A liquid crystalline phase in spermidine-condensed DNA

Over a large range of salt and spermidine concentrations, short DNA fragments precipitated by spermidine (a polyamine) sediment in a pellet from a dilute isotropic supernatant. We report here that the DNA-condensed phase consists of a cholesteric liquid crystal in equilibrium with a more concentrated phase. These results are discussed according to Flory's theory for the ordering of rigid polymers. The liquid crystal described here corresponds to an ordering in the presence of attractive interactions, in contrast with classical liquid crystalline DNA. Polyamines are often used in vitro to study the functional properties of DNA. We suggest that the existence of a liquid crystalline state in spermidine-condensed DNA is relevant to these studies

A first-in-human study of 11C-MTP38, a novel PET ligand for phosphodiesterase 7

PURPOSE: Phosphodiesterase 7 (PDE7) is an enzyme that selectively hydrolyses cyclic adenosine monophosphate, and its dysfunction is implicated in neuropsychiatric diseases. However, in vivo visualization of PDE7 in human brains has hitherto not been possible. Using the novel PET ligand 11C-MTP38, which we recently developed, we aimed to image and quantify PDE7 in living human brains. METHODS: Seven healthy males underwent a 90-min PET scan after injection of 11C-MTP38. We performed arterial blood sampling and metabolite analysis of plasma in six subjects to obtain a metabolite-corrected input function. Regional total distribution volumes (VTs) were estimated using compartment models, and Logan plot and Ichise multilinear analysis (MA1). We further quantified the specific radioligand binding using the original multilinear reference tissue model (MRTMO) and standardized uptake value ratio (SUVR) method with the cerebellar cortex as reference. RESULTS: PET images with 11C-MTP38 showed relatively high retentions in several brain regions, including in the striatum, globus pallidus, and thalamus, as well as fast washout from the cerebellar cortex, in agreement with the known distribution of PDE7. VT values were robustly estimated by two-tissue compartment model analysis (mean VT = 4.2 for the pallidum), Logan plot, and MA1, all in excellent agreement with each other, suggesting the reversibility of 11C-MTP38 binding. Furthermore, there were good agreements between binding values estimated by indirect method and those estimated by both MRTMO and SUVR, indicating that these methods could be useful for reliable quantification of PDE7. Because MRTMO and SUVR do not require arterial blood sampling, they are the most practical for the clinical use of 11C-MTP38-PET. CONCLUSION: We have provided the first demonstration of PET visualization of PDE7 in human brains. 11C-MTP38 is a promising novel PET ligand for the quantitative investigation of central PDE7

Positron emission tomography assessments of phosphodiesterase 10A in patients with schizophrenia

[Background and hypothesis] Phosphodiesterase 10A (PDE10A) is a highly expressed enzyme in the basal ganglia, where cortical glutamatergic and midbrain dopaminergic inputs are integrated. Therapeutic PDE10A inhibition effects on schizophrenia have been reported previously, but the status of this molecule in the living patients with schizophrenia remains elusive. Therefore, this study aimed to investigate the central PDE10A status in patients with schizophrenia and examine its relationship with psychopathology, cognition, and corticostriatal glutamate levels. [Study design] This study included 27 patients with schizophrenia, with 5 antipsychotic-free cases, and 27 healthy controls. Positron emission tomography with [18F]MNI-659, a specific PDE10A radioligand, was employed to quantify PDE10A availability by measuring non-displaceable binding potential (BPND) of the ligand in the limbic, executive, and sensorimotor striatal functional subregions, and in the pallidum. BPND estimates were compared between patients and controls while controlling for age and gender. BPND correlations were examined with behavioral and clinical measures, along with regional glutamate levels quantified by the magnetic resonance spectroscopy. [Study results] Multivariate analysis of covariance demonstrated a significant main effect of diagnosis on BPND (p = .03). A posthoc test showed a trend-level higher sensorimotor striatal BPND in patients, although it did not survive multiple comparison corrections. BPND in controls in this subregion was significantly and negatively correlated with the Tower of London scores, a cognitive subtest. Striatal or dorsolateral prefrontal glutamate levels did not correlate significantly with BPND in either group. [Conclusions] The results suggest altered striatal PDE10A availability and associated local neural dysfunctions in patients with schizophrenia

Tim3 binding to galectin-9 stimulates antimicrobial immunity

The interaction between Tim3 on Th1 cells and galectin-9 on Mycobacterium tuberculosis–infected macrophages restricts the bacterial growth by stimulating caspase-1–dependent IL-1β secretion

Proposal of blood volume-corrected model for quantification of regional cerebral blood flow with H2 15O-PET and its application to AVF.

Purpose. It is generally assumed that vascular tracer activity is negligible in the quantification of regional cerebral blood flow (gammaCBF) with H215O and positron emission tomography (PET) under normal conditions. We attempted to surpass the assumption of abnormal vascular conditions where the vascular tracer activity is significant by introducing the vascular component into the model.Materials and methods. H215O-dynamic and C15O PET scans were performed in an arterivenous fistula (AVF) patient. Time-activity curves of regions of interest (ROIs) were analyzed with nonlinear least -square approximation to estimate the gammaCBF and fractional arterial blood volume (va) simultaneously with the proposed model and the standard model.Results. The proposed model curve showed a fit to the time-activity curve of H215O at an ROI containing an enlarged vascular space induced by the AVF. The relation between the estimated va and CBV obtained with C15O-PET revealed that the ratio of va to CBV was approximately 0.23. The estimated gammaCBF with the proposed model in nonlesion ROIs corresponded to those of the standard model, with the estimated Vd 0.94ml/ml.Conclusion. The results supported the hypothesis that the blood volume-corrected model is applicable to the quantification of gammaCBF in a region with abnormal vascular structure. Furthermore,one of the advantages of the gammaCBF and arterial blood volume with dynamic-H215O PET scans

Quantification of 11C-PIB kinetics in mouse brain using metabolite-corrected arterial input function

Introduction: Recently, small animal PET studies have become of interest because various disease models have been developed by genetic manipulations primarily in mice. Advantages of PET study include in vivo evaluation of tracer kinetics that reflects physiological parameters quantitatively. Generally tracer kinetic analysis requires metabolite-corrected arterial input function obtained as plasma time-activity curve (pTAC). In this study, we performed pTAC measurement during the dynamic PET scan to test the capability of quantitative PET studies in mice using an amyloid tracer, 11C-PIB.\nMethods: Six male C57BL/6J mice (age, 9-10 weeks; body weight, 23-26g) were anesthetized with isoflurane throughout the experiment. The femoral artery was canulated to withdraw arterial blood, and mice were positioned in the micro-PET Focus 220 scanner. Simultaneously with the intravenous 11C-PIB (0.4-1.8 mCi, 0.2 ml) injection, 90-minute PET data acquisition and arterial blood sampling were started. Totally 12-14 blood samples were taken during the scan. The amount of each plasma sample was determined with micropipette and the radioactivity was measured with auto-gamma counter. A subset of plasma samples underwent HPLC analysis to determine unchanged 11C-PIB fraction. Tissue time-activity curves (tTACs) in the neocortex and cerebellum were obtained from dynamic PET data and total volume of distribution (VT) was estimated with one-tissue compartment model analysis (1TCM) and graphical analysis (GA) [1]. Furthermore, 2 mice were retested 4 weeks after the first experiment and reproducibility was assessed by variability: Variability (%) = 100x(test-retest)/{(test+retest)/2}.\nResults: Fig. 1 shows the time course of mean unchanged 11C-PIB fraction in plasma. Examples of pTAC and cerebeller tTAC from a test/retest study are shown in Figs. 2 and 3. VTs in the cerebellum and neocortex estimated with 1TCM were 1.1+/-0.2 and 0.82+/-0.22, respectively, and those estimated with GA were 1.4+/-0.4 and 1.1+/-0.4, respectively (ml/cm3; mean+/-sd). They were approximately 1/3 of those in humans reported previously [2]. Variability of estimated VT with 1TCM was ranging from 10% (cerebellum) to 27% (neocortex) and that with GA was from 5% (cecebellum) to 26% (neocortex). Conclusion: Our study has demonstrated the applicability of kinetic analysis using metabolite-corrected pTAC to PET measurements in small animals. The variability of estimated VT was mostly attributable to inaccurate mass measurement of small-volume plasma in the micro-liter range. With additional technical improvements, the present system would contribute to quantitative PET studies with diverse radiotracers, including those for which the reference region is not available.\n[1] Logan, J., et al. J Cereb Blood Flow Metab: 10, pp. 740-747, 1990.[2] Price, C.J., et al. J Cereb Blood Flow Metab: 25, pp. 1528-1547, 2005.The Seventh International Symposium on Neuroreceptor Mapping of Living Brai

Merging Biomathematical modelling and Machine learning to predict time-activity curves for PET CNS radioligand development

Purpose: The purpose of this study was the proposal of merging biomathematical model and machine learning approach to predict pharmacokinetics, time-activity curves (TACs), of candidate PET radioligand in brain for the development of PET CNS radioligands.Methods: Biomathematical model used in this study was based on the simplified one-tissue compartment model (1TCM) with the kinetic parameters (K1, k2 and BPND) in the human brain (Guo et al., JNNM, 2009). In silico apparent volume (Vx), lipohilisity (MlogP) of the ligand, in vitro affinity of the ligand (KD) to the target molecule and physiological parameter, the density of molecular target (Bmax) were used for the prediction of kinetic parameters (K1, k2 and BPND) (Arakawa et al., JNM, 2017). TAC can be calculated using K1, k2 and BPND and common arterial input function Cp (t). For merging this biomathematical model and machine learning, random forest (RF) algorithm, was introduced. As the training, 28 CNS radioligand database (Guo et al., JNNM, 2009), which includes mature, under developing and failed PET radioligands for various imaging targets was used. In total 280 datasets (28 CNS radioligand with 10 of Bmax values) was numerically created by biomathematical model. Input data for the prediction model was Vx, KD, Bmax, and MlogP, and the output was set to radioactive concentration [kBq/ml]. 3 PET radioligands ([11C]PIB, [11C]BF227, [18F]FACT)were used for the prediction of TACs. To verify the predicted radioactivity concentration, both predicted TACs from merged approach and from biomathematical model only were compared against averaged TAC from clinical PET study. Results: Correlation coefficient (R2) between training data and predicted radioactivity concentration [kBq/ml] was 0.97, 0.96, and 0.96 for time 5, 30 and 60 min, respectively. For the prediction of TACs for 3 PET radioligands, the merging approach resulted in poor prediction of TACs (different shape of TACs from clinical data) especially [18F]FACT compared with that from biomathematical model only. There are possible reasons for observed poor prediction, the number of training data, PET radioligand for the prediction, information of input datasets. Conclusion: In this study, we proposed the approach merging biomathematical model and machine learning to predict time-activity curves for PET radioligand. Further optimization of machine learning, and increase of applicable datasets would be necessary.2021 VIRTUAL IEEE NCLEAR SCIENCE SYMPOSIUM AND MEDICAL IMAGING CONFERENC