Extrauterine adenomyoma of the liver with a focally cellular smooth muscle component occurring in a patient with a history of myomectomy: case report and review of the literature

VIRTUAL SLIDES: The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/1327125766102291. Since first reported in 1986, 14 cases of extrauterine adenomyoma have been reported in the English literature, most often occurring in the ovaries. In this report, we present the first case of extrauterine adenomyoma involving the liver in a 29-year-old woman who presented with a 2-year history of low back pain with recent worsening and a history of laparoscopic myomectomy 5 years previously. Gross inspection of the specimen revealed a subcapsular mass that had a well-circumscribed margin with the adjacent liver tissue. By histopathologic examination, the multilobular mass was composed of a smooth muscle component and benign endometrioid glands and stroma. The smooth muscle component was focally cellular, and the endometrioid glands had secretory features. Both the smooth muscle component and endometrioid tissue were positive for ER and PR. The smooth muscle component was also positive for desmin and SMA, while the endometrioid stroma was positive for CD10. Other extrauterine lesions composed of a mixture of smooth muscle tissue and heterotopic endometrioid tissue, including endometriosis with a smooth muscle component, leiomyomatosis/leiomyomas associated with endometriosis and uterus-like masses, should be included in differential diagnoses. The patient was free from recurrence 5 months after liver tumor resection

Acoustic Tweezing Cytometry Induces Rapid Initiation of Human Embryonic Stem Cell Differentiation.

Mechanical forces play critical roles in influencing human embryonic stem cell (hESC) fate. However, it remains largely uncharacterized how local mechanical forces influence hESC behavior in vitro. Here, we used an ultrasound (US) technique, acoustic tweezing cytometry (ATC), to apply targeted cyclic subcellular forces to hESCs via integrin-bound microbubbles (MBs). We found that ATC-mediated cyclic forces applied for 30 min to hESCs near the edge of a colony induced immediate global responses throughout the colony, suggesting the importance of cell-cell connection in the mechanoresponsiveness of hESCs to ATC-applied forces. ATC application generated increased contractile force, enhanced calcium activity, as well as decreased expression of pluripotency transcription factors Oct4 and Nanog, leading to rapid initiation of hESC differentiation and characteristic epithelial-mesenchymal transition (EMT) events that depend on focal adhesion kinase (FAK) activation and cytoskeleton (CSK) tension. These results reveal a unique, rapid mechanoresponsiveness and community behavior of hESCs to integrin-targeted cyclic forces

Unique allosteric effect driven rapid adsorption of carbon dioxide on a new ionogel [P4444][2-Op]@MCM-41 with excellent cyclic stability and loading-dependent capacity

Allosteric effect-driven rapid stepwise CO2 adsorption of pyridine-containing anion functionalized ionic liquid [P4444][2-Op] confined into mesoporous silica MCM-41.</p

Blockchain-based Traditional Chinese Medicine Traceability Model

Traditional Chinese medicine is widely used in the medical field for its good curative effect and extremely small side effects,but the sources and preparation processes of traditional Chinese medicine materials lack transparency,Some traditional Chinese medicines have the defects of being easy to counterfeit and difficult to trace.Thus, a blockchain-based traditional Chinese medicine traceability model(BTCMT) is proposed in the paper.This model is based on the blockchain and uses a distributed storage mechanism. Under the control of the practical Byzantine algorithm and smart contract, it has achieved the distributed management of the whole process of planting, collecting, primary processing, preparation and sales of traditional Chinese medicine, ensuring the transparency, decentralization and anti-counterfeiting of traditional medicine on each block, and has realized the traceability and information sharing of traditional Chinese medicine. After simulation experiments, the accounting success rate of BTCMT was 100%, Failure rate is 0%, the shortest traceability response time was 212ms, and the maximum system traceability throughput was 4908.02bps. The model has good robustness and provides a reference for the traceability model of traditional Chinese medicine

A101: Effects of Combining Electrical Muscle Stimulation with Strength Training on Lower Limb Mechanics With PFPS

Background: Patellofemoral pain syndrome (PFPS) is a common causative factor of knee pain. Exercise and electrical muscle stimulation (EMS) have been shown to improve knee function and relieve pain in patients with PFPS. However, the effect of exercise and EMS training on lower limb mechanics is unclear. The purpose of this study was to investigate the lower limb mechanics during single-leg squats following 6-week training interventions of exercise and EMS. Methods: 46 participants were recruited and randomly assigned into the muscle strength training (MST) group (21.5±3.8 yr, 174.7±8.4 cm, 69.5±11.7 kg) and combining EMS with strength training (EMS) group (22.2±4.3 yr, 173.8±7.4 cm, 72.4±13.2 kg) from the local university. Lower limb mechanics during single-leg squats were collected at the baseline and following the 6-week intervention for each group by using infrared motion capture systems and force platform. Two-way (group by time) analysis of covariance tests with repeated measures were applied to analyze the data. Results: ANCOVA with repeated measures indicated that during single-leg squat, both groups showed significant decreases in peak patellofemoral joint stress (F=30.384, P \u3c 0.001, η²=0.459), peak patellofemoral joint stress force (F=25.063, P \u3c 0.001, η²=0.379), knee extension moment (F=13.603, P=0.001, η²=0.264), knee abduction moment (F=5.086, P=0.030, η²=0.113) and knee external rotation moment (F=4.277, P=0.045, η²=0.099) over time, but the EMS group had a larger change in peak patellofemoral joint stress (F=5.910, P=0.020, η²=0.138) over time compared to the MST group. Conclusions: EMS training is beneficial for altering lower limb movement patterns and joint loading during single-leg squats in patients with PFPS compared to MST

Exploring the Influence of Information Entropy Change in Learning Systems

In this work, we explore the influence of entropy change in deep learning systems by adding noise to the inputs/latent features. The applications in this paper focus on deep learning tasks within computer vision, but the proposed theory can be further applied to other fields. Noise is conventionally viewed as a harmful perturbation in various deep learning architectures, such as convolutional neural networks (CNNs) and vision transformers (ViTs), as well as different learning tasks like image classification and transfer learning. However, this paper aims to rethink whether the conventional proposition always holds. We demonstrate that specific noise can boost the performance of various deep architectures under certain conditions. We theoretically prove the enhancement gained from positive noise by reducing the task complexity defined by information entropy and experimentally show the significant performance gain in large image datasets, such as the ImageNet. Herein, we use the information entropy to define the complexity of the task. We categorize the noise into two types, positive noise (PN) and harmful noise (HN), based on whether the noise can help reduce the complexity of the task. Extensive experiments of CNNs and ViTs have shown performance improvements by proactively injecting positive noise, where we achieved an unprecedented top 1 accuracy of over 95% on ImageNet. Both theoretical analysis and empirical evidence have confirmed that the presence of positive noise can benefit the learning process, while the traditionally perceived harmful noise indeed impairs deep learning models. The different roles of noise offer new explanations for deep models on specific tasks and provide a new paradigm for improving model performance. Moreover, it reminds us that we can influence the performance of learning systems via information entropy change.Comment: Information Entropy, CNN, Transforme