The most common etiology of spontaneous, non-traumatic intracerebral hemorrhage is hypertension. One of the most decisive factors for the determination of etiology is location of the hemorrhage. Here, we report on a 53-year-old female patient who was admitted with left putaminal hemorrhage. She did not have prominent vascular risk factors other than fundoscopic signs of hypertensive retinopathy. Magnetic resonance imaging and angiography revealed arteriovenous malformation (AVM) in the basal ganglia, which was fed by the lateral lenticulostriate artery. This case suggests that we should consider an AVM-related hemorrhage in relatively young stroke patients, regardless of hematoma location or presence of accompanying hypertension.
Angiography ; Arteries ; Arteriovenous Malformations ; Basal Ganglia ; Cerebral Hemorrhage ; Female ; Hematoma ; Hemorrhage ; Humans ; Hypertension ; Hypertensive Retinopathy ; Magnetic Resonance Imaging ; Middle Aged ; Putaminal Hemorrhage ; Risk Factors ; Stroke

No abstract available.
Encephalomyelitis, Acute Disseminated ; Meningoencephalitis ; Orientia tsutsugamushi ; Scrub Typhus

No abstract available.
Amaurosis Fugax ; Antineoplastic Combined Chemotherapy Protocols ; Blindness ; Cyclophosphamide ; Doxorubicin ; Neurilemmoma ; Podophyllotoxin ; Vincristine

Background/Aims: Metabolic dysfunction-associated steatotic liver disease (MASLD) is characterized by fat accumulation in the liver. MASLD encompasses both steatosis and MASH. Since MASH can lead to cirrhosis and liver cancer, steatosis and MASH must be distinguished during patient treatment. Here, we investigate the genomes, epigenomes, and transcriptomes of MASLD patients to identify signature gene set for more accurate tracking of MASLD progression. Methods: Biopsy-tissue and blood samples from patients with 134 MASLD, comprising 60 steatosis and 74 MASH patients were performed omics analysis. SVM learning algorithm were used to calculate most predictive features. Linear regression was applied to find signature gene set that distinguish the stage of MASLD and to validate their application into independent cohort of MASLD. Results: After performing WGS, WES, WGBS, and total RNA-seq on 134 biopsy samples from confirmed MASLD patients, we provided 1,955 MASLD-associated features, out of 3,176 somatic variant callings, 58 DMRs, and 1,393 DEGs that track MASLD progression. Then, we used a SVM learning algorithm to analyze the data and select the most predictive features. Using linear regression, we identified a signature gene set capable of differentiating the various stages of MASLD and verified it in different independent cohorts of MASLD and a liver cancer cohort. Conclusions We identified a signature gene set (i.e., CAPG, HYAL3, WIPI1, TREM2, SPP1, and RNASE6) with strong potential as a panel of diagnostic genes of MASLD-associated disease.