Radiation dose considerations in digital radiography with an anti-scatter grid: A study using adult and pediatric phantoms.

BACKGROUND: When using an anti-scatter grid, a decrease in receptor dose caused by its X-ray absorption seems to lead to the misperception that radiation dose needs to be increased even in digital radiography (DR). OBJECTIVE: To demonstrate that there is no need to increase radiation dose in DR with a grid, based on a visual evaluation using an adult and a pediatric abdomen phantom (PAD and PPD , respectively). MATERIALS AND METHODS: Phantom images with and without a grid were obtained with exposure parameters determined based on a preliminarily measured signal-to-noise ratio improvement factor (SIF), an index for potential dose reduction when using a grid. In visual evaluation, four radiologists compared phantom images with a grid applied at different dose reduction rates (0% [no reduction], 18%, 36%, and 59% for PAD and 0% and 11% for PPD ) against an image without a grid at the baseline dose (as the reference). They graded the overall image quality of the former relative to that of the latter (reference) on a 3-point scale (3 = better, 2 = almost equal, 1 = worse). RESULTS: The mean scores for dose reduction rates of 0%, 18%, 36%, and 59% were 3.00, 3.00, 2.75, and 1.00, respectively, for PAD ; those for 0% and 11% were 2.13 and 1.63, respectively, for PPD . These results support the validity of our view that no dose increase is necessary when using an anti-scatter grid. Actually, there is even a potential for improvement in image quality with dose reduction rates of ≤36% for PAD . CONCLUSION: It is worth reconsidering the necessity of increasing radiation dose in the DR imaging of the adult and pediatric abdomens with an anti-scatter grid.

CT imaging comparison between intraductal papillary neoplasms of the bile duct and papillary cholangiocarcinomas.

OBJECTIVES: To identify imaging features that assist in discriminating intraductal papillary neoplasms of the bile duct (IPNBs) from papillary cholangiocarcinomas (PCCs). METHODS: This study was approved by the institutional review board. Using the recently proposed histological diagnostic criteria for biliary papillary neoplasms, IPNBs and PCCs were selected from 537 biliary neoplasms consecutively resected in a 12.5-year period. Clinical and imaging features were compared between the two groups. RESULTS: The histology review identified 19 IPNBs and 48 PCCs, representing an estimated prevalence of IPNBs among biliary neoplasms of 4%. Approximately one half of IPNBs were incidentally found on imaging conducted for other purposes. In terms of tumor location, 15/19 IPNBs (79%) developed in intrahepatic bile ducts, and 41/48 PCCs (85%) in the distal bile duct. Cystic appearance was highly suggestive for IPNBs (p < 0.001). Using these two parameters, 78% of papillary bile duct neoplasms could be classified into IPNBs or PCCs. Other imaging findings favoring IPNBs included frond-like mural nodule, downstream bile duct dilatation, and the lack of abnormal enhancement in the adjacent bile duct. Interestingly, two patients with non-invasive or microinvasive IPNB had undergone abdominal imaging studies > 3 years before, and a retrospective review of the previous images identified small nodular or cystic lesions, suggesting a less progressive nature of IPNBs than currently thought. CONCLUSIONS: Imaging findings useful for discriminating IPNBs from PCCs appear to be tumor location, shape of tumor, appearance of mural nodules, duct dilatation at unaffected duct, and abnormal enhancement of the adjacent bile duct. KEY POINTS: • Intrahepatic location and cystic dilatation of the affected bile duct are the strong discriminators between IPNBs and PCCs. • The shape of the mural nodule and appearance of the neighboring bile duct are helpful for distinguishing IPNBs and PCCs. • The less aggressive behavior of IPNBs compared with PCCs may facilitate less invasive management in patients with IPNB.

Guidelines for creating artificial neural network empirical interatomic potential from first-principles molecular dynamics data under specific conditions and its application to α-Ag2Se.

First-principles molecular dynamics (FPMD) simulations are highly accurate, but due to their high calculation cost, the computational scale is often limited to hundreds of atoms and few picoseconds under specific temperature and pressure conditions. We present here the guidelines for creating artificial neural network empirical interatomic potential (ANN potential) trained with such a limited FPMD data, which can perform long time scale MD simulations at least under the same conditions. The FPMD data for training are prepared on the basis of the convergence of radial distribution function [g(r)]. While training the ANN using total energy and atomic forces of the FPMD data, the error of pressure is also monitored and minimized. To create further robust potential, we add a small amount of FPMD data to reproduce the interaction between two atoms that are close to each other. ANN potentials for α-Ag2Se were created as an application example, and it has been confirmed that not only g(r) and mean square displacements but also the specific heat requiring a long time scale simulation matched the FPMD and the experimental values. In addition, the MD simulation using the ANN potential achieved over 104 acceleration over the FPMD one. The guidelines proposed here mitigate the creation difficulty of the ANN potential, and a lot of FPMD data sleeping on the hard disk after the research may be put on the front stage again.

Alcohol-related injury to peribiliary glands is a cause of peribiliary cysts: based on analysis of clinical and autopsy cases.

BACKGROUND AND GOAL: Peribiliary cysts, which are known to be associated with various hepatobiliary diseases including alcoholic liver disease, have been reported to originate in the peribiliary glands along the biliary tree. The causal relationship between the peribiliary cysts and alcohol-related hepatic and pancreatic disease were examined in this study. METHODS AND RESULTS: Peribiliary cysts were surveyed in the radiologic reports of out-patients and in-patients at our hospital (between 2007 and 2011), and a total of 31 patients with peribiliary cysts were found; 9 patients were associated with alcoholic liver disease and 2 patients with alcoholic pancreatitis. Among 202 consecutive autopsy cases with a history of heavy drinking (chronic alcoholics) at our Department (between 1990 and 2011), peribiliary cysts were found in 29 cases (14%), and the frequency of these cysts was correlated with the degree of alcohol-related hepatic fibrosis. Interestingly, peribiliary cysts were frequently associated with adenitis of the peribiliary glands (72%), and peribiliary adenitis and cyst formation correlated well with the degree of pancreatic fibrosis. CONCLUSIONS: These results suggest that peribiliary cysts are more likely to occur in chronic alcoholics. The frequent association of peribiliary cysts with the degree of alcohol-related hepatic fibrosis suggests the involvement of the hepatic fibrogenetic process in peribiliary cyst formation. The frequent association of peribiliary adenitis and cyst formation with the degree of pancreatic fibrosis in chronic alcoholics suggests the involvement of alcoholic injuries in the pancreas, resulting in progressive fibrosis, and peribiliary glands, resulting in adenitis and cyst formation.

Genetic Counseling for an Infertile Couple With Premature Chromatid Separation (PCS) Syndrome: A Case Report.

We report the first case of successful genetic counseling for an infertile couple with premature chromatid separation (PCS) syndrome. After our careful genetic counseling, the couple decided to continue infertility treatment. As a result, they gave birth to a baby (girl: 2,930 g) by caesarean section in May 2018. To our knowledge, there have not been any published reports regarding genetic counseling for an infertile couple with PCS after PubMed, EMBASE, and Web of Science searches until March 2024.

A multi-institutional study designed by members of Tokyo Guidelines (TG) Core Meeting to elucidate the clinical characteristics and pathogenesis of acute cholangitis after bilioenteric anastomosis and biliary stent insertion with a focus on biliary obstruction: Role of transient hepatic attenuation difference (THAD) and pneumobilia in improving TG diagnostic performance.

BACKGROUND/PURPOSE: The aim of this study was to clarify the clinical characteristics of acute cholangitis (AC) after bilioenteric anastomosis and stent-related AC in a multi-institutional retrospective study, and validate the TG18 diagnostic performance for various type of cholangitis. METHODS: We retrospectively reviewed 1079 AC patients during 2020, at 16 Tokyo Guidelines 18 (TG 18) Core Meeting institutions. Of these, the post-biliary reconstruction associated AC (PBR-AC), stent-associated AC (S-AC) and common AC (C-AC) were 228, 307, and 544, respectively. The characteristics of each AC were compared, and the TG18 diagnostic performance of each was evaluated. RESULTS: The PBR-AC group showed significantly milder biliary stasis compared to the C-AC group. Using TG18 criteria, definitive diagnosis rate in the PBR-AC group was significantly lower than that in the C-AC group (59.6% vs. 79.6%, p < .001) because of significantly lower prevalence of TG 18 imaging findings and milder bile stasis. In the S-AC group, the bile stasis was also milder, but definitive-diagnostic rate was significantly higher (95.1%) compared to the C-AC group. The incidence of transient hepatic attenuation difference (THAD) and pneumobilia were more frequent in PBR-AC than that in C-AC. The definitive-diagnostic rate of PBR-AC (59.6%-78.1%) and total cohort (79.6%-85.3%) were significantly improved when newly adding these items to TG18 diagnostic imaging findings. CONCLUSIONS: The diagnostic rate of PBR-AC using TG18 is low, but adding THAD and pneumobilia to TG imaging criteria may improve TG diagnostic performance.

Overexpression of enhancer of zeste homolog 2 and MUC1 may be related to malignant behaviour in intraductal papillary neoplasm of the bile duct.

AIMS: Intraductal papillary neoplasm of the bile duct (IPNB) usually has a favourable prognosis, but occasionally is associated with invasive carcinoma. Overexpression of the polycomb group protein enhancer of zeste homolog 2 (EZH2) is involved in the progression of malignant tumours. In this study, we examined the significance of EZH2 expression in IPNB and its association with clinicopathological features and the expression of p16(INK4a) , p53 and mucin core proteins. METHODS AND RESULTS: We examined immunohistochemically the expression of EZH2, p16(INK4a) , MUC mucin core proteins and p53 in 15 patients with IPNB without invasion, including the cystic variant [male/female ratio (M/F) = 9/6], and in 19 with IPNB associated with invasive carcinoma (M/F = 13/6). The expression levels of EZH2, p53 and MUC1 were significantly lower (P < 0.01), and of MUC6 were significantly higher (P < 0.05), in IPNB without invasion than in IPNB with invasion. Expression of EZH2 was significantly correlated with expression of MUC1 (P < 0.01) and inversely correlated with expression of MUC6 (P < 0.05). In cholangiocarcinoma cells (HuCTT-1 and TFK-1), knockdown of EZH2 and MUC1 by small interfering RNA decreased invasion and proliferation, whereas knockdown of MUC6 increased invasion. CONCLUSIONS: Overexpression of EZH2 may be associated with malignant behaviour in IPNB in parallel with up-regulated MUC1 expression and down-regulated MUC6 expression.

The Symplectic Adjoint Method: Memory-Efficient Backpropagation of Neural-Network-Based Differential Equations.

The combination of neural networks and numerical integration can provide highly accurate models of continuous-time dynamical systems and probabilistic distributions. However, if a neural network is used [Formula: see text] times during numerical integration, the whole computation graph can be considered as a network [Formula: see text] times deeper than the original. The backpropagation algorithm consumes memory in proportion to the number of uses times of the network size, causing practical difficulties. This is true even if a checkpointing scheme divides the computation graph into subgraphs. Alternatively, the adjoint method obtains a gradient by a numerical integration backward in time; although this method consumes memory only for single-network use, the computational cost of suppressing numerical errors is high. The symplectic adjoint method proposed in this study, an adjoint method solved by a symplectic integrator, obtains the exact gradient (up to rounding error) with memory proportional to the number of uses plus the network size. The theoretical analysis shows that it consumes much less memory than the naive backpropagation algorithm and checkpointing schemes. The experiments verify the theory, and they also demonstrate that the symplectic adjoint method is faster than the adjoint method and is more robust to rounding errors.

Deep learning-based cardiothoracic ratio measurement on chest radiograph: accuracy improvement without self-annotation.

Background: A reproducible and accurate automated approach to measuring cardiothoracic ratio on chest radiographs is warranted. This study aimed to develop a deep learning-based model for estimating the cardiothoracic ratio on chest radiographs without requiring self-annotation and to compare its results with those of manual measurements. Methods: The U-net architecture was designed to segment the right and left lungs and the cardiac shadow, from chest radiographs. The cardiothoracic ratio was then calculated using these labels by a mathematical algorithm. The initial model of deep learning-based cardiothoracic ratio measurement was developed using open-source 247 chest radiographs that had already been annotated. The advanced model was developed using a training dataset of 729 original chest radiographs, the labels of which were generated by the initial model and then screened. The cardiothoracic ratio of the two models was estimated in an independent test set of 120 original cases, and the results were compared to those obtained through manual measurement by four radiologists and the image-reading reports. Results: The means and standard deviations of the cardiothoracic ratio were 52.4% and 9.8% for the initial model, 51.0% and 9.3% for the advanced model, and 49.8% and 9.4% for the total of four manual measurements, respectively. The intraclass correlation coefficients (ICCs) of the cardiothoracic ratio ranged from 0.91 to 0.93 between the advanced model and the manual measurements, whereas those for the initial model and the manual measurements ranged from 0.77 to 0.82. Conclusions: Deep learning-based cardiothoracic ratio estimation on chest radiographs correlated favorably with the results obtained through manual measurements by radiologists. When the model was trained on additional local images generated by the initial model, the correlation with manual measurement improved even more than the initial model alone.

Learning vector quantized representation for cancer subtypes identification.

BACKGROUND AND OBJECTIVE: Defining and separating cancer subtypes is essential for facilitating personalized therapy modality and prognosis of patients. The definition of subtypes has been constantly recalibrated as a result of our deepened understanding. During this recalibration, researchers often rely on clustering of cancer data to provide an intuitive visual reference that could reveal the intrinsic characteristics of subtypes. The data being clustered are often omics data such as transcriptomics that have strong correlations to the underlying biological mechanism. However, while existing studies have shown promising results, they suffer from issues associated with omics data: sample scarcity and high dimensionality while they impose unrealistic assumptions to extract useful features from the data while avoiding overfitting to spurious correlations. METHODS: This paper proposes to leverage a recent strong generative model, Vector-Quantized Variational AutoEncoder, to tackle the data issues and extract discrete representations that are crucial to the quality of subsequent clustering by retaining only information relevant to reconstructing the input. RESULTS: Extensive experiments and medical analysis on multiple datasets comprising 10 distinct cancers demonstrate the proposed clustering results can significantly and robustly improve prognosis over prevalent subtyping systems. CONCLUSION: Our proposal does not impose strict assumptions on data distribution; while, its latent features are better representations of the transcriptomic data in different cancer subtypes, capable of yielding superior clustering performance with any mainstream clustering method.

A case of eosinophilic polyangiitis with granulomatosis that evolved to cardiac arrest due to advanced atrioventricular block.

Cardiac manifestations are the major cause of mortality in patients with eosinophilic granulomatosis with polyangiitis (EGPA). Among these manifestations in EGPA patients, in the literature, there are fewer reports describing bradycardia in EGPA patients than those describing tachycardia. A 50-year-old woman with a history of childhood-onset asthma. At age 28, she was diagnosed with eosinophilic gastroenteritis without the diagnosis of EGPA and was started on a systemic steroid and had maintenance daily dose of 2.5 mg after gradually tapered. She had experiencing dizziness and palpitations 2 weeks after discontinuation of the steroid treatment. At emergency visit, electrocardiography revealed an advanced atrioventricular block of 3:1 or less. Forty-eight minutes after the start of electrocardiography, only a P wave was observed and cardiac arrest occurred for 9 s and temporary emergency pacing was performed immediately. She was diagnosed as EGPA presenting leukocyte count, 16,500/µL, 42.8% of which were eosinophils and sinusitis in computed-tomography. She could be survival by treatment of steroid, following the patient to withdraw from an external pacemaker. She received prednisolone of 60 mg, intravenous cyclophosphamide and intravenous immunoglobulin. She had relapsed presenting peripheral eosinophilia, abdominal and numbness in the toes of the left leg pain, but not arrythmia after tapered of prednisolone. Following additional steroid pulse, she had an increase of prednisolone and continued by intravenous cyclophosphamide, intravenous immunoglobulin and started mepolizumab. We presented a severe case of EGPA presenting an advanced atrioventricular block into cardiac arrest.

Fat fraction and R2 * values of various liver masses: Initial experience with 6-point Dixon method on a 3T MRI system.

Purpose: To assess the feasibility of the 6-point Dixon method for evaluating liver masses. We also report our initial experience with the quantitative values in various liver masses on a 3T system. Materials and methods: Of 251 consecutive patients for whom 6-point Dixon was employed in abdominal magnetic resonance imaging scans between October 2020 and October 2021, 117 nodules in 117 patients with a mass diameter of more than 1 cm were included in the study. Images for measuring the proton density fat fraction (PDFF) and R2 * values were obtained using the iterative decomposition of water and fat with echo asymmetry and least-squares estimation-quantitative technique for liver imaging. Two radiologists independently measured PDFF (%) and R2 * (Hz). Inter-reader agreement and the differences between readers were examined using intra-class correlation coefficient (ICC) and the Bland-Altman method, respectively. PDFF and R2 * values in differentiating liver masses were examined. Results: The masses included hepatocellular carcinoma (n = 59), cyst (n = 20), metastasis (n = 14), hemangioma (n = 8), and others (n = 16). The ICCs for the region of interest (mm2), PDFF, and R2 * were 0.988 (95 % confidence interval (CI): 0.983, 0.992), 0.964 (95 % CI: 0.949, 0.975), and 0.962 (95 % CI: 0.941, 0.975), respectively. The differences of measurements between the readers showed that 5.1 % (6/117) and 6.0% (7/117) for PDFF and R2 * , respectively, were outside the 95 % CI. Conclusion: Our observation indicates that the 6-point Dixon method is applicable to liver masses.

Bile duct expression of pancreatic and duodenal homeobox 1 in perihilar cholangiocarcinogenesis.

AIMS: Pancreatic and duodenal homeobox 1 (Pdx1) is a transcription factor that is crucial in embryogenic development and differentiation of pancreas, and its overexpression is reportedly involved in the progression of many malignancies, including pancreatic carcinoma. In this study, the role of Pdx1 was examined in cholangiocarcinogenesis. METHODS AND RESULTS: Forty-three cases of human cholangiocarcinoma (CC) and 66 cases of hepatolithiasis or primary sclerosing cholangitis (PSC) with biliary intraepithelial neoplasia (BilIN) lesions and also eight fetal and 20 adult normal livers were examined immunohistochemically. Pdx1 was constantly expressed in the nuclei of fetal bile ducts, but was virtually absent in the large bile ducts of adults. By contrast, Hairy and enhancer of split 1 (Hes1), which represses pancreatic exocrine and endocrine differentiation, was expressed frequently in the adult bile ducts. Pdx1 was expressed in 67% of invasive CCs. In large bile ducts, expression of Pdx1 increased while that of Hes1 decreased during the progression of BilIN lesions to CC. Expression of Pdx1 correlated with proliferative activities in CCs. In an in vitro study, all three CC cell lines expressed Pdx1 mRNA and protein. CONCLUSION: Up-regulation of Pdx1 is a feature of cholangiocarcinogenesis associated with chronic cholangitis. Furthermore, expression of Pdx1 in CC is related to increased proliferative activity in CCs.

Intrahepatic cholangiocarcinomas in cirrhosis are hypervascular in comparison with those in normal livers.

BACKGROUND AND AIMS: Intrahepatic cholangiocarcinomas (ICCs) are usually adenocarcinomas with fibrotic and hypovascular stroma. Intrahepatic cholangiocarcinomas in cirrhosis and precirrhotic liver (ICC-cirrhosis) are increasingly being diagnosed, and can display hypervascular enhancement resembling a hepatocellular carcinoma on dynamic imaging. METHODS: In this study using ICC-cirrhosis (71 cases), ICC with non-specific reactive changes (ICC-reactive) (72 cases) and the cholangiocarcinoma component of combined hepatocellular cholangiocarcinoma (HCC-ICC) (30 cases), we tried to compare the tumour vasculature. RESULTS: It was found that ICC-cirrhosis and the cholangiocarcinoma component of HCC-ICC showed a higher density of arteries and microvessels (1.59 ± 0.58/mm(2) (mean ± SD) and 140 ± 43/mm(2) in ICC-cirrhosis and 1.74 ± 0.67/mm(2) and 131 ± 46/mm(2) in the cholangiocarcinoma component of HCC-ICC) than in ICC-reactive (1.26 ± 0.61/mm(2) and 103 ± 45/mm(2) ). Dynamic computed tomography (CT) and magnetic resonance imaging (MRI) showed that a majority of ICC-cirrhosis displayed strong hypervascular enhancement, whereas one-third of ICC-reactive each showed strong, weak and no or minimal enhancement respectively. The increased vascular density was positively correlated with enhanced arterial phase of dynamic CT and MRI. CONCLUSION: The density of arteries and microvessels of ICC-cirrhosis was higher than that in ICC-reactive and comparable to that in the cholangiocarcinoma component of HCC-ICC, and the higher density of arteries and microvessels in ICC may be responsible for the hypervascular enhancement of ICC-cirrhosis.

Deep Generative Model Using Unregularized Score for Anomaly Detection With Heterogeneous Complexity.

Accurate and automated detection of anomalous samples in an image dataset can be accomplished with a probabilistic model. Such images have heterogeneous complexity, however, and a probabilistic model tends to overlook simply shaped objects with small anomalies. The reason is that a probabilistic model assigns undesirable lower likelihoods to complexly shaped objects, which are nevertheless consistent with the current set standards. This difficulty is critical, especially for a defect detection task, where the anomaly can be a small scratch or grime. To overcome this difficulty, we propose an unregularized score for deep generative models (DGMs). We found that the regularization terms of the DGMs considerably influence the anomaly score depending on the complexity of the samples. By removing these terms, we obtain an unregularized score, which we evaluated on toy datasets, two in-house manufacturing datasets, and on open manufacturing and medical datasets. The empirical results demonstrate that the unregularized score is robust to the apparent complexity of given samples and detects anomalies selectively.

Specific immunoadsorption therapy using a tryptophan column in patients with refractory heart failure due to dilated cardiomyopathy.

BACKGROUND: Certain cardiac-specific autoantibodies found in patients with dilated cardiomyopathy (DCM) play a role in mediating myocardial damage and fatal ventricular arrhythmias resulting in sudden cardiac death. Immunoadsorption therapy (IA) is one of the therapeutic tools to remove such autoantibodies. Clinical studies from Germany have shown that nonspecific IA using columns loaded by sheep antihuman IgG or protein A improved hemodynamic data and affected favorably cardiac function and survival in patients with heart failure (HF) due to DCM. The goal of this study is to determine if IA therapy using the high-profile tryptophan column, which has high affinity for IgG3 subclass, affects favorably cardiac function in patients with severe HF who are refractory to conventional therapy. METHODS AND RESULTS: IA therapy was conducted in 16 patients with DCM (age 53 ± 4, male 8, New York Heart Association functional class III/IV, mean ejection fraction 18 ± 2%). Study subjects had autoantibodies directed against either ß1-adrenergic or M2-muscarinic receptors. Plasma brain natriuretic peptide levels were significantly decreased after IA (P = 0.016). Plasma inflammatory cytokines including interleukin-6 and tumor necrosis factor-α did not change after each session of IA. Six-minute walk distance was significantly increased after IA (P = 0.01). Left ventricular ejection fraction increased by 3% 3 months after IA (P = 0.039). CONCLUSIONS: Our initial experience demonstrated safety and short-term efficacy of IA using a new IgG3-specific tryptophan column for patients with advanced HF due to DCM. Long-term follow-up is needed to confirm the effects on cardiac function and morbidity/mortality in such patients.

Deep Generative Model of Individual Variability in fMRI Images of Psychiatric Patients.

Neuroimaging techniques, such as the resting-state functional magnetic resonance imaging (fMRI), have been investigated to find objective biomarkers of neuro-logical and psychiatric disorders. Objective biomarkers potentially provide a refined diagnosis and quantitative measurements of the effects of treatment. However, fMRI images are sensitive to individual variability, such as functional topography and personal attributes. Suppressing the irrelevant individual variability is crucial for finding objective biomarkers for multiple subjects. Herein, we propose a structured generative model based on deep learning (i.e., a deep generative model) that considers such individual variability. The proposed model builds a joint distribution of (preprocessed) fMRI images, state (with or without a disorder), and individual variability. It can thereby discriminate individual variability from the subject's state. Experimental results demonstrate that the proposed model can diagnose unknown subjects with greater accuracy than conventional approaches. Moreover, the diagnosis is fairer to gender and state, because the proposed model extracts subject attributes (age, gender, and scan site) in an unsupervised manner.

Independent and cumulative association of clinical and morphological heart failure with long-term outcome after percutaneous coronary intervention.

BACKGROUND: Heart failure (HF) is a risk factor for adverse post-procedural outcome after revascularization; however, it is unclear how left ventricular systolic dysfunction (LVSD) and clinical HF symptoms affect percutaneous coronary intervention (PCI) outcomes. We investigated the characteristics and long-term outcomes of patients with clinical HF or LVSD after PCI. METHODS: This was a Japanese multicenter registry study of adult patients receiving PCI. Among 4689 consecutive patients who underwent PCI at 15 hospitals from January 2009 to December 2012, we analyzed 2634 (56.2%) with documented left ventricular ejection fraction (LVEF). They were divided into four groups based on clinical HF (symptoms or HF hospitalization) and LVEF [≥35% and <35% (HF due to LVSD)]. The primary outcome was major adverse cardiovascular events (MACE), comprising all-cause death, acute coronary syndrome, HF hospitalization, performance of coronary artery bypass grafting, and stroke within 2 years after the initial PCI. RESULTS: Our findings revealed 354 patients (13.4%) with HF (clinical HF, n = 173, 48.9%; LVSD, n = 132, 37.3%; both, n = 49; 13.8%). The incidence of MACE was higher in patients with clinical HF or LVSD, and was largely due to higher non-cardiac death and HF hospitalization. After adjustment, clinical HF (hazard ratio 2.16, 95% confidence interval; 1.49-3.14) and lower LVEF (per 10%, hazard ratio 0.89, 95% confidence interval; 0.81-0.99) were independently associated with higher MACE risk. CONCLUSIONS: Clinical HF and LVSD were independently associated with adverse long-term clinical outcomes, particularly with non-cardiac death and HF readmission, in patients treated with PCI.

Peribiliary glands: development, dysfunction, related conditions and imaging findings.

Peribiliary glands are minute structures that are distributed along the intrahepatic large bile ducts, extrahepatic bile duct, and cystic duct. These glands regulate many physiological functions, such as enzyme secretion. Pancreatic exocrine tissues and enzymes are often observed in peribiliary glands; thus, peribiliary glands are involved in enzyme secretion. As such, these glands can be affected by conditions such as IgG4-related sclerosing cholangitis based on commonalities with their pancreatic counterparts. Cystic changes in peribiliary glands can occur de novo, as part of a congenital syndrome, or secondary to insults such as alcoholic cirrhosis. Biliary tree stem/progenitor cells have recently been identified in peribiliary glands. These cells are involved in turnover and regeneration of biliary epithelia as well as in sclerosing reactions in some pathological conditions, such as primary sclerosing cholangitis and hepatolithiasis. Notably, hepatolithiasis is involved in mucin secretion by the peribiliary glands. Additionally, these cells are associated with the manifestation of several neoplasms, including intraductal papillary neoplasm, cystic micropapillary neoplasm, and cholangiocarcinoma. Normal peribiliary glands themselves are particularly small structures that cannot be recognized using any available imaging modalities; however, these glands are closely associated with several diseases, as mentioned above, which have typical imaging features. Therefore, knowledge of the basic pathophysiology of peribiliary glands is helpful for understanding biliary diseases associated with the peribiliary glands.

Deep Neural Generative Model of Functional MRI Images for Psychiatric Disorder Diagnosis.

Accurate diagnosis of psychiatric disorders plays a critical role in improving the quality of life for patients and potentially supports the development of new treatments. Many studies have been conducted on machine learning techniques that seek brain imaging data for specific biomarkers of disorders. These studies have encountered the following dilemma: A direct classification overfits to a small number of high-dimensional samples but unsupervised feature-extraction has the risk of extracting a signal of no interest. In addition, such studies often provided only diagnoses for patients without presenting the reasons for these diagnoses. This study proposed a deep neural generative model of resting-state functional magnetic resonance imaging (fMRI) data. The proposed model is conditioned by the assumption of the subject's state and estimates the posterior probability of the subject's state given the imaging data, using Bayes' rule. This study applied the proposed model to diagnose schizophrenia and bipolar disorders. Diagnostic accuracy was improved by a large margin over competitive approaches, namely classifications of functional connectivity, discriminative/generative models of regionwise signals, and those with unsupervised feature-extractors. The proposed model visualizes brain regions largely related to the disorders, thus motivating further biological investigation.