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Ginger and its active compounds in cancer therapy: From folk uses to nano-therapeutic applications

Mahomoodally MF, Aumeeruddy MZ, Rengasamy KRR, Roshan S, Hammad S, Pandohee J, et al.

Semin Cancer Biol, 2021 Feb;69:140-149.
PMID: 31412298 DOI: 10.1016/j.semcancer.2019.08.009

Ginger is a spice that is renowned for its characteristic aromatic fragrance and pungent taste, with documented healing properties. Field studies conducted in several Asian and African countries revealed that ginger is used traditionally in the management of cancer. The scientific community has probed into the biological validation of its extracts and isolated compounds including the gingerols, shogaols, zingiberene, and zingerone, through in-vitro and in-vivo studies. Nonetheless, an updated compilation of these data together with a deep mechanistic approach is yet to be provided. Accordingly, this review highlights the mechanisms and therapeutics of ginger and its bioactive compounds focused on a cancer context and these evidence are based on the (i) cytotoxic effect against cancer cell lines, (ii) enzyme inhibitory action, (iii) combination therapy with chemotherapeutic and phenolic compounds, (iv) possible links to the microbiome and (v) the use of nano-formulations of ginger bioactive compounds as a more effective drug delivery strategy in cancer therapy.
Fulltext COVID-19 impact: Customised economic stimulus package recommender system using machine learning techniques

Kannan R, Wang IZW, Ong HB, Ramakrishnan K, Alamsyah A

F1000Res, 2021 09 16;10:932.
PMID: 34925768 DOI: 10.12688/f1000research.72976.2

Background: The Malaysian government reacted to the pandemic's economic effect with the Prihatin Rakyat Economic Stimulus Package (ESP) to cushion the novel coronavirus 2019 (COVID-19) impact on households. The ESP consists of cash assistance, utility discount, moratorium, Employee Provident Fund (EPF) cash withdrawals, credit guarantee scheme and wage subsidies. A survey carried out by the Department of Statistics Malaysia (DOSM) shows that households prefer different types of financial assistance. These preferences forge the need to effectively customise ESPs to manage the economic burden among low-income households. In this study, a recommender system for such ESPs was designed by leveraging data analytics and machine learning techniques. Methods: This study used a dataset from DOSM titled "Effects of COVID-19 on the Economy and Individual - Round 2," collected from April 10 to April 24, 2020. Cross-Industry Standard Process for Data Mining was followed to develop machine learning models to classify ESP receivers according to their preferred subsidies types. Four machine learning techniques-Decision Tree, Gradient Boosted Tree, Random Forest and Naïve Bayes-were used to build the predictive models for each moratorium, utility discount and EPF and Private Remuneration Scheme (PRS) cash withdrawals subsidies. The best predictive model was selected based on F-score metrics. Results: Among the four machine learning techniques, Gradient Boosted Tree outperformed the rest. This technique predicted the following: moratorium preferences with 93.8% sensitivity, 82.1% precision and 87.6% F-score; utilities discount with 86% sensitivity, 82.1% precision and 84% F-score; and EPF and PRS with 83.6% sensitivity, 81.2% precision and 82.4% F-score. Households that prefer moratorium subsidies did not favour other financial aids except for cash assistance. Conclusion: Findings present machine learning models that can predict individual household preferences from ESP. These models can be used to design customised ESPs that can effectively manage the financial burden of low-income households.
Fulltext Interference RNA in immune-mediated oral diseases - minireview

Dhanapal R, Somasundarapandian S, Wihaskoro S, Kannan R, Rajkumar G, Chidambaram R

Cent Eur J Immunol, 2017;42(3):301-304.
PMID: 29204096 DOI: 10.5114/ceji.2017.70974

Immune-mediated oral disorders are characterised by their chronicity, and some are refractory to treatment. Interference RNA (iRNA) has been implicated in the underlying mechanism of such immune-mediate oral and refractory inflammatory oral diseases. iRNA-based understanding of the mechanism in these diseases may help to produce non-invasive diagnostic methodologies and treatment modalities of such drug non-responsive diseases. Oral lesions in these immune-mediated diseases can precede the occurrence of lesions in other regions of the body. The early diagnosis and treatment of these drug non-responsive diseases might benefit the patient by reducing chronicity and probably even resolving the disease. This aim of the present minireview is to give an overview of the possible implications of iRNA on the pathogenesis, diagnosis, and treatments of immune-mediated and inflammatory oral diseases. The manuscript can form the framework for research on iRNA in these immune-mediated oral disorders.
Fulltext Machine Learning-Based Ensemble Classifiers for Anomaly Handling in Smart Home Energy Consumption Data

Kasaraneni PP, Venkata Pavan Kumar Y, Moganti GLK, Kannan R

Sensors (Basel), 2022 Nov 30;22(23).
PMID: 36502025 DOI: 10.3390/s22239323

Addressing data anomalies (e.g., garbage data, outliers, redundant data, and missing data) plays a vital role in performing accurate analytics (billing, forecasting, load profiling, etc.) on smart homes' energy consumption data. From the literature, it has been identified that the data imputation with machine learning (ML)-based single-classifier approaches are used to address data quality issues. However, these approaches are not effective to address the hidden issues of smart home energy consumption data due to the presence of a variety of anomalies. Hence, this paper proposes ML-based ensemble classifiers using random forest (RF), support vector machine (SVM), decision tree (DT), naive Bayes, K-nearest neighbor, and neural networks to handle all the possible anomalies in smart home energy consumption data. The proposed approach initially identifies all anomalies and removes them, and then imputes this removed/missing information. The entire implementation consists of four parts. Part 1 presents anomaly detection and removal, part 2 presents data imputation, part 3 presents single-classifier approaches, and part 4 presents ensemble classifiers approaches. To assess the classifiers' performance, various metrics, namely, accuracy, precision, recall/sensitivity, specificity, and F1 score are computed. From these metrics, it is identified that the ensemble classifier "RF+SVM+DT" has shown superior performance over the conventional single classifiers as well the other ensemble classifiers for anomaly handling.
Fulltext Job characteristics of a Malaysian bank's anti-money laundering system and its employees' job satisfaction

Kannan R, Reddiar Y, Ramakrishnan K, Eastaff MS, Ramesh S

F1000Res, 2021;10:1052.
PMID: 36225238 DOI: 10.12688/f1000research.73234.2

Background: Banks and financial institutions are vulnerable to money laundering (ML) as a result of crime proceeds infiltrating banks in the form of significant cash deposits. Improved financial crime compliance processes and systems enable anti-ML (AML) analysts to devote considerable time and effort to case investigation and process quality work, thereby lowering financial risks by reporting suspicious activity in a timely and effective manner. This study uses Job Characteristics Theory (JCT) to evaluate the AML system through the job satisfaction and motivation of its users. The purpose of this study is to determine how satisfied AML personnel are with their jobs and how motivated they are to work with the system. Methods: This cross-sectional study used JCT to investigate the important elements impacting employee satisfaction with the AML system. The five core dimensions of the job characteristics were measured using a job diagnostic survey. The respondents were employees working in the AML department of a Malaysian bank, and the sample group was chosen using a purposive sampling approach. A total of 100 acceptable replies were gathered and analysed using various statistical approaches. A motivating potential score was generated for each employee based on five main job characteristics. Results: Findings revealed that five core job characteristics, namely, skill diversity, task identity, task importance, autonomy and feedback, positively influence the AML system employees' job satisfaction. However, skill variety and autonomy are found to be low, which are reflected in the poor motivating potential score. Conclusion: This study examined the characteristics of the AML system and its users' job satisfaction. Findings revealed that task significance is the most widely recognised characteristic, followed by feedback and task identity. However, there is a lack of skill variety and autonomy, which must be addressed to improve employee satisfaction with the AML system.
New insights into the phytochemical composition, enzyme inhibition and antioxidant properties of desert cotton (Aerva javanica (Bum.f) Shult. -Amaranthaceae)

Saleem H, Zengin G, Khan KU, Ahmad I, Waqas M, Mahomoodally FM, et al.

Nat Prod Res, 2021 Feb;35(4):664-668.
PMID: 30919661 DOI: 10.1080/14786419.2019.1587427

This study sets out to probe into total bioactive contents, UHPLC-MS secondary metabolites profiling, antioxidant (DPPH, ABTS, FRAP, CUPRAC, phosphomolybdenum and metal chelating) and enzyme inhibitory (acetylcholinesterase- AChE, butyrylcholinesterase- BChE, α-amylase, α glucosidase, and tyrosinase) activities of methanol extract of Aerva javanica, also known as desert cotton or Kapok bush. Aerva javanica contains considerable phenolic (44.79 ± 3.12 mg GAE/g) and flavonoid (28.86 ± 0.12 mg QE/g) contents which tends to correlate with its significant antioxidant potential for ABTS, FRAP and CUPRAC assays with values of 101.41 ± 1.18, 124.10 ± 1.71 and 190.22 ± 5.70 mg TE/g, respectively. The UHPLC-MS analysis identified the presence of 45 phytochemicals belonging to six major groups: phenolic, flavonoids, lignin, terpenes, glycoside and alkaloid. Moreover, the plant extract also showed potent inhibitory action against AChE (3.73 ± 0.22 mg GALAE/g), BChE (3.31 ± 0.19 mg GALAE/g) and tyrosinase (126.05 ± 1.77 mg KAE/g). The observed results suggest A. javanica could be further explored as a natural source of bioactive compounds.
Combination of phenolic profiles, pharmacological properties and in silico studies to provide new insights on Silene salsuginea from Turkey

Zengin G, Rodrigues MJ, Abdallah HH, Custodio L, Stefanucci A, Aumeeruddy MZ, et al.

Comput Biol Chem, 2018 Dec;77:178-186.
PMID: 30336375 DOI: 10.1016/j.compbiolchem.2018.10.005

The genus Silene is renowned in Turkey for its traditional use as food and medicine. Currently, there are 138 species of Silene in Turkey, amongst which have been several studies for possible pharmacological potential and application in food industry. However, there is currently a paucity of data on Silene salsuginea Hub.-Mor. This study endeavours to access its antioxidant, enzyme inhibitory, and anti-inflammatory properties. Besides, reversed-phase high-performance liquid chromatography-diode array detector (RP-HPLC-DAD) was used to detect phenolic compounds, and molecular docking was performed to provide new insights for tested enzymes and phenolics. High amounts of apigenin (534 μg/g extract), ferulic acid (452 μg/g extract), p-coumaric acid (408 μg/g extract), and quercetin (336 μg/g extract) were detected in the methanol extract while rutin (506 μg/g extract) was most abundant in the aqueous extract. As for their biological properties, the methanol extract exhibited the best antioxidant effect in the DPPH and CUPRAC assays, and also the highest inhibition against tyrosinase. The aqueous extract was the least active enzyme inhibitor but showed the highest antioxidant efficacy in the ABTS, FRAP, and metal chelating assays. At a concentration of 15.6 μg/mL, the methanol extract resulted in a moderate decrease (25.1%) of NO production in lipopolysaccharide-stimulated cells. Among the phenolic compounds, epicatechin, (+)-catechin, and kaempferol showed the highest binding affinity towards the studied enzymes in silico. It can be concluded that extracts of S. salsuginea are a potential source of functional food ingredients but need further analytical experiments to explore its complexity of chemical compounds and pharmacological properties as well as using in vivo toxicity models to establish its maximum tolerated dose.
Pharmacological, phytochemical and in-vivo toxicological perspectives of a xero-halophyte medicinal plant: Zaleya pentandra (L.) Jeffrey

Saleem H, Zengin G, Locatelli M, Ahmad I, Khaliq S, Mahomoodally MF, et al.

Food Chem Toxicol, 2019 Sep;131:110535.
PMID: 31154083 DOI: 10.1016/j.fct.2019.05.043

This study endeavours to investigate the phytochemical composition, biological properties and in vivo toxicity of methanol and dichloromethane extracts of Zaleya pentandra (L.) Jeffrey. Total bioactive contents, antioxidant (phosphomolybdenum and metal chelating, DPPH, ABTS, FRAP and CUPRAC) and enzyme inhibition (cholinesterases, tyrosinase α-amylase, and α-glucosidase) potential were assessed utilizing in vitro bioassays. UHPLC-MS phytochemical profiling was carried out to identify the essential compounds. The methanol extract was found to contain highest phenolic (22.60 mg GAE/g) and flavonoid (31.49 mg QE/g) contents which correlate with its most significant radical scavenging, reducing potential and tyrosinase inhibition. The dichloromethane extract was most potent for phosphomolybdenum, ferrous chelation, α-amylase, α-glucosidase, and cholinesterase inhibition assays. UHPLC-MS analysis of methanol extract unveiled to identify 11 secondary metabolites belonging to five sub-groups, i.e., phenolic, alkaloid, carbohydrate, terpenoid, and fatty acid derivatives. Additionally, in vivo toxicity was conducted for 21 days and the methanol extract at different doses (150, 200, 250 and 300 mg/kg) was administered in experimental chicks divided into five groups each containing five individuals. Different physical, haematological and biochemical parameters along with the absolute and relative weight of visceral body organs were studied. Overall, no toxic effect was noted for the extract at tested doses.
Endothelialisation of poly (carbonate-siloxane-urea) urethane

Kannan RY, Sales KM, Salacinski HJ, Butler PE, Seifalian AM

Med J Malaysia, 2004 May;59 Suppl B:107-8.
PMID: 15468841
Cytotoxicity analysis of poly (carbonate-siloxane-urea) urethane

Kannan RY, Sales KM, Salacinski HJ, Butler PE, Seifalian AM

Med J Malaysia, 2004 May;59 Suppl B:99-100.
PMID: 15468837
Fulltext Identification of Helicobacter pylori in Saliva of Patients with and without Gastritis by Polymerase Chain Reaction

Sekhar Goud EVS, Kannan R, Rao UK, Joshua E, Tavaraja R, Jain Y

J Pharm Bioallied Sci, 2019 Nov;11(Suppl 3):S523-S529.
PMID: 31920269 DOI: 10.4103/jpbs.JPBS_260_18

Aims and Objective: The aim of this study was to identify the presence of Helicobacter pylori in saliva of patients with and without gastritis by polymerase chain reaction (PCR) method.
Materials and Methods: The study comprised 20 patients in Group I presenting with various symptoms of gastritis and 10 asymptomatic subjects in Group II. The intestinal endoscopy antral biopsies were collected from 20 symptomatic patients with gastroduodenal disorders. The saliva specimens were taken from all patients before endoscopy. PCR was performed using genomic DNA, isolated from the saliva and the biopsies of the patients as the template to detect the presence of the 16S ribosomal RNA gene in H. pylori.
Results: In Group I, 10 (50%) cases of clinical gastritis were positive for H. pylori by endoscopy biopsy and 10 (50%) were negative. Of the 10 endoscopy biopsy positive cases for H. pylori, eight were PCR positive in saliva and two were negative. Of the 10 endoscopy biopsy negative cases, three were PCR positive for H. pylori in saliva and seven were negative. In Groups II, four were symptomatic for gastritis and six were negative. Of the six gastritis negative cases, three were PCR positive, four were gastritis positive, and three were PCR positive. Sensitivity and specificity of PCR were found to be 80% and 70%, respectively. The positive predictive and negative predictive values of PCR in saliva were 72.7% and 77.7%, respectively.
Conclusion: PCR analysis of saliva may be handy in identification of H. pylori and serves as a noninvasive technique to diagnose and monitor the prognosis.

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