INVESTIGATING THE POTENTIAL OF AN ANTIDEPRESSANT INTRANASAL MUCOADHESIVE MICROEMULSION

Objective: The main aim of this study was to formulate, develop and optimized a duloxetine hydrochloride (dlx-hcl) loaded mucoadhesive microemulsion intended for intranasal administration.Methods: Established on solubility studies capmul mcm, transcutol-p, labrasol were used as oil, co-surfactant and surfactant respectively. The optimized mucoadhesive microemulsion prepared using water titration method was further characterized for particle size, polydispersity index, zeta potential and conductivity measurements followed by drug content, nasal cilio toxicity and biochemical estimation of the selected formulation.Results: All physicochemical parameters conducted, proved that dlx-hcl microemulsion was appropriate for nasal delivery. Chitosan, used as mucoadhesive polymer demonstrated enhanced retention time of the microemulsion in nasal mucosa with no signs of toxicity and epithelial damage. The particle size and zeta potential were found to be of 200 nm and-15 mV respectively considering the formulation safe for nasal delivery.Conclusion: This formulation strategy can be used as an effective targeting technique for the drugs having low bioavailability and poor brain penetration along with an effective method for the treatment long-term disease like depression

Differentially Expressed RNA from Public Microarray Data Identifies Serum Protein Biomarkers for Cross-Organ Transplant Rejection and Other Conditions

Serum proteins are routinely used to diagnose diseases, but are hard to find due to low sensitivity in screening the serum proteome. Public repositories of microarray data, such as the Gene Expression Omnibus (GEO), contain RNA expression profiles for more than 16,000 biological conditions, covering more than 30% of United States mortality. We hypothesized that genes coding for serum- and urine-detectable proteins, and showing differential expression of RNA in disease-damaged tissues would make ideal diagnostic protein biomarkers for those diseases. We showed that predicted protein biomarkers are significantly enriched for known diagnostic protein biomarkers in 22 diseases, with enrichment significantly higher in diseases for which at least three datasets are available. We then used this strategy to search for new biomarkers indicating acute rejection (AR) across different types of transplanted solid organs. We integrated three biopsy-based microarray studies of AR from pediatric renal, adult renal and adult cardiac transplantation and identified 45 genes upregulated in all three. From this set, we chose 10 proteins for serum ELISA assays in 39 renal transplant patients, and discovered three that were significantly higher in AR. Interestingly, all three proteins were also significantly higher during AR in the 63 cardiac transplant recipients studied. Our best marker, serum PECAM1, identified renal AR with 89% sensitivity and 75% specificity, and also showed increased expression in AR by immunohistochemistry in renal, hepatic and cardiac transplant biopsies. Our results demonstrate that integrating gene expression microarray measurements from disease samples and even publicly-available data sets can be a powerful, fast, and cost-effective strategy for the discovery of new diagnostic serum protein biomarkers

The kSORT Assay to Detect Renal Transplant Patients at High Risk for Acute Rejection: Results of the Multicenter AART Study

Development of noninvasive molecular assays to improve disease diagnosis and patient monitoring is a critical need. In renal transplantation, acute rejection (AR) increases the risk for chronic graft injury and failure. Noninvasive diagnostic assays to improve current late and nonspecific diagnosis of rejection are needed. We sought to develop a test using a simple blood gene expression assay to detect patients at high risk for AR. We developed a novel correlation-based algorithm by step-wise analysis of gene expression data in 558 blood samples from 436 renal transplant patients collected across eight transplant centers in the US, Mexico, and Spain between 5 February 2005 and 15 December 2012 in the Assessment of Acute Rejection in Renal Transplantation (AART) study. Gene expression was assessed by quantitative real-time PCR (QPCR) in one center. A 17-gene set—the Kidney Solid Organ Response Test (kSORT)—was selected in 143 samples for AR classification using discriminant analysis (area under the receiver operating characteristic curve [AUC] = 0.94; 95% CI 0.91–0.98), validated in 124 independent samples (AUC = 0.95; 95% CI 0.88–1.0) and evaluated for AR prediction in 191 serial samples, where it predicted AR up to 3 mo prior to detection by the current gold standard (biopsy). A novel reference-based algorithm (using 13 12-gene models) was developed in 100 independent samples to provide a numerical AR risk score, to classify patients as high risk versus low risk for AR. kSORT was able to detect AR in blood independent of age, time post-transplantation, and sample source without additional data normalization; AUC = 0.93 (95% CI 0.86–0.99). Further validation of kSORT is planned in prospective clinical observational and interventional trials. The kSORT blood QPCR assay is a noninvasive tool to detect high risk of AR of renal transplants

A Peripheral Blood Diagnostic Test for Acute Rejection in Renal Transplantation

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/93759/1/j.1600-6143.2012.04253.x.pd

Mastering the brain in critical conditions:an update

Acute brain injuries, such as traumatic brain injury and ischemic and hemorragic stroke, are a leading cause of death and disability worldwide. While characterized by clearly distict primary events-vascular damage in strokes and biomechanical damage in traumatic brain injuries-they share common secondary injury mechanisms influencing long-term outcomes. Growing evidence suggests that a more personalized approach to optimize energy substrate delivery to the injured brain and prognosticate towards families could be beneficial. In this context, continuous invasive and/or non-invasive neuromonitoring, together with clinical evaluation and neuroimaging to support strategies that optimize cerebral blood flow and metabolic delivery, as well as approaches to neuroprognostication are gaining interest. Recently, the European Society of Intensive Care Medicine organized a 2-day course focused on a practical case-based clinical approach of acute brain-injured patients in different scenarios and on future perspectives to advance the management of this population. The aim of this manuscript is to update clinicians dealing with acute brain injured patients in the intensive care unit, describing current knowledge and clinical practice based on the insights presented during this course

Mastering the brain in critical conditions:an update

Acute brain injuries, such as traumatic brain injury and ischemic and hemorragic stroke, are a leading cause of death and disability worldwide. While characterized by clearly distict primary events-vascular damage in strokes and biomechanical damage in traumatic brain injuries-they share common secondary injury mechanisms influencing long-term outcomes. Growing evidence suggests that a more personalized approach to optimize energy substrate delivery to the injured brain and prognosticate towards families could be beneficial. In this context, continuous invasive and/or non-invasive neuromonitoring, together with clinical evaluation and neuroimaging to support strategies that optimize cerebral blood flow and metabolic delivery, as well as approaches to neuroprognostication are gaining interest. Recently, the European Society of Intensive Care Medicine organized a 2-day course focused on a practical case-based clinical approach of acute brain-injured patients in different scenarios and on future perspectives to advance the management of this population. The aim of this manuscript is to update clinicians dealing with acute brain injured patients in the intensive care unit, describing current knowledge and clinical practice based on the insights presented during this course

Comparison of multiplex meta analysis techniques for understanding the acute rejection of solid organ transplants

<p>Abstract</p> <p>Background</p> <p>Combining the results of studies using highly parallelized measurements of gene expression such as microarrays and RNAseq offer unique challenges in meta analysis. Motivated by a need for a deeper understanding of organ transplant rejection, we combine the data from five separate studies to compare acute rejection versus stability after solid organ transplantation, and use this data to examine approaches to multiplex meta analysis.</p> <p>Results</p> <p>We demonstrate that a commonly used parametric effect size estimate approach and a commonly used non-parametric method give very different results in prioritizing genes. The parametric method providing a meta effect estimate was superior at ranking genes based on our gold-standard of identifying immune response genes in the transplant rejection datasets.</p> <p>Conclusion</p> <p>Different methods of multiplex analysis can give substantially different results. The method which is best for any given application will likely depend on the particular domain, and it remains for future work to see if any one method is consistently better at identifying important biological signal across gene expression experiments.</p

Measurement of the Bottom-Strange Meson Mixing Phase in the Full CDF Data Set

We report a measurement of the bottom-strange meson mixing phase \beta_s using the time evolution of B0_s -> J/\psi (->\mu+\mu-) \phi (-> K+ K-) decays in which the quark-flavor content of the bottom-strange meson is identified at production. This measurement uses the full data set of proton-antiproton collisions at sqrt(s)= 1.96 TeV collected by the Collider Detector experiment at the Fermilab Tevatron, corresponding to 9.6 fb-1 of integrated luminosity. We report confidence regions in the two-dimensional space of \beta_s and the B0_s decay-width difference \Delta\Gamma_s, and measure \beta_s in [-\pi/2, -1.51] U [-0.06, 0.30] U [1.26, \pi/2] at the 68% confidence level, in agreement with the standard model expectation. Assuming the standard model value of \beta_s, we also determine \Delta\Gamma_s = 0.068 +- 0.026 (stat) +- 0.009 (syst) ps-1 and the mean B0_s lifetime, \tau_s = 1.528 +- 0.019 (stat) +- 0.009 (syst) ps, which are consistent and competitive with determinations by other experiments.Comment: 8 pages, 2 figures, Phys. Rev. Lett 109, 171802 (2012