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Aim This review aimed to examine and describe the published research on nursing home (NH) nurses' turnover intentions in their workplace. Design This study is a systematic review following PRISMA guidelines. Methods An electronic search was conducted for English and Korean articles to identify research studies published between 2009–2019 using CINAHL, PubMed, Cochrane Library, PsycINFO, RISS, and DBpia. Results A total of six studies met the inclusion criteria and revealed NH nurses' turnover intentions. The factors influencing NH nurses' turnover intentions were identified and classified as individual and organizational factors. Among the various factors above, this study found that job satisfaction was the most influential factor in nurses' turnover intentions. Therefore, further efforts are required to increase NH nurses' job satisfaction to decrease turnover intention.

The internet is now a major source of health information. With the growth of internet users, eHealth literacy has emerged as a new concept for digital health care. Therefore, health professionals need to consider the eHealth literacy of consumers when providing care utilizing digital health technologies. This study aimed to identify currently available eHealth literacy instruments and evaluate their measurement properties to provide robust evidence to researchers and clinicians who are selecting an eHealth literacy instrument. We conducted a systematic review and meta-analysis of self-reported eHealth literacy instruments by applying the updated COSMIN (COnsensus-based Standards for the selection of health Measurement INstruments) methodology. This study included 7 instruments from 41 articles describing 57 psychometric studies, as identified in 4 databases (PubMed, CINAHL, Embase, and PsycInfo). No eHealth literacy instrument provided evidence for all measurement properties. The eHealth literacy scale (eHEALS) was originally developed with a single-factor structure under the definition of eHealth literacy before the rise of social media and the mobile web. That instrument was evaluated in 18 different languages and 26 countries, involving diverse populations. However, various other factor structures were exhibited: 7 types of two-factor structures, 3 types of three-factor structures, and 1 bifactor structure. The transactional eHealth literacy instrument (TeHLI) was developed to reflect the broader concept of eHealth literacy and was demonstrated to have a sufficient low-quality and very low-quality evidence for content validity (relevance, comprehensiveness, and comprehensibility) and sufficient high-quality evidence for structural validity and internal consistency; however, that instrument has rarely been evaluated. The eHealth literacy scale was the most frequently investigated instrument. However, it is strongly recommended that the instrument's content be updated to reflect recent advancements in digital health technologies. In addition, the transactional eHealth literacy instrument needs improvements in content validity and further psychometric studies to increase the credibility of its synthesized evidence.

Purpose The Depression Anxiety Stress Scales (DASS)-21 measures emotional symptoms of depression, anxiety, and stress, is relatively short, and is freely available in the public domain, which has resulted in it being applied to various clinical and non-clinical populations in many countries. The aim of this study was to systematically review the measurement properties of the DASS-21. Methods The MEDLINE, Embase, and CINAHL databases were searched. The methodological quality of each identified study was assessed using the updated COSMIN Risk of Bias checklist. The quality of the measurement properties of the studies was rated using the updated criteria for good measurement properties. The quality of evidence was rated using a modified version of the GRADE approach. Results This study included 48 studies in its review. The content validity of the DASS-21 demonstrated sufficient moderate-quality evidence. The instrument exhibited sufficient high-quality evidence for bifactor structural validity and internal consistency. The instrument also showed sufficient high-quality evidence for hypothesis testing of construct validity. Regarding criterion validity, only the DASS-21 Depression subscale demonstrated sufficient high-quality evidence. The measurement invariance across gender demonstrated inconsistent moderate-quality evidence. There was insufficient low-quality evidence for the reliability of each subscale. For responsiveness there was sufficient low-quality evidence for depression and stress subscales, and insufficient very-low-quality evidence for anxiety subscale. Conclusions The DASS-21 demonstrated sufficient high-quality evidence for bifactor structural validity, internal consistency (bifactor), criterion validity (Depression subscale), and hypothesis testing for construct validity. Further studies are required to assess the other measurement properties of the DASS-21.

Pipelines that satisfy k-anonymity alone remain vulnerable to attribute disclosure under skewed sensitive attributes. We studied real-time anonymization of high-throughput data streams under strict delay budgets (β). We jointly enforced k-anonymity and l-diversity via a delay-aware Monitor–Trigger–Repair controller that selects swap vs. merge by minimizing a weighted objective λΔIL + (1 − λ)ΔRT while bounding overhead with a neighbor cap (c) and a growth cap (γ). On UCI Adult stream replay, we identified operating regions where stricter privacy does not necessarily increase distortion: with moderate-to-high k and sufficiently large β, groups satisfy l preemptively, reducing reconfigurations and avoiding aggressive generalization, thereby mitigating information loss relative to k-only baselines. Privacy metrics (l-satisfaction rate and entropy) also improved. We further report a focused sensitivity analysis on λ, c, and γ and evaluate an entropy-driven adaptive lt controller, showing that these levers provide interpretable trade-offs between latency and distortion and can suppress excessive reconfiguration and tail latency.

The rapid development of large language models (LLMs) motivates us to explore how such state-of-the-art natural language processing systems can inform aphasia research. What kind of language indices can we derive from a pre-trained LLM? How do they differ from or relate to the existing language features in aphasia? To what extent can LLMs serve as an interpretable and effective diagnostic and measurement tool in a clinical context? To investigate these questions, we constructed predictive and correlational models, which utilize mean surprisals from LLMs as predictor variables. Using AphasiaBank archived data, we validated our models’ efficacy in aphasia diagnosis, measurement, and prediction. Our finding is that LLMs-surprisals can effectively detect the presence of aphasia and different natures of the disorder, LLMs in conjunction with the existing language indices improve models’ efficacy in subtyping aphasia, and LLMs-surprisals can capture common agrammatic deficits at both word and sentence level. Overall, LLMs have potential to advance automatic and precise aphasia prediction. A natural language processing pipeline can be greatly benefitted from integrating LLMs, enabling us to refine models of existing language disorders, such as aphasia.

BAP1, a deubiquitinase with a ubiquitin C-terminal hydrolase domain, functions as a tumor suppressor involved in diverse cellular processes, including DNA repair, genome stability, and apoptosis. Inactivating mutations in BAP1—particularly missense and deletion variants—are recurrent across multiple cancers, with a high prevalence in clear cell renal cell carcinoma (ccRCC), mesothelioma, and uveal melanoma. Among these, the Glu31Lys mutation in ccRCC impairs BAP1’s enzymatic activity, protein stability, and DNA repair functions. Here, we investigated the physiological impact of this recurrent mutation using isogenic KMRC-20 ccRCC cell clones in which the Glu31Lys substitution was precisely corrected to wild-type glutamate via CRISPR-Cas9–mediated adenine base editing. BAP1 reactivation restored anchorage-dependent growth in KMRC-20 cells—a hallmark of non-transformed epithelial cells—and increased apoptosis under non-adherent conditions, indicating reinstated sensitivity to anoikis. Mechanistically, this phenotypic switch was accompanied by post-transcriptional downregulation of N-cadherin and β-catenin under anchorage-free conditions, implicating BAP1 in the regulation of adhesion- and Wnt-related survival pathways. Furthermore, transcriptomic profiling revealed broad gene expression changes upon BAP1 restoration, suggesting that these combined alterations contribute to the re-establishment of anchorage-dependent growth in KMRC-20 cells. These findings uncover a previously unrecognized role for BAP1 in suppressing anchorage-independent survival, providing new insights into BAP1-driven tumorigenesis and underscoring the therapeutic potential of precise gene editing to restore tumor suppressor function in ccRCC.

•A novel deep learning framework for automatic regions selection and regions’ functional relation modeling for early MCI identification.•Analyzing and interpreting selected ROIs through association with previous neuroscience studies, and offering eMCI-related information and individual differences.•Validating the effectiveness of our proposed method using the ADNI public dataset by comparing its performance to other methods. Resting-state functional magnetic resonance imaging (rs-fMRI) has been widely adopted to investigate functional abnormalities in brain diseases. Rs-fMRI data is unsupervised in nature because the psychological and neurological labels are coarse-grained, and no accurate region-wise label is provided along with the complex co-activities of multiple regions. To the best of our knowledge, most studies regarding univariate group analysis or multivariate pattern recognition for brain disease identification have focused on discovering functional characteristics shared across subjects; however, they have paid less attention to individual properties of neural activities that result from different symptoms or degrees of abnormality. In this work, we propose a novel framework that can identify subjects with early-stage mild cognitive impairment (eMCI) and consider individual variability by learning functional relations from automatically selected regions of interest (ROIs) for each subject concurrently. In particular, we devise a deep neural network composed of a temporal embedding module, an ROI selection module, and a disease-identification module. Notably, the ROI selection module is equipped with a reinforcement learning mechanism so it adaptively selects ROIs to facilitate the learning of discriminative feature representations from a temporally embedded blood-oxygen-level-dependent signals. Furthermore, our method allows us to capture the functional relations of a subject-specific ROI subset through the use of a graph-based neural network. Our method considers individual characteristics for diagnosis, as opposed to most conventional methods that identify the same biomarkers across subjects within a group. Based on the ADNI cohort, we validate the effectiveness of our method by presenting the superior performance of our network in eMCI identification. Furthermore, we provide insightful neuroscientific interpretations by analyzing the regions selected for the eMCI classification.

Alzheimer’s disease, the most common age-related neurodegenerative disease, is characterized by tau aggregation and associated with disrupted circadian rhythms and dampened clock gene expression. REV-ERBα is a core circadian clock protein which also serves as a nuclear receptor and transcriptional repressor involved in lipid metabolism and macrophage function. Global REV-ERBα deletion has been shown to promote microglial activation and mitigate amyloid plaque formation. However, the cell-autonomous effects of microglial REV-ERBα in healthy brain and in tauopathy are unexplored. Here, we show that microglial REV-ERBα deletion enhances inflammatory signaling, disrupts lipid metabolism, and causes lipid droplet (LD) accumulation specifically in male microglia. These events impair microglial tau phagocytosis, which can be partially rescued by blockage of LD formation. In vivo, microglial REV-ERBα deletion exacerbates tau aggregation and neuroinflammation in two mouse tauopathy models, specifically in male mice. These data demonstrate the importance of microglial lipid droplets in tau accumulation and reveal REV-ERBα as a therapeutically accessible, sex-dependent regulator of microglial inflammatory signaling, lipid metabolism, and tauopathy. The circadian clock protein REV-ERBα has been implicated in neuroinflammation but mechanisms are poorly understood. Here, the authors show that microglial REV-ERBα regulates inflammatory signaling and lipid droplet formation to exert sex-specific effects on tau pathology in mice.

Wireless communications and sensing have notably advanced thanks to the recent developments in both software and hardware. Although various modulation schemes have been proposed to efficiently use the limited frequency resources by exploiting several degrees of freedom, antenna performance is essentially governed by frequency only. Here, we present an antenna design concept based on metasurfaces to manipulate antenna performances in response to the time width of electromagnetic pulses. We numerically and experimentally show that by using a proper set of spatially arranged metasurfaces loaded with lumped circuits, ordinary omnidirectional antennas can be reconfigured by the incident pulse width to exhibit directional characteristics varying over hundreds of milliseconds or billions of cycles, far beyond conventional performance. We demonstrate that the proposed concept can be applied for sensing, selective reception under simultaneous incidence and mutual communications as the first step to expand existing frequency resources based on pulse width. Metasurface-based antennas show variable beam-patterns in response to the time width of electromagnetic pulses. This concept advances the design of antennas and wireless communication environments by using the pulse width as a new degree of freedom.

A promising new therapeutic target for the treatment of Alzheimer's disease (AD) is the circadian system. Although patients with AD are known to have abnormal circadian rhythms and suffer sleep disturbances, the role of the molecular clock in regulating amyloid‐beta (Aβ) pathology is still poorly understood. Here, we explored how the circadian repressors REV‐ERBα and β affected Aβ clearance in mouse microglia. We discovered that, at Circadian time 4 (CT4), microglia expressed higher levels of the master clock protein BMAL1 and more rapidly phagocytosed fibrillary Aβ1‐42 (fAβ1‐42) than at CT12. BMAL1 directly drives transcription of REV‐ERB proteins, which are implicated in microglial activation. Interestingly, pharmacological inhibition of REV‐ERBs with the small molecule antagonist SR8278 or genetic knockdown of REV‐ERBs‐accelerated microglial uptake of fAβ1‐42 and increased transcription of BMAL1. SR8278 also promoted microglia polarization toward a phagocytic M2‐like phenotype with increased P2Y12 receptor expression. Finally, constitutive deletion of Rev‐erbα in the 5XFAD model of AD decreased amyloid plaque number and size and prevented plaque‐associated increases in disease‐associated microglia markers including TREM2, CD45, and Clec7a. Altogether, our work suggests a novel strategy for controlling Aβ clearance and neuroinflammation by targeting REV‐ERBs and provides new insights into the role of REV‐ERBs in AD. Genetic and pharmacological inhibition of REV‐ERBα is markedly increased microglial phagocytosis of Aβ by modulating P2Y12R expression with induction of Aβ internalization‐related receptors, leading to the M2 polarization in vitro and in vivo.