CD4 T cells are required for both development and maintenance of disease in a new mouse model of reversible colitis

Current therapies to treat inflammatory bowel diseases have limited efficacy, significant side effects, and often wane over time. Little is known about the cellular and molecular mechanisms operative in the process of mucosal healing from colitis. To study such events, we developed a new model of reversible colitis in which adoptive transfer of CD4(+)CD45RB(hi) T cells into Helicobacter typhlonius-colonized lymphopenic mice resulted in a rapid onset of colonic inflammation that was reversible through depletion of colitogenic T cells. Remission was associated with an improved clinical and histopathological score, reduced immune cell infiltration to the intestinal mucosa, altered intestinal gene expression profiles, regeneration of the colonic mucus layer, and the restoration of epithelial barrier integrity. Notably, colitogenic T cells were not only critical for induction of colitis but also for maintenance of disease. Depletion of colitogenic T cells resulted in a rapid drop in tumor necrosis factor α (TNFα) levels associated with reduced infiltration of inflammatory immune cells to sites of inflammation. Although neutralization of TNFα prevented the onset of colitis, anti-TNFα treatment of mice with established disease failed to resolve colonic inflammation. Collectively, this new model of reversible colitis provides an important research tool to study the dynamics of mucosal healing in chronic intestinal remitting-relapsing disorders.Mucosal Immunology advance online publication 16 September 2015; doi:10.1038/mi.2015.93.

A realistic validation study of a new nitrogen multiple-breath washout system

Background For reliable assessment of ventilation inhomogeneity, multiple-breath washout (MBW) systems should be realistically validated. We describe a new lung model for in vitro validation under physiological conditions and the assessment of a new nitrogen (N2)MBW system. Methods The N2MBW setup indirectly measures the N2 fraction (FN2) from main-stream carbon dioxide (CO2) and side-stream oxygen (O2) signals: FN2 = 1−FO2−FCO2−FArgon. For in vitro N2MBW, a double chamber plastic lung model was filled with water, heated to 37°C, and ventilated at various lung volumes, respiratory rates, and FCO2. In vivo N2MBW was undertaken in triplets on two occasions in 30 healthy adults. Primary N2MBW outcome was functional residual capacity (FRC). We assessed in vitro error (√[difference]2) between measured and model FRC (100–4174 mL), and error between tests of in vivo FRC, lung clearance index (LCI), and normalized phase III slope indices (Sacin and Scond). Results The model generated 145 FRCs under BTPS conditions and various breathing patterns. Mean (SD) error was 2.3 (1.7)%. In 500 to 4174 mL FRCs, 121 (98%) of FRCs were within 5%. In 100 to 400 mL FRCs, the error was better than 7%. In vivo FRC error between tests was 10.1 (8.2)%. LCI was the most reproducible ventilation inhomogeneity index. Conclusion The lung model generates lung volumes under the conditions encountered during clinical MBW testing and enables realistic validation of MBW systems. The new N2MBW system reliably measures lung volumes and delivers reproducible LCI values.

Bayesian statistics in oncology: a guide for the clinical investigator

The rise of evidence-based medicine as well as important progress in statistical methods and computational power have led to a second birth of the >200-year-old Bayesian framework. The use of Bayesian techniques, in particular in the design and interpretation of clinical trials, offers several substantial advantages over the classical statistical approach. First, in contrast to classical statistics, Bayesian analysis allows a direct statement regarding the probability that a treatment was beneficial. Second, Bayesian statistics allow the researcher to incorporate any prior information in the analysis of the experimental results. Third, Bayesian methods can efficiently handle complex statistical models, which are suited for advanced clinical trial designs. Finally, Bayesian statistics encourage a thorough consideration and presentation of the assumptions underlying an analysis, which enables the reader to fully appraise the authors' conclusions. Both Bayesian and classical statistics have their respective strengths and limitations and should be viewed as being complementary to each other; we do not attempt to make a head-to-head comparison, as this is beyond the scope of the present review. Rather, the objective of the present article is to provide a nonmathematical, reader-friendly overview of the current practice of Bayesian statistics coupled with numerous intuitive examples from the field of oncology. It is hoped that this educational review will be a useful resource to the oncologist and result in a better understanding of the scope, strengths, and limitations of the Bayesian approach.