whole-slide, Ki-67-stained brain tumor tissue
    Alomari YM, Sheikh Abdullah SN, MdZin RR, Omar K
    Comput Math Methods Med, 2015;2015:673658.
    PMID: 25793010 DOI: 10.1155/2015/673658
    Analysis of whole-slide tissue for digital pathology images has been clinically approved to provide a second opinion to pathologists. Localization of focus points from Ki-67-stained histopathology whole-slide tissue microscopic images is considered the first step in the process of proliferation rate estimation. Pathologists use eye pooling or eagle-view techniques to localize the highly stained cell-concentrated regions from the whole slide under microscope, which is called focus-point regions. This procedure leads to a high variety of interpersonal observations and time consuming, tedious work and causes inaccurate findings. The localization of focus-point regions can be addressed as a clustering problem. This paper aims to automate the localization of focus-point regions from whole-slide images using the random patch probabilistic density method. Unlike other clustering methods, random patch probabilistic density method can adaptively localize focus-point regions without predetermining the number of clusters. The proposed method was compared with the k-means and fuzzy c-means clustering methods. Our proposed method achieves a good performance, when the results were evaluated by three expert pathologists. The proposed method achieves an average false-positive rate of 0.84% for the focus-point region localization error. Moreover, regarding RPPD used to localize tissue from whole-slide images, 228 whole-slide images have been tested; 97.3% localization accuracy was achieved.
    Matched MeSH terms: Algorithms
  5. In vitro antifilarial effects of three plant species against adult worms of subperiodic Brugia malayi
    Zaridah MZ, Idid SZ, Omar AW, Khozirah S
    J Ethnopharmacol, 2001 Nov;78(1):79-84.
    PMID: 11585692
    Five aqueous extracts from three plant species, i.e., dried husks (HX), dried seeds (SX) and dried leaves (LX) of Xylocarpus granatum (Meliaceae), dried stems (ST) of Tinospora crispa (Menispermaceae) and dried leaves (LA) of Andrographis paniculata (Acanthaceae) were tested in vitro against adult worms of subperiodic Brugia malayi. The relative movability (RM) value of the adult worms over the 24-h observation period was used as a measure of the antifilarial activity of the aqueous extracts. SX extract of X. granatum demonstrated the strongest activity, followed by the LA extract of A. paniculata, ST extract of T. crispa, HX extract and LX extract of X. granatum.
    Matched MeSH terms: Algorithms
  6. Fulltext Fine-mapping of prostate cancer susceptibility loci in a large meta-analysis identifies candidate causal variants
    Dadaev T, Saunders EJ, Newcombe PJ, Anokian E, Leongamornlert DA, Brook MN, et al.
    Nat Commun, 2018 06 11;9(1):2256.
    PMID: 29892050 DOI: 10.1038/s41467-018-04109-8
    Prostate cancer is a polygenic disease with a large heritable component. A number of common, low-penetrance prostate cancer risk loci have been identified through GWAS. Here we apply the Bayesian multivariate variable selection algorithm JAM to fine-map 84 prostate cancer susceptibility loci, using summary data from a large European ancestry meta-analysis. We observe evidence for multiple independent signals at 12 regions and 99 risk signals overall. Only 15 original GWAS tag SNPs remain among the catalogue of candidate variants identified; the remainder are replaced by more likely candidates. Biological annotation of our credible set of variants indicates significant enrichment within promoter and enhancer elements, and transcription factor-binding sites, including AR, ERG and FOXA1. In 40 regions at least one variant is colocalised with an eQTL in prostate cancer tissue. The refined set of candidate variants substantially increase the proportion of familial relative risk explained by these known susceptibility regions, which highlights the importance of fine-mapping studies and has implications for clinical risk profiling.
    Matched MeSH terms: Algorithms
  7. Fulltext Evaluation of BRCA1 and BRCA2 mutations and risk-prediction models in a typical Asian country (Malaysia) with a relatively low incidence of breast cancer
    Thirthagiri E, Lee SY, Kang P, Lee DS, Toh GT, Selamat S, et al.
    Breast Cancer Res, 2008;10(4):R59.
    PMID: 18627636 DOI: 10.1186/bcr2118
    The cost of genetic testing and the limited knowledge about the BRCA1 and BRCA2 genes in different ethnic groups has limited its availability in medium- and low-resource countries, including Malaysia. In addition, the applicability of many risk-assessment tools, such as the Manchester Scoring System and BOADICEA (Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm) which were developed based on mutation rates observed primarily in Caucasian populations using data from multiplex families, and in populations where the rate of breast cancer is higher, has not been widely tested in Asia or in Asians living elsewhere. Here, we report the results of genetic testing for mutations in the BRCA1 or BRCA2 genes in a series of families with breast cancer in the multi-ethnic population (Malay, Chinese and Indian) of Malaysia.
    Matched MeSH terms: Algorithms
  8. Modified dynamic gracilis neosphincter for fecal incontinence: an analysis of functional outcome at a single institution
    Hassan MZ, Rathnayaka MM, Deen KI
    World J Surg, 2010 Jul;34(7):1641-7.
    PMID: 20180122 DOI: 10.1007/s00268-010-0489-1
    We undertook a prospective longitudinal study of patients with end-stage fecal incontinence who were undergoing transposition of the gracilis muscle as a neo-anal sphincter with external low-frequency electrical stimulation of the nerve to the gracilis combined with biofeedback.
    Matched MeSH terms: Algorithms
  9. Fulltext A machine learning approach of predicting high potential archers by means of physical fitness indicators
    Muazu Musa R, P P Abdul Majeed A, Taha Z, Chang SW, Ab Nasir AF, Abdullah MR
    PLoS One, 2019;14(1):e0209638.
    PMID: 30605456 DOI: 10.1371/journal.pone.0209638
    k-nearest neighbour (k-NN) has been shown to be an effective learning algorithm for classification and prediction. However, the application of k-NN for prediction and classification in specific sport is still in its infancy. The present study classified and predicted high and low potential archers from a set of physical fitness variables trained on a variation of k-NN algorithms and logistic regression. 50 youth archers with the mean age and standard deviation of (17.0 ± 0.56) years drawn from various archery programmes completed a one end archery shooting score test. Standard fitness measurements of the handgrip, vertical jump, standing broad jump, static balance, upper muscle strength and the core muscle strength were conducted. Multiple linear regression was utilised to ascertain the significant variables that affect the shooting score. It was demonstrated from the analysis that core muscle strength and vertical jump were statistically significant. Hierarchical agglomerative cluster analysis (HACA) was used to cluster the archers based on the significant variables identified. k-NN model variations, i.e., fine, medium, coarse, cosine, cubic and weighted functions as well as logistic regression, were trained based on the significant performance variables. The HACA clustered the archers into high potential archers (HPA) and low potential archers (LPA). The weighted k-NN outperformed all the tested models at itdemonstrated reasonably good classification on the evaluated indicators with an accuracy of 82.5 ± 4.75% for the prediction of the HPA and the LPA. Moreover, the performance of the classifiers was further investigated against fresh data, which also indicates the efficacy of the weighted k-NN model. These findings could be valuable to coaches and sports managers to recognise high potential archers from a combination of the selected few physical fitness performance indicators identified which would subsequently save cost, time and energy for a talent identification programme.
    Matched MeSH terms: Algorithms
  10. Statin intolerance: an updated, narrative review mainly focusing on muscle adverse effects
    Li JJ, Liu HH, Wu NQ, Yeo KK, Tan K, Ako J, et al.
    Expert Opin Drug Metab Toxicol, 2020 Sep;16(9):837-851.
    PMID: 32729743 DOI: 10.1080/17425255.2020.1802426
    INTRODUCTION: Statins have been established as the standard of care for dyslipidemia and preventing cardiovascular diseases while posing few safety concerns. However, misconceptions about statin intolerance lead to their underuse, indicating a need to improve the understanding of the safety of this treatment.

    AREAS COVERED: We searched PubMed and reviewed literatures related to statin intolerance published between February 2015 and February 2020. Important large-scale or landmark studies published before 2015 were also cited as key evidence.

    EXPERT OPINION: Optimal lowering of low-density lipoprotein cholesterol with statins substantially reduces the risk of cardiovascular events. Muscle adverse events (AEs) were the most frequently reported AEs by statin users in clinical practice, but they usually occurred at a similar rate with statins and placebo in randomized controlled trials and had a spurious causal relationship with statin treatment. We proposed a rigorous definition for identifying true statin intolerance and present the criteria for defining different forms of muscle AEs and an algorithm for their management. True statin intolerance is uncommon, and every effort should be made to exclude false statin intolerance and ensure optimal use of statins. For the management of statin intolerance, statin-based approaches should be prioritized over non-statin approaches.

    Matched MeSH terms: Algorithms
  11. Fulltext Modified J-CAPRA scoring system in predicting treatment outcomes of metastatic prostate cancer patients undergoing androgen deprivation therapy
    Lim J, Hinotsu S, Onozawa M, Malek R, Sundram M, Teh GC, et al.
    Cancer Med, 2020 12;9(24):9346-9352.
    PMID: 33098372 DOI: 10.1002/cam4.3548
    The J-CAPRA score is an assessment tool which stratifies risk and predicts outcome of primary androgen deprivation therapy (ADT) using prostate-specific antigen, Gleason score, and clinical TNM staging. Here, we aimed to assess the generalisability of this tool in multi-ethnic Asians. Performance of J-CAPRA was evaluated in 782 Malaysian and 16,946 Japanese patients undergoing ADT from the Malaysian Study Group of Prostate Cancer (M-CaP) and Japan Study Group of Prostate Cancer (J-CaP) databases, respectively. Using the original J-CAPRA, 69.6% metastatic (M1) cases without T and/or N staging were stratified as intermediate-risk disease in the M-CaP database. To address this, we first omitted clinical T and N stage variables, and calculated the score on a 0-8 scale in the modified J-CAPRA scoring system for M1 patients. Notably, treatment decisions of M1 cases were not directly affected by both T and N staging. The J-CAPRA score threshold was adjusted for intermediate (modified J-CAPRA score 3-5) and high-risk (modified J-CAPRA score ≥6) groups in M1 patients. Using J-CaP database, validation analysis showed that overall survival, prostate cancer-specific survival, and progression-free survival of modified intermediate and high-risk groups were comparable to those of original J-CAPRA (p > 0.05) with Cohen's coefficient of 0.65. Around 88% M1 cases from M-CaP database were reclassified into high-risk category. Modified J-CAPRA scoring system is instrumental in risk assessment and treatment outcome prediction for M1 patients without T and/or N staging.
    Matched MeSH terms: Algorithms
  12. Near-infrared spectroscopy (NIRS) and chemometric analysis of Malaysian and UK paracetamol tablets: a spectral database study
    Said MM, Gibbons S, Moffat AC, Zloh M
    Int J Pharm, 2011 Aug 30;415(1-2):102-9.
    PMID: 21645600 DOI: 10.1016/j.ijpharm.2011.05.057
    The influx of medicines from different sources into healthcare systems of developing countries presents a challenge to monitor their origin and quality. The absence of a repository of reference samples or spectra prevents the analysis of tablets by direct comparison. A set of paracetamol tablets purchased in Malaysian pharmacies were compared to a similar set of sample purchased in the UK using near-infrared spectroscopy (NIRS). Additional samples of products containing ibuprofen or paracetamol in combination with other actives were added to the study as negative controls. NIR spectra of the samples were acquired and compared by using multivariate modeling and classification algorithms (PCA/SIMCA) and stored in a spectral database. All analysed paracetamol samples contained the purported active ingredient with only 1 out of 20 batches excluded from the 95% confidence interval, while the negative controls were clearly classified as outliers of the set. Although the substandard products were not detected in the purchased sample set, our results indicated variability in the quality of the Malaysian tablets. A database of spectra was created and search methods were evaluated for correct identification of tablets. The approach presented here can be further developed as a method for identifying substandard pharmaceutical products.
    Matched MeSH terms: Algorithms
  13. Fulltext Flexible model-based clustering of mixed binary and continuous data: application to genetic regulation and cancer
    Zainul Abidin FN, Westhead DR
    Nucleic Acids Res, 2017 04 20;45(7):e53.
    PMID: 27994031 DOI: 10.1093/nar/gkw1270
    Clustering is used widely in 'omics' studies and is often tackled with standard methods, e.g. hierarchical clustering. However, the increasing need for integration of multiple data sets leads to a requirement for clustering methods applicable to mixed data types, where the straightforward application of standard methods is not necessarily the best approach. A particularly common problem involves clustering entities characterized by a mixture of binary data (e.g. presence/absence of mutations, binding, motifs and epigenetic marks) and continuous data (e.g. gene expression, protein abundance, metabolite levels). Here, we present a generic method based on a probabilistic model for clustering this type of data, and illustrate its application to genetic regulation and the clustering of cancer samples. We show that the resulting clusters lead to useful hypotheses: in the case of genetic regulation these concern regulation of groups of genes by specific sets of transcription factors and in the case of cancer samples combinations of gene mutations are related to patterns of gene expression. The clusters have potential mechanistic significance and in the latter case are significantly linked to survival. The method is available as a stand-alone software package (GNU General Public Licence) from http://github.com/BioToolsLeeds/FlexiCoClusteringPackage.git.
    Matched MeSH terms: Algorithms
  14. Automated characterization of cardiovascular diseases using relative wavelet nonlinear features extracted from ECG signals
    Adam M, Oh SL, Sudarshan VK, Koh JE, Hagiwara Y, Tan JH, et al.
    Comput Methods Programs Biomed, 2018 Jul;161:133-143.
    PMID: 29852956 DOI: 10.1016/j.cmpb.2018.04.018
    Cardiovascular diseases (CVDs) are the leading cause of deaths worldwide. The rising mortality rate can be reduced by early detection and treatment interventions. Clinically, electrocardiogram (ECG) signal provides useful information about the cardiac abnormalities and hence employed as a diagnostic modality for the detection of various CVDs. However, subtle changes in these time series indicate a particular disease. Therefore, it may be monotonous, time-consuming and stressful to inspect these ECG beats manually. In order to overcome this limitation of manual ECG signal analysis, this paper uses a novel discrete wavelet transform (DWT) method combined with nonlinear features for automated characterization of CVDs. ECG signals of normal, and dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy (HCM) and myocardial infarction (MI) are subjected to five levels of DWT. Relative wavelet of four nonlinear features such as fuzzy entropy, sample entropy, fractal dimension and signal energy are extracted from the DWT coefficients. These features are fed to sequential forward selection (SFS) technique and then ranked using ReliefF method. Our proposed methodology achieved maximum classification accuracy (acc) of 99.27%, sensitivity (sen) of 99.74%, and specificity (spec) of 98.08% with K-nearest neighbor (kNN) classifier using 15 features ranked by the ReliefF method. Our proposed methodology can be used by clinical staff to make faster and accurate diagnosis of CVDs. Thus, the chances of survival can be significantly increased by early detection and treatment of CVDs.
    Matched MeSH terms: Algorithms
  15. Fulltext In Silico Prediction and Validation of Gfap as an miR-3099 Target in Mouse Brain
    Abidin SZ, Leong JW, Mahmoudi M, Nordin N, Abdullah S, Cheah PS, et al.
    Neurosci Bull, 2017 Aug;33(4):373-382.
    PMID: 28597341 DOI: 10.1007/s12264-017-0143-0
    MicroRNAs are small non-coding RNAs that play crucial roles in the regulation of gene expression and protein synthesis during brain development. MiR-3099 is highly expressed throughout embryogenesis, especially in the developing central nervous system. Moreover, miR-3099 is also expressed at a higher level in differentiating neurons in vitro, suggesting that it is a potential regulator during neuronal cell development. This study aimed to predict the target genes of miR-3099 via in-silico analysis using four independent prediction algorithms (miRDB, miRanda, TargetScan, and DIANA-micro-T-CDS) with emphasis on target genes related to brain development and function. Based on the analysis, a total of 3,174 miR-3099 target genes were predicted. Those predicted by at least three algorithms (324 genes) were subjected to DAVID bioinformatics analysis to understand their overall functional themes and representation. The analysis revealed that nearly 70% of the target genes were expressed in the nervous system and a significant proportion were associated with transcriptional regulation and protein ubiquitination mechanisms. Comparison of in situ hybridization (ISH) expression patterns of miR-3099 in both published and in-house-generated ISH sections with the ISH sections of target genes from the Allen Brain Atlas identified 7 target genes (Dnmt3a, Gabpa, Gfap, Itga4, Lxn, Smad7, and Tbx18) having expression patterns complementary to miR-3099 in the developing and adult mouse brain samples. Of these, we validated Gfap as a direct downstream target of miR-3099 using the luciferase reporter gene system. In conclusion, we report the successful prediction and validation of Gfap as an miR-3099 target gene using a combination of bioinformatics resources with enrichment of annotations based on functional ontologies and a spatio-temporal expression dataset.
    Matched MeSH terms: Algorithms
  16. Rate adaptive pacing in an intracardiac pacemaker
    Lloyd M, Reynolds D, Sheldon T, Stromberg K, Hudnall JH, Demmer WM, et al.
    Heart Rhythm, 2017 02;14(2):200-205.
    PMID: 27871854 DOI: 10.1016/j.hrthm.2016.11.016
    BACKGROUND: The Micra transcatheter pacemaker was designed to have similar functionality to conventional transvenous VVIR pacing systems. It provides rate adaptive pacing using a programmable 3-axis accelerometer designed to detect patient activity in the presence of cardiac motion.

    OBJECTIVE: The purpose of this study was to evaluate the system's performance during treadmill tests to maximum exertion in a subset of patients within the Micra Transcatheter Pacing Study.

    METHODS: Patients underwent treadmill testing at 3 or 6 months postimplant with algorithm programming at physician discretion. Normalized sensor rate (SenR) relative to the programmed upper sensor rate was modeled as a function of normalized workload in metabolic equivalents (METS) relative to maximum METS achieved during the test. A normalized METS and SenR were determined at the end of each 1-minute treadmill stage. The proportionality of SenR to workload was evaluated by comparing the slope from this relationship to the prospectively defined tolerance margin (0.65-1.35).

    RESULTS: A total of 69 treadmill tests were attempted by 42 patients at 3 and 6 months postimplant. Thirty tests from 20 patients who completed ≥4 stages with an average slope of 0.86 (90% confidence interval 0.77-0.96) confirmed proportionality to workload. On an individual test basis, 25 of 30 point estimates (83.3%) had a normalized slope within the defined tolerance range (range 0.46-1.08).

    CONCLUSION: Accelerometer-based rate adaptive pacing was proportional to workload, thus confirming rate adaptive pacing commensurate to workload is achievable with an entirely intracardiac pacing system.

    Matched MeSH terms: Algorithms
  17. Fulltext Novel biphenyl ester derivatives as tyrosinase inhibitors: Synthesis, crystallographic, spectral analysis and molecular docking studies
    Kwong HC, Chidan Kumar CS, Mah SH, Chia TS, Quah CK, Loh ZH, et al.
    PLoS One, 2017;12(2):e0170117.
    PMID: 28241010 DOI: 10.1371/journal.pone.0170117
    Biphenyl-based compounds are clinically important for the treatments of hypertension and inflammatory, while many more are under development for pharmaceutical uses. In the present study, a series of 2-([1,1'-biphenyl]-4-yl)-2-oxoethyl benzoates, 2(a-q), and 2-([1,1'-biphenyl]-4-yl)-2-oxoethyl pyridinecarboxylate, 2(r-s) were synthesized by reacting 1-([1,1'-biphenyl]-4-yl)-2-bromoethan-1-one with various carboxylic acids using potassium carbonate in dimethylformamide at ambient temperature. Single-crystal X-ray diffraction studies revealed a more closely packed crystal structure can be produced by introduction of biphenyl moiety. Five of the compounds among the reported series exhibited significant anti-tyrosinase activities, in which 2p, 2r and 2s displayed good inhibitions which are comparable to standard inhibitor kojic acid at concentrations of 100 and 250 μg/mL. The inhibitory effects of these active compounds were further confirmed by computational molecular docking studies and the results revealed the primary binding site is active-site entrance instead of inner copper binding site which acted as the secondary binding site.
    Matched MeSH terms: Algorithms
  18. Assessing regional left ventricular thickening dysfunction and dyssynchrony via personalized modeling and 3D wall thickness measurements for acute myocardial infarction
    Khalid A, Lim E, Chan BT, Abdul Aziz YF, Chee KH, Yap HJ, et al.
    J Magn Reson Imaging, 2019 04;49(4):1006-1019.
    PMID: 30211445 DOI: 10.1002/jmri.26302
    BACKGROUND: Existing clinical diagnostic and assessment methods could be improved to facilitate early detection and treatment of cardiac dysfunction associated with acute myocardial infarction (AMI) to reduce morbidity and mortality.

    PURPOSE: To develop 3D personalized left ventricular (LV) models and thickening assessment framework for assessing regional wall thickening dysfunction and dyssynchrony in AMI patients.

    STUDY TYPE: Retrospective study, diagnostic accuracy.

    SUBJECTS: Forty-four subjects consisting of 15 healthy subjects and 29 AMI patients.

    FIELD STRENGTH/SEQUENCE: 1.5T/steady-state free precession cine MRI scans; LGE MRI scans.

    ASSESSMENT: Quantitative thickening measurements across all cardiac phases were correlated and validated against clinical evaluation of infarct transmurality by an experienced cardiac radiologist based on the American Heart Association (AHA) 17-segment model.

    STATISTICAL TEST: Nonparametric 2-k related sample-based Kruskal-Wallis test; Mann-Whitney U-test; Pearson's correlation coefficient.

    RESULTS: Healthy LV wall segments undergo significant wall thickening (P 50% transmurality) underwent remarkable wall thinning during contraction (thickening index [TI] = 1.46 ± 0.26 mm) as opposed to healthy myocardium (TI = 4.01 ± 1.04 mm). For AMI patients, LV that showed signs of thinning were found to be associated with a significantly higher percentage of dyssynchrony as compared with healthy subjects (dyssynchrony index [DI] = 15.0 ± 5.0% vs. 7.5 ± 2.0%, P 

    Matched MeSH terms: Algorithms
  19. Automated detection and classification of liver fibrosis stages using contourlet transform and nonlinear features
    Acharya UR, Raghavendra U, Koh JEW, Meiburger KM, Ciaccio EJ, Hagiwara Y, et al.
    Comput Methods Programs Biomed, 2018 Nov;166:91-98.
    PMID: 30415722 DOI: 10.1016/j.cmpb.2018.10.006
    BACKGROUND AND OBJECTIVE: Liver fibrosis is a type of chronic liver injury that is characterized by an excessive deposition of extracellular matrix protein. Early detection of liver fibrosis may prevent further growth toward liver cirrhosis and hepatocellular carcinoma. In the past, the only method to assess liver fibrosis was through biopsy, but this examination is invasive, expensive, prone to sampling errors, and may cause complications such as bleeding. Ultrasound-based elastography is a promising tool to measure tissue elasticity in real time; however, this technology requires an upgrade of the ultrasound system and software. In this study, a novel computer-aided diagnosis tool is proposed to automatically detect and classify the various stages of liver fibrosis based upon conventional B-mode ultrasound images.

    METHODS: The proposed method uses a 2D contourlet transform and a set of texture features that are efficiently extracted from the transformed image. Then, the combination of a kernel discriminant analysis (KDA)-based feature reduction technique and analysis of variance (ANOVA)-based feature ranking technique was used, and the images were then classified into various stages of liver fibrosis.

    RESULTS: Our 2D contourlet transform and texture feature analysis approach achieved a 91.46% accuracy using only four features input to the probabilistic neural network classifier, to classify the five stages of liver fibrosis. It also achieved a 92.16% sensitivity and 88.92% specificity for the same model. The evaluation was done on a database of 762 ultrasound images belonging to five different stages of liver fibrosis.

    CONCLUSIONS: The findings suggest that the proposed method can be useful to automatically detect and classify liver fibrosis, which would greatly assist clinicians in making an accurate diagnosis.

    Matched MeSH terms: Algorithms
  20. Fulltext TranCEP: Predicting the substrate class of transmembrane transport proteins using compositional, evolutionary, and positional information
    Alballa M, Aplop F, Butler G
    PLoS One, 2020;15(1):e0227683.
    PMID: 31935244 DOI: 10.1371/journal.pone.0227683
    Transporters mediate the movement of compounds across the membranes that separate the cell from its environment and across the inner membranes surrounding cellular compartments. It is estimated that one third of a proteome consists of membrane proteins, and many of these are transport proteins. Given the increase in the number of genomes being sequenced, there is a need for computational tools that predict the substrates that are transported by the transmembrane transport proteins. In this paper, we present TranCEP, a predictor of the type of substrate transported by a transmembrane transport protein. TranCEP combines the traditional use of the amino acid composition of the protein, with evolutionary information captured in a multiple sequence alignment (MSA), and restriction to important positions of the alignment that play a role in determining the specificity of the protein. Our experimental results show that TranCEP significantly outperforms the state-of-the-art predictors. The results quantify the contribution made by each type of information used.
    Matched MeSH terms: Algorithms
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