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Ultrastructural aspects of sylvatic dengue virus infection in Vero cell

Fish-Low CY, Abubakar S, Othman F, Chee HY

Malays J Pathol, 2019 Apr;41(1):41-46.
PMID: 31025636

INTRODUCTION: Dengue virus (DENV), the causative agent of dengue disease exists in sylvatic and endemic ecotypes. The cell morphological changes and viral morphogenesis of two dengue ecotypes were examined at the ultrastructural level to identify potential similarities and differences in the surrogate model of enzootic host.
MATERIALS AND METHODS: Vero cells were inoculated with virus at a multiplicity of infection (MOI) of 0.1. Cell cultures were harvested over a time course and processed for transmission electron microscopic imaging.
RESULTS: The filopodia protrusions on cell periphery preceded virus entry. Additionally, sylvatic DENV infection was found spreading slower than the endemic DENV. Morphogenesis of both dengue ecotypes was alike but at different level of efficiency in the permissive cells.
CONCLUSIONS: This is the first ultrastructural study on sylvatic DENV and this comparative study revealed the similarities and differences of cellular responses and morphogenesis of two dengue ecotypes in vitro. The study revealed the weaker infectivity of sylvatic DENV in the surrogate model of enzootic host, which supposed to support better replication of enzootic DENV than endemic DENV.
Ultrastructural changes of cell morphology and viral morphogenesis of two ecotypes of dengue virus infection in human monocytic U-937 cell

Fish-Low CY, Abu Bakar S, Othman F, Chee HY

Trop Biomed, 2018 Dec 01;35(4):1154-1159.
PMID: 33601863

Dengue virus (DENV) is maintained and circulated in both sylvatic/enzootic and endemic/human cycles and spill over infection of sylvatic DENV into human populations has been reported. Extensive deforestation and increase human activities in forest may increase the risk of human exposure to sylvatic dengue infection and this may become a threat to human. Present study investigated the changes in cell morphology and viral morphogenesis upon infection with sylvatic and endemic ecotypes in human monocytic U-937 cells using transmission electron microscopy. Autophagy, a process that is either pro-viral or anti-viral, was observed in U-937 cells of both infections, however only the replication of endemic DENV was evidenced. An insight into the infection responses of sylvatic progenitors of DENV in susceptible host cells may provide better understanding on dengue emergence in human populations.
The Potential of Eight Plasma Proteins as Biomarkers in Redefining Leptospirosis Diagnosis

Fish-Low CY, Than LTL, Ling KH, Sekawi Z

J Proteome Res, 2024 Sep 06;23(9):4027-4042.
PMID: 39150348 DOI: 10.1021/acs.jproteome.4c00376

Leptospirosis, a notifiable endemic disease in Malaysia, has higher mortality rates than regional dengue fever. Diverse clinical symptoms and limited diagnostic methods complicate leptospirosis diagnosis. The demand for accurate biomarker-based diagnostics is increasing. This study investigated the plasma proteome of leptospirosis patients with leptospiraemia and seroconversion compared with dengue patients and healthy subjects using isobaric tags for relative and absolute quantitation (iTRAQ)-mass spectrometry (MS). The iTRAQ analysis identified a total of 450 proteins, which were refined to a list of 290 proteins through a series of exclusion criteria. Differential expression in the plasma proteome of leptospirosis patients compared to the control groups identified 11 proteins, which are apolipoprotein A-II (APOA2), C-reactive protein (CRP), fermitin family homolog 3 (FERMT3), leucine-rich alpha-2-glycoprotein 1 (LRG1), lipopolysaccharide-binding protein (LBP), myosin-9 (MYH9), platelet basic protein (PPBP), platelet factor 4 (PF4), profilin-1 (PFN1), serum amyloid A-1 protein (SAA1), and thrombospondin-1 (THBS1). Following a study on a verification cohort, a panel of eight plasma protein biomarkers was identified for potential leptospirosis diagnosis: CRP, LRG1, LBP, MYH9, PPBP, PF4, SAA1, and THBS1. In conclusion, a panel of eight protein biomarkers offers a promising approach for leptospirosis diagnosis, addressing the limitations of the "one disease, one biomarker" concept.
Fulltext Plasma proteome profiling reveals differentially expressed lipopolysaccharide-binding protein among leptospirosis patients

Fish-Low CY, Than LTL, Ling KH, Lin Q, Sekawi Z

J Microbiol Immunol Infect, 2020 Feb;53(1):157-162.
PMID: 31029530 DOI: 10.1016/j.jmii.2018.12.015

BACKGROUND: Human leptospirosis, or commonly known as "rat urine disease" is a zoonotic disease that is caused by the bacteria called Leptospira sp. The incidence rate of leptospirosis has been under-reported due to its unspecific clinical symptoms and the limitations of current laboratory diagnostic methods. Leptospirosis can be effectively treated with antibiotics in the early stage, and it is a curable disease but the accuracy to diagnose the infection is rarely achieved.
METHODS: The present pilot study investigated plasma protein profiles of leptospirosis patients and compared them against two control groups which consisted of dengue patients and healthy individuals. The plasma protein digests were analyzed using shotgun approach by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Protein abundances were estimated from the exponentially modified protein abundance index (emPAI) values. Plasma proteins in leptospirosis patients with at least two-fold differential expression compared to dengue and healthy control groups (p
Fulltext Dataset on microstructural characteristics and mechanical performance of homogeneous and functionally graded fibrous scaffolds

Khoo W, Chung SM, Lim SC, Low CY, Shapiro JM, Koh CT

Data Brief, 2019 Dec;27:104718.
PMID: 31763388 DOI: 10.1016/j.dib.2019.104718

Data in this article are supplementary to the corresponding research article [1]. Morphological features of homogeneous and graded nanofibrous electrospun gelatin scaffolds were observed using scanning electron microscopy. Microstructural properties including fiber diameter and pore size were determined via image analysis, using ImageJ. Uniaxial tensile and fracture tests were performed on both homogeneous and graded scaffolds using a universal testing machine. Stress-strain curves of all scaffolds are presented. Computing software, MATLAB, was used to design fibrous networks with thickness-dependent density and alignment gradients (DAG). Finite element analysis software, Abaqus, was used to determine the effect of the number of layers on the fracture properties of DAG multilayer scaffolds.
Elucidating factors influencing machine learning algorithm prediction in spasticity assessment: a prospective observational study

Mohamad Hashim N, Yee J, Othman NA, Johar K, Low CY, Hanapiah FA, et al.

Comput Methods Biomech Biomed Engin, 2021 Oct 20.
PMID: 34668820 DOI: 10.1080/10255842.2021.1990270

The Machine Learning Model (MLM) has garnered popularity in rehabilitation, ranging from developing algorithms in outcome prediction, prognostication, and training artificial intelligence. High-quality data plays a critical role in algorithm development. Limited studies have explored factors that may influence the MLM algorithm performance in predicting spasticity severity level. The objectives of this study were to train and validate a MLM algorithm for spasticity assessment and determine the algorithm's prediction performance in predicting ambiguous spasticity datasets. Forty-seven persons with central nervous system pathology that fulfilled the inclusion and exclusion criteria were recruited. Four biomechanical properties of spasticity were obtained using off-the-shelf wearable sensors. The data were analyzed individually, and ambiguous datasets were separated. The acceptable inertial data were used to train and validate MLM in predicting spasticity. The trained and validated MLM algorithm was later deployed to predict the ambiguous spasticity datasets. A series of MLM were applied, including Support Vector Machine, Decision Tree, and Random Forest. The MLM's performance accuracy of the validation data was 96%, 52%, and 72%, respectively. The validated MLM accuracy performance level predicting ambiguous datasets reduces to 20%, 23%, and 23%, respectively. This study elucidates data biases and variances of disease background, pathophysiological and anatomical factors that have to be considered in MLM training.
Fulltext Hypocalcemia, hypochloremia, and eosinopenia as clinical predictors of leptospirosis: A retrospective study

Fish-Low CY, Balami AD, Than LTL, Ling KH, Mohd Taib N, Md Shah A, et al.

J Infect Public Health, 2020 Feb;13(2):216-220.
PMID: 31455598 DOI: 10.1016/j.jiph.2019.07.021

BACKGROUND: Underestimation of leptospirosis cases is happening in many countries. The most common factor of underreporting is misdiagnosis. Considering the limitations of direct detection of pathogen and serological diagnosis for leptospirosis, clinical features and blood tests though non-specific are usually referred in making presumptive diagnosis to decide disease management.
METHODS: In this single-centre retrospective study, comparative analysis on clinical presentations and laboratory findings was performed between confirmed leptospirosis versus non-leptospirosis cases.
RESULTS: In multivariate logistic regression evidenced by a Hosmer-Lemeshow significance value of 0.979 and Nagelkerke R square of 0.426, the predictors of a leptospirosis case are hypocalcemia (calcium <2.10mmol/L), hypochloremia (chloride <98mmol/L), and eosinopenia (absolute eosinophil count <0.040×109/L). The proposed diagnostic scoring model has a discriminatory power with area under the curve (AUC) 0.761 (p<0.001). A score value of 6 reflected a sensitivity of 0.762, specificity of 0.655, a positive predictive value of 0.38, negative predictive value of 0.91, a positive likelihood ratios of 2.21, and a negative likelihood ratios of 0.36.
CONCLUSION: With further validation in clinical settings, implementation of this diagnostic scoring model is helpful to manage presumed leptospirosis especially in the absence of leptospirosis confirmatory tests.
Fulltext Quality prediction and classification of resistance spot weld using artificial neural network with open-sourced, self-executable and GUI-based application tool Q-Check

Abd Halim S, Manurung YHP, Raziq MA, Low CY, Rohmad MS, Dizon JRC, et al.

Sci Rep, 2023 Feb 21;13(1):3013.
PMID: 36810419 DOI: 10.1038/s41598-023-29906-0

Optimizing Resistance spot welding, often used as a time and cost-effective process in many industrial sectors, is very time-consuming due to the obscurity inherent within process with numerous interconnected welding parameters. Small changes in values will give effect to the quality of welds which actually can be easily analysed using application tool. Unfortunately, existing software to optimize the parameters are expensive, licensed and inflexible which makes small industries and research centres refused to acquire. In this study, application tool using open-sourced and customized algorithm based on artificial neural networks (ANN) was developed to enable better, fast, cheap and practical predictions of major parameters such as welding time, current and electrode force on tensile shear load bearing capacity (TSLBC) and weld quality classifications (WQC). A supervised learning algorithm implemented in standard backpropagation neural network gradient descent (GD), stochastic gradient descent (SGD) and Levenberg-Marquardt (LM) was constructed using TensorFlow with Spyder IDE in python language. All the display and calculation processes are developed and compiled in the form of application tool of graphical user interface (GUI). Results showed that this low-cost application tool Q-Check based on ANN models can predict with 80% training and 20% test set on TSLBC with an accuracy of 87.220%, 92.865% and 93.670% for GD, SGD and LM algorithms respectively while on WQC 62.5% for GD and 75% for both SGD and LM. It is also expected that tool with flexible GUI can be widely used and enhanced by practitioner with minimum knowledge in the domain.
Silver nanoparticles: Advanced and promising technology in diabetic wound therapy

Choudhury H, Pandey M, Lim YQ, Low CY, Lee CT, Marilyn TCL, et al.

Mater Sci Eng C Mater Biol Appl, 2020 Jul;112:110925.
PMID: 32409075 DOI: 10.1016/j.msec.2020.110925

Wounds associated with diabetes mellitus are the most severe co-morbidities, which could be progressed to cause cell necrosis leading to amputation. Statistics on the recent status of the diabetic wounds revealed that the disease affects 15% of diabetic patients, where 20% of them undergo amputation of their limb. Conventional therapies are found to be ineffective due to changes in the molecular architecture of the injured area, urging novel deliveries for effective treatment. Therefore, recent researches are on the development of new and effective wound care materials. Literature is evident in providing potential tools in topical drug delivery for wound healing under the umbrella of nanotechnology, where nano-scaffolds and nanofibers have shown promising results. The nano-sized particles are also known to promote healing of wounds by facilitating proper movement through the healing phases. To date, focuses have been made on the efficacy of silver nanoparticles (AgNPs) in treating the diabetic wound, where these nanoparticles are known to exploit potential biological properties in producing anti-inflammatory and antibacterial activities. AgNPs are also known to activate cellular mechanisms towards the healing of chronic wounds; however, associated toxicities of AgNPs are of great concern. This review is an attempt to illustrate the use of AgNPs in wound healing to facilitate this delivery system in bringing into clinical applications for a superior dressing and treatment over wounds and ulcers in diabetes patients.