METHODS: We examined 81 common treeshrews from 14 areas in nine states and the Federal Territory of Peninsular Malaysia for filarial parasites. Once any filariae that were found had been isolated, we examined their morphological characteristics and determined the partial sequences of their mitochondrial cytochrome c oxidase subunit 1 (cox1) and 12S rRNA genes. Polymerase chain reaction (PCR) products of the internal transcribed spacer 1 (ITS1) region were then cloned into the pGEM-T vector, and the recombinant plasmids were used as templates for sequencing.
RESULTS: Malayfilaria sofiani Uni, Mat Udin & Takaoka, n. g., n. sp. is described based on the morphological characteristics of adults and microfilariae found in common treeshrews from Jeram Pasu, Kelantan, Malaysia. The Kimura 2-parameter distance between the cox1 gene sequences of the new species and W. bancrofti was 11.8%. Based on the three gene sequences, the new species forms a monophyletic clade with W. bancrofti and Brugia spp. The adult parasites were found in tissues surrounding the lymph nodes of the neck of common treeshrews.
CONCLUSIONS: The newly described species appears most closely related to Wuchereria spp. and Brugia spp., but differs from these in several morphological characteristics. Molecular analyses based on the cox1 and 12S rRNA genes and the ITS1 region indicated that this species differs from both W. bancrofti and Brugia spp. at the genus level. We thus propose a new genus, Malayfilaria, along with the new species M. sofiani.
METHODS: Seven hundered fifty five individuals from specialist clinics and community health screenings with LDL-c level ≥ 4.0 mmol/L were selected and diagnosed as FH using the DLCC, the SB Register criteria, the US MEDPED and the JFHMC. The sensitivity, specificity, efficiency, positive and negative predictive values of individuals screened with the SB register criteria, US MEDPED and JFHMC were assessed against the DLCC.
RESULTS: We found the SB register criteria identified more individuals with FH compared to the US MEDPED and the JFHMC (212 vs. 105 vs. 195; p
OBJECTIVE: The aim was to understand the local prevalence and factors associated with returning to work in Malaysia after a cardiac event.
METHODS: A cross sectional design was used. All patients attending the cardiac rehabilitation program after major cardiac event during an 11-months period (2011-2012) were included. Data relating to socio-demographic, work-related, risk factors and acute myocardial infarction were collected. The SF-36 questionnaire was used to assess quality of life. Regression analysis was used to determine the predicting factors to return to work.
RESULTS: A total of 398 files were screened, 112 respondents agreed to participate giving a response rate of 47.3%. The prevalence of returned to work (RTW) was 66.1% [95% CI: 57.2-75.0]. Factors associated with work resumption were age (Adj. OR: 0.92 (95% CI: 0.84-0.99), diabetes mellitus (Adj. OR: 3.70, 95% CI: 1.35-10.12), Mental Component Summary (MCS) score (Adj. OR: 1.05 (95% CI: 1.01-1.09) and baseline angiography findings. Patients with single vessel and two vessel disease were 8.9 times and 3.78 times more likely to return to work compared to those with 3 vessels (Adj. OR: 8.90 (95% CI: 2.29-34.64) and Adj. OR: 3.78, (95% CI: 1.12, 12.74).
CONCLUSIONS: We proposed a cardiac rehabilitation program to emphasize mental health as it may improve successful return to work after cardiac event.
Methods: Between May 2014 to April 2017, 268 patients (88% male, mean age 60.1 ± 10.8 years) with 291 coronary lesions were treated with AES. The primary endpoint was major adverse cardiac events (MACE) ie a composite of cardiovascular mortality, myocardial infarction (MI) and target lesion revascularization (TLR) at 12-month follow-up.
Results: The majority of patients presented with acute coronary syndrome (75%) and 75% had multi-vessel disease on angiography. Diabetes mellitus was present in 123 patients (46%). The most common target vessel for PCI was left anterior descending artery (43%) followed by right coronary artery (36%), left circumflex (10%) and left main (6%).The majority of lesions were type B-C (85%) by ACC/AHA lesion classification. An average of 1.25 ± 0.5 AES were used per patient, with mean AES diameter of 3.1 ± 0.4 mm and average total length of 34.8 ± 19.4 mm.At 12-month follow-up, 4% of patients developed MACE. MACE was mainly driven by cardiovascular mortality (1.5%), MI (2%) and TLR (1.5%). The rate of stent thrombosis was 1.5%.
Conclusion: In a contemporary all-comers South-East Asian registry with high rate of diabetes mellitus, AES was found to be efficacious with a low incidence of MACE observed at 12-month follow-up.
OBJECTIVE: Apply machine learning for the prediction and identification of factors associated with short and long-term mortality in Asian STEMI patients and compare with a conventional risk score.
METHODS: The National Cardiovascular Disease Database for Malaysia registry, of a multi-ethnic, heterogeneous Asian population was used for in-hospital (6299 patients), 30-days (3130 patients), and 1-year (2939 patients) model development. 50 variables were considered. Mortality prediction was analysed using feature selection methods with machine learning algorithms and compared to Thrombolysis in Myocardial Infarction (TIMI) score. Invasive management of varying degrees was selected as important variables that improved mortality prediction.
RESULTS: Model performance using a complete and reduced variable produced an area under the receiver operating characteristic curve (AUC) from 0.73 to 0.90. The best machine learning model for in-hospital, 30 days, and 1-year outperformed TIMI risk score (AUC = 0.88, 95% CI: 0.846-0.910; vs AUC = 0.81, 95% CI:0.772-0.845, AUC = 0.90, 95% CI: 0.870-0.935; vs AUC = 0.80, 95% CI: 0.746-0.838, AUC = 0.84, 95% CI: 0.798-0.872; vs AUC = 0.76, 95% CI: 0.715-0.802, p < 0.0001 for all). TIMI score underestimates patients' risk of mortality. 90% of non-survival patients are classified as high risk (>50%) by machine learning algorithm compared to 10-30% non-survival patients by TIMI. Common predictors identified for short- and long-term mortality were age, heart rate, Killip class, fasting blood glucose, prior primary PCI or pharmaco-invasive therapy and diuretics. The final algorithm was converted into an online tool with a database for continuous data archiving for algorithm validation.
CONCLUSIONS: In a multi-ethnic population, patients with STEMI were better classified using the machine learning method compared to TIMI scoring. Machine learning allows for the identification of distinct factors in individual Asian populations for better mortality prediction. Ongoing continuous testing and validation will allow for better risk stratification and potentially alter management and outcomes in the future.