The roles of nuclear orphan receptor NR2F6 in anti-viral innate immunity.

Proper transcription regulation by key transcription factors, such as IRF3, is critical for anti-viral defense. Dynamics of enhancer activity play important roles in many biological processes, and epigenomic analysis is used to determine the involved enhancers and transcription factors. To determine new transcription factors in anti-DNA-virus response, we have performed H3K27ac ChIP-Seq and identified three transcription factors, NR2F6, MEF2D and MAFF, in promoting HSV-1 replication. NR2F6 promotes HSV-1 replication and gene expression in vitro and in vivo, but not dependent on cGAS/STING pathway. NR2F6 binds to the promoter of MAP3K5 and activates AP-1/c-Jun pathway, which is critical for DNA virus replication. On the other hand, NR2F6 is transcriptionally repressed by c-Jun and forms a negative feedback loop. Meanwhile, cGAS/STING innate immunity signaling represses NR2F6 through STAT3. Taken together, we have identified new transcription factors and revealed the underlying mechanisms involved in the network between DNA viruses and host cells.

Synaptic density patterns in early Alzheimer's disease assessed by independent component analysis.

Synaptic loss is a primary pathology in Alzheimer's disease and correlates best with cognitive impairment as found in post-mortem studies. Previously, we observed in vivo reductions of synaptic density with [11C]UCB-J PET (radiotracer for synaptic vesicle protein 2A) throughout the neocortex and medial temporal brain regions in early Alzheimer's disease. In this study, we applied independent component analysis to synaptic vesicle protein 2A-PET data to identify brain networks associated with cognitive deficits in Alzheimer's disease in a blinded data-driven manner. [11C]UCB-J binding to synaptic vesicle protein 2A was measured in 38 Alzheimer's disease (24 mild Alzheimer's disease dementia and 14 mild cognitive impairment) and 19 cognitively normal participants. [11C]UCB-J distribution volume ratio values were calculated with a whole cerebellum reference region. Principal components analysis was first used to extract 18 independent components to which independent component analysis was then applied. Subject loading weights per pattern were compared between groups using Kruskal-Wallis tests. Spearman's rank correlations were used to assess relationships between loading weights and measures of cognitive and functional performance: Logical Memory II, Rey Auditory Verbal Learning Test-long delay, Clinical Dementia Rating sum of boxes and Mini-Mental State Examination. We observed significant differences in loading weights among cognitively normal, mild cognitive impairment and mild Alzheimer's disease dementia groups in 5 of the 18 independent components, as determined by Kruskal-Wallis tests. Only Patterns 1 and 2 demonstrated significant differences in group loading weights after correction for multiple comparisons. Excluding the cognitively normal group, we observed significant correlations between the loading weights for Pattern 1 (left temporal cortex and the cingulate gyrus) and Clinical Dementia Rating sum of boxes (r = -0.54, P = 0.0019), Mini-Mental State Examination (r = 0.48, P = 0.0055) and Logical Memory II score (r = 0.44, P = 0.013). For Pattern 2 (temporal cortices), significant associations were demonstrated between its loading weights and Logical Memory II score (r = 0.34, P = 0.0384). Following false discovery rate correction, only the relationship between the Pattern 1 loading weights with Clinical Dementia Rating sum of boxes (r = -0.54, P = 0.0019) and Mini-Mental State Examination (r = 0.48, P = 0.0055) remained statistically significant. We demonstrated that independent component analysis could define coherent spatial patterns of synaptic density. Furthermore, commonly used measures of cognitive performance correlated significantly with loading weights for two patterns within only the mild cognitive impairment/mild Alzheimer's disease dementia group. This study leverages data-centric approaches to augment the conventional region-of-interest-based methods, revealing distinct patterns that differentiate between mild cognitive impairment and mild Alzheimer's disease dementia, marking a significant advancement in the field.

Population-based deep image prior for dynamic PET denoising: A data-driven approach to improve parametric quantification.

The high noise level of dynamic Positron Emission Tomography (PET) images degrades the quality of parametric images. In this study, we aim to improve the quality and quantitative accuracy of Ki images by utilizing deep learning techniques to reduce the noise in dynamic PET images. We propose a novel denoising technique, Population-based Deep Image Prior (PDIP), which integrates population-based prior information into the optimization process of Deep Image Prior (DIP). Specifically, the population-based prior image is generated from a supervised denoising model that is trained on a prompts-matched static PET dataset comprising 100 clinical studies. The 3D U-Net architecture is employed for both the supervised model and the following DIP optimization process. We evaluated the efficacy of PDIP for noise reduction in 25%-count and 100%-count dynamic PET images from 23 patients by comparing with two other baseline techniques: the Prompts-matched Supervised model (PS) and a conditional DIP (CDIP) model that employs the mean static PET image as the prior. Both the PS and CDIP models show effective noise reduction but result in smoothing and removal of small lesions. In addition, the utilization of a single static image as the prior in the CDIP model also introduces a similar tracer distribution to the denoised dynamic frames, leading to lower Ki in general as well as incorrect Ki in the descending aorta. By contrast, as the proposed PDIP model utilizes intrinsic image features from the dynamic dataset and a large clinical static dataset, it not only achieves comparable noise reduction as the supervised and CDIP models but also improves lesion Ki predictions.

Prevention of postoperative nausea and vomiting after orthognathic surgery: a scoping review.

INTRODUCTION: Postoperative nausea and vomiting (PONV) is one of the most common adverse events following orthognathic surgery. It's a distressing feeling for patients and continues to be the cause of postoperative complications such as bleeding, delayed healing, and wound infection. This scoping review aims to identify effective PONV prophylaxis strategies during orthognathic surgery that have emerged in the past 15 years. METHODS: We searched Pubmed, Cochrane Controlled Register of Trials, and Embase from 2008 to May 2023. Studies meeting the following criteria were eligible for inclusion: (1) recruited patients undergo any orthognathic surgery; (2) evaluated any pharmacologic or non-pharmacologic method to prevent PONV. Studies meeting the following criteria were excluded: (1) case series, review papers, or retrospective studies; (2) did not report our prespecified outcomes. RESULTS: Twenty-one studies were included in this review. Pharmacological methods for PONV prevention include ondansetron and dexamethasone (3 studies), peripheral nerve block technique (4 studies), dexmedetomidine (1 study), pregabalin (2 studies), nefopam (2 studies), remifentanil (1 study), propofol (2 studies), and penehyclidine (1 study). Non-pharmacologic methods include capsicum plaster (1 study), throat packs (2 studies) and gastric aspiration (2 studies). CONCLUSIONS: Based on current evidence, we conclude that prophylactic antiemetics like dexamethasone, ondansetron, and penehyclidine are the first defense against PONV. Multimodal analgesia with nerve block techniques and non-opioid analgesics should be considered due to their notable opioid-sparing and PONV preventive effect. For the non-pharmacological methods, throat packs are not recommended for routine use because of their poor effect and serious complications. More prospective RCTs are required to confirm whether gastric aspiration can prevent PONV effectively for patients undergoing orthognathic surgery.

Transcription Factor Pdr3p Promotes Carotenoid Biosynthesis by Activating GAL Promoters in Saccharomyces cerevisiae.

Pleiotropic drug resistance (PDR) family proteins have been extensively studied for their roles in transporting hydrophobic substances, including carotenoids. Overexpression of the PDR family regulator Pdr3p was recently found to boost the biosynthesis of carotenoids, which could not be explained by enhanced product secretion due to the meager extracellular proportions. To provide insights into the possible mechanism, comparative transcriptomics, reverse metabolic engineering, and electrophoretic mobility shift assay (EMSA) were conducted. Transcriptomic data suggested an unexpected correlation between Pdr3p overexpression and the transcriptional levels of GAL promoter-driven genes. This assumption was verified using mCherry and the lycopene synthetic pathway as the reporters. qRT-PCR and EMSA provided further evidence for the activation of GAL promoters by Pdr3p binding to their upstream activation sequences (UASs). This work gives insight into the mechanism of Pdr3p-promoted carotenoid production and highlights the complicated metabolic networking between transcriptional factors and promoters in yeast.

The regional pattern of age-related synaptic loss in the human brain differs from gray matter volume loss: in vivo PET measurement with [11C]UCB-J.

PURPOSE: Aging is a major societal concern due to age-related functional losses. Synapses are crucial components of neural circuits, and synaptic density could be a sensitive biomarker to evaluate brain function. [11C]UCB-J is a positron emission tomography (PET) ligand targeting synaptic vesicle glycoprotein 2A (SV2A), which can be used to evaluate brain synaptic density in vivo. METHODS: We evaluated age-related changes in gray matter synaptic density, volume, and blood flow using [11C]UCB-J PET and magnetic resonance imaging (MRI) in a wide age range of 80 cognitive normal subjects (21-83 years old). Partial volume correction was applied to the PET data. RESULTS: Significant age-related decreases were found in 13, two, and nine brain regions for volume, synaptic density, and blood flow, respectively. The prefrontal cortex showed the largest volume decline (4.9% reduction per decade: RPD), while the synaptic density loss was largest in the caudate (3.6% RPD) and medial occipital cortex (3.4% RPD). The reductions in caudate are consistent with previous SV2A PET studies and likely reflect that caudate is the site of nerve terminals for multiple major tracts that undergo substantial age-related neurodegeneration. There was a non-significant negative relationship between volume and synaptic density reductions in 16 gray matter regions. CONCLUSION: MRI and [11]C-UCB-J PET showed age-related decreases of gray matter volume, synaptic density, and blood flow; however, the regional patterns of the reductions in volume and SV2A binding were different. Those patterns suggest that MR-based measures of GM volume may not be directly representative of synaptic density.

Assessment of Gray Matter Microstructure and Synaptic Density in Alzheimer's Disease: A Multimodal Imaging Study With DTI and SV2A PET.

OBJECTIVE: Multimodal imaging techniques have furthered our understanding of how different aspects of Alzheimer's disease (AD) pathology relate to one another. Diffusion tensor imaging (DTI) measures such as mean diffusivity (MD) may be a surrogate measure of the changes in gray matter structure associated with AD. Positron emission tomography (PET) imaging of synaptic vesicle glycoprotein 2A (SV2A) has been used to quantify synaptic loss, which is the major pathological correlate of cognitive impairment in AD. In this study, we investigated the relationship between gray matter microstructure and synaptic density. METHODS: DTI was used to measure MD and [11C]UCB-J PET to measure synaptic density in 33 amyloid-positive participants with AD and 17 amyloid-negative cognitively normal (CN) participants aged 50-83. Univariate regression analyses were used to assess the association between synaptic density and MD in both the AD and CN groups. RESULTS: Hippocampal MD was inversely associated with hippocampal synaptic density in participants with AD (r = -0.55, p <0.001, df = 31) but not CN (r = 0.13, p = 0.62, df = 15). Exploratory analyses across other regions known to be affected in AD suggested widespread inverse associations between synaptic density and MD in the AD group. CONCLUSION: In the setting of AD, an increase in gray matter MD is inversely associated with synaptic density. These co-occurring changes may suggest a link between synaptic loss and gray matter microstructural changes in AD. Imaging studies of gray matter microstructure and synaptic density may allow important insights into AD-related neuropathology.

Generation of Whole-Body FDG Parametric Ki Images from Static PET Images Using Deep Learning.

FDG parametric Ki images show great advantage over static SUV images, due to the higher contrast and better accuracy in tracer uptake rate estimation. In this study, we explored the feasibility of generating synthetic Ki images from static SUV ratio (SUVR) images using three configurations of U-Nets with different sets of input and output image patches, which were the U-Nets with single input and single output (SISO), multiple inputs and single output (MISO), and single input and multiple outputs (SIMO). SUVR images were generated by averaging three 5-min dynamic SUV frames starting at 60 minutes post-injection, and then normalized by the mean SUV values in the blood pool. The corresponding ground truth Ki images were derived using Patlak graphical analysis with input functions from measurement of arterial blood samples. Even though the synthetic Ki values were not quantitatively accurate compared with ground truth, the linear regression analysis of joint histograms in the voxels of body regions showed that the mean R2 values were higher between U-Net prediction and ground truth (0.596, 0.580, 0.576 in SISO, MISO and SIMO), than that between SUVR and ground truth Ki (0.571). In terms of similarity metrics, the synthetic Ki images were closer to the ground truth Ki images (mean SSIM = 0.729, 0.704, 0.704 in SISO, MISO and MISO) than the input SUVR images (mean SSIM = 0.691). Therefore, it is feasible to use deep learning networks to estimate surrogate map of parametric Ki images from static SUVR images.

Cooperation of MLL1 and Jun in controlling H3K4me3 on enhancers in colorectal cancer.

BACKGROUND: Enhancer dysregulation is one of the important features for cancer cells. Enhancers enriched with H3K4me3 have been implicated to play important roles in cancer. However, their detailed features and regulatory mechanisms have not been well characterized. RESULTS: Here, we profile the landscape of H3K4me3-enriched enhancers (m3Es) in 43 pairs of colorectal cancer (CRC) samples. M3Es are widely distributed in CRC and averagely possess around 10% of total active enhancers. We identify 1322 gain variant m3Es and 367 lost variant m3Es in CRC. The target genes of the gain m3Es are enriched in immune response pathways. We experimentally prove that repression of CBX8 and RPS6KA5 m3Es inhibits target gene expression in CRC. Furthermore, we find histone methyltransferase MLL1 is responsible for depositing H3K4me3 on the identified Vm3Es. We demonstrate that the transcription factor AP1/JUN interacts with MLL1 and regulates m3E activity. Application of a small chemical inhibitor for MLL1 activity, OICR-9429, represses target gene expression of the identified Vm3Es, enhances anti-tumor immunity and inhibits CRC growth in an animal model. CONCLUSIONS: Taken together, our study illustrates the genome-wide landscape and the regulatory mechanisms of m3Es in CRC, and reveals potential novel strategies for cancer treatment.

Inter-pass motion correction for whole-body dynamic PET and parametric imaging.

Whole-body dynamic FDG-PET imaging through continuous-bed-motion (CBM) mode multi-pass acquisition protocol is a promising metabolism measurement. However, inter-pass misalignment originating from body movement could degrade parametric quantification. We aim to apply a non-rigid registration method for inter-pass motion correction in whole-body dynamic PET. 27 subjects underwent a 90-min whole-body FDG CBM PET scan on a Biograph mCT (Siemens Healthineers), acquiring 9 over-the-heart single-bed passes and subsequently 19 CBM passes (frames). The inter-pass motion correction was executed using non-rigid image registration with multi-resolution, B-spline free-form deformations. The parametric images were then generated by Patlak analysis. The overlaid Patlak slope Ki and y-intercept Vb images were visualized to qualitatively evaluate motion impact and correction effect. The normalized weighted mean squared Patlak fitting errors (NFE) were compared in the whole body, head, and hypermetabolic regions of interest (ROI). In Ki images, ROI statistics were collected and malignancy discrimination capacity was estimated by the area under the receiver operating characteristic curve (AUC). After the inter-pass motion correction was applied, the spatial misalignment appearance between Ki and Vb images was successfully reduced. Voxel-wise normalized fitting error maps showed global error reduction after motion correction. The NFE in the whole body (p = 0.0013), head (p = 0.0021), and ROIs (p = 0.0377) significantly decreased. The visual performance of each hypermetabolic ROI in Ki images was enhanced, while 3.59% and 3.67% average absolute percentage changes were observed in mean and maximum Ki values, respectively, across all evaluated ROIs. The estimated mean Ki values had substantial changes with motion correction (p = 0.0021). The AUC of both mean Ki and maximum Ki after motion correction increased, possibly suggesting the potential of enhancing oncological discrimination capacity through inter-pass motion correction.

Principal component analysis of synaptic density measured with [11C]UCB-J PET in early Alzheimer's disease.

BACKGROUND: Synaptic loss is considered an early pathological event and major structural correlate of cognitive impairment in Alzheimer's disease (AD). We used principal component analysis (PCA) to identify regional patterns of covariance in synaptic density using [11C]UCB-J PET and assessed the association between principal components (PC) subject scores with cognitive performance. METHODS: [11C]UCB-J binding was measured in 45 amyloid + participants with AD and 19 amyloid- cognitively normal participants aged 55-85. A validated neuropsychological battery assessed performance across five cognitive domains. PCA was applied to the pooled sample using distribution volume ratios (DVR) standardized (z-scored) by region from 42 bilateral regions of interest (ROI). RESULTS: Parallel analysis determined three significant PCs explaining 70.2% of the total variance. PC1 was characterized by positive loadings with similar contributions across the majority of ROIs. PC2 was characterized by positive and negative loadings with strongest contributions from subcortical and parietooccipital cortical regions, respectively, while PC3 was characterized by positive and negative loadings with strongest contributions from rostral and caudal cortical regions, respectively. Within the AD group, PC1 subject scores were positively correlated with performance across all cognitive domains (Pearson r = 0.24-0.40, P = 0.06-0.006), PC2 subject scores were inversely correlated with age (Pearson r = -0.45, P = 0.002) and PC3 subject scores were significantly correlated with CDR-sb (Pearson r = 0.46, P = 0.04). No significant correlations were observed between cognitive performance and PC subject scores in CN participants. CONCLUSIONS: This data-driven approach defined specific spatial patterns of synaptic density correlated with unique participant characteristics within the AD group. Our findings reinforce synaptic density as a robust biomarker of disease presence and severity in the early stages of AD.

Removal of epigenetic repressive mark on inflammatory genes in fat liver.

Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease worldwide. The detailed epigenomic changes during fat accumulation in liver are not clear yet. Here, we performed ChIP-Seq analysis in the liver tissues of high-fat diet and regular chow diet mice and investigated the dynamic landscapes of H3K27ac and H3K9me3 marks on chromatin. We find that the activated typical enhancers marked with H3K27ac are enriched on lipid metabolic pathways in fat liver; however, super enhancers do not change much. The regions covered with H3K9me3 repressive mark seem to undergo great changes, and its peak number and intensity both decrease in fat liver. The enhancers located in lost H3K9me3 regions are enriched in lipid metabolism and inflammatory pathways; and motif analysis shows that they are potential targets for transcription factors involved in metabolic and inflammatory processes. Our study has revealed that H3K9me3 may play an important role during the pathogenesis of NAFLD through regulating the accessibility of enhancers.

MCP-Net: Introducing Patlak Loss Optimization to Whole-body Dynamic PET Inter-frame Motion Correction.

In whole-body dynamic positron emission tomography (PET), inter-frame subject motion causes spatial misalignment and affects parametric imaging. Many of the current deep learning inter-frame motion correction techniques focus solely on the anatomy-based registration problem, neglecting the tracer kinetics that contains functional information. To directly reduce the Patlak fitting error for 18F-FDG and further improve model performance, we propose an interframe motion correction framework with Patlak loss optimization integrated into the neural network (MCP-Net). The MCP-Net consists of a multiple-frame motion estimation block, an image-warping block, and an analytical Patlak block that estimates Patlak fitting using motion-corrected frames and the input function. A novel Patlak loss penalty component utilizing mean squared percentage fitting error is added to the loss function to reinforce the motion correction. The parametric images were generated using standard Patlak analysis following motion correction. Our framework enhanced the spatial alignment in both dynamic frames and parametric images and lowered normalized fitting error when compared to both conventional and deep learning benchmarks. MCP-Net also achieved the lowest motion prediction error and showed the best generalization capability. The potential of enhancing network performance and improving the quantitative accuracy of dynamic PET by directly utilizing tracer kinetics is suggested.

Drug characteristics derived from kinetic modeling: combined 11C-UCB-J human PET imaging with levetiracetam and brivaracetam occupancy of SV2A.

BACKGROUND: Antiepileptic drugs, levetiracetam (LEV) and brivaracetam (BRV), bind to synaptic vesicle glycoprotein 2A (SV2A). In their anti-seizure activity, speed of brain entry may be an important factor. BRV showed faster entry into the human and non-human primate brain, based on more rapid displacement of SV2A tracer 11C-UCB-J. To extract additional information from previous human studies, we developed a nonlinear model that accounted for drug entry into the brain and binding to SV2A using brain 11C-UCB-J positron emission tomography (PET) data and the time-varying plasma drug concentration, to assess the kinetic parameter K1 (brain entry rate) of the drugs. METHOD: Displacement (LEV or BRV p.i. 60 min post-tracer injection) and post-dose scans were conducted in five healthy subjects. Blood samples were collected for measurement of drug concentration and the tracer arterial input function. Fitting of nonlinear differential equations was applied simultaneously to time-activity curves (TACs) from displacement and post-dose scans to estimate 5 parameters: K1 (drug), K1(11C-UCB-J, displacement), K1(11C-UCB-J, post-dose), free fraction of 11C-UCB-J in brain (fND(11C-UCB-J)), and distribution volume of 11C-UCB-J (VT(UCB-J)). Other parameters (KD(drug), KD(11C-UCB-J), fP(drug), fP(11C-UCB-J, displacement), fP(11C-UCB-J, post-dose), fND(drug), koff(drug), koff(11C-UCB-J)) were fixed to literature or measured values. RESULTS: The proposed model described well the TACs in all subjects; however, estimates of drug K1 were unstable in comparison with 11C-UCB-J K1 estimation. To provide a conservative estimate of the relative speed of brain entry for BRV vs. LEV, we determined a lower bound on the ratio BRV K1/LEV K1, by finding the lowest BRV K1 or highest LEV K1 that were statistically consistent with the data. Specifically, we used the F test to compare the residual sum of squares with fixed BRV K1 to that with floating BRV K1 to obtain the lowest possible BRV K1; the same analysis was performed to find the highest LEV K1. The lower bound of the ratio BRV K1/LEV K1 was ~ 7. CONCLUSIONS: Under appropriate conditions, this advanced nonlinear model can directly estimate entry rates of drugs into tissue by analysis of PET TACs. Using a conservative statistical cutoff, BRV enters the brain at least sevenfold faster than LEV.

Real-world assessment of the clinical utility of whole body 18F-FDG PET/CT in the diagnosis of infection.

Few studies have aimed to capture the full spectrum of 18fluorodeoxyglucose-positron emission tomography/computed tomography (18F-FDG PET/CT) use for evaluation of infections in a real-world context. We performed a retrospective chart review of hospitalized patients who underwent 18F-FDG PET/CT for the workup of infection between April, 2013 and September, 2019. The clinical indications for and impact of 18F-FDG PET/CT on diagnostic and antimicrobial management were evaluated across different infectious indications. Sixty-one patients met the inclusion criteria. The most common indication was identifying a source of a known infection (46%), followed by fever of unknown etiology (FUE)/fever of unknown origin (FUO) (38%), and other (16%). 18F-FDG PET/CT was determined to have had a diagnostic or management clinical impact for a total of 22 patients (36%) including 12/28 (43%) of patients with known infection, 7/23 (30%) of patients with FUE/FUO, and 3/10 (30%) of patients with other indications. 18F-FDG PET/CT confirmed suspected prosthetic endovascular infection for 6/16 (38%) patients. In this study,18F-FDG PET/CT led to a clinical impact on diagnostic and treatment management of hospitalized patients across a variety of syndromes and particularly for source identification in the setting of known infection.

Epidemiology of liver cirrhosis and associated complications: Current knowledge and future directions.

Cirrhosis causes a heavy global burden. In this review, we summarized up-to-date epidemiological features of cirrhosis and its complications. Recent epidemiological studies reported an increase in the prevalence of cirrhosis in 2017 compared to in 1990 in both men and women, with 5.2 million cases of cirrhosis and chronic liver disease occurring in 2017. Cirrhosis caused 1.48 million deaths in 2019, an increase of 8.1% compared to 2017. Disability-adjusted life-years due to cirrhosis ranked 16th among all diseases and 7th in people aged 50-74 years in 2019. The global burden of hepatitis B virus and hepatitis C virus-associated cirrhosis is decreasing, while the burden of cirrhosis due to alcohol and nonalcoholic fatty liver disease (NAFLD) is increasing rapidly. We described the current epidemiology of the major complications of cirrhosis, including ascites, variceal bleeding, hepatic encephalopathy, renal disorders, and infections. We also summarized the epidemiology of hepatocellular carcinoma in patients with cirrhosis. In the future, NAFLD-related cirrhosis will likely become more common due to the prevalence of metabolic diseases such as obesity and diabetes, and the prevalence of alcohol-induced cirrhosis is increasing. This altered epidemiology should be clinically noted, and relevant interventions should be undertaken.

Systemic inflammation enhances stimulant-induced striatal dopamine elevation in tobacco smokers.

Immune-brain interactions influence the pathophysiology of addiction. Lipopolysaccharide (LPS)-induced systemic inflammation produces effects on reward-related brain regions and the dopamine system. We previously showed that LPS amplifies dopamine elevation induced by methylphenidate (MP), compared to placebo (PBO), in eight healthy controls. However, the effects of LPS on the dopamine system of tobacco smokers have not been explored. The goal of Study 1 was to replicate previous findings in an independent cohort of tobacco smokers. The goal of Study 2 was to combine tobacco smokers with the aforementioned eight healthy controls to examine the effect of LPS on dopamine elevation in a heterogenous sample for power and effect size determination. Eight smokers were each scanned with [11C]raclopride positron emission tomography three times-at baseline, after administration of LPS (0.8 ng/kg, intravenously) and MP (40 mg, orally), and after administration of PBO and MP, in a double-blind, randomized order. Dopamine elevation was quantified as change in [11C]raclopride binding potential (ΔBPND) from baseline. A repeated-measures ANOVA was conducted to compare LPS and PBO conditions. Smokers and healthy controls were well-matched for demographics, drug dosing, and scanning parameters. In Study 1, MP-induced striatal dopamine elevation was significantly higher following LPS than PBO (p = 0.025, 18 ± 2.9 % vs 13 ± 2.7 %) for smokers. In Study 2, MP-induced striatal dopamine elevation was also significantly higher under LPS than under PBO (p < 0.001, 18 ± 1.6 % vs 11 ± 1.5 %) in the combined sample. Smoking status did not interact with the effect of condition. This is the first study to translate the phenomenon of amplified dopamine elevation after experimental activation of the immune system to an addicted sample which may have implications for drug reinforcement, seeking, and treatment.

Virtual high-count PET image generation using a deep learning method.

PURPOSE: Recently, deep learning-based methods have been established to denoise the low-count positron emission tomography (PET) images and predict their standard-count image counterparts, which could achieve reduction of injected dosage and scan time, and improve image quality for equivalent lesion detectability and clinical diagnosis. In clinical settings, the majority scans are still acquired using standard injection dose with standard scan time. In this work, we applied a 3D U-Net network to reduce the noise of standard-count PET images to obtain the virtual-high-count (VHC) PET images for identifying the potential benefits of the obtained VHC PET images. METHODS: The training datasets, including down-sampled standard-count PET images as the network input and high-count images as the desired network output, were derived from 27 whole-body PET datasets, which were acquired using 90-min dynamic scan. The down-sampled standard-count PET images were rebinned with matched noise level of 195 clinical static PET datasets, by matching the normalized standard derivation (NSTD) inside 3D liver region of interests (ROIs). Cross-validation was performed on 27 PET datasets. Normalized mean square error (NMSE), peak signal to noise ratio (PSNR), structural similarity index (SSIM), and standard uptake value (SUV) bias of lesions were used for evaluation on standard-count and VHC PET images, with real-high-count PET image of 90 min as the gold standard. In addition, the network trained with 27 dynamic PET datasets was applied to 195 clinical static datasets to obtain VHC PET images. The NSTD and mean/max SUV of hypermetabolic lesions in standard-count and VHC PET images were evaluated. Three experienced nuclear medicine physicians evaluated the overall image quality of randomly selected 50 out of 195 patients' standard-count and VHC images and conducted 5-score ranking. A Wilcoxon signed-rank test was used to compare differences in the grading of standard-count and VHC images. RESULTS: The cross-validation results showed that VHC PET images had improved quantitative metrics scores than the standard-count PET images. The mean/max SUVs of 35 lesions in the standard-count and true-high-count PET images did not show significantly statistical difference. Similarly, the mean/max SUVs of VHC and true-high-count PET images did not show significantly statistical difference. For the 195 clinical data, the VHC PET images had a significantly lower NSTD than the standard-count images. The mean/max SUVs of 215 hypermetabolic lesions in the VHC and standard-count images showed no statistically significant difference. In the image quality evaluation by three experienced nuclear medicine physicians, standard-count images and VHC images received scores with mean and standard deviation of 3.34±0.80 and 4.26 ± 0.72 from Physician 1, 3.02 ± 0.87 and 3.96 ± 0.73 from Physician 2, and 3.74 ± 1.10 and 4.58 ± 0.57 from Physician 3, respectively. The VHC images were consistently ranked higher than the standard-count images. The Wilcoxon signed-rank test also indicated that the image quality evaluation between standard-count and VHC images had significant difference. CONCLUSIONS: A DL method was proposed to convert the standard-count images to the VHC images. The VHC images had reduced noise level. No significant difference in mean/max SUV to the standard-count images was observed. VHC images improved image quality for better lesion detectability and clinical diagnosis.

Imaging of Synaptic Density in Neurodegenerative Disorders.

PET technology has produced many radiopharmaceuticals that target specific brain proteins and other measures of brain function. Recently, a new approach has emerged to image synaptic density by targeting the synaptic vesicle protein 2A (SV2A), an integral glycoprotein in the membrane of synaptic vesicles and widely distributed throughout the brain. Multiple SV2A ligands have been developed and translated to human use. The most successful of these to date is 11C-UCB-J, because of its high uptake, moderate metabolism, and effective quantification with a 1-tissue-compartment model. Further, since SV2A is the target of the antiepileptic drug levetiracetam, human blocking studies have characterized specific binding and potential reference regions. Regional brain SV2A levels were shown to correlate with those of synaptophysin, another commonly used marker of synaptic density, providing the basis for SV2A PET imaging to have broad utility across neuropathologic diseases. In this review, we highlight the development of SV2A tracers and the evaluation of quantification methods, including compartment modeling and simple tissue ratios. Mouse and rat models of neurodegenerative diseases have been studied with small-animal PET, providing validation by comparison to direct tissue measures. Next, we review human PET imaging results in multiple neurodegenerative disorders. Studies on Parkinson disease and Alzheimer disease have progressed most rapidly at multiple centers, with generally consistent results of patterns of SV2A or synaptic loss. In Alzheimer disease, the synaptic loss patterns differ from those of amyloid, tau, and 18F-FDG, although intertracer and interregional correlations have been found. Smaller studies have been reported in other disorders, including Lewy body dementia, frontotemporal dementia, Huntington disease, progressive supranuclear palsy, and corticobasal degeneration. In conclusion, PET imaging of SV2A has rapidly developed, and qualified radioligands are available. PET studies on humans indicate that SV2A loss might be specific to disease-associated brain regions and consistent with synaptic density loss. The recent availability of new 18F tracers, 18F-SynVesT-1 and 18F-SynVesT-2, will substantially broaden the application of SV2A PET. Future studies are needed in larger patient cohorts to establish the clinical value of SV2A PET and its potential for diagnosis and progression monitoring of neurodegenerative diseases, as well as efficacy assessment of disease-modifying therapies.

Multifaceted regulation of enhancers in cancer.

Enhancer is one kind of cis-elements regulating gene transcription, whose activity is tightly controlled by epigenetic enzymes and histone modifications. Active enhancers are classified into typical enhancers, super-enhancers and over-active enhancers, according to the enrichment and location of histone modifications. Epigenetic factors control the level of histone modifications on enhancers to determine their activity, such as histone methyltransferases and acetylases. Transcription factors, cofactors and mediators co-operate together and are required for enhancer functions. In turn, abnormalities in these trans-acting factors affect enhancer activity. Recent studies have revealed enhancer dysregulation as one of the important features for cancer. Variations in enhancer regions and mutations of enhancer regulatory genes are frequently observed in cancer cells, and altering the activity of onco-enhancers is able to repress oncogene expression, and suppress tumorigenesis and metastasis. Here we summarize the recent discoveries about enhancer regulation in cancer and discuss their potential application in diagnosis and treatment.