On the Thermochemical Transition Depression of Cellulose Acetate Composite Membranes.

Gallic acid (GA) and quercetin (QU) are two important bioactive molecules with increased biomedical interest. Cellulose acetate (CA) is a polymer derived from cellulose and is used in various applications. In this work, differential scanning calorimetry (DSC), thermogravimetric analysis (TGA) and Fourier transform infrared spectroscopy (FTIR) were used to study the thermal behavior of electrospun CA membranes loaded with quercetin or gallic acid. It was found that gallic acid and quercetin depress the thermochemical transition (simultaneous softening and decomposition) of CA, in a mechanism similar to that of the glass transition depression of amorphous polymers by plasticizers. The extensive hydrogen bonding, besides the well-known effect of constraining polymer's softening by keeping macromolecules close to each other, has a secondary effect on the thermochemical transition, i.e., it weakens chemical bonds and, inevitably, facilitates decomposition. This second effect of hydrogen bonding can provide an explanation for an unexpected observation of this study: CA membranes loaded with quercetin or gallic acid soften at lower temperatures; however, at the same time, they decompose to a higher extent than pure CA. Besides optimization of CA processing, the fundamental understanding of the thermochemical transition depression could lead to the design of more sustainable processes for biomass recycling and conversion.

Machine learning and data mining frameworks for predicting drug response in cancer: An overview and a novel in silico screening process based on association rule mining.

A major challenge in cancer treatment is predicting the clinical response to anti-cancer drugs on a personalized basis. The success of such a task largely depends on the ability to develop computational resources that integrate big "omic" data into effective drug-response models. Machine learning is both an expanding and an evolving computational field that holds promise to cover such needs. Here we provide a focused overview of: 1) the various supervised and unsupervised algorithms used specifically in drug response prediction applications, 2) the strategies employed to develop these algorithms into applicable models, 3) data resources that are fed into these frameworks and 4) pitfalls and challenges to maximize model performance. In this context we also describe a novel in silico screening process, based on Association Rule Mining, for identifying genes as candidate drivers of drug response and compare it with relevant data mining frameworks, for which we generated a web application freely available at: https://compbio.nyumc.org/drugs/. This pipeline explores with high efficiency large sample-spaces, while is able to detect low frequency events and evaluate statistical significance even in the multidimensional space, presenting the results in the form of easily interpretable rules. We conclude with future prospects and challenges of applying machine learning based drug response prediction in precision medicine.