Every year, close to 8 million tons of waste crab, shrimp and lobster shells are produced globally, as well as 10 million tons of waste oyster, clam, scallop and mussel shells. The disposed shells are frequently dumped at sea or sent to landfill, where they modify soils, waters and marine ecosystems. Waste shells are a major by-product, which should become a new raw material to be used to the best of their potential. There are a number of applications for waste shells in many fields, such as agriculture, medicine, chemical production, construction, environmental protection, cosmetic industry, food and feed industry, and a plethora of other (often niche) applications, which are being developed by the day. This review provides a broad picture of crustacean and mollusc shell waste management and reutilization possibilities, reviewing well established, current, and potential strategies, particularly from the standpoint of sustainability challenges and energy demand.

Sulfated polysaccharide fucoidan isolated from brown algae shows a wide range of biological activities that are significantly dependent on its chemical composition, which is closely related to the applied technique and extraction parameters. Therefore, the objective of this study was to evaluate the influence of microwave assisted extraction (MAE) and pressurized liquid extraction (PLE) parameters (solvent, temperature, time, and number of cycles) on the Fucus virsoides and Cystoseira barbata polysaccharide yield (%PS) and chemical composition (total sugar, fucose, and sulfate group). The optimal MAE parameters that resulted in the highest polysaccharide extraction from F. virsoides and C. barbata were 0.1 M H₂SO₄ for 10 min at 80 °C, while the optimal PLE parameters were 0.1 M H₂SO₄, for two cycles of 15 min at 140 °C. Furthermore, the %PS, chemical structure, molecular properties, and antioxidant activity of the F. virsoides and C. barbata polysaccharide extracts obtained with MAE, PLE, and conventional extraction (CE) performed under previously determinate optimal conditions were compared. PLE resulted in a significantly higher %PS from F. virsoides, while for C. barbata, a similar yield was achieved with CE and PLE, as well as CE and MAE, for both algae. Furthermore, the polysaccharides obtained using PLE had the highest polydispersity index, fucose, and sulfate group content, and the lowest uronic acid content; however their antioxidant activity was lower.

Marine microalgae and cyanobacteria are sources of diverse bioactive compounds with potential biotechnological applications in food, feed, nutraceutical, pharmaceutical, cosmetic and biofuel industries. In this study, five microalgae, Nitzschia sp. S5, Nanofrustulum shiloi D1, Picochlorum sp. D3, Tetraselmis sp. Z3 and Tetraselmis sp. C6, and the cyanobacterium Euhalothece sp. C1 were isolated from the Adriatic Sea and characterized regarding their growth kinetics, biomass composition and specific products content (fatty acids, pigments, antioxidants, neutral and polar lipids). The strain Picochlorum sp. D3, showing the highest specific growth rate (0.009 h⁻¹), had biomass productivity of 33.98 ± 0.02 mg L⁻¹ day⁻¹. Proteins were the most abundant macromolecule in the biomass (32.83–57.94%, g g⁻¹). Nanofrustulum shiloi D1 contained significant amounts of neutral lipids (68.36%), while the biomass of Picochlorum sp. D3, Tetraselmis sp. Z3, Tetraselmis sp. C6 and Euhalothece sp. C1 was rich in glycolipids and phospholipids (75%). The lipids of all studied microalgae predominantly contained unsaturated fatty acids. Carotenoids were the most abundant pigments with the highest content of lutein and neoxanthin in representatives of Chlorophyta and fucoxanthin in strains belonging to the Bacillariophyta. All microalgal extracts showed antioxidant activity and antimicrobial activity against Gram-negative E. coli and S. typhimurium and Gram-positive S. aureus.

The purpose of this study was the identification and quantification of biochemical parameters over a 1-year cycle and to provide a detailed picture of seasonal changes in plasma metabolites and enzymes. Using the novel methods of machine learning techniques, the authors created and generated for the first time comprehensible classification models for exploring the importance of blood chemistry parameters, strength, mutual interactions or dependencies, and reliability of particular parameters within the seasonal groups