慶應義塾大学 · 医学
Satoko Hori教授の研究室は、血脳関門の機能制御と薬物送達のメカニズムを解明する研究を進めています。特に、脳血管内皮細胞の接着緊密接合(TJ)の維持に寄与するグリア細胞やペリサイド由来因子の同定、ならびに血脳関門に発現するABC transporter(ABCG2)の機能と調節機構を分子細胞生物学的手法で解明しています。また、患者がインターネット上で記録する体験記録を自然言語処理と深層学習で分析し、がん治療に伴う副作用の早期発見や患者の心理的負担の特定にも応用しています。
Figures are computed from collected data and may differ slightly.
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.
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