Meridianin C inhibits the growth of YD-10B human tongue cancer cells through macropinocytosis and the down-regulation of Dickkopf-related protein-3

Meridianin C is a marine natural product known for its anti‐cancer activity. At present, the anti‐tumour effects of meridianin C on oral squamous cell carcinoma are unknown. Here, we investigated the effect of meridianin C on the proliferation of four different human tongue cancer cells, YD‐8, YD‐10B, YD‐38 and HSC‐3. Among the cells tested, meridianin C most strongly reduced the growth of YD‐10B cells; the most aggressive and tumorigenic of the cell lines tested. Strikingly, meridianin C induced a significant accumulation of macropinosomes in the YD‐10B cells; confirmed by the microscopic and TEM analysis as well as the entry of FITC‐dextran, which was sensitive to the macropinocytosis inhibitor amiloride. SEM data also revealed abundant long and thin membrane extensions that resemble lamellipodia on the surface of YD‐10B cells treated with meridianin C, pointing out that meridianin C‐induced macropinosomes was the result of macropinocytosis. In addition, meridianin C reduced cellular levels of Dickkopf‐related protein‐3 (DKK‐3), a known negative regulator of macropinocytosis. A role for DKK‐3 in regulating macropinocytosis in the YD‐10B cells was confirmed by siRNA knockdown of endogenous DKK‐3, which led to a partial accumulation of vacuoles and a reduction in cell proliferation, and by exogenous DKK‐3 overexpression, which resulted in a considerable inhibition of the meridianin C‐induced vacuole formation and decrease in cell survival. In summary, this is the first study reporting meridianin C has novel anti‐proliferative effects via macropinocytosis in the highly tumorigenic YD‐10B cell line and the effects are mediated in part through down‐regulation of DKK‐3

Adjuvant cytokine-induced killer cell immunotherapy for hepatocellular carcinoma: a propensity score-matched analysis of real-world data

Background Several randomized controlled trials have shown that adjuvant immunotherapy with autologous cytokine-induced killer (CIK) cells prolongs recurrence-free survival (RFS) after curative treatment for hepatocellular carcinoma (HCC). We investigated the efficacy of adjuvant immunotherapy with activated CIK cells in real-world clinical practice. Methods A total of 59 patients who had undergone curative surgical resection or radiofrequency ablation for stage I or II HCC, and subsequently received adjuvant CIK cell immunotherapy at two large-volume centers in Korea were retrospectively included. Propensity score matching with a 1:1 ratio was conducted to avoid possible bias, and 59 pairs of matched control subjects were also generated. The primary endpoint was RFS and the secondary endpoints were overall survival and safety. Results The median follow-up duration was 28.0 months (interquartile range, 22.9–42.3 months). In a univariable analysis, the immunotherapy group showed significantly longer RFS than the control group (hazard ratio [HR], 0.42; 95% CI, 0.22–0.80; log-rank P = 0.006). The median RFS in the control group was 29.8 months, and the immunotherapy group did not reach a median RFS. A multivariable Cox proportional hazard analysis showed that immunotherapy was an independent predictor for HCC recurrence (adjusted HR, 0.38; 95% CI, 0.20–0.73; P = 0.004). The overall incidence of adverse events in the immunotherapy group was 16/59 (27.1%) and no patient experienced a grade 3 or 4 adverse event. Conclusions The adjuvant immunotherapy with autologous CIK cells after curative treatment safely prolonged the RFS of HCC patients in a real-world setting

Canine model of ischemic stroke with permanent middle cerebral artery occlusion: clinical and histopathological findings

The aim of the present study was to assess the clinical and histopathological findings in a canine model of ischemic stroke. Cerebral ischemic stroke was induced by middle cerebral artery occlusion in four healthy beagle dogs using silicone plugs. They showed neurological signs of forebrain dysfunction such as reduced responsiveness, head turning, circling, postural reaction deficits, perceptual deficits, and hemianopsia. These signs gradually regressed within 4 weeks without therapy. On magnetic resonance imaging, T2 hyperintensity and T1 hypointensity were found in the cerebral cortex and basal ganglia. These lesions were well-defined and sharply demarcated from adjacent brain parenchyma with a homogenous appearance. No abnormalities of the cerebrospinal fluid were observed. At necropsy, atrophic and necrotic lesions were observed in the cerebral cortex. The cerebral cortex, basal ganglia, and thalamus were partially unstained with triphenyl-tetrazolium chloride. Histopathologically, typical features of infarction were identified in cortical and thalamic lesions. This study demonstrates that our canine model resembles the conditions of real stroke patients

Minimal information for studies of extracellular vesicles (MISEV2023): From basic to advanced approaches

Extracellular vesicles (EVs), through their complex cargo, can reflect the state of their cell of origin and change the functions and phenotypes of other cells. These features indicate strong biomarker and therapeutic potential and have generated broad interest, as evidenced by the steady year-on-year increase in the numbers of scientific publications about EVs. Important advances have been made in EV metrology and in understanding and applying EV biology. However, hurdles remain to realising the potential of EVs in domains ranging from basic biology to clinical applications due to challenges in EV nomenclature, separation from non-vesicular extracellular particles, characterisation and functional studies. To address the challenges and opportunities in this rapidly evolving field, the International Society for Extracellular Vesicles (ISEV) updates its 'Minimal Information for Studies of Extracellular Vesicles', which was first published in 2014 and then in 2018 as MISEV2014 and MISEV2018, respectively. The goal of the current document, MISEV2023, is to provide researchers with an updated snapshot of available approaches and their advantages and limitations for production, separation and characterisation of EVs from multiple sources, including cell culture, body fluids and solid tissues. In addition to presenting the latest state of the art in basic principles of EV research, this document also covers advanced techniques and approaches that are currently expanding the boundaries of the field. MISEV2023 also includes new sections on EV release and uptake and a brief discussion of in vivo approaches to study EVs. Compiling feedback from ISEV expert task forces and more than 1000 researchers, this document conveys the current state of EV research to facilitate robust scientific discoveries and move the field forward even more rapidly

Determination of Internal Quality Indices in Oriental Melon Using Snapshot-Type Hyperspectral Image and Machine Learning Model

In this study, we aimed to develop a prediction model of the solid solutions concentration (SSC) and moisture content (MC) in oriental melon with snapshot-type hyperspectral imagery (Visible (VIS): 460–600 nm, 16 bands; Red-Near infrared (Red-NIR): 600–860 nm, 15 bands) using a machine learning model. The oriental melons were cultivated in a hydroponic greenhouse, Republic of Korea, and a total of 91 oriental melons that were harvested from March to April of 2022 were used as samples. The SSC and MC of the oriental melons were measured using destructive methods after taking hyperspectral imagery of the oriental melons. The reflectance spectrum obtained from the hyperspectral imagery was processed by the standard normal variate (SNV) method. Variable importance in projection (VIP) scores were used to select the bands related to SSC and MC. As a result, ten (609, 736, 561, 849, 818, 489, 754, 526, 683, and 597 nm) and six (609, 736, 561, 818, 849, and 489 nm) bands were selected for the SSC and MC, respectively. Four machine learning models, support vector regression (SVR), ridge regression (RR), K-nearest neighbors regression (K-NNR), and random forest regression (RFR), were used to develop models to predict SSC and MC, and their performances were compared. The SVR showed the best performance for predicting both the SSC and MC of the oriental melons. The SVR model achieved a relatively high accuracy with R2 values of 0.86 and 0.74 and RMSE values of 1.06 and 1.05 for SSC and MC, respectively. However, it will be necessary to carry out more experiments under various conditions, such as differing maturities of fruits and varying light sources and environments, to achieve more comprehensive predictions and apply them to monitoring robots in the future. Nevertheless, it is considered that the snapshot-type hyperspectral imagery aided by SVR would be a useful tool to predict the SSC and MC of oriental melon. In addition, if the maturity classification model for the oriental melon can be applied to fields, it could lead to less labor and result in high-quality oriental melon production

Predicting sensory evaluation of spinach freshness using machine learning model and digital images.

The visual perception of freshness is an important factor considered by consumers in the purchase of fruits and vegetables. However, panel testing when evaluating food products is time consuming and expensive. Herein, the ability of an image processing-based, nondestructive technique to classify spinach freshness was evaluated. Images of spinach leaves were taken using a smartphone camera after different storage periods. Twelve sensory panels ranked spinach freshness into one of four levels using these images. The rounded value of the average from all twelve panel evaluations was set as the true label. The spinach image was removed from the background, and then converted into a gray scale and CIE-Lab color space (L*a*b*) and Hue, Saturation and Value (HSV). The mean value, minimum value, and standard deviation of each component of color in spinach leaf were extracted as color features. Local features were extracted using the bag-of-words of key points from Oriented FAST (Features from Accelerated Segment Test) and Rotated BRIEF (Binary Robust Independent Elementary Features). The feature combinations selected from the spinach images were used to train machine learning models to recognize freshness levels. Correlation analysis between the extracted features and the sensory evaluation score showed a positive correlation (0.5 < r < 0.6) for four color features, and a negative correlation (‒0.6 < r < ‒0.5) for six clusters in the local features. The support vector machine classifier and artificial neural network algorithm successfully classified spinach samples with overall accuracy 70% in four-class, 77% in three-class and 84% in two-class, which was similar to that of the individual panel evaluations. Our findings indicate that a model using support vector machine classifiers and artificial neural networks has the potential to replace freshness evaluations currently performed by non-trained panels

Growth Characteristics of Lettuce Relative to Generation Position of Air Anions in a Closed-Type Plant Factory

(1) Background: We studied how the generation position of air anions in a closed-type plant factory affects the growth characteristics of lettuce and identified the optimal position. (2) Methods: We used LEDs (red/green/blue = 8:1:1) as a light source and set the temperature and RH of the plant factory to 20 ± 2 ℃ and 50% ± 5%, respectively. We grew lettuce under three air anion conditions—sideward, upward, and downward—and compared the growth characteristics to those of a control grown without air anions. We measured the growth characteristics of the lettuce at 3 and 4 weeks after sowing, and the measurement items were shoot fresh weight (FW) and dry weight (DW); leaf area (LA), length (LL), and width (LW); SPAD; antioxidant capacity; and total phenol content. (3) Results: At 4 weeks, FW in the downward treatment condition was 25.3% higher than in the control, and DW showed a similar difference. LA was about 1943.94 cm²/plant in the downward treatment condition, which was about 15.5% higher than in the control. (4) Conclusions: We conclude that air anion generation has a positive effect on lettuce growth, and the optimal generation position for air anions is downward

Potential of Snapshot-Type Hyperspectral Imagery Using Support Vector Classifier for the Classification of Tomatoes Maturity

It is necessary to convert to automation in a tomato hydroponic greenhouse because of the aging of farmers, the reduction in agricultural workers as a proportion of the population, COVID-19, and so on. In particular, agricultural robots are attractive as one of the ways for automation conversion in a hydroponic greenhouse. However, to develop agricultural robots, crop monitoring techniques will be necessary. In this study, therefore, we aimed to develop a maturity classification model for tomatoes using both support vector classifier (SVC) and snapshot-type hyperspectral imaging (VIS: 460&ndash;600 nm (16 bands) and Red-NIR: 600&ndash;860 nm (15 bands)). The spectral data, a total of 258 tomatoes harvested in January and February 2022, was obtained from the tomatoes&rsquo; surfaces. Spectral data that has a relationship with the maturity stages of tomatoes was selected by correlation analysis. In addition, the four different spectral data were prepared, such as VIS data (16 bands), Red-NIR data (15 bands), combination data of VIS and Red-NIR (31 bands), and selected spectral data (6 bands). These data were trained by SVC, respectively, and we evaluated the performance of trained classification models. As a result, the SVC based on VIS data achieved a classification accuracy of 79% and an F1-score of 88% to classify the tomato maturity into six stages (Green, Breaker, Turning, Pink, Light-red, and Red). In addition, the developed model was tested in a hydroponic greenhouse and was able to classify the maturity stages with a classification accuracy of 75% and an F1-score of 86%

Protective Effect of Red Light-Emitting Diode against UV-B Radiation-Induced Skin Damage in SKH:HR-2 Hairless Mice

In this in vivo study on hairless mice, we examined the effects of light-emitting diode (LED) treatment applied prior to ultraviolet B (UVB) irradiation. We found that pre-treating with LED improved skin morphological and histopathological conditions compared to those only exposed to UVB irradiation. In our study, histological evaluation of collagen and elastic fibers after LED treatment prior to UVB irradiation showed that this pretreatment significantly enhanced the quality of fibers, which were otherwise poor in density and irregularly arranged due to UV exposure alone. This suggests that LED treatment promotes collagen and elastin production, leading to improved skin properties. Additionally, we observed an increase in Claudin-1 expression and a reduction in nuclear factor-erythroid 2-related factor 2 (Nrf-2) and heme-oxygenase 1 (HO-1) expression within the LED-treated skin tissues, suggesting that LED therapy may modulate key skin barrier proteins and oxidative stress markers. These results demonstrate that pretreatment with LED light can enhance the skin’s resistance to UVB-induced damage by modulating gene regulation associated with skin protection. Further investigations are needed to explore the broader biological effects of LED therapy on other tissues such as blood vessels. This study underscores the potential of LED therapy as a non-invasive approach to enhance skin repair and counteract the effects of photoaging caused by UV exposure