Lipidomics analysis of juveniles' blue mussels (Mytilus edulis L. 1758), a key economic and ecological species

Blue mussels (Mytilus edulis L. 1758) are important components of coastal ecosystems and in the economy of rural and coastal areas. The understanding of their physiological processes at key life stages is important both within food production systems and in the management of wild populations. Lipids are crucial molecules for bivalve growth, but their diversity and roles have not been fully characterized. In this study, traditional lipid profiling techniques, such as fatty acid (FA) and lipid class analysis, are combined to un-targeted lipidomics to elucidate the lipid metabolism in newly settled spat fed on a range of diets. The evaluated diets included single strains treatments (Cylindrotheca fusiformis CCAP 1017/2 –CYL, Isochrysis galbana CCAP 927/1– ISO, Monodopsis subterranean CCAP 848/1 –MONO, Nannochloropsis oceanica CCAP 849/10– NANNO) and a commercial algae paste (SP). Spat growth was influenced by the diets, which, according to their efficacy were ranked as follows: ISO>NANNO/CYL>SP>MONO. A higher triacylglycerols (TG) content, ranging from 4.23±0.82 μg mgashfree Dry weight (DW)-1 at the beginning of the trial (T0) to 51±15.3 μg mgashfreeDW-1 in ISO, characterised significant growth in the spat, whereas, a reduction of TG (0.3±0.08 μg mgashfreeDW-1 in MONO), mono unsaturated FA–MUFA (from 8.52±1.02 μg mgFAashfreeDW-1 at T0 to 2.81±1.02 μg mgFAashfreeDW-1 in MONO) and polyunsaturated FA–PUFA (from 17.57±2.24 μg mgFAashfreeDW-1 at T0 to 6.19±2.49 μg mgFAashfreeDW-1 in MONO) content characterised poor performing groups. Untargeted lipidomics evidenced how the availability of dietary essential PUFA did not influence only neutral lipids but also the membrane lipids, with changes in lipid molecular species in relation to the essential PUFA provided via the diet. Such changes have the potential to affect spat production cycle and their ability to respond to the surrounding environment. This study evidenced the advantages of coupling different lipid analysis techniques, as each technique disclosed relevant information on nutritional requirements of M. edulis juveniles, expanding the existing knowledge on the physiology of this important species

From Ecology to Biotechnology, Study of the Defense Strategies of Algae and Halophytes (from Trapani Saltworks, NW Sicily) with a Focus on Antioxidants and Antimicrobial Properties

This study aimed at the characterization of the antioxidant power of polyphenol extracts (PE) obtained from the algae Cystoseira foeniculacea (CYS) (Phaeophyta) and from the halophyte Halocnemum strobilaceum (HAL), growing in the solar saltworks of western Sicily (Italy), and at the evaluation of their anti-microfouling properties, in order to correlate these activities to defense strategies in extreme environmental conditions. The antioxidant properties were assessed in the PE based on the total antioxidant activity test and the reducing power test; the anti-microfouling properties of the two PE were evaluated by measuring the growth inhibition of marine fish and shellfish pathogen bacteria as well as marine surface fouling bacteria and microalgae exposed to the fractions. Similar polyphenol content (CYS 5.88 ± 0.75 and HAL 6.03 ± 0.25 mg gallic acid equivalents (GAE) g−1 dried weight, DW) and similar reducing power percentage (93.91 ± 4.34 and 90.03 ± 6.19) were recorded for both species, even if they exhibited a different total antioxidant power (measured by the percentage of inhibition of the radical 2,2 diphenyl-1-picrylhydrazyl DPPH), with CYS (79.30) more active than HAL (59.90). Both PE showed anti-microfouling properties, being inhibitors of adhesion and growth of marine fish and shellfish pathogen bacteria (V. aestuarianus, V. carchariae, V. harveyi, P. elyakovii, H. aquamarina) and fouling bacteria (V. natriegens, V. proteolyticus, P. iirgensii, R. litoralis) with minimum inhibitory concentrations comparable to the commercial antifouling products used as a positive control (SEA-NINE™ 211N). Only CYS was a significant inhibitor of the microalgae strains tested, being able to reduce E. gayraliae and C. closterium growth (MIC 10 µg·mL−1) and the adhesion of all three strains tested (E. gayraliae, C. closterium and P. purpureum), suggesting its promise for use as an antifouling (AF) product

Artificial Intelligence Applications on Restaging [18F]FDG PET/CT in Metastatic Colorectal Cancer: A Preliminary Report of Morpho-Functional Radiomics Classification for Prediction of Disease Outcome

Featured Application Based on results defined in this study, new investigations might propose morpho-functional-based radiomics algorithms for risk stratification with possible impact on treatment management in colorectal cancer. The aim of this study was to investigate the application of [F-18]FDG PET/CT images-based textural features analysis to propose radiomics models able to early predict disease progression (PD) and survival outcome in metastatic colorectal cancer (MCC) patients after first adjuvant therapy. For this purpose, 52 MCC patients who underwent [F-18]FDGPET/CT during the disease restaging process after the first adjuvant therapy were analyzed. Follow-up data were recorded for a minimum of 12 months after PET/CT. Radiomics features from each avid lesion in PET and low-dose CT images were extracted. A hybrid descriptive-inferential method and the discriminant analysis (DA) were used for feature selection and for predictive model implementation, respectively. The performance of the features in predicting PD was performed for per-lesion analysis, per-patient analysis, and liver lesions analysis. All lesions were again considered to assess the diagnostic performance of the features in discriminating liver lesions. In predicting PD in the whole group of patients, on PET features radiomics analysis, among per-lesion analysis, only the GLZLM_GLNU feature was selected, while three features were selected from PET/CT images data set. The same features resulted more accurately by associating CT features with PET features (AUROC 65.22%). In per-patient analysis, three features for stand-alone PET images and one feature (i.e., HUKurtosis) for the PET/CT data set were selected. Focusing on liver metastasis, in per-lesion analysis, the same analysis recognized one PET feature (GLZLM_GLNU) from PET images and three features from PET/CT data set. Similarly, in liver lesions per-patient analysis, we found three PET features and a PET/CT feature (HUKurtosis). In discrimination of liver metastasis from the rest of the other lesions, optimal results of stand-alone PET imaging were found for one feature (SUVbwmin; AUROC 88.91%) and two features for merged PET/CT features analysis (AUROC 95.33%). In conclusion, our machine learning model on restaging [F-18]FDGPET/CT was demonstrated to be feasible and potentially useful in the predictive evaluation of disease progression in MCC