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Our results show the therapeutic potential of priprioca residue as a low-cost antiplasmodial agent.Honey is a natural product with a long use in traditional medicine and is well recognized to regulate different biological events. It is an important source of various biological or pharmacological molecules and, therefore, there is a strong interest to explore their properties. Evidence is growing that honey may have the potential to be an anticancer agent acting through several mechanisms. Here we observed for the first time in a cancer cell line a possible mechanism through which honey could induce an alteration in the intracellular reactive oxygen species and homeostatic balance of intracellular calcium concentration leading to cell death by apoptosis. This mechanism seems to be enhanced by manuka honey’s ability to maintain high H2O2 permeability through aquaporin-3.Understanding the various mechanisms that govern the development, activation, differentiation, and functions of T cells is crucial as it could provide opportunities for therapeutic interventions to disrupt immune pathogenesis. Immunometabolism is one such area that has garnered significant interest in the recent past as it has become apparent that cellular metabolism is highly dynamic and has a tremendous impact on the ability of T cells to grow, activate, and differentiate. In each phase of the lifespan of a T-cell, cellular metabolism has to be tailored to match the specific functional requirements of that phase. Resting T cells rely on energy-efficient oxidative metabolism but rapidly shift to a highly glycolytic metabolism upon activation in order to meet the bioenergetically demanding process of growth and proliferation. However, upon antigen clearance, T cells return to a more quiescent oxidative metabolism to support T cell memory generation. In addition, each helper T cell subset engages distinct metabolic pathways to support their functional needs. In this review, we provide an overview of the metabolic changes that occur during the lifespan of a T cell and discuss several important studies that provide insights into the regulation of the metabolic landscape of T cells and how they impact T cell development and function.

Relocation is a very important event in people’s lives in general, but really significant in old age. However, some predictors of relocation still need to be improved. The objective of this review was to synthesize qualitative evidence to understand the reasons of the participants to decide on the place of care of the older people.

Systematic review of qualitative studies was conducted in six databases Scopus, SciELO, PubMed, PsycINFO, Web of Science and CINAHL, from its beginning until 29 November 2017. Qualitative or mixed studies, written in English or Spanish and addressing the decision-making process (already experienced by participants) on the place of care of older persons (65 years or older), were included in the review. #link# PROSPERO ( find more ).

A total of 46 articles were finally included in the analysis. Our main result is the distinction of multiple reasons for each population group involved in the decision-making process, ranking these reasons into three factors Retention, pull and push.

This differentiation allows for a more detailed and in-depth analysis of the motivations of the different groups involved in this process.

This differentiation allows for a more detailed and in-depth analysis of the motivations of the different groups involved in this process.The current rate at which new DNA and protein sequences are being generated is too fast to experimentally discover the functions of those sequences, emphasizing the need for accurate Automatic Function Prediction (AFP) methods. AFP has been an active and growing research field for decades and has made considerable progress in that time. However, it is certainly not solved. In this paper, we describe challenges that the AFP field still has to overcome in the future to increase its applicability. The challenges we consider are how to (1) include condition-specific functional annotation, (2) predict functions for non-model species, (3) include new informative data sources, (4) deal with the biases of Gene Ontology (GO) annotations, and (5) maximally exploit the GO to obtain performance gains. We also provide recommendations for addressing those challenges, by adapting (1) the way we represent proteins and genes, (2) the way we represent gene functions, and (3) the algorithms that perform the prediction from gene to function. Together, we show that AFP is still a vibrant research area that can benefit from continuing advances in machine learning with which AFP in the 2020s can again take a large step forward reinforcing the power of computational biology.The problem of performing remote biomedical measurements using just a video stream of a subject face is called remote photoplethysmography (rPPG). The aim of this work is to propose a novel method able to perform rPPG using single-photon avalanche diode (SPAD) cameras. These are extremely accurate cameras able to detect even a single photon and are already used in many other applications. Moreover, a novel method that mixes deep learning and traditional signal analysis is proposed in order to extract and study the pulse signal. Experimental results show that this system achieves accurate results in the estimation of biomedical information such as heart rate, respiration rate, and tachogram. Lastly, thanks to the adoption of the deep learning segmentation method and dependability checks, this method could be adopted in non-ideal working conditions-for example, in the presence of partial facial occlusions.Machine learning techniques are widely used nowadays in the healthcare domain for the diagnosis, prognosis, and treatment of diseases. These techniques have applications in the field of hematopoietic cell transplantation (HCT), which is a potentially curative therapy for hematological malignancies. Herein, a systematic review of the application of machine learning (ML) techniques in the HCT setting was conducted. We examined the type of data streams included, specific ML techniques used, and type of clinical outcomes measured. A systematic review of English articles using PubMed, Scopus, Web of Science, and IEEE Xplore databases was performed. Search terms included “hematopoietic cell transplantation (HCT),” “autologous HCT,” “allogeneic HCT,” “machine learning,” and “artificial intelligence.” Only full-text studies reported between January 2015 and July 2020 were included. Data were extracted by two authors using predefined data fields. Following PRISMA guidelines, a total of 242 studies were identified, of which 27 studies met the inclusion criteria.