Chapters authored
Bayesian Estimation of Multivariate Autoregressive Hidden Markov Model with Application to Breast Cancer Biomarker Modeling
In this work, a first-order autoregressive hidden Markov model (AR(1)HMM) is proposed. It is one of the suitable models to characterize a marker of breast cancer disease progression essentially the progression that follows from a reaction to a treatment or caused by natural developments. The model supposes we have observations that increase or decrease with relation to a hidden phenomenon. We would like to discover if the information about those observations can let us learn about the progression of the phenomenon and permit us to evaluate the transition between its states (supposed discrete here). The hidden states governed by the Markovian process would be the disease stages, and the marker observations would be the depending observations. The parameters of the autoregressive model are selected at the first level according to a Markov process, and at the second level, the next observation is generated from a standard autoregressive model of first order (unlike other models considering the successive observations are independents). A Markov Chain Monte Carlo (MCMC) method is used for the parameter estimation, where we develop the posterior density for each parameter and we use a joint estimation of the hidden states or block update of the states.
Part of the book: Bayesian Inference
Natural Products as Cytotoxic Agents in Chemotherapy against Cancer
Nature continues to produce a great wealth of natural molecules endowed with cytotoxic activity toward a large panel of tumor cells. Some of these molecules are used in chemotherapy, and others have shown great anti-tumor and anti-metastatic potential in preclinical trials. This review discusses some examples of these molecules that have been studied in our laboratory and others. We report a differential cytotoxic activity of some monoterpenes (carvacrol, tymol, carveol, carvone, and isopulegol) against a panel of tumor cell lines. The carvacrol was the most cytotoxic molecule both in vitro and in vivo as demonstrated by preclinical studies using the DBA2/P815 mice model. On the other hand, polyphenols were also studied with respect to their cytotoxic effects. Interestingly, these compounds showed a prominent cytotoxic activity toward a panel of cancer cells with differential molecular mechanisms. In addition, we report a very strong antitumor efficacy of artemisinin, a sesquiterpen lactone from Artemisia annua, together with an antimetastatic potential as demonstrated by preclinical experiments. Furthermore, some of the molecular mechanisms involved in these effects are described.
Part of the book: Cytotoxicity