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Osteoarthritis (OA) poses a significant challenge in orthopedics. Inflammatory pathways are regarded as central mechanisms in the onset and progression of OA. Growing evidence suggests that senescence acts as a mediator in inflammation-induced OA. Given the lack of effective treatments for OA, there is an urgent need for a clearer understanding of its pathogenesis. In this review, we systematically summarize the cross-talk between cellular senescence and inflammation in OA. We begin by focusing on the mechanisms and hallmarks of cellular senescence, summarizing evidence that supports the relationship between cellular senescence and inflammation. We then discuss the mechanisms of interaction between cellular senescence and inflammation, including senescence-associated secretory phenotypes (SASP) and the effects of pro- and anti-inflammatory interventions on cellular senescence. Additionally, we focus on various types of cellular senescence in OA, including senescence in cartilage, subchondral bone, synovium, infrapatellar fat pad, stem cells, and immune cells, elucidating their mechanisms and impacts on OA. Finally, we highlight the potential of therapies targeting senescent cells in OA as a strategy for promoting cartilage regeneration.

Retinal illnesses such as age-related macular degeneration and diabetic macular edema will lead to irreversible blindness. With optical coherence tomography (OCT), doctors are able to see cross-sections of the retinal layers and provide patients with a diagnosis. Manual reading of OCT images is time-consuming, labor-intensive and even error-prone. Computer-aided diagnosis algorithms improve efficiency by automatically analyzing and diagnosing retinal OCT images. However, the accuracy and interpretability of these algorithms can be further improved through effective feature extraction, loss optimization and visualization analysis. In this paper, we propose an interpretable Swin-Poly Transformer network for performing automatically retinal OCT image classification. By shifting the window partition, the Swin-Poly Transformer constructs connections between neighboring non-overlapping windows in the previous layer and thus has the flexibility to model multi-scale features. Besides, the Swin-Poly Transformer modifies the importance of polynomial bases to refine cross entropy for better retinal OCT image classification. In addition, the proposed method also provides confidence score maps, assisting medical practitioners to understand the models’ decision-making process. Experiments in OCT2017 and OCT-C8 reveal that the proposed method outperforms both the convolutional neural network approach and ViT, with an accuracy of 99.80% and an AUC of 99.99%.

With the great success of coronavirus disease (COVID-19) messenger ribonucleic acid (mRNA) vaccines, mRNA therapeutics have gained significant momentum for the prevention and treatment of various refractory diseases. To function efficiently in vivo and overcome clinical limitations, mRNA demands safe and stable vectors and a reasonable administration route, bypassing multiple biological barriers and achieving organ-specific targeted delivery of mRNA. Nanoparticle (NP)-based delivery systems representing leading vector approaches ensure the successful intracellular delivery of mRNA to the target organ. In this review, chemical modifications of mRNA and various types of advanced mRNA NPs, including lipid NPs and polymers are summarized. The importance of passive targeting, especially endogenous targeting, and active targeting in mRNA nano-delivery is emphasized, and different cellular endocytic mechanisms are discussed. Most importantly, based on the above content and the physiological structure characteristics of various organs in vivo, the design strategies of mRNA NPs targeting different organs and cells are classified and discussed. Furthermore, the influence of administration routes on targeting design is highlighted. Finally, an outlook on the remaining challenges and future development toward mRNA targeted therapies and precision medicine is provided.

Development of the measurement-based carbon accounting means is of great importance to supplement the traditional inventory compilation. Mobile CO2/CH4 measurement provides a flexible way to inspect plant-scale CO2/CH4 emissions without the need to notify factories. In 2021, our team used a vehicle-based monitor system to conduct field campaigns in two cities and one industrial park in China, totaling 1143 km. Furthermore, we designed a model based on sample concentrations to evaluate CO2/CH4 emissions, EMISSION-PARTITION, which can be used to determine global optimal emission intensity and related dispersion parameters via intelligent algorithm (particle swarm optimization) and interior point penalty function. We evaluated the performance of EMISSION-PARTITION in chemical, coal washing, and waste incineration plants. The correlations between measured samples and rebuilt simulated ones were larger than 0.76, and RMSE was less than 11.7 mg m−3, even with much fewer samples (25). This study demonstrated the wide applications of a vehicle-based monitoring system in detecting greenhouse gas emission sources.

video captioning is a more challenging task that aims to generate abundant natural language descriptions, and it has become a promising direction for artificial intelligence. However, most existing methods are prone to ignore the problems of visual information redundancy and scene information omission due to the limitation of the sampling strategies. To address this problem, a semantic guidance network for video captioning is proposed. More specifically, a novel scene frame sampling strategy is first proposed to select key scene frames. Then, the vision transformer encoder is applied to learn visual and semantic information with a global view, alleviating information loss of modeling long-range dependencies caused in the encoder’s hidden layer. Finally, a non-parametric metric learning module is introduced to calculate the similarity value between the ground truth and the prediction result, and the model is optimized in an end-to-end manner. Experiments on the benchmark MSR-VTT and MSVD datasets show that the proposed method can effectively improve the description accuracy and generalization ability.

Early detection of tumors has great significance for formative detection and determination of treatment plans. However, cancer detection remains a challenging task due to the interference of diseased tissue, the diversity of mass scales, and the ambiguity of tumor boundaries. It is difficult to extract the features of small-sized tumors and tumor boundaries, so semantic information of high-level feature maps is needed to enrich the regional features and local attention features of tumors. To solve the problems of small tumor objects and lack of contextual features, this paper proposes a novel Semantic Pyramid Network with a Transformer Self-attention, named SPN-TS, for tumor detection. Specifically, the paper first designs a new Feature Pyramid Network in the feature extraction stage. It changes the traditional cross-layer connection scheme and focuses on enriching the features of small-sized tumor regions. Then, we introduce the transformer attention mechanism into the framework to learn the local feature of tumor boundaries. Extensive experimental evaluations were performed on the publicly available CBIS-DDSM dataset, which is a Curated Breast Imaging Subset of the Digital Database for Screening Mammography. The proposed method achieved better performance in these models, achieving 93.26% sensitivity, 95.26% specificity, 96.78% accuracy, and 87.27% Matthews Correlation Coefficient (MCC) value, respectively. The method can achieve the best detection performance by effectively solving the difficulties of small objects and boundaries ambiguity. The algorithm can further promote the detection of other diseases in the future, and also provide algorithmic references for the general object detection field.

Sepsis, the syndrome of infection complicated by acute organ dysfunction, is a serious and growing global problem, which not only leads to enormous economic losses but also becomes one of the leading causes of mortality in the intensive care unit. The detection of sepsis-related pathogens and biomarkers in the early stage plays a critical role in selecting appropriate antibiotics or other drugs, thereby preventing the emergence of dangerous phases and saving human lives. There are numerous demerits in conventional detection strategies, such as high cost, low efficiency, as well as lacking of sensitivity and selectivity. Recently, the aptamer-based biosensor is an emerging strategy for reasonable sepsis diagnosis because of its accessibility, rapidity, and stability. In this review, we first introduce the screening of suitable aptamer. Further, recent advances of aptamer-based biosensors in the detection of bacteria and biomarkers for the diagnosis of sepsis are summarized. Finally, the review proposes a brief forecast of challenges and future directions with highly promising aptamer-based biosensors.

Inflammatory osteolysis represents a critical complication following orthopedic interventions such as total joint replacement, primarily triggered by persistent inflammatory responses induced by prosthetic wear debris or bacterial components like lipopolysaccharides (LPS). Inflammatory osteolysis, a severe complication of orthopedic interventions like total joint replacement, is driven by prosthetic wear debris or lipopolysaccharides (LPS)-induced persistent inflammation and osteoclast activation. Current therapeutic strategies are limited by significant side effects and their inability to simultaneously halt the synergistic pathological processes of inflammation and osteoclast activation, highlighting an urgent need for novel therapeutic approaches. In this study, we synthesized ultrasmall Cu₄ nanoclusters with potent superoxide dismutase (SOD)-and catalase (CAT)-mimetic activities, enabling efficient reactive oxygen species (ROS) scavenging (80.43% • O 2 − and 93.17% H₂O₂ clearance at 200 µg/mL). we successfully synthesized ultrasmall Cu₄ clusters, which exhibit remarkable enzyme-mimetic activities (superoxide dismutase and catalase-like) and potent reactive oxygen species (ROS)-scavenging capabilities. These clusters specifically target mitochondria, effectively scavenging excessive ROS to mitigate oxidative stress. Furthermore, Cu₄ clusters activate the nuclear factor erythroid 2-related factor 2 (Nrf2) signaling pathway, inhibit the activation of key inflammatory pathways such as nuclear factor-κB (NF-κB), regulate iron homeostasis (Ferro-orange staining showed that the positive cell ratio in the lps group was as high as 54.1%, while it dropped to 9.96% in the 20 µg/mL Cu₄ clusters group) and lipid peroxidation to block ferroptosis, and reduce osteoclast formation. In LPS-induced calvarial osteolysis mice, Cu₄ clusters significantly alleviated bone resorption, restoring bone volume/tissue volume (BV/TV) by 57.6% (91.7% of control group) and reducing osteoclast number to 36.4% of the LPS group.Collectively, these actions result in significant alleviation of inflammation and bone resorption. This study highlights Cu₄ clusters as a promising therapeutic agent for inflammatory osteolysis, with substantial potential for clinical translation. Graphical Abstract Novel ultrasmall nanoclusters (Cu₄) scavenge ROS, modulate Nrf2/NF-κB pathways, block ferroptosis and suppress osteoclastogenesis to alleviate inflammatory osteolysis.

Dentin hypersensitivity (DH) causes considerable discomfort in many patients due to its characteristic symptoms, which primarily arise from exposed dentinal tubules (DTs). DTs also serve as a pathway for bacterial invasion. Therefore, a treatment that both effectively occludes DTs and prevents caries is needed. Polyaspartic acid (Pasp) and carboxymethyl chitosan (CMC) were combined with amorphous calcium phosphate (ACP) to develop PCA. PCA was compared with three polyelectrolyte complexes (PA, CA, PC). Recombinant collagen models assessed the mineralization-promoting effect of PCA, whereas DH models evaluated its ability to occlude DTs. The sealing performance and surface hardness of PCA-treated dentin discs were assessed by airtightness and hardness tests. Antibacterial effects were analyzed using live-dead staining, bacterial adherence assays, and colony-forming unit (CFU) counts. Biocompatibility was evaluated by live-dead cell staining, CCK-8 assays, and hemolysis tests. PCA contains ACP clusters approximately 2 nm in diameter and achieves effective intrafibrillar mineralization within 5 days. PCA completely covers the collagen fibers on the dentin surface and occludes the DTs. The remineralized dentin demonstrates excellent resistance to friction and acid exposure. PCA treatment significantly restores the airtightness and mechanical strength of demineralized dentin. Moreover, PCA exhibits strong antibacterial activity against Streptococcus mutans ( ). Biocompatibility tests confirm favorable safety profiles. This study demonstrates the dual function of PCA in DH management and caries prevention by occluding DTs and providing antibacterial protection, supporting its potential for clinical application.