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The mitochondrial 16 s rRNA reveals high anthropogenic influence on land snail diversity in a preliminary island survey

Abu-Bakar SB, Razali NM, Naggs F, Wade C, Mohd-Nor SA, Aileen-Tan SH

Mol Biol Rep, 2014 Mar;41(3):1799-805.
PMID: 24443224 DOI: 10.1007/s11033-014-3029-5

A total of 30 specimens belonging to five species, namely; Cryptozona siamensis, Sarika resplendens and Sarika sp. from the family Ariophantidae as well as Quantula striata and Quantula sp. from the family Dyakiidae were collected from the Langkawi Island in Northern Peninsular Malaysia. All specimens were identified through comparisons of shell morphology and amplification of a 500 bp segment of the 16S rRNA mtDNA gene. To assess phylogenetic insights, the sequences were aligned using ClustalW and phylogenetic trees were constructed. The analyses showed two major lineages in both Maximum Parsimony and Neighbour Joining phylogenetic trees. Each putative taxonomic group formed a monophyletic cluster. Our study revealed low species and intraspecies genetic diversities based on the 16S rRNA gene sequences. Thus, this study has provided an insight of land snail diversity in populations of an island highly influenced by anthropogenic activities through complementary use of shell morphological and molecular data.
Fulltext Performance and emission prediction using ANN (artificial neural network) on H2-assisted Garcinia gummi-gutta biofuel doped with nano additives

Venu H, Soudagar MEM, Kiong TS, Razali NM, Wei HR, Khan TMY, et al.

Sci Rep, 2025 Feb 18;15(1):5911.
PMID: 39966510 DOI: 10.1038/s41598-025-90165-2

The current work focuses on utilization of ANN (artificial neural network) for the prediction of performance and tailpipe emissions of Garcinia gummigutta methyl ester (GGME) enriched with H2 and TiO2 nano additives. For experimentation, H2 gas was introduced to the mixes containing TiO2 nanoparticles. Diesel, B10 blend (10% GGME biofuel + 90% Diesel), B20 (20% GGME biofuel + 80% Diesel), Diesel-TiO2 (Mineral Diesel with 100 ppm TiO2 nano additives), B10-H2-TiO2 (B10 blend with 100 ppm nano additives + 5 L/min of H2) and B20-H2-TiO2 (B20 blend with 100 ppm nanoparticles + 5 L/min of H2) were considered for experimentation. A constant mass flow rate of 10 L/min was used for the hydrogen flow throughout the test procedures. Test results were carefully analyzed to determine the performance and emission measures. Different speeds between 1800 and 2800 rpm were used for each test. When combined with pure Diesel and mixtures of biodiesel, these nanoparticles and hydrogen enhanced the performance data. For instance, the brake-specific fuel consumption was reduced but the power, torque, and thermal efficiency were increased. Although there was a modest rise in NO emissions, the primary goal of lowering CO, CO2, and other UHC emissions was met. The ANN models confirm and agreed the Diesel engine experimental work possesses minimal root mean square error (RMSE) and correlation coefficient values were estimated. This ideal model predicts and optimizes the engine output at a higher accuracy level, which gives better results compared with other empirical and theoretical models.
Fulltext Nanotechnology and LSTM machine learning algorithms in advanced fuel spray dynamics in CI engines with different bowl geometries

Venu H, Soudagar MEM, Kiong TS, Razali NM, Wei HR, Rajabi A, et al.

Sci Rep, 2025 Jan 06;15(1):983.
PMID: 39762341 DOI: 10.1038/s41598-024-83211-y

This study explores the integration of nanotechnology and Long Short-Term Memory (LSTM) machine learning algorithms to enhance the understanding and optimization of fuel spray dynamics in compression ignition (CI) engines with varying bowl geometries. The incorporation of nanotechnology, through the addition of nanoparticles to conventional fuels, improves fuel atomization, combustion efficiency, and emission control. Simultaneously, LSTM models are employed to analyze and predict the complex spray behavior under diverse operational and geometric conditions. Key parameters, including spray penetration, droplet size distribution, and evaporation rates, are modeled and validated against experimental data. The findings reveal that nanoparticle-enhanced fuels, coupled with LSTM-based predictive analytics, lead to superior combustion performance and lower pollutant formation. This interdisciplinary approach provides a robust framework for designing next-generation CI engines with improved efficiency and sustainability. Diesel engine performance and emissions were found to be influenced by variations in combustion chamber geometry, underwent validation through simulation using Diesel-RK. Re-entrant bowl profile in quaternary blend is found to exhibit 31.3% higher BTE and 8.65% lowered BSFC than the conventional HCC bowl at full load condition. Emission wise, re-entrant bowl induced 90.16% lowered CO, 59.95% lowered HC and 15.48% lowered smoke owing to improved spray penetration and faster burning of soot precursors. However, the NOx emissions of DBOPN-TRCC were found to be higher. The simulation outcomes, derived from Diesel-RK, were subsequently compared with empirical data obtained from real-world experiments. These experiments were systematically carried out under identical operating conditions, employing different piston bowl geometries.
Personalised management of Chronic Obstructive Pulmonary Disease (COPD): Malaysian consensus algorithm for appropriate use of inhaled corticosteroid (ICS) in COPD patients

Marzuki NM, Jaeb MZM, Ban A, Ismail AI, Ali IAH, Razali NM, et al.

Med J Malaysia, 2020 11;75(6):717-721.
PMID: 33219183

BACKGROUND: Regarding the long-term safety issues with the use of inhaled corticosteroids (ICS) and the clinical predominance of dual bronchodilators in enhancing treatment outcomes in chronic obstructive pulmonary disease (COPD), ICS is no longer a "preferred therapy" according to the Global Initiative for Chronic Obstructive Lung Disease except on top of a dual bronchodilator. This has necessitated a change in the current therapy for many COPD patients.
OBJECTIVE: To determine a standardised algorithm to reassess and personalise the treatment COPD patients based on the available evidence.
METHODS: A consensus statement was agreed upon by a panel of pulmonologists in from 11 institutes in Malaysia whose members formed this consensus group.
RESULTS: According to the consensus, which was unanimously adopted, all COPD patients who are currently receiving an ICS-based treatment should be reassessed based on the presence of co-existence of asthma or high eosinophil counts and frequency of moderate or severe exacerbations in the previous 12 months. When that the patients meet any of the aforementioned criteria, then the patient can continue taking ICS-based therapy. However, if the patients do not meet the criteria, then the treatment of patients need to be personalised based on whether the patient is currently receiving long-acting beta-agonists (LABA)/ICS or triple therapy.
CONCLUSION: A flowchart of the consensus providing a guidance to Malaysian clinicians was elucidated based on evidences and international guidelines that identifies the right patients who should receive inhaled corticosteroids and enable to switch non ICS based therapies in patients less likely to benefit from such treatments.