Simultaneous dynamic glucose-enhanced (DGE) MRI and fiber photometry measurements of glucose in the healthy mouse brain

Glucose is the main energy source in the brain and its regulated uptake and utilization are important biomarkers of pathological brain function. Glucose Chemical Exchange Saturation Transfer (GlucoCEST) and its time-resolved version Dynamic Glucose-Enhanced MRI (DGE) are promising approaches to monitor glucose and detect tumors, since it is radioactivity-free, does not require 13C labelling and it is easily translatable to the clinics. The main principle of DGE is clear. However, what remains to be established is to which extent the signal reflects vascular, extracellular or intracellular glucose. To elucidate the compartmental contributions to the DGE signal, we coupled it with FRET-based fiber photometry of genetically encoded sensors, a technique that combines quantitative glucose readout with cellular specificity. The glucose sensor FLIIP was used with fiber photometry to measure astrocytic and neuronal glucose changes upon injection of D-glucose, 3OMG and L-glucose, in the anaesthetized murine brain. By correlating the kinetic profiles of the techniques, we demonstrate the presence of a vascular contribution to the signal, especially at early time points after injection. Furthermore, we show that, in the case of the commonly used contrast agent 3OMG, the DGE signal actually anticorrelates with the glucose concentration in neurons and astrocytes. Keywords: fiber photometry; genetically encoded sensors; glucoCEST; kinetic modelling; two-photon microscopy

PRECYCLE: multicenter, randomized phase IV intergroup trial to evaluate the impact of eHealth-based patient-reported outcome (PRO) assessment on quality of life in patients with hormone receptor positive, HER2 negative locally advanced or metastatic breast cancer treated with palbociclib and an aromatase inhibitor or palbociclib and fulvestrant

Background Efficacy and quality of life (QoL) are key criteria for therapy selection in metastatic breast cancer (MBC). In hormone receptor positive (HR +) human epidermal growth factor receptor 2 negative (HER2 −) MBC, addition of targeted oral agents such as everolimus or a cycline-dependent kinase 4/6 (CDK 4/6) inhibitor (e.g., palbociclib, ribociclib, abemaciclib) to endocrine therapy substantially prolongs progression-free survival and in the case of a CDK 4/6i also overall survival. However, the prerequisite is adherence to therapy over the entire course of treatment. However, particularly with new oral drugs, adherence presents a challenge to disease management. In this context, factors influencing adherence include maintaining patients’ satisfaction and early detection/management of side effects. New strategies for continuous support of oncological patients are needed. An eHealth-based platform can help to support therapy management and physician–patient interaction. Methods PreCycle is a multicenter, randomized, phase IV trial in HR + HER2 − MBC. All patients (n = 960) receive the CDK 4/6 inhibitor palbociclib either in first (62.5%) or later line (37.5%) together with endocrine therapy (AI, fulvestrant) according to national guidelines. PreCycle evaluates and compares the time to deterioration (TTD) of QoL in patients supported by eHealth systems with substantially different functionality: CANKADO active vs. inform. CANKADO active is the fully functional CANKADO-based eHealth treatment support system. CANKADO inform is a CANKADO-based eHealth service with a personal login, documentation of daily drug intake, but no further functions. To evaluate QoL, the FACT-B questionnaire is completed at every visit. As little is known about relationships between behavior (e.g., adherence), genetic background, and drug efficacy, the trial includes both patient-reported outcome and biomarker screening for discovery of forecast models for adherence, symptoms, QoL, progression free survival (PFS), and overall survival (OS). Discussion The primary objective of PreCycle is to test the hypothesis of superiority for time to deterioration (TTD) in terms of DQoL = “Deterioration of quality of life” (FACT-G scale) in patients supported by an eHealth therapy management system (CANKADO active) versus in patients merely receiving eHealth-based information (CANKADO inform). EudraCT Number: 2016–004191-2

MR-double-zero-Proof-of-concept for a framework to autonomously discover MRI contrasts

Purpose: A framework for supervised design of MR sequences for any given target contrast is proposed, based on fully automatic acquisition and reconstruction of MR data on a real MR scanner. The proposed method does not require any modeling of MR physics and thus allows even unknown contrast mechanisms to be addressed. Methods: A derivative-free optimization algorithm is set up to repeatedly update and execute a parametrized sequence on the MR scanner to acquire data. In each iteration, the acquired data are mapped to a given target contrast by linear regression. Results: It is shown that with the proposed framework it is possible to find an MR sequence that yields a predefined target contrast. In the present case, as a proof-of principle, a sequence mapping absolute creatine concentration, which cannot be extracted from T1 or T2-weighted scans directly, is discovered. The sequence was designed in a comparatively short time and with no human interaction. Conclusions: New MR contrasts for mapping a given target can be discovered by derivative-free optimization of parametrized sequences that are directly executed on a real MRI scanner. This is demonstrated by 're-discovery' of a chemical exchange weighted sequence. The proposed method is considered to be a paradigm shift towards autonomous, model-free and target-driven sequence design

Simultaneous dynamic glucose-enhanced (DGE) MRI and fiber photometry measurements of glucose in the healthy mouse brain

Glucose is the main energy source in the brain and its regulated uptake and utilization are important biomarkers of pathological brain function. Glucose Chemical Exchange Saturation Transfer (GlucoCEST) and its time-resolved version Dynamic Glucose-Enhanced MRI (DGE) are promising approaches to monitor glucose and detect tumors, since they are radioactivity-free, do not require 13C labeling and are is easily translatable to the clinics. The main principle of DGE is clear. However, what remains to be established is to which extent the signal reflects vascular, extracellular or intracellular glucose. To elucidate the compartmental contributions to the DGE signal, we coupled it with FRET-based fiber photometry of genetically encoded sensors, a technique that combines quantitative glucose readout with cellular specificity. The glucose sensor FLIIP was used with fiber photometry to measure astrocytic and neuronal glucose changes upon injection of D-glucose, 3OMG and L-glucose, in the anaesthetized murine brain. By correlating the kinetic profiles of the techniques, we demonstrate the presence of a vascular contribution to the signal, especially at early time points after injection. Furthermore, we show that, in the case of the commonly used contrast agent 3OMG, the DGE signal actually anticorrelates with the glucose concentration in neurons and astrocytes.ISSN:1053-8119ISSN:1095-957

CD84 expression on human hematopoietic progenitor cells

OBJECTIVE: CD84 is a member of the CD2 subgroup of the immunoglobulin receptor superfamily. Members of this family have been implicated in the activation of T cells and NK cells. Expression of CD84 was originally described on most mononuclear blood cells as well as platelets. To elucidate its presence on other blood cell types, we analyzed the expression pattern of CD84 on human immature CD34+ and mature hematopoietic cells. METHODS: Expression analysis was carried out by flow cytometry. The differentiation potential of CD84+ progenitor cells was assessed by colony-forming assays and long-term cultures. RT-PCR was used to analyze CD84 mRNA isoforms. RESULTS: In addition to monocytes, macrophages, B cells, and some T cells, CD84 is expressed on the cell surface of the majority of granulocytes. In addition, 64%+/-5% of CD34+ progenitor cells isolated from peripheral blood and 30.5%+/-5% from bone marrow of healthy volunteers also express CD84. The majority of CD34+ cells coexpressing lineage antigens were CD84+. In methylcellulose CD34+CD84+ cells formed primarily erythroid colonies, whereas myeloid or mixed colonies were scarce. The frequency of long-term culture-initiating cells in peripheral blood was approximately fivefold higher in CD34+CD84- vs CD34+CD84+ cells. In short-term cultures, 95% of the initially CD34+CD84- cells became CD84+ after 72 hours. CONCLUSIONS: CD84 is expressed on cells from almost all hematopoietic lineages and on CD34+ hematopoietic progenitor cells. The proliferative potential of CD34+ cells decreases with increasing CD84 expression, suggesting that CD84 serves as a marker for committed hematopoietic progenitor cells