METHODS: We imputed the classical HLA alleles, amino acids, and haplotypes using the Immunochip genotyping data of 1260 RA cases (i.e., 530 Malays, 259 Chinese, 412 Indians, and 59 mixed ethnicities) and 1571 controls (i.e., 981 Malays, 205 Chinese, 297 Indians, and 87 mixed ethnicities) from the Malaysian Epidemiological Investigation of Rheumatoid Arthritis (MyEIRA) population-based case-control study. Stepwise logistic regression was performed to identify the independent genetic risk factors for RA within the HLA region.
RESULTS: We confirmed that the HLA-DRB1 amino acid at position 11 with valine residue conferred the strongest risk effect for ACPA-positive RA (OR = 4.26, 95% CI = 3.30-5.49, PGWAS = 7.22 × 10-29) in the Malays. Our study also revealed that HLA-DRB1 amino acid at position 96 with histidine residue was negatively associated with the risk of developing ACPA-positive RA in the Indians (OR = 0.48, 95% CI = 0.37-0.62, PGWAS = 2.58 × 10-08). Interestingly, we observed that HLA-DQB1*03:02 allele was inversely related to the risk of developing ACPA-positive RA in the Malays (OR = 0.17, 95% CI = 0.09-0.30, PGWAS = 1.60 × 10-09). No association was observed between the HLA variants and risk of developing ACPA-negative RA in any of the three major ethnic groups in Malaysia.
CONCLUSIONS: Our results demonstrate that the RA-associated genetic factors in the multi-ethnic Malaysian population are similar to those in the Caucasian population, despite significant differences in the genetic architecture of HLA region across populations. A novel and distinct independent association between the HLA-DQB1*03:02 allele and ACPA-positive RA was observed in the Malays. In common with the Caucasian population, there is little risk from HLA region for ACPA-negative RA.
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.
MATERIALS AND METHODS: Sixteen children with GDD underwent magnetic resonance imaging (MRI) and cross-sectional DTI. Formal developmental assessment of all GDD patients was performed using the Mullen Scales of Early Learning. An automated processing pipeline for the WMT assessment was implemented. The DTI-derived metrics of the children with GDD were compared to healthy children with normal development (ND).
RESULTS: Only two out of the 17 WMT demonstrated significant differences (p<0.05) in DTI parameters between the GDD and ND group. In the uncinate fasciculus (UF), the GDD group had lower mean values for fractional anisotropy (FA; 0.40 versus 0.44), higher values for mean diffusivity (0.96 versus 0.91×10-3 mm2/s) and radial diffusivity (0.75 versus 0.68×10-3 mm2/s) compared to the ND group. In the superior cerebellar peduncle (SCP), mean FA values were lower for the GDD group (0.38 versus 0.40). Normal myelination pattern of DTI parameters was deviated against age for GDD group for UF and SCP.
CONCLUSION: The UF and SCP WMT showed microstructural changes suggestive of compromised white matter maturation in children with GDD. The DTI metrics have potential as imaging markers for inadequate white matter maturation in GDD children.
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.
METHODS: Teaching and Learning (T&L) activities were conducted virtually on e-learning platforms. The students' experience and feedback were evaluated after 15 weeks.
RESULTS: We found that while students preferred face-to-face, physical teaching, they were able to adapt to the new norm of e-learning. More than 60% of the students agreed that pre-recorded lectures and viewing videos of practical sessions, plus answering short questions, were beneficial. Certain aspects, such as hands-on practical and clinical experience, could never be replaced. The e-learning and study-from-home environment accorded a lot of flexibility. However, students also found it challenging to focus because of distractions, lack of engagement and mental stress. Technical problems, such as poor Internet connectivity and limited data plans, also compounded the problem.
CONCLUSION: We expect e-learning to prevail in future. Hybrid learning strategies, which includes face-to-face classes and e-learning, will become common, at least in the medical physics programme of the University of Malaya even after the pandemic.
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.
SETTING: A single centre study, Malaysia.
PARTICIPANTS: Adults aged between 18 and 60 years with mTBI as a result of road traffic accident, with no previous history of head trauma, minimum of 9 years education and abnormal cognition at 3 months will be included. The exclusion criteria include pre-existing chronic illness or neurological/psychiatric condition, long-term medication that affects cognitive/psychological status, clinical evidence of substance intoxication at the time of injury and major polytrauma. Based on multiple estimated calculations, the minimum intended sample size is 50 participants (Cohen's d effect size=0.35; alpha level of 0.05; 85% power to detect statistical significance; 40% attrition rate).
INTERVENTIONS: Intervention group will receive individualised structured cognitive rehabilitation. Control group will receive the best patient-centred care for attention disorders. Therapy frequency for both groups will be 1 hour per week for 12 weeks.
OUTCOME MEASURES: Primary: Neuropsychological Assessment Battery-Screening Module (S-NAB) scores. Secondary: Diffusion Tensor Imaging (DTI) parameters and Goal Attainment Scaling score (GAS).
RESULTS: Results will include descriptive statistics of population demographics, CogniPlus cognitive program and metacognitive strategies. The effect of intervention will be the effect size of S-NAB scores and mean GAS T scores. DTI parameters will be compared between groups via repeated measure analysis. Correlation analysis of outcome measures will be calculated using Pearson's correlation coefficient.
CONCLUSION: This is a complex clinical intervention with multiple outcome measures to provide a comprehensive evidence-based treatment model.
ETHICS AND DISSEMINATION: The study protocol was approved by the Medical Research Ethics Committee UMMC (MREC ID NO: 2016928-4293). The findings of the trial will be disseminated through peer-reviewed journals and scientific conferences.
TRIAL REGISTRATION NUMBER: NCT03237676.
METHODS: Continuous raw PPG waveforms were blindly allocated into segments with an equal length (5s) without leveraging any pulse location information and were normalized with Z-score normalization methods. A 1-D-CNN was designed to automatically learn the intrinsic features of the PPG waveform, and perform the required classification. Several training hyperparameters (initial learning rate and gradient threshold) were varied to investigate the effect of these parameters on the performance of the network. Subsequently, this proposed network was trained and validated with 30 subjects, and then tested with eight subjects, with our local dataset. Moreover, two independent datasets downloaded from the PhysioNet MIMIC II database were used to evaluate the robustness of the proposed network.
RESULTS: A 13 layer 1-D-CNN model was designed. Within our local study dataset evaluation, the proposed network achieved a testing accuracy of 94.9%. The classification accuracy of two independent datasets also achieved satisfactory accuracy of 93.8% and 86.7% respectively. Our model achieved a comparable performance with most reported works, with the potential to show good generalization as the proposed network was evaluated with multiple cohorts (overall accuracy of 94.5%).
CONCLUSION: This paper demonstrated the feasibility and effectiveness of applying blind signal processing and deep learning techniques to PPG motion artifact detection, whereby manual feature thresholding was avoided and yet a high generalization ability was achieved.
METHODS: A 3-step framework was proposed, consisting of: (1) 3D LV model reconstruction from motion-corrected 4D cine-MRI; (2) Registration of 2D LGE-MRI with 4D cine-MRI; (3) LV contour extraction from the intersection of LGE slices with the LV model. The framework was evaluated against cardiac MRI data from 27 patients scanned within 6 months after acute myocardial infarction. We compared the use of local Pearson's correlation (LPC) and normalized mutual information (NMI) as similarity measures for the registration. The use of 2 and 6 long-axis (LA) cine-MRI scans was also compared. The accuracy of the framework was evaluated using manual segmentation, and the interobserver variability of the scar volume derived from the segmented LV was determined using Bland-Altman analysis.
RESULTS: LPC outperformed NMI as a similarity measure for the proposed framework using 6 LA scans, with Hausdorrf distance (HD) of 1.19 ± 0.53 mm versus 1.51 ± 2.01 mm (endocardial) and 1.21 ± 0.48 mm versus 1.46 ± 1.78 mm (epicardial), respectively. Segmentation using 2 LA scans was comparable to 6 LA scans with a HD of 1.23 ± 0.70 mm (endocardial) and 1.25 ± 0.74 mm (epicardial). The framework yielded a lower interobserver variability in scar volumes compared with manual segmentation.
CONCLUSION: The framework showed high accuracy and robustness in delineating LV in LGE-MRI and allowed for bidirectional mapping of information between LGE- and cine-MRI scans, crucial in personalized model studies for treatment planning.