The lipid and fatty acid profiles of 14 marine macroalgal species from the Madeira Archipelago, including two green (Ulvales and Dasycladales), three red (Corallinales, Bonnemaisoniales, and Ceramiales) and nine brown (Fucales, Dictyotales, and Sphacelariales) species were characterised in order to determine their potential use for animal and human nutrition. The total lipid content of species analysed was generally low, varying from 0.2 to 5.2% of dry weight. All species presented an omega 6/omega 3 (n-6/n-3) ratio lower than 10, as recommended by the World Health Organization for proper human health. Polyunsaturated fatty acids (PUFA), including linoleic acid and alpha-linolenic acid were exceptionally high in the green macroalga Ulva sp. Red macroalgae were rich in n-3 long-chain PUFA, particularly Asparagopsis taxiformis, which contained 6.6% of docosahexaenoic acid, and Halopithys incurva with 9.3% of eicosapentaenoic acid. Within Ochrophyta, Dictyota dichotoma is an interesting source of n-3 PUFA due to its high stearidonic acid proportion (8.0%). In addition, H. incurva contained a high proportion of both mono- and digalactosyldiacylglycerols. According to their lipid profiles, most macroalgae analysed might be considered of particular interest for their potential exploitation for human nutrition and livestock and aquaculture production.

Huntington’s disease (HD) is an autosomal dominant, incurable neurodegenerative disease caused by mutation in the huntingtin gene (HTT). HTT mutation leads to protein misfolding and aggregation, which affect cells’ functions and structural features. Because these changes might modify the scattering strength of affected cells, we propose that random lasing (RL) is an appropriate technique for detecting cells that express mutated HTT. To explore this hypothesis, we used a cell model of HD based on the expression of two different forms—pathogenic and non-pathogenic—of HTT. The RL signals from both cell profiles were compared. A multivariate statistical analysis of the RL signals based on the principal component analysis (PCA) and linear discriminant analysis (LDA) techniques revealed substantial differences between cells that expressed the pathogenic and the non-pathogenic forms of HTT.

Huntington's disease (HD) is an autosomal dominant neurodegenerative disease caused by an expansion of CAG triplet repeats in the gene that encodes the protein Huntingtin (HTT). Proteolytic fragments of the mutant HTT (mHTT) are accumulated in neurons leading to neurodegeneration. HD has no cure, and most research efforts are focused on finding disease-modifying therapies and biomarkers of disease progression and treatment efficacy. Random lasing (RL) has been successfully used in biomedicine to differentiate normal from pathological tissues showing robust morphological and structural differences. Here, we evaluate the potential of RL in discriminating brain samples of a transgenic mouse model of HD from those of its wild-type littermates. Furthermore, we also investigate the sensibility of RL to the effects of a mHTT lowering treatment in transgenic mice therapy. The results reveal that multivariate statistical analysis of RL signals discriminates between healthy and transgenic mice and also between treated and untreated transgenic mice. These findings open up perspectives for RL as a sensing tool in HD and, possibly, in other neurodegenerative diseases whose pathogenic hallmark is the accumulation of anomalous proteins.

Random lasing (RL) is an optical phenomenon that arises from the combination of light amplification with optical feedback through multiple scattering events. In this paper, we present our investigations of RL generation from human blood samples. We tested mixtures of rhodamine B dye solutions with different blood components, including platelets, lymphocytes, erythrocytes, and whole blood. Intense coherent RL was obtained in all cases at relatively low pump thresholds, except for erythrocytes. We also studied the potential of RL signal analysis for biosensing applications using blood samples from healthy individuals and patients suffering from Chronic Lymphocytic Leukemia (CLL). CLL is a blood disease characterized by a high count of lymphocytes with significant morphological changes. A statistical analysis of the RL spectra based on principal component and linear discriminant analyses was conducted for classification purposes. RL-based sample discrimination was conducted for whole blood, platelet, and lymphocyte samples, being especially successful (86.7%) for the latter. Our results highlight the potential of RL analysis as a sensing tool in blood.