LncRNA H19 Impairs Chemo and Radiotherapy in Tumorigenesis

Various treatments based on drug administration and radiotherapy have been devoted to preventing, palliating, and defeating cancer, showing high efficiency against the progression of this disease. Recently, in this process, malignant cells have been found which are capable of triggering specific molecular mechanisms against current treatments, with negative consequences in the prognosis of the disease. It is therefore fundamental to understand the underlying mechanisms, including the genes—and their signaling pathway regulators—involved in the process, in order to fight tumor cells. Long non-coding RNAs, H19 in particular, have been revealed as powerful protective factors in various types of cancer. However, they have also evidenced their oncogenic role in multiple carcinomas, enhancing tumor cell proliferation, migration, and invasion. In this review, we analyze the role of lncRNA H19 impairing chemo and radiotherapy in tumorigenesis, including breast cancer, lung adenocarcinoma, glioma, and colorectal carcinoma.     

Ensemble of Efficient Minimal Learning Machines for Classification and Regression

Minimal Learning Machine (MLM) is a recently proposed supervised learning algorithm with performance comparable to most state-of-the-art machine learning methods. In this work, we propose ensemble methods for classification and regression using MLMs. The goal of ensemble strategies is to produce more robust and accurate models when compared to a single classifier or regression model. Despite its successful application, MLM employs a computationally intensive optimization problem as part of its test procedure (out-of-sample data estimation). This becomes even more noticeable in the context of ensemble learning, where multiple models are used. Aiming to provide fast alternatives to the standard MLM, we also propose the Nearest Neighbor Minimal Learning Machine and the Cubic Equation Minimal Learning Machine to cope with classification and single-output regression problems, respectively. The experimental assessment conducted on real-world datasets reports that ensemble of fast MLMs perform comparably or superiorly to reference machine learning algorithms.     

Improving the performance and functionality of Mondrian open‐source OLAP systems

For a long time, the design of relational databases has focused on the optimization of atomic transactions (insert, select, update or delete). Currently, relational databases store tactical information of data warehouses, mainly for select‐like operations. However, the database paradigm has evolved, and nowadays on‐line analytical processing (OLAP) systems handle strategic information for further analysis. These systems enable fast, interactive and consistent information analysis of data warehouses, including shared calculations and allocations. OLAP and data warehouses jointly allow multidimensional data views, turning raw data into knowledge. OLAP allows ‘slice and dice’ navigation and a top‐down perspective of data hierarchies. In this paper, we describe our experience in the migration from a large relational database management system to an OLAP system on top of a relational layer (the data warehouse), and the resulting contributions in open‐source ROLAP optimization. Existing open‐source ROLAP technologies rely on summarized tables with materialized aggregate views to improve system performance (in terms of response time). The design and maintenance of those tables are cumbersome. Instead, we intensively exploit cache memory, where key data reside, yielding low response times. A cold start process brings summarized data from the relational database to cache memory, subsequently reducing the response time. We ensure concurrent access to the summarized data, as well as consistency when the relational database updates data. We also improve the OLAP functionality, by providing new features for automating the creation of calculated members. This makes it possible to define new measures on the fly using virtual dimensions, without re‐designing the multidimensional cube. We have chosen the XML/A de facto standard for service provision. Copyright © 2008 John Wiley & Sons, Ltd.     

Improving Scalability of Application-Level Checkpoint-Recovery by Reducing Checkpoint Sizes

The execution times of large-scale parallel applications on nowadays multi/many-core systems are usually longer than the mean time between failures. Therefore, parallel applications must tolerate hardware failures to ensure that not all computation done is lost on machine failures. Checkpointing and rollback recovery is one of the most popular techniques to implement fault-tolerant applications. However, checkpointing parallel applications is expensive in terms of computing time, network utilization and storage resources. Thus, current checkpoint-recovery techniques should minimize these costs in order to be useful for large scale systems. In this paper three different and complementary techniques to reduce the size of the checkpoints generated by application-level checkpointing are proposed and implemented. Detailed experimental results obtained on a multicore cluster show the effectiveness of the proposed methods to reduce checkpointing cost.     

Chronic-Antibiotics Induced Gut Microbiota Dysbiosis Rescues Memory Impairment and Reduces β-Amyloid Aggregation in a Preclinical Alzheimer’s Disease Model

Alzheimer’s disease (AD) is a multifactorial pathology characterized by β-amyloid (Aβ) deposits, Tau hyperphosphorylation, neuroinflammatory response, and cognitive deficit. Changes in the bacterial gut microbiota (BGM) have been reported as a possible etiological factor of AD. We assessed in offspring (F1) 3xTg, the effect of BGM dysbiosisdysbiosis in mothers (F0) at gestation and F1 from lactation up to the age of 5 months on Aβ and Tau levels in the hippocampus, as well as on spatial memory at the early symptomatic stage of AD. We found that BGM dysbiosisdysbiosis with antibiotics (Abx) treatment in F0 was vertically transferred to their F1 3xTg mice, as observed on postnatal day (PD) 30 and 150. On PD150, we observed a delay in spatial memory impairment and Aβ deposits, but not in Tau and pTau protein in the hippocampus at the early symptomatic stage of AD. These effects are correlated with relative abundance of bacteria and alpha diversity, and are specific to bacterial consortia. Our results suggest that this specific BGM could reduce neuroinflammatory responses related to cerebral amyloidosis and cognitive deficit and activate metabolic pathways associated with the biosynthesis of triggering or protective molecules for AD.     

The Future of Point-of-Care Nucleic Acid Amplification Diagnostics after COVID-19: Time to Walk the Walk

Since the onset of the COVID-19 pandemic, over 610 million cases have been diagnosed and it has caused over 6.5 million deaths worldwide. The crisis has forced the scientific community to develop tools for disease control and management at a pace never seen before. The control of the pandemic heavily relies in the use of fast and accurate diagnostics, that allow testing at a large scale. The gold standard diagnosis of viral infections is the RT-qPCR. Although it provides consistent and reliable results, it is hampered by its limited throughput and technical requirements. Here, we discuss the main approaches to rapid and point-of-care diagnostics based on RT-qPCR and isothermal amplification diagnostics. We describe the main COVID-19 molecular diagnostic tests approved for self-testing at home or for point-of-care testing and compare the available options. We define the influence of specimen selection and processing, the clinical validation, result readout improvement strategies, the combination with CRISPR-based detection and the diagnostic challenge posed by SARS-CoV-2 variants for different isothermal amplification techniques, with a particular focus on LAMP and recombinase polymerase amplification (RPA). Finally, we try to shed light on the effect the improvement in molecular diagnostics during the COVID-19 pandemic could have in the future of other infectious diseases.     

Immobilization of Lipase B from Candida antarctica in Octyl-Vinyl Sulfone Agarose: Effect of the Enzyme-Support Interactions on Enzyme Activity,Specificity, Structure and Inactivation Pathway

Lipase B from Candida antarctica was immobilized on heterofunctional support octyl agarose activated with vinyl sulfone to prevent enzyme release under drastic conditions. Covalent attachment was established, but the blocking step using hexylamine, ethylenediamine or the amino acids glycine (Gly) and aspartic acid (Asp) altered the results. The activities were lower than those observed using the octyl biocatalyst, except when using ethylenediamine as blocking reagent and p-nitrophenol butyrate (pNPB) as substrate. The enzyme stability increased using these new biocatalysts at pH 7 and 9 using all blocking agents (much more significantly at pH 9), while it decreased at pH 5 except when using Gly as blocking agent. The stress inactivation of the biocatalysts decreased the enzyme activity versus three different substrates (pNPB, S-methyl mandelate and triacetin) in a relatively similar fashion. The tryptophane (Trp) fluorescence spectra were different for the biocatalysts, suggesting different enzyme conformations. However, the fluorescence spectra changes during the inactivation were not too different except for the biocatalyst blocked with Asp, suggesting that, except for this biocatalyst, the inactivation pathways may not be so different.     

Towards the Search for Potential Biomarkers in Osteosarcoma: State-of-the-Art and Translational Expectations

Osteosarcoma represents a rare cause of cancer in the general population, accounting for <1% of malignant neoplasms globally. Nonetheless, it represents the main cause of malignant bone neoplasm in children, adolescents and young adults under 20 years of age. It also presents another peak of incidence in people over 50 years of age and is associated with rheumatic diseases. Numerous environmental risk factors, such as bone diseases, genetics and a history of previous neoplasms, have been widely described in the literature, which allows monitoring a certain group of patients. Diagnosis requires numerous imaging tests that make it possible to stratify both the local involvement of the disease and its distant spread, which ominously determines the prognosis. Thanks to various clinical trials, the usefulness of different chemotherapy regimens, radiotherapy and surgical techniques with radical intent has now been demonstrated; these represent improvements in both prognosis and therapeutic approaches. Osteosarcoma patients should be evaluated in reference centres by multidisciplinary committees with extensive experience in proper management. Although numerous genetic and rheumatological diseases and risk factors have been described, the use of serological, genetic or other biomarkers has been limited in clinical practice compared to other neoplasms. This limits both the initial follow-up of these patients and screening in populations at risk. In addition, we cannot forget that the diagnosis is mainly based on the direct biopsy of the lesion and imaging tests, which illustrates the need to study new diagnostic alternatives. Therefore, the purpose of this study is to review the natural history of the disease and describe the main biomarkers, explaining their clinical uses, prognosis and limitations.     

TNF-α Affects Signature Cytokines of Th1 and Th17 T Cell Subsets through Differential Actions on TNFR1 and TNFR2

Tumor necrosis factor (TNF)-α is a pleiotropic cytokine implicated in the etiology of several autoimmune diseases, including rheumatoid arthritis (RA). TNF-α regulates diverse effector functions through the activation of TNF-α receptor (TNFR)1 and TNFR2. Although the detrimental role of this cytokine has been addressed in distinct disease settings, the effects of TNF-α on cytokine production by isolated CD4⁺ T helper type 1 (Th1) and Th17 cells, two T cell subpopulations that contribute to the pathogenesis of RA, have not been completely elucidated. Here, we show that TNF-α promotes a reduction and expansion in the frequency of both T cell subsets producing IFN-γ and IL-17, respectively. Selective blockade of TNFR1 or TNFR2 on Th1 and Th17 cells revealed that TNFR2 mediates the decrease in IFN-γ production, while signaling through both receptors augments IL-17 production. We also demonstrate that Th1, but not Th17 cells from RA patients present lower levels of TNFR1 compared to healthy controls, whereas TNFR2 expression on both T cell types is similar between patients and controls. Since TNF-α receptors levels in RA patients are not significantly changed by the therapeutic blockade of TNF-α, we propose that targeting TNFR2 may represent an alternative strategy to normalize the levels of key cytokines that contribute to RA pathogenesis.