Judul: Penapisan Aktivitas dan Identifikasi Gugus Fungsi Komponen Antidiabetes Tanaman dengan Pendekatan Metabolomik
Abstrak: Diabetes menjadi masalah kesehatan serius dunia yang meningkatkan morbiditas, mortalitas, dan berkurangnya harapan hidup seseorang. Indonesia menempati peringkat keenam tertinggi dunia penderita diabetes usia 20-79 tahun. Berbagai ragam tanaman di Indonesia diketahui memiliki aktivitas sebagai antidiabetes misalnya pada beberapa tanaman sayur dan rempah yang sering dikonsumsi. Penelitian ini bertujuan untuk (1) mendapatkan data profil aktivitas biologis (penghambatan α-glukosidase, antioksidan), total fenol, dan spektra 1HNMR berbagai tanaman sayur dan rempah, (2) menentukan tanaman yang memiliki aktivitas biologis terbaik, dan (3) mengidentifikasi gugus fungsi komponen antidiabetes tanaman terpilih dengan pendekatan metabolomik. Tanaman uji yang digunakan pada penelitian ini adalah daun kedondong cina, daun jambu mete, daun pegagan, daun kelor, pakis, bunga pepaya, kencur, temu mangga, bawang kucai, dan daun bawang. Penelitian ini dilakukan melalui dua tahap yaitu (1) penapisan tanaman berdasarkan aktivitas biologis dan total fenol dan (2) mengidentifikasi gugus fungsi komponen antidiabetes dengan pendekatan metabolomik berbasis FTIR. Preparasi tanaman pada tahap penapisan tanaman dilakukan dengan cara dikeringkan tanaman menggunakan pengering beku, dibubukkan, diekstraksi dengan proses sonikasi menggunakan metanol 80%, dan diuapkan hingga didapatkan ekstrak kering. Ekstrak tanaman kering dianalisis aktivitas penghambatan α-glukosidase, antioksidan, total fenol, dan profil NMR-nya. Aktivitas penghambatan α-glukosidase tanaman berkisar antara 99.26-1.63% pada konsentrasi ekstrak 1000 μg mL-1. Aktivitas penghambatan α-glukosidase tertinggi terdapat pada daun jambu mete (99.26%±0.06), pakis (98.33%±0.29), dan daun kedondong cina (98.15%±0.53) dengan nilai IC50 berturut-turut adalah 108.04±0.47, 145.54±1.79, dan 107.79±0.67 μg mL-1. Aktivitas antioksidan tanaman uji berkisar antara 95.11-47.10% pada konsentrasi ekstrak 1000 μg mL-1. Aktivitas antioksidan tertinggi terlihat pada daun jambu mete (95.11%±0.03), pakis (94.24%±0.38), dan daun kedondong cina (94.86%±0.04) dengan nilai IC50 berturut-turut adalah 114.68±2.65, 179.00±5.81, dan 137.9±2.67 μg mL-1. Total fenol tanaman uji berkisar antara 22.72-5.27 μgGAE mL-1 pada konsentrasi ekstrak 1000 μg mL-1 dengan total fenol tertinggi dimiliki oleh daun jambu mete. Hasil analisis sidik ragam terhadap aktivitas biologis dan total fenol tanaman diketahui daun jambu mete memiliki aktivitas biologis dan total fenol terbaik. Spektra 1HNMR memperlihatkan daun jambu mete, daun kedondong cina, dan kencur mengandung flavonoid. Preparasi sampel pada tahap identifikasi gugus fungsi komponen antidiabetes dilakukan dengan cara fraksinasi ekstrak metanol daun jambu mete menggunakan heksana, kloroform, etil asetat, dan air (6 ulangan). Fraksi etil asetat memiliki aktivitas penghambatan α-glukosidase (93.76%), antioksidan (89.21%), dan total fenol (72.23 μgGAE mL-1) tertinggi. Proses identifikasi gugus fungsi komponen antidiabetes menggunakan pendekatan metabolomik dengan cara mengorelasikan aktivitas biologis (penghambatan α-glukosidase dan antioksidan) dan data FTIR melalui analisis data multivariat Orthogonal projection to latent structure (OPLS). Hasil OPLS diketahui gugus fungsi yang berkorelasi positif terhadap aktivitas penghambatan α-glukosidase adalah gugus fungsi alkana, ester, keton, asam karboksilat, alkena, dan aromatik. Gugus fungsi yang berkorelasi positif terhadap aktivitas antioksidan adalah eter (aromatik, oleifinik atau alifatik), alkana, ester, keton, asam karboksilat, dan aromatik. Gugus fungsi tersebut dapat digunakan sebagai penanda komponen aktif yang berpotensi sebagai penghambat α-glukosidase dan antioksidan pada daun jambu mete. Dibutuhkan pengkajian lebih lanjut untuk menentukan komponen aktif penghambatan α-glukosidase dan antioksidan dengan instrumen analisis yang lebih sensitif dan spesifik (robust).
Keyword: metabolomik, penghambatan α-glukosidase, antioksidan, total fenol, NMR, FTIR

Judul: Metabolomic Application in Identification α-Glucosidase Inhibitor from Vegetables and Spices
Abstrak: Diabetes mellitus is a degenerative disease with a high prevalence worldwide. Plant-based foods such as vegetables and spices were often reported to be able to reduce the risk of and even cure diabetes mellitus, especially type 2 (T2DM). Indonesia has many natural resources such as vegetables and spices that can be sources of antidiabetic foods. However, the antidiabetic activity of many vegetables and spices have not been evaluated. Besides, studies to observe the bioactive compounds responsible for the antidiabetic activity of vegetables and spices are still limited. One of the antidiabetic activities commonly studied from plant species is the α-glucosidase inhibition. Therefore, the objective of this study was to evaluate the potency of α-glucosidase inhibition of 11 vegetables and 4 spices commonly consumed in Indonesia and to identify their bioactive compound using a metabolomic approach. This study consisted of 3 stages: 1) Screening α-glucosidase inhibition activity of Indonesian vegetables and spices, (2) characterization of antioxidant compound from selected plant (obtained from the first stage) using UHPLC-HRMS and identification of its α-glucosidase inhibitor using NMR- and HPLC combined with UHPLC-HRMS metabolomics, and (3) verification of α-glucosidase inhibition activity of putative compounds (obtained from the second stage) through in silico analysis. The results of the first study showed that the highest antioxidant and α-glucosidase inhibition activity was found in leaves extract of Syzygium polyanthum followed by Pluchea indica with the values of the IC50 antioxidant activity being 2.46 and 4.34 μg/ml, respectively. The values of their IC50 of α-glucosidase inhibition were 11.76 and 12.17 μg/ml, respectively. Interestingly, the total phenolic content of these two samples was also higher than others with 25.37 and 37.99 μg GAE/mg extract, respectively. The 1H-NMR fingerprinting analysis showed that each sample had different spectral profile, especially at the aromatic region (chemical shift (δ) > 5.7). Further metabolite profiling on the 1H-NMR of S. polyanthum revealed the presence of gallic acid, syringic acid, and myricetin, whereas 5-O-caffeoylquinic acid, 4,5-di-O-caffeoylquinic acid methyl ester, and esculetin were found in the 1H-NMR of P. indica. Previous studies reported that all identified compounds had antidiabetic activity especially through α-glucosidase inhibition. Thus, those compounds might be associated with the α-glucosidase inhibition activity of S. polyanthum and P. Indica, which was investigated further in the next step. Based on the results of the first study, S. polyanthum leaves were chosen for the second stage. Dried leaves of S. polyanthum were fractionated using solvents with different polarity sequentially i.e. hexane, combination of hexane-acetone, acetone, combination of acetone-water and water to obtain 14 fractions. Analysis of antioxidant activity through the ability to reduce radical 2,2-diphenyl-1-picrylhydrazyl (DPPH) indicated that at a tested concentration of 500 μg/ml, Fraction 9/F9 (obtained from acetone:water = 4:1) gave the highest antioxidant activity followed by Fraction 1/F1 (obtained from hexane 100%), Fraction 10/F10 (obtained from acetone:water = 3:2) and Fraction 3 (obtained from hexane:acetone = 4:1) with the values of 95.21, 94.82, 93.37 and 93.19%, respectively. The characterization of the UHPLC-HRMS chromatogram of F9 identified the presence of gallic acid, trans-aconitic acid, and myricetin, while only gallic acid and trans-aconitic acid were identified in
Keyword: α-glucosidase, antioxidant, docking, HPLC-UHPLC-HRMS, metabolomics, NMR, Syzygium polyanthum

Judul: The Construction of Greedy SVP LLL Algorithm
Abstrak: A lattice is a set of all integer linear combination of a set of linearly independent vectors in ℝ𝑛���. The independent vectors are called bases of lattice. Any lattice can be generated from many bases, and these bases have the same cardinality. In the lattice, the most fundamental and renowned problem is the Shortest Vector Problem (SVP). SVP is a tracking problem of the shortest nonzero vector in a lattice with equivalent bases. In two dimensions, SVP problem has resolved exactly by Gauss’ algorithm. When the lattice dimension is higher than two, A.K. Lenstra, H.W. Lenstra and L. Lovasz gave a LLL algorithm to compute approximation of nonzero shortest vector in the lattice. Reduced bases solution obtained from LLL algorithm still be an approximation and has polynomial running time of arbitrary dimension which large enough. In 1994, Schnoor and Euchner modified this LLL algorithm as a new variant which later was named deep insertion LLL algorithm. This algorithm is a modified version of the LLL algorithm to the terms of the exchange step to improve the accuracy of output of LLL reduced bases and applying it to the subset sum problem. The purpose of this research are: (1) Reconstruct the LLL algorithm and deep insertion LLL algorithm using a geometric approach. (2) Construct a greedy SVP LLL algorithm. (3) Implement the three algorithms in a symbolic programming language, and evaluate them experimentally. A bases ℬ=[𝐛���1,𝐛���2,…,𝐛���𝑛���] in ℝ𝑚��� is called reduced LLL with parameter 𝛿��� if satisfies: (a) |𝜇���𝑗���𝑖���|≤12, for every integer 𝑖���,𝑗��� with 1≤𝑖���<𝑗���<𝑛���. (b)𝛿���‖𝜋���𝑗���(𝐛���𝑗���)‖2≤‖𝜋���𝑗���(𝐛���𝑗���+1)‖2, for 𝑗���=1,2,…,𝑛���−1,where 𝛿��� is a reduced parameter of real numbers with 14<𝛿���<1. The first requirement is the reduced basis 𝛿��� must “nearly orthogonal” and in its computation case, this requirement can be reached out by using the Gram-Schmidt’s orthogonalization. While the second requirement is called exchange step, or used to called as Lovasz condition. By using both of these requirements, then they establish the LLL algorithm. Then, Schnoor and Euchner modify the LLL algorithm based on the requirements of the exchange step called the deep insertion LLL algorithm, where the exchange step is based on the comparison of the projected vector in orthogonal complement [𝐛���1,𝐛���2,…,𝐛���𝑘���−1]⊥ after j-th reduction. To construct greedy SVP LLL algorithm, the requirements of the exchange step is based on purely by comparing norm of lattice 𝐛���𝑗��� with norm lattice 𝐛���𝑖��� for 𝑖���=1,2,3…,𝑗���−1. The process of insertion reduced vector after the exchange step also has done greedily. Then, we calculate the number of arithmetic operation in greedy SVP LLL algorithm for further analysis. The result of our exprimentation for three algorithms on the certain matrix size shows that the running time of the three algorithms increases propotional to the size of the matrix. The result also shows that by using 𝛿���=34 for the LLL algorithm and the deep insertion LLL algorithm, and the greedy SVP LLL algorithm which is a new variant made by using no parameter of 𝛿���, outperform of the other of two previous algorithm in terms of speed with the same output.
Keyword: LLL algorithm, LLL deep insertion algorithm, greedy SVP LLL algorithm