Judul: Potensi Ekstrak Daun Tin (Ficus carica L.) sebagai Antioksidan dan Aktivitas Hambatannya terhadap Proliferasi Sel Kanker HeLa
Abstrak: Fig fruit (Ficus carica L.) has antioxidant dan anticancer properties both empirically and scientific reseach. However, scientific research on the use of fig leaf as anticancer has not reported yet. This study examined the antioxidant and anticancer protency of fig leaf extract. Based on the phytochemicals test, simplicia of fig leaf contains flavonoids, tannins, steroids and alkaloids. Flavonoids, tannins, steroids of fig leaf were extracted by maceration technique using suitable solvent. The extracts were tested by 1,1-diphenyl-2-picrylhydrazyl antioxidant method and brine shrimp lethality test toxicity method. Flavonoid extract showed highest antioxidant activity with IC50 150 mg/L, while the LC50 for toxicity tests was 191.43 ppm. Flavonoids extract of fig leaf was fractionated using preparative thin layer chromatography and obtained seven fractions.Proliferation inhibition test against HeLa cancer cell by 3-[4,5-dimethylthiazol-2yl]-2,5-diphenyl tetrazolium bromide method showed the most active fraction was fraction F7 which inhibit 57.18 % proliferation of HeLa cancer cells at concentrations of 800 ppm. Based on the identification using ultraviolet spectrophotometer and fourier transform infrared, F7 fraction suspected to contain isoflavones or flavones compounds.
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Judul: Potential of Bioactive Compounds in Fig (Ficus carica L.) as Selective Estrogen Receptor Modulator (SERMs) In Silico
Abstrak: Kasus kanker payudara terbanyak didominasi jenis positif hormon reseptor, yang terkait pada keberadaan reseptor estrogen-α (ERα). Selective estrogen receptor modulator (SERMs) mampu mengikat ERα agar proliferasi terhambat. Tanaman tin memiliki aktivitas antikanker payudara positif estrogen, namun belum diketahui interaksi antagonisnya terhadap ERα. Penelitian ini bertujuan menentukan senyawa aktif yang berpotensi berperan sebagai antagonis terhadap reseptor ERα pada tanaman tin. Penambatan molekuler menggunakan AutoDock Vina. Jenis interaksi molekuler ligan-reseptor ditentukan melalui parameter energi afinitas (ΔG), konstanta inhibisi (Ki), dan binding site similarity (BSS). Hasil penambatan mendapatkan 13 ligan uji yang bersifat antagonis terhadap ERα. Potensi pengikatan antagonis terbaik terdapat pada ligan epikatekin (ΔG -8,4 kkal/mol dengan Ki 0,696 μM) dan apigenin (ΔG -8,1 kkal/mol dengan Ki 1,154 μM). Hasil penelitian menunjukkan bahwa epikatekin merupakan ligan dengan potensi terbaik dalam berikatan antagonis terhadap ERα berdasarkan energi afinitas, konstanta inhibisi, serta interaksi residu asam amino., Most of breast cancer cases is hormone receptor positive type of breast cancer. Selective estrogen receptor modulator (SERMs) can binding an estrogen receptor α (ERα) and inhibit proliferation. Fig has anticancer breast activity, but their antagonist’s interaction with the ERα still unknown. This study aims to determine the bioactive compounds on figs that potentially block the ERα as an antagonist. Molecular docking using AutoDock Vina. The type of ligand-receptor molecular interaction is determined by the parameters such as affinity energy (ΔG), inhibition constant (Ki), and binding site similarity (BSS). The result of molecular docking is 13 ligands from leaves, fruit, and latex fig that can block the ERα. Epicatechin (ΔG -8,4 kcal/mol with Ki 0,696 μM) and apigenin (ΔG -8,1 kcal/mol with Ki 1,154 μM) are compounds that have the best potential antagonist activities to ERα. The results showed that epicatechin is the test ligands with the best potential antagonist compound to the ERα based on affinity energy, inhibitory constant, and amino acid residues’s interaction.
Keyword: Estrogen receptor α, Fig, Molecular docking, SERMs

Judul: Implementation of Spatio-Temporal Change Query in Relational Database.
Abstrak: Geographic object changes in various views. Object changes are recorded using a model of static storage. No historical connection between old version data and new version data has been built. For that, a storage model of spatio-temporal queries that can analyze spatio-temporal changes has been developed. The object changes the geometry are grouped according to the taxonomy of Spatio-Temporal Pattern Change. This research has been able to apply the query patterns change including born, expand, merge, contract, split, normal and die. This query based on geometric (area) changes. The implementation of spatio-temporal change queries represents an object changes continuously with easy, change detection is done by comparing two versions of data. Keyword: spatio-temporal change query, spatio-temporal change pattern.
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