Harmful algal blooms (HABs) are among the most severe ecological marine problems worldwide. Under favorable climate and oceanographic conditions, toxin-producing microalgae species may proliferate, reach increasingly high cell concentrations in seawater, accumulate in shellfish, and threaten the health of seafood consumers. There is an urgent need for the development of effective tools to help shellfish farmers to cope and anticipate HAB events and shellfish contamination, which frequently leads to significant negative economic impacts. Statistical and machine learning forecasting tools have been developed in an attempt to better inform the shellfish industry to limit damages, improve mitigation measures and reduce production losses. This study presents a synoptic review covering the trends in machine learning methods for predicting HABs and shellfish biotoxin contamination, with a particular focus on autoregressive models, support vector machines, random forest, probabilistic graphical models, and artificial neural networks (ANN). Most efforts have been attempted to forecast HABs based on models of increased complexity over the years, coupled with increased multi-source data availability, with ANN architectures in the forefront to model these events. The purpose of this review is to help defining machine learning-based strategies to support shellfish industry to manage their harvesting/production, and decision making by governmental agencies with environmental responsibilities.

The diets and the trophic niche overlap between seven flatfish species were studied in a coastal nursery adjoining to the Tagus estuary (Portugal). Fish were sampled monthly, from March to November 1999, using a beach seine. Arnoglossus imperialis (Rafinesque, 1810), Arnoglossus laterna (Walbaum, 1792) and Arnoglossus thori Kyle, 1913, fed mainly on crustaceans. The diets of Buglossidium luteum (Risso, 1810) and Dicologoglossa cuneata (Moreau, 1881) were mainly composed of Bivalvia and Polychaeta, while for Scophthalmus rhombus (Linnaeus, 1758) the main food items were Mysidacea and Teleostei. The diet of Pegusa lascaris (Risso, 1810) was mainly composed by Cumacea, Bivalvia, Decapoda and Amphipoda. Based on diet similarities two main groups were identified: one composed of A. imperialis, A. laterna, A. thori and S. rhombus, and the other grouping B. luteum, P. lascaris and D. cuneata. For the most common flatfishes, a similar pattern of diet seasonal variation was found, such that Amphipoda presented higher indices values in the period from March to June, while from July to November, Decapoda were more abundant. Although high values of diet overlap were obtained among some of the species, the main items in the diet of flatfishes are probably the most abundant prey in this coastal area, which suggests a generalist and opportunistic utilization of these food resources. Furthermore, niche overlap between these species is probably minimized by differences in resource use in other niche dimensions, namely time and space.

Chronic kidney disease (CKD) poses a significant and growing global health challenge, making early detection and slowing disease progression essential for improving patient outcomes. Traditional diagnostic methods such as glomerular filtration rate and proteinuria are insufficient to capture the complexity of CKD. In contrast, omics technologies have shed light on the molecular mechanisms of CKD, helping to identify biomarkers for disease assessment and management. Artificial intelligence (AI) and machine learning (ML) could transform CKD care, enabling biomarker discovery for early diagnosis and risk prediction, and personalized treatment. By integrating multi‐omics datasets, AI can provide real‐time, patient‐specific insights, improve decision support, and optimize cost efficiency by early detection and avoidance of unnecessary treatments. Multidisciplinary collaborations and sophisticated ML methods are essential to advance diagnostic and therapeutic strategies in CKD. This review presents a comprehensive overview of the pipeline for translating CKD omics data into personalized treatment, covering recent advances in omics research, the role of ML in CKD, and the critical need for clinical validation of AI‐driven discoveries to ensure their efficacy, relevance, and cost‐effectiveness in patient care.