記述言語：英語   掲載種別：研究論文（学術雑誌）  

Synthetic data generation by Artificial Intelligence (AI) and other means has the potential to share and analyze data while preserving privacy and maintaining statistical characteristics, and various methods have been developed. In medical datasets, abnormal values are more critical than normal values for identifying diseases, making the accurate representation of distribution tails essential. However, existing evaluations of synthetic data generation methods often have not focused on distribution tails. This study generated synthetic data from actual specimen test results at a university hospital and analyzed the representation of distribution tails. As a result, we found that the Forest Diffusion model better represents the tails of distribution characteristics of the original data than the Gaussian Copula model or the Conditional generative adversarial networks (CTGAN) model. As distribution tails vary significantly across generation methods, careful consideration of tail characteristics is crucial when generating synthetic medical data.

DOI： 10.3233/SHTI250924

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

As the global population ages, the importance of reducing or eliminating social agism increases. However, although the medical field is known to be pervasive in terms of ageism against the older people, such assessments have not yet been conducted in terms of equity in large language models (LLMs). In this study, we created a dataset to assess the potential for medically related ageism among LLMs and attempted to visualize it. These results suggest that many LLMs exhibit stereotypical ageism in terms of reluctance to intervene in treatment and self-ageism. These results indicate the importance of developing a framework to avoid or reduce ageism in the social implementation of LLMs in the medical field.

DOI： 10.3233/SHTI250911

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

Assessing the ethical biases of large language models (LLMs) is crucial for their clinical application. To evaluate the ethical behavior of LLMs in clinical settings, we introduce a text corpus that focuses on ethical dilemmas. The corpus comprises 50 scenarios of clinical cases, each consisting of a clinical situation and options that impose ethical dilemmas. Each option is scored based on four ethical principles, enabling the evaluation of the ethical decision-making capabilities of LLMs based on their decision trajectories. The GPT-4 and GEMINI LLMs demonstrate significantly higher ethical scores than random selection across all ethical principles. Furthermore, both LLMs demonstrate adaptability to instructions for specific cases, revealing ethical vulnerabilities because they comply with ethically undesirable instructions. Future corpus expansion may allow evaluating diverse cultural and institutional contexts, thereby enhancing ethical evaluation.

DOI： 10.3233/SHTI250854

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

PURPOSE: Capecitabine, an oral anticancer agent, frequently causes hand-foot syndrome (HFS), affecting patients' quality of life and treatment adherence. However, such symptomatic toxicities are often difficult to detect in structured electronic health record (EHR) data. This study primarily aimed to validate a natural language processing (NLP) approach to identifying capecitabine-induced HFS from unstructured clinical text and demonstrate its application in evaluating medication-associated adverse event trends in real-world settings. METHODS: We conducted a retrospective cohort study using EHRs from the University of Tokyo Hospital (2004-2021). HFS cases were identified using the MedNERN-CR-JA NLP model. After propensity score matching, we compared capecitabine users with and without celecoxib and assessed time to HFS onset using Cox proportional hazards models. NLP-based HFS detection was validated through manual annotation of aggregated clinical notes. Negative control and sensitivity analyses ensured robustness. RESULTS: Among 44,502 patients with cancer, 669 capecitabine users were analyzed. HFS incidence was significantly higher among capecitabine users (hazard ratio [HR], 1.93 [95% CI, 1.48 to 2.52]; P < .001) compared with nonusers. Celecoxib use showed a suggestive association with a reduced HFS risk (HR, 0.51 [95% CI, 0.24 to 1.07]; P = .073). The NLP model demonstrated high accuracy in identifying HFS, achieving a precision of 0.875, recall of 1.000, and F1 score of 0.933, based on manual annotation of patient-level clinical notes. Outcome trends remained consistent when using manually annotated HFS case labels instead of NLP-detected events, supporting the method's robustness. CONCLUSION: These findings demonstrate the effectiveness of NLP in detecting HFS from real-world clinical records. The application to celecoxib-HFS detection illustrates the potential utility of this approach for retrospective safety analysis. Further work is needed to evaluate generalizability across diverse clinical settings.

DOI： 10.1200/CCI-25-00096

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

The optimal transfusion ratios for severe blunt trauma requiring massive transfusion remain unclear. This nationwide retrospective cohort study used data from the Japan Trauma Data Bank (2019-2022) and included patients receiving ≥ 10 units of packed red blood cells (pRBC) within 24 h. The fresh frozen plasma (FFP)-to-pRBC and platelet concentrate (PC)-to-pRBC ratios were categorized as 0-0.5, 0.5-1, 1-1.5, 1.5-2, and > 2. Among 2,849 eligible patients, an FFP-to-pRBC ratio of 1-1.5 was associated with significantly higher in-hospital survival than 0.5-1 (adjusted odds ratio [OR], 1.46; 95% confidence interval [CI], 1.12-1.92; P = 0.006). A PC-to-pRBC ratio of 1.5-2 also showed a trend toward improved survival (1.62; 1.00-2.69; P = 0.053). Patients were categorized into three phenotypes: truncal trauma with shock (70.3%), moderate head and extremity trauma (11.8%), and severe head trauma with consciousness disturbances (17.9%). In the truncal trauma with shock phenotype, FFP-to-pRBC ratios of 1-1.5 (1.56; 1.12-2.20; P = 0.010) and > 2 (2.32; 1.14-5.10; P = 0.027) were associated with improved survival. Higher FFP-to-pRBC and PC-to-pRBC ratios may be associated with improved survival, especially in truncal trauma with shock.

DOI： 10.1038/s41598-025-11338-7

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

BACKGROUND: Effective prediction of immunoglobulin A nephropathy (IgAN) progression is crucial for early intervention and management. We aimed to develop and validate distinct IgAN prediction models for clinical and research applications. METHODS: We analyzed data from the Japanese Nationwide Retrospective Cohort Study in IgAN (n = 1174) gathered over 10 years. The models were developed and tested using data from general physicians in primary care, specialists in tertiary care hospitals, and researchers at academic research institutes. Three tailored prediction models (Primary Care, Tertiary Care, and Research Institute Models) were created to address the unique needs of different clinical environments. The primary outcome was a composite renal event defined as a 1.5-fold increase in serum creatinine level or progression to kidney failure. The predictive performance was assessed using C-statistics. RESULTS: In the derivation cohort, the primary care model included predictors such as estimated glomerular filtration rate < 45 mL/min/1.73 m2, proteinuria ≥ 0.5 g/day, and non-use of corticosteroids, achieving a C-statistic of 0.796 (95% confidence interval [CI] 0.686-0.895). The tertiary care model showed a C-statistic of 0.807 (95% CI 0.713-0.886), using predictors such as glomerular number and histological severity. The research institute model, incorporating 38 variables, demonstrated a C-statistic of 0.802 (95% CI 0.686-0.906). CONCLUSIONS: The prediction models for primary and tertiary care settings provided effective tools for forecasting renal outcomes in IgAN patients and are competitive with more complex machine learning-based models used in research. These models can help guide clinical decisions in various healthcare settings.

DOI： 10.1007/s40620-025-02338-x

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

The association between age and timing of antihypertensive treatment initiation and its effect on outcomes of patients with hypertension remain unclear. We investigated the impact of the time to antihypertensive therapy initiation for cardiovascular event primary prevention in an age-stratified analysis using data from a nationwide health claims database. This observational cohort study analyzed claim and health examination data recorded between January 1, 2005, and April 30, 2021, in the Japan Medical Data Center database. Patients with hypertension treated with antihypertensive agents were grouped by time (years) to therapy initiation: <1 (reference group), 1-2, and ≥2. The primary outcome was a composite outcome encompassing cardiovascular death, acute coronary syndrome, heart failure, and cerebrovascular disease. The secondary outcome was all-cause mortality. Cox proportional hazard models were used to calculate hazard ratios and 95% confidence intervals adjusted for the time to treatment (TTI) group, age, male sex, systolic blood pressure, smoking status, dyslipidemia, diabetes, and visceral obesity. Among 520,669 participants, TTI ≥ 1 year conferred significantly higher hazard ratios for primary outcomes than TTI < 1 year in individuals aged ≥40 years. Hazard ratios (95% confidence intervals) for the primary outcome with TTI of 1-2 and >2 years were 1.215 (1.073-1.375) and 1.296 (1.163-1.444) in those aged 40-49 years and 1.268 (1.144-1.406) and 1.341 (1.224-1.468) in those aged 50-59 years, respectively. TTI ≥ 2 years was an independent prognostic factor for the secondary outcome of all-cause mortality in those aged ≥40 years.

DOI： 10.1038/s41440-025-02249-1

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

Advancements in artificial intelligence propelled the implementation of general-purpose multitasking agents called foundation models. However, it has been challenging for foundation models to handle structured longitudinal medical data due to the mixed data types and variable timestamps in these data. Acquiring large training data is another obstacle. This study proposes a generative foundation model to manage patient trajectory data of variable lengths with mixed data types (categorical and continuous variables). Additionally, we propose a data pipeline to supply real-world data large enough to support foundation models. We locally obtained a large clinical dataset with a reproducible data pipeline scheme that leveraged a national HL7 message standard. Our trained model acquired the ability to suggest clinically relevant medical concepts and continuous variables for general purposes. The model also synthesized a database of more than 10,000 realistic patient trajectories. Our results suggest promising future downstream clinical applications of the foundation model.

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

BACKGROUND: Preoperative risk assessment is very important to ensure surgical safety and predict postoperative complications. However, no large-scale studies have evaluated the risk of perioperative cardiovascular events in Japan. This study evaluated perioperative cardiovascular events using real-world data. In addition, the applicability of machine learning to risk stratification was examined to develop a predictive model for perioperative cardiovascular events. METHODS AND RESULTS: This was an observational cohort study using the Japan Medical Data Center database, which includes claim and health examination data in Japan, between January 2005 and April 2021. In all, 133,634 gastrointestinal surgeries were included in the analysis. The primary outcome was 30-day risk of major adverse cardiovascular events (MACE). The 30-day MACE incidence rate following surgery was 3.8%. Machine learning was used to perform a binary classification task to predict MACE occurrence within 30 days after surgery. A clustering algorithm was developed based on the Shapley additive explanation values obtained from training data, and generalizability was evaluated using test data. Of the variables, age, history of ischemic heart disease or heart failure, history of stroke, diabetes, hypertension, atrial fibrillation, cases of malignancy, and pancreatic biliary surgery were identified as factors associated with MACE occurrence. CONCLUSIONS: A machine learning model built from basic clinical information, comorbidities, and surgical information demonstrated the capacity to stratify MACE risk in patients undergoing gastrointestinal surgery.

DOI： 10.1253/circj.CJ-25-0032

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

In this retrospective observational study, we aimed to investigate the potential of natural language processing (NLP) for drug repositioning by analyzing the preventive effects of cardioprotective drugs against anthracycline-induced cardiotoxicity (AIC) using electronic medical records. We evaluated the effects of angiotensin II receptor blockers/angiotensin-converting enzyme inhibitors (ARB/ACEIs), beta-blockers (BBs), statins, and calcium channel blockers (CCBs) on AIC using signals extracted from clinical texts via NLP. The study included 2935 patients prescribed anthracyclines at a single hospital, with concomitant prescriptions of ARB/ACEIs, BBs, statins, and CCBs. Upon propensity score matching, groups with and without these medications were compared, and expressions suggestive of cardiotoxicity, extracted via NLP, were considered as the outcome. The hazard ratios for ARB/ACEIs, BBs, statins, and CCBs were 0.58 [95% CI: 0.38-0.88], 0.71 [95% CI: 0.35-1.44], 0.60 [95% CI 0.38-0.95], and 0.63 [95% CI: 0.45-0.88], respectively. ARB/ACEIs, statins, and CCBs significantly suppressed AIC, whereas BBs did not demonstrate statistical significance, possibly due to limited statistical power. NLP-extracted signals from clinical texts reflected the known effects of these medications, demonstrating the feasibility of NLP-based drug repositioning. Further investigation is needed to determine if similar results can be replicated using electronic medical records from other institutions.

DOI： 10.1038/s41598-025-91187-6

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

BACKGROUND: The Revised Cardiac Risk Index (RCRI) has been incorporated into preoperative assessment guidelines and is used for simple preoperative screening; however, validation studies within large populations are limited. Moreover, although sex differences in perioperative risk are recognized, their effect on the performance of the RCRI remains unclear. Therefore, in this study we evaluated whether sex differences exist in the risks within the strata classified by the RCRI. METHODS AND RESULTS: The Japan Medical Data Center database based on claim and health examination data in Japan between January 2005 and April 2021 was used. A total of 161,359 noncardiac surgeries performed during hospitalization were analyzed. The main outcome was the 30-day risk of major adverse cardiovascular events. Although there was no significant sex difference among those with an RCRI ≥1, males had a significant hazard rate (1.32 [95% confidence interval, 1.03-1.68]) of postoperative events in the low-risk group with an RCRI of 0. However, this significant difference was not detected in the population excluding those who underwent breast and gynecological surgeries. CONCLUSIONS: The RCRI achieved reasonable risk stratification in validation using Japanese real-world data regardless of sex. Although further detailed analysis is necessary to determine the sex differences, the validity of using the RCRI for screening purposes is supported at this stage.

DOI： 10.1253/circj.CJ-24-0846

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掲載種別：研究論文（学術雑誌）   出版者・発行元：Frontiers Media SA  

Large Language Models (LLM) are increasingly multimodal, and Zero-Shot Visual Question Answering (VQA) shows promise for image interpretation. If zero-shot VQA can be applied to a 12-lead electrocardiogram (ECG), a prevalent diagnostic tool in the medical field, the potential benefits to the field would be substantial. This study evaluated the diagnostic performance of zero-shot VQA with multimodal LLMs on 12-lead ECG images. The results revealed that multimodal LLM tended to make more errors in extracting and verbalizing image features than in describing preconditions and making logical inferences. Even when the answers were correct, erroneous descriptions of image features were common. These findings suggest a need for improved control over image hallucination and indicate that performance evaluation using the percentage of correct answers to multiple-choice questions may not be sufficient for performance assessment in VQA tasks.

DOI： 10.3389/fcvm.2025.1458289

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

Assessing the risk of postoperative cardiovascular events before performing non-cardiac surgery is clinically important. The current risk score systems for preoperative evaluation may not adequately represent a small subset of high-risk populations. Accordingly, this study aimed at applying iterative random forest to analyze combinations of factors that could potentially be clinically valuable in identifying these high-risk populations. To this end, we used the Japan Medical Data Center database, which includes claims data from Japan between January 2005 and April 2021, and employed iterative random forests to extract factor combinations that influence outcomes. The analysis demonstrated that a combination of a prior history of stroke and extremely low LDL-C levels was associated with a high non-cardiac postoperative risk. The incidence of major adverse cardiovascular events in the population characterized by the incidence of previous stroke and extremely low LDL-C levels was 15.43 events per 100 person-30 days [95% confidence interval, 6.66-30.41] in the test data. At this stage, the results only show correlation rather than causation; however, these findings may offer valuable insights for preoperative risk assessment in non-cardiac surgery.

DOI： 10.1038/s41598-024-78482-4

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

BACKGROUND: Diabetes affects millions worldwide. Primary care physicians provide a significant portion of care, and they often struggle with selecting appropriate medications. OBJECTIVE: This study aimed to develop a model that accurately predicts what drug an endocrinologist would prescribe based on the current measurements. The goal was to create a system that would assist nonspecialists in choosing medications, thereby potentially improving diabetes treatment outcomes. Based on the performance of previous studies, we set a performance target of achieving a receiver operating characteristic area under the curve (ROC-AUC) above 0.95. METHODS: A transformer-based encoder-decoder model predicts whether 44 types of diabetes drugs will be prescribed. The model uses sequences of age, sex, history for 12 laboratory tests, and prescribed drug history as inputs. We assessed the model using the electronic health records from 7034 patients with diabetes seeing endocrinologists between 2012 and 2022 at the University of Tokyo Hospital. We assessed model performance trained on data subsets spanning different time periods (2, 5, and 10 years) using micro- and macro-averaged ROC-AUC on a hold-out test set comprising data solely from 2022. The model's performance was compared against LightGBM. RESULTS: The model trained on data from the past 5 years (2017-2021) yielded the best predictive performance, achieving a microaverage (95% CI) ROC-AUC of 0.993 (0.992-0.994) and a macroaverage (95% CI) ROC-AUC of 0.988 (0.980-0.993). The model achieved an ROC-AUC above 0.95 for 43 out of 44 drugs. These results surpassed the predefined performance target and outperformed both previous studies and the LightGBM model's microaverage ROC-AUC of 0.988 (0.985-0.990) in terms of prediction accuracy. Furthermore, training the model with short-term data from the past 5 years yielded high accuracy compared to using data from the past 10 years, suggesting that learning from more recent prescribing patterns might be advantageous. CONCLUSIONS: The proposed model demonstrates the feasibility of accurately predicting the next prescribed drugs. This model, trained from the past prescriptions of endocrinologists, has the potential to provide information that can assist nonspecialists in making diabetes-treatment decisions. Future studies will focus on incorporating important factors such as prescription contraindications and constraints to enhance safety, as well as leveraging large-scale clinical data across multiple hospitals to improve the generalizability of the model.

DOI： 10.2196/67748

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

PURPOSE: While the prognosis of patients with cancer has improved, the time burden of treatment has recently been recognized as time toxicity; although, the actual clinical situation remains largely unexplored. This retrospective study aimed to elucidate the time toxicity of rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) in patients with B-cell lymphoma and the factors influencing it. METHODS: We used a nationwide hospital-based database between January 2010 and November 2021 in Japan. We extracted the claims data of patients with diffuse large B-cell lymphoma and follicular lymphoma who were hospitalized and/or visited hospitals for chemotherapy. RESULTS: Among the 7760 R-CHOP administered to 2006 patients, the rate of outpatient therapy increased over time (2010-2015: 17.9%; 2016-2021: 31.8%). In 2016, the median length of hospitalization was the shortest at 13 days (IQR 8-19), which coincided with the peak use of pegylated granulocyte colony-stimulating factor (Peg-G-CSF) during hospitalization in 2015-2016, likely driven by changes in the insurance system. In multivariate analysis, the factors associated with longer hospital stays were older age and poor activities of daily living, whereas the use of Peg-G-CSF, a reduced-dose regimen, and treatment at cancer-designated hospitals were associated with shorter stays. CONCLUSION: The time toxicity of R-CHOP has improved and may be influenced by the patient's condition, adequate supportive care, changes in the insurance system, and center-specific treatment proficiency.

DOI： 10.1007/s00520-025-09335-7

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掲載種別：研究論文（学術雑誌）  

DOI： 10.1016/j.heliyon.2024.e40566

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

This study demonstrates that adverse events (AEs) extracted using natural language processing (NLP) from clinical texts reflect the known frequencies of AEs associated with anticancer drugs. Using data from 44,502 cancer patients at a single hospital, we identified cases prescribed anticancer drugs (platinum, PLT; taxane, TAX; pyrimidine, PYA) and compared them to non-treatment (NTx) group using propensity score matching. Over 365 days, AEs (peripheral neuropathy, PN; oral mucositis, OM; taste abnormality, TA; appetite loss, AL) were extracted from clinical text using an NLP tool. The hazard ratios (HRs) for the anticancer drugs were: PN, 1.15-1.95; OM, 3.11-3.85; TA, 3.48-4.71; and AL, 1.98-3.84; the HRs were significantly higher than that of the NTx group. Sensitivity analysis revealed that the HR for TA may have been underestimated; however, the remaining three types of AEs extracted from clinical text by NLP were consistently associated with the three anticancer drugs.

DOI： 10.1038/s41746-024-01323-1

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記述言語：日本語   出版者・発行元：(一社)日本医療情報学会  

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

The browsing history of a medical institution's website can potentially reveal or identify information about the health condition of the website visitor through browser cookies and fingerprints. In Japan, although the Personal Information Protection Law was revised in April 2022, the use of access analysis services to collect browsing history on medical institution websites has not been investigated. Therefore, this study investigates the actual usage of access analysis services on Japanese medical institution websites and compares it with the current situation in France, which follows the General Data Protection Regulation. The results revealed that the larger the size of the hospital, the higher the percentage of adoption of access analytics services in Japan. However, the implementation of a system for obtaining consent for the use of access analysis in Japan was found to be poor compared to that of French medical institutions. While access analysis tools are used in the websites of several medical institutions in Japan, the implementation of the process of obtaining consent to acquire browsing history is poor.

DOI： 10.3233/SHTI240635

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

BACKGROUND: Type 2 diabetes (T2D) is a significant global health challenge. Physicians need to assess whether future glycemic control will be poor on the current trajectory of usual care and usual-care treatment intensifications so that they can consider taking extra treatment measures to prevent poor outcomes. Predicting poor glycemic control from trends in hemoglobin A1c (HbA1c) levels is difficult due to the influence of seasonal fluctuations and other factors. OBJECTIVE: We sought to develop a model that accurately predicts poor glycemic control among patients with T2D receiving usual care. METHODS: Our machine learning model predicts poor glycemic control (HbA1c≥8%) using the transformer architecture, incorporating an attention mechanism to process irregularly spaced HbA1c time series and quantify temporal relationships of past HbA1c levels at each time point. We assessed the model using HbA1c levels from 7787 patients with T2D seeing specialist physicians at the University of Tokyo Hospital. The training data include instances of poor glycemic control occurring during usual care with usual-care treatment intensifications. We compared prediction accuracy, assessed with the area under the receiver operating characteristic curve, the area under the precision-recall curve, and the accuracy rate, to that of LightGBM. RESULTS: The area under the receiver operating characteristic curve, the area under the precision-recall curve, and the accuracy rate (95% confidence limits) of the proposed model were 0.925 (95% CI 0.923-0.928), 0.864 (95% CI 0.852-0.875), and 0.864 (95% CI 0.86-0.869), respectively. The proposed model achieved high prediction accuracy comparable to or surpassing LightGBM's performance. The model prioritized the most recent HbA1c levels for predictions. Older HbA1c levels in patients with poor glycemic control were slightly more influential in predictions compared to patients with good glycemic control. CONCLUSIONS: The proposed model accurately predicts poor glycemic control for patients with T2D receiving usual care, including patients receiving usual-care treatment intensifications, allowing physicians to identify cases warranting extraordinary treatment intensifications. If used by a nonspecialist, the model's indication of likely future poor glycemic control may warrant a referral to a specialist. Future efforts could incorporate diverse and large-scale clinical data for improved accuracy.

DOI： 10.2196/56700

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

BACKGROUND: In some cases of patients with out-of-hospital cardiac arrest (OHCA) who underwent extracorporeal cardiopulmonary resuscitation (ECPR), negative pupillary light reflex (PLR) and mydriasis upon hospital arrival serve as common early indicator of poor prognosis. However, in certain patients with poor prognoses inferred by pupil findings upon hospital arrival, pupillary findings improve before and after the establishment of ECPR. The association between these changes in pupillary findings and prognosis remains unclear. This study aimed to clarify the association of pupillary examinations before and after the establishment of ECPR in patients with OHCA showing poor pupillary findings upon hospital arrival with their outcomes. To this end, we analysed retrospective multicentre registry data involving 36 institutions in Japan, including all adult patients with OHCA who underwent ECPR between January 2013 and December 2018. We selected patients with poor prognosis inferred by pupillary examinations, negative pupillary light reflex (PLR) and pupil mydriasis, upon hospital arrival. The primary outcome was favourable neurological outcome, defined as Cerebral Performance Category 1 or 2 at hospital discharge. Multivariable logistic regression analysis was performed to evaluate the association between favourable neurological outcome and pupillary examination after establishing ECPR. RESULTS: Out of the 2,157 patients enrolled in the SAVE-J II study, 723 were analysed. Among the patients analysed, 74 (10.2%) demonstrated favourable neurological outcome at hospital discharge. Multivariable analysis revealed that a positive PLR at ICU admission (odds ration [OR] = 11.3, 95% confidence intervals [CI] = 5.17-24.7) was significantly associated with favourable neurological outcome. However, normal pupil diameter at ICU admission (OR = 1.10, 95%CI = 0.52-2.32) was not significantly associated with favourable neurological outcome. CONCLUSION: Among the patients with OHCA who underwent ECPR and showed poor pupillary examination findings upon hospital arrival, 10.2% had favourable neurological outcome at hospital discharge. A positive PLR after the establishment of ECPR was significantly associated with favourable neurological outcome.

DOI： 10.1186/s13613-024-01265-7

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

AIM: To investigate the factors associated with favourable neurological outcomes in adult patients undergoing extracorporeal cardiopulmonary resuscitation (ECPR) for out-of-hospital cardiac arrest (OHCA). METHODS: This retrospective observational study used secondary analysis of the SAVE-J II multicentre registry data from 36 institutions in Japan. Between 2013 and 2018, 2157 patients with OHCA who underwent ECPR were enrolled in SAVE-J II. A total of 1823 patients met the study inclusion criteria. Adult patients (aged ≥ 18 years) with OHCA, who underwent ECPR before admission to the intensive care unit, were included in our secondary analysis. The primary outcome was a favourable neurological outcome at hospital discharge, defined as a Cerebral Performance Category score of 1 or 2. We used a multivariate logistic regression model to examine the association between factors measured at the incident scene or upon hospital arrival and favourable neurological outcomes. RESULTS: Multivariable analysis revealed that shockable rhythm at the scene [odds ratio (OR); 2.11; 95% confidence interval (CI), 1.16-3.95] and upon hospital arrival (OR 2.59; 95% CI 1.60-4.30), bystander CPR (OR 1.63; 95% CI 1.03-1.88), body movement during resuscitation (OR 7.10; 95% CI 1.79-32.90), gasping (OR 4.33; 95% CI 2.57-7.28), pupillary reflex on arrival (OR 2.93; 95% CI 1.73-4.95), and male sex (OR 0.43; 95% CI 0.24-0.75) significantly correlated with neurological outcomes. CONCLUSIONS: Shockable rhythm, bystander CPR, body movement during resuscitation, gasping, pupillary reflex, and sex were associated with favourable neurological outcomes in patients with OHCA treated with ECPR.

DOI： 10.1016/j.resplu.2024.100574

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

Transformation of patient data extracted from a database into fixed-length numerical vectors requires expertise in topical medical knowledge as well as data manipulation-thus, manual feature design is labor-intensive. In this study, we propose a machine learning-based method to for this purpose applicable to electronic medical data recorded during hospitalization, which utilizes unsupervised feature extraction based on graph embedding. Unsupervised learning is performed on a heterogeneous graph using Graph2Vec, and the inclusion of clinically useful data in the obtained embedding representation is evaluated by predicting readmission within 30 days of discharge based on it. The embedded representations are observed to improve predictive performance significantly as the information contained in the graph increases, indicating the suitability of the proposed method for feature design corresponding to clinical information.

DOI： 10.3233/SHTI231058

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

Both lectures and hands-on education are essential for the development of human resources that can use real-world data (RWD). The University of Tokyo has launched a new hybrid-style RWD educational program entitled "Medical Real World Data Utilization Human Resource Development Project" from FY2019 onwards. We present an overview of the overall picture of the project, including the development process of the educational program and the challenges associated with it.

DOI： 10.3233/SHTI231283

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

Counterfactual simulation-exploring hypothetical consequences under alternative clinical scenarios-holds promise for transformative applications such as personalized medicine and in-silico trials. However, it remains challenging due to methodological limitations. Here, we show that an autoregressive generative model, trained on real-world data from over 300,000 patients and 400 million patient timeline entries, can generate clinically plausible counterfactual trajectories. As a validation task, we applied the model to patients hospitalized with COVID-19 in 2023, modifying age, serum C-reactive protein (CRP), and serum creatinine to simulate 7-day outcomes. Increased in-hospital mortality was observed in counterfactual simulations with older age, elevated CRP, and elevated serum creatinine. Remdesivir prescriptions increased in simulations with higher CRP values and decreased in those with impaired kidney function. These counterfactual trajectories reproduced known clinical patterns. These findings suggest that autoregressive generative models trained on real-world data in a self-supervised manner can establish a foundation for counterfactual clinical simulation.

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

INTRODUCTION: Bronchopulmonary dysplasia (BPD) poses a substantial global health burden. Individualized treatment strategies based on early prediction of the development of BPD can mitigate preterm birth complications; however, previously suggested predictive models lack early postnatal applicability. We aimed to develop predictive models for BPD and mortality based on immediate postnatal clinical data. METHODS: Clinical information on very preterm and very low birth weight infants born between 2008 and 2018 was extracted from a nationwide Japanese database. The gradient boosting decision trees (GBDT) algorithm was adopted to predict BPD and mortality, using predictors within the first 6 h postpartum. We assessed the temporal validity and evaluated model adequacy using Shapley additive explanations (SHAP) values. RESULTS: We developed three predictive models using data from 39,488, 39,096, and 40,291 infants to predict "death or BPD," "death or severe BPD," and "death before discharge," respectively. These well-calibrated models achieved areas under the receiver operating characteristic curve of 0.828 (95% CI: 0.828-0.828), 0.873 (0.873-0.873), and 0.887 (0.887-0.888), respectively, outperforming the multivariable logistic regression models. SHAP value analysis identified predictors of BPD, including gestational age, size at birth, male sex, and persistent pulmonary hypertension. In SHAP value-based case clustering, the "death or BPD" prediction model stratified infants by gestational age and persistent pulmonary hypertension, whereas the other models for "death or severe BPD" and "death before discharge" commonly formed clusters of low mortality, extreme prematurity, low Apgar scores, and persistent pulmonary hypertension of the newborn. CONCLUSIONS: GBDT models for predicting BPD and mortality, designed for use within 6 h postpartum, demonstrated superior prognostic performance. SHAP value-based clustering, a data-driven approach, formed clusters of clinical relevance. These findings suggest the efficacy of a GBDT algorithm for the early postnatal prediction of BPD.

DOI： 10.1371/journal.pone.0300817

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

BACKGROUND: A better understanding of the relative contributions of various factors to patient outcomes is essential for optimal patient selection for extracorporeal cardiopulmonary resuscitation (ECPR) therapy for patients with out-of-hospital cardiac arrest (OHCA). However, evidence on the prognostic comparison based on the etiologies of cardiac arrest is limited. RESEARCH QUESTION: What is the etiology-based prognosis of patients undergoing ECPR for OHCA? STUDY DESIGN AND METHODS: This retrospective multicenter registry study involved 36 institutions in Japan and included all adult patients with OHCA who underwent ECPR between January 2013 and December 2018. The primary etiology for OHCA was determined retrospectively from all hospital-based data at each institution. We performed a multivariable logistic regression model to determine the association between etiology of cardiac arrest and two outcomes: favorable neurological outcomes and survival at hospital discharge. RESULTS: We identified 1,781 eligible patients, of whom 1,405 (78.9%) had cardiac arrest due to the cardiac causes. Multivariable logistic regression analysis for favorable neurological outcomes showed that accidental hypothermia (adjusted OR = 5.12; 95% CI = 2.98-8.80, P < 0.001) was associated with a significantly higher rate of favorable neurological outcomes than cardiac causes. Multivariable logistic regression analysis for survival showed that accidental hypothermia (adjusted OR = 5.19; 95% CI = 3.15-8.56, P < 0.001) had significantly higher rates of survival than cardiac causes. Acute aortic dissection/aneurysm (adjusted OR = 0.07, 95% CI = 0.02-0.28, P < 0.001) and primary cerebral disorders (adjusted OR = 0.12, 95% CI = 0.03-0.50, P = 0.004) had significantly lower rates of survival than cardiac causes. INTERPRETATION: In this retrospective multicenter cohort study, although most OHCA patients underwent ECPR for cardiac causes, accidental hypothermia was associated with favorable neurological outcomes and survival; in contrast, acute aortic dissection/aneurysm and primary cerebral disorders were associated with non-survival than cardiac causes.

DOI： 10.1016/j.chest.2023.10.022

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

IMPORTANCE: Younger females with out-of-hospital cardiac arrest (OHCA) in public locations have less likelihood to receive public access defibrillation and bystander cardiopulmonary resuscitation (CPR). However, the association between age- and sex-based disparities and neurological outcomes remains underexamined. OBJECTIVE: To investigate the association between sex and age and the rate of receiving bystander CPR, automated external defibrillator defibrillation, and neurological outcomes in patients with OHCA. DESIGN, SETTING, AND PARTICIPANTS: This cohort study used the All-Japan Utstein Registry, a prospective, population-based, nationwide database in Japan containing data on 1 930 273 patients with OHCA between January 1, 2005, and December 31, 2020. The cohort comprised patients with OHCA of cardiac origin that was witnessed by citizens and treated by emergency medical service personnel. The data were analyzed from September 3, 2022, to May 5, 2023. EXPOSURE: Sex and age. MAIN OUTCOMES AND MEASURES: The primary outcome was favorable neurological outcome at 30 days after an OHCA. Favorable neurological outcome was defined as a Cerebral Performance Category score of 1 (indicating good cerebral performance) or 2 (indicating moderate cerebral disability). The secondary outcomes were the rates of receiving public access defibrillation and bystander CPR. RESULTS: The 354 409 included patients who experienced bystander-witnessed OHCA of cardiac origin had a median (IQR) age of 78 (67-86) years and 136 520 were females (38.5%). The rate of receiving public access defibrillation was higher in males than females (3.2% vs 1.5%; P < .001). Stratified by age, age- and sex-based disparities in prehospital lifesaving interventions by bystanders and in neurological outcomes were observed. Although younger females had a lower rate of receiving public access defibrillation and bystander CPR than males, younger females had a higher favorable neurological outcome compared with males of the same age (odds ratio [OR], 1.19; 95% CI, 1.08-1.31). In younger females with OHCA that was witnessed by nonfamily bystanders, receiving public access defibrillation (OR, 3.51; 95% CI, 2.34-5.27) or bystander CPR (OR, 1.62; 95% CI, 1.20-2.22) was associated with a favorable neurological outcome. CONCLUSIONS AND RELEVANCE: Results of this study suggest a pattern of significant sex- and age-based differences in bystander CPR, public access defibrillation, and neurological outcomes in Japan. Improvement in neurological outcomes in patients with OHCA, especially younger females, was associated with increased use of public access defibrillation and bystander CPR.

DOI： 10.1001/jamanetworkopen.2023.21783

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

The diversity of patient information recorded on electronic medical records generally, presents a challenge for converting it into fixed-length vectors that align with clinical characteristics. To address this issue, this study aimed to utilize an unsupervised graph representation learning method to transform the unstructured inpatient information from electronic medical records into a fixed-length vector. Infograph, one of the unsupervised graph representation learning algorithms was applied to the graphed inpatient information, resulting in embedded vectors of fixed length. The embedded vectors were then evaluated for whether the clinical information was preserved in it. The results indicated that the embedded representation contained information that could predict readmission within 30 days, demonstrating the feasibility of using unsupervised graph representation learning to transform patient information into fixed-length vectors that retain clinical characteristics.

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掲載種別：研究論文（学術雑誌）   出版者・発行元：Elsevier BV  

DOI： 10.1016/j.bbrc.2022.09.108

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記述言語：日本語   出版者・発行元：(一社)日本医療情報学会  

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記述言語：日本語   出版者・発行元：(一社)日本医療情報学会  

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記述言語：日本語   出版者・発行元：(一社)日本医療情報学会  

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掲載種別：研究論文（学術雑誌）   出版者・発行元：JMIR Publications Inc.  

Background

Treatment discontinuation (TD) is one of the major prognostic issues in diabetes care, and several models have been proposed to predict a missed appointment that may lead to TD in patients with diabetes by using binary classification models for the early detection of TD and for providing intervention support for patients. However, as binary classification models output the probability of a missed appointment occurring within a predetermined period, they are limited in their ability to estimate the magnitude of TD risk in patients with inconsistent intervals between appointments, making it difficult to prioritize patients for whom intervention support should be provided.

Objective

This study aimed to develop a machine-learned prediction model that can output a TD risk score defined by the length of time until TD and prioritize patients for intervention according to their TD risk.

Methods

This model included patients with diagnostic codes indicative of diabetes at the University of Tokyo Hospital between September 3, 2012, and May 17, 2014. The model was internally validated with patients from the same hospital from May 18, 2014, to January 29, 2016. The data used in this study included 7551 patients who visited the hospital after January 1, 2004, and had diagnostic codes indicative of diabetes. In particular, data that were recorded in the electronic medical records between September 3, 2012, and January 29, 2016, were used. The main outcome was the TD of a patient, which was defined as missing a scheduled clinical appointment and having no hospital visits within 3 times the average number of days between the visits of the patient and within 60 days. The TD risk score was calculated by using the parameters derived from the machine-learned ranking model. The prediction capacity was evaluated by using test data with the C-index for the performance of ranking patients, area under the receiver operating characteristic curve, and area under the precision-recall curve for discrimination, in addition to a calibration plot.

Results

The means (95% confidence limits) of the C-index, area under the receiver operating characteristic curve, and area under the precision-recall curve for the TD risk score were 0.749 (0.655, 0.823), 0.758 (0.649, 0.857), and 0.713 (0.554, 0.841), respectively. The observed and predicted probabilities were correlated with the calibration plots.

Conclusions

A TD risk score was developed for patients with diabetes by combining a machine-learned method with electronic medical records. The score calculation can be integrated into medical records to identify patients at high risk of TD, which would be useful in supporting diabetes care and preventing TD.

DOI： 10.2196/37951

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

Failure of the right ventricle plays a critical role in any type of heart failure. However, the mechanism remains unclear, and there is no specific therapy. Here, we show that the right ventricle predominantly expresses alternative complement pathway-related genes, including Cfd and C3aR1. Complement 3 (C3)-knockout attenuates right ventricular dysfunction and fibrosis in a mouse model of right ventricular failure. C3a is produced from C3 by the C3 convertase complex, which includes the essential component complement factor D (Cfd). Cfd-knockout mice also show attenuation of right ventricular failure. Moreover, the plasma concentration of CFD correlates with the severity of right ventricular failure in patients with chronic right ventricular failure. A C3a receptor (C3aR) antagonist dramatically improves right ventricular dysfunction in mice. In summary, we demonstrate the crucial role of the C3-Cfd-C3aR axis in right ventricular failure and highlight potential therapeutic targets for right ventricular failure.

DOI： 10.1038/s41467-022-33152-9

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

Deep learning techniques have recently been applied to analyze associations between gene expression data and disease phenotypes. However, there are concerns regarding the black box problem: it is difficult to interpret why the prediction results are obtained using deep learning models from model parameters. New methods have been proposed for interpreting deep learning model predictions but have not been applied to genetics. In this study, we demonstrated that applying SHapley Additive exPlanations (SHAP) to a deep learning model using graph convolutions of genetic pathways can provide pathway-level feature importance for classification prediction of diffuse large B-cell lymphoma (DLBCL) gene expression subtypes. Using Kyoto Encyclopedia of Genes and Genomes pathways, a graph convolutional network (GCN) model was implemented to construct graphs with nodes and edges. DLBCL datasets, including microarray gene expression data and clinical information on subtypes (germinal center B-cell-like type and activated B-cell-like type), were retrieved from the Gene Expression Omnibus to evaluate the model. The GCN model showed an accuracy of 0.914, precision of 0.948, recall of 0.868, and F1 score of 0.906 in analysis of the classification performance for the test datasets. The pathways with high feature importance by SHAP included highly enriched pathways in the gene set enrichment analysis. Moreover, a logistic regression model with explanatory variables of genes in pathways with high feature importance showed good performance in predicting DLBCL subtypes. In conclusion, our GCN model for classifying DLBCL subtypes is useful for interpreting important regulatory pathways that contribute to the prediction.

DOI： 10.1371/journal.pone.0269570

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記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：International Heart Journal (Japanese Heart Journal)  

DOI： 10.1536/ihj.21-407

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記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：Elsevier BV  

BACKGROUND: Aortic regurgitation (AR) is a common heart disease, with a relatively high prevalence of 4.9% in the Framingham Heart Study. Because the prevalence increases with advancing age, an upward shift in the age distribution may increase the burden of AR. To provide an effective screening method for AR, we developed a deep learning-based artificial intelligence algorithm for the diagnosis of significant AR using electrocardiography (ECG). METHODS: Our dataset comprised 29,859 paired data of ECG and echocardiography, including 412 AR cases, from January 2015 to December 2019. This dataset was divided into training, validation, and test datasets. We developed a multi-input neural network model, which comprised a two-dimensional convolutional neural network (2D-CNN) using raw ECG data and a fully connected deep neural network (FC-DNN) using ECG features, and compared its performance with the performances of a 2D-CNN model and other machine learning models. In addition, we used gradient-weighted class activation mapping (Grad-CAM) to identify which parts of ECG waveforms had the most effect on algorithm decision making. RESULTS: The area under the receiver operating characteristic curve of the multi-input model (0.802; 95% CI, 0.762-0.837) was significantly greater than that of the 2D-CNN model alone (0.734; 95% CI, 0.679-0.783; p<0.001) and those of other machine learning models. Grad-CAM demonstrated that the multi-input model tended to focus on the QRS complex in leads I and aVL when detecting AR. CONCLUSIONS: The multi-input deep learning model using 12-lead ECG data could detect significant AR with modest predictive value.

DOI： 10.1016/j.jjcc.2021.08.029

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

Risk assessment of in-hospital mortality of patients at the time of hospitalization is necessary for determining the scale of required medical resources for the patient depending on the patient's severity. Because recent machine learning application in the clinical area has been shown to enhance prediction ability, applying this technique to this issue can lead to an accurate prediction model for in-hospital mortality prediction. In this study, we aimed to generate an accurate prediction model of in-hospital mortality using machine learning techniques. Patients 18 years of age or older admitted to the University of Tokyo Hospital between January 1, 2009 and December 26, 2017 were used in this study. The data were divided into a training/validation data set (n = 119,160) and a test data set (n = 33,970) according to the time of admission. The prediction target of the model was the in-hospital mortality within 14 days. To generate the prediction model, 25 variables (age, sex, 21 laboratory test items, length of stay, and mortality) were used to predict in-hospital mortality. Logistic regression, random forests, multilayer perceptron, and gradient boost decision trees were performed to generate the prediction models. To evaluate the prediction capability of the model, the model was tested using a test data set. Mean probabilities obtained from trained models with five-fold cross-validation were used to calculate the area under the receiver operating characteristic (AUROC) curve. In a test stage using the test data set, prediction models of in-hospital mortality within 14 days showed AUROC values of 0.936, 0.942, 0.942, and 0.938 for logistic regression, random forests, multilayer perceptron, and gradient boosting decision trees, respectively. Machine learning-based prediction of short-term in-hospital mortality using admission laboratory data showed outstanding prediction capability and, therefore, has the potential to be useful for the risk assessment of patients at the time of hospitalization.

DOI： 10.1371/journal.pone.0246640

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

Advances in deep learning technology have enabled complex task solutions. The accuracy of image classification tasks has improved owing to the establishment of convolutional neural networks (CNN). Cellular senescence is a hallmark of ageing and is important for the pathogenesis of ageing-related diseases. Furthermore, it is a potential therapeutic target. Specific molecular markers are used to identify senescent cells. Moreover senescent cells show unique morphology, which can be identified. We develop a successful morphology-based CNN system to identify senescent cells and a quantitative scoring system to evaluate the state of endothelial cells by senescence probability output from pre-trained CNN optimised for the classification of cellular senescence, Deep Learning-Based Senescence Scoring System by Morphology (Deep-SeSMo). Deep-SeSMo correctly evaluates the effects of well-known anti-senescent reagents. We screen for drugs that control cellular senescence using a kinase inhibitor library by Deep-SeSMo-based drug screening and identify four anti-senescent drugs. RNA sequence analysis reveals that these compounds commonly suppress senescent phenotypes through inhibition of the inflammatory response pathway. Thus, morphology-based CNN system can be a powerful tool for anti-senescent drug screening.

DOI： 10.1038/s41467-020-20213-0

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記述言語：日本語   出版者・発行元：(一社)日本医療情報学会  

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

BACKGROUND: Outcome prediction for patients with out-of-hospital cardiac arrest (OHCA) has the possibility to detect patients who could have been potentially saved. Advanced machine learning techniques have recently been developed and employed for clinical studies. In this study, we aimed to establish a prognostication model for OHCA with presumed cardiac aetiology using an advanced machine learning technique. METHODS AND RESULTS: Cohort data from a prospective multi-centre cohort study for OHCA patients transported by an ambulance in the Kanto area of Japan between January 2012 and March 2013 (SOS-KANTO 2012 study) were analysed in this study. Of 16,452 patients, data for OHCA patients aged ≥18 years with presumed cardiac aetiology were retrieved, and were divided into two groups (training set: n = 5718, between January 1, 2012 and December 12, 2012; test set: n = 1608, between January 1, 2013 and March 31, 2013). Of 421 variables observed during prehospital and emergency department settings, 35 prehospital variables, or 35 prehospital and 18 in-hospital variables, were used for outcome prediction of 1-year survival using a random forest method. In validation using the test set, prognostication models trained with 35 variables, or 53 variables for 1-year survival showed area under the receiver operating characteristics curve (AUC) values of 0.943 (95% CI [0.930, 0.955]) and 0.958 (95% CI [0.948, 0.969]), respectively. CONCLUSIONS: The advanced machine learning technique showed favourable prediction capability for 1-year survival of OHCA with presumed cardiac aetiology. These models can be useful for detecting patients who could have been potentially saved.

DOI： 10.1016/j.resuscitation.2019.06.006

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

Immunogenicity of immature pluripotent stem cells is a topic of intense debate. Immunogenic antigens, which are specific in pluripotent states, have not been described previously. In this study, we identified glypican-3 (GPC3), a known carcinoembryonic antigen, as a pluripotent state-specific immunogenic antigen. Additionally, we validated the applicability of human leukocyte antigen (HLA)-class I-restricted GPC3-reactive cytotoxic T lymphocytes (CTLs) in the removal of undifferentiated pluripotent stem cells (PSCs) from human induced pluripotent stem cell (hiPSC)-derivatives. HiPSCs uniquely express GPC3 in pluripotent states and were rejected by GPC3-reactive CTLs, which were sensitized with HLA-class I-restricted GPC3 peptides. Furthermore, GPC3-reactive CTLs selectively removed undifferentiated PSCs from hiPSC-derivatives in vitro and inhibited tumor formation in vivo. Our results demonstrate that GPC3 works as a pluripotent state-specific immunogenic antigen in hiPSCs and is applicable to regenerative medicine as a method of removing undifferentiated PSCs, which are the main cause of tumor formation.

DOI： 10.1016/j.bbrc.2019.02.094

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1016/j.healun.2018.11.002

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1016/j.stemcr.2018.04.007

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1016/j.stem.2017.11.010

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1016/j.stemcr.2017.08.025

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1002/1873-3468.12791

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1038/srep44312

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

Andersen-Tawil syndrome (ATS) is a rare inherited channelopathy. The cardiac phenotype in ATS is typified by a prominent U wave and ventricular arrhythmia. An effective treatment for this disease remains to be established. We reprogrammed somatic cells from three ATS patients to generate induced pluripotent stem cells (iPSCs). Multi-electrode arrays (MEAs) were used to record extracellular electrograms of iPSC-derived cardiomyocytes, revealing strong arrhythmic events in the ATS-iPSC-derived cardiomyocytes. Ca2+ imaging of cells loaded with the Ca2+ indicator Fluo-4 enabled us to examine intracellular Ca2+ handling properties, and we found a significantly higher incidence of irregular Ca2+ release in the ATS-iPSC-derived cardiomyocytes than in control-iPSC-derived cardiomyocytes. Drug testing using ATS-iPSC-derived cardiomyocytes further revealed that antiarrhythmic agent, flecainide, but not the sodium channel blocker, pilsicainide, significantly suppressed these irregular Ca2+ release and arrhythmic events, suggesting that flecainide's effect in these cardiac cells was not via sodium channels blocking. A reverse-mode Na+/Ca2+exchanger (NCX) inhibitor, KB-R7943, was also found to suppress the irregular Ca2+ release, and whole-cell voltage clamping of isolated guinea-pig cardiac ventricular myocytes confirmed that flecainide could directly affect the NCX current (INCX). ATS-iPSC-derived cardiomyocytes recapitulate abnormal electrophysiological phenotypes and flecainide suppresses the arrhythmic events through the modulation of INCX.

DOI： 10.1016/j.bbrep.2017.01.002

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1038/srep34198

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記述言語：日本語   掲載種別：研究論文（学術雑誌）  

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1016/j.stemcr.2016.04.015

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1016/j.cmet.2016.03.001

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

Despite significant advances in medical treatment, cardiovascular disease is still a major cause of morbidity and mortality in advanced countries. To improve the outcome, the further promotion of basic cardiovascular science has a pivotal role for the developing novel therapeutic approach. However, due to the inaccessibility of human heart tissue, we couldn't obtain the sufficient amount of patient's heart tissues. The discovery of human-induced pluripotent stem cells (iPSCs) is highly expected to provide the breakthrough to this obstruction. Through the patient-specific iPSCs-derived cardiomyocytes, we could analyze the patient-specific heart diseases directly and repetitively. Herein we introduce the outline of creation for cardiac disease modeling using patient-specific iPSCs. Within several topics, we present the actual representative methodologies throughout the process from the derivation of cardiomyocytes to those of functional analysis.

DOI： 10.1007/7651_2014_165

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.3791/53225

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1016/j.bbrc.2015.07.036

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1371/journal.pone.0133308

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1371/journal.pone.0133568

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1038/ncomms7745

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記述言語：英語  

DOI： 10.4252/wjsc.v7.i1.116

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1016/j.fob.2015.03.008

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.5966/sctm.2014-0072

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1161/JAHA.114.001263

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記述言語：英語  

DOI： 10.1016/j.cardfail.2014.07.290

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1016/j.yjmcc.2014.03.020

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1371/journal.pone.0097397

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1371/journal.pone.0085645

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1016/j.stem.2012.09.013

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記述言語：英語   掲載種別：研究論文（学術雑誌）   出版者・発行元：Hindawi Publishing Corporation  

DOI： 10.1155/2013/659739

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記述言語：英語  

DOI： 10.1152/ajpheart.00376.2012

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1093/cvr/cvs206

Web of Science

PubMed

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1038/nprot.2012.015

Web of Science

PubMed

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

This unit describes a protocol for the generation of induced pluripotent stem (iPS) cells from human peripheral circulating T cells. Initially, human dermal fibroblasts and retroviral vectors were used to generate human iPS cells. Invasive approaches, such as skin biopsy, and genomic insertion of transgenes into the host genome are not appropriate for routine clinical application. Peripheral circulating T cells are readily available from blood samples of patients and healthy volunteers. For the efficient generation of human iPS cells, efficient introduction of the transgene into host cells is necessary. Using a combination of activated T cell culture and Sendai virus allows for the easy and efficient introduction of transgenes into activated T cells and the generation of human iPS cells without genomic integration of extrinsic genes. The T cell-derived iPS (TiPS) cells exhibit monoclonal T cell receptor (TCR) rearrangement in their genome, a hallmark of mature terminally differentiated T cells.

DOI： 10.1002/9780470151808.sc04a03s18

PubMed

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記述言語：英語   出版者・発行元：The Japanese Society of Inflammation and Regeneration  

Pluripotent stem (iPS) cells are a very promising cell source for models of human genetic diseases and revolutionary new therapies. Successful reprogramming of human blood cells has been reported and is likely to advance the clinical application of iPS cells. In terms of a patient's own somatic cells, generating iPS cells from peripheral blood cells has advantages for clinical applications because these cells are an easily accessible cell source. Of the human peripheral blood cells, T cells can be readily cultured in vitro and proliferate rapidly. Furthermore, only a small amount of peripheral blood is needed to generate iPS cells from T cells, thus increasing the number of patients in whom the technique can be used. iPS cells that contain T-cell receptor (TCR) rearrangements in their genome also have the potential to be traceable markers when establishing novel transplantation therapies. The present review summarizes recent progress in the methods used to generate iPS cells and the future potential of human T cell-derived iPS cells.

DOI： 10.2492/inflammregen.31.393

CiNii Article

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1016/j.stem.2010.06.003

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PubMed

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1161/CIRCRESAHA.109.214130

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PubMed

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1016/j.stem.2010.01.002

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PubMed

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1016/j.bbrc.2009.05.073

Web of Science

PubMed

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記述言語：英語   掲載種別：研究論文（学術雑誌）  

DOI： 10.1385/EP:15:1:83

Scopus

PubMed

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記述言語：日本語   出版者・発行元：(一社)日本補体学会  

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J-GLOBAL

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記述言語：英語   掲載種別：研究発表ペーパー・要旨（国際会議）  

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J-GLOBAL

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記述言語：英語  

DOI： 10.4020/jhrs.27.PL

Scopus

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記述言語：日本語   出版者・発行元：(一社)日本循環器学会  

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記述言語：日本語  

DOI： 10.2169/naika.105.1287

J-GLOBAL

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記述言語：英語   掲載種別：研究発表ペーパー・要旨（国際会議）  

Web of Science

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記述言語：英語   掲載種別：研究発表ペーパー・要旨（国際会議）  

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記述言語：日本語   出版者・発行元：最新医学社  

CiNii Article

CiNii Books

J-GLOBAL

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記述言語：英語   掲載種別：研究発表ペーパー・要旨（国際会議）  

Web of Science

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記述言語：日本語  

J-GLOBAL

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記述言語：日本語  

J-GLOBAL

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記述言語：日本語   出版者・発行元：(一社)日本心臓病学会  

J-GLOBAL

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記述言語：日本語  

J-GLOBAL

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記述言語：英語   掲載種別：研究発表ペーパー・要旨（国際会議）  

Web of Science

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記述言語：日本語  

J-GLOBAL

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記述言語：日本語  

J-GLOBAL

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記述言語：日本語  

J-GLOBAL

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記述言語：日本語  

J-GLOBAL

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記述言語：日本語   出版者・発行元：医歯薬出版  

CiNii Article

CiNii Books

J-GLOBAL

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記述言語：日本語   出版者・発行元：メディカ出版  

CiNii Article

CiNii Books

J-GLOBAL

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記述言語：日本語   出版者・発行元：医学書院  

DOI： 10.11477/mf.1408101965

CiNii Article

CiNii Books

J-GLOBAL

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記述言語：日本語   出版者・発行元：オーム社  

CiNii Article

CiNii Books

J-GLOBAL

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記述言語：英語   掲載種別：研究発表ペーパー・要旨（国際会議）  

Web of Science

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記述言語：日本語   出版者・発行元：医学書院  

DOI： 10.11477/mf.1404101370

CiNii Article

CiNii Books

J-GLOBAL

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記述言語：英語   掲載種別：研究発表ペーパー・要旨（国際会議）  

Web of Science

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記述言語：英語   掲載種別：研究発表ペーパー・要旨（国際会議）  

Web of Science

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記述言語：英語   掲載種別：研究発表ペーパー・要旨（国際会議）  

Web of Science

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開催年月日： 2024年8月25日 - 2024年8月29日

記述言語：英語   会議種別：口頭発表（一般）  

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開催年月日： 2024年8月17日 - 2024年8月18日

記述言語：日本語  

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開催年月日： 2023年12月16日

記述言語：日本語   会議種別：公開講演，セミナー，チュートリアル，講習，講義等  

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開催年月日： 2023年11月11日 - 2023年11月15日

記述言語：英語   会議種別：口頭発表（一般）  

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記述言語：英語   会議種別：口頭発表（一般）  

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記述言語：日本語   会議種別：シンポジウム・ワークショップ パネル（公募）  

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記述言語：日本語   会議種別：口頭発表（招待・特別）  

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記述言語：日本語   会議種別：口頭発表（一般）  

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記述言語：英語   会議種別：口頭発表（一般）  

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記述言語：英語  

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