Glycine is a simple nonessential amino acid known to have neuroprotective properties. Treatment with glycine results in reduced infarct volume of the brain, neurologic function scores, and neuronal ...and microglial death in ischemic stroke injury. Neuroinflammation has been considered a major contributor to cerebral ischemia-induced brain damage. However, the role of glycine in neuroinflammation following ischemic stroke is unclear. The present study aimed to determine whether neuroinflammation is involved in the neuroprotective effects of glycine in cerebral ischemia injury. Ischemic stroke promotes M1 microglial polarization. Interestingly, we found that the injection of glycine in rats after injury can inhibit ischemia-induced inflammation and promote M2 microglial polarization in vivo (Sprague-Dawley rats) and in vitro (cortical microglia and BV-2 cells). We show that glycine suppresses Hif-1α by inhibiting the upregulation of NF-κB p65 after ischemia-reperfusion injury, resulting in the inhibition of proinflammatory activity. The activation of AKT mediates the inhibition of NF-κB p65/Hif-1α signaling by glycine. Moreover, we confirm that glycine-regulated AKT activation is mediated by the inhibition of PTEN in a PTEN depletion cell line, U251 cells. Glycine modulates microglial polarization after ischemic stroke, which indirectly inhibits ischemia-induced neuronal death and functional recovery. Taken together, our findings provide a new understanding of glycine in neuroprotection by inhibiting M1 microglial polarization and promoting anti-inflammation by suppressing NF-κB p65/Hif-1α signaling.
The whole-brain functional connectivity (FC) pattern obtained from resting-state functional magnetic resonance imaging data are commonly applied to study neuropsychiatric conditions such as autism ...spectrum disorder (ASD) by using different machine learning models. Recent studies indicate that both hyper- and hypo- aberrant ASD-associated FCs were widely distributed throughout the entire brain rather than only in some specific brain regions. Deep neural networks (DNN) with multiple hidden layers have shown the ability to systematically extract lower-to-higher level information from high dimensional data across a series of neural hidden layers, significantly improving classification accuracy for such data. In this study, a DNN with a novel feature selection method (DNN-FS) is developed for the high dimensional whole-brain resting-state FC pattern classification of ASD patients vs. typical development (TD) controls. The feature selection method is able to help the DNN generate low dimensional high-quality representations of the whole-brain FC patterns by selecting features with high discriminating power from multiple trained sparse auto-encoders. For the comparison, a DNN without the feature selection method (DNN-woFS) is developed, and both of them are tested with different architectures (i.e., with different numbers of hidden layers/nodes). Results show that the best classification accuracy of
is generated by the DNN-FS approach with 3 hidden layers and 150 hidden nodes (3/150). Remarkably, DNN-FS outperforms DNN-woFS for all architectures studied. The most significant accuracy improvement was
with the 3/150 architecture. The method also outperforms other feature selection methods, e.g., two sample
-test and elastic net. In addition to improving the classification accuracy, a Fisher's score-based biomarker identification method based on the DNN is also developed, and used to identify 32 FCs related to ASD. These FCs come from or cross different pre-defined brain networks including the default-mode, cingulo-opercular, frontal-parietal, and cerebellum. Thirteen of them are statically significant between ASD and TD groups (two sample
-test
< 0.05) while 19 of them are not. The relationship between the statically significant FCs and the corresponding ASD behavior symptoms is discussed based on the literature and clinician's expert knowledge. Meanwhile, the potential reason of obtaining 19 FCs which are not statistically significant is also provided.
Dark quark nuggets Bai, Yang; Long, Andrew J.; Lu, Sida
Physical review. D,
03/2019, Letnik:
99, Številka:
5
Journal Article
Recenzirano
Odprti dostop
“Dark quark nuggets,” a lump of dark quark matter, can be produced in the early universe for a wide range of confining gauge theories and serve as a macroscopic dark matter candidate. The two ...necessary conditions, a nonzero dark baryon number asymmetry and a first-order phase transition, can easily be satisfied for many asymmetric dark matter models and QCD-like gauge theories with a few massless flavors. For confinement scales from 10 keV to 100 TeV, these dark quark nuggets with a huge dark baryon number have their masses vary from 1023 g to 10−7 g and their radii from 108 cm to 10−15 cm. Such macroscopic dark matter candidates can be searched for by a broad scope of experiments and even new detection strategies. Specifically, we have found that the gravitational microlensing experiments can probe heavier dark quark nuggets or smaller confinement scales around 10 keV; collision of dark quark nuggets can generate detectable and transient electromagnetic radiation signals; the stochastic gravitational wave signals from the first-order phase transition can be probed by the pulsar timing array observations and other space-based interferometry experiments; the approximately massless dark mesons can behave as dark radiation to be tested by the next-generation cosmic microwave background experiments; the free dark baryons, as a subcomponent of dark matter, can have direct detection signals for a sufficiently strong interaction strength with the visible sector.
Porcine circovirus type 2 (PCV2), the causative agent of postweaning multisystemic wasting syndrome (PMWS), is a serious economic problem for the swine industry in China. In this study, we ...investigated the genetic variation of PCV2 in China using strains isolated from 2004-2008. Viruses were isolated from samples collected from pigs with multi-systemic lesions and clinical signs of PMWS from different regions of China, and the genomes of these viruses were sequenced. The assembled sequences were used to define the genotypes of these strains; PCR-RFLP methodology was used to distinguish isolates and capture ELISA was used to demonstrate the antigenic changes resulted from ORF2 gene mutation of the isolates.
We identified 19 PCV2 isolates, including four newly emerging PCV2 mutant strains. The 19 isolates were designated into three genotypes (PCV2a, PCV2b and PCV2d). PCV2d represented a novel genotype and a shift from PCV2a to PCV2b as the predominant genotype in China was identified. This is the first report of 1766 nt PCV2 harboring a base deletion at other new different positions. Amino acid sequence analysis identified two novel ORF2 mutations (resulting in ORF2 sequences 705 and 708 nt in length) in three deletion strains (1766 nt) and one strain with a genome 1767 nt in length. Finding of two amino acids elongation of the ORF2-encoded Cap protein is firstly observed among PCV2 strains all over the world. The isolates were distinguished into different genotypes by PCR-RFLP methodology and antigenic changes were present in Cap protein of mutation isolates by capture ELISA.
The results of this study provide evidence that PCV2 is undergoing constant genetic variation and that the predominant strain in China as well as the antigenic situation has changed in recent years. Furthermore, the PCR-RFLP method presented here may be useful for the differential identification of PCV2 strains in future studies.
Most studies of protein networks operate on a high level of abstraction, neglecting structural and chemical aspects of each interaction. Here, we characterize interactions by using atomic-resolution ...information from three-dimensional protein structures. We find that some previously recognized relationships between network topology and genomic features (e.g., hubs tending to be essential proteins) are actually more reflective of a structural quantity, the number of distinct binding interfaces. Subdividing hubs with respect to this quantity provides insight into their evolutionary rate and indicates that additional mechanisms of network growth are active in evolution (beyond effective preferential attachment through gene duplication).
•Identify noise local image features by comparing features from disease group and healthy control group.•Support vector machine is used to classify image features.•Disease related regions are ...identified from three different diseases (Alzheimer's disease, Parkinson's disease and bipolar disorder).•The algorithm can be used to analyze MR images from heterogeneous datasets.
Detecting brain structural changes from magnetic resonance (MR) images can facilitate early diagnosis and treatment of neurological and psychiatric diseases. Many existing methods require an accurate deformation registration, which is difficult to achieve and therefore prevents them from obtaining high accuracy. We develop a novel local feature based support vector machine (SVM) approach to detect brain structural changes as potential biomarkers. This approach does not require deformation registration and thus is less influenced by artifacts such as image distortion. We represent the anatomical structures based on scale invariant feature transform (SIFT). Likelihood scores calculated using feature-based morphometry is used as the criterion to categorize image features into three classes (healthy, patient and noise). Regional SVMs are trained to classify the three types of image features in different brain regions. Only healthy and patient features are used to predict the disease status of new brain images. An ensemble classifier is built from the regional SVMs to obtain better prediction accuracy. We apply this approach to 3D MR images of Alzheimer's disease, Parkinson's disease and bipolar disorder. The classification accuracy ranges between 70% and 87%. The highly predictive disease-related regions, which represent significant anatomical differences between the healthy and diseased, are shown in heat maps. The common and disease-specific brain regions are identified by comparing the highly predictive regions in each disease. All of the top-ranked regions are supported by literature. Thus, this approach will be a promising tool for assisting automatic diagnosis and advancing mechanism studies of neurological and psychiatric diseases.
Mucoid mucA22 Pseudomonas aeruginosa (PA) is an opportunistic lung pathogen of cystic fibrosis (CF) and chronic obstructive pulmonary disease (COPD) patients that is highly sensitive to acidified ...nitrite (A-NO2-). In this study, we first screened PA mutant strains for sensitivity or resistance to 20 mM A-NO2- under anaerobic conditions that represent the chronic stages of the aforementioned diseases. Mutants found to be sensitive to A-NO2- included PA0964 (pmpR, PQS biosynthesis), PA4455 (probable ABC transporter permease), katA (major catalase, KatA) and rhlR (quorum sensing regulator). In contrast, mutants lacking PA0450 (a putative phosphate transporter) and PA1505 (moaA2) were A-NO2- resistant. However, we were puzzled when we discovered that mucA22 mutant bacteria, a frequently isolated mucA allele in CF and to a lesser extent COPD, were more sensitive to A-NO2- than a truncated ΔmucA deletion (Δ157-194) mutant in planktonic and biofilm culture, as well as during a chronic murine lung infection. Subsequent transcriptional profiling of anaerobic, A-NO2--treated bacteria revealed restoration of near wild-type transcript levels of protective NO2- and nitric oxide (NO) reductase (nirS and norCB, respectively) in the ΔmucA mutant in contrast to extremely low levels in the A-NO2--sensitive mucA22 mutant. Proteins that were S-nitrosylated by NO derived from A-NO2- reduction in the sensitive mucA22 strain were those involved in anaerobic respiration (NirQ, NirS), pyruvate fermentation (UspK), global gene regulation (Vfr), the TCA cycle (succinate dehydrogenase, SdhB) and several double mutants were even more sensitive to A-NO2-. Bioinformatic-based data point to future studies designed to elucidate potential cellular binding partners for MucA and MucA22. Given that A-NO2- is a potentially viable treatment strategy to combat PA and other infections, this study offers novel developments as to how clinicians might better treat problematic PA infections in COPD and CF airway diseases.
Treatment of large bone defects derived from bone tumor surgery is typically performed in multiple separate operations, such as hyperthermia to extinguish residual malignant cells or implanting ...bioactive materials to initiate apatite remineralization for tissue repair; it is very challenging to combine these functions into a material. Herein, we report the first photothermal (PT) effect in bismuth (Bi)-doped glasses. On the basis of this discovery, we have developed a new type of Bi-doped bioactive glass that integrates both functions, thus reducing the number of treatment cycles. We demonstrate that Bi-doped bioglasses (BGs) provide high PT efficiency, potentially facilitating photoinduced hyperthermia and bioactivity to allow bone tissue remineralization. The PT effect of Bi-doped BGs can be effectively controlled by managing radiative and non-radiative processes of the active Bi species by quenching photoluminescence (PL) or depolymerizing glass networks. In vitro studies demonstrate that such glasses are biocompatible to tumor and normal cells and that they can promote osteogenic cell proliferation, differentiation, and mineralization. Upon illumination with near-infrared (NIR) light, the bioglass (BG) can efficiently kill bone tumor cells, as demonstrated via in vitro and in vivo experiments. This indicates excellent potential for the integration of multiple functions within the new materials, which will aid in the development and application of novel biomaterials.
Pseudomonas aeruginosa (PA) is a ubiquitous opportunistic pathogen that is capable of causing highly problematic, chronic infections in cystic fibrosis and chronic obstructive pulmonary disease ...patients. With the increased prevalence of multi-drug resistant PA, the conventional "one gene, one drug, one disease" paradigm is losing effectiveness. Network pharmacology, on the other hand, may hold the promise of discovering new drug targets to treat a variety of PA infections. However, given the urgent need for novel drug target discovery, a PA protein-protein interaction (PPI) network of high accuracy and coverage, has not yet been constructed. In this study, we predicted a genome-scale PPI network of PA by integrating various genomic features of PA proteins/genes by a machine learning-based approach. A total of 54,107 interactions covering 4,181 proteins in PA were predicted. A high-confidence network combining predicted high-confidence interactions, a reference set and verified interactions that consist of 3,343 proteins and 19,416 potential interactions was further assembled and analyzed. The predicted interactome network from this study is the first large-scale PPI network in PA with significant coverage and high accuracy. Subsequent analysis, including validations based on existing small-scale PPI data and the network structure comparison with other model organisms, shows the validity of the predicted PPI network. Potential drug targets were identified and prioritized based on their essentiality and topological importance in the high-confidence network. Host-pathogen protein interactions between human and PA were further extracted and analyzed. In addition, case studies were performed on protein interactions regarding anti-sigma factor MucA, negative periplasmic alginate regulator MucB, and the transcriptional regulator RhlR. A web server to access the predicted PPI dataset is available at http://research.cchmc.org/PPIdatabase/.
PDF
