Loss of Cytokine-STAT5 Signaling in the CNS and Pituitary Gland Alters Energy Balance and Leads to Obesity

Signal transducers and activators of transcription (STATs) are critical components of cytokine signaling pathways. STAT5A and STAT5B (STAT5), the most promiscuous members of this family, are highly expressed in specific populations of hypothalamic neurons in regions known to mediate the actions of cytokines in the regulation of energy balance. To test the hypothesis that STAT5 signaling is essential to energy homeostasis, we used Cre-mediated recombination to delete the Stat5 locus in the CNS. Mutant males and females developed severe obesity with hyperphagia, impaired thermal regulation in response to cold, hyperleptinemia and insulin resistance. Furthermore, central administration of GM-CSF mediated the nuclear accumulation of STAT5 in hypothalamic neurons and reduced food intake in control but not in mutant mice. These results demonstrate that STAT5 mediates energy homeostasis in response to endogenous cytokines such as GM-CSF

Unitary relation between a harmonic oscillator of time-dependent frequency and a simple harmonic oscillator with and without an inverse-square potential

The unitary operator which transforms a harmonic oscillator system of time-dependent frequency into that of a simple harmonic oscillator of different time-scale is found, with and without an inverse-square potential. It is shown that for both cases, this operator can be used in finding complete sets of wave functions of a generalized harmonic oscillator system from the well-known sets of the simple harmonic oscillator. Exact invariants of the time-dependent systems can also be obtained from the constant Hamiltonians of unit mass and frequency by making use of this unitary transformation. The geometric phases for the wave functions of a generalized harmonic oscillator with an inverse-square potential are given.Comment: Phys. Rev. A (Brief Report), in pres

Automated ventricular systems segmentation in brain CT images by combining low-level segmentation and high-level template matching

<p>Abstract</p> <p>Background</p> <p>Accurate analysis of CT brain scans is vital for diagnosis and treatment of Traumatic Brain Injuries (TBI). Automatic processing of these CT brain scans could speed up the decision making process, lower the cost of healthcare, and reduce the chance of human error. In this paper, we focus on automatic processing of CT brain images to segment and identify the ventricular systems. The segmentation of ventricles provides quantitative measures on the changes of ventricles in the brain that form vital diagnosis information.</p> <p>Methods</p> <p>First all CT slices are aligned by detecting the ideal midlines in all images. The initial estimation of the ideal midline of the brain is found based on skull symmetry and then the initial estimate is further refined using detected anatomical features. Then a two-step method is used for ventricle segmentation. First a low-level segmentation on each pixel is applied on the CT images. For this step, both Iterated Conditional Mode (ICM) and Maximum A Posteriori Spatial Probability (MASP) are evaluated and compared. The second step applies template matching algorithm to identify objects in the initial low-level segmentation as ventricles. Experiments for ventricle segmentation are conducted using a relatively large CT dataset containing mild and severe TBI cases.</p> <p>Results</p> <p>Experiments show that the acceptable rate of the ideal midline detection is over 95%. Two measurements are defined to evaluate ventricle recognition results. The first measure is a sensitivity-like measure and the second is a false positive-like measure. For the first measurement, the rate is 100% indicating that all ventricles are identified in all slices. The false positives-like measurement is 8.59%. We also point out the similarities and differences between ICM and MASP algorithms through both mathematically relationships and segmentation results on CT images.</p> <p>Conclusion</p> <p>The experiments show the reliability of the proposed algorithms. The novelty of the proposed method lies in its incorporation of anatomical features for ideal midline detection and the two-step ventricle segmentation method. Our method offers the following improvements over existing approaches: accurate detection of the ideal midline and accurate recognition of ventricles using both anatomical features and spatial templates derived from Magnetic Resonance Images.</p

A comparative analysis of multi-level computer-assisted decision making systems for traumatic injuries

<p>Abstract</p> <p>Background</p> <p>This paper focuses on the creation of a predictive computer-assisted decision making system for traumatic injury using machine learning algorithms. Trauma experts must make several difficult decisions based on a large number of patient attributes, usually in a short period of time. The aim is to compare the existing machine learning methods available for medical informatics, and develop reliable, rule-based computer-assisted decision-making systems that provide recommendations for the course of treatment for new patients, based on previously seen cases in trauma databases. Datasets of traumatic brain injury (TBI) patients are used to train and test the decision making algorithm. The work is also applicable to patients with traumatic pelvic injuries.</p> <p>Methods</p> <p>Decision-making rules are created by processing patterns discovered in the datasets, using machine learning techniques. More specifically, CART and C4.5 are used, as they provide grammatical expressions of knowledge extracted by applying logical operations to the available features. The resulting rule sets are tested against other machine learning methods, including AdaBoost and SVM. The rule creation algorithm is applied to multiple datasets, both with and without prior filtering to discover significant variables. This filtering is performed via logistic regression prior to the rule discovery process.</p> <p>Results</p> <p>For survival prediction using all variables, CART outperformed the other machine learning methods. When using only significant variables, neural networks performed best. A reliable rule-base was generated using combined C4.5/CART. The average predictive rule performance was 82% when using all variables, and approximately 84% when using significant variables only. The average performance of the combined C4.5 and CART system using significant variables was 89.7% in predicting the exact outcome (home or rehabilitation), and 93.1% in predicting the ICU length of stay for airlifted TBI patients.</p> <p>Conclusion</p> <p>This study creates an efficient computer-aided rule-based system that can be employed in decision making in TBI cases. The rule-bases apply methods that combine CART and C4.5 with logistic regression to improve rule performance and quality. For final outcome prediction for TBI cases, the resulting rule-bases outperform systems that utilize all available variables.</p

Histone deacetylase regulates high mobility group A2-targeting microRNAs in human cord blood-derived multipotent stem cell aging

Cellular senescence involves a reduction in adult stem cell self-renewal, and epigenetic regulation of gene expression is one of the main underlying mechanisms. Here, we observed that the cellular senescence of human umbilical cord blood-derived multipotent stem cells (hUCB-MSCs) caused by inhibition of histone deacetylase (HDAC) activity leads to down-regulation of high mobility group A2 (HMGA2) and, on the contrary, to up-regulation of p16INK4A, p21CIP1/WAF1 and p27KIP1. We found that let-7a1, let-7d, let-7f1, miR-23a, miR-26a and miR-30a were increased during replicative and HDAC inhibitor-mediated senescence of hUCB-MSCs by microRNA microarray and real-time quantitative PCR. Furthermore, the configurations of chromatins beading on these miRNAs were prone to transcriptional activation during HDAC inhibitor-mediated senescence. We confirmed that miR-23a, miR-26a and miR-30a inhibit HMGA2 to accelerate the progress of senescence. These findings suggest that HDACs may play important roles in cellular senescence by regulating the expression of miRNAs that target HMGA2 through histone modification

Dimerization of Translationally Controlled Tumor Protein Is Essential For Its Cytokine-Like Activity

BACKGROUND:Translationally Controlled Tumor Protein (TCTP) found in nasal lavage fluids of allergic patients was named IgE-dependent histamine-releasing factor (HRF). Human recombinant HRF (HrHRF) has been recently reported to be much less effective than HRF produced from activated mononuclear cells (HRFmn). METHODS AND FINDINGS:We found that only NH(2)-terminal truncated, but not C-terminal truncated, TCTP shows cytokine releasing activity compared to full-length TCTP. Interestingly, only NH(2)-terminal truncated TCTP, unlike full-length TCTP, forms dimers through intermolecular disulfide bonds. We tested the activity of dimerized full-length TCTP generated by fusing it to rabbit Fc region. The untruncated-full length protein (Fc-HrTCTP) was more active than HrTCTP in BEAS-2B cells, suggesting that dimerization of TCTP, rather than truncation, is essential for the activation of TCTP in allergic responses. We used confocal microscopy to evaluate the affinity of TCTPs to its putative receptor. We detected stronger fluorescence in the plasma membrane of BEAS-2B cells incubated with Del-N11TCTP than those incubated with rat recombinant TCTP (RrTCTP). Allergenic activity of Del-N11TCTP prompted us to see whether the NH(2)-terminal truncated TCTP can induce allergic airway inflammation in vivo. While RrTCTP had no influence on airway inflammation, Del-N11TCTP increased goblet cell hyperplasia in both lung and rhinal cavity. The dimerized protein was found in sera from allergic patients, and bronchoalveolar lavage fluids from airway inflamed mice. CONCLUSIONS:Dimerization of TCTP seems to be essential for its cytokine-like activity. Our study has potential to enhance the understanding of pathogenesis of allergic disease and provide a target for allergic drug development

Fatty Acid Binding Protein 1 Is Related with Development of Aspirin-Exacerbated Respiratory Disease

BACKGROUND: Aspirin-exacerbated respiratory disease (AERD) refers to the development of bronchoconstriction in asthmatics following the ingestion of aspirin. Although alterations in eicosanoid metabolites play a role in AERD, other immune or inflammatory mechanisms may be involved. We aimed to identify proteins that were differentially expressed in nasal polyps between patients with AERD and aspirin-tolerant asthma (ATA). METHODOLOGY/PRINCIPAL FINDINGS: Two-dimensional electrophoresis was adopted for differential display proteomics. Proteins were identified by liquid chromatography-tandem mass spectrometry (LC-MS). Western blotting and immunohistochemical staining were performed to compare the amount of fatty acid-binding protein 1 (FABP1) in the nasal polyps of patients with AERD and ATA. Fifteen proteins were significantly up- (seven spots) or down-regulated in the nasal polyps of patients with AERD (n = 5) compared to those with ATA (n = 8). LC-MS revealed an increase in seven proteins expression and a decrease in eight proteins expression in patients with AERD compared to those with ATA (P = 0.003-0.045). FABP1-expression based on immunoblotting and immunohistochemical analysis was significantly higher in the nasal polyps of patients with AERD compared to that in patients with ATA. FABP1 was observed in epithelial, eosinophils, macrophages, and the smooth-muscle cells of blood vessels in the polyps. CONCLUSIONS/SIGNIFICANCE: Our results indicate that alterations in 15 proteins, including FABP1, may be related to the development of AERD

Effects of Interleukin-10 Polymorphisms, Helicobacter pylori Infection, and Smoking on the Risk of Noncardia Gastric Cancer

OBJECTIVE: Both variations in the interleukin-10 (IL10) gene and environmental factors are thought to influence inflammation and gastric carcinogenesis. Therefore, we investigated the associations between IL10 polymorphisms, Helicobacter pylori (H. pylori) infection, and smoking in noncardia gastric carcinogenesis in Koreans. METHODS: We genotyped three promoter polymorphisms (-1082A>G, -819T>C, and -592 A>C) of IL10 in a case-control study of 495 noncardia gastric cancer patients and 495 sex- and age-matched healthy controls. Multiple logistic regression models were used to detect the effects of IL10 polymorphisms, H. pylori infection, and smoking on the risk of gastric cancer, which was stratified by the histological type of gastric cancer. RESULTS: The IL10-819C and -592C alleles were found to have complete linkage disequilibrium, and all three IL10 polymorphisms were associated with an increased risk of intestinal-type noncardia gastric cancer. These associations were observed only in H. pylori-positive subjects and current smokers. A statistically significant interaction between the IL10-592 genotype and H. pylori infection on the risk of intestinal-type gastric cancer was observed (P for interaction  = 0.047). In addition, H. pylori-positive smokers who were carriers of either the IL10-1082G (OR [95% CI]  = 17.76 [6.17-51.06]) or the -592C (OR [95% CI]  = 8.37 [2.79-25.16]) allele had an increased risk of intestinal-type gastric cancer compared to H. pylori-negative nonsmokers homozygous for IL10-1082A and -592A, respectively. The interaction between the IL10-1082 polymorphism and the combined effects of H. pylori infection and smoking tended towards significance (P for interaction  = 0.080). CONCLUSIONS: Inflammation-related genetic variants may interact with H. pylori infection and smoking to increase the risk of noncardia gastric cancer, particularly the intestinal-type. These findings may be helpful in identifying individuals at an increased risk for developing noncardia gastric cancer