End-to-end risk prediction of atrial fibrillation from the 12-Lead ECG by deep neural networks

Background: Atrial fibrillation (AF) is one of the most common cardiac arrhythmias that affects millions of people each year worldwide and it is closely linked to increased risk of cardiovas-cular diseases such as stroke and heart failure. Machine learning methods have shown promising results in evaluating the risk of developing atrial fibrillation from the electrocardiogram. We aim to develop and evaluate one such algorithm on a large CODE dataset collected in Brazil. Methods: We used the CODE cohort to develop and test a model for AF risk prediction for individual patients from the raw ECG recordings without the use of additional digital biomarkers. The cohort is a collection of ECG recordings and annotations by the Telehealth Network of Minas Gerais, in Brazil. A convolutional neural network based on a residual network architecture was implemented to produce class probabilities for the classification of AF. The probabilities were used to develop a Cox proportional hazards model and a Kaplan-Meier model to carry out survival analysis. Hence, our model is able to perform risk prediction for the development of AF in patients without the condition. Results: The deep neural network model identified patients without indication of AF in the presented ECG but who will develop AF in the future with an AUC score of 0.845. From our survival model, we obtain that patients in the high-risk group (i.e. with the probability of a future AF case being >0.7) are 50% more likely to develop AF within 40 weeks, while patients belonging to the minimal-risk group (i.e. with the probability of a future AF case being less than or equal to 0.1) have >85% chance of remaining AF free up until after seven years. Conclusion: We developed and validated a model for AF risk prediction. If applied in clinical practice, the model possesses the potential of providing valuable and useful information in decision- making and patient management processes

A Phase I Clinical Trial of Systemically Delivered NEMO Binding Domain Peptide in Dogs with Spontaneous Activated B-Cell like Diffuse Large B-Cell Lymphoma

<div><p>Activated B-Cell (ABC) Diffuse Large B-Cell Lymphoma (DLBCL) is a common, aggressive and poorly chemoresponsive subtype of DLBCL, characterized by constitutive canonical NF-κB signaling. Inhibition of NF-κB signaling leads to apoptosis of ABC-DLBCL cell lines, suggesting targeted disruption of this pathway may have therapeutic relevance. The selective IKK inhibitor, NEMO Binding Domain (NBD) peptide effectively blocks constitutive NF-κB activity and induces apoptosis in ABC-DLBCL cells <i>in vitro</i>. Here we used a comparative approach to determine the safety and efficacy of systemic NBD peptide to inhibit constitutive NF-κB signaling in privately owned dogs with spontaneous newly diagnosed or relapsed ABC-like DLBCL. Malignant lymph nodes biopsies were taken before and twenty-four hours after peptide administration to determine biological effects. Intravenous administration of <2 mg/kg NBD peptide was safe and inhibited constitutive canonical NF-κB activity in 6/10 dogs. Reductions in mitotic index and Cyclin D expression also occurred in a subset of dogs 24 hours post peptide and in 3 dogs marked, therapeutically beneficial histopathological changes were identified. Mild, grade 1 toxicities were noted in 3 dogs at the time of peptide administration and one dog developed transient subclinical hepatopathy. Long term toxicities were not identified. Pharmacokinetic data suggested rapid uptake of peptide into tissues. No significant hematological or biochemical toxicities were identified. Overall the results from this phase I study indicate that systemic administration of NBD peptide is safe and effectively blocks constitutive NF-κB signaling and reduces malignant B cell proliferation in a subset of dogs with ABC-like DLBCL. These results have potential translational relevance for human ABC-DLBCL.</p></div

Biosorption of Metals and Metalloids

Industrial activities such as mining operations, refining of ores and combustion of fuel oils play a relevant role in environmental pollution since their wastes contain high concentrations of toxic metals that can add significant contamination to natural water and other water sources if no decontamination is previously applied. As toxic metals and metalloids, including arsenic, cadmium, lead, mercury, thallium, vanadium, among others, are not biodegradable and tend to accumulate in living organisms, it is necessary to treat the contaminated industrial wastewaters prior to their discharge into the water bodies. There are different remediation techniques that have been developed to solve elemental pollution, but biosorption has arisen as a promising clean-up and low-cost biotechnology. Biosorption is one of the pillars of bioremediation and is governed by a variety of mechanisms, including chemical binding, ion exchange,physisorption, precipitation, and oxide-reduction. This involves operations(e.g. biosorbent reuse, immobilization, direct analysis of sample without destruction) that can be designed to minimize or avoid the use or generation of hazardous substances that have a negative impact on the environment and biota, thus following the concepts of "green chemistry" and promoting the environmental care. Furthermore, it has to be specially considered that the design of a biosorption process and the quality of a biosorbent are normally evaluated from the equilibrium, thermodynamic, and kinetic viewpoints.Therefore, a successful biosorption process can be only developed based on multidisciplinary knowledge that includes physical chemistry, biochemistryand technology, among other fields.In this chapter, we explain in detail all the aforementioned aspects. State of the art applications of biosorbents for metals and metalloids removal are carefully revised based on a complete analysis of the literature. Thus, it is evidenced in this chapter that the main points to consider regarding biosorption are the type of biomaterial (e.g. bacteria, fungi, algae, plant?derivatives and agricultural wastes, chitin/chitosan based materials) and the presence of a broad set of functional groups on their surface that are effective for the removal of different toxic metals and metalloids. In fact, removal percentages as high as 70-100% can be found in most works reported in the literature, which is demonstrating the excellent performance obtained with biosorbents. Also, biosorbents have evolved with the help of nanotechnology to modern bio-nano-hybrids materials having superlative sorption properties due to their high surface area coming from the nano-materials structures and multifunctional capacity incorporated from the several types of chemical groups of biomaterials. These, as well as other important aspects linked to biosorption are fully covered in the present chapter.Fil: Escudero, Leticia Belén. Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; ArgentinaFil: Quintas, Pamela Yanina. Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; ArgentinaFil: Wuilloud, Rodolfo German. Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; ArgentinaFil: Dotto, Guilherme L.. Universidade Federal de Santa Maria; Brasi