CXCR4-Targeted Macrophage-Derived Biomimetic Hybrid Vesicle Nanoplatform for Enhanced Cancer Therapy through Codelivery of Manganese and Doxorubicin.

Immune-cell-derived membranes have garnered significant attention as innovative delivery modalities in cancer immunotherapy for their intrinsic immune-modulating functionalities and superior biocompatibilities. Integrating additional parental cell membranes or synthetic lipid vesicles into cellular vesicles can further potentiate their capacities to perform combinatorial pharmacological activities in activating antitumor immunity, thus providing insights into the potential of hybrid cellular vesicles as versatile delivery vehicles for cancer immunotherapy. Here, we have developed a macrophage-membrane-derived hybrid vesicle that has the dual functions of transporting immunotherapeutic drugs and shaping the polarization of tumor-associated macrophages for cancer immunotherapy. The platform combines M1 macrophage-membrane-derived vesicles with CXCR4-binding-peptide-conjugated liposomes loaded with manganese and doxorubicin. The hybrid nanovesicles exhibited remarkable macrophage-targeting capacity through the CXCR4-binding peptide, resulting in enhanced macrophage polarization to the antitumoral M1 phenotype characterized by proinflammatory cytokine release. The manganese/doxorubicin-loaded hybrid vesicles in the CXCR4-expressing tumor cells evoked potent cancer cytotoxicity, immunogenic cell death of tumor cells, and STING activation. Moreover, cotreatment with manganese and doxorubicin promoted dendritic cell maturation, enabling effective tumor growth inhibition. In murine models of CT26 colon carcinoma and 4T1 breast cancer, intravenous administration of the manganese/doxorubicin-loaded hybrid vesicles elicited robust tumor-suppressing activity at a low dosage without adverse systemic effects. Local administration of hybrid nanovesicles also induced an abscessive effect in a bilateral 4T1 tumor model. This study demonstrates a promising biomimetic manganese/doxorubicin-based hybrid nanovesicle platform for effective cancer immunotherapy tailored to the tumor microenvironment, which may offer an innovative approach to combinatorial immunotherapy.

Encoding of multi-modal emotional information via personalized skin-integrated wireless facial interface.

Human affects such as emotions, moods, feelings are increasingly being considered as key parameter to enhance the interaction of human with diverse machines and systems. However, their intrinsically abstract and ambiguous nature make it challenging to accurately extract and exploit the emotional information. Here, we develop a multi-modal human emotion recognition system which can efficiently utilize comprehensive emotional information by combining verbal and non-verbal expression data. This system is composed of personalized skin-integrated facial interface (PSiFI) system that is self-powered, facile, stretchable, transparent, featuring a first bidirectional triboelectric strain and vibration sensor enabling us to sense and combine the verbal and non-verbal expression data for the first time. It is fully integrated with a data processing circuit for wireless data transfer allowing real-time emotion recognition to be performed. With the help of machine learning, various human emotion recognition tasks are done accurately in real time even while wearing mask and demonstrated digital concierge application in VR environment.

Digital Mechanical Metamaterial: Encoding Mechanical Information with Graphical Stiffness Pattern for Adaptive Soft Machines.

Inspired by the adaptive features exhibited by biological organisms like the octopus, soft machines that can tune their shape and mechanical properties have shown great potential in applications involving unstructured and continuously changing environments. However, current soft machines are far from achieving the same level of adaptability as their biological counterparts, hampered by limited real-time tunability and severely deficient reprogrammable space of properties and functionalities. As a steppingstone toward fully adaptive soft robots and smart interactive machines, an encodable multifunctional material that uses graphical stiffness patterns is introduced here to in situ program versatile mechanical capabilities without requiring additional infrastructure. Through independently switching the digital binary stiffness states (soft or rigid) of individual constituent units of a simple auxetic structure with elliptical voids, in situ and gradational tunability is demonstrated here in various mechanical qualities such as shape-shifting and -memory, stress-strain response, and Poisson's ratio under compressive load as well as application-oriented functionalities such as tunable and reusable energy absorption and pressure delivery. This digitally programmable material is expected to pave the way toward multienvironment soft robots and interactive machines.

Highly Compressible 3D-Printed Soft Magnetoelastic Sensors for Human-Machine Interfaces.

Incorporating perception into robots or objects holds great potential to revolutionize daily human life. To achieve this, critical factors include the design of an integrable three-dimensional (3D) soft sensor with self-powering capability, a wide working range, and tuneable functionalities. Here, we introduce a highly compressible 3D-printed soft magnetoelastic sensor with a wide strain sensing range. Inspired by the lattice metamaterial, which offers a highly porous structure with tuneable mechanical properties, we realized a remarkably compliant 3D self-powering sensor. Using magnetoelastic composite materials and 3D printing combined with sacrificial molding, a broad design space for constituent materials and structures is investigated, allowing for tuneable mechanical properties and sensor performances. These sensors are successfully integrated with two robotic systems as the robot operation and perception units, enabling robot control and recognition of diverse physical interactions with a user. Overall, we believe that this work represents a cornerstone for compliant 3D self-powered soft sensors, giving impetus to the development of advanced human-machine interfaces.

Nanosome-Mediated Delivery Of Hdac Inhibitors and Oxygen Molecules for the Transcriptional Reactivation of Latent Hiv-Infected Cd4+ T Cells.

The treatment of human immunodeficiency virus (HIV) infection is notoriously difficult due to the ability of this virus to remain latent in the host's CD4+ T cells. Histone deacetylases (HDACs) interfere with DNA transcription in HIV-infected hosts, resulting in viral latency. Therefore, HDAC inhibitors can be used to activate viral transcription in latently infected cells, after which the virus can be eliminated through a shock-and-kill strategy. Here, a drug delivery system is developed to effectively deliver HDAC inhibitors to latent HIV-infected cells. Given that the efficacy of HDAC inhibitors is reduced under hypoxic conditions, oxygen-containing nanosomes are used as drug carriers. Oxygen-containing nanosomes can improve the efficiency of chemotherapy by delivering essential oxygen to cells. Additionally, their phospholipid bilayer structure makes them uniquely well-suited for drug delivery. In this study, a novel drug delivery system is developed by taking advantage of the oxygen carriers in these oxygen nanosomes, incorporating a multi-drug strategy consisting of HDAC inhibitors and PKA activators, and introducing CXCR4 binding peptides to specifically target CD4+ T cells. Oxygen nanosomes with enhanced targeting capability through the introduction of the CXCR4 binding peptide mitigate drug toxicity and slow down drug release. The observed changes in the expression of p24, a capsid protein of HIV, indirectly confirm that the proposed drug delivery system can effectively induce transcriptional reactivation of HIV in latent HIV-infected cells.

Clinical Significance of Glycolytic Metabolic Activity in Hepatocellular Carcinoma.

High metabolic activity is a hallmark of cancers, including hepatocellular carcinoma (HCC). However, the molecular features of HCC with high metabolic activity contributing to clinical outcomes and the therapeutic implications of these characteristics are poorly understood. We aimed to define the features of HCC with high metabolic activity and uncover its association with response to current therapies. By integrating gene expression data from mouse liver tissues and tumor tissues from HCC patients (n = 1038), we uncovered three metabolically distinct HCC subtypes that differ in clinical outcomes and underlying molecular biology. The high metabolic subtype is characterized by poor survival, the strongest stem cell signature, high genomic instability, activation of EPCAM and SALL4, and low potential for benefitting from immunotherapy. Interestingly, immune cell analysis showed that regulatory T cells (Tregs) are highly enriched in high metabolic HCC tumors, suggesting that high metabolic activity of cancer cells may trigger activation or infiltration of Tregs, leading to cancer cells' evasion of anti-cancer immune cells. In summary, we identified clinically and metabolically distinct subtypes of HCC, potential biomarkers associated with these subtypes, and a potential mechanism of metabolism-mediated immune evasion by HCC cells.

3D-printed programmable tensegrity for soft robotics.

Tensegrity structures provide both structural integrity and flexibility through the combination of stiff struts and a network of flexible tendons. These structures exhibit useful properties: high stiffness-to-mass ratio, controllability, reliability, structural flexibility, and large deployment. The integration of smart materials into tensegrity structures would provide additional functionality and may improve existing properties. However, manufacturing approaches that generate multimaterial parts with intricate three-dimensional (3D) shapes suitable for such tensegrities are rare. Furthermore, the structural complexity of tensegrity systems fabricated through conventional means is generally limited because these systems often require manual assembly. Here, we report a simple approach to fabricate tensegrity structures made of smart materials using 3D printing combined with sacrificial molding. Tensegrity structures consisting of monolithic tendon networks based on smart materials supported by struts could be realized without an additional post-assembly process using our approach. By printing tensegrity with coordinated soft and stiff elements, we could use design parameters (such as geometry, topology, density, coordination number, and complexity) to program system-level mechanics in a soft structure. Last, we demonstrated a tensegrity robot capable of walking in any direction and several tensegrity actuators by leveraging smart tendons with magnetic functionality and the programmed mechanics of tensegrity structures. The physical realization of complex tensegrity metamaterials with programmable mechanical components can pave the way toward more algorithmic designs of 3D soft machines.

Modulation of immune responses with nanoparticles and reduction of their immunotoxicity.

Particles with a size range of 1-100 nm used in various fields of life sciences are called nanoparticles (NPs). Currently, nanotechnology has a wide range of applications in biomedical research, industries and in almost all types of modern technology. The growing applications of nanotechnology in medicine urge scientists to analyze the impact of NPs on human body tissues and the immune system. Easy surface modifications of the NPs enable the modulation of the immune system either by evading the immune system to prevent allergic reactions or by enhancing the immunogenic response. In this review, we discussed the various possible theories and practical implications reported to date for the applications of nanotechnology in immunostimulation and immunosuppression for favorable immune response, such as vaccine delivery and cancer treatments. In the last part of this paper, we also discussed the biocompatibility and unfavorable immunotoxicity of NPs and methods for lowering their toxicity.

Laterality Classification of Fundus Images Using Interpretable Deep Neural Network.

In this paper, we aimed to understand and analyze the outputs of a convolutional neural network model that classifies the laterality of fundus images. Our model not only automatizes the classification process, which results in reducing the labors of clinicians, but also highlights the key regions in the image and evaluates the uncertainty for the decision with proper analytic tools. Our model was trained and tested with 25,911 fundus images (43.4% of macula-centered images and 28.3% each of superior and nasal retinal fundus images). Also, activation maps were generated to mark important regions in the image for the classification. Then, uncertainties were quantified to support explanations as to why certain images were incorrectly classified under the proposed model. Our model achieved a mean training accuracy of 99%, which is comparable to the performance of clinicians. Strong activations were detected at the location of optic disc and retinal blood vessels around the disc, which matches to the regions that clinicians attend when deciding the laterality. Uncertainty analysis discovered that misclassified images tend to accompany with high prediction uncertainties and are likely ungradable. We believe that visualization of informative regions and the estimation of uncertainty, along with presentation of the prediction result, would enhance the interpretability of neural network models in a way that clinicians can be benefitted from using the automatic classification system.