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Purpose: The aim of this study was to evaluate the value of elastography in the differential diagnosis of benign versus malignant testicular lesions.Methods: The PubMed, Cochrane Library, and Embase databases were searched for relevant studies. The diagnostic accuracy of elastography was evaluated using pooled sensitivity, specificity, likelihood ratio, post-test probability, diagnostic odds ratio, and by summarizing the area under the hierarchical summary receiver operating characteristic (HSROC) curve.Results: Seven studies with 568 lesions were included. The pooled sensitivity and specificity were 87% (95% confidence interval [CI], 81% to 92%) and 81% (95% CI, 65% to 90%), respectively. The pooled estimates of the positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were 4.48 (95% CI, 2.37 to 8.47), 0.16 (95% CI, 0.10 to 0.25), and 28.11 (95% CI, 11.39 to 69.36), respectively. The area under the HSROC curve was 90% (95% CI, 88% to 93%).Conclusion: Elastography is useful for assessing the stiffness of testicular lesions and for differentiating benign from malignant lesions. Elastography can be an effective supplement to conventional ultrasonography.

BACKGROUND: A noninvasive, safe, and economic imaging technique is required to identify epileptogenic lesions in the brain. PURPOSE: Our aim was to perform a meta-analysis evaluating the accuracy of arterial spin-labeling in localizing the epileptic focus in the brain and the changes in the blood perfusion in these regions. DATA SOURCES: Our sources were the PubMed and EMBASE data bases. STUDY SELECTION: English language studies that assessed the diagnostic accuracy of arterial spin-labeling for detecting the epileptogenic zone up to July 2019 were included. DATA ANALYSIS: The symptomatogenic foci of seizures in the brain were determined and used as the references. The relevant studies were evaluated using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. The outcomes were evaluated using the pooled sensitivity, pooled specificity, pooled accuracy, diagnostic odds ratio, area under the summary receiver operating characteristic curve, and likelihood ratio. DATA SYNTHESIS: Six studies that included 174 patients qualified for this meta-analysis. The pooled sensitivity, pooled specificity, and area under the summary receiver operating characteristic curve were 0.74 (95% CI, 0.65–0.82), 0.35 (95% CI, 0.03–0.90), and 0.73 (95% CI, 0.69–0.76), respectively. The accuracy of arterial spin-labeling for localizing the epileptic focus was 0.88 (accuracy in arterial spin-labeling/all perfusion changes in arterial spin-labeling) in cases of a positive arterial spin-labeling result. The epileptogenic zone exhibited hyperperfusion or hypoperfusion. LIMITATIONS: Only a few studies were enrolled due to the strict inclusion criteria. CONCLUSIONS: Arterial spin-labeling can be used for assessing, monitoring, and reviewing, postoperatively, patients with epilepsy. Blood perfusion changes in the brain may be closely related to the seizure time and pattern.

To investigate how a DL model makes decisions in lesion classification with a newly defined region of evidence (ROE) by incorporating “explainable AI” (xAI) techniques. A data set of 785 2D breast ultrasound images acquired from 367 females. The DenseNet-121 was used to classify whether the lesion is benign or malignant. For performance assessment, classification results are evaluated by calculating accuracy, sensitivity, specificity, and receiver operating characteristic for experiments of both coarse and fine regions of interest (ROIs). The area under the curve (AUC) was evaluated, and the true-positive, false-positive, true-negative, and false-negative results with breakdown in high, medium, and low resemblance on test sets were also reported. The two models with coarse and fine ROIs of ultrasound images as input achieve an AUC of 0.899 and 0.869, respectively. The accuracy, sensitivity, and specificity of the model with coarse ROIs are 88.4%, 87.9%, and 89.2%, and with fine ROIs are 86.1%, 87.9%, and 83.8%, respectively. The DL model captures ROE with high resemblance of physicians’ consideration as they assess the image. We have demonstrated the effectiveness of using DenseNet to classify breast lesions with limited quantity of 2D grayscale ultrasound image data. We have also proposed a new ROE-based metric system that can help physicians and patients better understand how AI makes decisions in reading images, which can potentially be integrated as a part of evidence in early screening or triaging of patients undergoing breast ultrasound examinations. • The two models with coarse and fine ROIs of ultrasound images as input achieve an AUC of 0.899 and 0.869, respectively. The accuracy, sensitivity, and specificity of the model with coarse ROIs are 88.4%, 87.9%, and 89.2%, and with fine ROIs are 86.1%, 87.9%, and 83.8%, respectively. • The first model with coarse ROIs is slightly better than the second model with fine ROIs according to these evaluation metrics. • The results from coarse ROI and fine ROI are consistent and the peripheral tissue is also an impact factor in breast lesion classification.

Aim:Mitochondria are essential for energy metabolism in the tumor microenvironment and the survival of cancer cells.Background:ADP-ribosylation factor–like GTPase 5b (ARL5B) has been found to be associated with mitochondrial dysfunction and breast cancer (BC) progression, but the underlying mechanism needs to be further understood.Objective:We investigated the effects of ARL5B on the apoptosis and glycolysis of breast cancer cells and its underlying mechanisms.Method:Quantitative reverse transcription-PCR (qRT-PCR) and western blot assays were used to detect the expression of ARL5B in breast cancer tissues and cells. An ARL5B loss-of-function assay was performed to verify its role in BC development.Results:ARL5B was upregulated in breast cancer tissues and cell lines. ARL5B knockdown induced apoptosis and activated the mitochondrial pathway in breast cancer cells. Interestingly, the inhibition of ARL5B repressed the aerobic glycolysis of breast cancer cells. The role of ARL5B in breast cancer cells was exerted by mediating the activation of viral RNA sensor MDA5-evoked signaling. Silencing ARL5B triggered MDA5 signaling by upregulating the key proteins involved in the MDA5 pathway. Importantly, MDA5 silencing reversed the effects of ARL5B knockdown on mitochondrial-mediated apoptosis and glycolysis, whereas poly (I:C), as a ligand for MDA5, further enhanced ARL5B knockdown- facilitated mitochondrial apoptosis and the inhibition of glycolysis.Conclusion:The knockdown of ARL5B activated MDA5 signaling and thus led to the enhanced mitochondrial- mediated apoptosis and glycolysis inhibition in breast cancer cells. Our study suggested that ARL5B might be a potential therapy target for BC.

Aims: In this systematic review and meta-analysis, we discuss the value of grey-scale ultrasonography (US) in diagnosing adhesive capsulitis of the shoulder (ACS).Material and methods: We retrieved relevant studies from PubMed, Cochrane Library, and Embase before 8 April 2019. We selected 7 studies concerning 446 patients (490 shoulders) that used grey-scale US to diagnose ACS and magnetic resonance imaging (MRI) or arthroscopy as the reference standard. We assessed the diagnostic accuracy of US on the basis of combined sensitivity, specificity, likelihood ratio (LR), and the area under the summary receiver operating characteristic (SROC) curve (AUC).Results: The combined sensitivity, specificity, positive LR and negative LR were found to be 88% (95%CI: 74–95), 96% (95%CI: 88–99), 23.89 (95%CI: 6.31–90.51) and 0.12 (95%CI: 0.05–0.29), respectively. The AUC was 0.97 (95%CI: 0.96–0.98). ACS was diagnosed on the basis of four US features: coracohumeral ligament thickening, inferior capsule/axillary recess capsule thickening, rotator interval abnormality, and restriction of the range of motion. The corresponding sensitivities were 64.4 (95%CI: 48.8–78.1), 82.1 (95%CI: 73.8–88.7), 82.6 (95%CI: 74.1–89.2) and 94.3 (95%CI: 84.3–98.8), respectively, and specificities were 88.9 (95%CI: 76.0–96.3), 95.7 (95%CI: 90.3–98.6), 93.9 (95%CI: 89.8–96.7), and 90.9 (95%CI: 75.7–98.1), respectively.Conclusions: Our meta-analysis showed that grey-scale US plays a significant role in the diagnosis of ACS. Because of its high sensitivity and specificity, US can be added to the existing clinical diagnosis program.

Objective: To evaluate if an automatic classification of Rheumatoid arthritis (RA) metacarpophalangeal joint conditions in ultrasound images is feasible by deep learning (DL) method, to provide a more objective, automated and fast way of RA diagnosis in clinical setting. Materials and Methods: DenseNet-based DL model was used, both training and testing are implemented in TensorFlow 1.13.1 with Keras DL libraries. The area under curve (AUC), accuracy, sensitivity and specificity values with 95% CIs were reported. The statistical analysis was performed by using scikit-learn libraries in Python 3.7. Results: A total of 1337 RA ultrasound images were acquired from 208 patients, the number of images is 313, 657, 178 and 189 in OESS Grade L0, L1, L2, and L3 respectively. In Classification Scenario 1 S.P.-no vs. S.P.-yes, three experiments with region of interest of size 192*448 (Group 1), 96*224 (Group 2) and 96*224 stacked with pre-segmented annotated mask of S.P. area (Group 3) as input achieve an AUC of 0.863 (95% CI: 0.809, 0.917), 0.861 (95% CI: 0.805, 0.916) and 0.886 (95% CI: 0.836, 0.936), respectively. In Classification Scenario 2 Healthy vs. Diseased, experiments in Group 1, Group 2 and Group 3 achieve an AUC of 0.848 (95% CI: 0.799, 0.896), 0.864 (95% CI: 0.819, 0.909) and 0.916 (95% CI: 0.883, 0.952), respectively. Conclusion: We combined Dense Net model with ultrasound images for RA condition assessment. The feasibility of using DL to create an automatic RA condition classification system was also demonstrated. The proposed method can be an alternative to the initial screening of RA patients. Funding Statement: None. Declaration of Interests: The authors declare that they have no competing interests. Ethics Approval Statement: The study was approved by the ethics committee of Shenzhen people’s Hospital.

Studies have shown that numerosity-based arithmetic training can promote arithmetic learning in typically developing children as well as children with developmental dyscalculia (DD), but the cognitive mechanism underlying this training effect remains unclear. The main aim of the current study was to examine the role of visual form perception in arithmetic improvement through an 8-day numerosity training for DD children. Eighty DD children were selected from four Chinese primary schools. They were randomly divided into the intervention and control groups. The intervention group received training on an apple-collecting game, whereas the control group received an English dictation task. Children's cognitive and arithmetic performances were assessed before and after training. The results showed that the intervention group showed a significant improvement in arithmetic performance, approximate number system (ANS) acuity, and visual form perception, but not in spatial processing and sentence comprehension. The control group showed no significant improvement in any cognitive ability. Mediation analysis further showed that training-related improvement in arithmetic performance was fully mediated by the improvement in visual form perception. The results suggest that short-term numerosity training enhances the arithmetic performance of DD children by improving their visual form perception.