Molecular investigation of soybean protein for improving the stability of quinoa (Chenopodium quinoa willd.) milk substitute.

Soybean could greatly improve stability of quinoa milk substitute. However, the key compound and underlying mechanisms remained unclear. Here we showed that soybean protein was the key component for improving quinoa milk substitute stability but not oil or okara. Supplementary level of soybean protein at 0%, 2%, 4%, and 8% of quinoa (w/w) was optimized. Median level at 4% could effectively enhance physical stability, reduce particle size, narrow down particle size distribution, and decrease apparent viscosity of quinoa milk substitute. Microscopic observation further confirmed that soybean protein could prevent phase separation. Besides, soybean protein showed increased surface hydrophobicity. Molecular docking simulated that soybean protein but not quinoa protein, could provide over 10 anchoring points for the most abundant quinoa vanillic acid, through hydrogen bond and Van-der-Waals. These results contribute to improve stability of quinoa based milk substitute, and provide theoretical basis for the interaction of quinoa phenolics and soybean protein.

Prediction of the Ki-67 expression level in head and neck squamous cell carcinoma with machine learning-based multiparametric MRI radiomics: a multicenter study.

BACKGROUND: This study aimed to develop and validate a machine learning (ML)-based fusion model to preoperatively predict Ki-67 expression levels in patients with head and neck squamous cell carcinoma (HNSCC) using multiparametric magnetic resonance imaging (MRI). METHODS: A total of 351 patients with pathologically proven HNSCC from two medical centers were retrospectively enrolled in the study and divided into training (n = 196), internal validation (n = 84), and external validation (n = 71) cohorts. Radiomics features were extracted from T2-weighted images and contrast-enhanced T1-weighted images and screened. Seven ML classifiers, including k-nearest neighbors (KNN), support vector machine (SVM), logistic regression (LR), random forest (RF), linear discriminant analysis (LDA), naive Bayes (NB), and eXtreme Gradient Boosting (XGBoost) were trained. The best classifier was used to calculate radiomics (Rad)-scores and combine clinical factors to construct a fusion model. Performance was evaluated based on calibration, discrimination, reclassification, and clinical utility. RESULTS: Thirteen features combining multiparametric MRI were finally selected. The SVM classifier showed the best performance, with the highest average area under the curve (AUC) of 0.851 in the validation cohorts. The fusion model incorporating SVM-based Rad-scores with clinical T stage and MR-reported lymph node status achieved encouraging predictive performance in the training (AUC = 0.916), internal validation (AUC = 0.903), and external validation (AUC = 0.885) cohorts. Furthermore, the fusion model showed better clinical benefit and higher classification accuracy than the clinical model. CONCLUSIONS: The ML-based fusion model based on multiparametric MRI exhibited promise for predicting Ki-67 expression levels in HNSCC patients, which might be helpful for prognosis evaluation and clinical decision-making.

Caffeic acid combined with arabinoxylan or ß-glucan attenuates diet-induced obesity in mice via modulation of gut microbiota and metabolites.

Polyphenols and dietary fibers in whole grains are important bioactive compounds to reduce risks for obesity. However, whether the combination of the two components exhibits a stronger anti-obesity effect remains unclear. Caffeic acid is a major phenolic acid in cereals, and arabinoxylan and ß-glucan are biological macromolecules with numerous health benefits. Here, we investigated the effect of caffeic acid combined with arabinoxylan or ß-glucan on glucose and lipid metabolism, gut microbiota, and metabolites in mice fed a high-fat diet (HFD). Caffeic acid combined with arabinoxylan or ß-glucan significantly reduced the body weight, blood glucose, and serum free fatty acid concentrations. Caffeic acid combined with ß-glucan effectively decreased serum total cholesterol levels and hepatic lipid accumulation, modulated oxidative and inflammatory stress, and improved gut barrier function. Compared with arabinoxylan, ß-glucan, and caffeic acid alone, caffeic acid combined with arabinoxylan or ß-glucan exhibited a better capacity to modulate gut microbiota, including increased microbial diversity, reduced Firmicutes/Bacteroidetes ratio, and increased abundance of beneficial bacteria such as Bifidobacterium. Furthermore, caffeic acid combined with ß-glucan reversed HFD-induced changes in microbiota-derived metabolites involving tryptophan, purine, and bile acid metabolism. Thus, caffeic acid and ß-glucan had a synergistic anti-obesity effect by regulating specific gut microbiota and metabolites.

Machine Learning Radiomics-Based Prediction of Non-sentinel Lymph Node Metastasis in Chinese Breast Cancer Patients with 1-2 Positive Sentinel Lymph Nodes: A Multicenter Study.

RATIONALE AND OBJECTIVES: This study aimed to construct a machine learning radiomics-based model using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) images to evaluate non-sentinel lymph node (NSLN) metastasis in Chinese breast cancer (BC) patients who underwent total mastectomy (TM) and had 1-2 positive sentinel lymph nodes (SLNs). MATERIALS AND METHODS: In total, 494 patients were retrospectively enrolled from two hospitals, and were divided into the training (n = 286), internal validation (n = 122), and external validation (n = 86) cohorts. Features were extracted from DCE-MRI images for each patient and screened. Six ML classifies were trained and the best classifier was evaluated to calculate radiomics (Rad)-scores. A combined model was developed based on Rad-scores and clinical risk factors, then the calibration, discrimination, reclassification, and clinical usefulness were evaluated. RESULTS: 14 radiomics features were ultimately selected. The random forest (RF) classifier showed the best performance, with the highest average area under the curve (AUC) of 0.833 in the validation cohorts. The combined model incorporating RF-based Rad-scores, tumor size, lymphovascular invasion, and proportion of positive SLNs resulted in the best discrimination ability, with AUCs of 0.903, 0.890, and 0.836 in the training, internal validation, and external validation cohorts, respectively. Furthermore, the combined model significantly improved the classification accuracy and clinical benefit for NSLN metastasis prediction. CONCLUSION: A RF-based combined model using DCE-MRI images exhibited a promising performance for predicting NSLN metastasis in Chinese BC patients who underwent TM and had 1-2 positive SLNs, thereby aiding in individualized clinical treatment decisions.

Non-invasive prediction model of axillary lymph node status in patients with early-stage breast cancer: a feasibility study based on dynamic contrast-enhanced-MRI radiomics.

OBJECTIVES: Accurate axillary evaluation plays an important role in prognosis and treatment planning for breast cancer. This study aimed to develop and validate a dynamic contrast-enhanced (DCE)-MRI-based radiomics model for preoperative evaluation of axillary lymph node (ALN) status in early-stage breast cancer. METHODS: A total of 410 patients with pathologically confirmed early-stage invasive breast cancer (training cohort, N = 286; validation cohort, N = 124) from June 2018 to August 2022 were retrospectively recruited. Radiomics features were derived from the second phase of DCE-MRI images for each patient. ALN status-related features were obtained, and a radiomics signature was constructed using SelectKBest and least absolute shrinkage and selection operator regression. Logistic regression was applied to build a combined model and corresponding nomogram incorporating the radiomics score (Rad-score) with clinical predictors. The predictive performance of the nomogram was evaluated using receiver operator characteristic (ROC) curve analysis and calibration curves. RESULTS: Fourteen radiomic features were selected to construct the radiomics signature. The Rad-score, MRI-reported ALN status, BI-RADS category, and tumour size were independent predictors of ALN status and were incorporated into the combined model. The nomogram showed good calibration and favourable performance for discriminating metastatic ALNs (N + (≥1)) from non-metastatic ALNs (N0) and metastatic ALNs with heavy burden (N + (≥3)) from low burden (N + (1-2)), with the area under the ROC curve values of 0.877 and 0.879 in the training cohort and 0.859 and 0.881 in the validation cohort, respectively. CONCLUSIONS: The DCE-MRI-based radiomics nomogram could serve as a potential non-invasive technique for accurate preoperative evaluation of ALN burden, thereby assisting physicians in the personalized axillary treatment for early-stage breast cancer patients. ADVANCES IN KNOWLEDGE: This study developed a potential surrogate of preoperative accurate evaluation of ALN status, which is non-invasive and easy-to-use.