Lipid Nanocapsules Loaded with Rhenium-188 Reduce Tumor Progression in a Rat Hepatocellular Carcinoma Model

International audienceBACKGROUND: Due to their nanometric scale (50 nm) along with their biomimetic properties, lipid nanocapsules loaded with Rhenium-188 (LNC(188)Re-SSS) constitute a promising radiopharmaceutical carrier for hepatocellular carcinoma treatment as its size may improve tumor penetration in comparison with microspheres devices. This study was conducted to confirm the feasibility and to assess the efficacy of internal radiation with LNC(188)Re-SSS in a chemically induced hepatocellular carcinoma rat model. METHODOLOGY/PRINCIPAL FINDINGS: Animals were treated with an injection of LNC(188)Re-SSS (80 MBq or 120 MBq). The treated animals (80 MBq, n = 12; 120 MBq, n = 11) were compared with sham (n = 12), blank LNC (n = 7) and (188)Re-perrhenate (n = 4) animals. The evaluation criteria included rat survival, tumor volume assessment, and vascular endothelial growth factor quantification. Following treatment with LNC(188)Re-SSS (80 MBq) therapeutic efficiency was demonstrated by an increase in the median survival from 54 to 107% compared with control groups with up to 7 long-term survivors in the LNC(188)Re-SSS group. Decreased vascular endothelial growth factor expression in the treated rats could indicate alterations in the angiogenesis process. CONCLUSIONS/SIGNIFICANCE: Overall, these results demonstrate that internal radiation with LNC(188)Re-SSS is a promising new strategy for hepatocellular carcinoma treatment

Effect of Global Cardiac Ischemia on Human Ventricular Fibrillation: Insights from a Multi-scale Mechanistic Model of the Human Heart

Acute regional ischemia in the heart can lead to cardiac arrhythmias such as ventricular fibrillation (VF), which in turn compromise cardiac output and result in secondary global cardiac ischemia. The secondary ischemia may influence the underlying arrhythmia mechanism. A recent clinical study documents the effect of global cardiac ischaemia on the mechanisms of VF. During 150 seconds of global ischemia the dominant frequency of activation decreased, while after reperfusion it increased rapidly. At the same time the complexity of epicardial excitation, measured as the number of epicardical phase singularity points, remained approximately constant during ischemia. Here we perform numerical studies based on these clinical data and propose explanations for the observed dynamics of the period and complexity of activation patterns. In particular, we study the effects on ischemia in pseudo-1D and 2D cardiac tissue models as well as in an anatomically accurate model of human heart ventricles. We demonstrate that the fall of dominant frequency in VF during secondary ischemia can be explained by an increase in extracellular potassium, while the increase during reperfusion is consistent with washout of potassium and continued activation of the ATP-dependent potassium channels. We also suggest that memory effects are responsible for the observed complexity dynamics. In addition, we present unpublished clinical results of individual patient recordings and propose a way of estimating extracellular potassium and activation of ATP-dependent potassium channels from these measurements

Advances in Carcinogenic Metal Toxicity and Potential Molecular Markers

Metal compounds such as arsenic, cadmium, chromium, cobalt, lead, mercury, and nickel are classified as carcinogens affecting human health through occupational and environmental exposure. However, the underlying mechanisms involved in tumor formation are not well clarified. Interference of metal homeostasis may result in oxidative stress which represents an imbalance between production of free radicals and the system’s ability to readily detoxify reactive intermediates. This event consequently causes DNA damage, lipid peroxidation, protein modification, and possibly symptomatic effects for various diseases including cancer. This review discusses predominant modes of action and numerous molecular markers. Attention is paid to metal-induced generation of free radicals, the phenomenon of oxidative stress, damage to DNA, lipid, and proteins, responsive signal transduction pathways with major roles in cell growth and development, and roles of antioxidant enzymatic and DNA repair systems. Interaction of non-enzymatic antioxidants (carotenoids, flavonoids, glutathione, selenium, vitamin C, vitamin E, and others) with cellular oxidative stress markers (catalase, glutathione peroxidase, and superoxide dismutase) as well as certain regulatory factors, including AP-1, NF-κB, Ref-1, and p53 is also reviewed. Dysregulation of protective pathways, including cellular antioxidant network against free radicals as well as DNA repair deficiency is related to oncogenic stimulation. These observations provide evidence that emerging oxidative stress-responsive regulatory factors and DNA repair proteins are putative predictive factors for tumor initiation and progression

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

The Changing Landscape for Stroke\ua0Prevention in AF: Findings From the GLORIA-AF Registry Phase 2

Background GLORIA-AF (Global Registry on Long-Term Oral Antithrombotic Treatment in Patients with Atrial Fibrillation) is a prospective, global registry program describing antithrombotic treatment patterns in patients with newly diagnosed nonvalvular atrial fibrillation at risk of stroke. Phase 2 began when dabigatran, the first non\u2013vitamin K antagonist oral anticoagulant (NOAC), became available. Objectives This study sought to describe phase 2 baseline data and compare these with the pre-NOAC era collected during phase 1. Methods During phase 2, 15,641 consenting patients were enrolled (November 2011 to December 2014); 15,092 were eligible. This pre-specified cross-sectional analysis describes eligible patients\u2019 baseline characteristics. Atrial fibrillation disease characteristics, medical outcomes, and concomitant diseases and medications were collected. Data were analyzed using descriptive statistics. Results Of the total patients, 45.5% were female; median age was 71 (interquartile range: 64, 78) years. Patients were from Europe (47.1%), North America (22.5%), Asia (20.3%), Latin America (6.0%), and the Middle East/Africa (4.0%). Most had high stroke risk (CHA2DS2-VASc [Congestive heart failure, Hypertension, Age  6575 years, Diabetes mellitus, previous Stroke, Vascular disease, Age 65 to 74 years, Sex category] score  652; 86.1%); 13.9% had moderate risk (CHA2DS2-VASc = 1). Overall, 79.9% received oral anticoagulants, of whom 47.6% received NOAC and 32.3% vitamin K antagonists (VKA); 12.1% received antiplatelet agents; 7.8% received no antithrombotic treatment. For comparison, the proportion of phase 1 patients (of N = 1,063 all eligible) prescribed VKA was 32.8%, acetylsalicylic acid 41.7%, and no therapy 20.2%. In Europe in phase 2, treatment with NOAC was more common than VKA (52.3% and 37.8%, respectively); 6.0% of patients received antiplatelet treatment; and 3.8% received no antithrombotic treatment. In North America, 52.1%, 26.2%, and 14.0% of patients received NOAC, VKA, and antiplatelet drugs, respectively; 7.5% received no antithrombotic treatment. NOAC use was less common in Asia (27.7%), where 27.5% of patients received VKA, 25.0% antiplatelet drugs, and 19.8% no antithrombotic treatment. Conclusions The baseline data from GLORIA-AF phase 2 demonstrate that in newly diagnosed nonvalvular atrial fibrillation patients, NOAC have been highly adopted into practice, becoming more frequently prescribed than VKA in Europe and North America. Worldwide, however, a large proportion of patients remain undertreated, particularly in Asia and North America. (Global Registry on Long-Term Oral Antithrombotic Treatment in Patients With Atrial Fibrillation [GLORIA-AF]; NCT01468701

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing