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Paroxetine (PRX) is widely prescribed for treating psychiatric disorders. Emerging evidence suggests that PRX can act as an immunosuppressive agent, yet the molecular mechanisms underlying its effects are not fully understood. This study has investigated whether PRX influences phenotypic changes of monocytic cells and signaling pathways induced by immune oxysterol, like 27-hydroxycholesterol (27OHChol), that triggers an inflammatory response using THP-1 monocytic cells. Treatment with PRX impaired 27OHChol-induced transcription and production of the pro-inflammatory chemokine CCL2, which was associated with decreased migration of monocytic cells, and repressed the expression and activity of MMP-9. It reduced the expression of mature dendritic cell markers, like CD80, CD83, and CD88, and partially restored phagocytic function. PRX also impaired phosphorylation of Akt and the downstream targets of mTORC1, S6, and 4E-BP1. These results indicate that PRX suppresses 27OHChol-induced change of monocytic cells to a proinflammatory phenotype by influencing the Akt/mTORC1 pathway. We suggest that PRX exerts its anti-inflammatory effects by suppressing the activation of monocytic cells in response to immune oxysterol.

Low-cost light scattering particulate matter (PM) sensors have been widely researched and deployed in order to overcome the limitations of low spatio-temporal resolution of government-operated beta attenuation monitor (BAM). However, the accuracy of low-cost sensors has been questioned, thus impeding their wide adoption in practice. To evaluate the accuracy of low-cost PM sensors in the field, a multi-sensor platform has been developed and co-located with BAM in Dongjak-gu, Seoul, Korea from 15 January 2019 to 4 September 2019. In this paper, a sample variation of low-cost sensors has been analyzed while using three commercial low-cost PM sensors. Influences on PM sensor by environmental conditions, such as humidity, temperature, and ambient light, have also been described. Based on this information, we developed a novel combined calibration algorithm, which selectively applies multiple calibration models and statistically reduces residuals, while using a prebuilt parameter lookup table where each cell records statistical parameters of each calibration model at current input parameters. As our proposed framework significantly improves the accuracy of the low-cost PM sensors (e.g., RMSE: 23.94 → 4.70 μ g/m 3 ) and increases the correlation (e.g., R 2 : 0.41 → 0.89), this calibration model can be transferred to all sensor nodes through the sensor network.

Extranodal extension (ENE) of metastatic lymph nodes is a well-established adverse prognostic factor in head and neck cancer and has traditionally been confirmed pathologically. Imaging-detected ENE (iENE) enables pre-treatment assessment and provides prognostic information across different treatment settings. Recent studies and meta-analyses have demonstrated an independent association between iENE and adverse outcomes in both nasopharyngeal carcinoma (NPC) and human papillomavirus-positive oropharyngeal carcinoma, supporting its prognostic relevance, particularly in nonsurgical patient populations in whom pathological confirmation is unavailable. Growing evidence supporting the prognostic impact of iENE has influenced recent revisions to the American Joint Committee on Cancer 9th edition staging system, which now incorporates \"advanced radiological ENE\" as a criterion for the N3 category in NPC. However, the clinical interpretation of iENE remains constrained by substantial interobserver variability, heterogeneous imaging criteria, and an imperfect correlation with pathological ENE (pENE), underscoring the ongoing debate regarding its clinical validity. Recent international efforts have sought to standardize imaging definitions and grading systems for iENE, with the aim of reducing variability and facilitating more consistent application in clinical practice and research. This review summarizes the current evidence regarding the prognostic and diagnostic roles of iENE, outlines recent developments in its classification, and discusses future directions for its integration into risk-adapted treatment planning for head and neck cancer.

White blood cells (WBCs) act as mediators of inflammatory responses and are commonly measured in hospitals. Although several studies have reported a relation between WBC count and mortality, no systematic review or meta-analysis has been conducted. This study aimed to identify an association between WBC count and mortality. We conducted a systematic search on Embase using keywords such as “white blood cell” and “mortality.” We analyzed the hazard ratios (HRs) for WBC count of 1.0 × 109 cells/L regarding 2 criteria: the cause of mortality and the follow-up period. A total of 13 of 222 articles comprising a total of 62,904 participants were included in this study, meeting the criteria set. A positive association was observed between WBC count and mortality, as indicated by an HR of 1.10 (95% confidence interval [CI] 1.08 to 1.13). In additionally, WBC count emerged as a significant predictor of mortality in both groups, with an HR of 1.10 (95% CI 1.07 to 1.12) for patients with cardiovascular disease and an HR of 1.12 (95% CI 1.07 to 1.17) for the general population or patients with COVID-19. Furthermore, a higher WBC count demonstrated a significant association with long-term all-cause mortality (HR 1.09, 95% CI 1.07 to 1.12) and long-term cardiovascular mortality (HR 1.05, 95% CI 1.02 to 1.07). Similarly, a significant association was found between higher WBC count and short-term all-cause mortality (HR 1.12, 95% CI 1.09 to 1.16) and cardiovascular mortality (HR 1.12, 95% CI 1.07 to 1.17). Further research is necessary to explore the relation between WBC count and disease progression or death and to establish causality between elevated WBC count and disease progression.

The glymphatic system clears metabolic waste from the brain and can be probed using contrast-enhanced MRI. We investigated whether early-phase gadolinium-based contrast agent (GBCA) uptake varies with time of day and age. In this retrospective study, 447 patients who underwent brain MRI for suspected movement disorders received pre- and post-contrast T1 mapping a median of 5.9 minutes after GBCA injection. Scans were categorized as daytime (n = 307) or nighttime (n = 140). Regional ΔT1 values were measured in the cerebral and cerebellar cortices, white matter, basal ganglia perivascular spaces, and choroid plexus. Daytime cohorts showed significantly greater cortical enhancement than nighttime cohorts, particularly in the frontal, parietal, and temporal lobes, while no differences were observed in white matter, perivascular spaces, or choroid plexus. Increasing age was independently associated with stronger cortical enhancement across both day and night scans, with no significant interaction between age and time. These findings suggest that cortical GBCA retention is influenced by both circadian timing and aging, supporting early-phase T1 mapping as a practical approach to evaluate human glymphatic function.

This study proposes a new method for effectively manipulating a magnetic microrobot in a two-dimensional manner using a triad of electromagnetic coils (TEC). A TEC is a system consisting of three circular coils of the same type arranged in the form of a triangle. It has a simple structure and exhibits magnetic symmetry. This study sought to develop a method to more accurately manipulate and reduce the energy consumption of microrobots using a TEC. This was accomplished by selectively using individual coils of a TEC with respect to the robot’s position, moving direction, and other manipulating conditions based on the structural characteristics and magnetic field distribution pattern of the TEC. Effective calculation methods and operating procedures are also proposed. The proposed method was found to effectively generate the necessary actuation force to control microrobots by using either one or two of the coils of a TEC, depending on the given conditions. This type of process results in improved precision in magnetic field generation and a reduction in energy consumption while making it easier to control microrobots. Magnetic fields and actuation forces were generated using the proposed method under various experimental conditions, and these results were verified through simulations to confirm the validity of the proposed method. In addition, a TEC and a closed-loop control system were built and used to test the actuation of microrobots over various paths, and the results confirmed the superiority of the proposed method compared to existing methods.

Simulation with a reasonable physical model is important to develop control algorithms for robots quickly, accurately, and safely without damaging the associated physical systems in various environments. However, it is difficult to choose the suitable tool for simulating a specific project. To help users in selecting the best tool when simulating a given project, we compare the performance of the four widely used physics engines, namely, ODE, Bullet, Vortex, and MoJoco, for various simple and complex industrial scenarios. We first summarize the technical algorithms implemented in each physics engine. We also designed four simulation scenarios ranging from simple scenarios for which analytic solution exists to complex industrial scenarios to compare the performance of each physics engine. We then present the simulation results in the default settings of all the physics engines, and analyze the behavior and contact force of the simulated objects.

This paper presents a study on optimal design to determine the installation position and link lengths of a robot within a designated workspace for welding, aiming to minimize singularities during the robot’s motion. Bi-objective functions are formulated to minimize singularities while maximizing the volumes of linear velocity manipulability ellipsoid and angular velocity manipulability ellipsoid, respectively, ensuring isotropy. We have constructed a simulation environment incorporating PID control to account for robot tracking errors. This environment was utilized as a simulator to derive a Bi-objective function set within a genetic algorithm. Through this, we optimized four robot link length variables and two installation position variables, selecting the optimal design variables on the Pareto Front. In the standard work object, the volume average of the linear velocity manipulability ellipsoid was confirmed to have improved by 72% compared to the initial level, and the isotropy of the angular velocity manipulability ellipsoid was confirmed to have improved by 23% compared to the initial level. Furthermore, correlation analysis between design parameters identified those with a high correlation with the objective functions, and the analysis results are discussed.

Whole-body center of gravity (CG) movements in relation to the center of pressure (COP) offer insights into the balance control strategies of the human body. Existing CG measurement methods using expensive measurement equipment fixed in a laboratory environment are not intended for continuous monitoring. The development of wireless sensing technology makes it possible to expand the measurement in daily life. The insole system is a wearable device that can evaluate human balance ability by measuring pressure distribution on the ground. In this study, a novel protocol (data preparation and model training) for estimating the 3-axis CG trajectory from vertical plantar pressures was proposed and its performance was evaluated. Input and target data were obtained through gait experiments conducted on 15 adult and 15 elderly males using a self-made insole prototype and optical motion capture system. One gait cycle was divided into four semantic phases. Features specified for each phase were extracted and the CG trajectory was predicted using a bi-directional long short-term memory (Bi-LSTM) network. The performance of the proposed CG prediction model was evaluated by a comparative study with four prediction models having no gait phase segmentation. The CG trajectory calculated with the optoelectronic system was used as a golden standard. The relative root mean square error of the proposed model on the 3-axis of anterior/posterior, medial/lateral, and proximal/distal showed the best prediction performance, with 2.12%, 12.97%, and 12.47%. Biomechanical analysis of two healthy male groups was conducted. A statistically significant difference between CG trajectories of the two groups was shown in the proposed model. Large CG sway of the medial/lateral axis trajectory and CG fall of the proximal/distal axis trajectory is shown in the old group. The protocol proposed in this study is a basic step to have gait analysis in daily life. It is expected to be utilized as a key element for clinical applications.

Accurate prediction of electric energy consumption is critical for both user convenience and supplier efficiency. This study introduces an ensemble approach that integrates clustering algorithms with machine learning (ML) models to enhance prediction accuracy by identifying consumption patterns within buildings. The research focused on residential apartments in the metropolitan area of Korea, utilizing four evaluation methods (Elbow-Method, Silhouette Score, Calinski-Harabasz Index, and Dunn Index) across five data collection intervals (10 min, 1 h, 1 day, 1 week, and 1 month). Five ML models (CatBoost, Decision Tree, LightGBM, Random Forest, XGBoost) were assessed for their prediction performance across clusters. Additionally, ML models that exhibited high performance within each cluster were amalgamated into an ensemble model to assess the predictive performance regarding total electric energy consumption at the research site. Optimal clustering resulted in two clusters (142 houses for C0, 206 houses for C1) using monthly resampled power data. CatBoost and LightGBM exhibited the highest average prediction performance. Based on the possible combinations of the two models applied to each cluster, four ensemble models were developed: CB-CB, CB-LGBM, LGBM-CB, and LGBM-LGBM. Statistical analysis confirmed that all ensemble models significantly outperformed the control group’s traditional ML approaches without clustering ( p  < 0.05 or 0.01). The proposed clustering-based ML ensemble model in this study can predict the energy consumed in buildings more accurately by accounting for the unique consumption pattern of each house. It is anticipated to contribute effectively to energy consumption reduction.