Notes

MGRCDA: Metagraph Recommendation Means for Projecting CircRNA-Disease Association.
RANKL on T lymphocytes, Osteopontin and MIP-1β decreased with SRT treatment indicating probable reduction in osteoclast activity. Other secreted factors, Osteocalcin and RANKL/Osteoprotegerin did not change with the treatment status. Insights from the study highlight personalized differences between subjects and possible use of RANK pathway components as markers for bone disease progression.Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterized by progressive loss of motor neurons (MN). Importantly, MN degeneration is intimately linked to oligodendrocyte dysfunction and impaired capacity of oligodendrocyte precursor cells (OPCs) to regenerate the myelin sheath enwrapping and protecting neuronal axons. this website Thus, improving OPC reparative abilities represents an innovative approach to counteract MN loss. A pivotal regulator of OPC maturation is the P2Y-like G protein-coupled receptor 17 (GPR17), whose role in ALS has never been investigated. In other models of neurodegeneration, an abnormal increase of GPR17 has been invariably associated to myelin defects and its pharmacological manipulation succeeded in restoring endogenous remyelination. Here, we analyzed GPR17 alterations in the SOD1G93A ALS mouse model and assessed in vitro whether this receptor could be targeted to correct oligodendrocyte alterations. Western-blot and immunohistochemical analyses showed that GPR17 protein levels are significantly increased in spinal cord of ALS mice at pre-symptomatic stage; this alteration is exacerbated at late symptomatic phases. Concomitantly, mature oligodendrocytes degenerate and are not successfully replaced. Moreover, OPCs isolated from spinal cord of SOD1G93A mice display defective differentiation compared to control cells, which is rescued by treatment with the GPR17 antagonist montelukast. These data open novel therapeutic perspectives for ALS management.Tobacco (Nicotiana tabacum), is a world's major non-food agricultural crop widely cultivated for its economic value. Among several color change associated biological processes, plastid pigment metabolism is of trivial importance in postharvest plant organs during curing and storage. However, the molecular mechanisms involved in carotenoid and chlorophyll metabolism, as well as color change in tobacco leaves during curing, need further elaboration. Here, proteomic analysis at different curing stages (0 h, 48 h, 72 h) was performed in tobacco cv. Bi'na1 with an aim to investigate the molecular mechanisms of pigment metabolism in tobacco leaves as revealed by the iTRAQ proteomic approach. Our results displayed significant differences in leaf color parameters and ultrastructural fingerprints that indicate an acceleration of chloroplast disintegration and promotion of pigment degradation in tobacco leaves due to curing. In total, 5931 proteins were identified, of which 923 (450 up-regulated, 452 down-regulated, anive, which prompts us to pay attention to further screen key proteins in tobacco leaves during curing.Carbonic anhydrase II (CAII) is a metalloenzyme that catalyzes the reversible hydration/dehydration of CO2/HCO3-. In addition, CAII is attributed to other catalytic reactions, including esterase activity. Aspirin (acetyl-salicylic acid), an everyday over-the-counter drug, has both ester and carboxylic acid moieties. Recently, compounds with a carboxylic acid group have been shown to inhibit CAII. Hence, we hypothesized that Aspirin could act as a substrate for esterase activity, and the product salicylic acid (SA), an inhibitor of CAII. Here, we present the crystal structure of CAII in complex with SA, a product of CAII crystals pre-soaked with Aspirin, to 1.35Å resolution. In addition, we provide kinetic data to support the observation that CAII converts Aspirin to its deacetylated form, SA. This data may also explain the short half-life of Aspirin, with CAII so abundant in blood, and that Aspirin could act as a suicide inhibitor of CAII.This paper reviews applications of machine learning (ML) predictive models in the diagnosis of chronic diseases. Chronic diseases (CDs) are responsible for a major portion of global health costs. Patients who suffer from these diseases need lifelong treatment. Nowadays, predictive models are frequently applied in the diagnosis and forecasting of these diseases. In this study, we reviewed the state-of-the-art approaches that encompass ML models in the primary diagnosis of CD. This analysis covers 453 papers published between 2015 and 2019, and our document search was conducted from PubMed (Medline), and Cumulative Index to Nursing and Allied Health Literature (CINAHL) libraries. Ultimately, 22 studies were selected to present all modeling methods in a precise way that explains CD diagnosis and usage models of individual pathologies with associated strengths and limitations. Our outcomes suggest that there are no standard methods to determine the best approach in real-time clinical practice since each method has its advantages and disadvantages. Among the methods considered, support vector machines (SVM), logistic regression (LR), clustering were the most commonly used. These models are highly applicable in classification, and diagnosis of CD and are expected to become more important in medical practice in the near future.With increasing demand for ready-to-eat (RTE) fresh vegetables, it is important to understand how visual information cues, both intrinsic and extrinsic, affect consumer perception of these products. This study developed an emotional and wellness lexicon related to RTE salads. Subsequent questionnaires with images of salads were used to quantify consumer (N = 150) emotional and hedonic perceptions related to green color shade, shape/size of pieces, multicolor scheme, product name, and packaging. The different visual cues significantly impacted emotions and their intensities. Qualitatively, feelings of health and wellness predominated across salad samples. Negative emotions were more influenced by size of piece and green-color (intrinsic), while positive emotions were influenced by viewing salads of multiple colors (intrinsic) and packaging (extrinsic). Pale green salads were generally less liked than darker green ones. Values, in one case, ranged from 4.39 to 7.28 (on a 9-point hedonic scale), but naming the product ("iceberg lettuce") did raise the lowest score to 5.
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