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Fine particulate matter (PM2.5) exposure worsens chronic respiratory diseases through oxidative stress and inflammation. Perilla frutescens (L.) has potential respiratory protective properties, but the impact of growth stages on its beneficial metabolites is unclear. We aimed to evaluate how different growth stages affect phenolic acids, flavonoids, and polycosanols in perilla seeds and flowers and their efficacy in countering PM2.5-induced damage. Perilla seeds and flowers from five varieties at 10, 20, 30, and 40 days post-flowering were analyzed for metabolite content. Their antioxidant, anti-inflammatory, and respiratory protective effects were tested in RPMI 2650 cells. Our findings indicated that perilla flowers contained higher levels of functional components than seeds and exhibited significant variation with maturation. Phenolic acids of perilla flowers were highest at the early stages of maturation after flowering. However, individual flavones of perilla flowers were the highest at the late maturation stages after flowering. Extracts from perilla flowers harvested 20 days after flowering exhibited significant respiratory protection, effectively inhibiting inflammatory cytokines, mucus secretion, and oxidative stress markers. In conclusion, the flower parts of perilla, particularly those harvested 20 days after flowering, are useful materials for obtaining phenolic compounds, including rosmarinic acid, with high antioxidant and respiratory enhancement effects.

Fine particulate matter (PM2.5) is known to exacerbate chronic respiratory disorders, primarily by inducing inflammatory responses and mucus overproduction. Perilla leaves are reported to have significant health benefits, such as antioxidant, antibacterial, and antiallergic properties, attributed to phenolic compounds that vary depending on genetic diversity. In this study, flavonoid‐rich extracts (FRE) from 56 perilla leaf varieties and genetic resources were prepared and screened using a mass screening system. The screening focused on evaluating their anti‐inflammatory, mucus‐reducing, and respiratory protective effects against PM2.5‐induced damage in human nasal cells (RPMI2650). Parameters such as cell viability, nitric oxide (NO) levels, and mucus secretion factor (MUC5AC) concentrations were assessed. Among the 56 varieties, Perilla frutescens var. crispa (YCPL706), sourced from Ulleung Island, Korea, exhibited the highest cell viability (112.50%, 100 μg/mL), lowest NO concentration (9.98 μM, 100 μg/mL), and MUC5AC level (78.65 ng/mL, 100 μg/mL). Further evaluation of YCPL706 FRE demonstrated significant respiratory protective effects, including the inhibition of pro‐inflammatory cytokines (TNF‐α, IL‐6, and IL‐1β), MUC5AC, and oxidative stress factors (MDA and ROS), compared to the control cultivar Namcheon. YCPL706 also showed strong antibacterial activity against Pseudomonas aeruginosa (minimum inhibitory concentration: 5 mg/mL). These findings suggest that the genetic resource YCPL706 is a promising candidate for combating PM2.5‐induced respiratory damage due to its potent anti‐inflammatory, antioxidant, and antibacterial properties. This study demonstrates the potential of perilla leaves as a protective agent against PM2.5‐induced respiratory damage, highlighting genetic diversity's role in phytochemical accumulation. Among 56 varieties, the selected resource (YCPL706) protects the respiratory system by reducing oxidative stress, inflammation, mucus hypersecretion, and fibrosis, thereby preventing respiratory diseases induced by pathogenic bacteria, such as P. aeruginosa.

Emulsifiers, like egg yolk (EY), are necessary for the formation of mayonnaise, which is an oil-in-water type of colloid. This study aimed to assess the potential of defatted soybean powder treated with supercritical carbon dioxide (DSF) to enhance the quality of plant-based mayonnaise as plant-based alternatives gain popularity. This study involved the production of DSF and the comparison of its quality attributes to those of mayonnaise made with varying amounts of control soy flour (CSF), DSF, and EY. It was found that mayonnaise made with an increased quantity of DSF showed better emulsion stability, viscosity, and a smaller, more uniform particle size when compared with CSF mayonnaise. Additionally, DSF mayonnaise was generally rated higher in sensory evaluation. The addition of approximately 2% DSF positively influenced the emulsion and sensory properties of the vegan mayonnaise, indicating that DSF is a promising plant-based alternative emulsifier for the replacement of animal ingredients.

The lymphatic system plays a crucial role in maintaining physiological homeostasis and regulating immune responses. Traditional imaging modalities such as magnetic resonance imaging, computerized tomography, and positron emission tomography have been widely used to diagnose disorders in the lymphatic system, including lymphedema, lymphangioma, lymphatic metastasis, and Castleman disease. Nano-fluorescence technology has distinct advantages—including naked-eye visibility, operational simplicity, portability of the laser, and real-time visibility—and serves as an innovative alternative to traditional imaging techniques. This review explores recent advancements in nano-fluorescence imaging aimed at enhancing the resolution of lymphatic structure, function, and immunity. After delineating the fundamental characteristics of lymphatic systems, it elaborates on the development of various nano-fluorescence systems (including nanoparticles incorporating fluorescent dyes and those with intrinsic fluorescence) while addressing key challenges such as photobleaching, limited tissue penetration, biocompatibility, and signal interference from biomolecules. Furthermore, this review highlights the clinical applications of nano-fluorescence and its potential integration into standard diagnostic protocols. Ongoing advancements in nanoparticle technology underscore the potential of nano-fluorescence to revolutionize the diagnosis and treatment of lymphatic disease.

Fine particulate matter (PM2.5) exposure worsens chronic respiratory diseases through oxidative stress and inflammation. Perilla frutescens (L.) has potential respiratory protective properties, but the impact of growth stages on its beneficial metabolites is unclear. We aimed to evaluate how different growth stages affect phenolic acids, flavonoids, and polycosanols in perilla seeds and flowers and their efficacy in countering PM2.5-induced damage. Perilla seeds and flowers from five varieties at 10, 20, 30, and 40 days post-flowering were analyzed for metabolite content. Their antioxidant, anti-inflammatory, and respiratory protective effects were tested in RPMI 2650 cells. Our findings indicated that perilla flowers contained higher levels of functional components than seeds and exhibited significant variation with maturation. Phenolic acids of perilla flowers were highest at the early stages of maturation after flowering. However, individual flavones of perilla flowers were the highest at the late maturation stages after flowering. Extracts from perilla flowers harvested 20 days after flowering exhibited significant respiratory protection, effectively inhibiting inflammatory cytokines, mucus secretion, and oxidative stress markers. In conclusion, the flower parts of perilla, particularly those harvested 20 days after flowering, are useful materials for obtaining phenolic compounds, including rosmarinic acid, with high antioxidant and respiratory enhancement effects.

The flatfish is a major farmed species consumed globally in large quantities. However, due to the densely populated farming environment, flatfish are susceptible to lesions and diseases, making early lesion detection crucial. Traditionally, lesions were detected through visual inspection, but observing large numbers of fish is challenging. Automated approaches based on deep learning technologies have been widely used to address this problem, but accurate detection remains difficult due to the diversity of the fish and the lack of a fish lesion and disease dataset. This study augments fish lesion images using generative adversarial networks and image harmonization methods. Next, lesion detectors are trained separately for three body parts (head, fins, and body) to address individual lesions properly. Additionally, a flatfish lesion and disease image dataset, called FlatIMG, was created and verified using the proposed methods on the dataset. A flash salmon lesion dataset was also tested to validate the generalizability of the proposed methods. The results achieved 12% higher performance than the baseline framework. This study is the first attempt to create a high‐quality flatfish lesion image dataset with detailed annotations and proposes an effective lesion detection framework. Automatic lesion and disease monitoring can be achieved in farming environments using the proposed methods and dataset.

Extreme heat exposure has severe negative impacts on humans, and the issue is exacerbated by climate change. Estimating spatial heat stress such as mean radiant temperature (MRT) is currently difficult to apply at city scale. This study constructed a method for estimating the MRT of street canyons using Google Street View (GSV) images and investigated its large-scale spatial patterns at street level. We used image segmentation using deep learning to calculate the view factor (VF) and project panorama into fisheye images. We calculated sun paths to estimate MRT using panorama images from Google Street View. This paper shows that regression analysis can be used to validate between estimated short-wave, long-wave radiation and the measurement data at seven field measurements in the clear-sky (0.97 and 0.77, respectively). Additionally, we compared the calculated MRT and land surface temperature (LST) from Landsat 8 on a city scale. As a result of investigating spatial patterns of MRT in Seoul, South Korea, we found that a high MRT of street canyons (>59.4 °C) is mainly distributed in open space areas and compact low-rise density buildings where the sky view factor is 0.6–1.0 and the building view factor (BVF) is 0.35–0.5, or west-east oriented street canyons with an SVF of 0.3–0.55. However, high-density buildings (BVF: 0.4–0.6) or high-density tree areas (Tree View Factor, TVF: 0.6–0.99) showed low MRT (<47.6). The mapped MRT results had a similar spatial distribution to the LST; however, the MRT was lower than the LST in low tree density or low-rise high-density building areas. The method proposed in this study is suitable for a complex urban environment consisting of buildings, trees, and streets. This will help decision makers understand spatial patterns of heat stress at the street level.

Air pollution, particularly fine particulate matter (PM2.5), poses significant environmental and public health challenges in South Korea. The National Institute of Environmental Research (NIER) currently relies on numerical models such as the Community Multiscale Air Quality (CMAQ) model for PM2.5 forecasting. However, these models exhibit inherent uncertainties due to limitations in emission inventories, meteorological inputs, and model frameworks. To address these challenges, this study evaluates and compares the forecasting performance of two alternative models: Long Short-Term Memory (LSTM), a deep learning model, and Seasonal Auto Regressive Integrated Moving Average with Exogenous Variables (SARIMAX), a statistical model. The performance evaluation was focused on Seoul, South Korea, and took place over different forecast lead times (D00–D02). The results indicate that for short-term forecasts (D00), SARIMAX outperformed LSTM in all statistical metrics, particularly in detecting high PM2.5 concentrations, with a 19.43% higher Probability of Detection (POD). However, SARIMAX exhibited a sharp performance decline in extended forecasts (D01–D02). In contrast, LSTM demonstrated relatively stable accuracy over longer lead times, effectively capturing complex PM2.5 concentration patterns, particularly during high-concentration episodes. These findings highlight the strengths and limitations of statistical and deep learning models. While SARIMAX excels in short-term forecasting with limited training data, LSTM proves advantageous for long-term forecasting, benefiting from its ability to learn complex temporal patterns from historical data. The results suggest that an integrated air quality forecasting system combining numerical, statistical, and machine learning approaches could enhance PM2.5 forecasting accuracy.

The accurate monitoring and analysis of PM-2.5 are critical for improving air quality and formulating public health policies. However, environmental data often contain missing values due to equipment failures, data collection errors, or extreme weather conditions, which can hinder reliable analysis and predictions. This study evaluates the performance of various missing data imputation methods for PM-2.5 data in Seoul, Korea, using scenarios with artificially generated missing values during high- and low-concentration periods. The methods compared include FFILL, KNN, MICE, SARIMAX, DNN, and LSTM. The results indicate that KNN consistently achieved stable and balanced performance across different temporal intervals, with an RMSE of 5.65, 9.14, and 9.71 for 6 h, 12 h, and 24 h intervals, respectively. FFILL demonstrated superior performance for short intervals (RMSE 4.76 for 6 h) but showed significant limitations as intervals lengthened. SARIMAX performed well in long-term scenarios, with an RMSE of 9.37 for 24 h intervals, but required higher computational complexity. Conversely, deep learning models such as DNN and LSTM underperformed, highlighting the need for further optimization for time-series data. This study highlights the practicality of KNN as the most effective method for addressing missing PM-2.5 data in mid- to long-term applications due to its simplicity and efficiency. These findings provide valuable insights into the selection of imputation methods for environmental data analysis, contributing to the enhancement of data reliability and the development of effective air quality management policies.