Tomoyuki Sugimoto

The lactic acid bacterium Pediococcus acidilactici suppresses autoimmune encephalomyelitis by inducing IL-10-producing regulatory T cells.

Background Certain intestinal microflora are thought to regulate the systemic immune response. Lactic acid bacteria are one of the most studied bacteria in terms of their beneficial effects on health and autoimmune diseases; one of which is Multiple sclerosis (MS) which affects the central nervous system. We investigated whether the lactic acid bacterium Pediococcus acidilactici, which comprises human commensal bacteria, has beneficial effects on experimental autoimmune encephalomyelitis (EAE), an animal model of MS. Methodology/Principal Findings P. acidilactici R037 was orally administered to EAE mice to investigate the effects of R037. R037 treatment suppressed clinical EAE severity as prophylaxis and therapy. The antigen-specific production of inflammatory cytokines was inhibited in R037-treated mice. A significant increase in the number of CD4+ Interleukin (IL)-10-producing cells was observed in the mesenteric lymph nodes (MLNs) and spleens isolated from R037-treated naive mice, while no increase was observed in the number of these cells in the lamina propria. Because only a slight increase in the CD4+Foxp3+ cells was observed in MLNs, R037 may primarily induce Foxp3− IL10-producing T regulatory type 1 (Tr1) cells in MLNs, which contribute to the beneficial effect of R037 on EAE. Conclusions/Significance An orally administered single strain of P. acidilactici R037 ameliorates EAE by inducing IL10-producing Tr1 cells. Our findings indicate the therapeutic potential of the oral administration of R037 for treating multiple sclerosis.

Sample size determination in clinical trials with multiple co‐primary binary endpoints

Clinical trials often employ two or more primary efficacy endpoints. One of the major problems in such trials is how to determine a sample size suitable for multiple co-primary correlated endpoints. We provide fundamental formulae for the calculation of power and sample size in order to achieve statistical significance for all the multiple primary endpoints given as binary variables. On the basis of three association measures among primary endpoints, we discuss five methods of power and sample size calculation: the asymptotic normal method with and without continuity correction, the arcsine method with and without continuity correction, and Fishers exact method. For all five methods, the achieved sample size decreases as the value of association measure increases when the effect sizes among endpoints are approximately equal. In particular, a high positive association has a greater effect on the decrease in the sample size. On the other hand, such a relationship is not very strong when the effect sizes are different.

Elevation of Sema4A Implicates Th Cell Skewing and the Efficacy of IFN-β Therapy in Multiple Sclerosis

Multiple sclerosis (MS) is a demyelinating autoimmune disease of the CNS and a leading cause of lasting neurologic disabilities in young adults. Although the precise mechanism remains incompletely understood, Ag presentation and subsequent myelin-reactive CD4+ T cell activation/differentiation are essential for the pathogenesis of MS. Although semaphorins were initially identified as axon guidance cues during neural development, several semaphorins are crucially involved in various phases of immune responses. Sema4A is one of the membrane-type class IV semaphorins, which we originally identified from the cDNA library of dendritic cell (DC). Sema4A plays critical roles in T cell activation and Th1 differentiation during the course of experimental autoimmune encephalomyelitis, an animal model of MS; however, its pathological involvement in human MS has not been determined. In this study, we report that Sema4A is increased in the sera of patients with MS. The expression of Sema4A is increased on DCs in MS patients and shed from these cells in a metalloproteinase-dependent manner. DC-derived Sema4A is not only critical for Th1 but also for Th17 cell differentiation, and MS patients with high Sema4A levels exhibit Th17 skewing. Furthermore, patients with high Sema4A levels have more severe disabilities and are unresponsive to IFN-β treatment. Taken together, our results suggest that Sema4A is involved in the pathogenesis of MS by promoting Th17 skewing.

Diameter of splenic vein is a risk factor for portal or splenic vein thrombosis after laparoscopic splenectomy.

BACKGROUND Splenomegaly is a risk factor for post-splenectomy portal or splenic vein thrombosis (PSVT) due to large splenic vein stump. The relationship between splenic vein diameter (SVD) and PSVT has not been established. OBJECTIVES To investigate whether SVD is a risk factor for PSVT. METHODS Forty patients who underwent laparoscopic splenectomy were analyzed. Preoperative and postoperative enhanced helical computed tomographic scans were obtained in all patients, and subsequent follow-up was performed in patients with PSVT during anticoagulant therapy. SVDs at the junction of portal vein (PV) 2, 4, and 6 cm from the junction of PV were measured preoperatively and postoperatively. Multivariate analysis was performed using logistic regression model. RESULTS PSVT was diagnosed in 52.5% (21/40) patients. Preoperative SVD was significantly larger in patients with PSVT than in those without PSVT. Seventy-two percent of patients (16/22) with PSVT in splenic veins with a diameter of >8 mm developed PSVT. Multivariate analysis identified preoperative SVD as a significant and independent determinant of PSVT. At a cutoff value of 8 mm, receiver operator characteristic analysis for prediction of PSVT provided an area under the curve of 0.8552 (95% CI 0.821-1.000). CONCLUSION Preoperative SVD is a risk factor for post-splenectomy PSVT. We recommend measurement of SVD preoperatively in patients elected to undergo splenectomy, and a close follow-up of patients with SVD greater than 8 mm.

Deleterious effects of lymphocytes at the early stage of neurodegeneration in an animal model of amyotrophic lateral sclerosis

BackgroundNon-neuronal cells, such as microglia and lymphocytes, are thought to be involved in the pathogenesis of amyotrophic lateral sclerosis (ALS). Previous studies have demonstrated neuroprotective effects of lymphocytes at the end stage of ALS, partly through induction of alternatively activated microglia (M2 microglia), which are neuroprotective. In this study, we investigated the role of lymphocytes in the early stage of the disease using an animal model of inherited ALS.MethodsWe established a transgenic mouse line overexpressing the familial ALS-associated G93A-SOD1 mutation (harboring a single amino acid substitution of glycine to alanine at codon 93) with depletion of the Rag2 gene (mSOD1/RAG2-/- mice), an animal model of inherited ALS lacking mature lymphocytes. Body weights, clinical scores and motor performance (hanging wire test) of mSOD1/RAG2-/- mice were compared to those of mutant human SOD1 transgenic mice (mSOD1/RAG2+/+ mice). Activation of glial cells in the spinal cords of these mice was determined immunohistochemically, and the expression of mRNA for various inflammatory and anti-inflammatory molecules was evaluated.ResultsClinical onset in mSOD1/RAG2-/- mice was significantly delayed, and the number of lectin-positive cells in spinal cord was increased at the early stage of disease when compared to mSOD1/RAG2+/+ mice. Quantitative RT-PCR confirmed that mRNA for Ym1, an M2 microglial-related molecule, was significantly increased in mSOD1/RAG2-/- mouse spinal cords at the early disease stage.ConclusionsCompared with mSOD1/RAG2+/+ mice, mSOD1/RAG2-/- mice displayed delayed onset and increased M2 microglial activation at the early stage of disease. Thus, lymphocytes at the early pathological phase of ALS display a deleterious effect via inhibition of M2 microglial activation.

Sample Size Determination in Superiority Clinical Trials with Multiple Co-Primary Correlated Endpoints

In pharmaceutical drug development, for regulatory purposes, there are increasing discussions on the establishment of statistically significant results demonstrating the efficacy of a new treatment on multiple co-primary endpoints. At the design stage with multiple co-primary endpoints, it is critical to determine the appropriate sample size for indicating statistical significance for all co-primary endpoints with preserving the intended power set, since the type II error increases as the number of co-primary endpoints increases. We provide fundamental formulae for power and sample size calculation with multiple co-primary endpoints and illustrate the aspect of the provided methods through numerical tables and examples.

A convenient formula for sample size calculations in clinical trials with multiple co‐primary continuous endpoints

The clinical efficacy of a new treatment may often be better evaluated by two or more co-primary endpoints. Recently, in pharmaceutical drug development, there has been increasing discussion regarding establishing statistically significant favorable results on more than one endpoint in comparisons between treatments, which is referred to as a problem of multiple co-primary endpoints. Several methods have been proposed for calculating the sample size required to design a trial with multiple co-primary correlated endpoints. However, because these methods require users to have considerable mathematical sophistication and knowledge of programming techniques, their application and spread may be restricted in practice. To improve the convenience of these methods, in this paper, we provide a useful formula with accompanying numerical tables for sample size calculations to design clinical trials with two treatments, where the efficacy of a new treatment is demonstrated on continuous co-primary endpoints. In addition, we provide some examples to illustrate the sample size calculations made using the formula. Using the formula and the tables, which can be read according to the patterns of correlations and effect size ratios expected in multiple co-primary endpoints, makes it convenient to evaluate the required sample size promptly.

Sample size determination for clinical trials with co-primary outcomes: exponential event times

Clinical trials with event-time outcomes as co-primary contrasts are common in many areas such as infectious disease, oncology, and cardiovascular disease. We discuss methods for calculating the sample size for randomized superiority clinical trials with two correlated time-to-event outcomes as co-primary contrasts when the time-to-event outcomes are exponentially distributed. The approach is simple and easily applied in practice.

Sample size determination in clinical trials with multiple co‐primary endpoints including mixed continuous and binary variables

In the field of pharmaceutical drug development, there have been extensive discussions on the establishment of statistically significant results that demonstrate the efficacy of a new treatment with multiple co-primary endpoints. When designing a clinical trial with such multiple co-primary endpoints, it is critical to determine the appropriate sample size for indicating the statistical significance of all the co-primary endpoints with preserving the desired overall power because the type II error rate increases with the number of co-primary endpoints. We consider overall power functions and sample size determinations with multiple co-primary endpoints that consist of mixed continuous and binary variables, and provide numerical examples to illustrate the behavior of the overall power functions and sample sizes. In formulating the problem, we assume that response variables follow a multivariate normal distribution, where binary variables are observed in a dichotomized normal distribution with a certain point of dichotomy. Numerical examples show that the sample size decreases as the correlation increases when the individual powers of each endpoint are approximately and mutually equal.

Sample size determination in group-sequential clinical trials with two co-primary endpoints

We discuss sample size determination in group-sequential designs with two endpoints as co-primary. We derive the power and sample size within two decision-making frameworks. One is to claim the test interventions benefit relative to control when superiority is achieved for the two endpoints at the same interim timepoint of the trial. The other is when superiority is achieved for the two endpoints at any interim timepoint, not necessarily simultaneously. We evaluate the behaviors of sample size and power with varying design elements and provide a real example to illustrate the proposed sample size methods. In addition, we discuss sample size recalculation based on observed data and evaluate the impact on the power and Type I error rate.