31 research outputs found
Improving the fatigue life of printed structures using stochastic variations
Additive manufacturing allows designers to create geometries that would not be possible or economical to manufacture using traditional manufacturing processes. Production with these technologies does, however, introduce a large amount of variation and additional unknowns. These random variations from idealized geometry or material properties can harm the performance of the design. The current work presents an approach to improve the fatigue life of such structures, and simultaneously reduce its influence from random variations in local thickness. Following an initial numerical study, the results are experimentally validated. Experimental results show a significant improvement in fatigue life in the redesigned sample with a tailored thickness distribution
K(2P)18.1 translates T cell receptor signals into thymic regulatory T cell development
It remains largely unclear how thymocytes translate relative differences in T cell receptor (TCR) signal strength into distinct developmental programs that drive the cell fate decisions towards conventional (Tconv) or regulatory T cells (Treg). Following TCR activation, intracellular calcium (Ca2+) is the most important second messenger, for which the potassium channel K(2P)18.1 is a relevant regulator. Here, we identify K(2P)18.1 as a central translator of the TCR signal into the thymus-derived Treg (tTreg) selection process. TCR signal was coupled to NF-kappa B-mediated K(2P)18.1 upregulation in tTreg progenitors. K(2P)18.1 provided the driving force for sustained Ca2+ influx that facilitated NF-kappa B- and NFAT-dependent expression of FoxP3, the master transcription factor for Treg development and function. Loss of K(2P)18.1 ion-current function induced a mild lymphoproliferative phenotype in mice, with reduced Treg numbers that led to aggravated experimental autoimmune encephalomyelitis, while a gain-of-function mutation in K(2P)18.1 resulted in increased Treg numbers in mice. Our findings in human thymus, recent thymic emigrants and multiple sclerosis patients with a dominant-negative missense K(2P)18.1 variant that is associated with poor clinical outcomes indicate that K(2P)18.1 also plays a role in human Treg development. Pharmacological modulation of K(2P)18.1 specifically modulated Treg numbers in vitro and in vivo. Finally, we identified nitroxoline as a K(2P)18.1 activator that led to rapid and reversible Treg increase in patients with urinary tract infections. Conclusively, our findings reveal how K(2P)18.1 translates TCR signals into thymic T cell fate decisions and Treg development, and provide a basis for the therapeutic utilization of Treg in several human disorders.Peer reviewe
ECMO for COVID-19 patients in Europe and Israel
Since March 15th, 2020, 177 centres from Europe and Israel have joined the study, routinely reporting on the ECMO support they provide to COVID-19 patients. The mean annual number of cases treated with ECMO in the participating centres before the pandemic (2019) was 55. The number of COVID-19 patients has increased rapidly each week reaching 1531 treated patients as of September 14th. The greatest number of cases has been reported from France (n = 385), UK (n = 193), Germany (n = 176), Spain (n = 166), and Italy (n = 136) .The mean age of treated patients was 52.6 years (range 16–80), 79% were male. The ECMO configuration used was VV in 91% of cases, VA in 5% and other in 4%. The mean PaO2 before ECMO implantation was 65 mmHg. The mean duration of ECMO support thus far has been 18 days and the mean ICU length of stay of these patients was 33 days. As of the 14th September, overall 841 patients have been weaned from ECMO
support, 601 died during ECMO support, 71 died after withdrawal of ECMO, 79 are still receiving ECMO support and for 10 patients status n.a. . Our preliminary data suggest that patients placed
on ECMO with severe refractory respiratory or cardiac failure secondary to COVID-19 have a reasonable (55%) chance of survival. Further extensive data analysis is expected to provide invaluable information on the demographics, severity of illness, indications and different ECMO management strategies in these patients