Explore the vast range of titles available.

Purpose Delirium after transcatheter aortic valve implantation (TAVI) should be prevented because it is associated with worse patient outcomes. Perioperative administration of benzodiazepines is a risk factor for postoperative delirium; however, the association between remimazolam, a newer ultrashort-acting benzodiazepine for general anesthesia, and postoperative delirium remains unclear. This study aimed to evaluate whether remimazolam administration during TAVI under general anesthesia affected the incidence of postoperative delirium. Methods This single-center retrospective study recruited all adult patients who underwent transfemoral TAVI (TF-TAVI) under general anesthesia between March 2020 and May 2022. Patients were divided into the remimazolam (R) and propofol (P) groups according to the sedative used for anesthesia. In the R group, all patients received flumazenil after surgery. The primary endpoint was the incidence of delirium within 3 days after surgery. Factors associated with delirium after TF-TAVI were examined by multiple logistic regression analysis. Results Ninety-eight patients were included in the final analysis (R group, n  = 40; P group, n  = 58). The incidence of postoperative delirium was significantly lower in the R group than in the P group (8% vs. 26%, p  = 0.032). Multiple logistic regression analysis revealed that remimazolam (odds ratio 0.17, 95% CI 0.04–0.80, p  = 0.024) was independently associated with the incidence of postoperative delirium, even after adjustment for age, sex, preoperative cognitive function, history of stroke, and TF-TAVI approach. Conclusion Remimazolam may benefit TF-TAVI in terms of postoperative delirium; however, its usefulness must be further evaluated in extensive prospective studies.

One of the most significant risk factors for diseases is aging. Interestingly, some organisms, such as naked mole-rats and most turtles, do not exhibit typical aging-like symptoms or increased mortality as they become older. These aspects indicate that aging is not necessarily an essential event for animal life and are avoidable. Overcoming aging would free humans from age-associated diseases (AADs) and prolong lifespans. Recent studies have demonstrated that one of the causes of age-related organ dysfunction is excessive chronic inflammation caused by the accumulation of senescent cells (SNCs) and their senescence-associated secretory phenotypes (SASPs). Therefore, the development of drugs and medication to remove SNCs is ongoing. Natural killer (NK) cells are integral components of the innate immune system that are critical for clearing SNCs. Beyond this direct function, NK cells also orchestrate innate and adaptive immunity responses to survey and eradicate these compromised cells. Consequently, preserving NK cell function throughout the aging process is paramount for mitigating AADs and promoting robust health in later life. Simultaneously, NK cell-based senotherapy presents compelling avenues for addressing the multifaceted challenges associated with SNC accumulation and aging. Recent investigations into adoptive NK cell-based senotherapy have demonstrated considerable promise in rejuvenating immunosenescence, facilitating SNC elimination. The accumulating evidence provides a promising proof-of-concept for adoptive NK cell-based senotherapy, indicating its potential as a development in longevity therapeutics.

Glioblastoma (GBM) is the most aggressive and malignant primary brain tumor in adults. Despite multimodality treatment involving surgical resection, radiation therapy, chemotherapy, and tumor-treating fields, the median overall survival (OS) after diagnosis is approximately 2 years and the 5-year OS is poor. Considering the poor prognosis, novel treatment strategies are needed, such as immunotherapies, which include chimeric antigen receptor T-cell therapy, immune checkpoint inhibitors, vaccine therapy, and oncolytic virus therapy. However, these therapies have not achieved satisfactory outcomes. One reason for this is that these therapies are mainly based on activating T cells and controlling GBM progression. Natural killer (NK) cell-based immunotherapy involves the new feature of recognizing GBM via differing mechanisms from that of T cell-based immunotherapy. In this review, we focused on NK cell-based immunotherapy as a novel GBM treatment strategy.

In this study, we proposed two types of hybrid models based on the heterogeneous autoregressive (HAR) model and support vector regression (SVR) model to forecast realized volatility (RV). The first model is a residual-type model, where the RV is first predicted using the HAR model, and the residuals are used to train the SVR model. The residual component is then predicted using the SVR model, and the results from both the HAR and SVR models are combined to obtain the final prediction. The second model is a weight-based model, which is a combination of the HAR and SVR models and uses the same independent variables and dependent variables as the HAR model; we adjust the contribution of the two models to the predicted values by giving different weights to each model. In particular, four volatility models are used in RV forecasting as basic models. For empirical analysis, the RV of returns of the Tokyo stock price index and five individual stocks of TOPIX 30 is used as the dataset. The empirical results reveal that according to the model confidence set test, the weight-type model outperforms the HAR model and the residual-type HAR–SVR model.

Financial time-series data often exhibit statistically significant skewness and heavy tails, and numerous flexible distributions have been proposed to model them. In the context of the Log-linear Realized GARCH model with Skew-t (ST) distributions, our objective is to explore how the choice of prior distributions in the Adaptive Random Walk Metropolis method and initial parameter values in the Generalized Reduced Gradient (GRG) Solver method affect ST parameter and log-likelihood estimates. An empirical study was conducted using the FTSE 100 index to evaluate model performance. We provide a comprehensive step-by-step tutorial demonstrating how to perform estimation and sensitivity analysis using data tables in Microsoft Excel. Among seven ST distributions—namely, the asymmetric, epsilon, exponentiated half-logistic, Hansen, Jones–Faddy, Mittnik–Paolella, and Rosco–Jones–Pewsey distributions—Hansen’s ST distribution is found to be superior. This study also applied the GRG method to estimate new approaches, including Realized Real-Time GARCH, Realized ASHARV, and GARCH@CARR models. An empirical study showed that the GARCH@CARR model with the feedback effect provides the best goodness of fit. Out-of-sample forecasting evaluations further confirm the predictive dominance of models incorporating real-time information, particularly Realized Real-Time GARCH for volatility forecasting and Realized ASHARV for 1% VaR estimation. The findings offer actionable insights for portfolio managers and risk analysts, particularly in improving volatility forecasts and tail-risk assessments during market crises, thereby enhancing risk-adjusted returns and regulatory compliance. Although the GRG method is sensitive to initial values, its presence in the spreadsheet method can be a powerful and promising tool in working with probability density functions that have explicit forms and are unimodal, high-dimensional, and complex, without the need for programming experience.

This study clarifies how risks spread across economic sectors and indicates the sectors that are the most affected to help investors with asset allocation and to support them in risk management. Although the Japanese stock market is one of the relatively large global stock markets, no studies have explored volatility spillovers among its sectors. Using the forecast error variance decomposition of the vector autoregressive model, this study examines the volatility spillovers among sectors classified on the Tokyo Stock Exchange. Our findings show that the pattern of volatility spillovers across sectors in the Japanese stock market differs between a few years preceding the coronavirus disease 2019 (pre-COVID-19), from 2014 to 2019, and during the COVID-19 period, in 2020. Although the energy resources and bank sectors are risk receivers in the pre-COVID-19 period, these sectors are risk transmitters during the COVID-19 period. We also find that volatility spillovers in the Japanese stock market are mainly driven by negative realized semivariance. These results are useful for asset allocation and risk management.

It is well known that the volatility spillover increases when a large economic shock occurs, and then the volatility spillover pattern in the market changes. Accordingly, many papers note that clarifying the time-varying pattern of volatility transmission in domestic and international markets is useful for investors and policymakers. This paper focuses on information contagion across various industrial sectors, investigates portfolio strategies based on the volatility spillovers, and aims to clarify whether an investment strategy based on volatility spillovers benefits investors. Regarding portfolio reallocation, as soon as we observe an increase or a decrease in the effect/timing of a volatility spillover, we obtain a smaller number of reallocations and a more informative portfolio. Our results compare our proposed method with periodic portfolios, for example, daily or annually, showing that our proposed method has larger returns and a greater Sharpe ratio than the others.

To preserve language function, intraoperative functional brain mapping (IFBM) in and near the speech center is essential. We present a case of a 73-year-old right-handed woman with colon cancer. She presented with mild speech disturbance. Magnetic resonance imaging revealed a ringed enhancing lesion in the frontal operculum. The preservation of language function was critical; therefore, she underwent awake craniotomy using IFBM. Thus, the speech site was elicited by cortical electrical stimulation at the surface, near the location of the tumor. We made a safe corticotomy on the surface of the lesion and performed the resection of brain metastasis (BM) via a safety corridor. We achieved gross total resection of the BM while preserving the language function. After surgery, she recovered from speech disturbance. She returned to her normal life with improved language function. IFBM is a useful tool to undertake a safe approach via the speech center, avoiding permanent language deficits.

We modify the recently proposed forecasting model of high-dimensional covariance matrices (HDCM) of asset returns using high-dimensional principal component analysis (PCA). It is well-known that when the sample size is smaller than the dimension, eigenvalues estimated by classical PCA have a bias. In particular, a very small number of eigenvalues are extremely large and they are called spiked eigenvalues. High-dimensional PCA gives eigenvalues which correct the biases of the spiked eigenvalues. This situation also happens in the financial field, especially in situations where high-frequency and high-dimensional data are handled. The research aims to estimate the HDCM of asset returns using high-dimensional PCA for the realized covariance matrix using the Nikkei 225 data, it estimates 5- and 10-min intraday asset-returns intervals. We construct time-series models for eigenvalues which are estimated by each PCA, and forecast HDCM. Our simulation analysis shows that the high-dimensional PCA has better estimation performance than classical PCA for the estimating integrated covariance matrix. In our empirical analysis, we show that we will be able to improve the forecasting performance using the high-dimensional PCA and make a portfolio with smaller variance.

To avoid permanent neurologic deficits and preserve brain function, intraoperative electrical stimulation mapping (IESM) is essential for surgical resection. A 59-year-old right-handed woman with ovarian cancer who had undergone stereotactic radiotherapy for brain metastasis two years before, was introduced due to progressive left upper paresis. Magnetic resonance imaging showed a recurrence of the lesion. We performed awake surgery using IESM. Thus, the sensorimotor site was elicited on the precentral and postcentral gyrus. However, IESM elicited no disturbance of motor function on the surface of the posterior part of the precentral gyrus. We made a safe corticotomy on it, and performed the resection of recurrent BM. Preserving the motor and sensory function, we achieved the resection of BM. After surgery, she experienced a significant improvement in motor function. IESM is a useful tool to make a safe approach via the precentral gyrus avoiding permanent sensorimotor deficits.