Judul: Komposisi Kimia Dan Aktivitas Inhibitor Topoisomerase I Dari Kerang Mas Ngur (Atactodea striata)
Abstrak: Inhibitor topoisomerase merupakan salah satu target molekul untuk penemuan obat kanker. Topoisomerase terdiri dari 2 tipe yaitu topoisomerase I yang berperan dalam relaksasi supercoiled DNA dan memutus single stranded DNA; dan topoisomerase II yang berperan dalam relaksasi pada pemutusan double stranded DNA. Atactodea striata telah digunakan oleh masyarakat di kepulauan Kei Maluku Tenggara sebagai obat penyakit kuning. Penelitian ini bertujuan untuk mengetahui kandungan gizi kerang Mas Ngur (Atactodea striata) dan menemukan senyawa kimia yang memiliki aktivitas inhibitor topoisomerase I. Kerang Mas Ngur (Atactodea striata) yang digunakan dalam penelitian ini diperoleh dari pulau Kei Kecil Maluku Tenggara. Kerang kering diblender halus dan dianalisa komposisi kimianya serta asam amino, dengan metode standar (AOAC 1995). Selain itu bubuk kering diekstraksi menggunakan pelarut heksana, etil asetat dan metanol. Ekstrak yang diperoleh selanjutnya diuji aktivitasnya sebagai inhibitor topoisomerase I. Kemudian dilakukan karakterisasi ekstrak aktif, yang dilanjutkan dengan isolasi golongan senyawa kimia. Hasil penelitian menunjukkan bahwa ekstrak heksana, etil asetat, dan metanol memiliki aktifitas inhibitor topoisomerase I. Minimum Inhibitory Concentration (MIC) dari ekstrak metanol sebagai inhibitor topoisomerase I adalah 5 mg/ml. Hasil uji komposisi kimia bubuk kerang menunjukkan kadar air 7,84 %, abu 7,88 %, protein 56,08 %, lemak 5,95 %, serat kasar 1,25 %, dan karbohidrat 21 %. Ekstrak metanol mengandung senyawa alkaloid dan saponin, ekstrak etil asetat mengandung senyawa steroid dan ekstrak heksana mengandung saponin.
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Judul: Komposisi Kimia dan Aktivitas Inhibitor Topoisomerase I dari Kablang (Nerita albicilla)
Abstrak: DNA topoisomerase I dan II adalah enzim inti sel esensial untuk proliferasi sel dan merupakan target dalam pengembangan obat antikanker. Dalam penelitian ini dipelajari kadar proksimat dan komposisi asam amino serta aktivitas inhibisi dari ekstrak Kablang (Nerita albicilla) terhadap kerja dari enzim DNA topoisomerase I. Tahapan penelitian meliputi penentuan kadar proksimat dan asam amino Kablang, ekstraksi sampel dengan pelarut heksana, etil asetat dan metanol, penentuan aktivitas inhibitor topoisomerase I dengan menggunakan drug screening kit topoisomerase I dari TopoGen. Aktivitas inhibisi DNA topoisomerase I dianalisis dengan menggunakan gel elektroforesis. Komposisi kimia Nerita albicilla terdiri dari kadar air 12,44%; abu 9,17%; protein 62,05%; lemak 5,58%; serat kasar 6,60%, dan karbohidarat (by diference) 4,16%. Kandungan asam amino terbesar yaitu asam glutamat 8,89 % dan terkecil adalah serin 1,07%. Rendemen ekstrak heksana, etil asetat dan metanol masing-masing 2,05%, 1,56%, dan 6,99%. Ketiga ekstrak menunjukkan aktivitas sebagai inhibitor topoisomerase I. Ekstrak heksana menunjukkan mekanisme katalitik terhadap topoisomerase I, sedangkan ekstrak etil asetat dan ekstrak metanol bersifat poison. Minimum inhibitory concentration (MIC) dari ekstrak metanol adalah 2,5 μg/ml. Ekstrak aktif metanol mengandung golongan senyawa alkaloid dan steroid.
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Judul: Mixed effect models for high –dimensional longitudinal data with latent variables
Abstrak: A set of data that consist of repeated measurements on a large number of outcomes and covariates are known as high-dimensional longitudinal data. The high-dimensional longitudinal data may consist of several effects of interest that cannot be directly measured, known as latent factors or latent variables. During the last decade, the multivariate responses or high-dimensional in longitudinal data have been a big issue. The analysis of high-dimensional longitudinal data is complicated due to its complex correlation structures between outcomes. To understand changes over time of outcome variables, having correlations in individuals with explanatory variables is not enough. Hence, complex correlation structures between outcomes need to be considered. One approach that is commonly used to overcome high-dimensional longitudinal data is simultaneous equation modeling. A well-known simultaneous equation modeling method is the structural equation model (SEM). This approach has several appealing modeling abilities and can be used for high-dimensional longitudinal data. Under the SEM framework, the continuous time SEM is developed to avoid some issues associated with autoregressive and cross-lagged problems in SEM. Another simultaneous equation modeling method is by combining factor analysis and multivariate analysis methods to overcome high dimensional longitudinal data, namely latent factor linear mixed model (LFLMM). The factor analysis is used to reduce the high-dimensional outcomes, and the multivariate linear mixed model is used to study the longitudinal trends of several latent factors. One example of high-dimensional longitudinal data is the General Election Study. This study is carried out repeatedly to observe tendencies towards political attitudes and behavior over time in Belgium. The data contain political information, knowledge, perceptions, and preferences of a political party and the level of participation in politics. One of the most interesting things to study from the data is to analyze the change of political attitudes and behavior of respondents over time. Also, the relationship of changes in these outcomes is important to analyze. The General Election Study in Belgium was designed to include a representative sample of the target population under the Belgian electorate, so accurate estimates about the population could be made. This sampling design was created by the Institute for Social and Political Opinion (ISPO) and the Inter-university Center for Political Opinion Research (PIOP). The Belgian data set contained three subsamples, the Flemish (Dutch-speaking), the Walloon (French-speaking), and the Brussels Capital Region (Dutch and French-speaking). Several studies conducted on the Belgium data are carried out to understand the relationships between the latent variables Individualism (I), Ethnocentrism (E), and Authoritarianism (A) in Flanders. Cross-sectional or a longitudinal studies have also been carried out. In such cases, it is critical to capture the trend of the latent variables over time and, more importantly, whether there is any association or relationships between the development of nationalism (N), ethnocentrism (E), individualism (I), and authoritarianism (A) in Belgium. An empirical analysis of CT-SEM has been done to present the interdependencies among the four latent variables mentioned above on the basis of the General Election Studies for Belgium in 1991, 1995, and 1999 (Interuniversitair steunpunt politieke-opinieonderzoek, 1991, 1995, 1999). Although the four variables have been the subject of
Keyword: Belgium, CT-SEM, high-dimensional longitudinal data, LFLMM, Supplemented EM Algorithm