Noninvasive Assessment of Tumor Histological Grade in Invasive Breast Carcinoma Based on Ultrasound Radiomics and Clinical Characteristics: A Multicenter Study.

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.

PKM2: A Potential Regulator of Cardiac Injury Through Glycolytic and Non-glycolytic Pathways.

Adult animals are unable to regenerate heart cells due to postnatal cardiomyocyte cycle arrest, leading to higher mortality rates in cardiomyopathy. However, reprogramming of energy metabolism in cardiomyocytes provides a new perspective on the contribution of glycolysis to repair, regeneration, and fibrosis after cardiac injury. Pyruvate kinase (PK) is a key enzyme in the glycolysis process. This review focuses on the glycolysis function of PKM2, although PKM1 and PKM2 both play significant roles in the process after cardiac injury. PKM2 exists in both a low-activity dimer and a high-activity tetramer form. PKM2 dimers promote aerobic glycolysis but have low catalytic activity, leading to the accumulation of glycolytic intermediates. These intermediates enter the PPP pathway to promote cardiomyocyte proliferation and heart regeneration. Additionally, they activate KATP channels, protecting the heart against ischemic damage. PKM2 tetramers function similarly to PKM1 in glycolysis, promoting pyruvate oxidation and subsequently ATP generation to protect the heart from ischemic damage. They also activate KDM5 through the accumulation of αKG, thereby promoting cardiomyocyte proliferation and cardiac regeneration. Apart from glycolysis, PKM2 interacts with transcription factors like Jmjd4, RAC1, ß-catenin, and HIF-1α, playing various roles in homeostasis maintenance, remodeling, survival regulation, and neovascularization promotion. However, PKM2 has also been implicated in promoting cardiac fibrosis through mechanisms like SIRT3 deletion, TG2 expression enhancement, and activation of TGF-ß1/Smad2/3 and Jak2/Stat3 signals. Overall, PKM2 shows promising potential as a therapeutic target for promoting cardiomyocyte proliferation and cardiac regeneration, as well as addressing cardiac fibrosis after injury.

The Application of Aptamer and Research Progress in Liver Disease.

Aptamers, as a kind of small-molecule nucleic acid, have attracted much attention since their discovery. Compared with biological reagents such as antibodies, aptamers have the advantages of small molecular weight, low immunogenicity, low cost, and easy modification. At present, aptamers are mainly used in disease biomarker discovery, disease diagnosis, treatment, and targeted drug delivery vectors. In the process of screening and optimizing aptamers, it is found that there are still many problems need to be solved such as the design of the library, optimization of screening conditions, the truncation of screened aptamer, and the stability and toxicity of the aptamer. In recent years, the incidence of liver-related diseases is increasing year by year and the treatment measures are relatively lacking, which has attracted the people's attention in the application of aptamers in liver diseases. This article mainly summarizes the research status of aptamers in disease diagnosis and treatment, especially focusing on the application of aptamers in liver diseases, showing the crucial significance of aptamers in the diagnosis and treatment of liver diseases, and the use of Discovery Studio software to find the binding target and sequence of aptamers, and explore their possible interaction sites.

Inhibitory Effect of Jinkui Shenqi Pills on Glucocorticoid-Enhanced Axial Length Elongation in Experimentally Myopic Guinea Pigs.

OBJECTIVE: To explore the underlying mechanism of inhibition by Jinkui Shenqi Pills (JKSQP) on glucocorticoid-enhanced axial length elongation in experimental lens-induced myopia (LIM) guinea pigs. METHODS: Sixty 2-week old male guinea pigs were randomly divided into 4 groups with 15 guinea pigs in each group, according to the random numbers generated by SPSS software: control, LIM, saline and JKSQP groups. The control group includes animals with no treatment, while the guinea pigs in the other 3 groups received lens-induced myopization on the right eyes throughout the experiment (for 8 weeks). The saline and JKSQP groups were given daily intraperitoneal injections of 10 mg/kg hydrocortisone for 2 consecutive weeks at the same time, and then orally administered either saline or JKSQP [13.5 g/(kg•d) for 6 consecutive weeks. Body weight, anal temperature and animal appearance were observed and recorded to evaluate the GC-associated symptoms. The ocular parameters, including refraction and axial length, were measured by streak retinoscopy and A-scan ultrasonography, respectively. The levels of plasma hormones associated with the hypothalamic-pituitary-adrenal axis (HPAA), including free triiodothyronine, free thyroxine, estradiol and testosterone, were measured by radioimmunoassay, and cyclic adenosine monophosphate (cAMP) and cyclic guanosine monophosphate were measured by enzyme-linked immunosorbent assay. In addition, the mRNA and protein expressions of retinal amphiregulin (AREG) was measured by quantitative real-time polymerase chain reaction and Western blotting, respectively. RESULTS: JKSQP effectively increased body weight and anal temperature, improved animal appearance and suppressed axial length elongation in glucocorticoid-enhanced myopic guinea pigs with normalization of 4 HPAA-associated plasma hormones (all P<0.05). The plasma level of cAMP was significantly increased, whereas the plasma level of cGMP and the mRNA and protein expressions of retinal AREG were decreased after treatment with JKSQP (all P<0.05). CONCLUSION: JKSQP exhibited a significant inhibitory effect on axial length elongation with decreased expression of AREG in the retina, and normalized 4 HPAA-associated plasma hormones and the expression of cAMP and cGMP in GC-enhanced myopic guinea pigs.

Current Status and Challenges of Aptamers Screening and Optimization.

BACKGROUND: Aptamers, consisting of single-stranded DNA or RNA, have secondary and tertiary structures which could bind specifically to target molecules. They are characterized by strong specificity, high affinity, low molecular weight, and low immunogenicity; therefore, the current research focuses on their potential as a targeted drug carrier, a diagnostic probe for diseases, or as a direct therapeutic drug. OBJECTIVE: In this review, how to improve the success rate of adaptor screening and the optimization after screening is described. RESULTS: For aptamer screening, an efficient selection strategy is needed. In this article, by analyzing key aspects of SELEX such as initial library design, screening procedures, truncation and modification after screening, a comprehensive analysis of each step that might meet obstacles in SELEX is provided. CONCLUSION: Aptamers, which possess the specificity and affinity with the target, can serve as targeted drug carriers or biosensors for diagnosing a disease. If the problems in the screening process in cell-SELEX technology, truncation, and modification after screening are solved, it will have a broader range of applications.

The roles of ETS transcription factors in liver fibrosis.

E26 transformation specific or E twenty-six (ETS) protein family consists of 28 transcription factors, five of which, named ETS1/2, PU.1, ERG and EHF, are known to involve in the development of liver fibrosis, and are expected to become diagnostic markers or therapeutic targets of liver fibrosis. In recent years, some small molecule inhibitors of ETS protein family have been discovered, which might open up a new path for the liver fibrosis therapy targeting ETS. This article reviews the research progress of ETS family members in the development liver fibrosis as well as their prospect of clinical application.

Development and validation of an ultrasound-based radiomics nomogram for predicting the luminal from non-luminal type in patients with breast carcinoma.

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.

Development and Validation of an Ultrasound-Based Radiomics Nomogram for Identifying HER2 Status in Patients with Breast Carcinoma.

(1) Objective: To evaluate the performance of ultrasound-based radiomics in the preoperative prediction of human epidermal growth factor receptor 2-positive (HER2+) and HER2- breast carcinoma. (2) Methods: Ultrasound images from 309 patients (86 HER2+ cases and 223 HER2- cases) were retrospectively analyzed, of which 216 patients belonged to the training set and 93 patients assigned to the time-independent validation set. The region of interest of the tumors was delineated, and the radiomics features were extracted. Radiomics features underwent dimensionality reduction analyses using the intra-class correlation coefficient (ICC), Mann-Whitney U test, and the least absolute shrinkage and selection operator (LASSO) algorithm. The radiomics score (Rad-score) for each patient was calculated through a linear combination of the nonzero coefficient features. The support vector machine (SVM), K nearest neighbors (KNN), logistic regression (LR), decision tree (DT), random forest (RF), naive Bayes (NB) and XGBoost (XGB) machine learning classifiers were trained to establish prediction models based on the Rad-score. A clinical model based on significant clinical features was also established. In addition, the logistic regression method was used to integrate Rad-score and clinical features to generate the nomogram model. The leave-one-out cross validation (LOOCV) method was used to validate the reliability and stability of the model. (3) Results: Among the seven classifier models, the LR achieved the best performance in the validation set, with an area under the receiver operating characteristic curve (AUC) of 0.786, and was obtained as the Rad-score model, while the RF performed the worst. Tumor size showed a statistical difference between the HER2+ and HER2- groups (p = 0.028). The nomogram model had a slightly higher AUC than the Rad-score model (AUC, 0.788 vs. 0.786), but no statistical difference (Delong test, p = 0.919). The LOOCV method yielded a high median AUC of 0.790 in the validation set. (4) Conclusion: The Rad-score model performs best among the seven classifiers. The nomogram model based on Rad-score and tumor size has slightly better predictive performance than the Rad-score model, and it has the potential to be utilized as a routine modality for preoperatively determining HER2 status in BC patients non-invasively.

Integration of ultrasound radiomics features and clinical factors: A nomogram model for identifying the Ki-67 status in patients with breast carcinoma.

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.

Diagnostic performance of elastosonography in the differential diagnosis of benign and malignant salivary gland tumors: A meta-analysis.

Purpose: The clinical practice of elastosonography for the detection of salivary gland tumors is still a controversial issue. The objective of this meta-analysis was to evaluate the effect of elastosonography for the diagnosis of salivary gland tumors and to compare the diagnostic value of elastosonography and conventional ultrasound in the diagnosis of salivary gland tumors. Methods: A comprehensive literature search through PubMed, EMBASE, and Cochrane Library was carried out from inception to November 2021. Two researchers independently extracted the data from the enrolled papers using a standard data extraction form. The pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the curve (AUC) were calculated to evaluate the diagnostic performance of elastosonography. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was utilized to evaluate the quality of each included study. Meta-DiSc version 1.4, Review Manager 5.3, and StataSE 15 were used. Results: Sixteen studies with a total of 1105 patients with 1146 lesions were included in this meta-analysis. The pooled sensitivity, specificity, PLR, NLR, and DOR of elastosonography for the differentiation between benign and malignant salivary gland tumors were 0.73 (95%CI, 0.66-0.78), 0.64 (95%CI, 0.61-0.67), 2.83 (95%CI, 1.97-4.07), 0.45 (95%CI, 0.32-0.62), and 9.86 (95%CI, 4.49-21.62), respectively, with an AUC of 0.82. Four studies provided data regarding the conventional ultrasound for the differentiation between benign and malignant salivary gland tumors. The pooled sensitivity, specificity, and DOR were 0.62 (95%CI, 0.50-0.73), 0.93 (95%CI, 0.90-0.96), and 25.07 (95%CI, 4.28-146.65), respectively. The meta-regression and subgroup analyses found that assessment methods were associated with significant heterogeneity, and quantitative or semiquantitative elastosonography performed better than the qualitative one. Conclusions: Elastosonography showed a limited value for diagnosing malignant salivary gland tumors; it could be considered as a supplementary diagnostic technology to conventional ultrasound, and quantitative or semiquantitative elastosonography was superior to the qualitative one.