Spatial transcriptomic brain imaging reveals the effects of immunomodulation therapy on specific regional brain cells in a mouse dementia model.

Increasing evidence of brain-immune crosstalk raises expectations for the efficacy of novel immunotherapies in Alzheimer's disease (AD), but the lack of methods to examine brain tissues makes it difficult to evaluate therapeutics. Here, we investigated the changes in spatial transcriptomic signatures and brain cell types using the 10x Genomics Visium platform in immune-modulated AD models after various treatments. To proceed with an analysis suitable for barcode-based spatial transcriptomics, we first organized a workflow for segmentation of neuroanatomical regions, establishment of appropriate gene combinations, and comprehensive review of altered brain cell signatures. Ultimately, we investigated spatial transcriptomic changes following administration of immunomodulators, NK cell supplements and an anti-CD4 antibody, which ameliorated behavior impairment, and designated brain cells and regions showing probable associations with behavior changes. We provided the customized analytic pipeline into an application named STquantool. Thus, we anticipate that our approach can help researchers interpret the real action of drug candidates by simultaneously investigating the dynamics of all transcripts for the development of novel AD therapeutics.

Distinct subtypes of spatial brain metabolism patterns in Alzheimer's disease identified by deep learning-based FDG PET clusters.

PURPOSE: Alzheimer's disease (AD) is a heterogeneous disease that presents a broad spectrum of clinicopathologic profiles. To date, objective subtyping of AD independent of disease progression using brain imaging has been required. Our study aimed to extract representations of unique brain metabolism patterns different from disease progression to identify objective subtypes of AD. METHODS: A total of 3620 FDG brain PET images with AD, mild cognitive impairment (MCI), and cognitively normal (CN) were obtained from the ADNI database from 1607 participants at enrollment and follow-up visits. A conditional variational autoencoder model was trained on FDG brain PET images of AD patients with the corresponding condition of AD severity score. The k-means algorithm was applied to generate clusters from the encoded representations. The trained deep learning-based cluster model was also transferred to FDG PET of MCI patients and predicted the prognosis of subtypes for conversion from MCI to AD. Spatial metabolism patterns, clinical and biological characteristics, and conversion rate from MCI to AD were compared across the subtypes. RESULTS: Four distinct subtypes of spatial metabolism patterns in AD with different brain pathologies and clinical profiles were identified: (i) angular, (ii) occipital, (iii) orbitofrontal, and (iv) minimal hypometabolic patterns. The deep learning model was also successfully transferred for subtyping MCI, and significant differences in frequency (P < 0.001) and risk of conversion (log-rank P < 0.0001) from MCI to AD were observed across the subtypes, highest in S2 (35.7%) followed by S1 (23.4%). CONCLUSION: We identified distinct subtypes of AD with different clinicopathologic features. The deep learning-based approach to distinguish AD subtypes on FDG PET could have implications for predicting individual outcomes and provide a clue to understanding the heterogeneous pathophysiology of AD.

CellDART: cell type inference by domain adaptation of single-cell and spatial transcriptomic data.

Deciphering the cellular composition in genome-wide spatially resolved transcriptomic data is a critical task to clarify the spatial context of cells in a tissue. In this study, we developed a method, CellDART, which estimates the spatial distribution of cells defined by single-cell level data using domain adaptation of neural networks and applied it to the spatial mapping of human lung tissue. The neural network that predicts the cell proportion in a pseudospot, a virtual mixture of cells from single-cell data, is translated to decompose the cell types in each spatial barcoded region. First, CellDART was applied to a mouse brain and a human dorsolateral prefrontal cortex tissue to identify cell types with a layer-specific spatial distribution. Overall, the proposed approach showed more stable and higher accuracy with short execution time compared to other computational methods to predict the spatial location of excitatory neurons. CellDART was capable of decomposing cellular proportion in mouse hippocampus Slide-seq data. Furthermore, CellDART elucidated the cell type predominance defined by the human lung cell atlas across the lung tissue compartments and it corresponded to the known prevalent cell types. CellDART is expected to help to elucidate the spatial heterogeneity of cells and their close interactions in various tissues.

Discovery of molecular features underlying the morphological landscape by integrating spatial transcriptomic data with deep features of tissue images.

Profiling molecular features associated with the morphological landscape of tissue is crucial for investigating the structural and spatial patterns that underlie the biological function of tissues. In this study, we present a new method, spatial gene expression patterns by deep learning of tissue images (SPADE), to identify important genes associated with morphological contexts by combining spatial transcriptomic data with coregistered images. SPADE incorporates deep learning-derived image patterns with spatially resolved gene expression data to extract morphological context markers. Morphological features that correspond to spatial maps of the transcriptome were extracted by image patches surrounding each spot and were subsequently represented by image latent features. The molecular profiles correlated with the image latent features were identified. The extracted genes could be further analyzed to discover functional terms and exploited to extract clusters maintaining morphological contexts. We apply our approach to spatial transcriptomic data from different tissues, platforms and types of images to demonstrate an unbiased method that is capable of obtaining image-integrated gene expression trends.

Human neural stem cell-derived extracellular vesicles protect against Parkinson's disease pathologies.

BACKGROUND: Neural stem cells (NSCs) have the ability to generate a variety of functional neural cell types and have a high potential for neuronal cell regeneration and recovery. Thus, they been recognized as the best source of cell therapy for neurodegenerative diseases, such as Parkinson's disease (PD). Owing to the possibility of paracrine effect-based therapeutic mechanisms and easier clinical accessibility, extracellular vesicles (EVs), which possess very similar bio-functional components from their cellular origin, have emerged as potential alternatives in regenerative medicine. MATERIAL AND METHODS: EVs were isolated from human fibroblast (HFF) and human NSC (F3 cells). The supernatant of the cells was concentrated by a tangential flow filtration (TFF) system. Then, the final EVs were isolated using a total EV isolation kit. RESULTS: In this study, we demonstrate the potential protective effect of human NSC-derived EVs, showing the prevention of PD pathologies in 6-hydroxydopamine (6-OHDA)-induced in vitro and in vivo mouse models. Human NSC and F3 cell (F3)-derived EVs reduced the intracellular reactive oxygen species (ROS) and associated apoptotic pathways. In addition, F3-derived EVs induced downregulation of pro-inflammatory factors and significantly decreased 6-OHDA-induced dopaminergic neuronal loss in vivo. F3 specific microRNAs (miRNAs) such as hsa-mir-182-5p, hsa-mir-183-5p, hsa-mir-9, and hsa-let-7, which are involved in cell differentiation, neurotrophic function, and immune modulation, were found in F3-derived EVs. CONCLUSIONS: We report that human NSC-derived EVs show an effective neuroprotective property in an in vitro transwell system and in a PD model. The EVs clearly decreased ROS and pro-inflammatory cytokines. Taken together, these results indicate that NSC-derived EVs could potentially help prevent the neuropathology and progression of PD.

High-quantum yield alloy-typed core/shell CdSeZnS/ZnS quantum dots for bio-applications.

BACKGROUND: Quantum dots (QDs) have been used as fluorophores in various imaging fields owing to their strong fluorescent intensity, high quantum yield (QY), and narrow emission bandwidth. However, the application of QDs to bio-imaging is limited because the QY of QDs decreases substantially during the surface modification step for bio-application. RESULTS: In this study, we fabricated alloy-typed core/shell CdSeZnS/ZnS quantum dots (alloy QDs) that showed higher quantum yield and stability during the surface modification for hydrophilization compared with conventional CdSe/CdS/ZnS multilayer quantum dots (MQDs). The structure of the alloy QDs was confirmed using time-of-flight medium-energy ion scattering spectroscopy. The alloy QDs exhibited strong fluorescence and a high QY of 98.0%. After hydrophilic surface modification, the alloy QDs exhibited a QY of 84.7%, which is 1.5 times higher than that of MQDs. The QY was 77.8% after the alloy QDs were conjugated with folic acid (FA). Alloy QDs and MQDs, after conjugation with FA, were successfully used for targeting human KB cells. The alloy QDs exhibited a stronger fluorescence signal than MQD; these signals were retained in the popliteal lymph node area for 24 h. CONCLUSION: The alloy QDs maintained a higher QY in hydrophilization for biological applications than MQDs. And also, alloy QDs showed the potential as nanoprobes for highly sensitive bioimaging analysis.

Hippocampal glucose uptake as a surrogate of metabolic change of microglia in Alzheimer's disease.

BACKGROUND: Dynamically altered microglia play an important role in the progression of Alzheimer's disease (AD). Here, we found a close association of the metabolic reconfiguration of microglia with increased hippocampal glucose uptake on [18F]fluorodeoxyglucose (FDG) PET. METHODS: We used an AD animal model, 5xFAD, to analyze hippocampal glucose metabolism using both animal FDG PET and ex vivo FDG uptake test. Cells of the hippocampus were isolated to perform single-cell RNA-sequencing (scRNA-seq). The molecular features of cells associated with glucose metabolism were analyzed at a single-cell level. In order to apply our findings to human brain imaging study, brain FDG PET data obtained from the Alzheimer's Disease Neuroimaging Initiative were analyzed. FDG uptake in the hippocampus was compared according to the diagnosis, AD, mild cognitive impairment, and controls. The correlation analysis between hippocampal FDG uptake and soluble TREM2 in cerebrospinal fluid was performed. RESULTS: In the animal study, 8- and 12-month-old 5xFAD mice showed higher FDG uptake in the hippocampus than wild-type mice. Cellular FDG uptake tests showed that FDG activity in hippocampal microglia was increased in the AD model, while FDG activity in non-microglial cells of the hippocampus was not different between the AD model and wild-type. scRNA-seq data showed that changes in glucose metabolism signatures including glucose transporters, glycolysis and oxidative phosphorylation, mainly occurred in microglia. A subset of microglia with higher glucose transporters with defective glycolysis and oxidative phosphorylation was increased according to disease progression. In the human imaging study, we found a positive association between soluble TREM2 and hippocampal FDG uptake. FDG uptake in the hippocampus at the baseline scan predicted mild cognitive impairment conversion to AD. CONCLUSIONS: We identified the reconfiguration of microglial glucose metabolism in the hippocampus of AD, which could be evaluated by FDG PET as a feasible surrogate imaging biomarker for microglia-mediated inflammation.

Visual interpretation of [18F]Florbetaben PET supported by deep learning-based estimation of amyloid burden.

PURPOSE: Amyloid PET which has been widely used for noninvasive assessment of cortical amyloid burden is visually interpreted in the clinical setting. As a fast and easy-to-use visual interpretation support system, we analyze whether the deep learning-based end-to-end estimation of amyloid burden improves inter-reader agreement as well as the confidence of the visual reading. METHODS: A total of 121 clinical routines [18F]Florbetaben PET images were collected for the randomized blind-reader study. The amyloid PET images were visually interpreted by three experts independently blind to other information. The readers qualitatively interpreted images without quantification at the first reading session. After more than 2-week interval, the readers additionally interpreted images with the quantification results provided by the deep learning system. The qualitative assessment was based on a 3-point BAPL score (1: no amyloid load, 2: minor amyloid load, and 3: significant amyloid load). The confidence score for each session was evaluated by a 3-point score (0: ambiguous, 1: probably, and 2: definite to decide). RESULTS: Inter-reader agreements for the visual reading based on a 3-point scale (BAPL score) calculated by Fleiss kappa coefficients were 0.46 and 0.76 for the visual reading without and with the deep learning system, respectively. For the two reading sessions, the confidence score of visual reading was improved at the visual reading session with the output (1.27 ± 0.078 for visual reading-only session vs. 1.66 ± 0.63 for a visual reading session with the deep learning system). CONCLUSION: Our results highlight the impact of deep learning-based one-step amyloid burden estimation system on inter-reader agreement and confidence of reading when applied to clinical routine amyloid PET reading.

Deep learning-based interpretation of basal/acetazolamide brain perfusion SPECT leveraging unstructured reading reports.

PURPOSE: Basal/acetazolamide brain perfusion single-photon emission computed tomography (SPECT) has been used to evaluate functional hemodynamics in patients with carotid artery stenosis. We aimed to develop a deep learning model as a support system for interpreting brain perfusion SPECT leveraging unstructured text reports. METHODS: In total, 7345 basal/acetazolamide brain perfusion SPECT images and their text reports were retrospectively collected. A long short-term memory (LSTM) network was trained using 500 randomly selected text reports to predict manually labeled structured information, including abnormalities of basal perfusion and vascular reserve for each vascular territory. Using this trained LSTM model, we extracted structured information from the remaining 6845 text reports to develop a deep learning model for interpreting SPECT images. The model was based on a 3D convolutional neural network (CNN), and the performance was tested on the other 500 cases by measuring the area under the receiver-operating characteristic curve (AUC). We then applied the model to patients who underwent revascularization (n = 33) to compare the estimated output of the CNN model for pre- and post-revascularization SPECT and clinical outcomes. RESULTS: The AUC of the LSTM model for extracting structured labels was 1.00 for basal perfusion and 0.99 for vascular reserve for all 9 brain regions. The AUC of the CNN model designed to identify abnormal perfusion was 0.83 for basal perfusion and 0.89 for vascular reserve. The output of the CNN model was significantly improved according to the revascularization in the target vascular territory, and its changes in brain territories were concordant with clinical outcomes. CONCLUSION: We developed a deep learning model to support the interpretation of brain perfusion SPECT by converting unstructured text reports into structured labels. This model can be used as a support system not only to identify perfusion abnormalities but also to provide quantitative scores of abnormalities, particularly for patients who require revascularization.

Cognitive signature of brain FDG PET based on deep learning: domain transfer from Alzheimer's disease to Parkinson's disease.

PURPOSE: Although functional brain imaging has been used for the early and objective assessment of cognitive dysfunction, there is a lack of generalized image-based biomarker which can evaluate individual's cognitive dysfunction in various disorders. To this end, we developed a deep learning-based cognitive signature of FDG brain PET adaptable for Parkinson's disease (PD) as well as Alzheimer's disease (AD). METHODS: A deep learning model for discriminating AD from normal controls (NCs) was built by a training set consisting of 636 FDG PET obtained from Alzheimer's Disease Neuroimaging Initiative database. The model was directly transferred to images of mild cognitive impairment (MCI) patients (n = 666) for identifying who would rapidly convert to AD and another independent cohort consisting of 62 PD patients to differentiate PD patients with dementia. The model accuracy was measured by area under curve (AUC) of receiver operating characteristic (ROC) analysis. The relationship between all images was visualized by two-dimensional projection of the deep learning-based features. The model was also designed to predict cognitive score of the subjects and validated in PD patients. Cognitive dysfunction-related regions were visualized by feature maps of the deep CNN model. RESULTS: AUC of ROC for differentiating AD from NC was 0.94 (95% CI 0.89-0.98). The transfer of the model could differentiate MCI patients who would convert to AD (AUC = 0.82) and PD with dementia (AUC = 0.81). The two-dimensional projection mapping visualized the degree of cognitive dysfunction compared with normal brains regardless of different disease cohorts. Predicted cognitive score, an output of the model, was highly correlated with the mini-mental status exam scores. Individual cognitive dysfunction-related regions included cingulate and high frontoparietal cortices, while they showed individual variability. CONCLUSION: The deep learning-based cognitive function evaluation model could be successfully transferred to multiple disease domains. We suggest that this approach might be extended to an objective cognitive signature that provides quantitative biomarker for cognitive dysfunction across various neurodegenerative disorders.

Physical activity status in relation to quality of life and dietary habits in breast cancer survivors: subset analyses of KROG 14-09 nationwide questionnaire study.

PURPOSE: We investigated the relationship of physical activity with dietary habits and quality of life (QoL) in breast cancer survivors in accordance with the recommendations of the American Cancer Society. METHODS: Data of 928 breast cancer survivors were obtained from the KROG 14-09 study to measure QoL in early phase after adjuvant radiotherapy. According to the extent of physical activity, survivors were divided into four groups: inactivity (0-149 min/week, N = 144), regular activity (150-450 min/week, N = 309), moderate activity (451-900 min/week, N = 229), and marked activity (901-1800 min/week, N = 164) excluding hyperactivity (> 1800 min/week, N = 82) as it is a difficult condition to recommend to survivors. Global physical activity questionnaire, 5-dimensional questionnaire by EuroQoL (EQ-5D-3L), QoL Questionnaire-breast cancer (QLQ-BR23) from EORTC, and dietary habits were surveyed. A linear-to-linear association test for EQ-5D-3L and Kruskal-Wallis analysis for QLQ-BR23 and dietary habit were conducted. RESULTS: Overall, 15.5% respondents (144/928) were classified as physically inactive. The trends of frequent intake of fruits (p = 0.001) and vegetable (p = 0.005) and reluctance toward fatty food (p < 0.001) were observed in physically active groups. Mobility (p = 0.021) and anxiety (p = 0.030) of EQ-5D-3L, and systemic therapy side effect (p = 0.027) and future perspective (p = 0.008) of QLQ-BR23 were better in physically active groups besides body image (p = 0.003) for the survivors with breast-conserving surgery. However, moderate and marked activities did not further improve QoL than regular activity. CONCLUSION: Physicians and care-givers have to pay attention to inactive survivors to boost their physical activity, thereby facilitating a better QoL and dietary habit.

Prognostic value of metabolic tumour volume on baseline 18F-FDG PET/CT in addition to NCCN-IPI in patients with diffuse large B-cell lymphoma: further stratification of the group with a high-risk NCCN-IPI.

PURPOSE: The purpose of this study was to determine the prognostic value of metabolic volumetric parameters as a quantitative index on pre-treatment 18F-FDG PET/CT in addition to the National Comprehensive Cancer Network International Prognostic Index (NCCN-IPI) in patients with diffuse large B-cell lymphoma (DLBCL). METHODS: A total of 103 consecutive patients with DLBCL and baseline FDG PET/CT were retrospectively evaluated. Quantitative metabolic parameters, including total metabolic tumour volume (TMTV) using a standardized uptake value (SUV) of ≥2.5 as the threshold, were estimated. Receiver operating characteristic curve analysis was used to determine the optimal cut-off values for the metabolic parameters. The relationships between study variables and patient survival were tested using Cox regression analysis. Patient survival rates were derived from Kaplan-Meier curves and compared using the log-rank test. RESULTS: Median follow-up was 34 months. In patients with a low TMTV (<249 cm3), the 3-year progression free survival (PFS) rate was 83% and the overall survival (OS) rate was 92%, in contrast to 41% and 57%, respectively, in those with a high TMTV (≥249 cm3). In univariate analysis, a high TMTV and NCCN-IPI ≥4 were associated with inferior PFS and OS (P < 0.0001 for all), as was a high total lesion glycolysis (P = 0.004 and P = 0.005, respectively). In multivariate analysis, TMTV and NCCN-IPI were independent predictors of PFS (hazard ratio, HR, 3.11, 95% confidence interval, CI, 1.37-7.07, P = 0.007, and HR 3.42, 95% CI 1.36-8.59, P = 0.009, respectively) and OS (HR 3.41, 95% CI 1.24-9.38, P = 0.017, and HR 5.06, 95% CI 1.46-17.60, P = 0.014, respectively). TMTV was able to separate patients with a high-risk NCCN-IPI of ≥4 (n = 62) into two groups with significantly different outcomes; patients with low TMTV (n = 16) had a 3-year PFS rate of 75% and an OS rate of 88%, while those with a high TMTV had a 3-year PFS rate of 32% and an OS rate of 47% (χ2 = 7.92, P = 0.005, and χ2 = 8.26, P = 0.004, respectively). However, regardless of TMTV, patients with a low-risk NCCN-IPI of <4 (n = 41) had excellent outcomes (3-year PFS and OS rates of 85% and 95%, respectively). CONCLUSION: Pretreatment TMTV was an independent predictor of survival in patients with DLBCL. Importantly, TMTV had an additive prognostic value in patients with a high-risk NCCN-IPI. Thus, the combination of baseline TMTV with NCCN-IPI may improve the prognostication and may be helpful guide the decision for intensive therapy and clinical trials, especially in DLBCL patients with a high-risk NCCN-IPI.

Development of 99mTc-Labeled Human Serum Albumin with Prolonged Circulation by Chelate-then-Click Approach: A Potential Blood Pool Imaging Agent.

Technetium-99m-labeled human serum albumin (99mTc-HSA) has been utilized as a blood pool imaging agent in the clinic for several decades. However, 99mTc-HSA has a short circulation time, which is a critical shortcoming for a blood pool imaging agent. Herein, we developed a novel 99mTc-labeled HSA with a long circulation time using click chemistry and a chelator, 2,2'-dipicolylamine (DPA), (99mTc-DPA-HSA). Specifically, we examined the feasibility of copper-free strain-promoted alkyne-azide cycloaddition (SPAAC) for the incorporation of HSA to the [99mTc (CO)3(H2O)3]+ system by adopting a chelate-then-click approach. In this strategy, a potent chelate system, azide-functionalized DPA, was first complexed with [99mTc (CO)3(H2O)3]+, followed by the SPAAC click reaction with azadibenzocyclooctyne-functionalized HSA (ADIBO-HSA) under biocompatible conditions. Radiolabeling efficiency of azide-functionalized DPA (99mTc-DPA) was >98%. Click conjugation efficiency of 99mTc-DPA with ADIBO-HSA was between 76 and 99% depending on the number of ADIBO moieties attached to HSA. In whole-body in vivo single photon emission computed tomography images, the blood pool uptakes of 99mTc-DPA-HSA were significantly enhanced compared to those of 99mTc-HSA at 10 min, 2, and 6 h after the injection ( P < 0.001, 0.025, and 0.003, respectively). Furthermore, the blood activities of 99mTc-DPA-HSA were 8 times higher at 30 min and 10 times higher at 3 h after the injection compared to those of conventional 99mTc-HSA in ex vivo biodistribution experiment. The results exhibit the potential of 99mTc-DPA-HSA as a blood pool imaging agent and further illustrate the promise of the pre-labeling SPAAC approach for conjugation of heat-sensitive biological targeting vectors with [99mTc (CO)3(H2O)3]+.

Composite criteria using clinical and FDG PET/CT factors for predicting recurrence of hepatocellular carcinoma after living donor liver transplantation.

OBJECTIVES: Fluorodeoxyglucose (FDG) PET/CT is effective for predicting recurrence of hepatocellular carcinoma after liver transplantation. This study aimed to design composite criteria for predicting post-transplantation recurrence using clinical and FDG PET/CT factors. METHODS: We retrospectively enrolled 239 patients who underwent living donor transplantation in two independent centers between 2005 and 2013. On PET, maximum tumor-to-background ratio (TBRmax) was measured. Significant predictors for recurrence were selected by logistic regression and survival analyses. With varying cutoff values for the selected factors, composite criteria were designed to maximize the predictive performance for recurrence, and tenfold cross-validation was performed. Predictive values were compared between the composite criteria and the conventional recipient selection criteria. RESULTS: Tumor size, number, alpha-fetoprotein, and TBRmax were selected as significant predictors in both logistic regression and multivariate survival analyses. In combination of these factors, the highest diagnostic performance was sensitivity of 75.7% and specificity of 88.5% with cutoff values of tumor size < 6.0 cm, tumor number < 8, alpha-fetoprotein < 465 ng/mL, and TBRmax < 2.8. The composite criteria exhibited the highest performance for predicting recurrence and recurrence-free survival among the tested criteria including conventional ones. CONCLUSIONS: The composite criteria adding FDG PET findings to clinical factors are effective in selecting appropriate liver cancer patients who are candidates for liver transplantation. KEY POINTS: • In patients with HCC, tumor uptake on FDG PET/CT, tumor size, number, and serum AFP level are recognized individual predictors for tumor recurrence after LT. • A composite criterion set, combining tumor size, number, serum AFP level, and maximum tumor-to-background ratio (TBR max ), predicts post-LT recurrence most effectively when compared with conventional criteria sets in selecting candidates for living donor LT.

Advances in imaging instrumentation for nuclear cardiology.

Advances in imaging instrumentation and technology have greatly contributed to nuclear cardiology. Dedicated cardiac SPECT cameras incorporating novel, highly efficient detector, collimator, and system designs have emerged with the expansion of nuclear cardiology. Solid-state radiation detectors incorporating cadmium zinc telluride, which directly convert radiation to electrical signals and yield improved energy resolution and spatial resolution and enhanced count sensitivity geometries, are increasingly gaining favor as the detector of choice for application in dedicated cardiac SPECT systems. Additionally, hybrid imaging systems in which SPECT and PET are combined with X-ray CT are currently widely used, with PET/MRI hybrid systems having also been recently introduced. The improved quantitative SPECT/CT has the potential to measure the absolute quantification of myocardial blood flow and flow reserve. Rapid development of silicon photomultipliers leads to enhancement in PET image quality and count rates. In addition, the reduction of emission-transmission mismatch artifacts via application of accurate time-of-flight information, and cardiac motion de-blurring aided by anatomical images, are emerging techniques for further improvement of cardiac PET. This article reviews recent advances such as these in nuclear cardiology imaging instrumentation and technology, and the corresponding diagnostic benefits.

Endoscopic Prediction of Tumor Invasion Depth in Early Gastric Signet Ring Cell Carcinoma.

INTRODUCTION: Signet ring cell carcinoma (SRC) is a poorly differentiated cancer of the stomach. Recent studies imply that early gastric SRC can be well managed by endoscopic resection. Unfortunately, unlike differentiated cancers, the endoscopic features of early gastric SRC have not been well studied. This study evaluated the endoscopic features of early gastric SRC, as well as the risk factors for submucosal (SM) invasion. METHOD: The medical records of patients from 7 tertiary hospitals (Daejeon and Chungcheong province) were reviewed to examine endoscopic findings and clinical data. These patients underwent surgery or endoscopic resection between January 2011 and December 2016 and were divided into 2 groups (derivation group and validation group) in order to develop and validate an endoscopic scoring system for SM invasion. RESULTS: In total, 331 patients (129 in the derivation group and 202 in the validation group) were enrolled in this study. In the derivation group, the risk factors for SM invasion, namely, fold convergence, nodular mucosal change, and deep depression, were identified by logistic regression analysis (ORs 3.4, 5.9, and 6.0, p < 0.05). A depth-prediction score was created by assigning 1 point for fold convergence and 2 points for other factors. When validation lesions of 0.5 point or more were diagnosed as SM invasion, the sensitivity and specificity were 76.8-78.6% and 61.6-74.7% respectively. CONCLUSION: Fold convergence, nodular mucosal change, and deep depression are risk factors for SM invasion in early gastric SRC. Our depth-prediction scoring system may be useful for differentiating SM cancers.

Questionnaire study of the dietary habits of breast cancer survivors and their relationship to quality of life (KROG 14-09).

We evaluated the dietary habits of breast cancer survivors and investigated the relationship with quality of life (QoL), with 1,156 survivors recruited from 17 institutions. We used the Questionnaire Survey of Dietary Habits of Korean Adults (Q-DH-KOR) comprising 25 questions. The following indices were derived as follows: (1) quality of healthy dietary habits (Q-HD)-eight questions on number of meals, regularity, quantity, duration, skipping breakfast, dinner with companion(s), overeating and late-night snacks; (2) habits of nutritional balance (H-NB)-questions on consuming five food categories (grains, fruits, proteins, vegetables and dairy products); and (3) habits of unhealthy foods (H-UF)-questions on consuming three food categories (fatty, instant and fast foods). The times and regularity of meals, frequency of skipping breakfast, dinner with companion(s) and overeating were better in groups with high symptomatic and functional QoL. Symptomatic QoL positively affected Q-HD and H-NB (p < 0.001 and p = 0.024 respectively) and negatively affected H-UF (p = 0.02). Breast cancer survivors more frequently ate from the fruit, protein and vegetable categories than did the control group, with lower H-UF and higher Q-HD values (p < 0.001 and p < 0.001 respectively). Our findings supported the relationship between QoL and dietary habit and showed healthier dietary habits of breast cancer survivors than controls.

Graphene oxide-quenching-based fluorescence in situ hybridization (G-FISH) to detect RNA in tissue: Simple and fast tissue RNA diagnostics.

FISH-based RNA detection in paraffin-embedded tissue can be challenging, with complicated procedures producing uncertain results and poor image quality. Here, we developed a robust RNA detection method based on graphene oxide (GO) quenching and recovery of fluorescence in situ hybridization (G-FISH) in formalin-fixed paraffin-embedded (FFPE) tissues. Using a fluorophore-labeled peptide nucleic acid (PNA) attached to GO, the endogenous long noncoding RNA BC1, the constitutive protein ß-actin mRNA, and miR-124a and miR-21 could be detected in the cytoplasm of a normal mouse brain, primary cultured hippocampal neurons, an Alzheimer's disease model mouse brain, and glioblastoma multiforme tumor tissues, respectively. Coding and non-coding RNAs, either long or short, could be detected in deparaffinized FFPE or frozen tissues, as well as in clear lipid-exchanged anatomically rigid imaging/immunostaining-compatible tissue hydrogel (CLARITY)-transparent brain tissues. The fluorescence recovered by G-FISH correlated highly with the amount of miR-21, as measured by quantitative real time RT-PCR. We propose G-FISH as a simple, fast, inexpensive, and sensitive method for RNA detection, with a very low background, which could be applied to a variety of research or diagnostic purposes.

Acute and repeated toxicological study of Myelophil, an ethanol extract of a mixture of Astragali Radix and Salviae Miltiorrhizae Radix, in beagle dogs.

BACKGROUND: To evaluate the pharmaceutical safety of Myelophil, an ethanol extract of a mixture of Astragali Radix and Salviae Miltiorrhizae Radix, using both acute and repeated toxicological studies. METHODS: A total of 40 beagle dogs (20 each male and female) were fed doses up to 5,000 mg/kg for the acute study and up to 1,250 mg/kg for the 13-week repeated dose toxicological study. Adverse effects were examined intensively by comparing the differences between normal and drug-administered groups using clinical signs, autopsies, histopathological findings, hematology, urinalysis, and biochemical analysis. RESULTS: No mortality or drug-related clinical signs were observed in the Myelophil-treated groups, except for vomiting due to an excessive dose (5,000 mg/kg). Likewise, in the repeated toxicity test, compound-colored stools in the Myelophil-treated groups and soft stools in all groups, including the control, were observed. No drug-related abnormalities were found in the histopathology, hematology, urinalysis, and biochemical analyses for any doses of Myelophil. CONCLUSION: These results support the safety of Myelophil with a no observed adverse effect level (NOAEL) of 1250 mg/kg in beagle dogs, which corresponds to a human equivalent dose (HED) of 694 g/kg.

A Fully Integrated Bluetooth Low-Energy Transceiver with Integrated Single Pole Double Throw and Power Management Unit for IoT Sensors.

This paper presents a low power Gaussian Frequency-Shift Keying (GFSK) transceiver (TRX) with high efficiency power management unit and integrated Single-Pole Double-Throw switch for Bluetooth low energy application. Receiver (RX) is implemented with the RF front-end with an inductor-less low-noise transconductance amplifier and 25% duty-cycle current-driven passive mixers, and low-IF baseband analog with a complex Band Pass Filter(BPF). A transmitter (TX) employs an analog phase-locked loop (PLL) with one-point GFSK modulation and class-D digital Power Amplifier (PA) to reduce current consumption. In the analog PLL, low power Voltage Controlled Oscillator (VCO) is designed and the automatic bandwidth calibration is proposed to optimize bandwidth, settling time, and phase noise by adjusting the charge pump current, VCO gain, and resistor and capacitor values of the loop filter. The Analog Digital Converter (ADC) adopts straightforward architecture to reduce current consumption. The DC-DC buck converter operates by automatically selecting an optimum mode among triple modes, Pulse Width Modulation (PWM), Pulse Frequency Modulation (PFM), and retention, depending on load current. The TRX is implemented using 1P6M 55-nm Complementary Metal-Oxide-Semiconductor (CMOS) technology and the die area is 1.79 mm2. TRX consumes 5 mW on RX and 6 mW on the TX when PA is 0-dBm. Measured sensitivity of RX is -95 dBm at 2.44 GHz. Efficiency of the DC-DC buck converter is over 89% when the load current is higher than 2.5 mA in the PWM mode. Quiescent current consumption is 400 nA from a supply voltage of 3 V in the retention mode.