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BACKGROUND: The tumor burden within the axillary lymph nodes (ALNs) constitutes a pivotal factor in breast cancer, serving as the primary determinant for treatment decisions and exhibiting a close correlation with prognosis. OBJECTIVE: This study aimed to investigate the potential of ultrasound-based radiomics and clinical characteristics in non-invasively distinguishing between low tumor burden (1-2 positive nodes) and high tumor burden (more than 2 positive nodes) in patients with node-positive breast cancer. METHODS: A total of 215 patients with node-positive breast cancer, who underwent preoperative ultrasound examinations, were enrolled in this study. Among these patients, 144 cases were allocated to the training set, 37 cases to the validation set, and 34 cases to the testing set. Postoperative histopathology was used to determine the status of ALN tumor burden. The region of interest for breast cancer was delineated on the ultrasound image. Nine models were developed to predict high ALN tumor burden, employing a combination of three feature screening methods and three machine learning classifiers. Ultimately, the optimal model was selected and tested on both the validation and testing sets. In addition, clinical characteristics were screened to develop a clinical model. Furthermore, Shapley additive explanations (SHAP) values were utilized to provide explanations for the machine learning model. RESULTS: During the validation and testing sets, the models demonstrated area under the curve (AUC) values ranging from 0.577 to 0.733 and 0.583 to 0.719, and accuracies ranging from 64.9% to 75.7% and 64.7% to 70.6%, respectively. Ultimately, the Boruta_XGB model, comprising five radiomics features, was selected as the final model. The AUC values of this model for distinguishing low from high tumor burden were 0.828, 0.715, and 0.719 in the training, validation, and testing sets, respectively, demonstrating its superiority over the clinical model. CONCLUSIONS: The developed radiomics models exhibited a significant level of predictive performance. The Boruta_XGB radiomics model outperformed other radiomics models in this study.

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The hormone receptor (HR) status plays a significant role in breast cancer, serving as the primary guide for treatment decisions and closely correlating with prognosis. This study aims to investigate the predictive value of radiomics analysis in long-axis and short-axis ultrasound planes for distinguishing between HR-positive and HR-negative breast cancers. A cohort of 505 patients from two hospitals was stratified into discovery (Institute 1, 416 patients) and validation (Institute 2, 89 patients) cohorts. A comprehensive set of 788 ultrasound radiomics features was extracted from both long-axis and short-axis ultrasound planes, respectively. Utilizing least absolute shrinkage and selection operator (LASSO) regression analysis, distinct models were constructed for the long-axis and short-axis data. Subsequently, radiomics scores (Rad-scores) were computed for each patient. Additionally, a combined model was formulated by integrating data from long-axis and short-axis Rad-scores along with clinical factors. The diagnostic efficacy of all models was evaluated by the area under the receiver operating characteristic (ROC) curve (AUC). The long-axis and short-axis models, consisting of 11 features and 15 features, respectively, were established, yielding AUCs of 0.743 and 0.751 in the discovery cohort, and 0.795 and 0.744 in the validation cohort. The calculated long-axis and short-axis Rad-scores exhibited significant differences between HR-positive and HR-negative groups across all cohorts (all p < 0.001). Univariate analysis identified ultrasound-reported tumor size as an independent predictor. The combined model, incorporating long-axis and short-axis Rad-scores along with tumor size, achieved superior AUCs of 0.788 and 0.822 in the discovery and validation cohorts, respectively. The combined model effectively distinguishes between HR-positive and HR-negative breast cancers based on ultrasound radiomics features and tumor size, which may offer a valuable tool to facilitate treatment decision making and prognostic assessment.

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Rationale and Objectives: We aimed to develop and validate prediction models for histological grade of invasive breast carcinoma (BC) based on ultrasound radiomics features and clinical characteristics. Materials and Methods: A number of 383 patients with invasive BC were retrospectively enrolled and divided into a training set (207 patients), internal validation set (90 patients), and external validation set (86 patients). Ultrasound radiomics features were extracted from all the eligible patients. The Boruta method was used to identify the most useful features. Seven classifiers were adopted to developed prediction models. The output of the classifier with best performance was labeled as the radiomics score (Rad-score) and the classifier was selected as the Rad-score model. A combined model combining clinical factors and Rad-score was developed. The performance of the models was evaluated using receiver operating characteristic curve. Results: Seven radiomics features were selected from 788 candidate features. The logistic regression model performing best among the 7 classifiers in the internal and external validation sets was considered as Rad-score model, with areas under the receiver operating characteristic curve (AUC) values of 0.731 and 0.738. The tumor size was screened out as the risk factor and the combined model was developed, with AUC values of 0.721 and 0.737 in the internal and external validation sets. Furthermore, the 10-fold cross-validation demonstrated that the 2 models above were reliable and stable. Conclusion: The Rad-score model and combined model were able to predict histological grade of invasive BC, which may enable tailored therapeutic strategies for patients with BC in routine clinical use.

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BACKGROUND: Uterine leiomyomas are hormone-dependent benign tumors and often begin to shrink after menopause due to the reduction in ovarian steroids. The influence of pregnancy on uterine leiomyomas size remains unclear. Here, we present a case of spontaneous regression of a giant uterine leiomyoma after delivery. CASE PRESENTATION: A 40-year-old woman presented with multiple uterine leiomyomas, one of which is a giant uterine leiomyomas (approximately 8 cm in diameter) that gradually shrinked after delivery. At over two months postpartum, the large myometrial leiomyoma had transformed into a submucosal leiomyoma, and over 3 years postpartum, both the submucosal leiomyoma and multiple intramural leiomyomas completely regressed. CONCLUSION: Spontaneous regression of a giant uterine leiomyom is rare after delivery. Considering uterine leiomyoma regression until over 3 year postpartum,we need to observe the regression of uterine fibroid for a longer time postpartum in the absence of fibroid related complications. In addition, it will provide new insights for treatment options of uterine leiomyomas in the future.

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Objective: To examine the relationship between diastolic function and the ratio of early diastolic mitral inflow to early diastolic mitral annular velocity (E/e') in patients with chronic renal disease who had deep vein catheterization and internal fistula. Methods: The clinical data of 50 uremia patients treated at The Affiliated Dongyang Hospital of Wenzhou Medical University from January 2020 to January 2022 were retrospectively analyzed. To assess the differences in E/e' ratio and patients' diastolic function between the two groups, they were split into two teams according to the various therapy modalities: the internal fistula team (n = 42) and the deep vein catheterization team (n = 8). Results: After treatment, the left ventricular end-diastolic diameter (LVd), E peak, a peak and E/A value, the volume and area of four chambers of the left ventricle (LV), the volume and area of two chambers of LV in both groups were significantly lower than those before treatment (P < .001). After treatment, the LVd left ventricular end-systolic diameter (LVs), the four-chamber volume of LV, and the two-chamber volume and area of LV in patients with internal fistula were significantly lower than those in patients with deep vein catheterization (P < .001). After treatment, E peak, A peak and E/A value, e' interventricular septum, E/e' value of interventricular septum, e' lateral wall, and E of lateral wall in patients with internal fistula group. Conclusion: Both deep vein catheterization and internal fistula treatment can improve the diastolic function and reduce the pulmonary pressure of uremic patients to a certain extent, but internal fistula treatment is better than deep vein catheterization in reducing LVd, LVs, LV four-chamber volume, LV two-chamber volume and area, and the effects of both in improving the E/e ratio of patients are not obvious.

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Sisal is a leaf fiber crop with a high integrated value and a wide range of uses in the application of soil remediation of heavy metal contamination. This study provides a preliminary understanding of how sisal responds to Cd stress and presents a theoretical basis for exploring the potential of sisal in the remediation of Cd-contaminated soils. In this work, the activities of the antioxidant enzymes (SOD, POD, and CAT) of sisal were measured by hydroponics with the addition of CdCl2·2.5H2O and different concentrations of Cd stress. Whole transcriptome sequencing (RNA-Seq) analysis was performed with lllumina sequencing technology, and qRT-PCR was conducted to verify the differential genes. The results obtained were as follows: (1) Short-term low concentration of Cd stress (20 mg/kg) had a transient promotion effect on the growth of sisal roots, but Cd showed a significant inhibitory effect on the growth of sisal roots over time. (2) Under different concentrations of Cd stress, the Cd content in sisal root was greater than that in sisal leaf, and Cd accumulated mainly in sisal roots. (3) With the increase of Cd stress concentration, the antioxidant enzyme catalase activity increased, peroxidase activity showed a decreasing trend, and superoxide dismutase showed a trend of increasing and then decreasing. (4) Transcriptome sequencing analysis detected 123 differentially expressed genes (DEGs), among which 85 genes were up-regulated and 38 genes were down-regulated. The DEGs were mainly concentrated in flavonoid biosynthesis and glutathione metabolism, and both processes had some regulatory effects on the Cd tolerance characteristics of sisal. This study elucidated the physiological, biochemical and transcriptomic responses of sisal under cadmium stress, and provided a theoretical basis for the ecological restoration function of sisal.

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Introduction: The molecular subtype plays a significant role in breast carcinoma (BC), which is the main indicator to guide treatment and is closely associated with prognosis. The aim of this study was to investigate the feasibility and efficacy of an ultrasound-based radiomics nomogram in preoperatively discriminating the luminal from non-luminal type in patients with BC. Methods: A total of 264 BC patients who underwent routine ultrasound examination were enrolled in this study, of which 184 patients belonged to the training set and 80 patients to the test set. Breast tumors were delineated manually on the ultrasound images and then radiomics features were extracted. In the training set, the T test and least absolute shrinkage and selection operator (LASSO) were used for selecting features, and the radiomics score (Rad-score) for each patient was calculated. Based on the clinical risk features, Rad-score, and combined clinical risk features and Rad-score, three models were established, respectively. The performances of the models were validated with receiver operator characteristic (ROC) curve and decision curve analysis. Results: In all, 788 radiomics features per case were obtained from the ultrasound images. Through radiomics feature selection, 11 features were selected to constitute the Rad-score. The area under the ROC curve (AUC) of the Rad-score for predicting the luminal type was 0.828 in the training set and 0.786 in the test set. The nomogram comprising the Rad-score and US-reported tumor size showed AUCs of the training and test sets were 0.832 and 0.767, respectively, which were significantly higher than the AUCs of the clinical model in the training and test sets (0.691 and 0.526, respectively). However, there was no significant difference in predictive performance between the Rad-score and nomogram. Conclusion: Both the Rad-score and nomogram can be applied as useful, noninvasive tools for preoperatively discriminating the luminal from non-luminal type in patients with BC. Furthermore, this study might provide a novel technique to evaluate molecular subtypes of BC.

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Objective: The aim of this study was to develop and validate an ultrasound-based radiomics nomogram model by integrating the clinical risk factors and radiomics score (Rad-Score) to predict the Ki-67 status in patients with breast carcinoma. Methods: Ultrasound images of 284 patients (196 high Ki-67 expression and 88 low Ki-67 expression) were retrospectively analyzed, of which 198 patients belonged to the training set and 86 patients to the test set. The region of interest of tumor was delineated, and the radiomics features were extracted. Radiomics features underwent dimensionality reduction analysis by using the independent sample t test and least absolute shrinkage and selection operator (LASSO) algorithm. The support vector machine (SVM), logistic regression (LR), decision tree (DT), random forest (RF), naive Bayes (NB) and XGBoost (XGB) machine learning classifiers were trained to establish prediction model based on the selected features. The classifier with the highest AUC value was selected to convert the output of the results into the Rad-Score and was regarded as Rad-Score model. In addition, the logistic regression method was used to integrate Rad-Score and clinical risk factors to generate the nomogram model. The leave group out cross-validation (LGOCV) method was performed 200 times to verify the reliability and stability of the nomogram model. Results: Six classifier models were established based on the 15 non-zero coefficient features. Among them, the LR classifier achieved the best performance in the test set, with the area under the receiver operating characteristic curve (AUC) value of 0.786, and was obtained as the Rad-Score model, while the XGB performed the worst (AUC, 0.615). In multivariate analysis, independent risk factor for high Ki-67 status was age (odds ratio [OR] = 0.97, p = 0.04). The nomogram model based on the age and Rad-Score had a slightly higher AUC than that of Rad-Score model (AUC, 0.808 vs. 0.798) in the test set, but no statistical difference (p = 0.144, DeLong test). The LGOCV yielded a median AUC of 0.793 in the test set. Conclusions: This study proposed a convenient, clinically useful ultrasound radiomics nomogram model that can be used for the preoperative individualized prediction of the Ki-67 status in patients with BC.

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The ignition sensitivity and flame propagation of zirconium powder clouds are investigated with the influence of initial turbulence. The effect of initial turbulence on the zirconium powder explosion is studied by the change of ignition delay time and dispersion pressure. Hartmann apparatus and Godbert-Greenwald furnace are used to evaluate the minimum ignition energy and minimum ignition temperature, respectively. The high-speed camera is used to analyze the flame propagation behaviors of zirconium powder cloud. The experimental results show that the minimum ignition energy is between 1 mJ and 3 mJ and minimum ignition temperature is 503 K. The ignition energy reaches the minimum value of 30 mJ at the 0.7 MPa. The ignition energy with the effect of ignition delay time has revealed the similar rule. The maximum flame speed increases with the increase of dispersion pressure. Although, the instantaneous flame speed with the lowest dispersion pressure (0.4 MPa) is significantly higher than two others in the early stage of flame propagation.