Displaying publications 1 - 20 of 53 in total

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  1. Letchumanan N, Wong JHD, Tan LK, Ab Mumin N, Ng WL, Chan WY, et al.
    J Digit Imaging, 2023 Aug;36(4):1533-1540.
    PMID: 37253893 DOI: 10.1007/s10278-022-00753-1
    This study investigates the feasibility of using texture radiomics features extracted from mammography images to distinguish between benign and malignant breast lesions and to classify benign lesions into different categories and determine the best machine learning (ML) model to perform the tasks. Six hundred and twenty-two breast lesions from 200 retrospective patient data were segmented and analysed. Three hundred fifty radiomics features were extracted using the Standardized Environment for Radiomics Analysis (SERA) library, one of the radiomics implementations endorsed by the Image Biomarker Standardisation Initiative (IBSI). The radiomics features and selected patient characteristics were used to train selected machine learning models to classify the breast lesions. A fivefold cross-validation was used to evaluate the performance of the ML models and the top 10 most important features were identified. The random forest (RF) ensemble gave the highest accuracy (89.3%) and positive predictive value (66%) and likelihood ratio of 13.5 in categorising benign and malignant lesions. For the classification of benign lesions, the RF model again gave the highest likelihood ratio of 3.4 compared to the other models. Morphological and textural radiomics features were identified as the top 10 most important features from the random forest models. Patient age was also identified as one of the significant features in the RF model. We concluded that machine learning models trained against texture-based radiomics features and patient features give reasonable performance in differentiating benign versus malignant breast lesions. Our study also demonstrated that the radiomics-based machine learning models were able to emulate the visual assessment of mammography lesions, typically used by radiologists, leading to a better understanding of how the machine learning model arrive at their decision.
  2. Nair SR, Tan LK, Mohd Ramli N, Lim SY, Rahmat K, Mohd Nor H
    Eur Radiol, 2013 Jun;23(6):1459-66.
    PMID: 23300042 DOI: 10.1007/s00330-012-2759-9
    OBJECTIVE: To develop a decision tree based on standard magnetic resonance imaging (MRI) and diffusion tensor imaging to differentiate multiple system atrophy (MSA) from Parkinson's disease (PD).

    METHODS: 3-T brain MRI and DTI (diffusion tensor imaging) were performed on 26 PD and 13 MSA patients. Regions of interest (ROIs) were the putamen, substantia nigra, pons, middle cerebellar peduncles (MCP) and cerebellum. Linear, volumetry and DTI (fractional anisotropy and mean diffusivity) were measured. A three-node decision tree was formulated, with design goals being 100 % specificity at node 1, 100 % sensitivity at node 2 and highest combined sensitivity and specificity at node 3.

    RESULTS: Nine parameters (mean width, fractional anisotropy (FA) and mean diffusivity (MD) of MCP; anteroposterior diameter of pons; cerebellar FA and volume; pons and mean putamen volume; mean FA substantia nigra compacta-rostral) showed statistically significant (P < 0.05) differences between MSA and PD with mean MCP width, anteroposterior diameter of pons and mean FA MCP chosen for the decision tree. Threshold values were 14.6 mm, 21.8 mm and 0.55, respectively. Overall performance of the decision tree was 92 % sensitivity, 96 % specificity, 92 % PPV and 96 % NPV. Twelve out of 13 MSA patients were accurately classified.

    CONCLUSION: Formation of the decision tree using these parameters was both descriptive and predictive in differentiating between MSA and PD.

    KEY POINTS: • Parkinson's disease and multiple system atrophy can be distinguished on MR imaging. • Combined conventional MRI and diffusion tensor imaging improves the accuracy of diagnosis. • A decision tree is descriptive and predictive in differentiating between clinical entities. • A decision tree can reliably differentiate Parkinson's disease from multiple system atrophy.

  3. Tan LK, Wong JH, Ng KH
    AJR Am J Roentgenol, 2006 Mar;186(3):898-901.
    PMID: 16498128
    The purpose of this article was to develop a low-cost method for high-quality remote capturing and recording of multimedia presentations.
  4. Chia BL, Tan LK
    Am J Cardiol, 1984 May 01;53(9):1413.
    PMID: 6538741
  5. Chuah SH, Md Sari NA, Tan LK, Chiam YK, Chan BT, Abdul Aziz YF, et al.
    J Cardiovasc Transl Res, 2023 Oct;16(5):1110-1122.
    PMID: 37022611 DOI: 10.1007/s12265-023-10375-9
    Left ventricular adaptations can be a complex process under the influence of aortic stenosis (AS) and comorbidities. This study proposed and assessed the feasibility of using a motion-corrected personalized 3D + time LV modeling technique to evaluate the adaptive and maladaptive LV response to aid treatment decision-making. A total of 22 AS patients were analyzed and compared against 10 healthy subjects. The 3D + time analysis showed a highly distinct and personalized pattern of remodeling in individual AS patients which is associated with comorbidities and fibrosis. Patients with AS alone showed better wall thickening and synchrony than those comorbid with hypertension. Ischemic heart disease in AS caused impaired wall thickening and synchrony and systolic function. Apart from showing significant correlations to echocardiography and clinical MRI measurements (r: 0.70-0.95; p 
  6. Ramli N, Lim CH, Rajagopal R, Tan LK, Seow P, Ariffin H
    Pediatr Radiol, 2020 08;50(9):1277-1283.
    PMID: 32591982 DOI: 10.1007/s00247-020-04717-x
    BACKGROUND: Intrathecal and intravenous chemotherapy, specifically methotrexate, might contribute to neural microstructural damage.

    OBJECTIVE: To assess, by diffusion tensor imaging, microstructural integrity of white matter in paediatric patients with acute lymphoblastic leukaemia (ALL) following intrathecal and intravenous chemotherapy.

    MATERIALS AND METHODS: Eleven children diagnosed with de novo ALL underwent MRI scans of the brain with diffusion tensor imaging (DTI) prior to commencement of chemotherapy and at 12 months after diagnosis, using a 3-tesla (T) MRI scanner. We investigated the changes in DTI parameters in white matter tracts before and after chemotherapy using tract-based spatial statistics overlaid on the International Consortium of Brain Mapping DTI-81 atlas. All of the children underwent formal neurodevelopmental assessment at the two study time points.

    RESULTS: Whole-brain DTI analysis showed significant changes between the two time points, affecting several white matter tracts. The tracts demonstrated longitudinal changes of decreasing mean and radial diffusivity. The neurodevelopment of the children was near compatible for age at the end of ALL treatment.

    CONCLUSION: The quantification of white matter tracts changes in children undergoing chemotherapy showed improving longitudinal values in DTI metrics (stable fractional anisotropy, decreasing mean and radial diffusivity), which are incompatible with deterioration of microstructural integrity in these children.

  7. Lau YS, Tan LK, Chan CK, Chee KH, Liew YM
    Phys Med Biol, 2021 Dec 31;66(24).
    PMID: 34911053 DOI: 10.1088/1361-6560/ac4348
    Percutaneous coronary intervention (PCI) with stent placement is a treatment effective for coronary artery diseases. Intravascular optical coherence tomography (OCT) with high resolution is used clinically to visualize stent deployment and restenosis, facilitating PCI operation and for complication inspection. Automated stent struts segmentation in OCT images is necessary as each pullback of OCT images could contain thousands of stent struts. In this paper, a deep learning framework is proposed and demonstrated for the automated segmentation of two major clinical stent types: metal stents and bioresorbable vascular scaffolds (BVS). U-Net, the current most prominent deep learning network in biomedical segmentation, was implemented for segmentation with cropped input. The architectures of MobileNetV2 and DenseNet121 were also adapted into U-Net for improvement in speed and accuracy. The results suggested that the proposed automated algorithm's segmentation performance approaches the level of independent human obsevers and is feasible for both types of stents despite their distinct appearance. U-Net with DenseNet121 encoder (U-Dense) performed best with Dice's coefficient of 0.86 for BVS segmentation, and precision/recall of 0.92/0.92 for metal stent segmentation under optimal crop window size of 256.
  8. Tan LK, Liew YM, Lim E, Abdul Aziz YF, Chee KH, McLaughlin RA
    Med Biol Eng Comput, 2018 Jun;56(6):1053-1062.
    PMID: 29147835 DOI: 10.1007/s11517-017-1750-7
    In this paper, we develop and validate an open source, fully automatic algorithm to localize the left ventricular (LV) blood pool centroid in short axis cardiac cine MR images, enabling follow-on automated LV segmentation algorithms. The algorithm comprises four steps: (i) quantify motion to determine an initial region of interest surrounding the heart, (ii) identify potential 2D objects of interest using an intensity-based segmentation, (iii) assess contraction/expansion, circularity, and proximity to lung tissue to score all objects of interest in terms of their likelihood of constituting part of the LV, and (iv) aggregate the objects into connected groups and construct the final LV blood pool volume and centroid. This algorithm was tested against 1140 datasets from the Kaggle Second Annual Data Science Bowl, as well as 45 datasets from the STACOM 2009 Cardiac MR Left Ventricle Segmentation Challenge. Correct LV localization was confirmed in 97.3% of the datasets. The mean absolute error between the gold standard and localization centroids was 2.8 to 4.7 mm, or 12 to 22% of the average endocardial radius. Graphical abstract Fully automated localization of the left ventricular blood pool in short axis cardiac cine MR images.
  9. Anam C, Naufal A, Sutanto H, Arifin Z, Hidayanto E, Tan LK, et al.
    Biomed Phys Eng Express, 2023 May 30;9(4).
    PMID: 37216929 DOI: 10.1088/2057-1976/acd785
    Objective. To develop an algorithm to measure slice thickness running on three types of Catphan phantoms with the ability to adapt to any misalignment and rotation of the phantoms.Method. Images of Catphan 500, 504, and 604 phantoms were examined. In addition, images with various slice thicknesses ranging from 1.5 to 10.0 mm, distance to the iso-center and phantom rotations were also examined. The automatic slice thickness algorithm was carried out by processing only objects within a circle having a diameter of half the diameter of the phantom. A segmentation was performed within an inner circle with dynamic thresholds to produce binary images with wire and bead objects within it. Region properties were used to distinguish wire ramps and bead objects. At each identified wire ramp, the angle was detected using the Hough transform. Profile lines were then placed on each ramp based on the centroid coordinates and detected angles, and the full-width at half maximum (FWHM) was determined for the average profile. The slice thickness was obtained by multiplying the FWHM by the tangent of the ramp angle (23°).Results. Automatic measurements work well and have only a small difference (<0.5 mm) from manual measurements. For slice thickness variation, automatic measurement successfully performs segmentation and correctly locates the profile line on all wire ramps. The results show measured slice thicknesses that are close (<3 mm) to the nominal thickness at thin slices, but slightly deviated for thicker slices. There is a strong correlation (R2= 0.873) between automatic and manual measurements. Testing the algorithm at various distances from the iso-center and phantom rotation angle also produced accurate results.Conclusion. An automated algorithm for measuring slice thickness on three types of Catphan CT phantom images has been developed. The algorithm works well on various thicknesses, distances from the iso-center, and phantom rotations.
  10. Tan LK, Liew YM, Lim E, McLaughlin RA
    Med Image Anal, 2017 Apr 12;39:78-86.
    PMID: 28437634 DOI: 10.1016/j.media.2017.04.002
    Automated left ventricular (LV) segmentation is crucial for efficient quantification of cardiac function and morphology to aid subsequent management of cardiac pathologies. In this paper, we parameterize the complete (all short axis slices and phases) LV segmentation task in terms of the radial distances between the LV centerpoint and the endo- and epicardial contours in polar space. We then utilize convolutional neural network regression to infer these parameters. Utilizing parameter regression, as opposed to conventional pixel classification, allows the network to inherently reflect domain-specific physical constraints. We have benchmarked our approach primarily against the publicly-available left ventricle segmentation challenge (LVSC) dataset, which consists of 100 training and 100 validation cardiac MRI cases representing a heterogeneous mix of cardiac pathologies and imaging parameters across multiple centers. Our approach attained a .77 Jaccard index, which is the highest published overall result in comparison to other automated algorithms. To test general applicability, we also evaluated against the Kaggle Second Annual Data Science Bowl, where the evaluation metric was the indirect clinical measures of LV volume rather than direct myocardial contours. Our approach attained a Continuous Ranked Probability Score (CRPS) of .0124, which would have ranked tenth in the original challenge. With this we demonstrate the effectiveness of convolutional neural network regression paired with domain-specific features in clinical segmentation.
  11. Tan LK, Chua EH, Mohd Ghazali S, Cheah YK, Jayaraj VJ, Kee CC
    Nutrients, 2023 Dec 08;15(24).
    PMID: 38140302 DOI: 10.3390/nu15245043
    The healthy eating plate concept has been introduced in many countries, including Malaysia, as a visual guide for the public to eat healthily. The relationship between Malaysian Healthy Plate (MHP) and adequate fruit and vegetable (FV) intake among morbid Malaysian adults is unknown. Hence, we investigated the relationship between awareness of the MHP and FV intake among morbid Malaysian adults. National survey data on 9760 morbid Malaysian adults aged 18 years and above were analyzed. The relationship between awareness of MHP and FV intake among Malaysian adults with obesity, diabetes mellitus, hypertension, and hypercholesterolemia were determined using multivariable logistic regression controlling for sociodemographic characteristics and lifestyle risk factors. Our data demonstrated that MHP awareness is associated with adequate FV intake among the Malaysian adults with abdominal obesity (adjusted odds ratio (aOR): 1.86, 95% confidence interval (CI): 1.05-3.29), diabetes mellitus (aOR: 4.88, 95% CI: 2.13-22.18), hypertension (aOR: 4.39, 95% CI: 1.96-9.83), and hypercholesterolemia (aOR: 4.16, 95% CI: 1.48-11.72). Our findings indicated the necessity for ongoing efforts by policymakers, healthcare professionals, and nutrition educators to promote the concept of MHP and ensure that morbid Malaysian adults consume a sufficient intake of FV or adopt a healthy eating pattern to achieve and maintain optimal health.
  12. Roslan A, Kamsani SH, Nay TW, Tan KL, Hakim N, Tan AM, et al.
    Med J Malaysia, 2018 12;73(6):388-392.
    PMID: 30647209
    OBJECTIVE: Cardiac amyloidosis is under diagnosed and its prevalence is unknown. This is a retrospective, nonrandomised, single centre study of patients with endomyocardial biopsy-proven cardiac amyloidosis focusing on their echocardiographic and electrocardiogram (ECG) presentations. This is the first case series in Malaysia on this subject.

    METHODS: We identified all of our endomyocardial biopsyproven cardiac amyloidosis patients from January 2010 to January 2018 and reviewed their medical records. All patients echocardiographic and ECG findings reviewed and analysed comparing to basic mean population value.

    RESULTS: In total there are 13 biopsy-proven cardiac amyloidosis patients. All of the biopsies shows light chain (AL) amyloid. Majority of the patients (8, 61.5%) is male, and most of our patients (8, 61.5%) is Chinese. All seven patients on whom we performed deformation imaging have apical sparing pattern on longitudinal strain echocardiogram. Mean ejection fraction is 49.3%, (SD=7.9). All patients have concentric left ventricular hypertrophy and right ventricular hypertrophy. Diastolic dysfunction was present in all of our patients with nine out of 13 patients (69.2%) having restrictive filling patterns (E/A ≥2.0 E/e' ≥15). On electrocardiogram, 12 (92%) patients have prolonged PR interval (median 200ms, IQR 76.50ms) and 9 (69.2%) patients have pseudoinfarct pattern.

    CONCLUSION: Echocardiography plays an important role in diagnosing cardiac amyloidosis. The findings of concentric left ventricular hypertrophy with preserved ejection fraction without increased in loading condition should alert the clinician towards its possibility. This is further supported by right ventricular hypertrophy and particularly longitudinal strain imaging showing apical sparing pattern.

  13. Tan LK, Ooi PT, Carniel E, Thong KL
    PLoS One, 2014;9(8):e106329.
    PMID: 25170941 DOI: 10.1371/journal.pone.0106329
    Y. enterocolitica and Y. pseudotuberculosis are important food borne pathogens. However, the presence of competitive microbiota makes the isolation of Y. enterocolitica and Y. pseudotuberculosis from naturally contaminated foods difficult. We attempted to evaluate the performance of a modified Cefsulodin-Irgasan-Novobiocin (CIN) agar in the differentiation of Y. enterocolitica from non-Yersinia species, particularly the natural intestinal microbiota. The modified CIN enabled the growth of Y. enterocolitica colonies with the same efficiency as CIN and Luria-Bertani agar. The detection limits of the modified CIN for Y. enterocolitica in culture medium (10 cfu/ml) and in artificially contaminated pork (10(4) cfu/ml) were also comparable to those of CIN. However, the modified CIN provided a better discrimination of Yersinia colonies from other bacteria exhibiting Yersinia-like colonies on CIN (H2S-producing Citrobacter freundii, C. braakii, Enterobacter cloacae, Aeromonas hydrophila, Providencia rettgeri, and Morganella morganii). The modified CIN exhibited a higher recovery rate of Y. enterocolitica from artificially prepared bacterial cultures and naturally contaminated samples compared with CIN. Our results thus demonstrated that the use of modified CIN may be a valuable means to increase the recovery rate of food borne Yersinia from natural samples, which are usually contaminated by multiple types of bacteria.
  14. Soh LT, Squires RC, Tan LK, Pok KY, Yang H, Liew C, et al.
    Western Pac Surveill Response J, 2016 04 22;7(2):26-34.
    PMID: 27508088 DOI: 10.5365/WPSAR.2016.7.1.002
    OBJECTIVE: To conduct an external quality assessment (EQA) of dengue and chikungunya diagnostics among national-level public health laboratories in the Asia Pacific region following the first round of EQA for dengue diagnostics in 2013.

    METHODS: Twenty-four national-level public health laboratories performed routine diagnostic assays on a proficiency testing panel consisting of two modules. Module A contained serum samples spiked with cultured dengue virus (DENV) or chikungunya virus (CHIKV) for the detection of nucleic acid and DENV non-structural protein 1 (NS1) antigen. Module B contained human serum samples for the detection of anti-DENV antibodies.

    RESULTS: Among 20 laboratories testing Module A, 17 (85%) correctly detected DENV RNA by reverse transcription polymerase chain reaction (RT-PCR), 18 (90%) correctly determined serotype and 19 (95%) correctly identified CHIKV by RT-PCR. Ten of 15 (66.7%) laboratories performing NS1 antigen assays obtained the correct results. In Module B, 18/23 (78.3%) and 20/20 (100%) of laboratories correctly detected anti-DENV IgM and IgG, respectively. Detection of acute/recent DENV infection by both molecular (RT-PCR) and serological methods (IgM) was available in 19/24 (79.2%) participating laboratories.

    DISCUSSION: Accurate laboratory testing is a critical component of dengue and chikungunya surveillance and control. This second round of EQA reveals good proficiency in molecular and serological diagnostics of these diseases in the Asia Pacific region. Further comprehensive diagnostic testing, including testing for Zika virus, should comprise future iterations of the EQA.

  15. Pok KY, Squires RC, Tan LK, Takasaki T, Abubakar S, Hasebe F, et al.
    Western Pac Surveill Response J, 2015 Jun 30;6(2):73-81.
    PMID: 26306220 DOI: 10.5365/WPSAR.2015.6.1.017
    Accurate laboratory testing is a critical component of dengue surveillance and control. The objective of this programme was to assess dengue diagnostic proficiency among national-level public health laboratories in the World Health Organization (WHO) Western Pacific Region.
  16. Tan LK, McLaughlin RA, Lim E, Abdul Aziz YF, Liew YM
    J Magn Reson Imaging, 2018 07;48(1):140-152.
    PMID: 29316024 DOI: 10.1002/jmri.25932
    BACKGROUND: Left ventricle (LV) structure and functions are the primary assessment performed in most clinical cardiac MRI protocols. Fully automated LV segmentation might improve the efficiency and reproducibility of clinical assessment.

    PURPOSE: To develop and validate a fully automated neural network regression-based algorithm for segmentation of the LV in cardiac MRI, with full coverage from apex to base across all cardiac phases, utilizing both short axis (SA) and long axis (LA) scans.

    STUDY TYPE: Cross-sectional survey; diagnostic accuracy.

    SUBJECTS: In all, 200 subjects with coronary artery diseases and regional wall motion abnormalities from the public 2011 Left Ventricle Segmentation Challenge (LVSC) database; 1140 subjects with a mix of normal and abnormal cardiac functions from the public Kaggle Second Annual Data Science Bowl database.

    FIELD STRENGTH/SEQUENCE: 1.5T, steady-state free precession.

    ASSESSMENT: Reference standard data generated by experienced cardiac radiologists. Quantitative measurement and comparison via Jaccard and Dice index, modified Hausdorff distance (MHD), and blood volume.

    STATISTICAL TESTS: Paired t-tests compared to previous work.

    RESULTS: Tested against the LVSC database, we obtained 0.77 ± 0.11 (Jaccard index) and 1.33 ± 0.71 mm (MHD), both metrics demonstrating statistically significant improvement (P < 0.001) compared to previous work. Tested against the Kaggle database, the signed difference in evaluated blood volume was +7.2 ± 13.0 mL and -19.8 ± 18.8 mL for the end-systolic (ES) and end-diastolic (ED) phases, respectively, with a statistically significant improvement (P < 0.001) for the ED phase.

    DATA CONCLUSION: A fully automated LV segmentation algorithm was developed and validated against a diverse set of cardiac cine MRI data sourced from multiple imaging centers and scanner types. The strong performance overall is suggestive of practical clinical utility.

    LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.

  17. Fum WKS, Md Shah MN, Raja Aman RRA, Abd Kadir KA, Wen DW, Leong S, et al.
    Phys Eng Sci Med, 2023 Dec;46(4):1535-1552.
    PMID: 37695509 DOI: 10.1007/s13246-023-01317-5
    In fluoroscopy-guided interventions (FGIs), obtaining large quantities of labelled data for deep learning (DL) can be difficult. Synthetic labelled data can serve as an alternative, generated via pseudo 2D projections of CT volumetric data. However, contrasted vessels have low visibility in simple 2D projections of contrasted CT data. To overcome this, we propose an alternative method to generate fluoroscopy-like radiographs from contrasted head CT Angiography (CTA) volumetric data. The technique involves segmentation of brain tissue, bone, and contrasted vessels from CTA volumetric data, followed by an algorithm to adjust HU values, and finally, a standard ray-based projection is applied to generate the 2D image. The resulting synthetic images were compared to clinical fluoroscopy images for perceptual similarity and subject contrast measurements. Good perceptual similarity was demonstrated on vessel-enhanced synthetic images as compared to the clinical fluoroscopic images. Statistical tests of equivalence show that enhanced synthetic and clinical images have statistically equivalent mean subject contrast within 25% bounds. Furthermore, validation experiments confirmed that the proposed method for generating synthetic images improved the performance of DL models in certain regression tasks, such as localizing anatomical landmarks in clinical fluoroscopy images. Through enhanced pseudo 2D projection of CTA volume data, synthetic images with similar features to real clinical fluoroscopic images can be generated. The use of synthetic images as an alternative source for DL datasets represents a potential solution to the application of DL in FGIs procedures.
  18. Thong KL, Tan LK, Ooi PT
    J Sci Food Agric, 2018 Jan;98(1):87-95.
    PMID: 28542807 DOI: 10.1002/jsfa.8442
    BACKGROUND: The objectives of the present study were to determine the antimicrobial resistance, virulotypes and genetic diversity of Yersinia enterocolitica isolated from uncooked porcine food and live pigs in Malaysia.

    RESULTS: Thirty-two non-repeat Y. enterocolitica strains of three bioserotypes (3 variant/O:3, n = 27; 1B/O:8, n = 3; 1A/O:5, n = 2) were analysed. Approximately 90% of strains were multidrug-resistant with a multiple antibiotic resistance index < 0.2 and the majority of the strains were resistant to nalidixic acid, clindamycin, ampicillin, ticarcillin, tetracycline and amoxicillin. Yersinia enterocolitica could be distinguished distinctly into three clusters by pulsed-field gel electrophoresis, with each belonging to a particular bioserotype. Strains of 3 variant/O:3 were more heterogeneous than others. Eleven of the 15 virulence genes tested (hreP, virF, rfbC, myfA, sat, inv, ail, ymoA, ystA, tccC, yadA) and pYV virulence plasmid were present in all the bioserotpe 3 variant/03 strains.

    CONCLUSION: The occurrence of virulent strains of Y. enterocolitica in pigs and porcine products reiterated that pigs are important reservoirs for Y. enterocolitica. The increasing trend of multidrug resistant strains is a public health concern. This is the first report on the occurrence of potential pathogenic and resistant strains of Y. enterocolitica in pigs in Malaysia. © 2017 Society of Chemical Industry.

  19. Sidek S, Ramli N, Rahmat K, Ramli NM, Abdulrahman F, Tan LK
    Eur J Radiol, 2014 Aug;83(8):1437-41.
    PMID: 24908588 DOI: 10.1016/j.ejrad.2014.05.014
    To evaluate whether MR diffusion tensor imaging (DTI) of the optic nerve and optic radiation in glaucoma patients provides parameters to discriminate between mild and severe glaucoma and to determine whether DTI derived indices correlate with retinal nerve fibre layer (RNFL) thickness.
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