日期:
2020 年 — 2026 年
2020
2021
2022
2023
2024
2025
2026
影响因子:

Impact of Reporter Type on Signal Detection of Cancer Therapy-Induced Alopecia: A Hypothesis-Generating Study Using the FDA Adverse Event Reporting System

报告基因类型对癌症治疗诱发脱发信号检测的影响:一项基于FDA不良事件报告系统的假设生成研究

Yajima, Airi; Uesawa, Yoshihiro

Cardiovascular Adverse Events Associated With Carfilzomib, Bortezomib, and Ixazomib: A Disproportionality Analysis Using the Japanese Adverse Drug Event Report Database With Anticancer Agents as the Reference Group

以抗癌药物为参照组,基于日本药物不良事件报告数据库的卡非佐米、硼替佐米和伊沙佐米相关心血管不良事件:比例失调分析

Fujiwara, Masaki; Nagano, Shuji; Uesawa, Yoshihiro; Uchida, Mayako; Muroi, Nobuyuki; Shimizu, Tadashi

Exploring Adverse Event Associations of Predicted PXR Agonists Using the FAERS Database

利用FAERS数据库探索预测的PXR激动剂的不良事件关联

Yamada, Saki; Uesawa, Yoshihiro

Comprehensive Analysis of Gastrointestinal Injury Induced by Nonsteroidal Anti-Inflammatory Drugs Using Data from FDA Adverse Event Reporting System Database

利用FDA不良事件报告系统数据库数据对非甾体类抗炎药引起的胃肠道损伤进行全面分析

Kei, Motoki; Uesawa, Yoshihiro

Development of a Medication-Related Osteonecrosis of the Jaw Prediction Model Using the FDA Adverse Event Reporting System Database and Machine Learning

利用FDA不良事件报告系统数据库和机器学习技术开发药物相关性颌骨坏死预测模型

Toriumi, Shinya; Shimokawa, Komei; Yamamoto, Munehiro; Uesawa, Yoshihiro

A Comprehensive Analysis of Adverse Events Associated with HER2 Inhibitors Approved for Breast Cancer Using the FDA Adverse Event Report System (FAERS)

利用FDA不良事件报告系统(FAERS)对已批准用于治疗乳腺癌的HER2抑制剂相关不良事件进行全面分析

Yajima, Airi; Uesawa, Yoshihiro

Steroid-Induced Thrombosis: A Comprehensive Analysis Using the FAERS Database

类固醇诱发血栓形成:基于FAERS数据库的综合分析

Watanabe, Ayame; Uesawa, Yoshihiro

Adverse events of antibody-drug conjugates: comparative analysis of agents with a common payload using the adverse event spontaneous reporting database

抗体药物偶联物的不良事件:利用不良事件自发报告数据库对具有相同有效载荷的药物进行比较分析

Yamaoka, Kenta; Masago, Sho; Uchida, Mayako; Uesawa, Yoshihiro; Muroi, Nobuyuki; Shimizu, Tadashi

Comparative analysis of OECD guideline data and Tox21 assays to improve reproductive and developmental toxicity prediction

对经合组织指南数据和Tox21检测方法进行比较分析,以提高生殖和发育毒性预测的准确性

Kwon, Hee Jung; Lee, Hyomin; Lee, Sunyi; Ko, Woori; Yun, Shin Jea; Uesawa, Yoshihiro; Jung, Joohee

Consensus Modeling Strategies for Predicting Transthyretin Binding Affinity from Tox24 Challenge Data

基于Tox24挑战数据预测转甲状腺素蛋白结合亲和力的共识建模策略

Cirino, Thalita; Pinto, Luis; Iwan, Mateusz; Dougha, Alexis; Lučić, Bono; Kraljević, Antonija; Navoyan, Zaven; Tevosyan, Ani; Yeghiazaryan, Hrach; Khondkaryan, Lusine; Abelyan, Narek; Atoyan, Vahe; Babayan, Nelly; Iwashita, Yuma; Kimura, Kyosuke; Komasaka, Tomoya; Shishido, Koki; Nakamura, Taichi; Asada, Mizuho; Jain, Sankalp; Zakharov, Alexey V; Wang, Haobo; Liu, Wenjia; Chupakhin, Vladimir; Uesawa, Yoshihiro