LMO2-associated clonal T cell proliferation in two patients after gene therapy for SCID-X1.

We have previously shown correction of X-linked severe combined immunodeficiency [SCID-X1, also known as gamma chain (gamma(c)) deficiency] in 9 out of 10 patients by retrovirus-mediated gamma(c) gene transfer into autologous CD34 bone marrow cells. However, almost 3 years after gene therapy, uncontrolled exponential clonal proliferation of mature T cells (with gammadelta+ or alphabeta+ T cell receptors) has occurred in the two youngest patients. Both patients' clones showed retrovirus vector integration in proximity to the LMO2 proto-oncogene promoter, leading to aberrant transcription and expression of LMO2. Thus, retrovirus vector insertion can trigger deregulated premalignant cell proliferation with unexpected frequency, most likely driven by retrovirus enhancer activity on the LMO2 gene promoter

Calix[n] imidazolium as a new class of positively charged homo-calix compounds

Macrocycles based on neutral calixarenes and calixpyrroles have been extensively explored for ion binding, molecular assembly and related applications. Given that only these two types of calix compounds and their analogs are available, the introduction of new forms of widely usable calix macrocycles is an outstanding challenge. Here we report the quadruply/quintuply charged imidazole-based homo-calix compounds, calix[4/5] imidazolium. The noncovalent (C-H)(+)/pi(+) -anion interactions of the imidazolium rings with anions inside and outside the cone are the stabilizing factors for crystal packing, resulting in self-assembled arrays of cone-shaped calix-imidazolium molecules. Calix[4] imidazolium senses fluoride selectively even in aqueous solutions. Calix[5] imidazolium recognizes neutral fullerenes through pi(+) -pi interactions and makes them soluble in water, which could be useful in fullerene chemistry. Not only derivatization and ring expansion of calix[n] imidazolium, but also their utilization in ionic liquids, carbene chemistry and nanographite/graphene exfoliation could be exploited.close10

The use of reinforcement learning algorithms to meet the challenges of an artificial pancreas

Blood glucose control, for example, in diabetes mellitus or severe illness, requires strict adherence to a protocol of food, insulin administration and exercise personalized to each patient. An artificial pancreas for automated treatment could boost quality of glucose control and patients' independence. The components required for an artificial pancreas are: i) continuous glucose monitoring (CGM), ii) smart controllers and iii) insulin pumps delivering the optimal amount of insulin. In recent years, medical devices for CGM and insulin administration have undergone rapid progression and are now commercially available. Yet, clinically available devices still require regular patients' or caregivers' attention as they operate in open-loop control with frequent user intervention. Dosage-calculating algorithms are currently being studied in intensive care patients [1] , for short overnight control to supplement conventional insulin delivery [2] , and for short periods where patients rest and follow a prescribed food regime [3] . Fully automated algorithms that can respond to the varying activity levels seen in outpatients, with unpredictable and unreported food intake, and which provide the necessary personalized control for individuals is currently beyond the state-of-the-art. Here, we review and discuss reinforcement learning algorithms, controlling insulin in a closed-loop to provide individual insulin dosing regimens that are reactive to the immediate needs of the patient