EOMES and IL-10 regulate antitumor activity of T regulatory type 1 CD4 + T cells in chronic lymphocytic leukemia

The transcription factor eomesodermin (EOMES) promotes interleukin (IL)-10 expression in CD4(+) T cells, which has been linked to immunosuppressive and cytotoxic activities. We detected cytotoxic, programmed cell death protein-1 (PD-1) and EOMES co-expressing CD4(+) T cells in lymph nodes (LNs) of patients with chronic lymphocytic leukemia (CLL) or diffuse large B-cell lymphoma. Transcriptome and flow cytometry analyses revealed that EOMES does not only drive IL-10 expression, but rather controls a unique transcriptional signature in CD4(+) T cells, that is enriched in genes typical for T regulatory type 1 (T(R)1) cells. The T(R)1 cell identity of these CD4(+) T cells was supported by their expression of interferon gamma and IL-10, as well as inhibitory receptors including PD-1. T(R)1 cells with cytotoxic capacity accumulate also in Eµ-TCL1 mice that develop CLL-like disease. Whereas wild-type CD4(+) T cells control TCL1 leukemia development after adoptive transfer in leukopenic Rag2(−/)(−) mice, EOMES-deficient CD4(+) T cells failed to do so. We further show that T(R)1 cell-mediated control of TCL1 leukemia requires IL-10 receptor (IL-10R) signaling, as Il10rb-deficient CD4(+) T cells showed impaired antileukemia activity. Altogether, our data demonstrate that EOMES is indispensable for the development of IL-10-expressing, cytotoxic T(R)1 cells, which accumulate in LNs of CLL patients and control TCL1 leukemia in mice in an IL-10R-dependent manner

Model confidence sets and forecast combination: an application to age-specific mortality

Background: Model averaging combines forecasts obtained from a range of models, and it often produces more accurate forecasts than a forecast from a single model. Objective: The crucial part of forecast accuracy improvement in using the model averaging lies in the determination of optimal weights from a finite sample. If the weights are selected sub-optimally, this can affect the accuracy of the model-averaged forecasts. Instead of choosing the optimal weights, we consider trimming a set of models before equally averaging forecasts from the selected superior models. Motivated by Hansen et al. (2011), we apply and evaluate the model confidence set procedure when combining mortality forecasts. Data & Methods: The proposed model averaging procedure is motivated by Samuels and Sekkel (2017) based on the concept of model confidence sets as proposed by Hansen et al. (2011) that incorporates the statistical significance of the forecasting performance. As the model confidence level increases, the set of superior models generally decreases. The proposed model averaging procedure is demonstrated via national and sub-national Japanese mortality for retirement ages between 60 and 100+. Results: Illustrated by national and sub-national Japanese mortality for ages between 60 and 100+, the proposed model-average procedure gives the smallest interval forecast errors, especially for males. Conclusion: We find that robust out-of-sample point and interval forecasts may be obtained from the trimming method. By robust, we mean robustness against model misspecification

First-in-human Study to Evaluate a Single Injection of KiOmedine®CM-Chitosan for Treating Symptomatic Knee Osteoarthritis

Background: Single-injection viscosupplementation is currently performed with cross-linked hyaluronan (e.g. Durolane®) for treating symptomatic knee osteoarthritis. Objective: This first-in-human study evaluated the safety and performance of single-injection treatment with non-crosslinked KiOmedine®CM-Chitosan. Methods: Patients with painful knee osteoarthritis were randomly assigned to the KiOmedine®CM-Chitosan (n=63) or Durolane® (n=32) group. Patients were blinded to treatment and followed up for 26 weeks. Durolane® was used as scientific control to ensure the validity of the study and reliability of results. No direct comparison was performed between the two groups. The primary objective was defined as an intra-group effect size of 0.8 at 13 weeks post-injection compared to baseline on WOMAC-A (pain). Secondary outcomes included self-reported knee stiffness and knee function, responder rate, quality-of-life questionnaires, and safety. Results: The primary objective for both the KiOmedine®CM-Chitosan and the Durolane® groups was met: mean pain reduction of 62.5% (effect size 2.08) for the KiOmedine®CM-Chitosan group and 62.4% (effect size 2.28) for the Durolane® group. Secondary performance outcomes showed all clinically relevant treatment effects over 26 weeks for both groups (p<0.05). Treatment-related adverse events were more often reported in the KiOmedine®CM-Chitosan than Durolane® group and were limited to local reactions. No serious treatment-related adverse events were reported. Conclusion: A single intra-articular injection of non-crosslinked KiOmedine®CM-Chitosan is safe and effective for treating symptomatic knee osteoarthritis with a high responder rate. Pain reduction is maintained for 6 months with a high responder rate. The clinical trial registration number: NCT03679208.SCOPUS: ar.jinfo:eu-repo/semantics/publishe