Evaluation of Layered Graphene Prepared via Hydroxylation of Potassium-Graphite Intercalation Compounds

Layered graphene was prepared via the hydroxylation of potassium-graphite intercalation compound (KC8) produced from exfoliated graphite flake powder. When a small amount of water was dropped onto the KC8 in an oxygen-free atmosphere, the stage structure of the intercalation compounds was broken, and agglomerated graphene was obtained. Sonication of this agglomerated graphene in buffered water containing sodium dodecyl sulfate followed by rinsing with hot water yielded gray samples. Using scanning electron microscopy, X-ray diffraction analysis, and Raman spectroscopy, the gray samples were determined to be composed of a few layers of graphene with an area of 20–100 μm2 and thickness of 1.7 nm. They were thinner than those obtained when starting with natural graphite

Impact of atrial mitral and tricuspid regurgitation on atrial fibrillation recurrence after ablation

Background: Atrial fibrillation (AF) induces functional mitral regurgitation (FMR) and tricuspid regurgitation (FTR) during atrial remodeling. FMR and FTR are associated with AF prognosis, but the effects for AF recurrence after ablation have not been determined conclusively. Methods: Two hundred thirty nine patients who underwent AF ablation were enrolled. Forty five patients were excluded. In total, 194 patients were analyzed. FMR and FTR were assessed by echocardiography. The left atrial volume index (LAVI) was evaluated by contrast-enhanced computed tomography. Results: Significant FMR and moderate FTR were observed in 15 (7.7%) and in 25 (12.9%) patients, respectively. The severity of tricuspid regurgitation (TR) significant correlated with age, NT-proBNP, and LAVI. During a 13.4 month follow-up period of, 39 patients (20.1%) demonstrated AF recurrence. In the Cox proportional-hazards model, E/e’, FTR, and LAVI, were termed as predictor factors of AF recurrence (E/e’. hazard ratio [HR] = 1.117; P = 0.019, significant FTR. HR = 4.679; P = 0.041, LAVI. HR = 1.057; P = 0.003). Kaplan–Meier analysis showed that AF recurrence was more frequent in FTR compared with the nonsignificant FTR cases (log-rank, P = 0.001). Although survival analysis showed no difference with or without FMR, the presence of FMR and FTR was strongly associated with high-AF recurrence (log-rank, P = 0.004). Conclusions: AF recurrence was associated with E/e’, LAVI, and extensive FTR. Specifically, the combination of FTR and FMR markedly worsens the AF prognosis

Risk of Mortality Prediction Involving Time-Varying Covariates for Patients with Heart Failure Using Deep Learning

Heart failure (HF) is challenging public medical and healthcare systems. This study aimed to develop and validate a novel deep learning-based prognostic model to predict the risk of all-cause mortality for patients with HF. We also compared the performance of the proposed model with those of classical deep learning- and traditional statistical-based models. The present study enrolled 730 patients with HF hospitalized at Toho University Ohashi Medical Center between April 2016 and March 2020. A recurrent neural network-based model (RNNSurv) involving time-varying covariates was developed and validated. The proposed RNNSurv showed better prediction performance than those of a deep feed-forward neural network-based model (referred as “DeepSurv”) and a multivariate Cox proportional hazard model in view of discrimination (C-index: 0.839 vs. 0.755 vs. 0.762, respectively), calibration (better fit with a 45-degree line), and ability of risk stratification, especially identifying patients with high risk of mortality. The proposed RNNSurv demonstrated an improved prediction performance in consideration of temporal information from time-varying covariates that could assist clinical decision-making. Additionally, this study found that significant risk and protective factors of mortality were specific to risk levels, highlighting the demand for an individual-specific clinical strategy instead of a uniform one for all patients

Exploring and Identifying Prognostic Phenotypes of Patients with Heart Failure Guided by Explainable Machine Learning

Identifying patient prognostic phenotypes facilitates precision medicine. This study aimed to explore phenotypes of patients with heart failure (HF) corresponding to prognostic condition (risk of mortality) and identify the phenotype of new patients by machine learning (ML). A unsupervised ML was applied to explore phenotypes of patients in a derivation dataset (n = 562) based on their medical records. Thereafter, supervised ML models were trained on the derivation dataset to classify these identified phenotypes. Then, the trained classifiers were further validated on an independent validation dataset (n = 168). Finally, Shapley additive explanations were used to interpret decision making of phenotype classification. Three patient phenotypes corresponding to stratified mortality risk (high, low, and intermediate) were identified. Kaplan–Meier survival curves among the three phenotypes had significant difference (pairwise comparison p < 0.05). Hazard ratio of all-cause mortality between patients in phenotype 1 (n = 91; high risk) and phenotype 3 (n = 329; intermediate risk) was 2.08 (95%CI 1.29–3.37, p = 0.003), and 0.26 (95%CI 0.11–0.61, p = 0.002) between phenotype 2 (n = 142; low risk) and phenotype 3. For phenotypes classification by random forest, AUCs of phenotypes 1, 2, and 3 were 0.736 ± 0.038, 0.815 ± 0.035, and 0.721 ± 0.03, respectively, slightly better than the decision tree. Then, the classifier effectively identified the phenotypes for new patients in the validation dataset with significant difference on survival curves and hazard ratios. Finally, age and creatinine clearance rate were identified as the top two most important predictors. ML could effectively identify patient prognostic phenotypes, facilitating reasonable management and treatment considering prognostic condition

Risk of Mortality Prediction Involving Time-Varying Covariates for Patients with Heart Failure Using Deep Learning

Heart failure (HF) is challenging public medical and healthcare systems. This study aimed to develop and validate a novel deep learning-based prognostic model to predict the risk of all-cause mortality for patients with HF. We also compared the performance of the proposed model with those of classical deep learning- and traditional statistical-based models. The present study enrolled 730 patients with HF hospitalized at Toho University Ohashi Medical Center between April 2016 and March 2020. A recurrent neural network-based model (RNNSurv) involving time-varying covariates was developed and validated. The proposed RNNSurv showed better prediction performance than those of a deep feed-forward neural network-based model (referred as “DeepSurv”) and a multivariate Cox proportional hazard model in view of discrimination (C-index: 0.839 vs. 0.755 vs. 0.762, respectively), calibration (better fit with a 45-degree line), and ability of risk stratification, especially identifying patients with high risk of mortality. The proposed RNNSurv demonstrated an improved prediction performance in consideration of temporal information from time-varying covariates that could assist clinical decision-making. Additionally, this study found that significant risk and protective factors of mortality were specific to risk levels, highlighting the demand for an individual-specific clinical strategy instead of a uniform one for all patients

分類が困難であった初老期痴呆の一例

症例は55歳,男性。初発症状や画像所見および臨床症状がPick病に一致していたが痴呆の進行があまりにも急速でありC-J病が疑われた。しかしミオクローヌスや周期性同期性放電は見られず,さらに滞続言語を認め,C-J病は否定的であった。その他球麻痺症状を認め,また呼吸筋麻痺の存在があると考えられ運動ニューロン疾患(MND)を伴う痴呆も疑われたが,筋萎縮が認められず,MND伴う痴呆も否定的であった。最近,前頭側頭型痴呆(FTD)という概念が提唱されており,本症例もFTDに属する可能性が高いと考えられたが,FTDの臨床的特徴にある「緩徐な進行」という点では一致しなかった。本症例は従来の痴呆疾患の分類では一致するものはなかった。FTDを含む非Alzheimer型痴呆は臨床像からの分類が困難な症例も存在し,その病態はほとんど解明されていないのが現状であり,このような症例の詳細な臨床的観察と病理学的検索の集積により将来新しい疾患の発見,分類が期待される。A 55-year-old man, who had been diagnosed as having Pick\u27s disease based on clinical and neuroimaging findings, was admitted to our hospital. From 4 months before the admission, he was noticed to have become short-tempered, and his behaviors were not appropriate for occasions. In parallel, his disability in performing social activities became obvious. The magnetic resonance imaging of his brain indicated marked frontotemporal atrophy and bilateral dilatation of the lateral ventricle as well as mild atrophy of other brain regions. By use of single photon emission computed tomography with ^I-IMP, the regional cerebral blood flow was found to be markedly decreased in bilateral frontal lobes and the right temporal lobe. His condition severely deteriorated during a short period after the admission, showing symptoms such as disorientation, poor rapport with others, "stehende Redensart" (C. Schneider), and extinct initiative in thought, speech and behavior. Dysarthria and dysphagia appeared 7 months after the admission, which further deteriorated his condition, leading to his death due to respiratory failure within 9 months after the admission. This very acute course of deterioration seemed to be unusual for Pick\u27s disease. Creutzfeldt-Jakob disease (CJD) was not feasible, because neither periodic synchronous discharges nor myoclonic movements were observed whereas "stehende Redensart", which is seldom seen in CJD patients, became outstanding. It was also difficult to define this case as dementia with motor neuron disease, because neither muscle atrophy nor fasciculation was notable. Although we infer that this case is closely related to the frontotemporal dementia (FTD) proposed by the Lund and Manchester group, the clinical and neuroimaging findings may not safely be attributed to this subgroup of dementia. Since little is known about non-Alzheimer degenerative dementia including FTD, further accumulation of dementia cases having clinical features inconsistent with known classifications is needed to understand dementia and to define subgroups