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Fulltext CO2/CH4 Separation in Amino Acid Ionic Liquids, Polymerized Ionic Liquids, and Mixed Matrix Membranes

Selvaraj G, Wilfred CD

Molecules, 2024 Mar 19;29(6).
PMID: 38542993 DOI: 10.3390/molecules29061357

The ability to efficiently separate CO2 from other light gases using membrane technology has received a great deal of attention due to its importance in applications such as improving the efficiency of natural gas and reducing greenhouse gas emissions. A wide range of materials has been employed for the fabrication of membranes. This paper highlights the work carried out to develop novel advanced membranes with improved separation performance. We integrated a polymerizable and amino acid ionic liquid (AAIL) with zeolite to fabricate mixed matrix membranes (MMMs). The MMMs were prepared with (vinylbenzyl)trimethylammonium chloride [VBTMA][Cl] and (vinylbenzyl)trimethylammonium glycine [VBTMA][Gly] as the polymeric support with 5 wt% zeolite particles, and varying concentrations of 1-butyl-3-methylimidazolium glycine, [BMIM][Gly] (5-20 wt%) blended together. The membranes were fabricated through photopolymerization. The extent of polymerization was confirmed using FTIR. FESEM confirmed the membranes formed are dense in structure. The thermal properties of the membranes were measured using TGA and DSC. CO2 and CH4 permeation was studied at room temperature and with a feed side pressure of 2 bar. [VBTMA][Gly]-based membranes recorded higher CO2 permeability and CO2/CH4 selectivity compared to [VBTMA][Cl]-based membranes due to the facilitated transport of CO2. The best performing membrane Gly-Gly-20 recorded permeance of 4.17 GPU and ideal selectivity of 5.49.
Fulltext An Asian validation of the TIMI risk score for ST-segment elevation myocardial infarction

Selvarajah S, Fong AY, Selvaraj G, Haniff J, Uiterwaal CS, Bots ML

PLoS One, 2012;7(7):e40249.
PMID: 22815733 DOI: 10.1371/journal.pone.0040249

Risk stratification in ST-elevation myocardial infarction (STEMI) is important, such that the most resource intensive strategy is used to achieve the greatest clinical benefit. This is essential in developing countries with wide variation in health care facilities, scarce resources and increasing burden of cardiovascular diseases. This study sought to validate the Thrombolysis In Myocardial Infarction (TIMI) risk score for STEMI in a multi-ethnic developing country.
Impact of cardiac care variation on ST-elevation myocardial infarction outcomes in Malaysia

Selvarajah S, Fong AY, Selvaraj G, Haniff J, Hairi NN, Bulgiba A, et al.

Am J Cardiol, 2013 May 1;111(9):1270-6.
PMID: 23415636 DOI: 10.1016/j.amjcard.2013.01.271

Developing countries face challenges in providing the best reperfusion strategy for patients with ST-segment elevation myocardial infarction because of limited resources. This causes wide variation in the provision of cardiac care. The aim of this study was to assess the impact of variation in cardiac care provision and reperfusion strategies on patient outcomes in Malaysia. Data from a prospective national registry of acute coronary syndromes were used. Thirty-day all-cause mortality in 4,562 patients with ST-segment elevation myocardial infarctions was assessed by (1) cardiac care provision (specialist vs nonspecialist centers), and (2) primary reperfusion therapy (thrombolysis or primary percutaneous coronary intervention [P-PCI]). All patients were risk adjusted by Thrombolysis In Myocardial Infarction (TIMI) risk score. Thrombolytic therapy was administered to 75% of patients with ST-segment elevation myocardial infarctions (12% prehospital and 63% in-hospital fibrinolytics), 7.6% underwent P-PCI, and the remainder received conservative management. In-hospital acute reperfusion therapy was administered to 68% and 73% of patients at specialist and nonspecialist cardiac care facilities, respectively. Timely reperfusion was low, at 24% versus 31%, respectively, for in-hospital fibrinolysis and 28% for P-PCI. Specialist centers had statistically significantly higher use of evidence-based treatments. The adjusted 30-day mortality rates for in-hospital fibrinolytics and P-PCI were 7% (95% confidence interval 5% to 9%) and 7% (95% confidence interval 3% to 11%), respectively (p = 0.75). In conclusion, variation in cardiac care provision and reperfusion strategy did not adversely affect patient outcomes. However, to further improve cardiac care, increased use of evidence-based resources, improvement in the quality of P-PCI care, and reduction in door-to-reperfusion times should be achieved.
Fulltext Short- and long-term mortality prediction after an acute ST-elevation myocardial infarction (STEMI) in Asians: A machine learning approach

Aziz F, Malek S, Ibrahim KS, Raja Shariff RE, Wan Ahmad WA, Ali RM, et al.

PLoS One, 2021;16(8):e0254894.
PMID: 34339432 DOI: 10.1371/journal.pone.0254894

BACKGROUND: Conventional risk score for predicting short and long-term mortality following an ST-segment elevation myocardial infarction (STEMI) is often not population specific.
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.