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      UPLC-MS-Based Metabolomics Profiling for α-Glucosidase Inhibiting Property of Parkia speciosa Pods

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          Abstract

          Parkia speciosa is a food plant that grows indigenously in Southeast Asia. A great deal of interest has been paid to this plant due to its traditional uses in the treatment of several diseases. The pods contain many beneficial secondary metabolites with potential applications in medicine and cosmetics. However, studies on their phytochemical properties are still lacking. Therefore, the present study was undertaken to profile the bioactive compounds of P. speciosa pods collected from six different regions of Malaysia through ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS) and α-glucosidase inhibitory potential. This study applied metabolomics to elucidate the differences between P. speciosa populations found naturally in the different locations and to characterize potential α-glucosidase inhibitors from P. speciosa pods. P. speciosa collected from different regions of Malaysia showed good α-glucosidase inhibitory activity, with a median inhibitory concentration (IC 50) of 0.45–0.76 μg/mL. The samples from the northern and northeastern parts of Peninsular Malaysia showed the highest activity. Using UHPLC-QTOF-MS/MS analysis, 25 metabolites were identified in the pods of P. speciosa. The findings unveiled that the pods of P. speciosa collected from different locations exhibit different levels of α-glucosidase inhibitory activity. The pods are a natural source of potent antidiabetic bioactive compounds.

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          A Cross-platform Toolkit for Mass Spectrometry and Proteomics

          Mass-spectrometry-based proteomics has become an important component of biological research. Numerous proteomics methods have been developed to identify and quantify the proteins in biological and clinical samples 1 , identify pathways affected by endogenous and exogenous perturbations 2 , and characterize protein complexes 3 . Despite successes, the interpretation of vast proteomics datasets remains a challenge. There have been several calls for improvements and standardization of proteomics data analysis frameworks, as well as for an application-programming interface for proteomics data access 4,5 . In response, we have developed the ProteoWizard Toolkit, a robust set of open-source, software libraries and applications designed to facilitate proteomics research. The libraries implement the first-ever, non-commercial, unified data access interface for proteomics, bridging field-standard open formats and all common vendor formats. In addition, diverse software classes enable rapid development of vendor-agnostic proteomics software. Additionally, ProteoWizard projects and applications, building upon the core libraries, are becoming standard tools for enabling significant proteomics inquiries.
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            MZmine 2: Modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data

            Background Mass spectrometry (MS) coupled with online separation methods is commonly applied for differential and quantitative profiling of biological samples in metabolomic as well as proteomic research. Such approaches are used for systems biology, functional genomics, and biomarker discovery, among others. An ongoing challenge of these molecular profiling approaches, however, is the development of better data processing methods. Here we introduce a new generation of a popular open-source data processing toolbox, MZmine 2. Results A key concept of the MZmine 2 software design is the strict separation of core functionality and data processing modules, with emphasis on easy usability and support for high-resolution spectra processing. Data processing modules take advantage of embedded visualization tools, allowing for immediate previews of parameter settings. Newly introduced functionality includes the identification of peaks using online databases, MSn data support, improved isotope pattern support, scatter plot visualization, and a new method for peak list alignment based on the random sample consensus (RANSAC) algorithm. The performance of the RANSAC alignment was evaluated using synthetic datasets as well as actual experimental data, and the results were compared to those obtained using other alignment algorithms. Conclusions MZmine 2 is freely available under a GNU GPL license and can be obtained from the project website at: http://mzmine.sourceforge.net/. The current version of MZmine 2 is suitable for processing large batches of data and has been applied to both targeted and non-targeted metabolomic analyses.
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              Type 2 diabetes.

              415 million people live with diabetes worldwide, and an estimated 193 million people have undiagnosed diabetes. Type 2 diabetes accounts for more than 90% of patients with diabetes and leads to microvascular and macrovascular complications that cause profound psychological and physical distress to both patients and carers and put a huge burden on health-care systems. Despite increasing knowledge regarding risk factors for type 2 diabetes and evidence for successful prevention programmes, the incidence and prevalence of the disease continues to rise globally. Early detection through screening programmes and the availability of safe and effective therapies reduces morbidity and mortality by preventing or delaying complications. Increased understanding of specific diabetes phenotypes and genotypes might result in more specific and tailored management of patients with type 2 diabetes, as has been shown in patients with maturity onset diabetes of the young. In this Seminar, we describe recent developments in the diagnosis and management of type 2 diabetes, existing controversies, and future directions of care.
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                Author and article information

                Journal
                Life (Basel)
                Life (Basel)
                life
                Life
                MDPI
                2075-1729
                22 January 2021
                February 2021
                : 11
                : 2
                : 78
                Affiliations
                [1 ]Department of Pharmacology, Faculty of Medicine, Universiti Kebangsaan Malaysia, Cheras, Kuala Lumpur 56000, Malaysia; medsaleh@ 123456ukm.edu.my (M.S.M.S.); fitrifareez@ 123456ppukm.ukm.edu.my (F.F.R.)
                [2 ]Drug and Herbal Research Centre, Faculty of Pharmacy, Universiti Kebangsaan Malaysia, Kuala Lumpur 50300, Malaysia; juriyatijalil@ 123456ukm.edu.my (J.J.); norhidayahmustafa91@ 123456gmail.com (N.H.M.)
                [3 ]Unit of Pharmacology, Faculty of Medicine and Defence Health, Universiti Pertahanan Nasional Malaysia, Kem Sungai Besi, Kuala Lumpur 57000, Malaysia; draayusof@ 123456gmail.com
                Author notes
                [* ]Correspondence: kamisah_y@ 123456ppukm.ukm.edu.my or kamisah_y@ 123456yahoo.com ; Tel.: +603-9145-9575; Fax: +603-9145-9547
                Author information
                https://orcid.org/0000-0001-9449-6489
                https://orcid.org/0000-0003-4030-7616
                Article
                life-11-00078
                10.3390/life11020078
                7910992
                33499128
                8257bca4-d487-4c23-821c-9b7197fd767c
                © 2021 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 05 January 2021
                : 19 January 2021
                Categories
                Article

                α-glucosidase inhibitory activity,antidiabetic,flavonoids,phenolics,type 2 diabetes mellitus

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