Keio University · 의학
Satoko Hori 교수의 연구실은 혈뇌장벽의 분자 기전과 약물 운반체 기능을 중심으로 신경혈관 단위의 기초 생물학을 연구하고 있습니다. 특히 아스트로시트 및 퍼시테 세포가 혈뇌장벽의 티ght 제약단백질과 ABCG2 운반체 조절에 미치는 영향를 규명하며 뇌내 약물 투여의 효율성 향상에 기여하고 있습니다. 또한 환자들이 인터넷 블로그 등에서 기록하는 경험 기반의 약물 부작용 신호를 자연어 처리 기술을 활용해 탐지하는 디지털 헬스 연구도 진행하고 있습니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
Although tight-junctions (TJs) at the blood-brain barrier (BBB) are important to prevent non-specific entry of compounds into the CNS, molecular mechanisms regulating TJ maintenance remain still unclear. The purpose of this study was therefore to identify molecules, which regulate occludin expression, derived from astrocytes and pericytes that ensheathe brain microvessels by using conditionally immortalized adult rat brain capillary endothelial (TR-BBB13), type II astrocyte (TR-AST4) and brain p
The purpose of the present study was to clarify the expression, transport properties and regulation of ATP-binding cassette G2 (ABCG2) transporter at the rat blood-brain barrier (BBB). The rat homologue of ABCG2 (rABCG2) was cloned from rat brain capillary fraction. In rABCG2-transfected HEK293 cells, rABCG2 was detected as a glycoprotein complex bridged by disulfide bonds, possibly a homodimer. The protein transported mitoxantrone and BODIPY-prazosin. In rat brain capillary fraction, rABCG2 pro
Early detection and management of adverse drug reactions (ADRs) is crucial for improving patients' quality of life. Hand-foot syndrome (HFS) is one of the most problematic ADRs for cancer patients. Recently, an increasing number of patients post their daily experiences to internet community, for example in blogs, where potential ADR signals not captured through routine clinic visits can be described. Therefore, this study aimed to identify patients with potential ADRs, focusing on HFS, from inte
This study showed that the BERT model can extract multiple worries from text generated from patients with breast cancer. This is the first application of a multilabel classifier using the BERT model to extract multiple worries from patient-generated text. The results will be helpful to identify breast cancer patients' worries and give them timely social support.