Recurrent rhabdomyolysis in a patient with a history of rhabdomyolysis due to severe fever with thrombocytopenia syndrome.

Severe fever with thrombocytopenia syndrome (SFTS) is a hemorrhagic fever syndrome that is endemic to East Asia. Here, we describe a case of rhabdomyolysis, thought to have been caused by pemafibrate (which was prescribed for hyperlipidemia) or bacterial infection, in a patient who had experienced SFTS-induced rhabdomyolysis 4 years ago. This case suggests that SFTS causes muscle degeneration and can lead to recurrent rhabdomyolysis as a long-term complication.

Phase Lag Analysis Scalp Electroencephalography May Predict Seizure Frequencies in Patients with Childhood Epilepsy with Centrotemporal Spikes.

Background: Childhood epilepsy with centrotemporal spikes (CECTS) is the most common epilepsy syndrome in school-aged children. However, predictors for seizure frequency are yet to be clarified using the phase lag index (PLI) analyses. We investigated PLI of scalp electroencephalography data at onset to identify potential predictive markers for seizure times. Methods: We compared the PLIs of 13 patients with CECTS and 13 age- and sex-matched healthy controls. For the PLI analysis, we used resting-state electroencephalography data (excluding paroxysmal discharges), and analyzed the mean PLIs among all electrodes and between interest electrodes (C3, C4, P3, P4, T3, and T4) and other electrodes. Furthermore, we compared PLIs between CECTS and control data and analyzed the associations between PLIs and total seizure times in CECTS patients. Results: No differences were detected in clinical profiles or visual electroencephalography examinations between patients with CECTS and control participants. In patients with CECTS, the mean PLIs among all electrodes and toward interest electrodes were higher at the theta and alpha bands and lower at the delta and gamma bands than those in control participants. Additionally, the mean PLIs toward interest electrodes in the beta frequency band were negatively associated with seizure times (P = 0.02). Conclusion: The resting-state delta, theta, alpha, and gamma band PLIs might reflect an aberrant brain network in patients with CECTS. The resting-state PLI among the selected electrodes of interest in the beta frequency band may be a predictive marker of seizure times in patients with CECTS.

A case of eosinophilic cholangitis without bile duct stenosis diagnosed by bile duct biopsy.

Eosinophilic cholangitis (EC) is a rare benign disease that is often misdiagnosed as a malignancy due to the development of biliary stricture. This disease is generally diagnosed by liver biopsy or surgery. Herein, we report a case of EC diagnosed in an 86-year-old Japanese woman, who presented with fever, elevated eosinophil count, and elevated liver enzyme level, based on intraductal ultrasound evaluation showing bile duct wall thickening and bile duct biopsy of the same site. We diagnosed this case as EC based on the triad of wall thickening of the biliary system, histopathological findings of eosinophilic infiltration of the biliary tract, and reversibility of biliary abnormalities without treatment. Bile duct biopsy during endoscopic retrograde cholangiopancreatography (ERCP) is rarely used to confirm the diagnosis of EC without bile duct stenosis. For EC and cholecystitis associated with eosinophilia, bile duct biopsy under ERCP, which is less invasive, should be considered. This patient was older than the previously reported patients, and the value of a minimally invasive diagnosis was high.

Facial deblur inference using subspace analysis for recognition of blurred faces.

This paper proposes a novel method for recognizing faces degraded by blur using deblurring of facial images. The main issue is how to infer a Point Spread Function (PSF) representing the process of blur on faces. Inferring a PSF from a single facial image is an ill-posed problem. Our method uses learned prior information derived from a training set of blurred faces to make the problem more tractable. We construct a feature space such that blurred faces degraded by the same PSF are similar to one another. We learn statistical models that represent prior knowledge of predefined PSF sets in this feature space. A query image of unknown blur is compared with each model and the closest one is selected for PSF inference. The query image is deblurred using the PSF corresponding to that model and is thus ready for recognition. Experiments on a large face database (FERET) artificially degraded by focus or motion blur show that our method substantially improves the recognition performance compared to existing methods. We also demonstrate improved performance on real blurred images on the FRGC 1.0 face database. Furthermore, we show and explain how combining the proposed facial deblur inference with the local phase quantization (LPQ) method can further enhance the performance.