Microglial responses around intrinsic CNS neurons are correlated with axonal regeneration

<p>Abstract</p> <p>Background</p> <p>Microglia/macrophages and lymphocytes (T-cells) accumulate around motor and primary sensory neurons that are regenerating axons but there is little or no microglial activation or T-cell accumulation around axotomised intrinsic CNS neurons, which do not normally regenerate axons. We aimed to establish whether there was an inflammatory response around the perikarya of CNS neurons that were induced to regenerate axons through a peripheral nerve graft.</p> <p>Results</p> <p>When neurons of the thalamic reticular nucleus (TRN) and red nucleus were induced to regenerate axons along peripheral nerve grafts, a marked microglial response was found around their cell bodies, including the partial enwrapping of some regenerating neurons. T-cells were found amongst regenerating TRN neurons but not rubrospinal neurons. Axotomy alone or insertion of freeze-killed nerve grafts did not induce a similar perineuronal inflammation. Nerve grafts in the corticospinal tracts did not induce axonal regeneration or a microglial or T-cell response in the motor cortex.</p> <p>Conclusions</p> <p>These results strengthen the evidence that perineuronal microglial accumulation (but not T-cell accumulation) is involved in axonal regeneration by intrinsic CNS and other neurons.</p

Serum overexpression of miR-301a and miR-23a in patients with colorectal cancer

BACKGROUND: Extracellular vesicles (EVs) are a heterogeneous group of membrane-bound vesicles with complex cargoes including proteins, lipids, and nucleic acids. EVs have received significant attention due to their specific features including stability under harsh conditions and involvement in cell-to-cell communication. Circulating EVs and the molecules associated with them are important in the diagnosis and prognosis of cancers. MicroRNAs (miRNAs) are a group of small noncoding RNAs that have a role in regulating gene expression. Current literature shows that circulating miRNAs can be used as noninvasive biomarkers for early detection of cancers. The present study was set to investigate the potential role of serum exosomal miRNA expression levels in colorectal cancer (CRC) patients and evaluate their correlation with clinicopathologic features. METHODS: Exosome-enriched fractions were isolated from the serum of 25 CRC patients and 13 age- and sex-matched healthy controls using a polymer-based precipitation method. During the pilot phase, real-time polymerase chain reaction (RT-PCR) was carried out on 12 CRC patients and eight healthy participants to evaluate the expression difference of 11 candidate miRNAs between CRC patients and tumor free subjects. Finally, the results were validated in a separate group, which was similar in size to the pilot group. The clinicopathologic data were also collected and the relationship between aberrant miRNA expression and clinicopathological parameters were investigated. RESULTS: There were high expressions of exosomal miR-23a and miR-301a in serum samples of CRC patients compared to normal controls in training and validation phases; these differences were not significantly correlated with clinicopathologic features. Receiver operating characteristic curve analysis showed that miR-301a and miR-23a were able to discriminate CRC patients from normal subjects. CONCLUSION: The findings provide evidence on the roles of miR-301a and miR-23a in CRC development and their potential roles as noninvasive biomarkers for early detection of CRC

Intestinal barrier dysfunction plays an integral role in arthritis pathology and can be targeted to ameliorate disease

Background: Evidence suggests an important role for gut-microbiota dysbiosis in the development of rheumatoid arthritis (RA). The link between changes in gut bacteria and the development of joint inflammation is missing. Here, we address whether there are changes to the gut environment and how they contribute to arthritis pathogenesis. Methods: We analyzed changes in markers of gut permeability, damage, and inflammation in peripheral blood and serum of RA patients. Serum, intestines, and lymphoid organs isolated from K/BxN mice with spontaneous arthritis or from wild-type, genetically modified interleukin (IL)-10R−/− or claudin-8−/− mice with induced arthritis were analyzed by immunofluorescence/histology, ELISA, and flow cytometry. Findings: RA patients display increased levels of serum markers of gut permeability and damage and cellular gut-homing markers, both parameters positively correlating with disease severity. Arthritic mice display increased gut permeability from early stages of disease, as well as bacterial translocation, inflammatory gut damage, increases in interferon γ (IFNγ)+ and decreases in IL-10+ intestinal-infiltrating leukocyte frequency, and reduced intestinal epithelial IL-10R expression. Mechanistically, both arthritogenic bacteria and leukocytes are required to disrupt gut-barrier integrity. We show that exposing intestinal organoids to IFNγ reduces IL-10R expression by epithelial cells and that mice lacking epithelial IL-10R display increased intestinal permeability and exacerbated arthritis. Claudin-8−/− mice with constitutively increased gut permeability also develop worse joint disease. Treatment of mice with AT-1001, a molecule that prevents development of gut permeability, ameliorates arthritis. Conclusions: We suggest that breakdown of gut-barrier integrity contributes to arthritis development and propose restoration of gut-barrier homeostasis as a new therapeutic approach for RA

Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials

INTRODUCTION: The Alzheimer's Disease Neuroimaging Initiative (ADNI) has continued development and standardization of methodologies for biomarkers and has provided an increased depth and breadth of data available to qualified researchers. This review summarizes the over 400 publications using ADNI data during 2014 and 2015. METHODS: We used standard searches to find publications using ADNI data. RESULTS: (1) Structural and functional changes, including subtle changes to hippocampal shape and texture, atrophy in areas outside of hippocampus, and disruption to functional networks, are detectable in presymptomatic subjects before hippocampal atrophy; (2) In subjects with abnormal β-amyloid deposition (Aβ+), biomarkers become abnormal in the order predicted by the amyloid cascade hypothesis; (3) Cognitive decline is more closely linked to tau than Aβ deposition; (4) Cerebrovascular risk factors may interact with Aβ to increase white-matter (WM) abnormalities which may accelerate Alzheimer's disease (AD) progression in conjunction with tau abnormalities; (5) Different patterns of atrophy are associated with impairment of memory and executive function and may underlie psychiatric symptoms; (6) Structural, functional, and metabolic network connectivities are disrupted as AD progresses. Models of prion-like spreading of Aβ pathology along WM tracts predict known patterns of cortical Aβ deposition and declines in glucose metabolism; (7) New AD risk and protective gene loci have been identified using biologically informed approaches; (8) Cognitively normal and mild cognitive impairment (MCI) subjects are heterogeneous and include groups typified not only by "classic" AD pathology but also by normal biomarkers, accelerated decline, and suspected non-Alzheimer's pathology; (9) Selection of subjects at risk of imminent decline on the basis of one or more pathologies improves the power of clinical trials; (10) Sensitivity of cognitive outcome measures to early changes in cognition has been improved and surrogate outcome measures using longitudinal structural magnetic resonance imaging may further reduce clinical trial cost and duration; (11) Advances in machine learning techniques such as neural networks have improved diagnostic and prognostic accuracy especially in challenges involving MCI subjects; and (12) Network connectivity measures and genetic variants show promise in multimodal classification and some classifiers using single modalities are rivaling multimodal classifiers. DISCUSSION: Taken together, these studies fundamentally deepen our understanding of AD progression and its underlying genetic basis, which in turn informs and improves clinical trial desig

Estimating the Number of Significant Canonical Coordinates

Breast mass is one of the most distinctive signs for the diagnosis of breast cancer, and the accurate segmentation of masses is critical for improving the accuracy of breast cancer detection and reducing the mortality rate. It is time-consuming for a physician to review the film. Besides, traditional medical segmentation techniques often require prior knowledge or manual extraction of features, which often lead to a subjective diagnosis. Therefore, developing an automatic image segmentation method is important for clinical application. In this paper, a fully automatic method based on deep learning for breast mass segmentation is proposed, which combines densely connected U-Net with attention gates (AGs). It contains an encoder and a decoder. The encoder is a densely connected convolutional network and the decoder is the decoder of U-Net integrated with AGs. The proposed method is tested on the public and authoritative database-Digital Database for Screening Mammography (DDSM) database. F1-score, mean intersection over union, sensitivity, specificity, and overall accuracy are used to evaluate the effectiveness of the proposed method. The experimental results show that dense U-Net integrated AGs achieve better segmentation results than U-Net, attention U-Net, DenseNet, and state-of-the-art methods

An Improved Fuzzy Neural Network for Solving Uncertainty in Pattern Classification and Identification

Dealing with uncertainty is one of the most critical problems in complicatedpattern recognition subjects. In this paper, we modify the structure of a useful UnsupervisedFuzzy Neural Network (UFNN) of Kwan and Cai, and compose a new FNN with 6 types offuzzy neurons and its associated self organizing supervised learning algorithm. Thisimproved five-layer feed forward Supervised Fuzzy Neural Network (SFNN) is used forclassification and identification of shifted and distorted training patterns. It is generallyuseful for those flexible patterns which are not certainly identifiable upon their features. Toshow the identification capability of our proposed network, we used fingerprint, as the mostflexible and varied pattern. After feature extraction of different shapes of fingerprints, thepattern of these features, “feature-map”, is applied to the network. The network firstfuzzifies the pattern and then computes its similarities to all of the learned pattern classes.The network eventually selects the learned pattern of highest similarity and returns itsspecific class as a non fuzzy output. To test our FNN, we applied the standard (NISTdatabase) and our databases (with 176×224 dimensions). The feature-maps of thesefingerprints contain two types of minutiae and three types of singular points, each of themis represented by 22×28 pixels, which is less than real size and suitable for real timeapplications. The feature maps are applied to the FNN as training patterns. Upon its settingparameters, the network discriminates 3 to 7 subclasses for each main classes assigned toone of the subjects

Tensile fracturing of anisotropic Brisbane phyllite

Rocks are generally more or less anisotropic, depending on their structure at the scale of interest. The anisotropic rocks in civil and mining engineering projects cause non-symmetric deformation and their behaviour is unpredictable. The authors present the results of Brazilian Indirect Tensile (BIT) strength and Cracked Chevron Notched Brazilian Disc (CCNBD) tests for the determination of the fundamental tensile fracturing parameters of Brisbane phyllite specimens. In general, the influence of orientation angle (ψ) and foliation-loading angle (β) were found to influence both fracturing and the failure load