Research Archives - Page 24 of 60 - Yokohama City University

2022-06-21

Volume-Based Radiofrequency Thermocoagulation for Pediatric Insulo-Opercular Epilepsy: A Feasibility Study

BACKGROUND: Stereotactic ablation surgeries including radiofrequency thermocoagulation (RFTC) and laser interstitial thermal therapy are recent less invasive treatment methods for insular epilepsy. Volume-based RFTC after stereoelectroencephalography was first proposed by a French group as a more effective method for seizure relief in insular epilepsy patients than stereoelectroencephalography-guided RFTC.

2022-06-14

Chronic Kidney Disease and Clinical Outcomes in Patients with COVID-19 in Japan

Background Identifying predictive factors for coronavirus disease 2019 (COVID-19) is crucial for risk stratification and intervention. Kidney dysfunction contributes to the severity of various infectious diseases. However, the association between on-admission kidney dysfunction and the clinical outcome in COVID-19 patients is unclear.

2022-06-14

Indole-3-pyruvic acid regulates TAA1 activity, which plays a key role in coordinating the two steps of auxin biosynthesis

Significance The plant hormone auxin (indole-3-acetic acid [IAA]) plays a central role in regulating plant growth and development, but the regulation of IAA biosynthesis is poorly understood. Here, we identified a key push and pull feedback mechanism, which governs auxin biosynthesis through the tryptophan aminotransferase TAA1. We demonstrate that novel inhibitors of TAA1 function in both monocots and eudicots and present a catalytic mechanism that explains both its substrate specificity and its inhibition.

2022-05-30

Rapid screening using pathomorphological interpretation to detect BRAFV600E mutation and microsatellite instability in colorectal cancer

Purpose: Rapid decision-making is essential in precision medicine for initiating molecular targeted therapy for patients with cancer. This study aimed to extract pathomorphologic features that enable the accurate prediction of genetic abnormalities in cancer from hematoxylin and eosin images using deep learning (DL).