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Despite its growing promise in cancer treatment, ferrotherapy has low therapeutic efficacy due to compromised Fenton catalytic efficiency in tumor milieu. We herein report a hybrid semiconducting nanozyme (HSN) with high photothermal conversion efficiency for photoacoustic (PA) imaging-guided second near-infrared photothermal ferrotherapy. HSN comprises an amphiphilic semiconducting polymer as photothermal converter, PA emitter and iron-chelating Fenton catalyst. Upon photoirradiation, HSN generates heat not only to induce cytotoxicity but also to enhance Fenton reaction. The increased ·OH generation promotes both ferroptosis and apoptosis, oxidizes HSN (42 nm) and transforms it into tiny segments (1.7 nm) with elevated intratumoral permeability. The non-invasive seamless synergism leads to amplified therapeutic effects including a deep ablation depth (9 mm), reduced expression of metastasis-related proteins and inhibition of metastasis from primary tumor to distant organs. Thereby, our study provides a generalized nanozyme strategy to compensate both ferrotherapy and phototherapeutics for complete tumor regression. Due to tumour microenvironment, Fenton reactions have low therapeutic efficiency. Here the authors report on the application of NIR-II hybrid semiconducting nanozymes for combined photothermal therapy and enhanced ferrotherapy with photoacoustic imaging and show application in vivo in tumour models.

The application of traditional pressure sensors in health monitoring is limited by their initial rigidity. Flexible pressure sensors have thus received extensive attention owing to their excellent device flexibility. In this paper, we demonstrate a method of constructing flexible pressure sensors by inverting porous skeleton nickel foam based on multi-wall carbon nanotubes (MWCNTs) and polydimethylsiloxane (PDMS). MWCNTs and PDMS were mixed to form a composite conductive film, and the mass fraction of MWCNTs was optimized by evaluating the resistance change rate of the composite film. The optimized value of the mass fraction was 5%, which was used to prepare the flexible pressure sensors. The response, hysteresis, and stability of the sensors were further characterized. Pulse signals of humans were detected through flexible sensors, which can be used to evaluate cardiovascular conditions of the human body. These performance characteristics and the application demonstration show that our flexible pressure sensors have good prospects in human health care.

Federated learning is served as a novel distributed training framework that enables multiple clients of the internet of things to collaboratively train a global model while the data remains local. However, the implement of federated learning faces many problems in practice, such as the large number of training for convergence due to the size of model and the lack of adaptivity by the stochastic gradient-based update at the client side. Meanwhile, it is sensitive to noise during the optimization process that can affect the performance of the final model. For these reasons, we propose Federated Adaptive learning based on Derivative Term, called FedADT in this paper, which incorporates adaptive step size and difference of gradient in the update of local model. To further reduce the influence of noise on the derivative term that is estimated by difference of gradient, we use moving average decay on the derivative term. Moreover, we analyze the convergence performance of the proposed algorithm for non-convex objective function, i.e., the convergence rate of 1/nT can be achieved by choosing appropriate hyper-parameters, where n is the number of clients and T is the number of iterations, respectively. Finally, various experiments for the image classification task are conducted by training widely used convolutional neural network on MNIST and Fashion MNIST datasets to verify the effectiveness of FedADT. In addition, the receiver operating characteristic curve is used to display the result of the proposed algorithm by predicting the categories of clothing on the Fashion MNIST dataset.

Surgical tasks in small tortuous lumens demand interventional instruments with controllable mechanical adaptability. However, current microcatheters lack a non-disruptive, integration-ready strategy for dynamic stiffness tuning—critical for meeting the divergent mechanical demands for compliant steering and stable advancement. Here, we present a microrobotic system based on a helix-shaped magnetic soft microrobot (Helixoft) that compatibly integrates with commercial microcatheters (down to 300 μm in diameter), enabling continuous stiffness tuning (up to 40-fold) and precise steering, both controlled magnetically, free of any other potentially harmful stimuli. Stiffness tuning and active steering are independently controlled via a decoupled control strategy by the helical motion and torque-driven bending of independent microrobot components. This stiffness and structure reconfiguration allow the integrated microcatheter to perform large-angle navigation, precision payload delivery, and localised tissue biopsy without unintended buckling or tissue damage. We validate the system in both ex-vivo oviduct biopsy and in-vivo drug delivery to the fourth-generation bronchi of live pigs. The Helixoft system provides a minimally disruptive robotic strategy for the mechanical reconfiguration in confined and sensitive luminal environments. Surgical tasks within tortuous and narrow lumens require interventional instruments with controllable mechanical adaptability. Here, the authors present the Helixoft microrobotic system, which enables continuous stiffness tuning and active steering of microcatheters with diameters down to 300 μm controlled by weak magnetic fields.

A novel phosphorylated graphene oxide (PGO)-reinforced polybenzimidazole (PBI) composite membrane was prepared for high-temperature proton exchange membrane fuel cell (HT-PEMFC). PGO was prepared by phosphorylation of graphene oxide (GO) with phosphoric acid (PA), and its structure was characterized by Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), Raman spectroscopy, X-ray photoelectron spectroscopy (XPS), energy dispersive spectrometry (EDS), and ion exchange capacity (IEC) measurement. Then, PGO was added into PBI to prepare composite membranes, with excellent oxidation and thermal stability, outstanding mechanical properties, strong PA uptake and high proton conductivity. The PBI composite membrane with 1.5 wt.% of PGO exhibits the highest tensile strength (38.4 MPa) and excellent proton conductivity (17.6 mS·cm −1 at 180 ℃ without moisture) after immersion in PA aqueous solution, which is 1.31 and 1.15 times higher than that of pure PBI and PBI/GO composite membranes, respectively. Thus, PGO-reinforced PBI composite membranes have promising applications in HT-PEMFC.

Human papillomavirus type 16 (HPV16) is the most common type of infection in head and neck squamous cell carcinoma (HNSCC).The HPV16-encoded miRNA, HPV16-miR-H1, has been validated in cervical cancer tissues and cells, but its role in the pathogenesis of HNSCC remains unclear. The expression level of HPV16-miR-H1 in HNSCC cells was detected, and its mechanism of action was analyzed using bioinformatics tools and RNA sequencing. Overexpression and knockdown of HPV16-miR-H1 were performed to validate its regulatory effects on the PTEN/AKT axis and its impact on cell proliferation, migration, the cell cycle, and glycolysis. HPV16-miR-H1 was highly expressed in HPV16-positive HNSCC cells. Bioinformatics prediction and gene enrichment analysis indicated that HPV16-miR-H1 regulates PTEN transcription and the glycolysis process. Dual-luciferase reporter assays further confirmed that HPV16-miR-H1 targets the 3’-UTR region of PTEN. Functional experiments demonstrated that HPV16-miR-H1 affects HNSCC cell proliferation, migration, the cell cycle, and glycolysis. The expression of HPV16-miR-H1 in HNSCC cells was validated for the first time. Further studies revealed that it regulates the PTEN/AKT axis by targeting the 3’UTR region of PTEN, thereby enhancing the proliferation, migration, and glycolytic processes in HNSCC cells.

In smart homes, the computational offloading technology of edge cloud computing (ECC) can effectively deal with the large amount of computation generated by smart devices. In this paper, we propose a computational offloading strategy for minimizing delay based on the back-pressure algorithm (BMDCO) to get the offloading decision and the number of tasks that can be offloaded. Specifically, we first construct a system with multiple local smart device task queues and multiple edge processor task queues. Then, we formulate an offloading strategy to minimize the queue length of tasks in each time slot by minimizing the Lyapunov drift optimization problem, so as to realize the stability of queues and improve the offloading performance. In addition, we give a theoretical analysis on the stability of the BMDCO algorithm by deducing the upper bound of all queues in this system. The simulation results show the stability of the proposed algorithm, and demonstrate that the BMDCO algorithm is superior to other alternatives. Compared with other algorithms, this algorithm can effectively reduce the computation delay.

Whether extensive intraoperative peritoneal lavage (EIPL) after gastrectomy is beneficial to patients with locally advanced gastric cancer (AGC) is not clear. This phase 3, multicenter, parallel-group, prospective randomized study (NCT02745509) recruits patients between April 2016 and November 2017. Eligible patients who had been histologically proven AGC with T3/4NxM0 stage are randomly assigned (1:1) to either surgery alone or surgery plus EIPL. The results of the two groups are analyzed in the intent-to-treat population. A total of 662 patients with AGC (329 patients in the surgery alone group, and 333 in the surgery plus EIPL group) are included in the study. The primary endpoint is 3-year overall survival (OS). The secondary endpoints include 3-year disease free survival (DFS), 3-year peritoneal recurrence-free survival (reported in this manuscript) and 30-day postoperative complication and mortality (previously reported). The trial meets pre-specified endpoints. Estimated 3-year OS rates are 68.5% in the surgery alone group and 70.6% in the surgery plus EIPL group (log-rank p = 0.77). 3-year DFS rates are 61.2% in the surgery alone group and 66.0% in the surgery plus EIPL group (log-rank p = 0.24). The pattern of disease recurrence is similar in the two groups. In conclusion, EIPL does not improve the 3-year survival rate in AGC patients. Extensive intraoperative peritoneal lavage (EIPL) has been proposed as an approach to reduce peritoneal metastasis in patients with locally advanced gastric cancer undergoing gastrectomy. Here the authors report the results of the multicentric phase III SEIPLUS trial, showing that EIPL does not improve 3-year survival rate in patients with advanced gastric cancer.

The fall armyworm, Spodoptera frugiperda, a lepidopteran pest from the family Noctuidae, has become a major invasive pest since 2016. Using RNAi methods to control S. frugiperda is currently under investigation. This study is the first to target the V-ATPaseD gene of S. frugiperda using RNAi. Injection of dsRNA-V-ATPaseD into the hemolymph of 4th-instar larvae significantly suppressed gene expression at 24 and 48 h post-injection. Treated larvae showed delayed development and reduced pupation after 7 days. Subsequently, V-ATPaseD silencing was achieved through topical or oral administration of chitosan/dsRNA-V-ATPaseD nanoparticles. Larvae fed these nanoparticles exhibited significant reductions in V-ATPaseD mRNA at 72 h, persisting until 96 h before normalizing. Additionally, the treated larvae displayed disrupted molting and impaired pupation. Furthermore, larvae fed chitosan/dsRNA-V-ATPaseD were more susceptible to emamectin benzoate–lufenuron at LC30 concentrations, resulting in 68% mortality—27% higher than the pesticide alone—72 h post-exposure. Combining chitosan/dsRNA-V-ATPaseD nanoparticles with emamectin benzoate–lufenuron significantly enhanced pest control efficacy, providing new insights into pesticide reduction and sustainable pest control methods for this invasive species.

The multi-armed bandit (MAB) problem is a typical problem of exploration and exploitation. As a classical MAB problem, the stochastic multi-armed bandit (SMAB) is the basis of reinforcement learning recommendation. However, most existing SMAB and MAB algorithms have two limitations: (1) they do not make full use of feedback from the environment or agent, such as the number of arms and rewards contained in user feedback; (2) they overlook the utilization of different action selections, which can affect the exploration and exploitation of the algorithm. These limitations motivate us to propose a novel dynamic grouping within the minimax optimal strategy in the stochastic case (DG-MOSS) algorithm for reinforcement learning recommendation for small and medium-sized data scenarios. DG-MOSS does not require additional contextual data and can be used for recommendation of various types of data. Specifically, we designed a new exploration calculation method based on dynamic grouping which uses the feedback information automatically in the selection process and adopts different action selections. During the thorough training of the algorithm, we designed an adaptive episode length to effectively improve the training efficiency. We also analyzed and proved the upper bound of DG-MOSS’s regret. Our experimental results for different scales, densities, and field datasets show that DG-MOSS can yield greater rewards than nine baselines with sufficiently trained recommendation and demonstrate that it has better robustness.