Displaying publications 1 - 20 of 29 in total

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  1. Fulltext Dengue virus infection - a review of pathogenesis, vaccines, diagnosis and therapy
    Kok BH, Lim HT, Lim CP, Lai NS, Leow CY, Leow CH
    Virus Res, 2023 Jan 15;324:199018.
    PMID: 36493993 DOI: 10.1016/j.virusres.2022.199018
    The transmission of dengue virus (DENV) from an infected Aedes mosquito to a human, causes illness ranging from mild dengue fever to fatal dengue shock syndrome. The similar conserved structure and sequence among distinct DENV serotypes or different flaviviruses has resulted in the occurrence of cross reaction followed by antibody-dependent enhancement (ADE). Thus far, the vaccine which can provide effective protection against infection by different DENV serotypes remains the biggest hurdle to overcome. Therefore, deep investigation is crucial for the potent and effective therapeutic drugs development. In addition, the cross-reactivity of flaviviruses that leads to false diagnosis in clinical settings could result to delay proper intervention management. Thus, the accurate diagnostic with high specificity and sensitivity is highly required to provide prompt diagnosis in respect to render early treatment for DENV infected individuals. In this review, the recent development of neutralizing antibodies, antiviral agents, and vaccine candidates in therapeutic platform for DENV infection will be discussed. Moreover, the discovery of antigenic cryptic epitopes, principle of molecular mimicry, and application of single-chain or single-domain antibodies towards DENV will also be presented.
  2. A model-based bone milling state identification method via force sensing for a robotic surgical system
    Al-Abdullah KI, Lim CP, Najdovski Z, Yassin W
    Int J Med Robot, 2019 Jun;15(3):e1989.
    PMID: 30721570 DOI: 10.1002/rcs.1989
    BACKGROUND: This paper presents a model-based bone milling state identification method that provides intraoperative bone quality information during robotic bone milling. The method helps surgeons identify bone layer transitions during bone milling.

    METHODS: On the basis of a series of bone milling experiments with commercial artificial bones, an artificial neural network force model is developed to estimate the milling force of different bone densities as a function of the milling feed rate and spindle speed. The model estimations are used to identify the bone density at the cutting zone by comparing the actual milling force with the estimated one.

    RESULTS: The verification experiments indicate the ability of the proposed method to distinguish between one cortical and two cancellous bone densities.

    CONCLUSIONS: The significance of the proposed method is that it can be used to discriminate a set of different bone density layers for a range of the milling feed rate and spindle speed.

  3. Plasma, aqueous and vitreous homocysteine levels in proliferative diabetic retinopathy
    Lim CP, Loo AV, Khaw KW, Sthaneshwar P, Khang TF, Hassan M, et al.
    Br J Ophthalmol, 2012 May;96(5):704-7.
    PMID: 22353698 DOI: 10.1136/bjophthalmol-2011-301044
    To compare homocysteine (Hcy) concentration in the blood plasma, vitreous and aqueous of eyes with proliferative diabetic retinopathy (PDR) against control, and to investigate associations between Hcy concentration in blood plasma with that of aqueous and vitreous in these two groups.
  4. Fulltext Angiotensin receptor-neprilysin inhibitor improves New York Heart Association class and N-terminal-pro B-type natriuretic peptide levels: initial experience in a Singapore single-centre cohort
    Koh N, Goh VJ, Teoh CK, Hon JS, Teo LLY, Lim CP, et al.
    Singapore Med J, 2021 Jul;62(7):359-361.
    PMID: 34409482 DOI: 10.11622/smedj.2021092
  5. Fulltext Digitalization enhancement in the pharmaceutical supply network using a supply chain risk management approach
    Wong WP, Saw PS, Jomthanachai S, Wang LS, Ong HF, Lim CP
    Sci Rep, 2023 Dec 15;13(1):22287.
    PMID: 38097696 DOI: 10.1038/s41598-023-49606-z
    One major issue in pharmaceutical supply chain management is the supply shortage, and determining the root causes of medicine shortages necessitates an in-depth investigation. The concept of risk management is proposed in this study to identify significant risk factors in the pharmaceutical supply chain. Fuzzy failure mode and effect analysis and data envelopment analysis were used to evaluate the risks of the pharmaceutical supply chain. Based on a case study on the Malaysian pharmaceutical supply chain, it reveals that the pharmacy node is the riskiest link. The unavailability of medicine due to unexpected demand, as well as the scarcity of specialty or substitute drugs, pose the most significant risk factors. These risks could be mitigated by digital technology. We propose an appropriate digital technology platform consisting of big data analytics and blockchain technologies to undertake these challenges of supply shortage. By addressing risk factors through the implementation of a digitalized supply chain, organizations can fortify their supply networks, fostering resilience and efficiency, and thereby playing a pivotal role in advancing the Pharma 4.0 era.
  6. Safety assessment of methanol extract of red dragon fruit (Hylocereus polyrhizus): acute and subchronic toxicity studies
    Hor SY, Ahmad M, Farsi E, Yam MF, Hashim MA, Lim CP, et al.
    Regul Toxicol Pharmacol, 2012 Jun;63(1):106-14.
    PMID: 22440551 DOI: 10.1016/j.yrtph.2012.03.006
    Recently, the fruits of Hylocereus polyrhizus, known as red dragon fruit, have received much attention from growers worldwide. However, there is little toxicological information regarding the safety of repeated exposure to these fruits. The present study evaluated the potential toxicity of a methanol extract of H. polyrhizus fruit after acute and subchronic administration in rats. In the acute toxicity study, single doses of fruit extract (1250, 2500 and 5000 mg/kg) were administered to rats by oral gavage, and the rats were then monitored for 14 days. In the subchronic toxicity study, the fruit extract was administered orally to rats at doses of 1250, 2500 and 5000 mg/kg/day for 28 days. There was no mortality or signs of acute or subchronic toxicity. There was no significant difference in body weight, relative organ weight or hematological parameters in the subchronic toxicity study. Biochemical analysis showed some significant changes, including creatinine, globulin, total protein and urea levels. No abnormality of internal organs was observed between treatment and control groups. The lethal oral dose of the fruit extract is more than 5000 mg/kg and the no-observed-adverse-effect level (NOAEL) of the extract for both male and female rats is considered to be 5000 mg/kg per day for 28 days.
  7. Fulltext Credit card fraud detection using a hierarchical behavior-knowledge space model
    Nandi AK, Randhawa KK, Chua HS, Seera M, Lim CP
    PLoS One, 2022 01 20;17(1):e0260579.
    PMID: 35051184 DOI: 10.1371/journal.pone.0260579
    With the advancement in machine learning, researchers continue to devise and implement effective intelligent methods for fraud detection in the financial sector. Indeed, credit card fraud leads to billions of dollars in losses for merchants every year. In this paper, a multi-classifier framework is designed to address the challenges of credit card fraud detections. An ensemble model with multiple machine learning classification algorithms is designed, in which the Behavior-Knowledge Space (BKS) is leveraged to combine the predictions from multiple classifiers. To ascertain the effectiveness of the developed ensemble model, publicly available data sets as well as real financial records are employed for performance evaluations. Through statistical tests, the results positively indicate the effectiveness of the developed model as compared with the commonly used majority voting method for combination of predictions from multiple classifiers in tackling noisy data classification as well as credit card fraud detection problems.
  8. Safety assessment of standardised methanol extract of Cinnamomum burmannii
    Ahmad M, Lim CP, Akowuah GA, Ismail NN, Hashim MA, Hor SY, et al.
    Phytomedicine, 2013 Sep 15;20(12):1124-30.
    PMID: 23827665 DOI: 10.1016/j.phymed.2013.05.005
    The present study aims to evaluate the safety of methanol extract of Cinnamomum burmannii (MECB) by acute 14-day (single dose) and sub-chronic 28-day (repeated doses) oral administration to Sprague-Dawley rats. Our results showed that no toxicity was found in either acute or sub-chronic toxicity studies. MECB (containing 0.07% and 0.20% (w/w) of coumarin and trans-cinnamaldehyde, respectively), which was given orally at doses of 500, 1000 and 2000 mg/kg caused neither visible signs of toxicity nor mortality. No significant differences were observed in general condition, growth, organ weight, hematological parameters, biochemical values, or the gross and microscopic appearance of the organs from the treatment groups as compared to the control group. In conclusion, MECB did not cause any mortality nor did it cause any abnormalities in the necropsy and histopathology findings of treated rats. The LD50 for the MECB was found to be more than 2000 mg/kg. No adverse effects were observed in the treated rats at all the doses tested. The no-observed-adverse-effect level (NOAEL) for the 28-day study was determined to be 2000 mg/kg body weight/day.
  9. Use of artificial neural networks to predict drug dissolution profiles and evaluation of network performance using similarity factor
    Peh KK, Lim CP, Quek SS, Khoh KH
    Pharm Res, 2000 Nov;17(11):1384-8.
    PMID: 11205731
    PURPOSE: To use artificial neural networks for predicting dissolution profiles of matrix-controlled release theophylline pellet preparation, and to evaluate the network performance by comparing the predicted dissolution profiles with those obtained from physical experiments using similarity factor.

    METHODS: The Multi-Layered Perceptron (MLP) neural network was used to predict the dissolution profiles of theophylline pellets containing different ratios of microcrystalline cellulose (MCC) and glyceryl monostearate (GMS). The concepts of leave-one-out as well as a time-point by time-point estimation basis were used to predict the rate of drug release for each matrix ratio. All the data were used for training, except for one set which was selected to compare with the predicted output. The closeness between the predicted and the reference dissolution profiles was investigated using similarity factor (f2).

    RESULTS: The f2 values were all above 60, indicating that the predicted dissolution profiles were closely similar to the dissolution profiles obtained from physical experiments.

    CONCLUSION: The MLP network could be used as a model for predicting the dissolution profiles of matrix-controlled release theophylline pellet preparation in product development.

  10. Fulltext Recent trends in next generation immunoinformatics harnessed for universal coronavirus vaccine design
    Lim CP, Kok BH, Lim HT, Chuah C, Abdul Rahman B, Abdul Majeed AB, et al.
    Pathog Glob Health, 2023 Mar;117(2):134-151.
    PMID: 35550001 DOI: 10.1080/20477724.2022.2072456
    The ongoing pandemic of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has globally devastated public health, the economies of many countries and quality of life universally. The recent emergence of immune-escaped variants and scenario of vaccinated individuals being infected has raised the global concerns about the effectiveness of the current available vaccines in transmission control and disease prevention. Given the high rate mutation of SARS-CoV-2, an efficacious vaccine targeting against multiple variants that contains virus-specific epitopes is desperately needed. An immunoinformatics approach is gaining traction in vaccine design and development due to the significant reduction in time and cost of immunogenicity studies and increasing reliability of the generated results. It can underpin the development of novel therapeutic methods and accelerate the design and production of peptide vaccines for infectious diseases. Structural proteins, particularly spike protein (S), along with other proteins have been studied intensively as promising coronavirus vaccine targets. Numbers of promising online immunological databases, tools and web servers have widely been employed for the design and development of next generation COVID-19 vaccines. This review highlights the role of immunoinformatics in identifying immunogenic peptides as potential vaccine targets, involving databases, and prediction and characterization of epitopes which can be harnessed for designing future coronavirus vaccines.
  11. Evaluation of anti-cancer activity of Acanthester planci extracts obtained by different methods of extraction
    Mutee AF, Salhimi SM, Ghazali FC, Aisha AF, Lim CP, Ibrahim K, et al.
    Pak J Pharm Sci, 2012 Oct;25(4):697-703.
    PMID: 23009983
    Acanthaster planci, the crown-of-thorns starfish, naturally endowed with the numerous toxic spines around the dorsal area of its body. Scientific investigations demonstrated several toxico-pharmacological efficacies of A. planci such as, myonecrotic activity, hemorrhagic activity, hemolytic activity, mouse lethality, phospholipase A2 (PLA2) activity, capillary permeability-increasing activity, edema-forming activity, anticoagulant activity and histamine-releasing activity from mast cells. The present study was performed to evaluate the cytotoxic activity of A. planci extracts obtained by different methods of extraction on MCF-7 and HCT-116, human breast and colon cancer cell lines, respectively. Results of the cell proliferation assay showed that PBS extract exhibited very potent cytotoxic activity against both MCF-7 and HCT-116 cell lines with IC(50) of 13.48 μg/mL and 28.78 μg/mL, respectively, while the extracts prepared by Bligh and Dyer method showed moderate cytotoxicity effect against MCF-7 and HCT-116 cell lines, for chloroform extract, IC(50) = 121.37 μg/mL (MCF-7) and 77.65 μg/mL (HCT-116), and for methanol extract, IC(50) = 46.11 μg/mL (MCF-7) and 59.29 μg/mL (HCT-116). However, the extracts prepared by sequential extraction procedure from dried starfish found to be ineffective. This study paves the way for further investigation on the peptide composition in the PBS extract of the starfish to discover potential chemotherapeutic agents.
  12. A new hyperbox selection rule and a pruning strategy for the enhanced fuzzy min-max neural network
    Mohammed MF, Lim CP
    Neural Netw, 2017 Feb;86:69-79.
    PMID: 27890606 DOI: 10.1016/j.neunet.2016.10.012
    In this paper, we extend our previous work on the Enhanced Fuzzy Min-Max (EFMM) neural network by introducing a new hyperbox selection rule and a pruning strategy to reduce network complexity and improve classification performance. Specifically, a new k-nearest hyperbox expansion rule (for selection of a new winning hyperbox) is first introduced to reduce the network complexity by avoiding the creation of too many small hyperboxes within the vicinity of the winning hyperbox. A pruning strategy is then deployed to further reduce the network complexity in the presence of noisy data. The effectiveness of the proposed network is evaluated using a number of benchmark data sets. The results compare favorably with those from other related models. The findings indicate that the newly introduced hyperbox winner selection rule coupled with the pruning strategy are useful for undertaking pattern classification problems.
  13. Prediction of drug release profiles using an intelligent learning system: an experimental study in transdermal iontophoresis
    Lim CP, Quek SS, Peh KK
    J Pharm Biomed Anal, 2003 Feb 05;31(1):159-68.
    PMID: 12560060
    This paper investigates the use of a neural-network-based intelligent learning system for the prediction of drug release profiles. An experimental study in transdermal iontophoresis (TI) is employed to evaluate the applicability of a particular neural network (NN) model, i.e. the Gaussian mixture model (GMM), in modeling and predicting drug release profiles. A number of tests are systematically designed using the face-centered central composite design (CCD) approach to examine the effects of various process variables simultaneously during the iontophoresis process. The GMM is then applied to model and predict the drug release profiles based on the data samples collected from the experiments. The GMM results are compared with those from multiple regression models. In addition, the bootstrap method is used to assess the reliability of the network predictions by estimating confidence intervals associated with the results. The results demonstrate that the combination of the face-centered CCD and GMM can be employed as a useful intelligent tool for the prediction of time-series profiles in pharmaceutical and biomedical experiments.
  14. Acute and subchronic oral toxicity of Coriolus versicolor standardized water extract in Sprague-Dawley rats
    Hor SY, Ahmad M, Farsi E, Lim CP, Asmawi MZ, Yam MF
    J Ethnopharmacol, 2011 Oct 11;137(3):1067-76.
    PMID: 21767625 DOI: 10.1016/j.jep.2011.07.007
    Coriolus versicolor, which is known as Yun Zhi, is one of the commonly used Chinese medicinal herbs. Recent studies have demonstrated its antitumor activities on cancer cells which led to its widespread use in cancer patient. However, little toxicological information is available regarding its safety. The present study evaluated the potential toxicity of Coriolus versicolor standardized water extract after acute and subchronic administration in rats.
  15. Toxicity evaluation of a standardised 50% ethanol extract of Orthosiphon stamineus
    Mohamed EA, Lim CP, Ebrika OS, Asmawi MZ, Sadikun A, Yam MF
    J Ethnopharmacol, 2011 Jan 27;133(2):358-63.
    PMID: 20937371 DOI: 10.1016/j.jep.2010.10.008
    The present investigation was carried out to evaluate the safety of standardised 50% ethanol extract of Orthosiphon stamineus plant by determining its potential toxicity after acute and subchronic administration in rats.
  16. Fulltext Antioxidant and hepatoprotective effects of Murdannia bracteata methanol extract
    Yam MF, Ang LF, Lim CP, Ameer OZ, Salman IM, Ahmad M, et al.
    J Acupunct Meridian Stud, 2010 Sep;3(3):197-202.
    PMID: 20869021 DOI: 10.1016/S2005-2901(10)60036-2
    Murdannia bracteata (C. B. Clarke) is a local plant that is widely used in Malaysia as a traditional remedy for various diseases of the kidney and liver, including inflammation and cancer. In the present study, we investigated the antioxidant and hepatoprotective activities of M. bracteata methanol extract (MB). 2,2'-diphenyl-1-picrylhydrazyl radical scavenging activity, lipid peroxidation inhibition and trolox equivalent antioxidant capacity of MB were determined. The hepatoprotective activity of MB was studied using a CCl(4)-induced liver toxicity model in rats. The hepatoprotective effect was assessed by monitoring the plasma malondialdehyde level and serum alanine transaminase and aspartate transaminase activities. Histopathological changes of hepatic tissue were also investigated. The results indicated that MB possessed potential antioxidant, lipid peroxidation inhibition and free radical scavenging activities. Pretreatment of rats with MB (500 mg/kg and 1000 mg/kg per os) before induction of CCl(4)-induced hepatotoxicity showed a dose-dependent reduction in the necrotic changes in hepatic tissue. The increases in plasma malondialdehyde level, serum alanine transaminase and aspartate transaminase activities were also significantly inhibited by MB. The total phenolic content of MB determined using Folin-Ciocalteu assay was found to be 10%. The results of the present study indicated that the hepatoprotective effect of MB is most likely due to its antioxidant and free radical scavenging properties.
  17. A hybrid neural network system for pattern classification tasks with missing features
    Lim CP, Leong JH, Kuan MM
    IEEE Trans Pattern Anal Mach Intell, 2005 Apr;27(4):648-53.
    PMID: 15794170
    A hybrid neural network comprising Fuzzy ARTMAP and Fuzzy C-Means Clustering is proposed for pattern classification with incomplete training and test data. Two benchmark problems and a real medical pattern classification task are employed to evaluate the effectiveness of the hybrid network. The results are analyzed and compared with those from other methods.
  18. Predicting drug dissolution profiles with an ensemble of boosted neural networks: a time series approach
    Goh WY, Lim CP, Peh KK
    IEEE Trans Neural Netw, 2003;14(2):459-63.
    PMID: 18238031 DOI: 10.1109/TNN.2003.809420
    Applicability of an ensemble of Elman networks with boosting to drug dissolution profile predictions is investigated. Modifications of AdaBoost that enables its use in regression tasks are explained. Two real data sets comprising in vitro dissolution profiles of matrix-controlled-release theophylline pellets are employed to assess the effectiveness of the proposed system. Statistical evaluation and comparison of the results are performed. This work positively demonstrates the potentials of the proposed system for predicting desired drug dissolution characteristics in pharmaceutical product formulation tasks.
  19. Improved GART neural network model for pattern classification and rule extraction with application to power systems
    Yap KS, Lim CP, Au MT
    IEEE Trans Neural Netw, 2011 Dec;22(12):2310-23.
    PMID: 22067292 DOI: 10.1109/TNN.2011.2173502
    Generalized adaptive resonance theory (GART) is a neural network model that is capable of online learning and is effective in tackling pattern classification tasks. In this paper, we propose an improved GART model (IGART), and demonstrate its applicability to power systems. IGART enhances the dynamics of GART in several aspects, which include the use of the Laplacian likelihood function, a new vigilance function, a new match-tracking mechanism, an ordering algorithm for determining the sequence of training data, and a rule extraction capability to elicit if-then rules from the network. To assess the effectiveness of IGART and to compare its performances with those from other methods, three datasets that are related to power systems are employed. The experimental results demonstrate the usefulness of IGART with the rule extraction capability in undertaking classification problems in power systems engineering.
  20. A hybrid ART-GRNN online learning neural network with a epsilon -insensitive loss function
    Yap KS, Lim CP, Abidin IZ
    IEEE Trans Neural Netw, 2008 Sep;19(9):1641-6.
    PMID: 18779094 DOI: 10.1109/TNN.2008.2000992
    In this brief, a new neural network model called generalized adaptive resonance theory (GART) is introduced. GART is a hybrid model that comprises a modified Gaussian adaptive resonance theory (MGA) and the generalized regression neural network (GRNN). It is an enhanced version of the GRNN, which preserves the online learning properties of adaptive resonance theory (ART). A series of empirical studies to assess the effectiveness of GART in classification, regression, and time series prediction tasks is conducted. The results demonstrate that GART is able to produce good performances as compared with those of other methods, including the online sequential extreme learning machine (OSELM) and sequential learning radial basis function (RBF) neural network models.
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