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Background: The overall survival rate of advanced cancer patients has improved thanks to the development of modern medical treatments, particularly new and innovative chemotherapeutic agents such as targeted therapies and immune checkpoint inhibitors.Current Concepts: Chemotherapy is administered in neoadjuvant, adjuvant, and palliative settings, and its ultimate goal is to improve overall survival. Chemotherapy has several proven and valuable clinical benefits, but also many side effects that cannot be ignored, especially in patients with poor European Cancer Oncology Group performance status. Therefore, we must carefully weigh and trade off the benefits and harms from many chemotherapy agents. In fact, it can be difficult to determine whether advanced cancer patients really benefit from chemotherapy, which is why a number of value measurement tools such as the American Society Clinical Oncology-Value Framework and the European Society for Medical Oncology-Magnitude of Clinical Benefit Scale have been developed.Discussion and Conclusion: We need to include individual cancer patients in decision-making processes and use appropriate shared decision-making to decide whether or not to administer chemotherapy. Furthermore, we should perform rational trade-offs in consideration of limited health resources.

Background/Aims: The rapidity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-specific memory B or T cell response in vaccinated individuals is important for our understanding of immunopathogenesis of coronavirus disease 2019 (COVID-19). We therefore compared the timing of adequate immune responses between the first and booster doses of COVID-19 vaccines in infection-naïve healthcare workers.Methods: We enrolled healthcare workers who received two doses of either the BNT162b2 vaccine or the ChAdOx1 vaccine, all of whom received the BNT162b2 vaccine as the booster (the third) dose. Spike 1 (S1)-immunoglobulin G (IgG) antibodies and interferon gamma producing T cell responses were measured at 0, 7, 14, and 21 days after the first dose, and at 0 and between 2 to 7 days after the booster dose.Results: After the first-dose vaccination, the S1-IgG antibody responses were elicited within 14 days in the BNT162b2 group and within 21 days in the ChAdOx1 group. After the booster dose, the S1-IgG antibody responses were elicited within 5 days in both groups. The SARS-CoV-2-specific T cell responses appeared at 7 days after the first dose and at 4 days after the booster dose.Conclusions: SARS-CoV-2-specific immune responses by memory B cells and T cells may be expected to appear around 4 to 5 days after the booster dose.

Along with rapid economic growth, the United Arab Emirates (UAE) has undergone enormous sociocultural changes. Consequently, sociocultural and psychological factors, along with malnutrition and physical inactivity, have contributed to the high obesity rate. The objective of this study is to assess the long-term impact of these new emerging factors on obesity among women in the UAE via mathematical modeling.A differential equation model was developed considering psychological/social factors in population dynamics. It predicts the long-term prevalence of obesity among women in the UAE under these factors by 2070. Computer simulations and a sensitivity analysis of the model were conducted to measure the impact of these factors on obesity.The model predicts the following: 80.07% of female UAE nationals will become overweight or have obesity and 60.19% will have obesity by 2070, and the population with abnormal eating behavior will increase to 15% by 2070. Psychological/social factors aggravate the obesity problems and can cause abnormal eating behavior to develop with little effect on weight reduction.The obesity rate of female UAE nationals will continue to rise by 2070. Rising abnormal eating behavior caused by psychological/social factors is an emerging issue and should be recognized as a sign of escalating obesity problems in the UAE.

The strategic design of the cathode is a critical feature for high-performance and long-lasting reversibility of an energy storage system. In particular, the round-trip efficiency and cycling performance of nonaqueous lithium–oxygen batteries are governed by minimizing the discharge products, such as Li2O and Li2O2. Recently, a metal–organic framework has been directly pyrolyzed into a carbon frame with controllable pore volume and size. Furthermore, selective metallic catalysts can also be obtained by adjusting metal ions for outstanding electrochemical reactions. In this study, various bimetallic zeolitic imidazolate framework (ZIF)-derived carbons were designed by varying the ratio of Zn to Co ions. Moreover, carbon nanotubes (CNTs) are added to improve the electrical conductivity further, ultimately leading to better electrochemical stability in the cathode. As a result, the optimized bimetallic ZIF–carbon/CNT composite exhibits a high discharge capacity of 16,000 mAh·g−1, with a stable cycling performance of up to 137 cycles. This feature is also beneficial for lowering the overpotential of the cathode during cycling, even at the high current density of 2,000 mA·g−1.

The spread of social media has accelerated the formation and dissemination of user review data, which contain subjective opinions of users on products, in an e-commerce environment. Because these reviews significantly influence other users, opinion mining has garnered substantial attention in analyzing the positive and negative opinions of users and deriving solutions based on these analytical results. Terms that include sentimental information and used in user reviews serve as the most crucial element in sentimental classification. In this regard, it is crucial to distinguish the most influential terms in user reviews. This study proposed a document-level sentiment classification model based on the collection and application of user reviews generated in an e-commerce environment. Here, a term information extraction method was applied to the proposed model to select core terms, classify the selected terms according to parts of speech (POS), determine terms that can increase information power and influence, and adopt these terms in opinion mining research, based on SVM, SVM+, and SVM+MTL techniques. The results obtained from evaluating the proposed model indicate that it exhibited excellent sentiment analysis performance. The proposed model is expected to be effectively utilized in providing enhanced services for users and increasing competitiveness in the e-commerce environment.

Obesity is of grave concern as a comorbidity of coronavirus disease 2019 (COVID-19). We examined the factors associated with weight gain among Korean adults during the COVID-19 pandemic.We conducted an online survey of 1,000 adults (515 men and 485 women aged 20-59 years) in March 2021. Multivariable logistic regression analysis was performed to evaluate the factors associated with weight gain. The analysis was adjusted for sex, age, region, depressive mood, anxiety, eating out, late-night meals, alcohol consumption, exercise, sleep disturbance, meal pattern, subjective body image, comorbidities, marital status, living alone, and income.After adjusting for confounding variables, the odds for weight gain increased in the group aged 20-34 years compared with the group aged 50-59 years (1.82; 95% confidence interval [CI], 1.01-3.32). Women were more associated with the risk of weight gain compared with men. The odds for weight gain increased in the lack of exercise group compared with the exercise group (4.89; 95% CI, 3.09-7.88). The odds for weight gain increased in the eating-out and late-night meal groups compared with that in the groups not eating out and not having late-night meals. Individuals watching a screen for 3-6 hr/day were more associated with the risk of weight gain compared with those who rarely watched a screen. The odds for weight gain increased in participants who considered themselves obese compared with those who did not consider themselves obese.A healthy diet and regular physical activity tend to be the best approach to reduce obesity, a risk factor for COVID-19.

BackgroundIsocitrate dehydrogenase (IDH) mutation and 1p19q codeletion are important beneficial prognosticators in glioma. IDH and 1p19q diagnosis requires tissue sampling and there are likely benefits of presurgical diagnosis. Research supports the potential of MRI-based IDH and 1p19q diagnosis, however there is a paucity of external validation outside the widely used The Cancer Imaging Archive (TCIA) dataset. We present a combined IDH and 1p19q classification algorithm and assess performance on a local retrospective cohort (NZ) and the Erasmus Glioma Database (EGD).Methods2D convolutional neural networks are trained to provide IDH and 1p19q classification. Inputs are T1 post-contrast, T2, and FLAIR sequences. Training data consists of preoperative imaging from the TCIA dataset (n=184) and a locally obtained NZ dataset (n=349). Evaluation data consists of the most recent cases from the NZ dataset (n=205) and the EGD (n=420).ResultsIDH classification accuracy was 93.3% and 91.5% on the NZ and EDG, with AUC values of 95.4% and 95.8%, respectively. 1p19q accuracy was 94.5% and 87.5% with AUC values of 92.5% and 85.4% on the NZ and EGD datasets. Combined IDH and 1p19q accuracy was 90.4% and 84.3% on the NZ and EGD, with AUC values of 92.4% and 91.2%.ConclusionsHigh IDH and 1p19q classification performance was achieved on the NZ retrospective cohort. Performance generalised to the EGD demonstrating the potential for clinical translation. This method makes use of readily available imaging and has high potential impact in glioma diagnostics.Key Points-IDH and 1p19q are the main molecular markers in glioma.-Accurate predictions can be obtained from preoperative MRI without changes to imaging protocols.-Non-invasive diagnosis will likely enhance treatment planning and facilitate targeted preoperative therapies.Importance of the StudyThe 2021 WHO CNS tumour classification system formalises the increasing recognition of molecular factors like IDH and 1p19q in the prognostication and treatment of glioma. Emerging research shows the potential of artificial intelligence methods applied to preoperative MRI sequences to noninvasively predict molecular status. A limitation of the literature published to date is a lack of generalisation and external validation outside the widely used TCIA dataset. Here we present the performance of an MRI-based IDH and 1p19q classification tool evaluated on a large consecutive cohort from New Zealand and an independent publicly available dataset of MR images from the Netherlands. We demonstrate high predictive performance with robust generalisation, indicating the potential usefulness of this method in the workup of glioma. Reliable preoperative tumour characterisation may facilitate tailored treatment approaches and early decision making without the need for additional imaging.

There has been a concerted effort by the neuroimaging community to establish standards for computational methods for data analysis that promote reproducibility and portability. In particular, the Brain Imaging Data Structure (BIDS) specifies a standard for storing imaging data, and the related BIDS App methodology provides a standard for implementing containerized processing environments that include all necessary dependencies to process BIDS datasets using image processing workflows. We present the BrainSuite BIDS App, which encapsulates the core MRI processing functionality of BrainSuite within the BIDS App framework. Specifically, the BrainSuite BIDS App implements a participant-level workflow comprising three pipelines and a corresponding set of group-level analysis workflows for processing the participant-level outputs. The BrainSuite Anatomical Pipeline (BAP) extracts cortical surface models from a T1-weighted (T1w) MRI. It then performs surface-constrained volumetric registration to align the T1w MRI to a labeled anatomical atlas, which is used to delineate anatomical regions of interest in the MRI brain volume and on the cortical surface models. The BrainSuite Diffusion Pipeline (BDP) processes diffusion-weighted imaging (DWI) data, with steps that include coregistering the DWI data to the T1w scan, correcting for geometric image distortion, and fitting diffusion models to the DWI data. The BrainSuite Functional Pipeline (BFP) performs fMRI processing using a combination of FSL, AFNI, and BrainSuite tools. BFP coregisters the fMRI data to the T1w image, then transforms the data to the anatomical atlas space and to the Human Connectome Project’s grayordinate space. Each of these outputs can then be processed during group-level analysis. The outputs of BAP and BDP are analyzed using the BrainSuite Statistics in R (bssr) toolbox, which provides functionality for hypothesis testing and statistical modeling. The outputs of BFP can be analyzed using atlas-based or atlas-free statistical methods during group-level processing. These analyses include the application of BrainSync, which synchronizes the time-series data temporally and enables comparison of resting-state or task-based fMRI data across scans. We also present the BrainSuite Dashboard quality control system, which provides a browser-based interface for reviewing the outputs of individual modules of the participant-level pipelines across a study in real-time as they are generated. BrainSuite Dashboard facilitates rapid review of intermediate results, enabling users to identify processing errors and make adjustments to processing parameters if necessary. The comprehensive functionality included in the BrainSuite BIDS App provides a mechanism for rapidly deploying the BrainSuite workflows into new environments to perform large-scale studies. We demonstrate the capabilities of the BrainSuite BIDS App using structural, diffusion, and functional MRI data from the Amsterdam Open MRI Collection’s Population Imaging of Psychology dataset.

Purpose This paper aims to investigate the relationship between sustainability reporting and firm value, and subsequently, ascertains the moderating effect of assurance and the type of assurer on the sustainability reporting–firm value nexus. Design/methodology/approach The study is based on sample firms from the Johannesburg Stock Exchange (JSE) in South Africa. The fixed‐effect panel data analysis method is used to estimate the coefficients of the variables. Findings A significant positive relationship is found between sustainability reporting and firm value. The results also suggest that sustainability assurance has significant explanatory power on firm value. Furthermore, the authors found that the market is unable to distinguish between sustainability assurance services provided by Big 4 audit firms and specialist consultant firms. Practical implications The authors expect managers will see sustainability reporting and assurance as a business strategy with incremental market value. The study should also serve as a reference for stakeholders engaged in the advocacy for the adoption of sustainability assurance practices on the JSE and other emerging markets. Social implications The study finds that the South African market rewards firms that purchase third-party assurance to guarantee the integrity of their corporate social responsibility reports. This understanding could help encourage more firms to embrace the concept of sustainability assurance. Originality/value The study offers a first-hand information on how market participants in Johannesburg, an emerging economy, view sustainability assurance and the services provided by the different assurers.