Displaying publications 1 - 20 of 62 in total

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  1. The goat as a model for studies of pneumonic pasteurellosis caused by Pasteurella multocida
    Zamri-Saad M, Effendy WM, Maswati MA, Salim N, Sheikh-Omar AR
    Br. Vet. J., 1996 Jul;152(4):453-8.
    PMID: 8791853
    A model of pneumonic pasteurellosis has been established in goats using Pasteurella multocida harvested from pneumonic lungs of goats (types A and D), rabbits (type A) and sheep (type D). The resultant infections were acute, subacute or chronic. The gross and histological lesions of the subacute and chronic infections were typical of pneumonic pasteurellosis. P. multocida type D produced significantly (P < 0.01) more severe lesions when compared with other isolates. There were strong correlations between the clinical signs and the severity of lesions.
  2. Coccidial infections of goats in Selangor, peninsular Malaysia
    Jalila A, Dorny P, Sani R, Salim NB, Vercruysse J
    Vet Parasitol, 1998 Jan 31;74(2-4):165-72.
    PMID: 9561704
    Coccidial infections were studied in goats in the state of Selangor (peninsular Malaysia) during a 12-month period. The study included 10 smallholder farms on which kids were monitored for faecal oocyst counts from birth until 1-year old. Eimeria oocysts were found in 725 (89%) of 815 faecal samples examined. Nine species of Eimeria were identified. The most prevalent were E. arloingi, found in 71% of the samples, E. ninakohlyakimovae (67%), E. christenseni (63%) and E. alijevi (61%). The other species found were, E. hirci, E. jolchijevi, E. caprovina, E. caprina and E. pallida, present in 34, 22, 12, 9 and 4% of the samples, respectively. Oocyst counts were significantly higher in animals of less than 4-months old (P < 0.05). High oocyst counts were mainly caused by non-pathogenic species. Poor hygienic conditions were found to be associated with a higher intensity of coccidial infections. Mortality rates in kids could not be related to the intensity of coccidial infections.
  3. Similarity-based virtual screening with a bayesian inference network
    Abdo A, Salim N
    ChemMedChem, 2009 Feb;4(2):210-8.
    PMID: 19072820 DOI: 10.1002/cmdc.200800290
    Many methods have been developed to capture the biological similarity between two compounds for use in drug discovery. A variety of similarity metrics have been introduced, the Tanimoto coefficient being the most prominent. Many of the approaches assume that molecular features or descriptors that do not relate to the biological activity carry the same weight as the important aspects in terms of biological similarity. Herein, a novel similarity searching approach using a Bayesian inference network is discussed. Similarity searching is regarded as an inference or evidential reasoning process in which the probability that a given compound has biological similarity with the query is estimated and used as evidence. Our experiments demonstrate that the similarity approach based on Bayesian inference networks is likely to outperform the Tanimoto similarity search and offer a promising alternative to existing similarity search approaches.
  4. Fulltext Ligand-based virtual screening using Bayesian networks
    Abdo A, Chen B, Mueller C, Salim N, Willett P
    J Chem Inf Model, 2010 Jun 28;50(6):1012-20.
    PMID: 20504032 DOI: 10.1021/ci100090p
    A Bayesian inference network (BIN) provides an interesting alternative to existing tools for similarity-based virtual screening. The BIN is particularly effective when the active molecules being sought have a high degree of structural homogeneity but has been found to perform less well with structurally heterogeneous sets of actives. In this paper, we introduce an alternative network model, called a Bayesian belief network (BBN), that seeks to overcome this limitation of the BIN approach. Simulated virtual screening experiments with the MDDR, WOMBAT and MUV data sets show that the BIN and BBN methods allow effective screening searches to be carried out. However, the results obtained are not obviously superior to those obtained using a much simpler approach that is based on the use of the Tanimoto coefficient and of the square roots of fragment occurrence frequencies.
  5. New fragment weighting scheme for the Bayesian inference network in ligand-based virtual screening
    Abdo A, Salim N
    J Chem Inf Model, 2011 Jan 24;51(1):25-32.
    PMID: 21155550 DOI: 10.1021/ci100232h
    Many of the conventional similarity methods assume that molecular fragments that do not relate to biological activity carry the same weight as the important ones. One possible approach to this problem is to use the Bayesian inference network (BIN), which models molecules and reference structures as probabilistic inference networks. The relationships between molecules and reference structures in the Bayesian network are encoded using a set of conditional probability distributions, which can be estimated by the fragment weighting function, a function of the frequencies of the fragments in the molecule or the reference structure as well as throughout the collection. The weighting function combines one or more fragment weighting schemes. In this paper, we have investigated five different weighting functions and present a new fragment weighting scheme. Later on, these functions were modified to combine the new weighting scheme. Simulated virtual screening experiments with the MDL Drug Data Report (23) and maximum unbiased validation data sets show that the use of new weighting scheme can provide significantly more effective screening when compared with the use of current weighting schemes.
  6. Implementing relevance feedback in ligand-based virtual screening using Bayesian inference network
    Abdo A, Salim N, Ahmed A
    J Biomol Screen, 2011 Oct;16(9):1081-8.
    PMID: 21862688 DOI: 10.1177/1087057111416658
    Recently, the use of the Bayesian network as an alternative to existing tools for similarity-based virtual screening has received noticeable attention from researchers in the chemoinformatics field. The main aim of the Bayesian network model is to improve the retrieval effectiveness of similarity-based virtual screening. To this end, different models of the Bayesian network have been developed. In our previous works, the retrieval performance of the Bayesian network was observed to improve significantly when multiple reference structures or fragment weightings were used. In this article, the authors enhance the Bayesian inference network (BIN) using the relevance feedback information. In this approach, a few high-ranking structures of unknown activity were filtered from the outputs of BIN, based on a single active reference structure, to form a set of active reference structures. This set of active reference structures was used in two distinct techniques for carrying out such BIN searching: reweighting the fragments in the reference structures and group fusion techniques. Simulated virtual screening experiments with three MDL Drug Data Report data sets showed that the proposed techniques provide simple ways of enhancing the cost-effectiveness of ligand-based virtual screening searches, especially for higher diversity data sets.
  7. Outcome of corticosteroid injection versus physiotherapy in the treatment of mild trigger fingers
    Salim N, Abdullah S, Sapuan J, Haflah NH
    J Hand Surg Eur Vol, 2012 Jan;37(1):27-34.
    PMID: 21816888 DOI: 10.1177/1753193411415343
    We compared the effectiveness of physiotherapy and corticosteroid injection treatment in the management of mild trigger fingers. Mild trigger fingers are those with mild crepitus, uneven finger movements and actively correctable triggering. This is a single-centred, prospective, block randomized study with 74 patients; 39 patients for steroid injection and 35 patients for physiotherapy. The study duration was from Jun 2009 until August 2010. Evaluation was done at 6 weeks, 3 months and 6 months post-treatment. At 3 months, the success rate (absence of pain and triggering) for those receiving steroid injection was 97.4% and physiotherapy 68.6%. The group receiving steroid injection also had lower pain score, higher rate of satisfaction, stronger grip strength and early recovery to near normal function (findings were all significant, p 
  8. Ligand expansion in ligand-based virtual screening using relevance feedback
    Abdo A, Saeed F, Hamza H, Ahmed A, Salim N
    J Comput Aided Mol Des, 2012 Mar;26(3):279-87.
    PMID: 22249773 DOI: 10.1007/s10822-012-9543-4
    Query expansion is the process of reformulating an original query to improve retrieval performance in information retrieval systems. Relevance feedback is one of the most useful query modification techniques in information retrieval systems. In this paper, we introduce query expansion into ligand-based virtual screening (LBVS) using the relevance feedback technique. In this approach, a few high-ranking molecules of unknown activity are filtered from the outputs of a Bayesian inference network based on a single ligand molecule to form a set of ligand molecules. This set of ligand molecules is used to form a new ligand molecule. Simulated virtual screening experiments with the MDL Drug Data Report and maximum unbiased validation data sets show that the use of ligand expansion provides a very simple way of improving the LBVS, especially when the active molecules being sought have a high degree of structural heterogeneity. However, the effectiveness of the ligand expansion is slightly less when structurally-homogeneous sets of actives are being sought.
  9. Fulltext Voting-based consensus clustering for combining multiple clusterings of chemical structures
    Saeed F, Salim N, Abdo A
    J Cheminform, 2012 Dec 17;4(1):37.
    PMID: 23244782 DOI: 10.1186/1758-2946-4-37
    BACKGROUND: Although many consensus clustering methods have been successfully used for combining multiple classifiers in many areas such as machine learning, applied statistics, pattern recognition and bioinformatics, few consensus clustering methods have been applied for combining multiple clusterings of chemical structures. It is known that any individual clustering method will not always give the best results for all types of applications. So, in this paper, three voting and graph-based consensus clusterings were used for combining multiple clusterings of chemical structures to enhance the ability of separating biologically active molecules from inactive ones in each cluster.

    RESULTS: The cumulative voting-based aggregation algorithm (CVAA), cluster-based similarity partitioning algorithm (CSPA) and hyper-graph partitioning algorithm (HGPA) were examined. The F-measure and Quality Partition Index method (QPI) were used to evaluate the clusterings and the results were compared to the Ward's clustering method. The MDL Drug Data Report (MDDR) dataset was used for experiments and was represented by two 2D fingerprints, ALOGP and ECFP_4. The performance of voting-based consensus clustering method outperformed the Ward's method using F-measure and QPI method for both ALOGP and ECFP_4 fingerprints, while the graph-based consensus clustering methods outperformed the Ward's method only for ALOGP using QPI. The Jaccard and Euclidean distance measures were the methods of choice to generate the ensembles, which give the highest values for both criteria.

    CONCLUSIONS: The results of the experiments show that consensus clustering methods can improve the effectiveness of chemical structures clusterings. The cumulative voting-based aggregation algorithm (CVAA) was the method of choice among consensus clustering methods.

  10. Fulltext Ligand-based virtual screening using Bayesian inference network and reweighted fragments
    Ahmed A, Abdo A, Salim N
    ScientificWorldJournal, 2012;2012:410914.
    PMID: 22623895 DOI: 10.1100/2012/410914
    Many of the similarity-based virtual screening approaches assume that molecular fragments that are not related to the biological activity carry the same weight as the important ones. This was the reason that led to the use of Bayesian networks as an alternative to existing tools for similarity-based virtual screening. In our recent work, the retrieval performance of the Bayesian inference network (BIN) was observed to improve significantly when molecular fragments were reweighted using the relevance feedback information. In this paper, a set of active reference structures were used to reweight the fragments in the reference structure. In this approach, higher weights were assigned to those fragments that occur more frequently in the set of active reference structures while others were penalized. Simulated virtual screening experiments with MDL Drug Data Report datasets showed that the proposed approach significantly improved the retrieval effectiveness of ligand-based virtual screening, especially when the active molecules being sought had a high degree of structural heterogeneity.
  11. Fulltext Modification of palm kernel oil esters nanoemulsions with hydrocolloid gum for enhanced topical delivery of ibuprofen
    Salim N, Basri M, Rahman MB, Abdullah DK, Basri H
    Int J Nanomedicine, 2012;7:4739-47.
    PMID: 22973096 DOI: 10.2147/IJN.S34700
    During recent years, there has been growing interest in the use of nanoemulsion as a drug-carrier system for topical delivery. A nanoemulsion is a transparent mixture of oil, surfactant and water with a very low viscosity, usually the product of its high water content. The present study investigated the modification of nanoemulsions with different hydrocolloid gums, to enhanced drug delivery of ibuprofen. The in vitro characterization of the initial and modified nanoemulsions was also studied.
  12. Fulltext CMD: A Database to Store the Bonding States of Cysteine Motifs with Secondary Structures
    Bostan H, Salim N, Hussein ZA, Klappa P, Shamsir MS
    Adv Bioinformatics, 2012;2012:849830.
    PMID: 23091487 DOI: 10.1155/2012/849830
    Computational approaches to the disulphide bonding state and its connectivity pattern prediction are based on various descriptors. One descriptor is the amino acid sequence motifs flanking the cysteine residue motifs. Despite the existence of disulphide bonding information in many databases and applications, there is no complete reference and motif query available at the moment. Cysteine motif database (CMD) is the first online resource that stores all cysteine residues, their flanking motifs with their secondary structure, and propensity values assignment derived from the laboratory data. We extracted more than 3 million cysteine motifs from PDB and UniProt data, annotated with secondary structure assignment, propensity value assignment, and frequency of occurrence and coefficiency of their bonding status. Removal of redundancies generated 15875 unique flanking motifs that are always bonded and 41577 unique patterns that are always nonbonded. Queries are based on the protein ID, FASTA sequence, sequence motif, and secondary structure individually or in batch format using the provided APIs that allow remote users to query our database via third party software and/or high throughput screening/querying. The CMD offers extensive information about the bonded, free cysteine residues, and their motifs that allows in-depth characterization of the sequence motif composition.
  13. Graph-Based Consensus Clustering for Combining Multiple Clusterings of Chemical Structures
    Saeed F, Salim N, Abdo A, Hentabli H
    Mol Inform, 2013 Feb;32(2):165-78.
    PMID: 27481278 DOI: 10.1002/minf.201200110
    Consensus clustering methods have been successfully used for combining multiple classifiers in many areas such as machine learning, applied statistics, pattern recognition and bioinformatics. In this paper, consensus clustering is used for combining the clusterings of chemical structures to enhance the ability of separating biologically active molecules from inactive ones in each cluster. Two graph-based consensus clustering methods were examined. The Quality Partition Index method (QPI) was used to evaluate the clusterings and the results were compared to the Ward's clustering method. Two homogeneous and heterogeneous subsets DS1-DS2 of MDL Drug Data Report database (MDDR) were used for experiments and represented by two 2D fingerprints. The results, obtained by a combination of multiple runs of an individual clustering and a single run of multiple individual clusterings, showed that graph-based consensus clustering methods can improve the effectiveness of chemical structures clusterings.
  14. Consensus methods for combining multiple clusterings of chemical structures
    Saeed F, Salim N, Abdo A
    J Chem Inf Model, 2013 May 24;53(5):1026-34.
    PMID: 23581471 DOI: 10.1021/ci300442u
    The goal of consensus clustering methods is to find a consensus partition that optimally summarizes an ensemble and improves the quality of clustering compared with single clustering algorithms. In this paper, an enhanced voting-based consensus method was introduced and compared with other consensus clustering methods, including co-association-based, graph-based, and voting-based consensus methods. The MDDR and MUV data sets were used for the experiments and were represented by three 2D fingerprints: ALOGP, ECFP_4, and ECFC_4. The results were evaluated based on the ability of the clustering method to separate active from inactive molecules in each cluster using four criteria: F-measure, Quality Partition Index (QPI), Rand Index (RI), and Fowlkes-Mallows Index (FMI). The experiments suggest that the consensus methods can deliver significant improvements for the effectiveness of chemical structures clustering.
  15. Information Theory and Voting Based Consensus Clustering for Combining Multiple Clusterings of Chemical Structures
    Saeed F, Salim N, Abdo A
    Mol Inform, 2013 Jul;32(7):591-8.
    PMID: 27481767 DOI: 10.1002/minf.201300004
    Many consensus clustering methods have been applied in different areas such as pattern recognition, machine learning, information theory and bioinformatics. However, few methods have been used for chemical compounds clustering. In this paper, an information theory and voting based algorithm (Adaptive Cumulative Voting-based Aggregation Algorithm A-CVAA) was examined for combining multiple clusterings of chemical structures. The effectiveness of clusterings was evaluated based on the ability of the clustering method to separate active from inactive molecules in each cluster, and the results were compared with Ward's method. The chemical dataset MDL Drug Data Report (MDDR) and the Maximum Unbiased Validation (MUV) dataset were used. Experiments suggest that the adaptive cumulative voting-based consensus method can improve the effectiveness of combining multiple clusterings of chemical structures.
  16. Fulltext Alteration of cell cytoskeleton and functions of cell recovery of normal human osteoblast cells caused by factors associated with real space flight
    Kapitonova MY, Salim N, Othman S, Muhd Kamauzaman TM, Ali AM, Nawawi HM, et al.
    Malays J Pathol, 2013 Dec;35(2):153-63.
    PMID: 24362479 MyJurnal
    Experiments involving short-term space flight have shown an adverse effect on the physiology, morphology and functions of cells investigated. The causes for this effect on cells are: microgravity, temperature fluctuations, mechanical stress, hypergravity, nutrient restriction and others. However, the extent to which these adverse effects can be repaired by short-term space flown cells when recultured in conditions of normal gravity remains unclear. Therefore this study aimed to investigate the effect of short-term spaceflight on cytoskeleton distribution and recovery of cell functions of normal human osteoblast cells. The ultrastructure was evaluated using ESEM. Fluorescent staining was done using Hoechst, Mito Tracker CMXRos and Tubulin Tracker Green for cytoskeleton. Gene expression of cell functions was quantified using qPCR. As a result, recovered cells did not show any apoptotic markers when compared with control. Tubulin volume density (p<0.001) was decreased significantly when compared to control, while mitochondria volume density was insignificantly elevated. Gene expression for IL-6 (p<0.05) and sVCAM-1 (p<0.001) was significantly decreased while alkaline phosphatase (p<0.001), osteocalcin and sICAM (p<0.05) were significantly increased in the recovered cells compared to the control ones. The changes in gene and protein expression of collagen 1A, osteonectin, osteoprotegerin and beta-actin, caused by short-term spaceflight, were statistically not significant. These data indicate that short term space flight causes morphological changes in osteoblast cells which are consistent with hypertrophy, reduced cell differentiation and increased release of monocyte attracting proteins. The long-term effect of these changes on bone density and remodeling requires more detailed studies.
  17. Morphological and phenotypical characteristics of human osteoblasts after short-term space mission
    Kapitonova MY, Kuznetsov SL, Salim N, Othman S, Kamauzaman TM, Ali AM, et al.
    Bull. Exp. Biol. Med., 2014 Jan;156(3):393-8.
    PMID: 24771384 DOI: 10.1007/s10517-014-2357-8
    Morphological and phenotypical signs of cultured readaptation osteoblasts were studied after a short-term space mission. The ultrastructure and phenotype of human osteoblasts after Soyuz TMA-11 space flight (2007) were evaluated by scanning electron microscopy, laser confocal microscopy, and ELISA. The morphofunctional changes in cell cultures persisted after 12 passages. Osteoblasts retained the drastic changes in their shape and size, contour deformation, disorganization of the microtubular network, redistribution of organelles and specialized structures of the plasmalemma in comparison with the ground control cells. On the other hand, the expression of osteoprotegerin and osteocalcin (bone metabolism markers) increased; the expression of bone resorption markers ICAM-1 and IL-6 also increased, while the expression of VCAM-1 decreased. Hence, space flight led to the development of persistent shifts in cultured osteoblasts indicating injuries to the cytoskeleton and the phenotype changes, indicating modulation of bone metabolism biomarkers.
  18. Combining multiple clusterings of chemical structures using cluster-based similarity partitioning algorithm
    Saeed F, Salim N, Abdo A
    Int J Comput Biol Drug Des, 2014 01 09;7(1):31-44.
    PMID: 24429501 DOI: 10.1504/IJCBDD.2014.058584
    Many types of clustering techniques for chemical structures have been used in the literature, but it is known that any single method will not always give the best results for all types of applications. Recent work on consensus clustering methods is motivated because of the successes of combining multiple classifiers in many areas and the ability of consensus clustering to improve the robustness, novelty, consistency and stability of individual clusterings. In this paper, the Cluster-based Similarity Partitioning Algorithm (CSPA) was examined for improving the quality of chemical structures clustering. The effectiveness of clustering was evaluated based on the ability to separate active from inactive molecules in each cluster and the results were compared with the Ward's clustering method. The chemical dataset MDL Drug Data Report (MDDR) database was used for experiments. The results, obtained by combining multiple clusterings, showed that the consensus clustering method can improve the robustness, novelty and stability of chemical structures clustering.
  19. Weighted voting-based consensus clustering for chemical structure databases
    Saeed F, Ahmed A, Shamsir MS, Salim N
    J Comput Aided Mol Des, 2014 Jun;28(6):675-84.
    PMID: 24830925 DOI: 10.1007/s10822-014-9750-2
    The cluster-based compound selection is used in the lead identification process of drug discovery and design. Many clustering methods have been used for chemical databases, but there is no clustering method that can obtain the best results under all circumstances. However, little attention has been focused on the use of combination methods for chemical structure clustering, which is known as consensus clustering. Recently, consensus clustering has been used in many areas including bioinformatics, machine learning and information theory. This process can improve the robustness, stability, consistency and novelty of clustering. For chemical databases, different consensus clustering methods have been used including the co-association matrix-based, graph-based, hypergraph-based and voting-based methods. In this paper, a weighted cumulative voting-based aggregation algorithm (W-CVAA) was developed. The MDL Drug Data Report (MDDR) benchmark chemical dataset was used in the experiments and represented by the AlogP and ECPF_4 descriptors. The results from the clustering methods were evaluated by the ability of the clustering to separate biologically active molecules in each cluster from inactive ones using different criteria, and the effectiveness of the consensus clustering was compared to that of Ward's method, which is the current standard clustering method in chemoinformatics. This study indicated that weighted voting-based consensus clustering can overcome the limitations of the existing voting-based methods and improve the effectiveness of combining multiple clusterings of chemical structures.
  20. Fulltext A new graph-based molecular descriptor using the canonical representation of the molecule
    Hentabli H, Saeed F, Abdo A, Salim N
    ScientificWorldJournal, 2014;2014:286974.
    PMID: 25140330 DOI: 10.1155/2014/286974
    Molecular similarity is a pervasive concept in drug design. The basic idea underlying molecular similarity is the similar property principle, which states that structurally similar molecules will exhibit similar physicochemical and biological properties. In this paper, a new graph-based molecular descriptor (GBMD) is introduced. The GBMD is a new method of obtaining a rough description of 2D molecular structure in textual form based on the canonical representations of the molecule outline shape and it allows rigorous structure specification using small and natural grammars. Simulated virtual screening experiments with the MDDR database show clearly the superiority of the graph-based descriptor compared to many standard descriptors (ALOGP, MACCS, EPFP4, CDKFP, PCFP, and SMILE) using the Tanimoto coefficient (TAN) and the basic local alignment search tool (BLAST) when searches were carried.
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