Recent advances and challenges in the recovery and purification of cellular exosomes

Exosomes are nanovesicles secreted by most cellular types that carry important biochemical compounds throughout the body with different purposes, playing a preponderant role in cellular communication. Because of their structure, physicochemical properties and stability, recent studies are focusing in their use as nanocarriers for different therapeutic compounds for the treatment of different diseases ranging from cancer to Parkinson's disease. However, current bioseparation protocols and methodologies are selected based on the final exosome application or intended use and present both advantages and disadvantages when compared among them. In this context, this review aims to present the most important technologies available for exosome isolation while discussing their advantages and disadvantages and the possibilities of being combined with other strategies. This is critical since the development of novel exosome‐based therapeutic strategies will be constrained to the effectiveness and yield of the selected downstream purification methodologies for which a thorough understanding of the available technological resources is needed.     

Solidifying framework nucleic acids

正Living organisms have developed their unique strategies during the natural evolution for building hard tissues with minerals, including silica, calcium carbonate, calcium phosphate, and ferric oxide [1]. Such biomineralized materials generally have complex hierarchical structures with excellent mechanical properties. Although bioinspired approaches have led to the creation of well-defined synthetic structural materials ranging from micro to macro scales, the rational design of discrete biomimetic structures at the nanoscale remains a grand challenge.     

Effects of multiple delays on dynamics of a five-neuron network model

A five-neuron network model with multiple delays is proposed. This paper presents the combined effect of different delays on the dynamics of the proposed network. Pitchfork bifurcation is discussed in detail with the variation of the value of coupled weight or attenuation rate of internal neurons. By analyzing the corresponding characteristic equation, some stable criteria on delay-dependence and delay-independence are derived including multiple delays and coupled weights and the periodic oscillation arises bifurcated from the trivial equilibrium after the network loses its stability. Stable regions on delay-dependence are displayed in the two delayed parameter plane. It is shown that multiple delays can produce stability switching between resting state and periodic activity. Finally, theoretical results are justified by providing two illustrative examples.     

Infinitely many reducts of homogeneous structures

It is shown that the countably infinite dimensional pointed vector space (the vector space equipped with a constant) over a finite field has infinitely many first order definable reducts. This implies that the countable homogeneous Boolean-algebra has infinitely many reducts.     

A Comparative Analysis of Machine Learning Techniques for Muon Count in UHECR Extensive Air-Showers

The main goal of this work is to adapt a Physics problem to the Machine Learning (ML) domain and to compare several techniques to solve it. The problem consists of how to perform muon count from the signal registered by particle detectors which record a mix of electromagnetic and muonic signals. Finding a good solution could be a building block on future experiments. After proposing an approach to solve the problem, the experiments show a performance comparison of some popular ML models using two different hadronic models for the test data. The results show that the problem is suitable to be solved using ML as well as how critical the feature selection stage is regarding precision and model complexity.     

Identification of metabolic markers in patients with type 2 Diabetes by Ultrafast gas chromatography coupled to electronic nose. A pilot study

Metabolomics is a potential tool for the discovery of new biomarkers in the early diagnosis of diseases. An ultra-fast gas chromatography system equipped to an electronic nose detector (FGC eNose) was used to identify the metabolomic profile of Volatile Organic Compounds (VOCs) in type 2 diabetes (T2D) urine from Mexican population. A cross-sectional, comparative, and clinical study with translational approach was performed. We recruited twenty T2D patients and twenty-one healthy subjects. Urine samples were taken and analyzed by FGC eNose. Eighty-eight compounds were identified through Kovats's indexes. A natural variation of 30% between the metabolites, expressed by study groups, was observed in Principal Component 1 and 2 with a significant difference (p < 0.001). The model, performed through a Canonical Analysis of Principal coordinated (CAP), allowed a correct classification of 84.6% between healthy and T2D patients, with a 15.4% error. The metabolites 2-propenal, 2-propanol, butane- 2,3-dione and 2-methylpropanal, were increased in patients with T2D, and they were strongly correlated with discrimination between clinically healthy people and T2D patients. This study identified metabolites in urine through FGC eNose that can be used as biomarkers in the identification of T2D patients. However, more studies are needed for its implementation in clinical practice.     

The Jordan property of Cremona groups and essential dimension

We use a recent advance in birational geometry to prove new lower bounds on the essential dimension of some finite groups.