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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Generative adversarial network based on chaotic time series.

Generative adversarial networks (GANs) are becoming increasingly important in the artificial construction of natural images and related functionalities, wherein two types of networks called generators and discriminators evolve through adversarial mechanisms. Using deep convolutional neural networks and related techniques, high-resolution and highly realistic scenes, human faces, etc. have been generated. GANs generally require large amounts of genuine training data sets, as well as vast amounts of pseudorandom numbers. In this study, we utilized chaotic time series generated experimentally by semiconductor lasers for the latent variables of a GAN, whereby the inherent nature of chaos could be reflected or transformed into the generated output data. We show that the similarity in proximity, which describes the robustness of the generated images with respect to minute changes in the input latent variables, is enhanced, while the versatility overall is not severely degraded. Furthermore, we demonstrate that the surrogate chaos time series eliminates the signature of the generated images that is originally observed corresponding to the negative autocorrelation inherent in the chaos sequence. We also address the effects of utilizing chaotic time series to retrieve images from the trained generator.

Early detection of intrahepatic cholangiocarcinoma.

Cholangiocarcinoma (CC) is a malignant tumor which arises from the biliary epithelium and most cases represent adenocarcinoma. CC can be classified into intrahepatic CC (ICC), perihilar CC, and distal CC, based on the site of anatomic origin. The incidence of ICC is increasing in both Western and Eastern countries, while that of extrahepatic cholangiocarcinoma remains fairly stable. ICC infiltrates into adjacent nerves and lymphatic vessels, resulting in progressive disease with a poor prognosis; thus, early detection of ICC is critical for achieving better outcomes and providing better patient care. However, it is difficult for clinicians to detect an ICC, especially in its early stage. Different from hepatocellular carcinoma, the lack of surveillance system for the high-risk group of CC does not allow for a reliable screening examination. In this context, for early detection and diagnosis of ICC, radiologists need to know predisposing conditions that can lead to the development of ICC, such as chronic biliary or hepatic inflammation, primary sclerosing cholangitis, congenital biliary diseases, and other conditions. In this article, we discuss and illustrate the radiologic features of ICC with special attention to early disease stages and of predisposing conditions of ICC.

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.

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.

Classification and characterisation of brain network changes in chronic back pain: A multicenter study.

Background. Chronic pain is a common, often disabling condition thought to involve a combination of peripheral and central neurobiological factors. However, the extent and nature of changes in the brain is poorly understood. Methods. We investigated brain network architecture using resting-state fMRI data in chronic back pain patients in the UK and Japan (41 patients, 56 controls), as well as open data from USA. We applied machine learning and deep learning (conditional variational autoencoder architecture) methods to explore classification of patients/controls based on network connectivity. We then studied the network topology of the data, and developed a multislice modularity method to look for consensus evidence of modular reorganisation in chronic back pain. Results. Machine learning and deep learning allowed reliable classification of patients in a third, independent open data set with an accuracy of 63%, with 68% in cross validation of all data. We identified robust evidence of network hub disruption in chronic pain, most consistently with respect to clustering coefficient and betweenness centrality. We found a consensus pattern of modular reorganisation involving extensive, bilateral regions of sensorimotor cortex, and characterised primarily by negative reorganisation - a tendency for sensorimotor cortex nodes to be less inclined to form pairwise modular links with other brain nodes. Furthermore, these regions were found to display increased connectivity with the pregenual anterior cingulate cortex, a region known to be involved in endogenous pain control. In contrast, intraparietal sulcus displayed a propensity towards positive modular reorganisation, suggesting that it might have a role in forming modules associated with the chronic pain state. Conclusion. The results provide evidence of consistent and characteristic brain network changes in chronic pain, characterised primarily by extensive reorganisation of the network architecture of the sensorimotor cortex.

Qualitative and quantitative evaluation for morphological changes of the splenic artery in autoimmune pancreatitis: novel imaging findings for differentiation from pancreatic adenocarcinoma.

PURPOSE: To determine whether morphological changes can occur in the splenic artery (SPA) of autoimmune pancreatitis (AIP) cases, and if present, to compare them with those in pancreatic adenocarcinoma (PAC) to clarify any arterial morphological differences between AIP and PAC. METHODS: A total of 101 AIP cases were included in this study. The presence or absence of morphological change in the SPA was assessed, using arterial phase axial computed tomography images. Subsequently, imaging parameters (imaging pattern, capsule-like rim, other organ involvement, splenic vein [SPV] stenosis, and SPA calcification) were compared between cases with and without morphological changes. Additionally, comparison analyses (visual SPA assessment and % minimal lumen diameter [MLD] stenosis) among normal pancreas, PAC, and AIP groups were performed using early arterial phase (EAP) reconstructed images. RESULTS: In 25 (24.8%) AIP cases, marginal irregularities of the SPA were present. The presence of the capsule-like rim and SPV stenosis were significantly associated with the arterial morphological changes. All cases with morphological changes had a capsule-like rim. Visual assessment using EAP reconstructed images revealed irregularities of the SPA in 9 of 38 AIP cases (23.7%); however, arterial narrowing was not detected in any cases. % MLD stenosis in AIP group was significantly lower than that in PAC group (p < 0.0001). CONCLUSIONS: Although approximately one-quarter of AIP cases potentially demonstrate marginal irregularity in the SPA when it is surrounded by an apparent capsule-like rim, arterial luminal narrowing rarely occurs in contrast to PAC. These arterial findings can help to distinguish AIP from PAC.

Conduction Delay Learning Model for Unsupervised and Supervised Classification of Spatio-Temporal Spike Patterns.

Precise spike timing is considered to play a fundamental role in communications and signal processing in biological neural networks. Understanding the mechanism of spike timing adjustment would deepen our understanding of biological systems and enable advanced engineering applications such as efficient computational architectures. However, the biological mechanisms that adjust and maintain spike timing remain unclear. Existing algorithms adopt a supervised approach, which adjusts the axonal conduction delay and synaptic efficacy until the spike timings approximate the desired timings. This study proposes a spike timing-dependent learning model that adjusts the axonal conduction delay and synaptic efficacy in both unsupervised and supervised manners. The proposed learning algorithm approximates the Expectation-Maximization algorithm, and classifies the input data encoded into spatio-temporal spike patterns. Even in the supervised classification, the algorithm requires no external spikes indicating the desired spike timings unlike existing algorithms. Furthermore, because the algorithm is consistent with biological models and hypotheses found in existing biological studies, it could capture the mechanism underlying biological delay learning.