RESULTS: Thus, this study presents comprehensive robustness evaluations of seven widely used pathway activity inference methods using six cancer datasets based on two assessments. The first assessment seeks to investigate the robustness of pathway activity in pathway activity inference methods, while the second assessment aims to assess the robustness of risk-active pathways and genes predicted by these methods. The mean reproducibility power and total number of identified informative pathways and genes were evaluated. Based on the first assessment, the mean reproducibility power of pathway activity inference methods generally decreased as the number of pathway selections increased. Entropy-based Directed Random Walk (e-DRW) distinctly outperformed other methods in exhibiting the greatest reproducibility power across all cancer datasets. On the other hand, the second assessment shows that no methods provide satisfactory results across datasets.
CONCLUSION: However, PTB methods generally appear to perform better in producing greater reproducibility power and identifying potential cancer markers compared to non-TB methods.
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
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.
METHODS: We surveyed HFrEF patients from two hospitals in Malaysia, using Malay, English or Chinese versions of EQ-5D-5L. EQ-5D-5L dimensional scores were converted to utility scores using the Malaysian value set. A confirmatory factor analysis longitudinal model was constructed. The utility and visual analog scale (VAS) scores were evaluated for validity (convergent, known-group, responsiveness), and measurement equivalence of the three language versions.
RESULTS: 200 HFrEF patients (mean age = 61 years), predominantly male (74%) of Malay ethnicity (55%), completed the admission and discharge EQ-5D-5L questionnaire in Malay (49%), English (26%) or Chinese (25%) languages. 173 patients (86.5%) were followed up at 1-month post-discharge (1MPD). The standardized factor loadings and average variance extracted were ≥ 0.5 while composite reliability was ≥ 0.7, suggesting convergent validity. Patients with older age and higher New York Heart Association (NYHA) class reported significantly lower utility and VAS scores. The change in utility and VAS scores between admission and discharge was large, while the change between discharge and 1MPD was minimal. The minimal clinically important difference for utility and VAS scores was ±0.19 and ±11.01, respectively. Malay and English questionnaire were equivalent while the equivalence of Malay and Chinese questionnaire was inconclusive.
LIMITATION: This study only sampled HFrEF patients from two teaching hospitals, thus limiting the generalizability of results to the entire heart failure population.
CONCLUSION: EQ-5D-5L is a valid questionnaire to measure health-related quality of life and estimate utility values among HFrEF patients in Malaysia. The Malay and English versions of EQ-5D-5L appear equivalent for clinical and economic assessments.
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.
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.
OBJECTIVE: To derive a single algorithm using deep learning and machine learning for the prediction and identification of factors associated with in-hospital mortality in Asian patients with ACS and to compare performance to a conventional risk score.
METHODS: The Malaysian National Cardiovascular Disease Database (NCVD) registry, is a multi-ethnic, heterogeneous database spanning from 2006-2017. It was used for in-hospital mortality model development with 54 variables considered for patients with STEMI and Non-STEMI (NSTEMI). Mortality prediction was analyzed using feature selection methods with machine learning algorithms. Deep learning algorithm using features selected from machine learning was compared to Thrombolysis in Myocardial Infarction (TIMI) score.
RESULTS: A total of 68528 patients were included in the analysis. Deep learning models constructed using all features and selected features from machine learning resulted in higher performance than machine learning and TIMI risk score (p < 0.0001 for all). The best model in this study is the combination of features selected from the SVM algorithm with a deep learning classifier. The DL (SVM selected var) algorithm demonstrated the highest predictive performance with the least number of predictors (14 predictors) for in-hospital prediction of STEMI patients (AUC = 0.96, 95% CI: 0.95-0.96). In NSTEMI in-hospital prediction, DL (RF selected var) (AUC = 0.96, 95% CI: 0.95-0.96, reported slightly higher AUC compared to DL (SVM selected var) (AUC = 0.95, 95% CI: 0.94-0.95). There was no significant difference between DL (SVM selected var) algorithm and DL (RF selected var) algorithm (p = 0.5). When compared to the DL (SVM selected var) model, the TIMI score underestimates patients' risk of mortality. TIMI risk score correctly identified 13.08% of the high-risk patient's non-survival vs 24.7% for the DL model and 4.65% vs 19.7% of the high-risk patient's non-survival for NSTEMI. Age, heart rate, Killip class, cardiac catheterization, oral hypoglycemia use and antiarrhythmic agent were found to be common predictors of in-hospital mortality across all ML feature selection models in this study. The final algorithm was converted into an online tool with a database for continuous data archiving for prospective validation.
CONCLUSIONS: ACS patients were better classified using a combination of machine learning and deep learning in a multi-ethnic Asian population when compared to TIMI scoring. Machine learning enables the identification of distinct factors in individual Asian populations to improve mortality prediction. Continuous testing and validation will allow for better risk stratification in the future, potentially altering management and outcomes.