Nanozyme-based Clusterphene for Enhanced Electrically Catalytic Cancer Therapy

Nanozyme mediated catalytic therapy is an attractive strategy for cancer therapy. However, the nanozymes are tended to assemble into 3D architectures, resulting in poor catalytic efficiency for therapy. This study designs the assembly of nanozymes and natural enzymes into the layered structures featuring hexagonal pores as nanozyme clusterphene and investigates their catalytic therapy with the assistance of electric field. The nanozyme-based clusterphene consists of polyoxometalate (POM) and natural glucose oxidase (GOx), named POMG-based clusterphene, which facilitate multi-enzyme activities including peroxidase (POD), catalase (CAT), and glutathione oxidase (GPx). The highly ordered layers with hexagonal pores of POMG units significantly improve the peroxidase-like (POD-like) activity of the nanozyme and thus the sustained production of reactive oxygen species (ROS). At the same time, GOx can increase endogenous H2O2 and produce gluconic acid while consuming glucose, the nutrient of tumor cell growth. The results indicate that the POD-like activity of POMG-based clusterphene increase approximately sevenfold under electrical stimulation compared with Nd-substituted keggin type POM cluster (NdPW11). The experiments both in vitro and in vivo show that the proposed POMG-based clusterphene mediated cascade catalytic therapy is capable of efficient tumor inhibiting and preventing tumor proliferation in tumor-bearing mice model, promising as an excellent candidate for catalytic therapy.

FeP-Based Nanotheranostic Platform for Enhanced Phototherapy/Ferroptosis/Chemodynamic Therapy

Ferroptosis is an iron-dependent and lipid peroxides (LPO)-overloaded programmed damage cell death, induced by glutathione (GSH) depletion and glutathione peroxide 4 (GPX4) inactivation. However, the inadequacy of endogenous iron and reactive oxygen species (ROS) restricts the efficacy of ferroptosis. To overcome this obstacle, a near-infrared photo-responsive FeP@PEG NPs is fabricated. Exogenous iron pool can enhance the effect of ferroptosis via the depletion of GSH and further regulate GPX4 inactivation. Generation of ·OH derived from the Fenton reaction is proved by increased accumulation of lipid peroxides. The heat generated by photothermal therapy and ROS generated by photodynamic therapy can enhance cell apoptosis under near-infrared (NIR-808 nm) irradiation, as evidenced by mitochondrial dysfunction and further accumulation of lipid peroxide content. FeP@PEG NPs can significantly inhibit the growth of several types of cancer cells in vitro and in vivo, which is validated by theoretical and experimental results. Meanwhile, FeP@PEG NPs show excellent T2-weighted magnetic resonance imaging (MRI) property. In summary, the FeP-based nanotheranostic platform for enhanced phototherapy/ferroptosis/chemodynamic therapy provides a reliable opportunity for clinical cancer theranostics.

Design and Control of the Magnetically Actuated Micro/Nanorobot Swarm toward Biomedical Applications

Recently, magnetically actuated micro/nanorobots hold extensive promises in biomedical applications due to their advantages of noninvasiveness, fuel-free operation, and programmable nature. While effectively promised in various fields such as targeted delivery, most past investigations are mainly displayed in magnetic control of individual micro/nanorobots. Facing practical medical use, the micro/nanorobots are required for the development of swarm control in a closed-loop control manner. This review outlines the recent developments in magnetic micro/nanorobot swarms, including their actuating fundamentals, designs, controls, and biomedical applications. The fundamental principles and interactions involved in the formation of magnetic micro/nanorobot swarms are discussed first. The recent advances in the design of artificial and biohybrid micro/nanorobot swarms, along with the control devices and methods used for swarm manipulation, are presented. Furthermore, biomedical applications that have the potential to achieve clinical application are introduced, such as imaging-guided therapy, targeted delivery, embolization, and biofilm eradication. By addressing the potential challenges discussed toward the end of this review, magnetic micro/nanorobot swarms hold promise for clinical treatments in the future.

KGE-UNIT: toward the unification of molecular interactions prediction based on knowledge graph and multi-task learning on drug discovery

The prediction of molecular interactions is vital for drug discovery. Existing methods often focus on individual prediction tasks and overlook the relationships between them. Additionally, certain tasks encounter limitations due to insufficient data availability, resulting in limited performance. To overcome these limitations, we propose KGE-UNIT, a unified framework that combines knowledge graph embedding (KGE) and multi-task learning, for simultaneous prediction of drug-target interactions (DTIs) and drug-drug interactions (DDIs) and enhancing the performance of each task, even when data availability is limited. Via KGE, we extract heterogeneous features from the drug knowledge graph to enhance the structural features of drug and protein nodes, thereby improving the quality of features. Additionally, employing multi-task learning, we introduce an innovative predictor that comprises the task-aware Convolutional Neural Network-based (CNN-based) encoder and the task-aware attention decoder which can fuse better multimodal features, capture the contextual interactions of molecular tasks and enhance task awareness, leading to improved performance. Experiments on two imbalanced datasets for DTIs and DDIs demonstrate the superiority of KGE-UNIT, achieving high area under the receiver operating characteristics curves (AUROCs) (0.942, 0.987) and area under the precision-recall curve ( AUPRs) (0.930, 0.980) for DTIs and high AUROCs (0.975, 0.989) and AUPRs (0.966, 0.988) for DDIs. Notably, on the LUO dataset where the data were more limited, KGE-UNIT exhibited a more pronounced improvement, with increases of 4.32$\%$ in AUROC and 3.56$\%$ in AUPR for DTIs and 6.56$\%$ in AUROC and 8.17$\%$ in AUPR for DDIs. The scalability of KGE-UNIT is demonstrated through its extension to protein-protein interactions prediction, ablation studies and case studies further validate its effectiveness.

In Situ Raman Study of Surface Reconstruction of FeOOH/Ni3 S2 Oxygen Evolution Reaction Electrocatalysts

Construction of heterojunctions is an effective strategy to enhanced electrocatalytic oxygen evolution reaction (OER), but the structural evolution of the active phases and synergistic mechanism still lack in-depth understanding. Here, an FeOOH/Ni3 S2 heterostructure supported on nickel foam (NF) through a two-step hydrothermal-chemical etching method is reported. In situ Raman spectroscopy study of the surface reconstruction behaviors of FeOOH/Ni3 S2 /NF indicates that Ni3 S2 can be rapidly converted to NiOOH, accompanied by the phase transition from α-FeOOH to β-FeOOH during the OER process. Importantly, a deep analysis of Ni─O bond reveals that the phase transition of FeOOH can regulate the lattice disorder of NiOOH for improved catalytic activity. Density functional theory (DFT) calculations further confirm that NiOOH/FeOOH heterostructure possess strengthened adsorption for O-containing intermediates, as well as lower energy barrier toward the OER. As a result, FeOOH/Ni3 S2 /NF exhibits promising OER activity and stability in alkaline conditions, requiring an overpotential of 268 mV @ 100 mA cm-2 and long-term stability over 200 h at a current density of 200 mA cm-2 . This work provides a new perspective for understanding the synergistic mechanism of heterogeneous electrocatalysts during the OER process.

Fast autofocusing of recorded planes by salient feature region for coherent diffraction imaging

In multi-distance coherent diffraction imaging, the task of distance calculation for multi-diffraction images is cumbersome. The information features are hard-to-extract and the region of interest extraction algorithms are difficult to be adopted. A universal salient feature region selection algorithm by using the area with the highest density of corners is proposed to extract the most representative feature region. In addition, equally spaced recording modes and mismatched diffraction distances will result in system noise and destroy image quality. The polydirectional maximum gradient is offered as a sharpness criterion to weigh a quantitative feature for the final pattern. A fast, sensitive, and high-accuracy autofocusing and sample reconstruction can be achieved using only a small number of images while ensuring that morphological properties and quantification of the reconstructions are not compromised. The proposed method is promising for biological and medical dynamic observations for computational imaging systems.

THItoGene: a deep learning method for predicting spatial transcriptomics from histological images

Spatial transcriptomics unveils the complex dynamics of cell regulation and transcriptomes, but it is typically cost-prohibitive. Predicting spatial gene expression from histological images via artificial intelligence offers a more affordable option, yet existing methods fall short in extracting deep-level information from pathological images. In this paper, we present THItoGene, a hybrid neural network that utilizes dynamic convolutional and capsule networks to adaptively sense potential molecular signals in histological images for exploring the relationship between high-resolution pathology image phenotypes and regulation of gene expression. A comprehensive benchmark evaluation using datasets from human breast cancer and cutaneous squamous cell carcinoma has demonstrated the superior performance of THItoGene in spatial gene expression prediction. Moreover, THItoGene has demonstrated its capacity to decipher both the spatial context and enrichment signals within specific tissue regions. THItoGene can be freely accessed at https://github.com/yrjia1015/THItoGene.

A Detailed Insight into the Effects of Morphologies of Cerium Oxide on Fenton-like Reactions for Different Applications

As an exceptional Fenton-like reagent, cerium oxide (CeO2 ) finds applications in biomedical science and organic pollutants treatment. The Fenton-like reaction catalyzed by CeO2 typically encompasses two distinct processes: one resembling the classical Fenton reaction, wherein cerium (Ce3+ ) triggers the decomposition of hydrogen peroxide (H2 O2 ) to yield reactive oxygen species (ROS), and the other involves the complexation of H2 O2 on the Ce3+ surface, leading to the formation of peroxides. However, the influence of diverse CeO2 morphologies on these two reaction pathways has not been comprehensively explored. In this study, CeO2 exhibiting three typical morphologies, rods, cubes, and spheres, were prepared. The generation of ROS and peroxides was evaluated using the 3,3,5,5-tetramethylbenzidine (TMB) oxidation reaction and the reduction current of H2 O2 , respectively. Moreover, the impacts of pH variations and CeO2 /H2 O2 concentrations on the production and conversion of these two reaction products were investigated. To corroborate the distinctions between the resultant products and their applicability, apoptosis assays and acid orange 7 (AO7) degradation analyses were performed. Notably, CeO2 rods exhibited the highest proportion of Ce3+ , predominantly engaging in complexation with H2 O2 to foster peroxide formation, thereby facilitating the robust degradation of AO7. However, the generated peroxides appeared to occupy Ce3+ sites, thereby impeding the H2 O2 decomposition process. Conversely, Ce3+ species on the surface of CeO2 cubes were primarily involved in H2 O2 decomposition, leading to heightened ROS production, and thus showcasing substantial potential for damaging A549 tumor cells. It is worth noting that the ability of these Ce3+ species to form peroxides through complexation with H2 O2 was comparatively reduced. In summation, this study sheds light on the intricate interplay between distinct CeO2 morphologies and their divergent impacts on Fenton-like reactions. These findings expand our comprehension of the influences on its reactivity of CeO2 morphologies and open new insights for applications in diverse domains, from organic dye degradation to tumor therapy.

Wireless and Closed-Loop Smart Dressing for Exudate Management and On-Demand Treatment of Chronic Wounds

Chronic wounds have become a significant threat to people's physical and mental health and have increased the burden of social medical care. Intelligent wound dressing (IWD) with wound condition monitoring and closed-loop on-demand drug therapy can shorten the healing process and alleviate patient suffering. However, single-function wound dressings cannot meet the current needs of chronic wound treatment. Here, a wearable IWD consisting of wound exudate management, sensor monitoring, closed-loop therapy, and flexible circuit modules is reported, which can achieve effective synergy between wound exudate management and on-demand wound therapy. The dressing is attached to the wound site, and the wound exudate is spontaneously pumped into the microfluidic channel for storage. Meanwhile, the IWD can detect the state of the wound through the temperature and humidity sensor, and use this as feedback to control the liquid metal (LM) heater through a smartphone, thereby realizing the on-demand drug release from the hydrogel. In a mouse model of infected wounds, IWD accelerates wound healing by reducing inflammatory responses, promoting angiogenesis and collagen deposition.

SAGDTI: self-attention and graph neural network with multiple information representations for the prediction of drug-target interactions

Motivation

Accurate identification of target proteins that interact with drugs is a vital step in silico, which can significantly foster the development of drug repurposing and drug discovery. In recent years, numerous deep learning-based methods have been introduced to treat drug-target interaction (DTI) prediction as a classification task. The output of this task is binary identification suggesting the absence or presence of interactions. However, existing studies often (i) neglect the unique molecular attributes when embedding drugs and proteins, and (ii) determine the interaction of drug-target pairs without considering biological interaction information.

Results

In this study, we propose an end-to-end attention-derived method based on the self-attention mechanism and graph neural network, termed SAGDTI. The aim of this method is to overcome the aforementioned drawbacks in the identification of DTI. SAGDTI is the first method to sufficiently consider the unique molecular attribute representations for both drugs and targets in the input form of the SMILES sequences and three-dimensional structure graphs. In addition, our method aggregates the feature attributes of biological information between drugs and targets through multi-scale topologies and diverse connections. Experimental results illustrate that SAGDTI outperforms existing prediction models, which benefit from the unique molecular attributes embedded by atom-level attention and biological interaction information representation aggregated by node-level attention. Moreover, a case study on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) shows that our model is a powerful tool for identifying DTIs in real life.

Availability and implementation

The data and codes underlying this article are available in Github at https://github.com/lixiaokun2020/SAGDTI.

Amoeba-Inspired Magnetic Venom Microrobots

Nature provides a successful evolutionary direction for single-celled organisms to solve complex problems and complete survival tasks - pseudopodium. Amoeba, a unicellular protozoan, can produce temporary pseudopods in any direction by controlling the directional flow of protoplasm to perform important life activities such as environmental sensing, motility, predation, and excretion. However, creating robotic systems with pseudopodia to emulate environmental adaptability and tasking capabilities of natural amoeba or amoeboid cells remains challenging. Here, this work presents a strategy that uses alternating magnetic fields to reconfigure magnetic droplet into Amoeba-like microrobot, and the mechanisms of pseudopodia generation and locomotion are analyzed. By simply adjusting the field direction, microrobots switch in monopodia, bipodia, and locomotion modes, performing all pseudopod operations such as active contraction, extension, bending, and amoeboid movement. The pseudopodia endow droplet robots with excellent maneuverability to adapt to environmental variations, including spanning 3D terrains and swimming in bulk liquids. Inspired by the "Venom," the phagocytosis and parasitic behaviors have also been investigated. Parasitic droplets inherit all the capabilities of amoeboid robot, expanding their applicable scenarios such as reagent analysis, microchemical reactions, calculi removal, and drug-mediated thrombolysis. This microrobot may provide fundamental understanding of single-celled livings, and potential applications in biotechnology and biomedicine.

A Nonswelling Hydrogel with Regenerable High Wet Tissue Adhesion for Bioelectronics

Reducing the swelling of tissue-adhesive hydrogels is crucial for maintaining stable tissue adhesion and inhibiting tissue inflammation. However, reported strategies for reducing swelling always result in a simultaneous decrease in the tissue adhesive strength of the hydrogel. Furthermore, once the covalent bonds break in the currently reported hydrogels, they cannot be rebuilt, and the hydrogel loses its tissue adhesive ability. In this work, a nonswelling hydrogel (named as "PAACP") possessing regenerable high tissue adhesion is synthesized by copolymerizing and crosslinking poly(vinyl butyral) with acrylic acid, gelatin, and chitosan-grafted N-acetyl-l-cysteine. The tissue adhesive strength of the obtained PAACP reaches 211.4 kPa, which is approximately ten times higher than that of the reported nonswelling hydrogels, and the hydrogel can be reused for multiple cycles. The as-prepared hydrogel shows great potential in soft bioelectronics, as muscle fatigue is successfully monitored via the electrode array and strain sensor integrated on PAACP substrates. The success of these bioelectronics offers potential applicability in the long-term diagnosis of muscle-related health conditions and prosthetic manipulations.

Breaking through Barriers: Ultrafast Microbullet Based on Cavitation Bubble

Micromotors hold great promise for extensive practical applications such as those in biomedical domains and reservoir exploration. However, insufficient propulsion of the micromotor limits its application in crossing biological barriers and breaking reservoir boundaries. In this study, an ultrafast microbullet based on laser cavitation that can utilize the energy of a cavitation bubble and realize its own hurtling motion is reported. The experiments are performed using high-speed photography. A boundary integral method is adopted to reveal the motion mechanism of a polystyrene (PS)/magnetic nanoparticle (MNP) microbullet under the action of laser cavitation. Furthermore, the influence of certain factors (including laser intensity, microbullet size, and ambient temperature) on the motion of the microbullet was explored. For the PS/MNP microbullet driven by laser cavitation, the instantaneous velocity obtained can reach 5.23 m s^-1 . This strategy of driving the PS/MNP microbullet provides strong penetration ability and targeted motion. It is believed that the reported propulsion mechanism opens up new possibilities for micromotors in a wide range of engineering applications.