L-arginine: A unique amino acid for improving depressed wound immune function following hemorrhage

Objective: To determine whether L-arginine has any salutary effects on wound immune cell function following trauma-hemorrhage. Background. Depressed wound immune function contributes to an increased incidence of wound infections following hemorrhage. Although administration of L-arginine has been shown to restore depressed cell-mediated immune responses following hemorrhage potentially by maintaining organ blood flow, it remains unknown whether Larginine has any salutary effects on the depressed local immune response at the wound site. Methods: Male mice were subjected to a midline laparotomy and polyvinyl sponges were implanted subcutaneously in the abdominal wound prior to hemorrhage (35 +/- 5 mm Hg for 90 min and resuscitation) or sham operation. During resuscitation mice received 300 mg/kg body weight L-arginine or saline (vehicle). Sponges were harvested 24 h thereafter, wound fluid collected and wound immune cells cultured for 24 h in the presence of LPS. Pro- (IL-1beta, IL-6) and anti-inflammatory (IL-10) cytokines were determined in the supernatants and the wound fluid. In addition, wounds were stained for IL-6 immunohistochemically. In a separate set of animals, skin and muscle blood flow was determined by microspheres. Results: The capacity of wound immune cells to release IL-1beta and IL-6 in vitro was significantly depressed in hemorrhaged mice receiving vehicle. Administration of L-arginine, however, improved wound immune cell function. In contrast, in vivo the increased IL-6 release at the wound site was decreased in L-arginine-treated mice following hemorrhage. Moreover, IL-10 levels were significantly increased in the wound fluid in hemorrhaged animals receiving L-arginine compared to vehicle-treated mice. In addition, the depressed skin and muscle blood flow after hemorrhage was restored by L-arginine. Conclusions: Thus, L-arginine might improve local wound cell function by decreasing the inflammatory response at the wound site. Since L-arginine protected wound immune cell function this amino acid might represent a novel and useful adjunct to fluid resuscitation for decreasing wound complications following hemorrhage. Copyright beta 2002 S. Karger AG, Basel

Application of Hansch’s Model to Capsaicinoids and Capsinoids: A Study Using the Quantitative Structure−Activity Relationship. A Novel Method for the Synthesis of Capsinoids

We describe a synthetic approach for two families of compounds, the capsaicinoids and capsinoids, as part of a study of the quantitative relationship between structure and activity

Axial Torsion-Dirac spin Effect in Rotating Frame with Relativistic Factor

In the framework of spacetime with torsion and without curvature, the Dirac particle spin precession in the rotational system is studied. We write out the equivalent tetrad of rotating frame, in the polar coordinate system, through considering the relativistic factor, and the resultant equivalent metric is a flat Minkowski one. The obtained rotation-spin coupling formula can be applied to the high speed rotating case, which is consistent with the expectation.Comment: 6 page

Torsion Gravity: a Reappraisal

The role played by torsion in gravitation is critically reviewed. After a description of the problems and controversies involving the physics of torsion, a comprehensive presentation of the teleparallel equivalent of general relativity is made. According to this theory, curvature and torsion are alternative ways of describing the gravitational field, and consequently related to the same degrees of freedom of gravity. However, more general gravity theories, like for example Einstein-Cartan and gauge theories for the Poincare and the affine groups, consider curvature and torsion as representing independent degrees of freedom. By using an active version of the strong equivalence principle, a possible solution to this conceptual question is reviewed. This solution favors ultimately the teleparallel point of view, and consequently the completeness of general relativity. A discussion of the consequences for gravitation is presented.Comment: RevTeX, 34 pages. Review article to be published by Int. J. Mod. Phys.

Does abscisic acid affect strigolactone biosynthesis?

Strigolactones are considered a novel class of plant hormones that, in addition to their endogenous signalling function, are exuded into the rhizosphere acting as a signal to stimulate hyphal branching of arbuscular mycorrhizal (AM) fungi and germination of root parasitic plant seeds. Considering the importance of the strigolactones and their biosynthetic origin (from carotenoids), we investigated the relationship with the plant hormone abscisic acid (ABA). Strigolactone production and ABA content in the presence of specific inhibitors of oxidative carotenoid cleavage enzymes and in several tomato ABA-deficient mutants were analysed by LC-MS/MS. In addition, the expression of two genes involved in strigolactone biosynthesis was studied. * • The carotenoid cleavage dioxygenase (CCD) inhibitor D2 reduced strigolactone but not ABA content of roots. However, in abamineSG-treated plants, an inhibitor of 9-cis-epoxycarotenoid dioxygenase (NCED), and the ABA mutants notabilis, sitiens and flacca, ABA and strigolactones were greatly reduced. The reduction in strigolactone production correlated with the downregulation of LeCCD7 and LeCCD8 genes in all three mutants. * • The results show a correlation between ABA levels and strigolactone production, and suggest a role for ABA in the regulation of strigolactone biosynthesis

Impact of an Early Invasive Strategy versus Conservative Strategy for Unstable Angina and Non-ST Elevation Acute Coronary Syndrome in Patients with Chronic Kidney Disease: A Systematic Review.

BACKGROUND: Clinical practice guidelines support an early invasive approach after NSTE-ACS in patients with chronic kidney disease (CKD). There is no direct randomised controlled trial evidence in the CKD population, and whether the benefit of an early invasive approach is maintained across the spectrum of severity of CKD remains controversial. METHODS: We conducted a systematic review to evaluate the association between an early invasive approach and all-cause mortality in patients with CKD. We searched MEDLINE and EMBASE (1990-May 2015) and article reference lists. Data describing study design, participants, invasive management strategies, renal function, all-cause mortality and risk of bias were extracted. RESULTS: 3,861 potentially relevant studies were identified. Ten studies, representing data on 147,908 individuals with NSTE-ACS met the inclusion criteria. Qualitative heterogeneity in the definitions of early invasive approach, comparison groups and renal dysfunction existed. Meta-analysis of the RCT derived and observational data were generally supportive of an early invasive approach in CKD (RR0.76 (95% CI 0.49-1.17) and RR0.50 (95%CI 0.42-0.59) respectively). Meta-analysis of the observational studies demonstrated a large degree of heterogeneity (I2 79%) driven in part by study size and heterogeneity across various kidney function levels. CONCLUSIONS: The observational data support that an early invasive approach after NSTE-ACS confers a survival benefit in those with early-moderate CKD. Local opportunities for quality improvement should be sought. Those with severe CKD and the dialysis population are high risk and under-studied. Novel and inclusive approaches for CKD and dialysis patients in cardiovascular clinical trials are needed

Currency Unions

A currency union is when several independent sovereign nations share a common currency. This has been a recurring phenomenon in monetary history. In this article I study the theoretical foundations of such unions, and discuss some important currency unions in history, most notably the case of the US. Finally I contrast the design of the EMU with economic theories and historical experiences of currency unions

Candidate gene prioritization by network analysis of differential expression using machine learning approaches

<p>Abstract</p> <p>Background</p> <p>Discovering novel disease genes is still challenging for diseases for which no prior knowledge - such as known disease genes or disease-related pathways - is available. Performing genetic studies frequently results in large lists of candidate genes of which only few can be followed up for further investigation. We have recently developed a computational method for constitutional genetic disorders that identifies the most promising candidate genes by replacing prior knowledge by experimental data of differential gene expression between affected and healthy individuals.</p> <p>To improve the performance of our prioritization strategy, we have extended our previous work by applying different machine learning approaches that identify promising candidate genes by determining whether a gene is surrounded by highly differentially expressed genes in a functional association or protein-protein interaction network.</p> <p>Results</p> <p>We have proposed three strategies scoring disease candidate genes relying on network-based machine learning approaches, such as kernel ridge regression, heat kernel, and Arnoldi kernel approximation. For comparison purposes, a local measure based on the expression of the direct neighbors is also computed. We have benchmarked these strategies on 40 publicly available knockout experiments in mice, and performance was assessed against results obtained using a standard procedure in genetics that ranks candidate genes based solely on their differential expression levels (<it>Simple Expression Ranking</it>). Our results showed that our four strategies could outperform this standard procedure and that the best results were obtained using the <it>Heat Kernel Diffusion Ranking </it>leading to an average ranking position of 8 out of 100 genes, an AUC value of 92.3% and an error reduction of 52.8% relative to the standard procedure approach which ranked the knockout gene on average at position 17 with an AUC value of 83.7%.</p> <p>Conclusion</p> <p>In this study we could identify promising candidate genes using network based machine learning approaches even if no knowledge is available about the disease or phenotype.</p