Sea organisms as a source of collagen-derived polymers for skin tissue engineering applications

Advanced therapies combating acute and chronic skin wounds are likely to be brought about using our knowledge of regenerative medicine coupled with appropriately tissue engineered skin substitutes. At the present time, there are no models of an artificial skin that completely replicate normal uninjured skin and they are usually accompanied by fibrotic reactions that result in the production of a scar. Natural biopolymers such as collagen have been a lot investigated as potential source of biomaterial for skin replacement in Tissue Engineering. Collagens are the most abundant high molecular weight proteins in both invertebrate and vertebrate organisms, including mammals, and possess mainly a structural role in connective tissues. From this, they have been elected as one of the key biological materials in tissue regeneration approaches, as skin tissue engineering. In addition, industry is constantly searching for new natural sources of collagen and upgraded methodologies for their production. The most common sources are skin and bone from bovine and porcine origin. However, these last carry high risk of bovine spongiform encephalopathy or transmissible spongiform encephalopathy and immunogenic responses. On the other hand, the increase of jellyfish has led us to consider this marine organism as potential collagen source for tissue engineering applications. In the present study, novel form of acid and pepsin soluble collagen were extracted from dried Rhopilema hispidum jellyfish species in an effort to obtain an alternative and safer collagen. We studied different methods of collagen purification (tissues and experimental procedures). The best collagen yield was obtained using pepsin extraction method (34.16 mg collagen/g of tissue). The isolated collagen was characterized by SDS-polyacrylamide gel electrophoresis and circular dichroism spectroscopy.

Synthetic data generation for the assessment of antimicrobial resistance through machine learning

As a consequence of the diffusion of next generation sequencing techniques, metagenomics databases have become one of the most promising repositories of information about features and behavior of microorganisms. One of the subjects that can be studied from those data are bacteria populations. Next generation sequencing techniques allow to study the bacteria population within an environment by sampling genetic material directly from it, without the needing of culturing a similar population in vitro and observing its behavior. As a drawback, it is quite complex to extract information from those data and usually there is more than one way to do that; AMR is no exception. In this study we will discuss how the quantified AMR, which regards the genotype of the bacteria, can be related to the bacteria phenotype and its actual level of resistance against the specific substance. In order to have a quantitative information about bacteria genotype, we will evaluate the resistome from the read libraries, aligning them against CARD database. With those data, we will test various machine learning algorithms for predicting the bacteria phenotype. The samples that we exploit should resemble those that could be obtained from a natural context, but are actually produced by a read libraries simulation tool. In this way we are able to design the populations with bacteria of known genotype, so that we can relay on a secure ground truth for training and testing our algorithms.