Supramolecular Modular Assembly of Imaging-Trackable Enzymatic Nanomotors

Self-propelled micro/nanomotors (MNMs) have shown great application potential in biomedicine, sensing, environmental remediation, etc. In the past decade, various strategies or technologies have been used to prepare and functionalize MNMs. However, the current preparation strategies of the MNMs were mainly following the pre-designed methods based on specific tasks to introduce expected functional parts on the various micro/nanocarriers, which lacks a universal platform and common features, making it difficult to apply to different application scenarios. Here, we have developed a modular assembly strategy based on host-guest chemistry, which enables the on-demand construction of imaging-trackable nanomotors mounted with suitable driving and imaging modules using a universal assembly platform, according to different application scenarios. These assembled nanomotors exhibited enhanced diffusion behavior driven by enzymatic reactions. The loaded imaging functions were used to dynamically trace the swarm motion behavior of assembled nanomotors with corresponding fuel conditions both in vitro and in vivo. The modular assembly strategy endowed with host-guest interaction provides a universal approach to producing multifunctional MNMs in a facile and controllable manner, which paves the way for the future development of MNMs systems with programmable functions.

Modularized microrobot with lock-and-detachable modules for targeted cell delivery in bile duct

The magnetic microrobots promise benefits in minimally invasive cell-based therapy. However, they generally suffer from an inevitable compromise between their magnetic responsiveness and biomedical functions. Herein, we report a modularized microrobot consisting of magnetic actuation (MA) and cell scaffold (CS) modules. The MA module with strong magnetism and pH-responsive deformability and the CS module with cell loading-release capabilities were fabricated by three-dimensional printing technique. Subsequently, assembly of modules was performed by designing a shaft-hole structure and customizing their relative dimensions, which enabled magnetic navigation in complex environments, while not deteriorating the cellular functionalities. On-demand disassembly at targeted lesion was then realized to facilitate CS module delivery and retrieval of the MA module. Furthermore, the feasibility of proposed system was validated in an in vivo rabbit bile duct. Therefore, this work presents a modular design-based strategy that enables uncompromised fabrication of multifunctional microrobots and stimulates their development for future cell-based therapy.

Deep Reinforcement Learning Framework-Based Flow Rate Rejection Control of Soft Magnetic Miniature Robots

Soft magnetic miniature robots (SMMRs) have potential biomedical applications due to their flexible size and mobility to access confined environments. However, navigating the robot to a goal site with precise control performance and high repeatability in unstructured environments, especially in flow rate conditions, still remains a challenge. In this study, drawing inspiration from the control requirements of drug delivery and release to the goal lesion site in the presence of dynamic biofluids, we propose a flow rate rejection control strategy based on a deep reinforcement learning (DRL) framework to actuate an SMMR to achieve goal-reaching and hovering in fluidic tubes. To this end, an SMMR is first fabricated, which can be operated by an external magnetic field to realize its desired functionalities. Subsequently, a simulator is constructed based on neural networks to map the relationship between the applied magnetic field and robot locomotion states. With minimal prior knowledge about the environment and dynamics, a gated recurrent unit (GRU)-based DRL algorithm is formulated by considering the designed history state-action and estimated flow rates. In addition, the randomization technique is applied during training to distill the general control policy for the physical SMMR. The results of numerical simulations and experiments are illustrated to demonstrate the robustness and efficacy of the presented control framework. Finally, in-depth analyses and discussions indicate the potentiality of DRL for soft magnetic robots in biomedical applications.

Reactive wetting enabled anchoring of non-wettable iron oxide in liquid metal for miniature soft robot

Magnetic liquid metal (LM) soft robots attract considerable attentions because of distinctive immiscibility, deformability and maneuverability. However, conventional LM composites relying on alloying between LM and metallic magnetic powders suffer from diminished magnetism over time and potential safety risk upon leakage of metallic components. Herein, we report a strategy to composite inert and biocompatible iron oxide (Fe3O4) magnetic nanoparticles into eutectic gallium indium LM via reactive wetting mechanism. To address the intrinsic interfacial non-wettability between Fe3O4 and LM, a silver intermediate layer was introduced to fuse with indium component into AgxIny intermetallic compounds, facilitating the anchoring of Fe3O4 nanoparticles inside LM with improved magnetic stability. Subsequently, a miniature soft robot was constructed to perform various controllable deformation and locomotion behaviors under actuation of external magnetic field. Finally, practical feasibility of applying LM soft robot in an ex vivo porcine stomach was validated under in-situ monitoring by endoscope and X-ray imaging.

High-fat diet promotes tumor growth in the patient-derived orthotopic xenograft (PDOX) mouse model of ER positive endometrial cancer

Endometrial cancer, one of the common gynecological malignancies, is affected by several influencing factors. This study established a unique patient-derived orthotopic xenograft (PDOX) nude mouse model for the study of influencing factors in ER positive endometrial cancer. The aim of this study was to demonstrate that a high-fat diet can affect the growth of ER positive endometrial cancer PDOX model tumors. The tumor tissues were expanded by subcutaneous transplantation in nude mice, and then the subcutaneous tumor tissues were orthotopically implanted into the nude mouse uterus to establish the PDOX model. After modeling, they were divided into high-fat diet group and normal diet group for 8 weeks of feeding, which showed that high-fat diet significantly promoted tumor growth (P < 0.001) and increased the protein expression level of ERα in tumor tissues. This study demonstrates that PDOX models of endometrial cancer can embody the role of dietary influences on tumor growth and that this model has the potential for preclinical studies of cancer promoting factors.

Deep Learning-Based Multi-Modality Segmentation of Primary Gross Tumor Volume in CT and MRI for Nasopharyngeal Carcinoma

PURPOSE/OBJECTIVE(S)

The delineation of primary gross tumor volume (GTV) of nasopharyngeal carcinoma (NPC) is an essential step for radiotherapy planning. In clinical practice, radiation oncologists manually delineate the GTV in planning CT with the help of diagnostic MRI. This is because NPC tumors are closely adjacent to many important anatomic structures, and CT and MRI provide complementary strength to accurately determine the tumor extension boundary. Manual delineation is time-consuming with the potential registration errors between MRI and CT decreasing the delineation accuracy. In this study, we propose a fully automated GTV segmentation method based on CT and MRI by first aligning MRI to CT, and then, segmenting the GTV using a multi-modality deep learning model.

MATERIALS/METHODS

We collected 104 nasopharyngeal carcinoma patients with both planning CT and diagnostic MRI scans (T1 & T2 phases). An experienced radiation oncologists manually delineated the GTV, which was further examined by another senior radiation oncologist. Then, a coarse to fine cross-modality registration from MRI to CT was conducted as follows: (1) A rigid transformation was performed on MRI to roughly align MRI to CT with similar anatomic position. (2) Then, the region of interest (RoI) on both CT and rigid-transformed MRI were cropped. (3) A leading cross-modality deformable registration algorithm, named DEEDS, was applied on the cropped MRI and CT RoIs to find an accurate local alignment. Next, using CT and registered MRI as the combined input, a multi-modality deep segmentation network based on nnUNet was trained to generate the GTV prediction. 20% patients were randomly selected as the unseen testing set to quantitatively evaluate the performance.

RESULTS

The quantitative NPC GTV segmentation performance is summarized in Table 1. The deep segmentation model using CT alone achieved reasonable high performance with 76.6% Dice score and 1.34mm average surface distance (ASD). When both CT and registered MRI were used, the segmentation model further improved the performance by 0.9% Dice score increase and 11% relative ASD error reduction, demonstrating the complementary strength of CT and MRI in determining NPC GTV. Notably, the achieved 77.5% Dice score and 1.19mm ASD by the multimodality model is among the top performing results reported in recent automatic NPC GTV segmentation using either CT or MRI modality.

CONCLUSION

We developed a fully automated multi-modal deep-learning model for NPC GTV segmentation. The developed model can segment the NPC GTV in high accuracy. With further optimization and validation, this automated model has potential to standardize the NPC GTV segmentation and significantly decrease the workload of radiation oncologists in clinical practice.

Deep Learning for Automatic Prediction of Lymph Node Station Metastasis in Esophageal Cancer Patients from Contrast-Enhanced CT

PURPOSE/OBJECTIVE(S)

The diagnosis of lymph node (LN) metastasis in computed tomography (CT) is an essential yet challenging task in esophageal cancer staging and treatment planning. Although criteria (e.g., RECIST, morphological/texture features) are proposed to predict LN metastasis, the diagnostic accuracy remains low with sensitivity <50% and specificity <75%, as reported in previous studies. Deep learning (DL) has the potential to address this issue by learning from large-scale labeled data. However, due to the practical surgery procedure in lymph node dissection, it is difficult to pair the metastasis of individual LN reported in the pathology report to the LN instance found in the CT image. Hence, in this study, we first use pathology reports to determine the LNS metastasis, then develop a multiple instance deep learning (MIDL) model to predict lymph node station (LNS) metastasis.

MATERIALS/METHODS

We collected 1200 esophageal cancer patients with preoperative contrast-enhanced CT before surgery. A recently developed automatic mediastinal LNS segmentation model was first applied to segment LNS of 1 to 8 based on the IASLC protocol. For each LNS, the local CT region of interest (ROI) was cropped to generate a station-wise CT patch, where the LNS was labeled as metastatic if at least one metastatic LN was indicated in the pathology report. Using the station-wise CT patch and LNS label, we train a 3D MIDL model, MobileNetV3, to predict LNS metastasis. To better provide the LN position priors in MIDL, LN instances (with a short axis >4mm) were also segmented using an automatic LN detection algorithm and were added to the MIDL model as an auxiliary input. Five-fold cross-validation was conducted to evaluate the MIDL performance.

RESULTS

The MIDL model's performance is summarized in Table 1. The MIDL model incorporating an additional LN instance mask demonstrated a superior overall AUC of 0.7539, surpassing the model without the LN mask input by 2.93%. The specificity was evaluated at a threshold resulting in a recall of 0.7, and the best model outperformed the CT input model in terms of specificity by 2.11%. This highlights the value of including the LN position prior to the MIDL model. Notably, when a threshold was set to result in a specificity of 75%, the best MIDL model demonstrated a significantly higher recall compared to the previously reported clinical diagnostic recall (39.7% vs. 63.21%).

CONCLUSION

We developed a MIDL classification model to predict LNS metastasis using CT scans of 1200 patients. Our findings suggest that the MIDL model can substantially improve LNS metastasis prediction and has the potential to play an essential role in cancer staging, treatment planning, and prognostic analysis.

Anatomy-Guided Deep Learning Model for Accurate and Robust Gross Tumor Volume Segmentation in Lung Cancer Radiation Therapy

PURPOSE/OBJECTIVE(S)

In lung cancer radiation therapy, clinicians must outline the gross tumor volume (GTV) precisely on the planning computed tomography (pCT) for accurate radiation dose delivery. However, due to the limited contrast between tumor and normal tissues in lung parenchyma, accurate delineation of tumor boundaries is difficult leading to large inter-observer variation. In this study, we develop an anatomy-guided lung GTV deep segmentation model using a training cohort of multi-center datasets. The quantitative segmentation performance is evaluated on an independent dataset, where the inter-observer delineation variation is also assessed.

MATERIALS/METHODS

We collected and curated four publicly available lung datasets with GTV annotations (Lung-PET-CT-Dx, LIDC-IDRI, NSCLC-Radiogenomics and RIDER-CT) for deep learning model development. A total of 871 CT scans of patients, who were diagnosed with T1-T4 NSCLC, were available for training after data curation. The GTV annotations of primary tumor were examined and edited by two experienced radiation oncologists following the RTOG 1106 protocol. An anatomy-guided deep learning model was proposed, which consisted two deep networks. The first deep network used CT scan as input and segmented 4 anatomic organs (airway, heart, pulmonary artery and pulmonary vein), while the second deep network took both CT scan and these pre-segmented 4 organs as input and segmented the lung GTV. With the help of anatomic priors from 4 pre-segmented organs, the second deep network could more easily locate the GTV. We used nnUNet as the deep segmentation network. For evaluation, we used NSCLC-Radiomics as the testing dataset, which contains 20 CT scans each annotated by 5 radiation oncologists. The auto-segmented GTV were compared against each of the manual GTV reference. Inter-observer variation was also assessed using the 5 manual GTV references.

RESULTS

The proposed anatomic-guided lung GTV segmentation model achieved a mean Dice score of 82.4% and 95% Hausdorff distance (HD95) of 6.9mm when averaged cross 20 patients and 5 GTV references (Table 1), which outperformed the basic deep GTV segmentation model by markedly reducing 19.4% HD95 error. The performance of proposed model was also comparable to the inter-observer variation (Dice score: 82.4% vs. 81.9%, HD95 6.9 vs. 6.4mm), indicating that our model had similar reproducibility as human observers.

CONCLUSION

We developed and tested an anatomy-guided deep learning model for segmenting GTV in NSCLC patients. The model achieves high quantitative segmentation performance, which is comparable to the human observer variation. It can be potentially used in radiotherapy practice to improve GTV delineation consistency and reduce workloads of radiation oncologists.

A novel prognostic model based on AFP, tumor burden score and Albumin-Bilirubin grade for patients with hepatocellular carcinoma undergoing radiofrequency ablation

PURPOSE

The aim of this study was to develop prognostic scores, including the tumor burden score (TBS) and albumin-bilirubin (ALBI) grade, for evaluating the outcomes of hepatocellular carcinoma (HCC) patients after radiofrequency ablation (RFA).

MATERIALS AND METHODS

This retrospective study enrolled treatment-naïve HCC patients with BCLC 0-A who underwent RFA between January 2009 and December 2019. Regular follow-up was conducted after RFA to determine progression-free survival (PFS) and overall survival (OS). The patients were randomly allocated to the training or validation datasets in a 1:1 ratio. Preoperative prognostic scores were developed based on the results of multivariate analysis. The discriminatory ability of the scores was assessed using time-dependent AUC and compared with other models.

RESULTS

Serum alpha-fetoprotein (AFP) level and TBS were identified as independent prognostic factors for PFS, while serum AFP, TBS, and ALBI were identified as independent prognostic factors for OS in HCC patients after RFA. The time-dependent AUCs of the AFP-TBS score for the 1-, 3-, and 5-year PFS were 0.651, 0.667, and 0.620, respectively, in the training set, and 0.657, 0.687, and 0.704, respectively, in the validation set. For the 1-, 3-, and 5-year OS, the time-dependent AUCs were 0.680, 0.712, and 0.666, respectively, in the training set, and 0.712, 0.706 and 0.726 in the validation set for the AFP-TBS-ALBI score (ATA). The C-indices and AIC demonstrated that the scores provided better clinical benefits compared to other models.

CONCLUSION

The ATA/AT score, derived from clinical and objective laboratory variables, can assist in individually predicting the prognosis of HCC patients undergoing curative RFA.

[Impact of different diagnostic criteria for assessing mild micro-hepatic encephalopathy in liver cirrhosis: an analysis based on a prospective, multicenter, real-world study]

Objective: To compare the differences in the prevalence of mild micro-hepatic encephalopathy (MHE) among patients with cirrhosis by using the psychometric hepatic encephalopathy score (PHES) and the Stroop smartphone application (Encephal App) test. Methods: This prospective, multi-center, real-world study was initiated by the National Clinical Medical Research Center for Infectious Diseases and the Portal Hypertension Alliance and registered with International ClinicalTrials.gov (NCT05140837). 354 cases of cirrhosis were enrolled in 19 hospitals across the country. PHES (including digital connection tests A and B, digital symbol tests, trajectory drawing tests, and serial management tests) and the Stroop test were conducted in all of them. PHES was differentiated using standard diagnostic criteria established by the two studies in China and South Korea. The Stroop test was evaluated based on the criteria of the research and development team. The impact of different diagnostic standards or methods on the incidence of MHE in patients with cirrhosis was analyzed. Data between groups were differentiated using the t-test, Mann-Whitney U test, and χ (2) test. A kappa test was used to compare the consistency between groups. Results: After PHES, the prevalence of MHE among 354 cases of cirrhosis was 78.53% and 15.25%, respectively, based on Chinese research standards and Korean research normal value standards. However, the prevalence of MHE was 56.78% based on the Stroop test, and the differences in pairwise comparisons among the three groups were statistically significant (kappa = -0.064, P < 0.001). Stratified analysis revealed that the MHE prevalence in three groups of patients with Child-Pugh classes A, B, and C was 74.14%, 83.33%, and 88.24%, respectively, according to the normal value standards of Chinese researchers, while the MHE prevalence rates in three groups of patients with Child-Pugh classes A, B, and C were 8.29%, 23.53%, and 38.24%, respectively, according to the normal value standards of Korean researchers. Furthermore, the prevalence rates of MHE in the three groups of patients with Child-Pugh grades A, B, and C were 52.68%, 58.82%, and 73.53%, respectively, according to the Stroop test standard. However, among the results of each diagnostic standard, the prevalence of MHE showed an increasing trend with an increasing Child-Pugh grade. Further comparison demonstrated that the scores obtained by the number connection test A and the number symbol test were consistent according to the normal value standards of the two studies in China and South Korea (Z = -0.982, -1.702; P = 0.326, 0.089), while the other three sub-tests had significant differences (P < 0.001). Conclusion: The prevalence rate of MHE in the cirrhotic population is high, but the prevalence of MHE obtained by using different diagnostic criteria or methods varies greatly. Therefore, in line with the current changes in demographics and disease spectrum, it is necessary to enroll a larger sample size of a healthy population as a control. Moreover, the establishment of more reliable diagnostic scoring criteria will serve as a basis for obtaining accurate MHE incidence and formulating diagnosis and treatment strategies in cirrhotic populations.

Ultrasonic-Enabled Nondestructive and Substrate-Independent Liquid Metal Ink Sintering

Printing or patterning particle-based liquid metal (LM) ink is a good strategy to overcome poor wettability of LM for its circuits' preparation in flexible and printed electronics. Subsequently, a crucial step is to recover conductivity of LM circuits consisting of insulating LM micro/nano-particles. However, most widely used mechanical sintering methods based on hard contact such as pressing, may not be able to contact the LM patterns' whole surface conformally, leading to insufficient sintering in some areas. Hard contact may also break delicate shapes of the printed patterns. Hereby, an ultrasonic-assisted sintering strategy that can not only preserve original morphology of the LM circuits but also sinter circuits on various substrates of complex surface topography is proposed. The influencing factors of the ultrasonic sintering are investigated empirically and interpreted with theoretical understanding by simulation. LM circuits encapsulated inside soft elastomer are successfully sintered, proving feasibility in constructing stretchable or flexible electronics. By using water as energy transmission medium, remote sintering without any direct contact with substrate is achieved, which greatly protect LM circuits from mechanical damage. In virtue of such remote and non-contact manipulation manner, the ultrasonic sintering strategy would greatly advance the fabrication and application scenarios of LM electronics.

Active Micro/Nanoparticles in Colloidal Microswarms

Colloidal microswarms have attracted increasing attention in the last decade due to their unique capabilities in various complex tasks. Thousands or even millions of tiny active agents are gathered with distinctive features and emerging behaviors, demonstrating fascinating equilibrium and non-equilibrium collective states. In recent studies, with the development of materials design, remote control strategies, and the understanding of pair interactions between building blocks, microswarms have shown advantages in manipulation and targeted delivery tasks with high adaptability and on-demand pattern transformation. This review focuses on the recent progress in active micro/nanoparticles (MNPs) in colloidal microswarms under the input of an external field, including the response of MNPs to external fields, MNP-MNP interactions, and MNP-environment interactions. A fundamental understanding of how building blocks behave in a collective system provides the foundation for designing microswarm systems with autonomy and intelligence, aiming for practical application in diverse environments. It is envisioned that colloidal microswarms will significantly impact active delivery and manipulation applications on small scales.

Swarming self-adhesive microgels enabled aneurysm on-demand embolization in physiological blood flow

The recent rise of swarming microrobotics offers great promise in the revolution of minimally invasive embolization procedure for treating aneurysm. However, targeted embolization treatment of aneurysm using microrobots has significant challenges in the delivery capability and filling controllability. Here, we develop an interventional catheterization-integrated swarming microrobotic platform for aneurysm on-demand embolization in physiological blood flow. A pH-responsive self-healing hydrogel doped with magnetic and imaging agents is developed as the embolic microgels, which enables long-term self-adhesion under biological condition in a controllable manner. The embolization strategy is initiated by catheter-assisted deployment of swarming microgels, followed by the application of external magnetic field for targeted aggregation of microrobots into aneurysm sac under the real-time guidance of ultrasound and fluoroscopy imaging. Mild acidic stimulus is applied to trigger the welding of microgels with satisfactory bio-/hemocompatibility and physical stability and realize complete embolization. Our work presents a promising connection between the design and control of microrobotic swarms toward practical applications in dynamic environments.

Enzyme-Powered Tubular Microrobotic Jets as Bioinspired Micropumps for Active Transmembrane Drug Transport

In nature, there exist a variety of transport proteins on cell membranes capable of actively moving cargos across biological membranes, which plays a vital role in the living activities of cells. Emulating such biological pumps in artificial systems may bring in-depth insights on the principles and functions of cell behaviors. However, it poses great challenges due to difficulty in the sophisticated construction of active channels at the cellular scale. Here, we report the development of bionic micropumps for active transmembrane transportation of molecular cargos across living cells that is realized by enzyme-powered microrobotic jets. By immobilizing urease onto the surface of a silica-based microtube, the prepared microjet is capable of catalyzing the decomposition of urea in surrounding environments and generating microfluidic flow through the inside channel for self-propulsion, which is verified by both numerical simulation and experimental results. Therefore, once naturally endocytosed by the cell, the microjet enables the diffusion and, more importantly, active transportation of molecular substances between the extracellular and intracellular ends with the assistance of generated microflow, thus serving as an artificial biomimetic micropump. Furthermore, by constructing enzymatic micropumps on cancer cell membranes, enhanced delivery of anticancer doxorubicin into cells as well as improved killing efficacy are achieved, which demonstrates the effectiveness of the active transmembrane drug transport strategy in cancer treatment. This work not only extends the applications of micro/nanomachines in biomedical fields but also provides a promising platform for future cell biology research at cellular and subcellular scales.

Wirelessly powered deformable electronic stent for noninvasive electrical stimulation of lower esophageal sphincter

Electrical stimulation is a promising method to modulate gastrointestinal disorders. However, conventional stimulators need invasive implantation and removal surgeries associated with risks of infection and secondary injuries. Here, we report a battery-free and deformable electronic esophageal stent for wireless stimulation of the lower esophageal sphincter in a noninvasive fashion. The stent consists of an elastic receiver antenna infilled with liquid metal (eutectic gallium-indium), a superelastic nitinol stent skeleton, and a stretchable pulse generator that jointly enables 150% axial elongation and 50% radial compression for transoral delivery through the narrow esophagus. The compliant stent adaptive to the dynamic environment of the esophagus can wirelessly harvest energy through deep tissue. Continuous electrical stimulations delivered by the stent in vivo using pig models significantly increase the pressure of the lower esophageal sphincter. The electronic stent provides a noninvasive platform for bioelectronic therapies in the gastrointestinal tract without the need for open surgery.

Correlation between capsule endoscopy classification and CT lymphangiography of primary intestinal lymphangiectasia

AIM

To investigate the correlation between capsule endoscopy (CE) classification of primary intestinal lymphangiectasia (PIL) and computed tomography (CT) lymphangiography (CTL).

MATERIALS AND METHODS

A total of 52 patients with diagnosed PIL were enrolled. All patients were examined using CTL and small intestinal CE before surgery. CE assessments included the morphology, scope, colour, and size of lesions. CTL assessments included intestinal wall, lymphatic vessel dilatation, lymph fluid reflux, and lymphatic fistula. Patients were divided into three groups according to type diagnosed by CE, and the CTL characteristics were analysed among the groups.

RESULTS

CE showed 15 patients with type I, 27 with II, and 10 with type III. Intestinal wall thickening was observed in 15 type I, 21 type II, and seven type III. Pericardial effusion was observed in only three type I patients; the difference among types was statistically significant (p=0.02). Abnormal contrast agent distribution in the intestinal wall and mesentery was observed in 15 type II patients, and the difference was significantly greater than that of types I and III (p=0.02). Abnormal contrast agent distribution in the abdominal cavity was observed in 12 type II, and the difference was statistically significant (p=0.03).

CONCLUSION

The CE PIL classification reflects the extent and scope of intestinal mucosa lesions; CTL more systematically demonstrates abnormal lymphatic vessels or reflux, and its manifestations of PIL are related to the CE classification. The combination of CTL with CE is useful for accurately evaluating PIL, and provides guidance for preoperative assessment and treatment management of PIL patients.

Matrix reconstruction with reliable neighbors for predicting potential MiRNA-disease associations

Numerous experimental studies have indicated that alteration and dysregulation in mircroRNAs (miRNAs) are associated with serious diseases. Identifying disease-related miRNAs is therefore an essential and challenging task in bioinformatics research. Computational methods are an efficient and economical alternative to conventional biomedical studies and can reveal underlying miRNA-disease associations for subsequent experimental confirmation with reasonable confidence. Despite the success of existing computational approaches, most of them only rely on the known miRNA-disease associations to predict associations without adding other data to increase the prediction accuracy, and they are affected by issues of data sparsity. In this paper, we present MRRN, a model that combines matrix reconstruction with node reliability to predict probable miRNA-disease associations. In MRRN, the most reliable neighbors of miRNA and disease are used to update the original miRNA-disease association matrix, which significantly reduces data sparsity. Unknown miRNA-disease associations are reconstructed by aggregating the most reliable first-order neighbors to increase prediction accuracy by representing the local and global structure of the heterogeneous network. Five-fold cross-validation of MRRN produced an area under the curve (AUC) of 0.9355 and area under the precision-recall curve (AUPR) of 0.2646, values that were greater than those produced by comparable models. Two different types of case studies using three diseases were conducted to demonstrate the accuracy of MRRN, and all top 30 predicted miRNAs were verified.

Dynamic morphological transformations in soft architected materials via buckling instability encoded heterogeneous magnetization

The geometric reconfigurations in three-dimensional morphable structures have a wide range of applications in flexible electronic devices and smart systems with unusual mechanical, acoustic, and thermal properties. However, achieving the highly controllable anisotropic transformation and dynamic regulation of architected materials crossing different scales remains challenging. Herein, we develop a magnetic regulation approach that provides an enabling technology to achieve the controllable transformation of morphable structures and unveil their dynamic modulation mechanism as well as potential applications. With buckling instability encoded heterogeneous magnetization profiles inside soft architected materials, spatially and temporally programmed magnetic inputs drive the formation of a variety of anisotropic morphological transformations and dynamic geometric reconfiguration. The introduction of magnetic stimulation could help to predetermine the buckling states of soft architected materials, and enable the formation of definite and controllable buckling states without prolonged magnetic stimulation input. The dynamic modulations can be exploited to build systems with switchable fluidic properties and are demonstrated to achieve capabilities of fluidic manipulation, selective particle trapping, sensitivity-enhanced biomedical analysis, and soft robotics. The work provides new insights to harness the programmable and dynamic morphological transformation of soft architected materials and promises benefits in microfluidics, programmable metamaterials, and biomedical applications.

EphA2 Promotes the Development of Cervical Cancer through the CXCL11/PD-L1 Pathway

Erythropoietin-producing hepatoma receptor A2 (EphA2), receptor tyrosine kinase, the most widespread member of the largest receptor tyrosine kinase family, plays a critical role in physiological and pathological conditions. In recent years, the role of EphA2 in the occurrence and development of cancer has become a research hotspot and is considered a promising potential target. Our previous studies have shown that EphA2 has an indisputable cancer-promoting role in cervical cancer, but its related mechanism requires further research. In this study, high-throughput sequencing was performed on EphA2 knockdown cervical cancer cells and the control group. An analysis of differentially expressed genes revealed that EphA2 may exert its cancer-promoting effect through C-X-C motif chemokine ligand 11 (CXCL11). In addition, we found that EphA2 could further regulate programmed cell death ligand 1 (PD-L1) through CXCL11. This has also been further demonstrated in in vivo experiments. Our study demonstrated that EphA2 plays a tumor-promoting role in cervical carcinoma through the CXCL11/PD-L1 pathway, providing new guidance for the targeted therapy and combination therapy of cervical carcinoma.

Probing Fast Transformation of Magnetic Colloidal Microswarms in Complex Fluids

The rapidly transformed morphology of natural swarms enables fast response to environmental changes. Artificial microswarms can reconfigure their swarm patterns like natural swarms, which have drawn extensive attention due to their active adaptability in complex environments. However, as a prerequisite for biomedical applications of microswarms in confined environments, achieving on-demand control of pattern transformation rates remains a challenge. In this work, we report a strategy for optimizing pattern transformation rates of colloidal microswarms by coordinating the inner interactions. The influences of magnetic field parameters on pattern transformation rates are theoretically and experimentally studied, which elucidates the mechanism for optimal transformation rate control. The feasibility of the strategy is then validated in viscous Newtonian fluids and non-Newtonian biofluids. Moreover, the strategy is further validated in dynamic flow environments, exhibiting a promising future for practical applications in targeted delivery tasks with an optimal pattern transformation manner.

Rapid and Multimaterial 4D Printing of Shape-Morphing Micromachines for Narrow Micronetworks Traversing

Micromachines with high environmental adaptability have the potential to deliver targeted drugs in complex biological networks, such as digestive, neural, and vascular networks. However, the low processing efficiency and single processing material of current 4D printing methods often limit the development and application of shape-morphing micromachines (SMMs). Here, two 4D printing strategies are proposed to fabricate SMMs with pH-responsive hydrogels for complex micro-networks traversing. On the one hand, the 3D vortex light single exposure technique can rapidly fabricate a tubular SMM with controllable size and geometry within 0.1 s. On the other hand, the asymmetric multimaterial direct laser writing (DLW) method is used to fabricate SMMs with designable 3D structures composed of hydrogel and platinum nanoparticles (Pt NPs). Based on the presence of ferroferric oxide (Fe₃ O₄ ) and Pt NPs in the SMMs, efficient magnetic, bubble, and hybrid propulsion modes are achieved. Finally, it is demonstrated that the spatial shape conversion capabilities of these SMMs can be used for narrow micronetworks traversing, which will find potential applications in targeted cargo delivery in microcapillaries.

PARP inhibitor resistance in breast and gynecological cancer: Resistance mechanisms and combination therapy strategies

Breast cancer and gynecological tumors seriously endanger women’s physical and mental health, fertility, and quality of life. Due to standardized surgical treatment, chemotherapy, and radiotherapy, the prognosis and overall survival of cancer patients have improved compared to earlier, but the management of advanced disease still faces great challenges. Recently, poly (ADP-ribose) polymerase (PARP) inhibitors (PARPis) have been clinically approved for breast and gynecological cancer patients, significantly improving their quality of life, especially of patients with BRCA1/2 mutations. However, drug resistance faced by PARPi therapy has hindered its clinical promotion. Therefore, developing new drug strategies to resensitize cancers affecting women to PARPi therapy is the direction of our future research. Currently, the effects of PARPi in combination with other drugs to overcome drug resistance are being studied. In this article, we review the mechanisms of PARPi resistance and summarize the current combination of clinical trials that can improve its resistance, with a view to identify the best clinical treatment to save the lives of patients.

Correlation between glenoid bone structure and recurrent anterior dislocation of the shoulder joint

BACKGROUND

The aim of the study was to investigate the anatomical characteristics and symmetry of the bilateral glenoid structures of Chinese people and to explore the relationship between the glenoid bone structure and recurrent anterior dislocation.

MATERIALS AND METHODS

The control group included 131 individuals with no history of shoulder dislocation. The dislocation group consisted of 131 patients with a history of unilateral shoulder dislocation. All subjects underwent computed tomography scans. Glenoid shape (pear-shaped, inverted comma-shaped, oval-shaped), width, height, depth, version angle, area, maximum fitting circle area and volume were measured.

RESULTS

There was no significant difference in normal bilateral glenoid of Chinese people (p > 0.05). There were statistically significant differences in depth, height to width ratio, maximum fitting circle area and shape between the dislocation and control groups (p < 0.05). Regression analyses showed that the glenoid depth (odds ratio [OR] 0.48; p < 0.01), the glenoid height to width ratio (OR 28.61; p < 0.01), the glenoid maximum fitting circle area (OR 1.01; p < 0.01) and the glenoid shape (p <0.05; pear-shaped OR 0.432; inverted comma-shaped OR 0.954) were associated with anterior shoulder instability. Pear-shaped and inverted comma-shaped glenoid had lower risk of recurrent anterior shoulder dislocation compared to oval glenoid. Receiver operating characteristic curve analysis showed that individuals with anterior shoulder instability had smaller glenoid depth and larger height to width ratio and the glenoid maximum fitting circle area compared with the control group.

CONCLUSIONS

The normal bilateral glenoids of Chinese people are basically symmetrical. The glenoid shape, depth, height to width ratio and maximum fitting circle area are risk factors for recurrent anterior shoulder dislocation. Evaluation of the glenoid bone structure enables more accurate prediction of the risk of recurrent shoulder dislocation.

Untethered small-scale magnetic soft robot with programmable magnetization and integrated multifunctional modules

Intelligent magnetic soft robots capable of programmable structural changes and multifunctionality modalities depend on material architectures and methods for controlling magnetization profiles. While some efforts have been made, there are still key challenges in achieving programmable magnetization profile and creating heterogeneous architectures. Here, we directly embed programmed magnetization patterns (magnetization modules) into the adhesive sticker layers to construct soft robots with programmable magnetization profiles and geometries and then integrate spatially distributed functional modules. Functional modules including temperature and ultraviolet light sensing particles, pH sensing sheets, oil sensing foams, positioning electronic component, circuit foils, and therapy patch films are integrated into soft robots. These test beds are used to explore multimodal robot locomotion and various applications related to environmental sensing and detection, circuit repairing, and gastric ulcer coating, respectively. This proposed approach to engineering modular soft material systems has the potential to expand the functionality, versatility, and adaptability of soft robots.

Decoupling and Reprogramming the Wiggling Motion of Midge Larvae Using a Soft Robotic Platform

The efficient motility of invertebrates helps them survive under evolutionary pressures. Reconstructing the locomotion of invertebrates and decoupling the influence of individual basic motion are crucial for understanding their underlying mechanisms, which, however, generally remain a challenge due to the complexity of locomotion gaits. Herein, a magnetic soft robot to reproduce midge larva's key natural swimming gaits is developed, and the coupling effect between body curling and rotation on motility is investigated. Through the authors' systematically decoupling studies using programmed magnetic field inputs, the soft robot (named LarvaBot) experiences various coupled gaits, including biomimetic side-to-side flexures, and unveils that the optimal rotation amplitude and the synchronization of curling and rotation greatly enhance its motility. The LarvaBot achieves fast locomotion and upstream capability at the moderate Reynolds number regime. The soft robotics-based platform provides new insight to decouple complex biological locomotion, and design programmed swimming gaits for the fast locomotion of soft-bodied swimmers.

Real-Time Ultrasound Doppler Tracking and Autonomous Navigation of a Miniature Helical Robot for Accelerating Thrombolysis in Dynamic Blood Flow

Untethered small-scale robots offer great promise for medical applications in complex biological environments. However, challenges remain in the control and medical imaging of a robot for targeted delivery inside a living body, especially in flowing conditions (e.g., blood vessels). In this work, we report a strategy to autonomously navigate a miniature helical robot in dynamic blood flow under ultrasound Doppler imaging guidance. A magnetic torque and force-hybrid control approach is implemented, enabling the actuation of a millimeter-scale helical robot against blood flow under a rotating magnetic field with a controllable field gradient. Experimental results demonstrate that the robot (length 7.30 mm; diameter 2.15 mm) exhibits controlled navigation in vascular environments, including upstream and downstream navigation in flowing and pulsatile flowing blood with flow rates up to 24 mL/min (mean flow velocity: 14.15 mm/s). During navigation, the rotating robot-induced Doppler signals enable real-time localization and tracking in flowing and pulsatile flowing blood environments. Moreover, the robot can be selectively navigated along different paths by actively controlling the robot's orientation. We apply this autonomous strategy for localizing thrombus and accelerating thrombolysis rate. Compared with conventional tissue plasminogen activator (tPA) thrombolysis, the robot-enhanced shear stress and tPA convection near the clot-blood interface increase the unblocking and thrombolysis efficiency up to 4.8- and 3.5-fold, respectively. Such a medical imaging-guided navigation strategy provides simultaneous robot navigation and localization in complex dynamic biological environments, providing an intelligent approach toward real-time targeted delivery and diagnostic applications in vivo.

Collective Behaviors of Magnetic Active Matter: Recent Progress toward Reconfigurable, Adaptive, and Multifunctional Swarming Micro/Nanorobots

Active matter refers to the nonequilibrium system composed of interacting units that continually dissipate energy at a single-unit level and transduce it into mechanical force or motion. Such systems are ubiquitous in nature and span most of the biological scales, ranging from cytoskeleton protein polymers at the molecular level to bacterial colonies at the cellular level to swarms of insects, flocks of birds, schools of fish, and even crowds of humans on the organismal scale. The consumption of energy within systems tends to induce the self-organization of active matter as well as the spontaneous emergence of dynamic, complex, and collective states with extraordinary properties, such as adaptability, reconfigurability, taxis, and so on. The research into active matter is expected to deepen the understanding of the underlying mechanisms of how the units in living systems interact with each other and regulate the flow of energy to improve the survival efficiency, which in turn can provide valuable insights into the engineering of artificial active systems with novel and practical collective functionalities.Because of the striking similarity in collective states, a colloidal system is an emerging approach to understanding the guiding principles of the coordinated activities in living systems. Thanks to the capabilities in batch fabrication, size control, and the modulation of interactions (e.g., dipole-dipole interactions, capillary forces, electrostatic interactions, and so on), various complex collective states have been reproduced and programmed in colloidal suspension through the elaborate design of compositions and unit-unit interactions. Among the developed colloidal systems, magnetic colloids energized by alternating magnetic fields demonstrate several unique advantages, including the high-degree-of-freedom and simple modulation of the magnetic field parameters as well as the excellent compatibility of the magnetic field with many application scenarios. Therefore, magnetic active matter not only constitutes a useful platform that leads to a discovery of fascinating emergent collective behaviors but also promises enormous potential in a variety of engineering fields.In this Account, we summarize and highlight the key efforts carried out by our group and others on the investigation of the collective behavior of magnetic active matter in the past 5 years. First, we elucidate the generation mechanisms of the emergent coordinated behaviors, which are classified according to the dominating interactions among agents, that is, the magnetic dipole-dipole interaction, hydrodynamic interaction, and weak interaction. Then we illustrate the construction of magnetic active matter with a higher level of collective effects and functionalities (e.g., reconfigurability, environmental adaptability, 3D swarming, cooperative multifunctionality, and so on) via the synergistic effects between magnetic fields and other fields. Next, potential applications of magnetic active matter are discussed, which mainly focus on the exploration in revolutionizing traditional biomedical fields. Finally, an outlook of future opportunities is presented to promote the development of magnetic active matter, which facilitates a better understanding of living counterparts and the further realization of practical applications.

Environmentally Adaptive Shape-Morphing Microrobots for Localized Cancer Cell Treatment

Microrobots have attracted considerable attention due to their extensive applications in microobject manipulation and targeted drug delivery. To realize more complex micro-/nanocargo manipulation (e.g., encapsulation and release) in biological applications, it is highly desirable to endow microrobots with a shape-morphing adaptation to dynamic environments. Here, environmentally adaptive shape-morphing microrobots (SMMRs) have been developed by programmatically encoding different expansion rates in a pH-responsive hydrogel. Due to a combination with magnetic propulsion, a shape-morphing microcrab (SMMC) is able to perform targeted microparticle delivery, including gripping, transporting, and releasing by "opening-closing" of a claw. As a proof-of-concept demonstration, a shape-morphing microfish (SMMF) is designed to encapsulate a drug (doxorubicin (DOX)) by closing its mouth in phosphate-buffered saline (PBS, pH ∼ 7.4) and release the drug by opening its mouth in a slightly acidic solution (pH < 7). Furthermore, localized HeLa cell treatment in an artificial vascular network is realized by "opening-closing" of the SMMF mouth. With the continuous optimization of size, motion control, and imaging technology, these magnetic SMMRs will provide ideal platforms for complex microcargo operations and on-demand drug release.

Domino Reaction Encoded Heterogeneous Colloidal Microswarm with On-Demand Morphological Adaptability

Emulating natural swarm intelligence with group-level functionality in artificial micro/nanorobotic systems offers an opportunity to sublimate the limited functions of individuals and revolutionize their applications. However, achieving synchronous operation of microswarms with environmental adaptability and cooperative tasking capability remains a challenge. Here, an adaptive and heterogeneous colloidal magnetic microswarm with domino reaction encoded cooperative functions is presented. Through programming external magnetic fields, the system self-organizes into two swarm states, that is, vortex and ribbon microswarms, which can switch between each other reversibly within seconds, allowing to traverse tortuous, branched, and confined environments through adaptive morphological transformation. By specializing subgroups of building blocks with separate functions, cooperative tasking capability is integrated into the heterogeneous system following a "division of labor" manner. Given targeted therapy as a proof-of-concept task, the coordinated delivery of heterogeneous colloidal system across a complex environment with an access rate higher than 90% is demonstrated, and the specialization and cooperation between building blocks to disrupt multiple growth pathways of cancer cells via domino reaction are realized. The reconfigurable microswarm with hierarchical functionality presents a bioinspired approach to adapt to environmental variations and address multitasking requirements, which advances the development of microrobotic swarms and promises major benefits in biomedical fields.

Independent Pattern Formation of Nanorod and Nanoparticle Swarms under an Oscillating Field

Natural swarms can be formed by various creatures. The swarms can conduct demanded behaviors to adapt to their living environments, such as passing through harsh terrains and protecting each other from predators. At micrometer and nanometer scales, formation of a swarm pattern relies on the physical or chemical interactions between the agents owing to the absence of an on-board device. Independent pattern formation of different swarms, especially under the same input, is a more challenging task. In this work, a swarm of nickel nanorods is proposed and by exploiting its different behavior with the nanoparticle swarm, independent pattern formation of diverse microrobotic swarms under the same environment can be conducted. A mathematical model for the nanorod swarm is constructed, and the mechanism is illustrated. Two-region pattern changing of the nanorod swarm is discovered and compared with the one-region property of the nanoparticle swarm. Experimental characterization of the nanorod swarm pattern is conducted to prove the concept and validate the effectiveness of the theoretical analysis. Furthermore, independent pattern formation of different microrobotic swarms was demonstrated. The pattern of the nanorod swarm could be adjusted while the other swarm was kept unchanged. Simultaneous pattern changing of two swarms was achieved as well. As a fundamental research on the microrobotic swarm, this work presents how the nanoscale magnetic anisotropy of building agents affects their macroscopic swarm behaviors and promotes further development on the independent control of microrobotic swarms under a global field input.

Curcumin decreases dibutyl phthalate-induced renal dysfunction in Kunming mice via inhibiting oxidative stress and apoptosis

Curcumin (Cur) has been used extensively in dietary supplement with antioxidant and anti-apoptotic properties. Although dibutyl phthalate (DBP) has adverse effects on the kidney, any association between DBP exposure and the role of Cur is unclear. We tested the hypothesis that exposure to DBP has adverse consequences on renal dysfunction in mice and the potential protective role of Cur in decreasing DBP-induced renal dysfunction via inhibiting oxidative stress and apoptosis. Kidney function, oxidative stress biomarkers, and apoptosis factors as well as Bcl-2 and Bax were investigated. The results showed a marked increase of renal dysfunction, oxidative stress and apoptosis level after DBP exposure compared to the control. While administration of Cur to DBP-treated mice may reduce these adverse biochemical changes compared with DBP-alone group. Overall, these results suggest that oxidative stress and apoptosis are involved in DBP-induced renal disorder, whereas Cur plays a protective role in inhibiting these two pathways.

Deep medical image analysis with representation learning and neuromorphic computing

Deep learning is increasingly used in medical imaging, improving many steps of the processing chain, from acquisition to segmentation and anomaly detection to outcome prediction. Yet significant challenges remain: (i) image-based diagnosis depends on the spatial relationships between local patterns, something convolution and pooling often do not capture adequately; (ii) data augmentation, the de facto method for learning three-dimensional pose invariance, requires exponentially many points to achieve robust improvement; (iii) labelled medical images are much less abundant than unlabelled ones, especially for heterogeneous pathological cases; and (iv) scanning technologies such as magnetic resonance imaging can be slow and costly, generally without online learning abilities to focus on regions of clinical interest. To address these challenges, novel algorithmic and hardware approaches are needed for deep learning to reach its full potential in medical imaging.

Ultrasound Doppler-guided real-time navigation of a magnetic microswarm for active endovascular delivery

Swarming micro/nanorobots offer great promise in performing targeted delivery inside diverse hard-to-reach environments. However, swarm navigation in dynamic environments challenges delivery capability and real-time swarm localization. Here, we report a strategy to navigate a nanoparticle microswarm in real time under ultrasound Doppler imaging guidance for active endovascular delivery. A magnetic microswarm was formed and navigated near the boundary of vessels, where the reduced drag of blood flow and strong interactions between nanoparticles enable upstream and downstream navigation in flowing blood (mean velocity up to 40.8 mm/s). The microswarm-induced three-dimensional blood flow enables Doppler imaging from multiple viewing configurations and real-time tracking in different environments (i.e., stagnant, flowing blood, and pulsatile flow). We also demonstrate the ultrasound Doppler-guided swarm formation and navigation in the porcine coronary artery ex vivo. Our strategy presents a promising connection between swarm control and real-time imaging of microrobotic swarms for localized delivery in dynamic environments.