Inhibition or knock out of Inducible nitric oxide synthase result in resistance to bleomycin-induced lung injury

BACKGROUND: In the present study, by comparing the responses in wild-type mice (WT) and mice lacking (KO) the inducible (or type 2) nitric oxide synthase (iNOS), we investigated the role played by iNOS in the development of on the lung injury caused by bleomycin administration. When compared to bleomycin-treated iNOSWT mice, iNOSKO mice, which had received bleomycin, exhibited a reduced degree of the (i) lost of body weight, (ii) mortality rate, (iii) infiltration of the lung with polymorphonuclear neutrophils (MPO activity), (iv) edema formation, (v) histological evidence of lung injury, (vi) lung collagen deposition and (vii) lung Transforming Growth Factor beta1 (TGF-β1) expression. METHODS: Mice subjected to intratracheal administration of bleomycin developed a significant lung injury. Immunohistochemical analysis for nitrotyrosine revealed a positive staining in lungs from bleomycin-treated iNOSWT mice. RESULTS: The intensity and degree of nitrotyrosine staining was markedly reduced in tissue section from bleomycin-iNOSKO mice. Treatment of iNOSWT mice with of GW274150, a novel, potent and selective inhibitor of iNOS activity (5 mg/kg i.p.) also significantly attenuated all of the above indicators of lung damage and inflammation. CONCLUSION: Taken together, our results clearly demonstrate that iNOS plays an important role in the lung injury induced by bleomycin in the mice

Bezlotoxumab for Prevention of Recurrent Clostridium difficile Infection

BACKGROUND Clostridium difficile is the most common cause of infectious diarrhea in hospitalized patients. Recurrences are common after antibiotic therapy. Actoxumab and bezlotoxumab are human monoclonal antibodies against C. difficile toxins A and B, respectively. METHODS We conducted two double-blind, randomized, placebo-controlled, phase 3 trials, MODIFY I and MODIFY II, involving 2655 adults receiving oral standard-of-care antibiotics for primary or recurrent C. difficile infection. Participants received an infusion of bezlotoxumab (10 mg per kilogram of body weight), actoxumab plus bezlotoxumab (10 mg per kilogram each), or placebo; actoxumab alone (10 mg per kilogram) was given in MODIFY I but discontinued after a planned interim analysis. The primary end point was recurrent infection (new episode after initial clinical cure) within 12 weeks after infusion in the modified intention-to-treat population. RESULTS In both trials, the rate of recurrent C. difficile infection was significantly lower with bezlotoxumab alone than with placebo (MODIFY I: 17% [67 of 386] vs. 28% [109 of 395]; adjusted difference, −10.1 percentage points; 95% confidence interval [CI], −15.9 to −4.3; P<0.001; MODIFY II: 16% [62 of 395] vs. 26% [97 of 378]; adjusted difference, −9.9 percentage points; 95% CI, −15.5 to −4.3; P<0.001) and was significantly lower with actoxumab plus bezlotoxumab than with placebo (MODIFY I: 16% [61 of 383] vs. 28% [109 of 395]; adjusted difference, −11.6 percentage points; 95% CI, −17.4 to −5.9; P<0.001; MODIFY II: 15% [58 of 390] vs. 26% [97 of 378]; adjusted difference, −10.7 percentage points; 95% CI, −16.4 to −5.1; P<0.001). In prespecified subgroup analyses (combined data set), rates of recurrent infection were lower in both groups that received bezlotoxumab than in the placebo group in subpopulations at high risk for recurrent infection or for an adverse outcome. The rates of initial clinical cure were 80% with bezlotoxumab alone, 73% with actoxumab plus bezlotoxumab, and 80% with placebo; the rates of sustained cure (initial clinical cure without recurrent infection in 12 weeks) were 64%, 58%, and 54%, respectively. The rates of adverse events were similar among these groups; the most common events were diarrhea and nausea. CONCLUSIONS Among participants receiving antibiotic treatment for primary or recurrent C. difficile infection, bezlotoxumab was associated with a substantially lower rate of recurrent infection than placebo and had a safety profile similar to that of placebo. The addition of actoxumab did not improve efficacy. (Funded by Merck; MODIFY I and MODIFY II ClinicalTrials.gov numbers, NCT01241552 and NCT01513239.

Immunogenomic analyses associate immunological alterations with mismatch repair defects in prostate cancer.

Background Understanding the integrated immunogenomic landscape of advanced prostate cancer (APC) could impact stratified treatment selection.Methods Defective mismatch repair (dMMR) status was determined by either loss of mismatch repair protein expression on IHC or microsatellite instability (MSI) by PCR in 127 APC biopsies from 124 patients (Royal Marsden [RMH] cohort); MSI by targeted panel next-generation sequencing (MSINGS) was then evaluated in the same cohort and in 254 APC samples from the Stand Up To Cancer/Prostate Cancer Foundation (SU2C/PCF). Whole exome sequencing (WES) data from this latter cohort were analyzed for pathogenic MMR gene variants, mutational load, and mutational signatures. Transcriptomic data, available for 168 samples, was also performed.Results Overall, 8.1% of patients in the RMH cohort had some evidence of dMMR, which associated with decreased overall survival. Higher MSINGS scores associated with dMMR, and these APCs were enriched for higher T cell infiltration and PD-L1 protein expression. Exome MSINGS scores strongly correlated with targeted panel MSINGS scores (r = 0.73, P < 0.0001), and higher MSINGS scores associated with dMMR mutational signatures in APC exomes. dMMR mutational signatures also associated with MMR gene mutations and increased immune cell, immune checkpoint, and T cell-associated transcripts. APC with dMMR mutational signatures overexpressed a variety of immune transcripts, including CD200R1, BTLA, PD-L1, PD-L2, ADORA2A, PIK3CG, and TIGIT.Conclusion These data could impact immune target selection, combination therapeutic strategy selection, and selection of predictive biomarkers for immunotherapy in APC.Funding We acknowledge funding support from Movember, Prostate Cancer UK, The Prostate Cancer Foundation, SU2C, and Cancer Research UK

Robust tracking of multiple soccer robots using random finite sets

Having a good estimation of the robot-players positions is becoming imperative to accomplish high level tasks in any RoboCup League. Classical approaches use a vector representation of the robot positions and Bayesian filters to propagate them over time. However, these approaches have data association problems in real game situations. In order to tackle this issue, this paper presents a new method for building robot maps using Random Finite Sets (RFS). The method is applied to the problem of estimating the position of the teammates and opponents in the SPL league. Considering the computational capabilities of Nao robots, the GM-PHD implementation of RFS is used. In this implementation, the estimations of the robot positions and the robot observations are represented using Mixture of Gaussians, but instead of associating a robot or an observation to a given Gaussian, the weight of each Gaussian maintains an estimation of the number of robots that it represents. The proposed method is validated in several real game situations and compared with a classical EKF based approach. The proposed GM-PHD method shows a much better performance, being able to deal with most of the data association problems, even being able to manage complex situations such as robot kidnappings

Vision-Based, Terrain-Aided Navigation with Decentralized Fusion and Finite Set Statistics

Terrain-related information, in the form of features extracted from images, presents a rich data source that can be harvested to facilitate drastic improvements in navigation when conventional data sources, such as GPS, are not available. Conventional implementation of such data types requires image correlation techniques that interrupt streamlined transmission of statistics through a navigation filter, oftentimes leading to time-wise correlations that are erroneously ignored. This paper proposes leveraging finite set statistics to recast the terrain feature data into a simultaneous localization and mapping problem. Decentralized data fusion is employed to augment a standard extended Kalman filter-based navigation with the terrain data. Theoretical results are supported with a simulated descent to landing navigation scenario that demonstrates the improvements offered by augmenting standard navigation with terrain aiding