Metal bioaccumulation and metallothionein (MT) expression were investigated in the gills and liver of the red-blooded Antarctic teleost Trematomus hansoni to evaluate the possibility for this species to face, with adequate physiological responses, an increase of copper and cadmium concentrations in its tissues. Specimens of this Antarctic fish were collected from Terra Nova Bay (Ross Sea) and used for a metal exposure experiment in controlled laboratory conditions. The two treatments led to a significant accumulation of both metals and increased gene transcription only for the MT-1. The biosynthesis of MTs was verified especially in specimens exposed to Cd, but most of these proteins were soon oxidized, probably because they were involved in cell protection against oxidative stress risk by scavenging reactive oxygen species. The obtained data highlighted the phenotypic plasticity of T. hansoni, a species that evolved in an environment characterized by naturally high concentrations of Cu and Cd, and maybe the possibility for the Antarctic fish to face the challenges of a world that is becoming more toxic every day.

Glutathione peroxidases (GPxs) form a broad family of antioxidant proteins essential for maintaining redox homeostasis in eukaryotic cells. In this study, we used an integrative approach that combines bioinformatics, molecular biology, and biochemistry to investigate the role of GPxs in reactive oxygen species detoxification in the unicellular eukaryotic model organism Tetrahymena thermophila. Both phylogenetic and mechanistic empirical model analyses provided indications about the evolutionary relationships among the GPXs of Tetrahymena and the orthologous enzymes of phylogenetically related species. In-silico gene characterization and text mining were used to predict the functional relationships between GPxs and other physiologically-relevant processes. The GPx genes contain conserved transcriptional regulatory elements in the promoter region, which suggest that transcription is under tight control of specialized signaling pathways. The bioinformatic findings were next experimentally validated by studying the time course of gene transcription and enzymatic activity after copper (Cu) exposure. Results emphasize the role of GPxs in the detoxification pathways that, by complex regulation of GPx gene expression, enable Tethraymena to survive in high Cu concentrations and the associated redox environment.

Clinicians rely on various data modalities-such as patient history, clinical signs, imaging, and laboratory results-to improve decision making. Multimodal artificial intelligence (AI) systems are emerging as powerful tools to process these diverse data types; however, the clinical adoption of multimodal AI systems is challenging because of data heterogeneity and integration complexities. The 2024 Temerty Centre for AI Research and Education in Medicine symposium, held on June 17, 2024, in Toronto, Canada, explored the potential and challenges of implementing multimodal AI in health care. In this Review, we summarise insights from the symposium. We discuss current applications, such as those used in early diagnosis of sepsis and cardiology, and identify key barriers, including fusion techniques, model selection, generalisation, fairness, safety, security, and international considerations on the responsible deployment of multimodal AI in health care. We outline practical strategies to overcome these obstacles, emphasising technologies such as federated learning to reduce bias and promote equitable health care. By addressing these challenges, multimodal AI can transform clinical practice and improve patient outcomes worldwide.

Metallothioneins (MTs) are low-molecular weight sulfur-rich proteins, widely distributed in nature. They play a homeostatic role in the control and detoxification of metal ions. Previous studies indicated that MTs also have the capacity to scavenge reactive oxygen species. This study aimed to investigate the evolution of the protein in the notothenioid fish, evolved under the selective pressure of relatively high oxygen partial pressures, characteristics of cold Antarctic seawaters, and relatively high concentrations of metal ions, Cd and Cu in particular. The cDNA sequences of MT isoforms were characterized in members of the Nototheniidae, Bathydraconidae, Artedidraconidae, and Channichthyidae families. The phylogenetic relationships of MTs from these families and other teleosts were inferred by using Maximum Likelihood and Bayesian methods. The analysis of coding region and untranslated (UTR) sequences indicated the presence of two MT clades, each containing one of the two MT isoforms, MT-1 and MT-2. Our results indicated, for the first time for these proteins, that the evolution of MT genes has been characterized by strong purifying selection, whereas it did not observe any evidence of positive selection. In addition, phylogenetic analysis of the UTRs suggested that functional changes, in particular related to the MT-1 gene expression, had accompanied the duplication event.

Background: Although prior research has identified multiple risk factors for diabetic ketoacidosis (DKA), clinicians continue to lack clinic-ready models to predict dangerous and costly episodes of DKA. We asked whether we could apply deep learning, specifically the use of a long short-term memory (LSTM) model, to accurately predict the 180-day risk of DKA-related hospitalization for youth with type 1 diabetes (T1D). Objective: We aimed to describe the development of an LSTM model to predict the 180-day risk of DKA-related hospitalization for youth with T1D. Methods: We used 17 consecutive calendar quarters of clinical data (January 10, 2016, to March 18, 2020) for 1745 youths aged 8 to 18 years with T1D from a pediatric diabetes clinic network in the Midwestern United States. The input data included demographics, discrete clinical observations (laboratory results, vital signs, anthropometric measures, diagnosis, and procedure codes), medications, visit counts by type of encounter, number of historic DKA episodes, number of days since last DKA admission, patient-reported outcomes (answers to clinic intake questions), and data features derived from diabetes- and nondiabetes-related clinical notes via natural language processing. We trained the model using input data from quarters 1 to 7 (n=1377), validated it using input from quarters 3 to 9 in a partial out-of-sample (OOS-P; n=1505) cohort, and further validated it in a full out-of-sample (OOS-F; n=354) cohort with input from quarters 10 to 15. Results: DKA admissions occurred at a rate of 5% per 180-days in both out-of-sample cohorts. In the OOS-P and OOS-F cohorts, the median age was 13.7 (IQR 11.3-15.8) years and 13.1 (IQR 10.7-15.5) years; median glycated hemoglobin levels at enrollment were 8.6% (IQR 7.6%-9.8%) and 8.1% (IQR 6.9%-9.5%); recall was 33% (26/80) and 50% (9/18) for the top-ranked 5% of youth with T1D; and 14.15% (213/1505) and 12.7% (45/354) had prior DKA admissions (after the T1D diagnosis), respectively. For lists rank ordered by the probability of hospitalization, precision increased from 33% to 56% to 100% for positions 1 to 80, 1 to 25, and 1 to 10 in the OOS-P cohort and from 50% to 60% to 80% for positions 1 to 18, 1 to 10, and 1 to 5 in the OOS-F cohort, respectively. Conclusions: The proposed LSTM model for predicting 180-day DKA-related hospitalization was valid in this sample. Future research should evaluate model validity in multiple populations and settings to account for health inequities that may be present in different segments of the population (eg, racially or socioeconomically diverse cohorts). Rank ordering youth by probability of DKA-related hospitalization will allow clinics to identify the most at-risk youth. The clinical implication of this is that clinics may then create and evaluate novel preventive interventions based on available resources.

Typical 2-Cys peroxiredoxins (2-Cys Prdxs) are proteins with antioxidant properties belonging to the thioredoxin peroxidase family. With their peroxidase activity, they contribute to the homeostatic control of reactive oxygen species (ROS) and, therefore, participate in various physiological functions, such as cell proliferation, differentiation, and apoptosis. Although Prdxs have been shown to be potential biomarkers for monitoring aquatic environments, minimal scientific attention has been devoted to describing their molecular architecture and function in marine invertebrates. Our study aims to clarify the protective role against stress induced by exposure to metals (Cu, Zn, and Cd) of three Prdxs (Prdx2, Prdx3, and Prdx4) in the solitary ascidian Ciona robusta, an invertebrate chordate. Here, we report a detailed pre- and post-translational regulation of the three Prdx isoforms. Data on intestinal mRNA expression, provided by qRT-PCR analyses, show a generalized increase for Prdx2, -3, and -4, which is correlated to metal accumulation. Furthermore, the increase in tissue enzyme activity observed after Zn exposure is slower than that observed with Cu and Cd. The obtained results increase our knowledge of the evolution of anti-stress proteins in invertebrates and emphasize the importance of the synthesis of Prdxs as an efficient way to face adverse environmental conditions.