RESUMEN
For progressive prostate cancer, intermittent androgen deprivation (IAD) is one of the most common and effective treatments. Although this treatment is usually initially effective at regressing tumors, most patients eventually develop castration-resistant prostate cancer (CRPC), for which there is no effective treatment and is generally fatal. Although several biologic mechanisms leading to CRPC development and their relative frequencies have been identified, it is difficult to determine which mechanisms of resistance are developing in a given patient. Personalized therapy that identifies and targets specific mechanisms of resistance developing in individual patients is likely one of the most promising methods of future cancer therapy. Prostate-specific antigen (PSA) is a biomarker for monitoring tumor progression. We incorporated a cell death rate (CDR) function into a previous dynamical PSA model that was highly accurate at fitting clinical PSA data for 7 patients. The mechanism of action of IAD is largely induction of apoptosis, and each mechanism of resistance varies in its CDR dynamics. Thus, we analyze the CDR levels and their time-dependent oscillations to identify mechanisms of resistance to IAD developing in individual patients.
Asunto(s)
Andrógenos/metabolismo , Antineoplásicos Hormonales/uso terapéutico , Resistencia a Antineoplásicos , Neoplasias de la Próstata/tratamiento farmacológico , Neoplasias de la Próstata/metabolismo , Algoritmos , Biomarcadores de Tumor , Simulación por Computador , Progresión de la Enfermedad , Humanos , Masculino , Modelos Biológicos , Orquiectomía , Antígeno Prostático Específico , Neoplasias de la Próstata/patologíaRESUMEN
The hepatitis delta virus (HDV) is a rarest form of viral hepatitis, but has the worst outcomes for patients.It is a subviral satellite dependent on coinfection with hepatitis B (HBV) to replicate within the host liver.To date, there has been little to no modeling effort for HDV. Deriving and analyzing such a mathematical model poses difficulty as it requires the inclusion of (HBV). Here we begin with a well-studied HBV model from the literature and expand it to incorporate HDV. We investigate two models, one with and one without infected hepatocyte replication. Additionally, we consider treatment by the drug lamivudine. Comparison of model simulations with experimental results of lamivudine treatment indicate that infected cell proliferation may play a significant role in chronic HDV infection. Our results also shed light on several questions surrounding HDV and illustrate the need for more data.
Asunto(s)
Coinfección/inmunología , Virus de la Hepatitis B/inmunología , Hepatitis B/inmunología , Hepatitis D/inmunología , Virus de la Hepatitis Delta/inmunología , Modelos Inmunológicos , Proliferación Celular/efectos de los fármacos , Coinfección/tratamiento farmacológico , Coinfección/virología , Simulación por Computador , Hepatitis B/tratamiento farmacológico , Hepatitis B/virología , Hepatitis D/tratamiento farmacológico , Hepatitis D/virología , Hepatocitos/virología , Humanos , Lamivudine/farmacología , Lamivudine/uso terapéutico , Replicación Viral/inmunologíaRESUMEN
Many green microalgae significantly increased their cellular neutral lipid content when cultured in nitrogen limited or high light conditions. Due to their lipid production potential, these algae have been suggested as promising feedstocks for biofuel production. However, no models for algal lipid synthesis with respect to nutrient and light have been developed to predict lipid production and to help improve the production process. A mathematical model is derived describing the growth dynamics and neutral lipid production of green microalgae grown in batch cultures. The model assumed that as the nitrogen was depleted, photosynthesis became uncoupled from growth, resulting in the synthesis and accumulation of neutral lipids. Simulation results were compared with experimental data for the green microalgae Pseudochlorococcum sp. For growth media with low nitrogen concentration, the model agreed closely with the data; however, with high nitrogen concentration the model overestimated the biomass. It is likely that additional limiting factors besides nitrogen could be responsible for this discrepancy.