A Prognostic Model for Survival in Patients with Gastric Signet Ring Cell Carcinoma.

INTRODUCTION: The objective of our study was to develop a nomogram to predict overall survival (OS) and cancer-specific survival (CSS) in patients with gastric signet ring cell carcinoma (GSRCC). METHODS: A total of 3,408 GSRCC patients between 1975 and 2017 were screened from the Surveillance, Epidemiology, and End Results (SEER) database and randomly divided into training and validation cohorts. Univariate and multivariate Cox analyses were conducted to identify independent prognostic factors for the construction of a nomogram. The performance of the model was then assessed by the concordance index (C-index), calibration plot, and area under the receiver operating characteristic curve (AUC). Then, the novel nomogram was further assessed by 64 GSRCC patients from our hospital as the external cohort. RESULTS: We identified age, tumor lymph node metastasis (TNM) staging system, surgery, and chemotherapy as significant independent elements of prognosis. On this basis, a nomogram was constructed, with a C-index of OS in the training and validation cohorts of 0.763 (95% CI: 0.751-0.774) and 0.766 (95% CI: 0.748-0.784) and a C-index of CSS of 0.765 (95% CI: 0.753-0.777) and 0.773 (95% CI: 0.755-0.791), respectively. The AUCs of the nomogram for predicting 2- and 5-year OS were 0.848 and 0.885, respectively, and those for predicting CSS were 0.854 and 0.899, respectively, demonstrating the excellent predictive value of the constructed nomogram compared to the traditional AJCC staging system. Similar results were also observed in both the internal and external validation sets. CONCLUSION: The nomogram provided an accurate tool to predict OS and CSS in patients with GSRCC, which can assist clinicians in making predictions about individual patient survival.

Principles and methods of liquid metal actuators.

As a promising material, liquid metals (LMs) have gained considerable interest in the field of soft robotics due to their ability to move as designed routines or change their shape dramatically under external stimuli. Inspired by the science fiction film Terminator, tremendous efforts have been devoted to liquid robots with high compliance and intelligence. How to manipulate LM droplets is crucial to achieving this goal. Accordingly, this review is dedicated to presenting the principles driving LMs and summarizing the potential methods to develop LM actuators of high maneuverability. Moreover, the recent progress of LM robots based on these methods is overviewed. The challenges and prospects of implementing autonomous robots have been proposed.

Turing Instability of Liquid-Solid Metal Systems.

The classical Turing morphogenesis often occurs in nonmetallic solution systems due to the sole competition of reaction and diffusion processes. Here, this work conceives that gallium (Ga) based liquid metals (LMs) possess the ability to alloy, diffuse, and react with a range of solid metals (SMs) and thus should display Turing instability leading to a variety of nonequilibrium spatial concentration patterns. This work discloses a general mechanism for obtaining labyrinths, stripes, and spots-like stationary Turing patterns in the LM-SM reaction-diffusion systems (GaX-Y), taking the gallium indium alloy and silver substrate (GaIn-Ag) system as a proof of concept. It is only when Ga atoms diffuse over Y much faster than X while X reacts with Y preferentially, that Turing instability occurs. In such a metallic system, Ga serves as an inhibitor and X as an activator. The dominant factors in tuning the patterning process include temperature and concentration. Intermetallic compounds contained in the Turing patterns and their competitive reactions have also been further clarified. This LM Turing instability mechanism opens many opportunities for constructing microstructure systems utilizing condensed matter to experimentally explore the general morphogenesis process.

Biliary hemorrhage caused by a malignant small round cell tumor in the common bile duct: A case report.

BACKGROUND: Malignant small round cell tumor (MSRCT) metastasis to the common bile duct associated with recurrent biliary hemorrhage is extremely rare. Thus far, there have been no reports of metastatic small round cell tumors of the common bile duct. CASE SUMMARY: Herein, we report the case of a 77-year-old female patient with an MSRCT in the common bile duct. The patient was admitted to hospital due to gastrointestinal hemorrhage and abdominal pain. We found a neoplasm in the common bile duct with active bleeding through a spyglass. We performed biopsy through the spyglass and placed a metal stent to stop bleeding. The pathological result suggested that it was an MSRCT metastasized from the back to the common bile duct. Later, we found using fluorescence in situ hybridization that the SS18 gene break test was negative, ruling out the diagnosis of synovial sarcoma. CONCLUSION: MSRCT is a group of tumors with similar cell morphology and diffuse histological structure. Complete tumor resection results in improved survival in patients with MSRCT. Roux-en-Y cholangiojejunostomy was performed. After excision of the common bile duct tumor, the patient felt that the abdominal pain improved and hemorrhage disappeared. The patient underwent routine fecal examination one month after surgery, indicating a negative fecal occult blood test. On May 22, 2023, the patient was reexamined by abdominal computed tomography, and no abdominal space occupying lesions or abdominal lymphadenopathy was found.

Sonoelastography for Testicular Tumor Identification: A Systematic Review and Meta-Analysis of Diagnostic Test Accuracy.

The objective of this review was to summarize the applications of sonoelastography in testicular tumor identification and inquire about their test performances. Two authors independently searched English journal articles and full conference papers from CINAHL, Embase, IEEE Xplore®, PubMed, Scopus, and Web of Science from inception and organized them into a PIRO (patient, index test, reference test, outcome) framework. Eleven studies (n = 11) were eligible for data synthesis, nine of which (n = 9) utilized strain elastography and two (n = 2) employed shear-wave elastography. Meta-analyses were performed on the distinction between neoplasm (tumor) and non-neoplasm (non-tumor) from four study arms and between malignancy and benignity from seven study arms. The pooled sensitivity of classifying malignancy and benignity was 86.0% (95%CI, 79.7% to 90.6%). There was substantial heterogeneity in the classification of neoplasm and non-neoplasm and in the specificity of classifying malignancy and benignity, which could not be addressed by the subgroup analysis of sonoelastography techniques. Heterogeneity might be associated with the high risk of bias and applicability concern, including a wide spectrum of testicular pathologies and verification bias in the reference tests. Key technical obstacles in the index test were manual compression in strain elastography, qualitative observation of non-standardized color codes, and locating the Regions of Interest (ROI), in addition to decisions in feature extractions. Future research may focus on multiparametric sonoelastography using deep learning models and ensemble learning. A decision model on the benefits-risks of surgical exploration (reference test) could also be developed to direct the test-and-treat strategy for testicular tumors.

Computational Biomechanics of Sleep: A Systematic Mapping Review.

Biomechanical studies play an important role in understanding the pathophysiology of sleep disorders and providing insights to maintain sleep health. Computational methods facilitate a versatile platform to analyze various biomechanical factors in silico, which would otherwise be difficult through in vivo experiments. The objective of this review is to examine and map the applications of computational biomechanics to sleep-related research topics, including sleep medicine and sleep ergonomics. A systematic search was conducted on PubMed, Scopus, and Web of Science. Research gaps were identified through data synthesis on variants, outcomes, and highlighted features, as well as evidence maps on basic modeling considerations and modeling components of the eligible studies. Twenty-seven studies (n = 27) were categorized into sleep ergonomics (n = 2 on pillow; n = 3 on mattress), sleep-related breathing disorders (n = 19 on obstructive sleep apnea), and sleep-related movement disorders (n = 3 on sleep bruxism). The effects of pillow height and mattress stiffness on spinal curvature were explored. Stress on the temporomandibular joint, and therefore its disorder, was the primary focus of investigations on sleep bruxism. Using finite element morphometry and fluid-structure interaction, studies on obstructive sleep apnea investigated the effects of anatomical variations, muscle activation of the tongue and soft palate, and gravitational direction on the collapse and blockade of the upper airway, in addition to the airflow pressure distribution. Model validation has been one of the greatest hurdles, while single-subject design and surrogate techniques have led to concerns about external validity. Future research might endeavor to reconstruct patient-specific models with patient-specific loading profiles in a larger cohort. Studies on sleep ergonomics research may pave the way for determining ideal spine curvature, in addition to simulating side-lying sleep postures. Sleep bruxism studies may analyze the accumulated dental damage and wear. Research on OSA treatments using computational approaches warrants further investigation.

Liquid Metal Combinatorics toward Materials Discovery.

Liquid metals and their derivatives provide several opportunities for fundamental and practical exploration worldwide. However, the increasing number of studies and shortage of desirable materials to fulfill different needs also pose serious challenges. Herein, to address this issue, a generalized theoretical frame that is termed as "Liquid Metal Combinatorics" (LMC) is systematically presented, and summarizes promising candidate technical routes toward new generation material discovery. The major categories of LMC are defined, and eight representative methods for manufacturing advanced materials are outlined. It is illustrated that abundant targeted materials can be efficiently designed and fabricated via LMC through deep physical combinations, chemical reactions, or both among the main bodies of liquid metals, surface chemicals, precipitated ions, and other materials. This represents a large class of powerful, reliable, and modular methods for innovating general materials. The achieved combinatorial materials not only maintained the typical characteristics of liquid metals but also displayed distinct tenability. Furthermore, the fabrication strategies, wide extensibility, and pivotal applications of LMC are classified. Finally, by interpreting the developmental trends in the area, a perspective on the LMC is provided, which warrants its promising future for society.

Cushing syndrome caused by an ectopic ACTH-producing pituitary adenoma of the clivus region: A rare case report and literature review.

RATIONALE: Ectopic ACTH-producing pituitary adenoma (EAPA) of the clivus region is extraordinarily infrequent condition and merely a few reports have been reported to date. PATIENT CONCERNS: The patient was a 53-year-old woman who presented with Cushing-like appearances and a soft tissue mass in the clivus region. DIAGNOSES: The final diagnosis of clivus region EAPA was established by clinical, radiological and histopathological findings. INTERVENTIONS: The patient underwent gross total clivus tumor resection via transsphenoidal endoscopy. OUTCOMES: Half a year after surgery, the patient Cushing-like clinical manifestations improved significantly, and urinary free cortisol and serum adrenocorticotropin (ACTH) returned to normal. LESSONS: Given the extreme scarcity of these tumors and their unique clinical presentations, it may be possible to misdiagnose and delayed treatment. Accordingly, it is especially crucial to summarize such lesions through our present case and review the literature for their precise diagnosis and the selection of optimal treatment strategies.

Vision Transformers (ViT) for Blanket-Penetrating Sleep Posture Recognition Using a Triple Ultra-Wideband (UWB) Radar System.

Sleep posture has a crucial impact on the incidence and severity of obstructive sleep apnea (OSA). Therefore, the surveillance and recognition of sleep postures could facilitate the assessment of OSA. The existing contact-based systems might interfere with sleeping, while camera-based systems introduce privacy concerns. Radar-based systems might overcome these challenges, especially when individuals are covered with blankets. The aim of this research is to develop a nonobstructive multiple ultra-wideband radar sleep posture recognition system based on machine learning models. We evaluated three single-radar configurations (top, side, and head), three dual-radar configurations (top + side, top + head, and side + head), and one tri-radar configuration (top + side + head), in addition to machine learning models, including CNN-based networks (ResNet50, DenseNet121, and EfficientNetV2) and vision transformer-based networks (traditional vision transformer and Swin Transformer V2). Thirty participants (n = 30) were invited to perform four recumbent postures (supine, left side-lying, right side-lying, and prone). Data from eighteen participants were randomly chosen for model training, another six participants' data (n = 6) for model validation, and the remaining six participants' data (n = 6) for model testing. The Swin Transformer with side and head radar configuration achieved the highest prediction accuracy (0.808). Future research may consider the application of the synthetic aperture radar technique.

Design, synthesis and neuraminidase inhibitory activity of 4-methyl-5-(3-phenylacryloyl) thiazoles.

A series of 4-methyl-5-(3-phenylacryloyl)thiazoles based on chalcones were designed, synthesized and evaluated for their influenza neuraminidase (NA) inhibitory activity in vitro. A preliminary structure-activity relationship (SAR) analysis showed that thiazoles bearing amide had greater potency. It also showed that mono-hydroxyl group at 4-position on phenyl ring was more effective than other electron-releasing groups or electron-withdraw groups. Compounds A2 and A26 were more potent against NA with IC50 values of 8.2 ± 0.5 µg/mL and 6.2 ± 1.4 µg/mL, respectively. Molecular docking study demonstrated that thiazoles skeleton was benefit for the NA inhibitory activity.

Endocrine Tumor Classification via Machine-Learning-Based Elastography: A Systematic Scoping Review.

Elastography complements traditional medical imaging modalities by mapping tissue stiffness to identify tumors in the endocrine system, and machine learning models can further improve diagnostic accuracy and reliability. Our objective in this review was to summarize the applications and performance of machine-learning-based elastography on the classification of endocrine tumors. Two authors independently searched electronic databases, including PubMed, Scopus, Web of Science, IEEEXpress, CINAHL, and EMBASE. Eleven (n = 11) articles were eligible for the review, of which eight (n = 8) focused on thyroid tumors and three (n = 3) considered pancreatic tumors. In all thyroid studies, the researchers used shear-wave ultrasound elastography, whereas the pancreas researchers applied strain elastography with endoscopy. Traditional machine learning approaches or the deep feature extractors were used to extract the predetermined features, followed by classifiers. The applied deep learning approaches included the convolutional neural network (CNN) and multilayer perceptron (MLP). Some researchers considered the mixed or sequential training of B-mode and elastographic ultrasound data or fusing data from different image segmentation techniques in machine learning models. All reviewed methods achieved an accuracy of ≥80%, but only three were ≥90% accurate. The most accurate thyroid classification (94.70%) was achieved by applying sequential training CNN; the most accurate pancreas classification (98.26%) was achieved using a CNN-long short-term memory (LSTM) model integrating elastography with B-mode and Doppler images.

Liesegang Phenomenon of Liquid Metals on Au Film.

Room temperature liquid metals (LM) such as gallium (Ga) own the potential to react with specific materials which would incubate new application categories. Here, diverse self-organized ring patterns due to nonequilibrium reaction-diffusion and spreading-limitation of Ga-based LM clusters on gold (Au) film are reported, among which diffusion is the controlling step and the self-limiting oxide layer plays the role of kinetic barrier. Such phenomena, classically known as the Liesegang rings, mainly occur in electrolyte media. Unlike existing systems, the present periodic crystallization mechanism enables highly symmetric spatiotemporal periodic Liesegang rings on a smaller scale under ambient conditions. Typically, the Ga-Au and eutectic gallium-indium alloy (EGaIn)-Au reaction-diffusion-spreading systems are constructed, obtaining the revert type and hybrid type concentric Liesegang patterns, respectively. The competitive patterning behavior of the intermediate phase products AuGa2 and AuIn2 in hybrid Liesegang patterns is further analyzed by altering the initial Ga/In mass ratio, first-principles calculations, and molecular dynamic simulations. When the mass ratio of In in GaIn alloy exceeds 15%, it will preferentially react with Au. The discovery of LM Liesegang phenomenon is expected to be a flashpoint for self-organized reaction-diffusion systems and offers promising rules for diverse areas such as materials synthesis and the jewelry design industry.

Depth-Camera-Based Under-Blanket Sleep Posture Classification Using Anatomical Landmark-Guided Deep Learning Model.

Emerging sleep health technologies will have an impact on monitoring patients with sleep disorders. This study proposes a new deep learning model architecture that improves the under-blanket sleep posture classification accuracy by leveraging the anatomical landmark feature through an attention strategy. The system used an integrated visible light and depth camera. Deep learning models (ResNet-34, EfficientNet B4, and ECA-Net50) were trained using depth images. We compared the models with and without an anatomical landmark coordinate input generated with an open-source pose estimation model using visible image data. We recruited 120 participants to perform seven major sleep postures, namely, the supine posture, prone postures with the head turned left and right, left- and right-sided log postures, and left- and right-sided fetal postures under four blanket conditions, including no blanket, thin, medium, and thick. A data augmentation technique was applied to the blanket conditions. The data were sliced at an 8:2 training-to-testing ratio. The results showed that ECA-Net50 produced the best classification results. Incorporating the anatomical landmark features increased the F1 score of ECA-Net50 from 87.4% to 92.2%. Our findings also suggested that the classification performances of deep learning models guided with features of anatomical landmarks were less affected by the interference of blanket conditions.

LM-Jelly: Liquid Metal Enabled Biomimetic Robotic Jellyfish.

Jellyfish have attracted worldwide attention owing to their fantastic moving styles, which also inspired development in soft robotics to meet the demands of underwater surveillance. In this study, a soft robotic jellyfish integrated liquid metal coil, and magnetic field is proposed for the first time to mimic the soft rowing propulsion of oblate jellyfish. The soft robotic jellyfish is actuated by the entirely soft electromagnetic actuators that enabled the gentle motion. Through conceptual experiments and computational fluid dynamics simulations, we systematically interpreted the mechanism of this robotic jellyfish and various factors to dominate its movement behaviors, which involve vortex formation and ascending modalities. Besides, underwater monitoring and bio-friendliness of robotic jellyfish were also demonstrated to illustrate its potential application scenarios and gentle motion characteristics. This study will help to broaden the vistas for liquid metal enabled bionic robotics in a wide range of underwater applications.

Breast Tumour Classification Using Ultrasound Elastography with Machine Learning: A Systematic Scoping Review.

Ultrasound elastography can quantify stiffness distribution of tissue lesions and complements conventional B-mode ultrasound for breast cancer screening. Recently, the development of computer-aided diagnosis has improved the reliability of the system, whilst the inception of machine learning, such as deep learning, has further extended its power by facilitating automated segmentation and tumour classification. The objective of this review was to summarize application of the machine learning model to ultrasound elastography systems for breast tumour classification. Review databases included PubMed, Web of Science, CINAHL, and EMBASE. Thirteen (n = 13) articles were eligible for review. Shear-wave elastography was investigated in six articles, whereas seven studies focused on strain elastography (5 freehand and 2 Acoustic Radiation Force). Traditional computer vision workflow was common in strain elastography with separated image segmentation, feature extraction, and classifier functions using different algorithm-based methods, neural networks or support vector machines (SVM). Shear-wave elastography often adopts the deep learning model, convolutional neural network (CNN), that integrates functional tasks. All of the reviewed articles achieved sensitivity ³ 80%, while only half of them attained acceptable specificity ³ 95%. Deep learning models did not necessarily perform better than traditional computer vision workflow. Nevertheless, there were inconsistencies and insufficiencies in reporting and calculation, such as the testing dataset, cross-validation, and methods to avoid overfitting. Most of the studies did not report loss or hyperparameters. Future studies may consider using the deep network with an attention layer to locate the targeted object automatically and online training to facilitate efficient re-training for sequential data.

Skp2 promotes APL progression through the stabilization of oncoprotein PML-RARα and the inhibition of JunB expression.

AIMS: To investigate the role of Skp2 and JunB on acute promyelocytic leukemia (APL) progression and the related mechanism. MATERIALS AND METHODS: The expression of Skp2 in NB4 cell line was depleted to explore its effect on proliferation and differentiation both in vitro and in vivo assays. Western blot and quantitative RT-PCR analysis were performed to explore Skp2-regulated downstream target genes. Luciferase and co-immunoprecipitation analysis indicated that PML-RARα inhibited the transactivation of JunB by interacting with the PU.1 protein. The western blot analysis confirmed that Skp2 could maintain the stability of PML-RARα. KEY FINDINGS: We report that the progression of APL and the attenuation of APL sensitivity to ATRA are positively associated with Skp2. Elevated Skp2 expression promotes APL progression by decreasing the expression of lncRNA HOTAIRM1 and inactivation of GSK3ß, causing autophagy inhibition followed by the suppression of PML-RARα ubiquitylation and degradation, which represses JunB transcriptional activation through PU.1/PML-RARα transcriptional complex to block cell differentiation. Coupled with ATRA or GSK3ß inhibitor treatment, genetic or pharmacological inhibition of Skp2 strikingly induces JunB expression by accelerating the degradation of PML-RARα, which contributes to the eradication of APL. Additionally, the expressions of Skp2 and JunB are negatively correlated in mice subcutaneous leukemia xenograft tumors. SIGNIFICANCE: Collectively, this study uncovers the roles of Skp2 in PML-RARα stabilization and in APL oncogenic functions. We reveal a novel mechanism of PML-RARα degradation and JunB regulation that constitute an important signaling network of Skp2-GSK3ß-PML/RARα-JunB.

Self-Assembled Copper Film-Enabled Liquid Metal Core-Shell Composite.

Liquid metal (LM) droplets covered with functional materials, especially metallic, often make breakthroughs in performance and functionality. In this study, self-assembly was used to synthesize copper films on the surface of LM. Herein, using CuO nanoparticles as the monomers, driven by the electrostatic interaction between CuO and eutectic gallium-indium (EGaIn) in the alkaline environment, EGaIn@Cu is realized by taking advantage of the reducing property of the EGaIn-alkaline interface. The copper film is smooth and dense, and under its protection, a layer of gallium oxide remains on the reaction interface between copper and LM, which enabled EGaIn@Cu to possess the volt-ampere curves similar to the Schottky mode, showing that the proposed mechanism has the potential to be used in the bottom-up synthesis of the semiconductor junction. Owing to the support of the copper film, the stiffness coefficient of the LM droplet can be increased by 56.9%. Coupled with the melting latent heat of 55.46 J/g and the natural high density of metal, EGaIn@Cu is also a potential phase change capsule. In addition, a method based on stream jetting and self-breaking up mechanisms of LM to batch-produce sub-millimeter capsules was also introduced. The above structural and functional characteristics demonstrate the value of this work in related fields.

Electrochemical synthesis of 1,2,4-oxadiazoles from amidoximes through dehydrogenative cyclization.

A convenient and efficient method for the generation of the iminoxy radical through anodic oxidation was developed for the synthesis of 3,5-disubstituted 1,2,4-oxadiazoles from N-benzyl amidoximes. The transformation proceeds through 1.5-Hydrogen Atom Transfer (1,5-HAT) and intramolecular cyclization. The process features simple operation, mild conditions, broad substrate scope and high functional group compatibility, and provides a facile and practical way for the preparation of 1,2,4-oxadiazoles.

[lncRNA CRNDE promotes proliferation and inhibits apoptosis of U937 cells by downregulating miR-136-5p and upregulating MCM5].

Objective To investigate the effect of lncRNA CRNDE on proliferation, apoptosis, and cell cycle of U937 cells and its mechanism. Methods The expression level of CRNDE in bone marrow cells of AML patients was analyzed by GEPIA database; the mRNA expression levels of miR-136-5p, CRNDE, and minichromosome maintenance 5(MCM5) in AML cell lines were detected by quantitative real-time PCR (qRT-PCR). The lentiviral vector with CRNDE knocked down was constructed and transfected into U937 cells which were randomized into CRNDE knockdown group (sh-CRNDE group) and negative control group (sh-NC group); miR-136-5p mimic and miR-136-5p inhibitor were transfected respectively to overexpress and knock down miR-136-5p in U937 cells which were randomized into miR-136-5p-mimic group, NC-mimic group, miR-136-5p-inhibitor group, and NC-inhibitor group. The effect of CRNDE and miR-136-5p on proliferation was detected by CCK-8 assay and cell counting assay, and the effect of them on cell cycle and apoptosis was detected by flow cytometry. The mRNA expressions of miR-136-5p, CRNDE, and MCM5 were detected by qRT-PCR, and the protein expressions of MCM5, Bcl2, cyclin D1, and cyclin A2 were detected by Western blotting. Results CRNDE was highly expressed in the bone marrow and cell lines of AML patients. Knockdown of CRNDE upregulated miR-136-5p, inhibited the MCM5 mRNA and protein expressions and the cell proliferation, promoted the cell apoptosis, and blocked the cell cycle in G1 phase. Overexpression of miR-136-5p also inhibited the expression of MCM5 at both mRNA and protein levels, while knockdown of miR-136-5p reversed those effects. Conclusion CRNDE promotes the proliferation and inhibits the apoptosis of U937 cells by downregulating miR-136-5p and upregulating MCM5.

A new hydrodynamic interpretation of liquid metal droplet motion induced by an electrocapillary phenomenon.

The Marangoni effect, induced by the surface tension gradient resulting from the gradient of temperature, concentration, or electric potential gradient along a surface, is commonly utilized to manipulate a droplet. It is also the reason for unique behaviors of liquid metal such as moving, breathing, and large-scale deformation under an electric field, which have aroused tremendous interest in academics. However, liquid metal droplets are usually treated as solid marbles, which neglect their fluidic features and can hardly explain some unusual phenomena, such as a droplet under a stationary electric field that moves in the opposite direction in different solutions. To better clarify these discrepancies, this study reveals that the movement of liquid metal is directly driven by viscous forces of solution rather than interfacial tension. This mechanism was determined by analyzing flow characteristics on a liquid metal surface. Additionally, experiments with liquid metal free falling in solution, liquid metal droplet movement experiments on substrates with different roughness, and liquid metal droplet movement experiments under high current density were additionally conducted to verify the theoretical interpretation. This research is instrumental for a greater understanding of the movement of liquid metal under an electric field and lays the foundation for the applications of liquid metal droplets in pumping, fluid mixing, and many other microfluidic fields.