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Mismatch Repair Deficiency (dMMR)/Microsatellite Instability (MSI) is a key biomarker in colorectal cancer (CRC). Universal screening of CRC patients for MSI status is now recommended, but contributes to increased workload for pathologists and delayed therapeutic decisions. Deep learning has the potential to ease dMMR/MSI testing and accelerate oncologist decision making in clinical practice, yet no comprehensive validation of a clinically approved tool has been conducted. We developed MSIntuit, a clinically approved artificial intelligence (AI) based pre-screening tool for MSI detection from haematoxylin-eosin (H&E) stained slides. After training on samples from The Cancer Genome Atlas (TCGA), a blind validation is performed on an independent dataset of 600 consecutive CRC patients. Inter-scanner reliability is studied by digitising each slide using two different scanners. MSIntuit yields a sensitivity of 0.96–0.98, a specificity of 0.47-0.46, and an excellent inter-scanner agreement (Cohen’s κ: 0.82). By reaching high sensitivity comparable to gold standard methods while ruling out almost half of the non-MSI population, we show that MSIntuit can effectively serve as a pre-screening tool to alleviate MSI testing burden in clinical practice. Microsatellite instability is a known risk factor for colorectal cancer development and treatment response. Here, the authors utilise deep learning to develop MSIntuit, a pre-screening tool to detect MSI from H&E stained slides.

Although we are currently experiencing worldwide biodiversity loss, local species richness does not always decline under anthropogenic pressure. This conservation paradox may also apply in protected areas but has not yet received conclusive evidence in marine ecosystems. Here, we survey fish assemblages in six Mediterranean no-take reserves and their adjacent fishing grounds using environmental DNA (eDNA) while controlling for environmental conditions. We detect less fish species in marine reserves than in nearby fished areas. The paradoxical gradient in species richness is accompanied by a marked change in fish species composition under different managements. This dissimilarity is mainly driven by species that are often overlooked by classical visual surveys but detected with eDNA: cryptobenthic, pelagic, and rare fishes. These results do not negate the importance of reserves in protecting biodiversity but shed new light on how under-represented species groups can positively react to fishing pressure and how conservation efforts can shape regional biodiversity patterns.

Background An external control arm is a cohort of control patients that are collected from data external to a single-arm trial. To provide an unbiased estimation of efficacy, the clinical profiles of patients from single and external arms should be aligned, typically using propensity score approaches. There are alternative approaches to infer efficacy based on comparisons between outcomes of single-arm patients and machine-learning predictions of control patient outcomes. These methods include G-computation and Doubly Debiased Machine Learning (DDML) and their evaluation for External Control Arms (ECA) analysis is insufficient. Methods We consider both numerical simulations and a trial replication procedure to evaluate the different statistical approaches: propensity score matching, Inverse Probability of Treatment Weighting (IPTW), G-computation, and DDML. The replication study relies on five type 2 diabetes randomized clinical trials granted by the Yale University Open Data Access (YODA) project. From the pool of five trials, observational experiments are artificially built by replacing a control arm from one trial by an arm originating from another trial and containing similarly-treated patients. Results Among the different statistical approaches, numerical simulations show that DDML has the smallest bias followed by G-computation. In terms of mean squared error, G-computation usually minimizes mean squared error. Compared to other methods, DDML has varying Mean Squared Error performances that improves with increasing sample sizes. For hypothesis testing, all methods control type I error and DDML is the most conservative. G-computation is the best method in terms of statistical power, and DDML has comparable power at n = 1000 but inferior ones for smaller sample sizes. The replication procedure also indicates that G-computation minimizes mean squared error whereas DDML has intermediate performances in between G-computation and propensity score approaches. The confidence intervals of G-computation are the narrowest whereas confidence intervals obtained with DDML are the widest for small sample sizes, which confirms its conservative nature. Conclusions For external control arm analyses, methods based on outcome prediction models can reduce estimation error and increase statistical power compared to propensity score approaches.

Non-alcoholic fatty liver disease (NAFLD) is often the hepatic expression of metabolic syndrome and its comorbidities that comprise, among others, obesity and insulin-resistance. NAFLD involves a large spectrum of clinical conditions. These range from steatosis, a benign liver disorder characterized by the accumulation of fat in hepatocytes, to non-alcoholic steatohepatitis (NASH), which is characterized by inflammation, hepatocyte damage, and liver fibrosis. NASH can further progress to cirrhosis and hepatocellular carcinoma. The etiology of NAFLD involves both genetic and environmental factors, including an unhealthy lifestyle. Of note, unhealthy eating is clearly associated with NAFLD development and progression to NASH. Both macronutrients (sugars, lipids, proteins) and micronutrients (vitamins, phytoingredients, antioxidants) affect NAFLD pathogenesis. Furthermore, some evidence indicates disruption of metabolic homeostasis by food contaminants, some of which are risk factor candidates in NAFLD. At the molecular level, several models have been proposed for the pathogenesis of NAFLD. Most importantly, oxidative stress and mitochondrial damage have been reported to be causative in NAFLD initiation and progression. The aim of this review is to provide an overview of the contribution of nutrients and food contaminants, especially pesticides, to oxidative stress and how they may influence NAFLD pathogenesis.

Assessing species geographic distributions is critical to approximate their ecological niches, understand how global change may reshape their occurrence patterns, and predict their extinction risks. Yet, species records are over-aggregated across taxonomic, geographic, environmental, and anthropogenic dimensions. The under-sampling of remote locations biases the quantification of species geographic distributions and ecological niche for most species. Here, we used nearly one thousand environmental DNA (eDNA) samples across the world’s oceans, including polar regions and tropical remote islands, to determine the extent to which the geographic and ecological niche ranges of marine fishes are underestimated through the lens of global occurrence records based on conventional surveys. Our eDNA surveys revealed that the known geographic ranges for 93% of species and the ecological niche ranges for 7% of species were underestimated, and contributed to filling them. We show that the probability to detect a range filling for a given species is primarily shaped by the GBIF/OBIS sampling effort in a cell, but also by the number of occurrences available for the species. Most gap fillings were achieved by addressing a methodological sampling bias, notably when eDNA facilitated the detection of small fishes in previously sampled locations using conventional methods. Using a machine learning model, we found that a local effort of 10 eDNA samples would detect 24 additional fish species on average and a maximum of 98 species in previously unsampled tropical areas. Yet, a null model revealed that only half of ecological niche range fillings would be due to eDNA surveys, beyond a random allocation of classical sampling effort. Altogether, our results suggest that sampling in remote areas and performing eDNA surveys in over-sampled areas may both increase fish ecological niche ranges toward unexpected values with consequences in biodiversity modeling, management, and conservation.

Non-alcoholic fatty liver disease (NAFLD) is a major health issue worldwide, frequently associated with obesity and type 2 diabetes. Steatosis is the initial stage of the disease, which is characterized by lipid accumulation in hepatocytes, which can progress to non-alcoholic steatohepatitis (NASH) with inflammation and various levels of fibrosis that further increase the risk of developing cirrhosis and hepatocellular carcinoma. The pathogenesis of NAFLD is influenced by interactions between genetic and environmental factors and involves several biological processes in multiple organs. No effective therapy is currently available for the treatment of NAFLD. Peroxisome proliferator-activated receptors (PPARs) are nuclear receptors that regulate many functions that are disturbed in NAFLD, including glucose and lipid metabolism, as well as inflammation. Thus, they represent relevant clinical targets for NAFLD. In this review, we describe the determinants and mechanisms underlying the pathogenesis of NAFLD, its progression and complications, as well as the current therapeutic strategies that are employed. We also focus on the complementary and distinct roles of PPAR isotypes in many biological processes and on the effects of first-generation PPAR agonists. Finally, we review novel and safe PPAR agonists with improved efficacy and their potential use in the treatment of NAFLD.

External control arms can inform early clinical development of experimental drugs and provide efficacy evidence for regulatory approval. However, accessing sufficient real-world or historical clinical trials data is challenging. Indeed, regulations protecting patients’ rights by strictly controlling data processing make pooling data from multiple sources in a central server often difficult. To address these limitations, we develop a method that leverages federated learning to enable inverse probability of treatment weighting for time-to-event outcomes on separate cohorts without needing to pool data. To showcase its potential, we apply it in different settings of increasing complexity, culminating with a real-world use-case in which our method is used to compare the treatment effect of two approved chemotherapy regimens using data from three separate cohorts of patients with metastatic pancreatic cancer. By sharing our code, we hope it will foster the creation of federated research networks and thus accelerate drug development. External Control Arm methods for clinical trials were developed to compare the efficacy of a treatment to a control group that is built with data from external sources. Here, the authors present FedECA, a privacy-enhancing method for analyzing treatment effects across institutions, streamlining multi-centric trial design and thereby accelerating drug development while minimizing patient data exposure.

Ceramides are known to promote insulin resistance in a number of metabolically important tissues including skeletal muscle, the predominant site of insulin-stimulated glucose disposal. Depending on cell type, these lipid intermediates have been shown to inhibit protein kinase B (PKB/Akt), a key mediator of the metabolic actions of insulin, via two distinct pathways: one involving the action of atypical protein kinase C (aPKC) isoforms, and the second dependent on protein phosphatase-2A (PP2A). The main aim of this study was to explore the mechanisms by which ceramide inhibits PKB/Akt in three different skeletal muscle-derived cell culture models; rat L6 myotubes, mouse C2C12 myotubes and primary human skeletal muscle cells. Our findings indicate that the mechanism by which ceramide acts to repress PKB/Akt is related to the myocellular abundance of caveolin-enriched domains (CEM) present at the plasma membrane. Here, we show that ceramide-enriched-CEMs are markedly more abundant in L6 myotubes compared to C2C12 myotubes, consistent with their previously reported role in coordinating aPKC-directed repression of PKB/Akt in L6 muscle cells. In contrast, a PP2A-dependent pathway predominantly mediates ceramide-induced inhibition of PKB/Akt in C2C12 myotubes. In addition, we demonstrate for the first time that ceramide engages an aPKC-dependent pathway to suppress insulin-induced PKB/Akt activation in palmitate-treated cultured human muscle cells as well as in muscle cells from diabetic patients. Collectively, this work identifies key mechanistic differences, which may be linked to variations in plasma membrane composition, underlying the insulin-desensitising effects of ceramide in different skeletal muscle cell models that are extensively used in signal transduction and metabolic studies.

Provisioning activities in wildlife tourism often lead to short-term animal aggregations during the feeding events. However, the presence of groups does not necessarily mean that individuals interact among each other and form social networks. At the Shark Reef Marine Reserve in Fiji, several dozen bull sharks ( Carcharhinus leucas ) regularly visit a site, where direct feeding is conducted during tourism driven shark dives. On 3,063 shark feeding dives between 2003 and 2016, we visually confirmed the presence of 91 individual bull sharks based on external and long-lasting identification markings. We measured the intensity of associations between pairs of individuals by calculating the Simple Ratio Index (SRI) and calculated Generalized Affiliation Indices (GAIs) to distinguish true associations between dyads from structural predictor factors. Although the resulting mean SRIs were low, ranging from 0.01 to 0.12 (SRI mean = 0.06; mean SRI max = 0.21), preferred long-term companionships were observed between individuals. Avoidances were also observed within pairs of individuals during the second half of the study. The best fitting model describing the temporal association patterns of bull sharks revealed a social structure which is characterized by preferred companionships and casual acquaintances. Our results suggest that the aggregation resulting from direct feeding has served to facilitate the development of social associations.

The exceptional diversity of shallow‐water marine fishes contributes to the nutrition of millions of people worldwide through coastal wild‐capture fisheries, with different species having diverse nutritional profiles. Fishes in ecosystems are reservoirs of micronutrients with benefits to human health. Yet, the amount of micronutrients contained in fish species on coral reefs and in shallow tropical waters is challenging to estimate, and the micronutrients caught by fisheries remain uncertain. To assess whether micronutrient deficiencies could be addressed through specific fisheries management actions, we first require a quantification of the potentially available micronutrients contained in biodiverse reef fish assemblages. Here, we therefore undertake a broad heuristic assessment of available micronutrients on tropical reefs using ensemble species distribution modelling and identify potential mismatches with micronutrients derived from summarising coastal fisheries landings data. We find a mismatch between modelled estimates of micronutrients available in the ecosystem on the one hand and the micronutrients in small‐scale fisheries landings data. Fisheries had lower micronutrients than expected from fishes in the modelled assemblage. Further, fisheries were selective for vitamin A, thus resulting in a trade‐off with other micronutrients. Our results remained unchanged after accounting for the under‐sampling of fish communities and under‐reporting of small‐scale fisheries catches—two major sources of uncertainty. This reported mismatch indicates that current estimates of fished micronutrients are not adequate to fully assess micronutrient inventories. However, small‐scale fisheries in some countries were already selective towards micronutrient mass, indicating policies that target improved access, distribution and consumption of fish could leverage this existing high micronutrient mass. Enhanced taxonomic resolution of catches and biodiversity inventories using localised species consumption surveys could improve understanding of nature‐people linkages. Improving fisheries reporting and monitoring of reef fish assemblages will advance the understanding of micronutrient mismatches, which overall indicate a weak uptake of nutritional goals in fisheries practices. The decoupling between micronutrients in ecosystems and in fisheries catches indicates that social, economic, and biodiversity management goals are not shaped around nutritional targets—but this is key to achieve a sustainable and healthy planet for both people and nature. Read the free Plain Language Summary for this article on the Journal blog. Read the free Plain Language Summary for this article on the Journal blog.