RESUMEN
This paper highlights the potential of Sargassum algae, recovered from raw beach seaweed wastes, as a valid source of valuable sodium alginate. Alginate is a biodegradable, highly attractive polysaccharide widely used in food, pharmaceuticals, and biomedicine applications. The aim of this work is to employ a new eco-sustainable and cost-effective extractive method to obtain alginate as a raw material from pollutant organic Sargassum seaweeds. Algae were exposed to microwave pre-treatment under static and dynamic conditions, and three different extractive protocols were followed: (a) conventional, (b) hot water and (c) alkaline method. All samples were characterized by GPC, SEM, FTIR/ATR and TGA. It was found that alginate's best performances were obtained by the microwave dynamic pre-treatment method followed by alkaline extractive protocol. Nevertheless, the microwave pre-treatment of algae allowed the easiest breaking of their cell walls and the following fast releasing of sodium alginate. The authors demonstrated that microwave-enhanced extraction is an effective way to obtain sodium alginate from Sargassum-stranded seaweed waste materials in a cost-effective and eco-sustainable approach. They also assessed their applications as mulching films for agricultural applications.
RESUMEN
Mathematical models based on partial differential equations (PDEs) can be exploited to handle clinical data with space/time dimensions, e.g. tumor growth challenged by neoadjuvant therapy. A model based on simplified assessment of tumor malignancy and pharmacodynamics efficiency was exercised to discover new metrics of patient prognosis in the OLTRE trial. We tested in a 17-patients cohort affected by early-stage triple negative breast cancer (TNBC) treated with 3 weeks of olaparib, the capability of a PDEs-based reactive-diffusive model of tumor growth to efficiently predict the response to olaparib in terms of SUVmax detected at 18FDG-PET/CT scan, by using specific terms to characterize tumor diffusion and proliferation. Computations were performed with COMSOL Multiphysics. Driving parameters governing the mathematical model were selected with Pearson's correlations. Discrepancies between actual and computed SUVmax values were assessed with Student's t test and Wilcoxon rank sum test. The correlation between post-olaparib true and computed SUVmax was assessed with Pearson's r and Spearman's rho. After defining the proper mathematical assumptions, the nominal drug efficiency (εPD) and tumor malignancy (rc) were computationally evaluated. The former parameter reflected the activity of olaparib on the tumor, while the latter represented the growth rate of metabolic activity as detected by SUVmax. εPD was found to be directly dependent on basal tumor-infiltrating lymphocytes (TILs) and Ki67% and was detectable through proper linear regression functions according to TILs values, while rc was represented by the baseline Ki67-to-TILs ratio. Predicted post-olaparib SUV*max did not significantly differ from original post-olaparib SUVmax in the overall, gBRCA-mutant and gBRCA-wild-type subpopulations (p > 0.05 in all cases), showing strong positive correlation (r = 0.9 and rho = 0.9, p < 0.0001 both). A model of simplified tumor dynamics was exercised to effectively produce an upfront prediction of efficacy of 3-week neoadjuvant olaparib in terms of SUVmax. Prospective evaluation in independent cohorts and correlation of these outcomes with more recognized efficacy endpoints is now warranted for model confirmation and tailoring of escalated/de-escalated therapeutic strategies for early-TNBC patients.