Immune-related adverse events with severe pain and ureteral expansion as the main manifestations: a case report of tislelizumab-induced ureteritis/cystitis and review of the literature

Immune checkpoint inhibitor (ICI) is an up-to-date therapy for cancer with a promising efficacy, but it may cause unique immune-related adverse events (irAEs). Although irAEs could affect any organ, irAEs-induced whole urinary tract expansion was rarely reported. Herein, we reported a 27-year-old male patient with thymic carcinoma who received the treatment of tislelizumab, paclitaxel albumin and carboplatin. He was hospitalized for severe bellyache and lumbago after 6 courses of treatment. Antibiotic and antispasmodic treatment did not relieve his symptoms. The imaging examinations reported whole urinary tract expansion and cystitis. Therefore, we proposed that the patient’s pain was caused by tislelizumab-induced ureteritis/cystitis. After the discontinuation of tislelizumab and the administration of methylprednisolone, his symptoms were markedly alleviated. Herein, we reported a rare case of ICI-induced ureteritis/cystitis in the treatment of thymic cancer and reviewed other cases of immunotherapy-related cystitis and tislelizumab-related adverse events, which will provide a reference for the diagnosis and treatment of ICI-related irAEs

Rudder roll stabilization with robust predictive control based on fuzzy rules

ObjectiveIn order to solve the problem of an underactuated ship responding slowly to heading changes during rudder roll stabilization (RRS) caused by fixed weight values in model predictive control (MPC), a RRS control method based on the finite time extended states observer (FTESO), fuzzy rules and robust predictive control is proposed. MethodsA fixed speed linear underactuated ship model is established for controller design. The FTESO is used to estimate the ship's motion states and external disturbances. By analyzing the conditions of the ship's course-keeping and heading change, the objective function weights under the two conditions and the fuzzy rules between the states and weights are designed respectively. Robust predictive control is used to solve the multi-objective cooperative control problem with constraints. The closed-loop stability of the proposed control method is then proven theoretically. ResultsAccording to a numerical simulation of a multi-purpose naval vessel, the proposed control method is compared with a disturbance compensation MPC and disturbance observer enhanced MPC, and is shown to have a higher roll stabilization rate by 5.74% and 0.898 3%, respectively. The response time of the proposed method for a 30° heading change is also reduced by 1.8 s and 7.3 s respectively. ConclusionThe effectiveness of the proposed method in underactuated ship rolling reduction is proven

Few-Shot Website Fingerprinting Attack with Data Augmentation

This work introduces a novel data augmentation method for few-shot website fingerprinting (WF) attack where only a handful of training samples per website are available for deep learning model optimization. Moving beyond earlier WF methods relying on manually-engineered feature representations, more advanced deep learning alternatives demonstrate that learning feature representations automatically from training data is superior. Nonetheless, this advantage is subject to an unrealistic assumption that there exist many training samples per website, which otherwise will disappear. To address this, we introduce a model-agnostic, efficient, and harmonious data augmentation (HDA) method that can improve deep WF attacking methods significantly. HDA involves both intrasample and intersample data transformations that can be used in a harmonious manner to expand a tiny training dataset to an arbitrarily large collection, therefore effectively and explicitly addressing the intrinsic data scarcity problem. We conducted expensive experiments to validate our HDA for boosting state-of-the-art deep learning WF attack models in both closed-world and open-world attacking scenarios, at absence and presence of strong defense. For instance, in the more challenging and realistic evaluation scenario with WTF-PAD-based defense, our HDA method surpasses the previous state-of-the-art results by nearly 3% in classification accuracy in the 20-shot learning case

Semi-Autonomous Learning Algorithm for Remote Image Object Detection Based on Aggregation Area Instance Refinement

Semi-autonomous learning for object detection has attracted more and more attention in recent years, which usually tends to find only one object instance with the highest score in each image. However, this strategy usually highlights the most representative part of the object instead of the whole object, which may lead to the loss of a lot of important information. To solve this problem, a novel end-to-end aggregate-guided semi-autonomous learning residual network is proposed to perform object detection. Firstly, a progressive modified residual network (MRN) is applied to the backbone network to make the detector more sensitive to the boundary features of the object. Then, an aggregate-based region-merging strategy (ARMS) is designed to select high-quality instances by selecting aggregation areas and merging these regions. The ARMS selects the aggregation areas that are highly related to the object through association coefficient, and then evaluates the aggregation areas through a similarity coefficient and fuses them to obtain high-quality object instance areas. Finally, a regression-locating branch is further developed to refine the location of the object, which can be optimized jointly with regional classification. Extensive experiments demonstrate that the proposed method is superior to state-of-the-art methods

Semi-Autonomous Learning Algorithm for Remote Image Object Detection Based on Aggregation Area Instance Refinement

Semi-autonomous learning for object detection has attracted more and more attention in recent years, which usually tends to find only one object instance with the highest score in each image. However, this strategy usually highlights the most representative part of the object instead of the whole object, which may lead to the loss of a lot of important information. To solve this problem, a novel end-to-end aggregate-guided semi-autonomous learning residual network is proposed to perform object detection. Firstly, a progressive modified residual network (MRN) is applied to the backbone network to make the detector more sensitive to the boundary features of the object. Then, an aggregate-based region-merging strategy (ARMS) is designed to select high-quality instances by selecting aggregation areas and merging these regions. The ARMS selects the aggregation areas that are highly related to the object through association coefficient, and then evaluates the aggregation areas through a similarity coefficient and fuses them to obtain high-quality object instance areas. Finally, a regression-locating branch is further developed to refine the location of the object, which can be optimized jointly with regional classification. Extensive experiments demonstrate that the proposed method is superior to state-of-the-art methods

Synergistic Antibacterial Effect of Zinc Oxide Nanoparticles and Polymorphonuclear Neutrophils

Zinc oxide nanoparticles (ZnONPs) are inorganic nano-biomaterials with excellent antimicrobial properties. However, their effects on the anti-infection ability of the innate immune system remains poorly understood. The aim of the present study was to explore the potential immunomodulatory effects of ZnONPs on the innate immune system, represented by polymorphonuclear leukocytes (PMNs), and determine whether they can act synergistically to resist pathogen infections. In vitro experiment showed that ZnONPs not only exhibit obvious antibacterial activity at biocompatible concentrations but also enhance the antibacterial property of PMNs. In vivo experiments demonstrated the antibacterial effect of ZnONPs, accompanied by more infiltration of subcutaneous immune cells. Further ex vivo and in vitro experiments revealed that ZnONPs enhanced the migration of PMNs, promoted their bacterial phagocytosis efficiency, proinflammatory cytokine (TNF-α, IL-1β, and IL-6) expression, and reactive oxygen species (ROS) production. In summary, this study revealed potential synergistic effects of ZnONPs on PMNs to resist pathogen infection and the underlying mechanisms. The findings suggest that attempts should be made to fabricate and apply biomaterials in order to maximize their synergy with the innate immune system, thus promoting the host’s resistance to pathogen invasion

Synergistic Antibacterial Effect of Zinc Oxide Nanoparticles and Polymorphonuclear Neutrophils

Zinc oxide nanoparticles (ZnONPs) are inorganic nano-biomaterials with excellent antimicrobial properties. However, their effects on the anti-infection ability of the innate immune system remains poorly understood. The aim of the present study was to explore the potential immunomodulatory effects of ZnONPs on the innate immune system, represented by polymorphonuclear leukocytes (PMNs), and determine whether they can act synergistically to resist pathogen infections. In vitro experiment showed that ZnONPs not only exhibit obvious antibacterial activity at biocompatible concentrations but also enhance the antibacterial property of PMNs. In vivo experiments demonstrated the antibacterial effect of ZnONPs, accompanied by more infiltration of subcutaneous immune cells. Further ex vivo and in vitro experiments revealed that ZnONPs enhanced the migration of PMNs, promoted their bacterial phagocytosis efficiency, proinflammatory cytokine (TNF-α, IL-1β, and IL-6) expression, and reactive oxygen species (ROS) production. In summary, this study revealed potential synergistic effects of ZnONPs on PMNs to resist pathogen infection and the underlying mechanisms. The findings suggest that attempts should be made to fabricate and apply biomaterials in order to maximize their synergy with the innate immune system, thus promoting the host’s resistance to pathogen invasion

Identification and Characterization of Salt-Responsive MicroRNAs in Taxodium hybrid ‘Zhongshanshan 405’ by High-Throughput Sequencing

MicroRNAs (miRNAs) are a type of noncoding RNA participating in the post-transcriptional regulation of gene expression that regulates plant responses to salt stress. Small RNA sequencing was performed in this study to discover the miRNAs responding to salt stress in Taxodium hybrid ‘Zhongshanshan 405’, which is tolerant to salinity stress. A total of 52 miRNAs were found to be differentially expressed. The target genes were enriched with gene ontology (GO), including protein phosphorylation, cellular response to stimulus, signal transduction, ATP and ADP binding, showing that miRNAs may play key roles in regulating the tolerance to salt stress in T. hybrid ‘Zhongshanshan 405’. Notably, a G-type lectin S-receptor-like serine/threonine-protein kinase (GsSRK) regulated by novel_77 and novel_2 miRNAs and a mitogen-activated protein kinase kinase kinase (MAPKKK) regulated by novel_41 miRNA were discovered under both short- and long-term salt treatments and can be selected for future research. This result provides new insights into the regulatory functions of miRNAs in the salt response of T. hybrid ‘Zhongshanshan 405’