Microplastic in industrial aquaculture: Occurrence in the aquatic environment, feed and organisms (Dicentrarchus labrax).

The increasing use of plastics and the growing concern about their impact on the environment and living beings makes it necessary to study how microplastics (MP) affect aquaculture systems. In order to gain an in-depth understanding of these systems, this study covers the water intake, the purification treatment at the inlet, the water in the culture tanks, as well as the feed used in the feeding and the organism itself. For this purpose, five samples were taken, both in the water line, feed and sea bass during the weeks of the experiment. It is shown that the available purification systems reduce the amount of MP entering from the receiving environment. However, new MP are observed in the sea bass tank, which may be due mainly to those added through the feed and found in the feed, as well as in the piping and other materials used in current aquaculture systems (PTFE, PA, among others). If focusing on the feed that can reach the consumer, in the case of this study, carried out with sea bass, some types of MP (PE, PTFE, PS and PA) were found in 4 head samples and 4 skin/muscle samples. Although inlet water purification systems manage to reduce a high percentage of MPs in the system, it is observed that there are other access routes that should be considered and reduced in aquaculture facilities to prevent them from reaching the human consumer.

Integrative Clinical, Molecular, and Computational Analysis Identify Novel Biomarkers and Differential Profiles of Anti-TNF Response in Rheumatoid Arthritis.

Background: This prospective multicenter study developed an integrative clinical and molecular longitudinal study in Rheumatoid Arthritis (RA) patients to explore changes in serologic parameters following anti-TNF therapy (TNF inhibitors, TNFi) and built on machine-learning algorithms aimed at the prediction of TNFi response, based on clinical and molecular profiles of RA patients. Methods: A total of 104 RA patients from two independent cohorts undergoing TNFi and 29 healthy donors (HD) were enrolled for the discovery and validation of prediction biomarkers. Serum samples were obtained at baseline and 6 months after treatment, and therapeutic efficacy was evaluated. Serum inflammatory profile, oxidative stress markers and NETosis-derived bioproducts were quantified and miRNomes were recognized by next-generation sequencing. Then, clinical and molecular changes induced by TNFi were delineated. Clinical and molecular signatures predictors of clinical response were assessed with supervised machine learning methods, using regularized logistic regressions. Results: Altered inflammatory, oxidative and NETosis-derived biomolecules were found in RA patients vs. HD, closely interconnected and associated with specific miRNA profiles. This altered molecular profile allowed the unsupervised division of three clusters of RA patients, showing distinctive clinical phenotypes, further linked to the TNFi effectiveness. Moreover, TNFi treatment reversed the molecular alterations in parallel to the clinical outcome. Machine-learning algorithms in the discovery cohort identified both, clinical and molecular signatures as potential predictors of response to TNFi treatment with high accuracy, which was further increased when both features were integrated in a mixed model (AUC: 0.91). These results were confirmed in the validation cohort. Conclusions: Our overall data suggest that: 1. RA patients undergoing anti-TNF-therapy conform distinctive clusters based on altered molecular profiles, which are directly linked to their clinical status at baseline. 2. Clinical effectiveness of anti-TNF therapy was divergent among these molecular clusters and associated with a specific modulation of the inflammatory response, the reestablishment of the altered oxidative status, the reduction of NETosis, and the reversion of related altered miRNAs. 3. The integrative analysis of the clinical and molecular profiles using machine learning allows the identification of novel signatures as potential predictors of therapeutic response to TNFi therapy.