Extracellular ATP enhances radiation-induced brain injury through microglial activation and paracrine signaling via P2X7 receptor

AbstractActivation of purinergic receptors by extracellular ATP (eATP) released from injured cells has been implicated in the pathogenesis of many neuronal disorders. The P2X7 receptor (P2X7R), an ion-selective purinergic receptor, is associated with microglial activation and paracrine signaling. However, whether ATP and P2X7R are involved in radiation-induced brain injury (RBI) remains to be determined. Here, we found that the eATP level was elevated in the cerebrospinal fluid (CSF) of RBI patients and was associated with the clinical severity of the disorder. In our experimental model, radiation treatment increased the level of eATP in the supernatant of primary cultures of neurons and glial cells and in the CSF of irradiated mice. In addition, ATP administration activated microglia, induced the release of the inflammatory mediators such as cyclooxygenase-2, tumor necrosis factor α and interleukin 6, and promoted neuronal apoptosis. Furthermore, blockade of ATP–P2X7R interaction using P2X7 antagonist Brilliant Blue G or P2X7 knockdown suppressed radiation-induced microglial activation and proliferation in the hippocampus, and restored the spatial memory of irradiated mice. Finally, we found that the PI3K/AKT and nuclear factor κB mediated pathways were downstream of ATP–P2X7R signaling in RBI. Taken together, our results unveiled the critical role of ATP–P2X7R in brain damage in RBI, suggesting that inhibition of ATP–P2X7R axis might be a potential strategy for the treatment of patients with RBI

Predicting Diabetes Mellitus With Machine Learning Techniques

Diabetes mellitus is a chronic disease characterized by hyperglycemia. It may cause many complications. According to the growing morbidity in recent years, in 2040, the world’s diabetic patients will reach 642 million, which means that one of the ten adults in the future is suffering from diabetes. There is no doubt that this alarming figure needs great attention. With the rapid development of machine learning, machine learning has been applied to many aspects of medical health. In this study, we used decision tree, random forest and neural network to predict diabetes mellitus. The dataset is the hospital physical examination data in Luzhou, China. It contains 14 attributes. In this study, five-fold cross validation was used to examine the models. In order to verity the universal applicability of the methods, we chose some methods that have the better performance to conduct independent test experiments. We randomly selected 68994 healthy people and diabetic patients’ data, respectively as training set. Due to the data unbalance, we randomly extracted 5 times data. And the result is the average of these five experiments. In this study, we used principal component analysis (PCA) and minimum redundancy maximum relevance (mRMR) to reduce the dimensionality. The results showed that prediction with random forest could reach the highest accuracy (ACC = 0.8084) when all the attributes were used

Psychological Disorders, Cognitive Dysfunction and Quality of Life in Nasopharyngeal Carcinoma Patients with Radiation-Induced Brain Injury

PURPOSE:To evaluate factors affecting psychology, cognitive function and quality of life (QOL) of nasopharyngeal carcinoma (NPC) patients with radiation-induced brain injury (RI). METHODS AND MATERIALS:46 recurrence-free NPC patients with RI and 46 matched control patients without RI were recruited in our study. Subjective and objective symptoms of RI were evaluated with the LENT/SOMA systems. Psychological assessment was measured with Self-Rating Anxiety Scale (SAS) and Self-Rating Depression Scale (SDS). Montreal Cognitive Assessment (MoCA) was carried out in these patients for assessing their cognitive function. QOL was evaluated by means of WHOQOL BREF. RESULTS:Of the patients with RI, 39(84.8%) had depression and 40(87.0%) had anxiety. The patients with RI got higher scores both in SDS and SAS than those without RI (SDS, 63.48±8.11 vs. 58.67±7.52, p = 0.008; SAS, 67.36±10.41 vs. 60.34±9.76, p = 0.005). Score in MoCA of patients with RI was significantly lower than that of patients without RI (21.32±2.45 vs. 25.98±1.73, p<0.001). SAS was positive correlated with post-radiotherapy interval. Both SAS and SDS had a significantly positive correlation with the rank of SOMA, while MoCA had a significantly negative correlation with SOMA. Chemotherapy was a risk factor for cognitive dysfunction. In addition, patients with RI got significantly lower scores in physical health (16.50±11.05 vs. 35.02±10.43, p<0.001), psychological health (17.70±10.33 vs. 39.48±12.00, p<0.001) and social relationship (48.00±18.65 vs. 67.15±19.70, p<0.001) compared with those in patients without RI. Multiple linear regression analysis revealed that anxiety and cognitive impairment were significant predictors of global QOL. CONCLUSIONS:NPC patients with RI exhibit negative emotions, impaired cognitive function and QOL. The severity of clinical symptoms of RI plays an important role in both emotions and cognitive function. Anxiety and cognitive impairment are associated with decreased QOL

Boosting the performance of single-atom catalysts via external electric field polarization

Single-atom catalysts represent a unique catalytic system with high atomic utilization and tunable reaction pathway. Despite current successes in their optimization and tailoring through structural and synthetic innovations, there is a lack of dynamic modulation approach for the single-atom catalysis. Inspired by the electrostatic interaction within specific natural enzymes, here we show the performance of model single-atom catalysts anchored on two-dimensional atomic crystals can be systematically and efficiently tuned by oriented external electric fields. Superior electrocatalytic performance have been achieved in single-atom catalysts under electrostatic modulations. Theoretical investigations suggest a universal “onsite electrostatic polarization” mechanism, in which electrostatic fields significantly polarize charge distributions at the single-atom sites and alter the kinetics of the rate determining steps, leading to boosted reaction performances. Such field-induced on-site polarization offers a unique strategy for simulating the catalytic processes in natural enzyme systems with quantitative, precise and dynamic external electric fields

A Three-Dimensional Radiation Transfer Model to Evaluate Performance of Compound Parabolic Concentrator-Based Photovoltaic Systems

In the past, two-dimensional radiation transfer models (2-D models) were widely used to investigate the optical performance of linear compound parabolic concentrators (CPCs), in which the radiation transfer on the cross-section of CPC troughs is considered. However, the photovoltaic efficiency of solar cells depends on the real incidence angle instead of the projection incidence angle, thus 2-D models can’t reasonably evaluate the photovoltaic performance of CPC-based photovoltaic systems (CPVs). In this work, three-dimensional radiation transfer (3-D model) within CPC-θa/θe, the CPC with a maximum exit angle θe for radiation within its acceptance angle (θa), is investigated by means of vector algebra, solar geometry and imaging principle of plane mirror, and effects of geometry of CPV-θa/θe on its annual electricity generation are studied. Analysis shows that, as compared to similar photovoltaic (PV) panels, the use of CPCs makes the incident angle of solar rays on solar cells increase thus lowers the photovoltaic conversion efficiency of solar cells. Calculations show that, 2-D models can reasonably predict the optical performance of CPVs, but such models always overestimate the photovoltaic performance of CPVs, and even can’t predict the variation trend of annual power output of CPV-θa/θe with θe. Results show that, for full CPV-θa/θe with a given θa, the annual power output increases with θe first and then comes to a halt as θe &gt; 83°, whereas for truncated CPV-θa/θe with a given geometric concentration (Ct), the annual power output decreases with θe

Performance and Design Optimization of Two-Mirror Composite Concentrating PV Systems

The reflectors of a linear solar concentrator investigated in this work consisted of two plane mirrors (2MCC), and they were designed in such a way that made all radiation within the acceptance angle (&theta;a) arrive on flat-plate absorber, after less than two reflections. To investigate the performance of an east&ndash;west aligned 2MCC-based photovoltaic (PV) system (2MCPV), a mathematical procedure was suggested based on the three-dimensional radiation transfer and was validated by the ray-tracing analysis. Analysis indicated that the performance of 2MCPV was dependent on the geometry of 2MCC, the reflectivity of mirrors (&rho;), and solar resources in a site, thus, given &theta;a, an optimal geometry of 2MCC for maximizing the annual collectible radiation (ACR) and annual electricity generation (AEG) of 2MCPV in a site could be respectively found through iterative calculations. Calculation results showed that when the &rho; was high, the optimal design of 2MCC for maximizing its geometric concentration (Cg) could be utilized for maximizing the ACR and AEG of 2MCPV. As compared to similar compound parabolic concentrator (CPC)-based PV systems, the 2MCPV with the tilt-angle of the aperture yearly fixed (1T-2MCPV), annually generated more electricity when the &rho; was high; and the one with the tilt-angle adjusted yearly four times at three tilts (3T-2MCPV), performed better when &theta;a &lt; 25&deg; and &rho; &gt; 0.7, even in sites with poor solar resources

Identification and analysis of senescence-related genes in caudal fin cells of triploid crucian carp

This research aims to identify the hub genes associated with the senescence of triploid caudal fin cells. Transcriptomic data are obtained from the high and low generation (P6, P60) of triploid crucian carp caudal fin cells by high-throughput sequencing technology. Initially, all differential genes between the high and low generations are screened, yielding 4140 significantly upregulated genes and 3724 significantly downregulated genes. Subsequently, an aging gene set containing 950 genes is downloaded from the CellAge database to extract the differentially expressed genes associated with caudal fin cell aging, totaling 29 genes. GO and KEGG enrichment analyses are performed on these 29 aging differential genes. The GO analysis shows enrichment mainly in cellular processes related to aging, such as regulation of cell division, chromatin organization, cell cycle regulation. KEGG analysis reveals that the 29 aging-related genes are primarily involved in cell cycle and cellular senescence pathways. A PPI network of aging-related genes is constructed using the STRING database and Cytoscape software. Top-ranked genes were identified by using Degree, MCC, MNC, and Closeness algorithms in the Cytohubba plugin in Cytoscape, resulting in hub genes EZH2, JUN, MYD88, RBL2, BMP4, CCND1, NFKB2, MMP9. Lastly, qRT-PCR validation of these eight hub genes further confirmed the involvement of four genes: EZH2, RBL2, BMP4, and CCND1. The hub gene screened in this study may become a potential biomarker of fish caudal fin cell senescence, which provides a valuable experimental basis for the senescence of fish caudal fin cells, especially the senescence of caudal fin cells in polyploid fish, and the reproduction and breeding improvement of polyploid fish. It also provides meaningful data for elucidating the molecular mechanism of polyploid formation in animals, as well as the formation of aging and tumour in human beings