Judul: Sitotoksisitas Fraksi Aktif Biji Mahoni (Swietenia mahagoni) pada Sel Kanker Payudara T47D
Abstrak: Ethyl asetate fraction was reported to be toxic to Artemia salina and has a potency as medicine. The purpose of this study was to examine cytotoxicity of active fraction from Swietenia mahagoni seed toward breast cancer T47D. Ethyl acetate fraction was fractionated used clasic cromatography coloum with silica gel as the stationary phase and chloroform-ethyl acetate as the mobile phase gradiently and yielded 8 fraction. Fraction 2 with 50 lethal concentration 74.30 ppm could inhibit the growth of T47D cell with 50 inhibition concentration of 49.12 ppm. Based on phytocemical assay, fraction 2 consisted of alkaloid and steroid/triterpenoid.
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Judul: Fraksi Ekstrak Biji Mahoni (Swietenia macrophylla) dan Studi Pustaka Antikanker Sel MCF-7, T47D, serta HCT116
Abstrak: Kanker merupakan salah satu penyakit tidak menular yang menjadi masalah kesehatan masyarakat. Pertambahan jumlah penderita kanker yang semakin banyak, mendorong dilakukannya pencarian senyawa antikanker baru. Ekstrak biji mahoni (Swietenia macrophylla) adalah produk tumbuhan yang dilaporkan memiliki aktivitas antikanker. Penelitian ini bertujuan mendapatkan fraksi aktif yang memiliki aktivitas antikanker dari spesies tersebut dan menentukan aktivitas dari ekstrak atau fraksinya terhadap sel MCF-7, T47D, dan HCT116. Fraksionasi ekstrak etil asetat menggunakan kromatografi cair vakum menghasilkan 5 fraksi dengan rendemen antara 3.73% (Fraksi 4) dan 42.22% (Fraksi 3). Hasil studi pustaka ekstrak etanol menunjukkan aktivitas antikanker terhadap sel HCT116 dan MCF-7. Ekstrak etil asetat dan fraksinya dilaporkan sitotoksik terhadap sel kanker MCF-7 dan T47D. Senyawa aktif tersebut diduga adalah golongan limonoid.
Keyword: Sel HCT116, MCF-7, T47D,, Swietenia macrophylla

Judul: Which one use to overcome multicollinearity ? Ridge regression or principal component regression
Abstrak: In multiple linear regression the effect of nonorthogonality of the prediction vectors is to pull the least squares estimates of the regression coefficient away from the true coefficient that one is trying to estimate. Separating the effects of each factor of the regressors has been found to be difficult because of high correlation among independent variables. Although the multicollinearity problem has been recognized by many ways, ordinary least squares is still the main technique used in many fields, but the estimation of regression coefficient can present problems when the data vectors for the predictors are not orthogonal, or in this case are multicollinear. In using ordinary least squares, the regression coefficient may be unstable, too large in absolute value, and incorrect with respect to sign. Introduced biased estimation refer to unbiased one that measure the average closeness to the parameter being estimated corresponding to its mean square error (MSE). Ridge regression and principal component can produce better estimates by its own way than those given by ordinary least squares. It may well be preferred estimator since it will have larger probability of being close to true parameter value. Two of the biased regression methods, ridge regression and principal component regression will be applied into this research and discuss briefly for each of its properties. These methods have its particular idiosyncrasy. The biased regression methods attack the collinearity problem by computationally suppressing the effects of the collinearity. Ridge regression does this correctly by modifying the correlation matrix adding a biased constant driving through nonsingular matrix. Principal component regression attacks the problem by regressing Y on the important principal component and then parceling out the effect of the principal component variables to the original variables. Finally translated back the obtained variables into original variables yield the same sign and nearly the same coefficients for both methods.
Keyword: Regresssion analysis