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We aimed to develop the Korean Hospital Frailty Risk Score (K-HFRS) by applying the International Classification of Diseases-10 codes to community-dwelling older adults' medical data. We selected data from 2,761 people with no missing main variable values from the Korean Frailty and Aging Cohort Data (KFACD) and National Health Insurance Database (NHID) for analysis. Frailty was determined based on modified Fried's phenotype [MFP] and Korean Frailty Index for Primary Care [KFI-PC] in the KFACD. A previously established method calculated the K-HFRS, verified by the area under the receiver operating characteristic (ROC) curve. The calculated cutoff value predicted the medical use. The respective K-HFRSs of the frailty group using the MFP and KFI-PC criteria ranged from 3.64 (±3.03) to 8.15 (±5.72) and 4.07 (±3.42) to 9.10 (±6.28), with 7.67 (±5.40) and 8.59 (±6.03) when four diagnoses were included. The K-HFRS of the frailty group using the KFI-PC criteria was higher than that using the MFP criteria. With four diagnoses included using the MFP criteria, the adjusted odds ratio (OR) for medical expenditures in the frailty group compared to the non-frailty group was 3.01 (95% confidence interval [CI] 2.52-3.60, p < .001); for the number of emergency room (ER) visits was 2.19 (95% CI 1.77-2.70, p < .001); for inpatient days was 2.48 (95% CI 2.08-2.96, p < .001). With four diagnoses included using the KFI-PC criteria, the adjusted OR value for medical expenditures was 2.77 (95% CI 2.35-3.27, p < .001); for the number of ER visits was 1.87 (95% CI 1.51-2.32, p < .001); for inpatient days was 2.07 (95% CI 1.75-2.45, p < .001). This study substantiated that the K-HFRS can measure frailty efficiently at a lower cost. Follow-up studies are needed for additional validity.

The reinforcing fibers in filament winding fiber-reinforced polymer (FFRP) are not arranged in the axial direction; thus, the members are vulnerable to bending and shear stresses. To address the limitations, this study evaluated FRP-concrete composite piles with reinforcing fiber arranged in circumferential directions. In particular, modular pultruded FRP (PFRP) members were fabricated with reinforcing fibers arranged in the axial and circumferential directions. The exterior of the fabricated PFRP members was reinforced with FFRP, and the flexural performance of these members was investigated through flexural strength tests. The results obtained from the flexural tests and flexural-stiffness prediction formula differed by approximately 0.72–1.36 times. A comparison between the results of the flexural test and flexural-strength prediction equation showed an error of approximately 1 to 10%.

The aim of this study was to investigate the mediating effects of health literacy on the relationship between frailty and health-related quality of life (HRQOL) among community-dwelling older adults. This study used the Korean Frailty and Aging Cohort Database (KFACD) for secondary data analysis. We selected data from 1,631 people without missing main variable values for analysis. Frailty was determined based on the modified Fried's phenotype [MFP], and HRQOL was measured using the Korean version of the 5-level EuroQol questionnaire (EQ-5D-5L). Health literacy was assessed using the questions on the Behavioral Risk Factor Surveillance System (BRFSS) used by the U.S. Center for Disease Control and Prevention. To examine the mediating role of health literacy in the relationship between frailty and HRQOL, Baron & Kenny's three-step mediating effect verification method was utilized. The participants had a mean frailty score of 1.37±1.02, health literacy score of 8.56±2.59, and HRQOL score of 0.84±0.10. Frailty was negatively correlated with health literacy (r = -0.27, p < .001) and HRQOL (r = -0.32, p < .001), while health literacy was positively correlated with HRQOL (r = 0.34, p < .001). We observed that health literacy played a partial mediating role in the relationship between frailty and HRQOL. To increase older adults' HRQOL, measures that directly prevent and manage frailty as well as interventions that target the enhancement of health literacy are needed.

Low-density DNA microarrays are crucial in molecular diagnostics due to their cost-effectiveness and high sensitivity. However, reliable spot localization remains challenging due to positional variations and image artifacts. Traditional intensity-based methods often struggle with weak fluorescence signals. To address this, we propose a rapid spot localization method that combines template matching with point pattern matching, enhanced through vectorized programming and square (box) templates. Vectorized programming accelerated the most time-consuming calculation by 82 times on a PC and was 6000 times faster on a Raspberry Pi compared to a for-loop implementation. While this improvement applies to the vectorized square calculation alone, substantial performance gains were still achieved in the overall process. Additionally, replacing circular templates with square templates resulted in a fourfold reduction in processing time without compromising detection performance. The proposed method effectively reduces computational overhead, making it suitable for high-throughput and resource-constrained applications. The method was validated using HPV genotyping images from commercial DNA microarrays, demonstrating its practical applicability and robust performance in clinical settings.

Emotional awareness perception is a largely growing field that allows for more natural interactions between people and machines. Electroencephalography (EEG) has emerged as a convenient way to measure and track a user’s emotional state. The non-linear characteristic of the EEG signal produces a high-dimensional feature vector resulting in high computational cost. In this paper, characteristics of multiple neural networks are combined using Deep Feature Clustering (DFC) to select high-quality attributes as opposed to traditional feature selection methods. The DFC method shortens the training time on the network by omitting unusable attributes. First, Empirical Mode Decomposition (EMD) is applied as a series of frequencies to decompose the raw EEG signal. The spatiotemporal component of the decomposed EEG signal is expressed as a two-dimensional spectrogram before the feature extraction process using Analytic Wavelet Transform (AWT). Four pre-trained Deep Neural Networks (DNN) are used to extract deep features. Dimensional reduction and feature selection are achieved utilising the differential entropy-based EEG channel selection and the DFC technique, which calculates a range of vocabularies using k-means clustering. The histogram characteristic is then determined from a series of visual vocabulary items. The classification performance of the SEED, DEAP and MAHNOB datasets combined with the capabilities of DFC show that the proposed method improves the performance of emotion recognition in short processing time and is more competitive than the latest emotion recognition methods.

Parkinson’s disease (PD) is an incurable movement disorder characterized by dopaminergic cell loss, neuroinflammation, and α-synuclein pathology. Herein, we investigated the therapeutic effects of necrosulfonamide (NSA), a specific inhibitor of mixed lineage kinase domain-like protein (MLKL), in a subacute 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) mouse model of PD. MLKL is an executor of necroptosis, a programmed cell death pathway that causes inflammation. Repeated administration of NSA resulted in the recovery of impaired motor performance and dopaminergic degeneration. Furthermore, NSA inhibited the phosphorylation, ubiquitylation, and oligomerization of MLKL, all of which are associated with MLKL cell death-inducing activity in dopaminergic cells in the substantia nigra (SN). NSA also inhibited microglial activation and reactive astrogliosis as well as the MPTP-induced expression of proinflammatory molecules such as tumor necrosis factor-α, interleukin-1β, inducible nitric oxide synthase, and cystatin F. Furthermore, NSA inhibited α-synuclein oligomerization and phosphorylation in the SN of MPTP-treated mice by inhibiting the activity of glycogen synthase kinase 3β and matrix metalloproteinase-3. In conclusion, NSA has anti-necroptotic, anti-inflammatory, and anti-synucleinopathic effects on PD pathology. Therefore, NSA is a potential therapeutic candidate for PD.

Apoptosis inhibitor of macrophage (AIM) modulates the signaling in inflammatory responses, including infection, cancer, or other immune diseases. Recent studies suggest that like interleukin-10 (IL-10), AIM is involved in alternatively activated (M2) macrophage polarization. We aimed to understand whether and how AIM is involved in IL-10-induced inhibition of inflammasome activation and resolution of inflammation. First, we demonstrated that IL-10 induced increases in mRNA and protein expression of AIM in murine bone marrow-derived macrophages (BMDM). In addition, genetic and pharmacologic inhibition of STAT3 (signal transducer and activator of transcription 3) reduced IL-10-induced AIM expression. We also found that IL-10-induced STAT3 activity enhanced the AIM promoter activity by directly binding the promoter of the AIM gene. Additionally, reduction of LPS/adenosine triphosphate (ATP)-induced IL-1β production and caspase-1 activation by IL-10 was reversed in BMDM from AIM −/− mice. Treatment of BMDM from both wild type (WT) and IL-10 −/− mice with recombinant AIM showed the inhibitory effects on IL-1β and IL-18 production and caspase-1 activation. Endogenous and exogenous AIM inhibited apoptosis-associated speck-like protein containing a caspase activation and recruitment domain (ASC) speck formation. In LPS-induced acute peritonitis, inhibition of IL-1β and IL-18 production in peritoneal lavage fluid (PLF) and serum, reduction of caspase-1 activation in peritoneal macrophages, and reduction of numbers of neutrophils and peritoneal macrophages in PLF by administration of IL-10 were not evident in AIM −/− mice. Our in vitro and in vivo data reveal a novel role of AIM in the inhibition of inflammasome-mediated caspase-1 activation and IL-1β and IL-18 production.

We present a compact and cost-effective real-time PCR system designed for point-of-care testing (POCT), utilizing a PCB-based disposable chip and an open-platform CMOS camera. The system integrates precise thermal cycling with software-synchronized fluorescence detection and provides real-time analysis through a dedicated user interface. To minimize cost and complexity, a polycarbonate reaction chamber was integrated with a PCB-based heater and thermistor. A slanted LED illumination setup and an open-platform USB camera were employed for fluorescence imaging. Signal alignment was enhanced using device-specific region-of-interest (ROI) tracking based on copper pad corner detection. Thermal cycling performance achieved a heating rate of 8.0 °C/s and a cooling rate of −9.3 °C/s, with steady-state accuracy within ±0.1 °C. Fluorescence images exhibited high dynamic range without saturation, and the 3σ-based ROI correction method improved signal reliability. System performance was validated using Chlamydia trachomatis DNA standard (103 copies), yielding consistent amplification curves with a Ct standard deviation below 0.3 cycles. These results demonstrate that the proposed system enables rapid, accurate, and reproducible nucleic acid detection, making it a strong candidate for field-deployable molecular diagnostics.

Background Neuroinflammation plays a pivotal role in the pathogenesis of Parkinson’s disease (PD). Thus, the development of agents that can control neuroinflammation has been suggested as a promising therapeutic strategy for PD. In the present study, we investigated whether the phosphodiesterase (PDE) 10 inhibitor has anti-inflammatory and neuroprotective effects in neuroinflammation and PD mouse models. Methods Papaverine (PAP) was utilized as a selective inhibitor of PDE10. The effects of PAP on the expression of pro-inflammatory molecules were examined in lipopolysaccharide (LPS)–stimulated BV2 microglial cells by ELISA, RT-PCR, and Western blot analysis. The effects of PAP on transcription factors were analyzed by the electrophoretic mobility shift assay, the reporter gene assay, and Western blot analysis. Microglial activation and the expression of proinflammatory molecules were measured in the LPS- or MPTP-injected mouse brains by immunohistochemistry and RT-PCR analysis. The effect of PAP on dopaminergic neuronal cell death and neurotrophic factors were determined by immunohistochemistry and Western blot analysis. To assess mouse locomotor activity, rotarod and pole tests were performed in MPTP-injected mice. Results PAP inhibited the production of nitric oxide and proinflammatory cytokines in LPS-stimulated microglia by modulating various inflammatory signals. In addition, PAP elevated intracellular cAMP levels and CREB phosphorylation. Treatment with H89, a PKA inhibitor, reversed the anti-inflammatory effects of PAP, suggesting the critical role of PKA signaling in the anti-inflammatory effects of PAP. We verified the anti-inflammatory effects of PAP in the brains of mice with LPS-induced systemic inflammation. PAP suppressed microglial activation and proinflammatory gene expression in the brains of these mice, and these effects were reversed by H89 treatment. We further examined the effects of PAP on MPTP-injected PD model mice. MPTP-induced dopaminergic neuronal cell death and impaired locomotor activity were recovered by PAP. In addition, PAP suppressed microglial activation and proinflammatory mediators in the brains of MPTP-injected mice. Conclusions PAP has strong anti-inflammatory and neuroprotective effects and thus may be a potential candidate for treating neuroinflammatory disorders such as PD.