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Fulltext Three Level Thoracolumbar Spondylectomy for Recurrent Giant Cell Tumour of the Spine: A Case Report

Faruk NA, Mohd-Amin MZ, Awang-Ojep DN, Teo YY, Wong CC

Malays Orthop J, 2018 Nov;12(3):50-52.
PMID: 30555648 MyJurnal DOI: 10.5704/MOJ.1811.013

Giant cell tumour (GCT) is a benign tumour but can be locally aggressive and with the potential to metastasise especially to the lungs. Successful treatments have been reported for long bone lesions; however, optimal surgical and medical treatment for spinal and sacral lesions are not well established. In treating spinal GCTs, the aim is to achieve complete tumour excision, restore spinal stability and decompress the neural tissues. The ideal surgical procedure is an en bloc spondylectomy or vertebrectomy, where all tumour cells are removed as recurrence is closely related to the extent of initial surgical excision. However, such a surgery has a high complication rate, such as dura tear and massive blood loss. We report a patient with a missed pathological fracture of T12 treated initially with a posterior subtraction osteotomy, who had recurrence three years after the index surgery and subsequently underwent a three level vertebrectomy and posterior spinal fusion.
Fulltext DeepMoney: counterfeit money detection using generative adversarial networks

Ali T, Jan S, Alkhodre A, Nauman M, Amin M, Siddiqui MS

PeerJ Comput Sci, 2019;5:e216.
PMID: 33816869 DOI: 10.7717/peerj-cs.216

Conventional paper currency and modern electronic currency are two important modes of transactions. In several parts of the world, conventional methodology has clear precedence over its electronic counterpart. However, the identification of forged currency paper notes is now becoming an increasingly crucial problem because of the new and improved tactics employed by counterfeiters. In this paper, a machine assisted system-dubbed DeepMoney-is proposed which has been developed to discriminate fake notes from genuine ones. For this purpose, state-of-the-art models of machine learning called Generative Adversarial Networks (GANs) are employed. GANs use unsupervised learning to train a model that can then be used to perform supervised predictions. This flexibility provides the best of both worlds by allowing unlabelled data to be trained on whilst still making concrete predictions. This technique was applied to Pakistani banknotes. State-of-the-art image processing and feature recognition techniques were used to design the overall approach of a valid input. Augmented samples of images were used in the experiments which show that a high-precision machine can be developed to recognize genuine paper money. An accuracy of 80% has been achieved. The code is available as an open source to allow others to reproduce and build upon the efforts already made.
Fulltext Screening of commercial meat products from supermarket chains for feline derivatives using SP-PCR-RLFP and lab-on-a-chip

Al Amin M, Mahfujur Rahman M, Razimi MSA, Chowdhury ZZ, Hussain MNM, Desa MNM

J Food Compost Anal, 2020 Sep;92:103565.
PMID: 32546895 DOI: 10.1016/j.jfca.2020.103565

Determination of feline meat in food products is an important issue for social, health, economic and religious concern. Hence this paper documented the application of species specific polymerase chain reaction-restriction fragment length polymorphism (SP-PCR-RFLP) assay targeting a short-fragments (69 bp) of mitochondrial cytochrome b (cytb) gene to screen feline meat in commercial meat products using lab-on-a-chip. The SP-PCR assay proved its specificity theoretically and experimentally while testing with different common animal, aquatic and plant species of DNA. The feline specific (69 bp, 43- and 26-bp) characteristic molecular DNA pattern was observed by SP-PCR and RFLP analysis. For assay performance, it was tested in three different types of commercial dummy meat products such as frankfurters, nuggets and meatballs and digested with AluI-restriction enzyme. The highest sensitivity of the assay using lab-on-a-chip was as low as 0.1 pg or 0.01 % (w/w) in commercial dummy meat products. We have also applied this assay to screen three important commercial meat products of six different brand from six supermarket chains located at three different states of Malaysia. Thus total 378 samples were tested to validate the specificity, sensitivity, stability of the assay and utilization of it for commercial meat product screening.
Fulltext Potential Arabidopsis thaliana glucosinolate genes identified from the co-expression modules using graph clustering approach

Harun S, Afiqah-Aleng N, Karim MB, Altaf Ul Amin M, Kanaya S, Mohamed-Hussein ZA

PeerJ, 2021;9:e11876.
PMID: 34430080 DOI: 10.7717/peerj.11876

Background: Glucosinolates (GSLs) are plant secondary metabolites that contain nitrogen-containing compounds. They are important in the plant defense system and known to provide protection against cancer in humans. Currently, increasing the amount of data generated from various omics technologies serves as a hotspot for new gene discovery. However, sometimes sequence similarity searching approach is not sufficiently effective to find these genes; hence, we adapted a network clustering approach to search for potential GSLs genes from the Arabidopsis thaliana co-expression dataset.
Methods: We used known GSL genes to construct a comprehensive GSL co-expression network. This network was analyzed with the DPClusOST algorithm using a density of 0.5. 0.6. 0.7, 0.8, and 0.9. Generating clusters were evaluated using Fisher's exact test to identify GSL gene co-expression clusters. A significance score (SScore) was calculated for each gene based on the generated p-value of Fisher's exact test. SScore was used to perform a receiver operating characteristic (ROC) study to classify possible GSL genes using the ROCR package. ROCR was used in determining the AUC that measured the suitable density value of the cluster for further analysis. Finally, pathway enrichment analysis was conducted using ClueGO to identify significant pathways associated with the GSL clusters.
Results: The density value of 0.8 showed the highest area under the curve (AUC) leading to the selection of thirteen potential GSL genes from the top six significant clusters that include IMDH3, MVP1, T19K24.17, MRSA2, SIR, ASP4, MTO1, At1g21440, HMT3, At3g47420, PS1, SAL1, and At3g14220. A total of Four potential genes (MTO1, SIR, SAL1, and IMDH3) were identified from the pathway enrichment analysis on the significant clusters. These genes are directly related to GSL-associated pathways such as sulfur metabolism and valine, leucine, and isoleucine biosynthesis. This approach demonstrates the ability of the network clustering approach in identifying potential GSL genes which cannot be found from the standard similarity search.
Designing novel bioconjugates of hydroxyethyl cellulose and salicylates for potential pharmaceutical and pharmacological applications

Abbas K, Amin M, Hussain MA, Sher M, Bukhari SNA, Jantan I, et al.

Int J Biol Macromol, 2017 Oct;103:441-450.
PMID: 28526350 DOI: 10.1016/j.ijbiomac.2017.05.061

This deals with fabrication of macromolecular prodrugs (MPDs) of salicylic acid (SA) and aspirin (ASP) based on a hydrophilic cellulose ether, hydroxyethyl cellulose (HEC). Degrees of substitution (DS) of SA and ASP per HEC repeating unit (HEC-RU) were achieved ranging from 0.60 to 2.18 and 0.53 to1.50, respectively. The amphiphilic HEC-SA conjugate 2 assembled into nanowire-like structures, while HEC-ASP conjugate 6 formed nanoparticles (diameter 300-00nm) at a water/DMSO interface. After oral administration in rabbit models, conjugates 2 and 6 showed plasma half-life of 6.96 and 7.01h with maximum plasma concentration (Cmax) of 15.27 and 23.01μg L-1, respectively, and each reached peak plasma concentration (tmax) at 4.0h. Immunomodulatory assays (interleukin 6 and tumor necrosis factor-α values) revealed that anti-inflammatory properties of SA and ASP were unaltered in conjugates. Swelling inhibition of 61 and 71% was observed for conjugates 2 and 6, respectively, in a carrageenan induced paw edema test. Cytotoxic profiling (MTT assay) showed that conjugates were safe for administration in the concentration range of 2-10mM up to 24h. Thermal analyses revealed that Tdm values of SA and ASP conjugates were increased by 99 and 154̊C, respectively, indicating extraordinary thermal stability imparted to drugs after MPD formation.
Future climate change impact assessment of watershed scale hydrologic processes in Peninsular Malaysia by a regional climate model coupled with a physically-based hydrology modelo

Amin MZM, Shaaban AJ, Ercan A, Ishida K, Kavvas ML, Chen ZQ, et al.

Sci Total Environ, 2017 Jan 01;575:12-22.
PMID: 27723460 DOI: 10.1016/j.scitotenv.2016.10.009

Impacts of climate change on the hydrologic processes under future climate change conditions were assessed over Muda and Dungun watersheds of Peninsular Malaysia by means of a coupled regional climate and physically-based hydrology model utilizing an ensemble of future climate change projections. An ensemble of 15 different future climate realizations from coarse resolution global climate models' (GCMs) projections for the 21st century was dynamically downscaled to 6km resolution over Peninsular Malaysia by a regional climate model, which was then coupled with the watershed hydrology model WEHY through the atmospheric boundary layer over Muda and Dungun watersheds. Hydrologic simulations were carried out at hourly increments and at hillslope-scale in order to assess the impacts of climate change on the water balances and flooding conditions in the 21st century. The coupled regional climate and hydrology model was simulated for a duration of 90years for each of the 15 realizations. It is demonstrated that the increase in mean monthly flows due to the impact of expected climate change during 2040-2100 is statistically significant from April to May and from July to October at Muda watershed. Also, the increase in mean monthly flows is shown to be significant in November during 2030-2070 and from November to December during 2070-2100 at Dungun watershed. In other words, the impact of the expected climate change will be significant during the northeast and southwest monsoon seasons at Muda watershed and during the northeast monsoon season at Dungun watershed. Furthermore, the flood frequency analyses for both watersheds indicated an overall increasing trend in the second half of the 21st century.
Assessment of vaccination with gamma radiationattenuated infective Toxocara canis eggs on murine toxocariasis

Hafez EN, Awadallah FM, Ibrahim SA, Amin MM, El-Nawasera NZ

Trop Biomed, 2020 Mar 01;37(1):89-102.
PMID: 33612721

Toxocara canis is a major parasite that infects many animals with high risk of human infections. This study aims at assessing the immunization with gamma radiationattenuated infective stage on rats challenged with non-irradiated dose. Level of vaccine protection was evaluated in liver and lung regarding parasitological, histopathological, biochemical and molecular parameters. Fifty rats were enrolled in three groups: group A (10 rats) as normal control; group B (20 rats) subdivided into subgroup B1 (infected control) and subgroup B2 infected then challenged after 14 days with the same dose of infection (challenged infected control); and group C (20 rats) subdivided into subgroup C1 vaccinated with a dose of 800 gray (Gy) gamma-radiated infective eggs (vaccine control) and subgroup C2 vaccinated then challenged on 14th day with same number of infective eggs (vaccinated-challenged). Tissues were stained with Haematoxylin and Eosin (H and E) for histopathological studies. Biochemical studies through detection of nitric oxide (NO) and Caspase-3 were conducted. Extent of DNA damage by Comet assay was assessed. Vaccinated-challenged subgroup revealed a marked reduction in larvae in tissues with mild associated histological changes. In addition there was accompanied reduction of NO, Casepase-3 level and DNA damage compared to the control infected group. It could be concluded that vaccination of rats with a dose of 800Gy gamma radiation-attenuated infective stage improves immune response to challenge infection and drastically reduces the morbidity currently seen.
Fulltext Chemoinformatics-driven classification of Angiosperms using sulfur-containing compounds and machine learning algorithm

Abdullah-Zawawi MR, Govender N, Karim MB, Altaf-Ul-Amin M, Kanaya S, Mohamed-Hussein ZA

Plant Methods, 2022 Nov 05;18(1):118.
PMID: 36335358 DOI: 10.1186/s13007-022-00951-6

BACKGROUND: Phytochemicals or secondary metabolites are low molecular weight organic compounds with little function in plant growth and development. Nevertheless, the metabolite diversity govern not only the phenetics of an organism but may also inform the evolutionary pattern and adaptation of green plants to the changing environment. Plant chemoinformatics analyzes the chemical system of natural products using computational tools and robust mathematical algorithms. It has been a powerful approach for species-level differentiation and is widely employed for species classifications and reinforcement of previous classifications.
RESULTS: This study attempts to classify Angiosperms using plant sulfur-containing compound (SCC) or sulphated compound information. The SCC dataset of 692 plant species were collected from the comprehensive species-metabolite relationship family (KNApSAck) database. The structural similarity score of metabolite pairs under all possible combinations (plant species-metabolite) were determined and metabolite pairs with a Tanimoto coefficient value > 0.85 were selected for clustering using machine learning algorithm. Metabolite clustering showed association between the similar structural metabolite clusters and metabolite content among the plant species. Phylogenetic tree construction of Angiosperms displayed three major clades, of which, clade 1 and clade 2 represented the eudicots only, and clade 3, a mixture of both eudicots and monocots. The SCC-based construction of Angiosperm phylogeny is a subset of the existing monocot-dicot classification. The majority of eudicots present in clade 1 and 2 were represented by glucosinolate compounds. These clades with SCC may have been a mixture of ancestral species whilst the combinatorial presence of monocot-dicot in clade 3 suggests sulphated-chemical structure diversification in the event of adaptation during evolutionary change.
CONCLUSIONS: Sulphated chemoinformatics informs classification of Angiosperms via machine learning technique.
Inhibitory Activity of Extract, Fractions, and Compounds from Zingiber montanum Rhizomes on the Migration of Breast Cancer Cells

Al-Amin M, Eltayeb NM, Hossain CF, Khairuddean M, Fazalul Rahiman SS, Salhimi SM

Planta Med, 2020 Apr;86(6):387-394.
PMID: 32168546 DOI: 10.1055/a-1129-7026

Zingiber montanum rhizomes are traditionally used for the treatment of numerous human ailments. The present study was carried out to investigate the inhibitory activity of the crude extract, chromatographic fractions, and purified compounds from Z. montanum rhizomes on the migration of MDA-MB-231 cells. The effect of the extract on cell migration was investigated by a scratch assay, which showed significant inhibition in a concentration-dependent manner. Vacuum liquid chromatography on silica gel afforded four fractions (Frs. 1 - 4), which were tested on cell migration in the scratch assay. Frs. 1 and 2 showed the most significant inhibition of MDA-MB-231 cell migration. The effect of the most potent fraction (Fr. 2) was further confirmed in a transwell migration assay. The study of Frs. 1 and 2 by gelatin zymography showed significant inhibition of MMP-9 enzyme activity. Chromatographic separation of Frs. 1 and 2 afforded buddledone A (1: ), zerumbone (2: ), (2E,9E)-6-methoxy-2,9-humuradien-8-one (3: ), zerumbone epoxide (4: ), stigmasterol (5: ), and daucosterol (6: ). In a cell viability assay, compounds 1: - 4: inhibited the viability of MDA-MB-231 cells in a concentration-dependent manner. The study of buddledone A (1: ) and zerumbone epoxide (4: ) on cell migration revealed that 4: significantly inhibited the migration of MDA-MB-231 cells in both scratch and transwell migration assays. The results of the present study may lead to further molecular studies behind the inhibitory activity of zerumbone epoxide (4: ) on cell migration and support the traditional use of Z. montanum rhizomes for the treatment of cancer.
Fulltext Life cycle assessment of iron-biomass supported catalyst for Fischer Tropsch synthesis

Amin M, Shah HH, Naveed AB, Iqbal A, Gamil Y, Najeh T

Front Chem, 2024;12:1374739.
PMID: 38601886 DOI: 10.3389/fchem.2024.1374739

The iron-based biomass-supported catalyst has been used for Fischer-Tropsch synthesis (FTS). However, there is no study regarding the life cycle assessment (LCA) of biomass-supported iron catalysts published in the literature. This study discusses a biomass-supported iron catalyst's LCA for the conversion of syngas into a liquid fuel product. The waste biomass is one of the source of activated carbon (AC), and it has been used as a support for the catalyst. The FTS reactions are carried out in the fixed-bed reactor at low or high temperatures. The use of promoters in the preparation of catalysts usually enhances C5+ production. In this study, the collection of precise data from on-site laboratory conditions is of utmost importance to ensure the credibility and validity of the study's outcomes. The environmental impact assessment modeling was carried out using the OpenLCA 1.10.3 software. The LCA results reveals that the synthesis process of iron-based biomass supported catalyst yields a total impact score in terms of global warming potential (GWP) of 1.235E + 01 kg CO2 equivalent. Within this process, the AC stage contributes 52% to the overall GWP, while the preparation stage for the catalyst precursor contributes 48%. The comprehensive evaluation of the iron-based biomass supported catalyst's impact score in terms of human toxicity reveals a total score of 1.98E-02 kg 1,4-dichlorobenzene (1,4-DB) equivalent.
Fulltext Clustering analysis for classifying fake real estate listings

Mohd Amin M, Sani NS, Nasrudin MF, Abdullah S, Chhabra A, Abd Kadir F

PeerJ Comput Sci, 2024;10:e2019.
PMID: 38983188 DOI: 10.7717/peerj-cs.2019

With the rapid growth of online property rental and sale platforms, the prevalence of fake real estate listings has become a significant concern. These deceptive listings waste time and effort for buyers and sellers and pose potential risks. Therefore, developing effective methods to distinguish genuine from fake listings is crucial. Accurately identifying fake real estate listings is a critical challenge, and clustering analysis can significantly improve this process. While clustering has been widely used to detect fraud in various fields, its application in the real estate domain has been somewhat limited, primarily focused on auctions and property appraisals. This study aims to fill this gap by using clustering to classify properties into fake and genuine listings based on datasets curated by industry experts. This study developed a K-means model to group properties into clusters, clearly distinguishing between fake and genuine listings. To assure the quality of the training data, data pre-processing procedures were performed on the raw dataset. Several techniques were used to determine the optimal value for each parameter of the K-means model. The clusters are determined using the Silhouette coefficient, the Calinski-Harabasz index, and the Davies-Bouldin index. It was found that the value of cluster 2 is the best and the Camberra technique is the best method when compared to overlapping similarity and Jaccard for distance. The clustering results are assessed using two machine learning algorithms: Random Forest and Decision Tree. The observational results have shown that the optimized K-means significantly improves the accuracy of the Random Forest classification model, boosting it by an impressive 96%. Furthermore, this research demonstrates that clustering helps create a balanced dataset containing fake and genuine clusters. This balanced dataset holds promise for future investigations, particularly for deep learning models that require balanced data to perform optimally. This study presents a practical and effective way to identify fake real estate listings by harnessing the power of clustering analysis, ultimately contributing to a more trustworthy and secure real estate market.
Fulltext Prevalence of Antibiotic-Resistant Seafood-Borne Pathogens in Retail Seafood Sold in Malaysia: A Systematic Review and Meta-Analysis

Odeyemi OA, Amin M, Dewi FR, Kasan NA, Onyeaka H, Stratev D, et al.

Antibiotics (Basel), 2023 Apr 28;12(5).
PMID: 37237733 DOI: 10.3390/antibiotics12050829

The objective of this study was to examine the frequency and extent of antibiotic-resistant pathogens in seafood sold in Malaysia, using a systematic review and meta-analysis approach to analyze primary research studies. Four bibliographic databases were systematically searched for primary studies on occurrence. Meta-analysis using a random-effect model was used to understand the prevalence of antibiotic-resistant bacteria in retail seafood sold in Malaysia. A total of 1938 primary studies were initially identified, among which 13 met the inclusion criteria. In the included primary studies, a total of 2281 seafoods were analyzed for the presence of antibiotic-resistant seafood-borne pathogens. It was observed that 51% (1168/2281) of the seafood was contaminated with pathogens. Overall, the prevalence of antibiotic-resistant seafood-borne pathogens in retail seafood was 55.7% (95% CI: 0.46-0.65). Antibiotic-resistant Salmonella species had an overall prevalence of 59.9% (95% CI: 0.32-0.82) in fish, Vibrio species had an overall prevalence of 67.2% (95% CI: 0.22-0.94) in cephalopods, and MRSA had an overall prevalence of 70.9% (95% CI: 0.36-0.92) in mollusks. It could be concluded that there is a high prevalence of antibiotic-resistant seafood-borne pathogens in the retail seafood sold in Malaysia, which could be of public health importance. Therefore, there is a need for proactive steps to be taken by all stakeholders to reduce the widespread transmission of antibiotic-resistant pathogens from seafood to humans.
Fulltext Green synthesis of silver nanoparticles through reduction with Solanum xanthocarpum L. berry extract: characterization, antimicrobial and urease inhibitory activities against Helicobacter pylori

Amin M, Anwar F, Janjua MRSA, Iqbal MA, Rashid U

Int J Mol Sci, 2012;13(8):9923-9941.
PMID: 22949839 DOI: 10.3390/ijms13089923

A green synthesis route for the production of silver nanoparticles using methanol extract from Solanum xanthocarpum berry (SXE) is reported in the present investigation. Silver nanoparticles (AgNps), having a surface plasmon resonance (SPR) band centered at 406 nm, were synthesized by reacting SXE (as capping as well as reducing agent) with AgNO(3) during a 25 min process at 45 °C. The synthesized AgNps were characterized using UV-Visible spectrophotometry, powdered X-ray diffraction, and transmission electron microscopy (TEM). The results showed that the time of reaction, temperature and volume ratio of SXE to AgNO(3) could accelerate the reduction rate of Ag(+) and affect the AgNps size and shape. The nanoparticles were found to be about 10 nm in size, mono-dispersed in nature, and spherical in shape. In vitro anti-Helicobacter pylori activity of synthesized AgNps was tested against 34 clinical isolates and two reference strains of Helicobacter pylori by the agar dilution method and compared with AgNO(3) and four standard drugs, namely amoxicillin (AMX), clarithromycin (CLA), metronidazole (MNZ) and tetracycline (TET), being used in anti-H. pylori therapy. Typical AgNps sample (S1) effectively inhibited the growth of H. pylori, indicating a stronger anti-H. pylori activity than that of AgNO(3) or MNZ, being almost equally potent to TET and less potent than AMX and CLA. AgNps under study were found to be equally efficient against the antibiotic-resistant and antibiotic-susceptible strains of H. pylori. Besides, in the H. pylori urease inhibitory assay, S1 also exhibited a significant inhibition. Lineweaver-Burk plots revealed that the mechanism of inhibition was noncompetitive.
Bioactive compounds from Curcuma aeruginosa and the effect of comosone II on the migration and invasion of breast cancer cells

Al-Amin M, Eltayeb NM, Hossain CF, Rahiman SSF, Khairuddean M, Muhamad Salhimi S

J Asian Nat Prod Res, 2022 Jun 04.
PMID: 35658750 DOI: 10.1080/10286020.2022.2081562

Bioassay-guided separation afforded furanodienone 1,10-epoxide (9) as the new compound, curcolone (10) as partially described compound and ten known compounds; germacrone (1), furanodienone (2), curzerenone (3), curcumenol (4), zederone (5), comosone II (6), (1E,4E,8R)-8-hydroxygermacra-1(10),4,7(11)-trieno-12,8-lactone (7), 13-hydroxygermacrone (8), curcuzederone (11) and demethoxycurcumin (12). The study showed that germacrone, furanodienone, curzerenone, comosone II, 13-hydroxygermacrone, curcuzederone and demethoxycurcumin are the bioactive compounds of C. aeruginosa rhizomes. Comosone II significantly inhibited MDA-MB-231 cell migration and invasion through the inhibition of MMP-9 enzyme. The present study may lead to further anticancer studies of comosone II and supports the traditional uses of C. aeruginosa rhizomes.
Fulltext Addressing Internet of Things security by enhanced sine cosine metaheuristics tuned hybrid machine learning model and results interpretation based on SHAP approach

Dobrojevic M, Zivkovic M, Chhabra A, Sani NS, Bacanin N, Mohd Amin M

PeerJ Comput Sci, 2023;9:e1405.
PMID: 37409075 DOI: 10.7717/peerj-cs.1405

An ever increasing number of electronic devices integrated into the Internet of Things (IoT) generates vast amounts of data, which gets transported via network and stored for further analysis. However, besides the undisputed advantages of this technology, it also brings risks of unauthorized access and data compromise, situations where machine learning (ML) and artificial intelligence (AI) can help with detection of potential threats, intrusions and automation of the diagnostic process. The effectiveness of the applied algorithms largely depends on the previously performed optimization, i.e., predetermined values of hyperparameters and training conducted to achieve the desired result. Therefore, to address very important issue of IoT security, this article proposes an AI framework based on the simple convolutional neural network (CNN) and extreme machine learning machine (ELM) tuned by modified sine cosine algorithm (SCA). Not withstanding that many methods for addressing security issues have been developed, there is always a possibility for further improvements and proposed research tried to fill in this gap. The introduced framework was evaluated on two ToN IoT intrusion detection datasets, that consist of the network traffic data generated in Windows 7 and Windows 10 environments. The analysis of the results suggests that the proposed model achieved superior level of classification performance for the observed datasets. Additionally, besides conducting rigid statistical tests, best derived model is interpreted by SHapley Additive exPlanations (SHAP) analysis and results findings can be used by security experts to further enhance security of IoT systems.
Fulltext Characterization of size-fractionated carbonaceous particles in the small to nano-size range in Batam city, Indonesia

Amin M, Prajati G, Humairoh GP, Putri RM, Phairuang W, Hata M, et al.

Heliyon, 2023 May;9(5):e15936.
PMID: 37215863 DOI: 10.1016/j.heliyon.2023.e15936

A cascade impactor type sampler equipped with an inertial filter was used to collect size-segregated particles down to ultrafine particles (UFPs or PM0.1) on Batam Island in Sumatra, Indonesia, bordered by Singapore and Malaysia during a wet and the COVID-19 pandemic season in 2021. Carbonaceous species, including organic carbon (OC) and elemental carbon (EC), were analyzed by a thermal/optical carbon analyzer to determine the carbon species and their indices. The average UFP was 3.1 ± 0.9 μg/m3, which was 2-4 times lower than in other cities in Sumatra during the same season in the normal condition. The PMs mass concentration was largely affected by local emissions but long-range transportation of particles from Singapore and Malaysia was also not negligible. The air mass arrived at the sampling site passed the ocean, which introduced out clean air with a low level of PMs. The backward trajectory of the air mass and the largest fraction of OC2 and OC3 in all sizes was identified as being transported from the 2 above countries. OC is the dominant fraction in TC and the ratio of carbonaceous components indicated that origin of all particle sizes was predominantly vehicle emissions. UFPs were dominantly emitted from vehicles exhaust emission, while coarser particles (>10 μm) were influenced by the non-exhaust emissions, such as tire wear. Other particles (0.5-1.0; 1.0-2.5; and 2.5-10 μm) were slightly affected by biomass burning. The effective carbon ratio (ECR) and inhalation dose (ID) related EC indicated that finer particles or UFPs and PM0.5-1 contributed more to human health and global warming.
Fulltext Cucurbitacin L 2-O-β-Glucoside Demonstrates Apoptogenesis in Colon Adenocarcinoma Cells (HT-29): Involvement of Reactive Oxygen and Nitrogen Species Regulation

Abdelwahab SI, Hassan LE, Abdul Majid AM, Yagi SM, Mohan S, Elhassan Taha MM, et al.

Evid Based Complement Alternat Med, 2012;2012:490136.
PMID: 22685485 DOI: 10.1155/2012/490136

Emerging evidence suggests that reactive oxygen (ROS) and nitrogen (RNS) species can contribute to diverse signalling pathways of inflammatory and tumour cells. Cucurbitacins are a group of highly oxygenated triterpenes. Many plants used in folk medicine to treat cancer have been found to contain cucurbitacins displaying potentially important anti-inflammatory actions. The current study was designed to investigate the anti-ROS and -RNS effects of cucurbitacin L 2-O-β-glucoside (CLG) and the role of these signaling factors in the apoptogenic effects of CLG on human colon cancer cells (HT-29). This natural cucurbitacin was isolated purely from Citrullus lanatus var. citroides (Cucurbitaceae). The results revealed that CLG was cytotoxic to HT-29. CLG increased significantly (P < 0.05) RNA and protein levels of caspase-3 in HT-29 cells when verified using a colorimetric assay and realtime qPCR, respectively. The results showed that lipopolysaccharide/interferon-gamma (LPS/INF-γ) increased nitrous oxide (NO) production inR AW264.7macrophages, whereas N(G)-nitro-L-argininemethyl ester (L-NAME) and CLG curtailed it. This compound did not reveal any cytotoxicity on RAW264.7 macrophages and human normal liver cells (WRL-68) when tested using the MTT assay. Findings of ferric reducing antioxidant power (FRAP) and oxygen radical absorption capacity (ORAC) assays demonstrate the antioxidant properties of CLG. The apoptogenic property of CLG on HT-29 cells is thus related to inhibition of reactive nitrogen and oxygen reactive species and the triggering of caspase-3-regulated apoptosis.
Fulltext Development and mining of a volatile organic compound database

Abdullah AA, Altaf-Ul-Amin M, Ono N, Sato T, Sugiura T, Morita AH, et al.

Biomed Res Int, 2015;2015:139254.
PMID: 26495281 DOI: 10.1155/2015/139254

Volatile organic compounds (VOCs) are small molecules that exhibit high vapor pressure under ambient conditions and have low boiling points. Although VOCs contribute only a small proportion of the total metabolites produced by living organisms, they play an important role in chemical ecology specifically in the biological interactions between organisms and ecosystems. VOCs are also important in the health care field as they are presently used as a biomarker to detect various human diseases. Information on VOCs is scattered in the literature until now; however, there is still no available database describing VOCs and their biological activities. To attain this purpose, we have developed KNApSAcK Metabolite Ecology Database, which contains the information on the relationships between VOCs and their emitting organisms. The KNApSAcK Metabolite Ecology is also linked with the KNApSAcK Core and KNApSAcK Metabolite Activity Database to provide further information on the metabolites and their biological activities. The VOC database can be accessed online.
Fulltext Investigation of Coatings, Corrosion and Wear Characteristics of Machined Biomaterials through Hydroxyapatite Mixed-EDM Process: A Review

Al-Amin M, Abdul-Rani AM, Danish M, Rubaiee S, Mahfouz AB, Thompson HM, et al.

Materials (Basel), 2021 Jun 28;14(13).
PMID: 34203154 DOI: 10.3390/ma14133597

Together, 316L steel, magnesium-alloy, Ni-Ti, titanium-alloy, and cobalt-alloy are commonly employed biomaterials for biomedical applications due to their excellent mechanical characteristics and resistance to corrosion, even though at times they can be incompatible with the body. This is attributed to their poor biofunction, whereby they tend to release contaminants from their attenuated surfaces. Coating of the surface is therefore required to mitigate the release of contaminants. The coating of biomaterials can be achieved through either physical or chemical deposition techniques. However, a newly developed manufacturing process, known as powder mixed-electro discharge machining (PM-EDM), is enabling these biomaterials to be concurrently machined and coated. Thermoelectrical processes allow the migration and removal of the materials from the machined surface caused by melting and chemical reactions during the machining. Hydroxyapatite powder (HAp), yielding Ca, P, and O, is widely used to form biocompatible coatings. The HAp added-EDM process has been reported to significantly improve the coating properties, corrosion, and wear resistance, and biofunctions of biomaterials. This article extensively explores the current development of bio-coatings and the wear and corrosion characteristics of biomaterials through the HAp mixed-EDM process, including the importance of these for biomaterial performance. This review presents a comparative analysis of machined surface properties using the existing deposition methods and the EDM technique employing HAp. The dominance of the process factors over the performance is discussed thoroughly. This study also discusses challenges and areas for future research.
Fulltext 4E analysis of a two-stage refrigeration system through surrogate models based on response surface methods and hybrid grey wolf optimizer

Ahmed R, Mahadzir S, Mota-Babiloni A, Al-Amin M, Usmani AY, Ashraf Rana Z, et al.

PLoS One, 2023;18(2):e0272160.
PMID: 36735732 DOI: 10.1371/journal.pone.0272160

Refrigeration systems are complex, non-linear, multi-modal, and multi-dimensional. However, traditional methods are based on a trial and error process to optimize these systems, and a global optimum operating point cannot be guaranteed. Therefore, this work aims to study a two-stage vapor compression refrigeration system (VCRS) through a novel and robust hybrid multi-objective grey wolf optimizer (HMOGWO) algorithm. The system is modeled using response surface methods (RSM) to investigate the impacts of design variables on the set responses. Firstly, the interaction between the system components and their cycle behavior is analyzed by building four surrogate models using RSM. The model fit statistics indicate that they are statistically significant and agree with the design data. Three conflicting scenarios in bi-objective optimization are built focusing on the overall system following the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Linear Programming Technique for Multidimensional Analysis of Preference (LINMAP) decision-making methods. The optimal solutions indicate that for the first to third scenarios, the exergetic efficiency (EE) and capital expenditure (CAPEX) are optimized by 33.4% and 7.5%, and the EE and operational expenditure (OPEX) are improved by 27.4% and 19.0%. The EE and global warming potential (GWP) are also optimized by 27.2% and 19.1%, where the proposed HMOGWO outperforms the MOGWO and NSGA-II. Finally, the K-means clustering technique is applied for Pareto characterization. Based on the research outcomes, the combined RSM and HMOGWO techniques have proved an excellent solution to simulate and optimize two-stage VCRS.

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