Comparative Analysis of Polyphenolic Compounds in Different Amaranthus Species: Influence of Genotypes and Harvesting Year.

Amaranth is a nutritionally valuable crop, as it contains phenolic acids and flavonoids, yielding diverse plant secondary metabolites (PSMs) like phytosterol, tocopherols, and carotenoids. This study explored the variations in the contents of seventeen polyphenolic compounds within the leaves of one hundred twenty Amaranthus accessions representing nine Amaranthus species. The investigation entailed the analysis of phenolic content across nine Amaranthus species, specifically A. hypochondriacus, A. cruentus, A. caudatus, A. tricolor, A. dubius, A. blitum, A. crispus, A. hybridus, and A. viridis, utilizing ultra performance liquid chromatography with photodiode array detection (UPLC-PDA). The results revealed significant differences in polyphenolic compounds among accessions in which rutin content was predominant in all Amaranthus species in both 2018 and 2019. Among the nine Amaranthus species, the rutin content ranged from 95.72 ± 199.17 µg g-1 (A. dubius) to 1485.09 ± 679.51 µg g-1 (A. viridis) in 2018 and from 821.59 ± 709.95 µg g-1 (A. tricolor) to 3166.52 ± 1317.38 µg g-1 (A. hypochondriacus) in 2019. Correlation analysis revealed, significant positive correlations between rutin and kaempferol-3-O-ß-rutinoside (r = 0.93), benzoic acid and ferulic acid (r = 0.76), and benzoic acid and kaempferol-3-O-ß-rutinoside (r = 0.76), whereas gallic acid showed consistently negative correlations with each of the 16 phenolic compounds. Wide variations were identified among accessions and between plants grown in the two years. The nine species and one hundred twenty Amaranthus accessions were clustered into six groups based on their seventeen phenolic compounds in each year. These findings contribute to expanding our understanding of the phytochemical traits of accessions within nine Amaranthus species, which serve as valuable resources for Amaranthus component breeding and functional material development.

Value of glucose transport protein 1 expression in detecting lymph node metastasis in patients with colorectal cancer.

BACKGROUND: There are limited data on the use of glucose transport protein 1 (GLUT-1) expression as a biomarker for predicting lymph node metastasis in patients with colorectal cancer. GLUT-1 and GLUT-3, hexokinase (HK)-II, and hypoxia-induced factor (HIF)-1 expressions may be useful biomarkers for detecting primary tumors and lymph node metastasis when combined with fluorodeoxyglucose (FDG) uptake on positron emission tomography/computed tomography (PET/CT). AIM: To evaluate GLUT-1, GLUT-3, HK-II, and HIF-1 expressions as biomarkers for detecting primary tumors and lymph node metastasis with 18F-FDG-PET/CT. METHODS: This retrospective study included 169 patients with colorectal cancer who underwent colectomy and preoperative 18F-FDG-PET/CT at Chungbuk National University Hospital between January 2009 and May 2012. Two tissue cores from the central and peripheral areas of the tumors were obtained and were examined by a dedicated pathologist, and the expressions of GLUT-1, GLUT-3, HK-II, and HIF-1 were determined using immunohistochemical staining. We analyzed the correlations among their expressions, various clinicopathological factors, and the maximum standardized uptake value (SUVmax) of PET/CT. RESULTS: GLUT-1 was found at the center or periphery of the tumors in 109 (64.5%) of the 169 patients. GLUT-1 positivity was significantly correlated with the SUVmax of the primary tumor and lymph nodes, regardless of the biopsy site (tumor center, P < 0.001 and P = 0.012; tumor periphery, P = 0.030 and P = 0.010, respectively). GLUT-1 positivity and negativity were associated with higher and lower sensitivities of PET/CT, respectively, for the detection of lymph node metastasis, regardless of the biopsy site. GLUT3, HK-II, and HIF-1 expressions were not significantly correlated with the SUVmax of the primary tumor and lymph nodes. CONCLUSION: GLUT-1 expression was significantly correlated with the SUVmax of 18F-FDG-PET/CT for primary tumors and lymph nodes. Clinicians should consider GLUT-1 expression in preoperative endoscopic biopsy in interpreting PET/CT findings.

Ionic Strength-Dependent Structure of Complex Coacervate Core Micelles.

Salt concentration-dependent structure of complex coacervate core micelles (C3Ms), formed by polyether-based block copolyelectrolytes containing cationic ammonium (A) or anionic sulfonate (S) groups in aqueous media, is investigated by light scattering and small-angle X-ray/neutron scattering (SAX/NS). As the salt concentration increases, both a core radius (Rcore) and an aggregation number (Nagg) significantly decrease, but a corona thickness (Lcorona) is nearly unchanged. Larger salt concentrations can lower the interfacial tension between the coacervate cores and aqueous media, resulting in an increased interfacial area per chain and a more relaxed conformation of the core blocks. Based on the structure characterization, the scaling relationship between structure parameters (i.e., Rcore, Nagg, and Lcorona) and salt concentration is obtained and compared to the theoretical description estimated by the free energy balance between the entropic penalty of core stretching and the interfacial energy. We propose that the free energy contribution of the core block stretching is not negligible in C3Ms because of the highly swollen cores caused by water.

The Association Among Post-hemodialysis Blood Pressure, Nocturnal Hypertension, and Cardiovascular Risk Factors.

Background: Most hemodialysis (HD) patients suffer from hypertension and have a heightened cardiovascular risk. While blood pressure (BP) control is essential to end-stage kidney disease (ESKD) patients, overly stringent control can lead to intradialytic hypotension (IDH). This study aimed to examine BP variations during and after HD to determine whether these variations correlate with IDH risk. Methods: BP measurements during dialysis were taken from 28 ESKD patients, and ambulatory BP monitoring was applied post-dialysis. Laboratory parameters and risk factors, including diabetes, coronary disease, and LV mass index, were compared between IDH and non-IDH groups using an independent t-test. Results: Of the 28 patients with an average age of 57.4 years, 16 (57.1%) had diabetes, 5 (17.9%) had coronary artery disease, and 1 (3.6%) had cerebrovascular disease. The mean systolic blood pressure (SBP) during and post-HD was 142.26 mmHg and 156.05 mmHg, respectively (p=0.0003). Similarly, the mean diastolic blood pressure (DBP) also demonstrated a significant increase, from 74.59 mmHg during HD to 86.82 mmHg post-HD (p<0.0001). Patients with IDH exhibited a more substantial SBP difference (delta SBP, 36.38 vs. 15.07 mmHg, p=0.0033; age-adjusted OR=1.58, p=0.0168) and a lower post-dialysis BUN level (12.75 vs. 18.77 mg/dL, p=0.0015; age-adjusted OR=0.76, p=0.0242). No significant variations were observed in daytime and nocturnal BP between the IDH and non-IDH groups. Conclusion: Hemodialysis patients exhibited a marked increase in post-dialysis BP and lacked a nocturnal BP dip, suggesting augmented cardiovascular risks. This highlights the importance of more stringent BP control after hemodialysis.

Scaling Relationship of Complex Coacervate Core Micelles: Role of Core Block Stretching.

The scaling relationship of complex coacervate core micelles (C3Ms) has been investigated experimentally and theoretically. The C3Ms are formed by mixing two oppositely charged block copolyelectrolyte solutions (i.e., AB + AC system) and are characterized by small-angle neutron (SANS) and X-ray scattering (SAXS). Scaling relationships for micellar structure parameters, including core radius, total radius, corona thickness, and aggregation number, all with respect to the core block length, are determined. A scaling theory is also proposed by minimizing the free energy per chain, leading to four regimes depending on the core and corona chain conformations. Although the corona block is significantly longer than the core block, the structure of our C3Ms is consistent with that of the crew-cut I regime. A highly swollen core by water enables the core blocks to be stretched significantly and corona chains to be minimally overlapped.

New machine learning-based automatic high-throughput video tracking system for assessing water toxicity using Daphnia Magna locomotory responses.

Daphnia magna is an important organism in ecotoxicity studies because it is sensitive to toxic substances and easy to culture in laboratory conditions. Its locomotory responses as a biomarker are highlighted in many studies. Over the last several years, multiple high-throughput video tracking systems have been developed to measure the locomotory responses of Daphnia magna. These high-throughput systems, used for high-speed analysis of multiple organisms, are essential for efficiently testing ecotoxicity. However, existing systems are lacking in speed and accuracy. Specifically, speed is affected in the biomarker detection stage. This study aimed to develop a faster and better high-throughput video tracking system using machine learning methods. The video tracking system consisted of a constant temperature module, natural pseudo-light, multi-flow cell, and an imaging camera for recording videos. To measure Daphnia magna movements, we developed a tracking algorithm for automatic background subtraction using k-means clustering, Daphnia classification using machine learning methods (random forest and support vector machine), and tracking each Daphnia magna location using the simple online real-time tracking algorithm. The proposed tracking system with random forest performed the best in terms of identification (ID) precision, ID recall, ID F1 measure, and ID switches, with scores of 79.64%, 80.63%, 78.73%, and 16, respectively. Moreover, it was faster than existing tracking systems such as Lolitrack and Ctrax. We conducted an experiment to observe the impact of toxicants on behavioral responses. Toxicity was measured manually in the laboratory and automatically using the high-throughput video tracking system. The median effective concentration of Potassium dichromate measured in the laboratory and using the device was 1.519 and 1.414, respectively. Both measurements conformed to the guideline provided by the Environmental Protection Agency of the United States; therefore, our method can be used for water quality monitoring. Finally, we observed Daphnia magna behavioral responses in different concentrations after 0, 12, 18, and 24 h and found that there was a difference in movement according to the concentration at all hours.

Interpretation of ensemble learning to predict water quality using explainable artificial intelligence.

Algal bloom is a significant issue when managing water quality in freshwater; specifically, predicting the concentration of algae is essential to maintaining the safety of the drinking water supply system. The chlorophyll-a (Chl-a) concentration is a commonly used indicator to obtain an estimation of algal concentration. In this study, an XGBoost ensemble machine learning (ML) model was developed from eighteen input variables to predict Chl-a concentration. The composition and pretreatment of input variables to the model are important factors for improving model performance. Explainable artificial intelligence (XAI) is an emerging area of ML modeling that provides a reasonable interpretation of model performance. The effect of input variable selection on model performance was estimated, where the priority of input variable selection was determined using three indices: Shapley value (SHAP), feature importance (FI), and variance inflation factor (VIF). SHAP analysis is an XAI algorithm designed to compute the relative importance of input variables with consistency, providing an interpretable analysis for model prediction. The XGB models simulated with independent variables selected using three indices were evaluated with root mean square error (RMSE), RMSE-observation standard deviation ratio, and Nash-Sutcliffe efficiency. This study shows that the model exhibited the most stable performance when the priority of input variables was determined by SHAP. This implies that on-site monitoring can be designed to collect the selected input variables from the SHAP analysis to reduce the cost of overall water quality analysis. The independent variables were further analyzed using SHAP summary plot, force plot, target plot, and partial dependency plot to provide understandable interpretation on the performance of the XGB model. While XAI is still in the early stages of development, this study successfully demonstrated a good example of XAI application to improve the interpretation of machine learning model performance in predicting water quality.

Anomaly Detection with Feature Extraction Based on Machine Learning Using Hydraulic System IoT Sensor Data.

Hydraulic systems are advanced in function and level as they are used in various industrial fields. Furthermore, condition monitoring using internet of things (IoT) sensors is applied for system maintenance and management. In this study, meaningful features were identified through extraction and selection of various features, and classification evaluation metrics were presented through machine learning and deep learning to expand the diagnosis of abnormalities and defects in each component of the hydraulic system. Data collected from IoT sensor data in the time domain were divided into clusters in predefined sections. The shape and density characteristics were extracted by cluster. Among 2335 newly extracted features, related features were selected using correlation coefficients and the Boruta algorithm for each hydraulic component and used for model learning. Linear discriminant analysis (LDA), logistic regression, support vector classifier (SVC), decision tree, random forest, XGBoost, LightGBM, and multi-layer perceptron were used to calculate the true positive rate (TPR) and true negative rate (TNR) for each hydraulic component to detect normal and abnormal conditions. Valve condition, internal pump leakage, and hydraulic accumulator data showed TPR performance of 0.94 or more and a TNR performance of 0.84 or more. This study's findings can help to determine the stable and unstable states of each component of the hydraulic system and form the basis for engineers' judgment.

Trends in the Approval and Quality Management of Artificial Intelligence Medical Devices in the Republic of Korea.

Artificial intelligence (AI) is being implemented in many areas of medicine, such as patient-customized diagnosis. Growth in the artificial intelligence medical device (AIMD) field is expected in the coming years. Major countries are currently establishing systems and policies to gain a leading position in the medical artificial intelligence market. The Republic of Korea has initiated the Act on Nurturing the Medical Devices Industry and Supporting Innovative Medical Devices for the development of AIMDs and is implementing it preemptively. As a result, the country has achieved an effective strategy for coping with the COVID-19 pandemic, an increase in the number of AIMD approvals (85 approved as of September 2021), and the creation of a document pertaining to internationally harmonized guidelines on AIMD-related terms and definitions. However, in order to develop and activate more AIMD products, it is necessary to improve post-market management such as product change and quality control in addition to approval. Here, we review the current regulatory status of AIMD in the Republic of Korea and what needs to be improved for AIMD to be more developed and activated.

Development of a Fundus Image-Based Deep Learning Diagnostic Tool for Various Retinal Diseases.

Artificial intelligence (AI)-based diagnostic tools have been accepted in ophthalmology. The use of retinal images, such as fundus photographs, is a promising approach for the development of AI-based diagnostic platforms. Retinal pathologies usually occur in a broad spectrum of eye diseases, including neovascular or dry age-related macular degeneration, epiretinal membrane, rhegmatogenous retinal detachment, retinitis pigmentosa, macular hole, retinal vein occlusions, and diabetic retinopathy. Here, we report a fundus image-based AI model for differential diagnosis of retinal diseases. We classified retinal images with three convolutional neural network models: ResNet50, VGG19, and Inception v3. Furthermore, the performance of several dense (fully connected) layers was compared. The prediction accuracy for diagnosis of nine classes of eight retinal diseases and normal control was 87.42% in the ResNet50 model, which added a dense layer with 128 nodes. Furthermore, our AI tool augments ophthalmologist's performance in the diagnosis of retinal disease. These results suggested that the fundus image-based AI tool is applicable for the medical diagnosis process of retinal diseases.

Brassinosteroid-BZR1/2-WAT1 module determines the high level of auxin signalling in vascular cambium during wood formation.

The tight regulation of local auxin homeostasis and signalling maxima in xylem precursor cells specifies the organising activity of the vascular cambium and consequently promotes xylem differentiation and wood formation. However, the molecular mechanisms underlying the local auxin signalling maxima in the vascular cambium are largely unknown. Here, we reveal that brassinosteroid (BR)-activated WALLS ARE THIN1 (WAT1) facilitates wood formation by enhancing local auxin signalling in the vascular cambium in Solanum lycopersicum. Growth defects and low auxin signalling readouts in the BR-deficient tomato cultivar, Micro-Tom, were associated with a novel recessive allele, Slwat1-copi, created by the insertion of a retrotransposon in the last exon of the SlWAT1 locus. Molecular and genetic studies by generating the gain-of-function and loss-of-function tomato mutants revealed that SlWAT1 is a critical regulator for fine tuning local auxin homeostasis and signalling outputs in vascular cambium to facilitate secondary growth. Finally, we discovered that BR-regulated SlBZR1/2 directly activated downstream auxin responses by SlWAT1 upregulation in xylem precursor cells to facilitate xylem differentiation and subsequent wood formation. Our data suggest that the BR-SlBZR1/2-WAT1 signalling network contributes to the high level of auxin signalling in the vascular cambium for secondary growth.

Molecular Exchange Kinetics in Complex Coacervate Core Micelles: Role of Associative Interaction.

Molecular exchange dynamics between spherical complex coacervate core micelles (C3Ms) are documented using time-resolved small-angle neutron scattering measurements (TR-SANS), and the effects of salt concentration, type of charges, and core block polydispersity to the chain exchange are quantified. Isotopically labeled block copolyelectrolytes were prepared by postpolymerization modification of two nearly identical poly(ethylene oxide-b-allyl glycidyl ether), one with normal and the other with deuterated PEO blocks (i.e., hPEO-PAGE and dPEO-PAGE). The observed rates at multiple salt concentrations are consolidated using time-salt superposition shift factors representing chain exchange rates and analyzed. Our comprehensive analytical relaxation function based on the sticky-Rouse model and the thermodynamic barrier for core block extraction successfully describes the molecular exchange kinetics between the isotopically labeled C3Ms. We believe this work provides fundamental design criteria for C3Ms with engineered chain exchange dynamics.

Metagenomics approach and canonical correspondence analysis of novel nitrifiers and ammonia-oxidizing archaea in full scale anaerobic-anoxic-oxic (A2/O) and oxidation ditch processes.

Various microorganisms are involved in nitrogen removal, and their group compositions depend closely on operating parameters. The structures and functions of nitrification microorganisms in full-scale anaerobic-anoxic-oxic (A2/O) and oxidation ditch processes were analyzed using metagenomics and canonical correspondence analysis. The community structure of ammonia-oxidizing archaea in the oxidation ditch was 3.8 (winter) - 6.3 (summer) times higher than in A2/O, and the complete ammonia oxidizer was only found in the oxidation ditch process. The canonical correspondence analysis of various environmental variables showed that Nitrosomonadales, Crenarchaeota, and Nitrospira inopinata correlate highly with nitrification, and Nitrospira was involved in NO2--N oxidation rather than Nitrobacter. The longer solid and hydraulic retention times in the oxidation ditch were more effective in achieving a wider range of novel nitrification than A2/O. This result indicates that microbial communities of novel nitrifiers and ammonia-oxidizing archaea improved in the oxidation ditch process, significantly contributing to stable nitrogen removal.

Universal inherent fluctuations in statistical counting of large particles in slurry used for semiconductor manufacturing.

In the chemical mechanical polishing process of semiconductor manufacturing, the concentration of 'large' particles ([Formula: see text]0.5 µm) in the slurry, which is considerably larger in size than the main abrasives ([Formula: see text] 0.1 µm), is a critical parameter that strongly influences manufacturing defects, yields, and reliabilities of large-scale-integrated circuits. Various instruments, so-called particle counters, based on light scattering, light extinction, and holography techniques have been developed to measure and monitor the large particle concentration in semiconductor fabs in real time. However, sizeable fluctuation in the measured particle concentration complicates the statistical process control in the fabs worldwide. Here, we show that an inherent fluctuation exists in the counting of large particles, which is universal, independent of instrument type, and quantitatively determined by the instrument's operation parameters. We analytically derive a statistical theory of the fluctuation based on Poisson statistics and validate the theory through experiments and Monte-Carlo simulation. Furthermore, we provide a strategy to enhance the measurement accuracy by statistically adjusting the instrumental parameters commonly involved in the particle counters. The present results and analyses could be useful for statistical process control in semiconductor fabs to prevent large particle-induced defects such as micro-scratches and pits on wafers.

Comparison between Statistical Models and Machine Learning Methods on Classification for Highly Imbalanced Multiclass Kidney Data.

This study aims to compare the classification performance of statistical models on highly imbalanced kidney data. The health examination cohort database provided by the National Health Insurance Service in Korea is utilized to build models with various machine learning methods. The glomerular filtration rate (GFR) is used to diagnose chronic kidney disease (CKD). It is calculated using the Modification of Diet in Renal Disease method and classified into five stages (1, 2, 3A and 3B, 4, and 5). Different CKD stages based on the estimated GFR are considered as six classes of the response variable. This study utilizes two representative generalized linear models for classification, namely, multinomial logistic regression (multinomial LR) and ordinal logistic regression (ordinal LR), as well as two machine learning models, namely, random forest (RF) and autoencoder (AE). The classification performance of the four models is compared in terms of accuracy, sensitivity, specificity, precision, and F1-Measure. To find the best model that classifies CKD stages correctly, the data are divided into a 10-fold dataset with the same rate for each CKD stage. Results indicate that RF and AE show better performance in accuracy than the multinomial and ordinal LR models when classifying the response variable. However, when a highly imbalanced dataset is modeled, the accuracy of the model performance can distort the actual performance. This occurs because accuracy is high even if a statistical model classifies a minority class into a majority class. To solve this problem in performance interpretation, we not only consider accuracy from the confusion matrix but also sensitivity, specificity, precision, and F-1 measure for each class. To present classification performance with a single value for each model, we calculate the macro-average and micro-weighted values for each model. We conclude that AE is the best model classifying CKD stages correctly for all performance indices.

Development of a Deep-Learning-Based Artificial Intelligence Tool for Differential Diagnosis between Dry and Neovascular Age-Related Macular Degeneration.

The use of deep-learning-based artificial intelligence (AI) is emerging in ophthalmology, with AI-mediated differential diagnosis of neovascular age-related macular degeneration (AMD) and dry AMD a promising methodology for precise treatment strategies and prognosis. Here, we developed deep learning algorithms and predicted diseases using 399 images of fundus. Based on feature extraction and classification with fully connected layers, we applied the Visual Geometry Group with 16 layers (VGG16) model of convolutional neural networks to classify new images. Image-data augmentation in our model was performed using Keras ImageDataGenerator, and the leave-one-out procedure was used for model cross-validation. The prediction and validation results obtained using the AI AMD diagnosis model showed relevant performance and suitability as well as better diagnostic accuracy than manual review by first-year residents. These results suggest the efficacy of this tool for early differential diagnosis of AMD in situations involving shortages of ophthalmology specialists and other medical devices.

Oral diabetes medication and risk of dementia in elderly patients with type 2 diabetes.

AIM: To examine the effect of oral diabetes medication on the risk of dementia in an elderly cohort with type 2 diabetes. METHODS: This was a population-based cohort study using the Korean National Health Insurance claims data from 2002 to 2013. Elderly subjects (60 years of age or older) with and without type 2 diabetes were included; patients with new-onset type 2 diabetes were further divided into the oral diabetes medication group and no-medication group. RESULTS: Among 278,290 patients with type 2 diabetes, 56,587 developed dementia (20.3%) over 11 years of follow-up. Type 2 diabetes was associated with a 1.69-fold increased risk of dementia (95% CI 1.66-1.72). Among patients with newly diagnosed type 2 diabetes, the risk of dementia was lower in the oral diabetes medication group than in the no-medication group (adjusted hazard ratio [aHR], 0.79; 95% CI 0.77-0.81). Lower risk of dementia was particularly noticeable in all of the combination therapy groups and especially lower in the combination therapy group treated with dipeptidyl peptidase 4 inhibitor (aHR 0.48, 95% CI 0.45-0.51). CONCLUSION: Overall, the use of oral diabetes medication in type 2 diabetes patients significantly decreased the risk of dementia.

Effect of Ionic Group on the Complex Coacervate Core Micelle Structure.

Pairs of ionic group dependence of the structure of a complex coacervate core micelle (C3M) in an aqueous solution was investigated using DLS, cryo-TEM, and SANS with a contrast matching technique and a detailed model analysis. Block copolyelectrolytes were prepared by introducing an ionic group (i.e., ammonium, guanidinium, carboxylate, and sulfonate) to poly(ethylene oxide-b-allyl glycidyl ether) (NPEO = 227 and NPAGE = 52), and C3Ms were formed by simple mixing of two oppositely-charged block copolyelectrolyte solutions with the exactly same degree of polymerization. All four C3Ms are spherical with narrow distribution of micelle dimension, and the cores are significantly swollen by water, resulting in relatively low brush density of PEO chains on the core surface. With the pair of strong polyelectrolytes, core radius and aggregation number increases, which reflects that the formation of complex coacervates are significantly sensitive to the pairs of ionic groups rather than simple charge pairing.

Evaluation of Hypertriglyceridemia as a Mediator Between Endocrine Diseases and Pancreatitis in Dogs.

The role of hypertriglyceridemia (HTG) secondary to endocrine diseases in the occurrence of pancreatitis in dogs has not been fully investigated. The objective of the present study was to evaluate HTG as a mediator between endocrine diseases and pancreatitis in dogs. The study design was a retrospective case-control study. Medical records of dogs newly diagnosed with acutely presenting pancreatitis between 2012 and 2014 were reviewed for the presence or absence of hyperadrenocorticism (HAC), diabetes mellitus (DM), and hypothyroidism. A matched case-control analysis was performed, and the association between endocrine diseases and pancreatitis was evaluated using multiple logistic regression analysis. In dogs with pancreatitis, the odds of HAC (P < .001) and DM (P < .001) were 4.5 and 12.4 times that of dogs without pancreatitis, respectively. HTG significantly mediated the association between DM and pancreatitis but not between HAC and pancreatitis. Additional studies will be necessary to confirm these findings and to further elucidate the associations between endocrine diseases and pancreatitis.