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Background Tyrosine kinase inhibitors (TKIs) such as sunitinib are multitarget antiangiogenic agents in clear cell renal cell carcinoma (ccRCC). They are widely used in the treatment of advanced/metastatic renal cancer. However, resistance to TKIs is common in the clinic, particularly after long-term treatment. YTHDC1 is the main nuclear reader protein that binds with m 6 A to regulate the splicing, export and stability of mRNA. However, the specific role and corresponding mechanism of YTHDC1 in renal cancer cells are still unclear. Methods The Cancer Genome Atlas (TCGA) dataset was used to study the expression of YTHDC1 in ccRCC. Cell counting kit-8 (CCK-8), wound healing, Transwell and xenograft assays were applied to explore the biological function of YTHDC1 in ccRCC. Western blot, quantitative real time PCR (RT‒qPCR), RNA immunoprecipitation PCR (RIP-qPCR), methylated RIP-qPCR (MeRIP-qPCR) and RNA sequencing (RNA-seq) analyses were applied to study the YY1/HDAC2/YTHDC1/ANXA1 axis in renal cancer cells. The CCK-8 assay and xenograft assay were used to study the role of YTHDC1 in determining the sensitivity of ccRCC to sunitinib. Results Our results demonstrated that YTHDC1 is downregulated in ccRCC tissues compared with normal tissues. Low expression of YTHDC1 is associated with a poor prognosis in patients with ccRCC. Subsequently, we showed that YTHDC1 inhibits the progression of renal cancer cells via downregulation of the ANXA1/MAPK pathways. Moreover, we also showed that the YTHDC1/ANXA1 axis modulates the sensitivity of tyrosine kinase inhibitors. We then revealed that HDAC2 inhibitors resensitize ccRCC to tyrosine kinase inhibitors through the YY1/HDAC2 complex. We have identified a novel YY1/HDAC2/YTHDC1/ANXA1 axis modulating the progression and chemosensitivity of ccRCC. Conclusion We identified a novel YY1/HDAC2/YTHDC1/ANXA1 axis modulating the progression and chemosensitivity of ccRCC.

Bispecific antibodies represent an innovative paradigm in cancer therapy, offering broader therapeutic potential compared to conventional monoclonal antibodies. To increase tumor selectivity while mitigating off-target effects in normal tissues, the concept of prodrug-based bispecific antibodies has emerged. This review delineates the various mechanisms underlying the action of prodrug-based bispecific antibodies, including protease-mediated activation, steric hindrance release via proteolytic processing, activation by soluble factors, conditional assembly, and chain exchange-mediated activation. We also address the critical challenges that must be overcome to optimize the development and clinical application of these sophisticated therapeutic agents.

Fitness movement recognition and evaluation systems play a crucial role in accurately, quantitatively, and efficiently guiding the fitness process, thereby enhancing fitness effectiveness and safety.However, existing research in this area still has some drawbacks, such as high computational complexity, insufficient real-time performance, and low stability. To address these issues, a real-time fitness action recognition and evaluation system is developed based on the lightweight BlazePose model. The system integrates the K Nearest Neighbor (KNN) algorithm for action recognition and classification. In addition, an improved dynamic time warping (S - WFDTW) algorithm is introduced. By utilizing similarity calculations, this algorithm quantifies the metrics and significantly improves the real-time alignment of the time series. Moreover, it provides joint angle analysis and motion trajectory feedback, which enables real-time counting and evaluation of fitness movements. The experimental results demonstrate that the system achieves 98.33% accuracy in movement assessment and counting under complex conditions. In a comparison experiment with the COCO dataset, the average processing rate of the BlazePose model used by the system is 13.3 times faster than that of OpenPose. This significant improvement greatly enhances the efficiency and responsiveness of the system. In conclusion, the developed system achieves efficient and stable recognition of fitness movements, and is able to accurately assess and quantify exercise performance. It can also help users optimize their movements through visual feedback, thus significantly improving the efficiency and accuracy of fitness movement recognition and evaluation.

Background Post-infarction cardiovascular remodeling and heart failure are the leading cause of myocardial infarction (MI)-driven death during the past decades. Experimental observations have involved intestinal microbiota in the susceptibility to MI in mice; however, in humans, identifying whether translocation of gut bacteria to systemic circulation contributes to cardiovascular events post-MI remains a major challenge. Results Here, we carried out a metagenomic analysis to characterize the systemic bacteria in a cohort of 49 healthy control individuals, 50 stable coronary heart disease (CHD) subjects, and 100 ST-segment elevation myocardial infarction (STEMI) patients. We report for the first time higher microbial richness and diversity in the systemic microbiome of STEMI patients. More than 12% of post-STEMI blood bacteria were dominated by intestinal microbiota ( Lactobacillus , Bacteroides , and Streptococcus ). The significantly increased product of gut bacterial translocation (LPS and d -lactate) was correlated with systemic inflammation and predicted adverse cardiovascular events. Following experimental MI, compromised left ventricle (LV) function and intestinal hypoperfusion drove gut permeability elevation through tight junction protein suppression and intestinal mucosal injury. Upon abrogation of gut bacterial translocation by antibiotic treatment, both systemic inflammation and cardiomyocyte injury in MI mice were alleviated. Conclusions Our results provide the first evidence that cardiovascular outcomes post-MI are driven by intestinal microbiota translocation into systemic circulation. New therapeutic strategies targeting to protect the gut barrier and eliminate gut bacteria translocation may reduce or even prevent cardiovascular events post-MI.

Trophoblast cell surface antigen 2 (Trop2) exhibits limited expression in normal tissues but is over-expressed across various solid tumors. The effectiveness of anti-Trop2 antibody-drug conjugate (ADC) in managing breast cancer validates Trop2 as a promising therapeutic target for cancer treatment. However, excessive toxicity and a low response rate of ADCs pose ongoing challenges. Safer and more effective strategies should be developed for Trop2-positive cancers. The dynamic structural attributes and the oligomeric assembly of Trop2 present formidable obstacles to the progression of innovative targeted therapeutics. In this review, we summarize recent advancements in understanding Trop2’s structure and provide an overview of the epitope characteristics of Trop2-targeted agents. Furthermore, we discuss the correlation between anti-Trop2 agents’ epitopes and their respective functions, particularly emphasizing their efficacy and specificity in targeted therapies.

Virus-like particles (VLPs) are multimeric nanostructures composed of one or more structural proteins of a virus in the absence of genetic material. Having similar morphology to natural viruses but lacking any pathogenicity or infectivity, VLPs have gradually become a safe substitute for inactivated or attenuated vaccines. VLPs can achieve tissue-specific targeting and complete and effective cell penetration. With highly ordered epitope repeats, VLPs have excellent immunogenicity and can induce strong cellular and humoral immune responses. In addition, as a type of nanocarrier, VLPs can be used to display antigenic epitopes or deliver small molecules. VLPs have thus become powerful tools for vaccinology and biomedical research. This review highlights the versatility of VLPs in antigen presentation, drug delivery, and vaccine technology.

The assignment of tasks for unmanned aerial vehicles (UAVs) during forest fire reconnaissance is a highly complex and large-scale problem. Current task allocation methods struggle to strike a balance between solution speed and effectiveness. In this paper, a two-phase centralized UAV task assignment model based on expectation maximization (EM) clustering and the multidimensional knapsack model (MKP) is proposed for the forest fire reconnaissance task assignment. The fire situation information is acquired using the sensors carried by satellites at first. Then, the EM algorithm based on the Gaussian mixture model (GMM) is applied to get the initial position of every UAV. In the end, the MKP is applied for UAV task assignment based on the initial positions of the UAVs. An improved genetic algorithm (GA) based on the fireworks algorithm (FWA) is proposed for faster iteration speed. A simulation was carried out against the background of forest fires in Liangshan Prefecture, Sichuan Province, and the simulation’s results demonstrate that the task assignment model can quickly and effectively address task allocation problems on a large scale. In addition, the FW–GA hybrid algorithm has great advantages over the traditional GA, particularly in solving time, iteration convergence speed, and solution effectiveness. It can reduce up to 556% of the iteration time and increase objective function value by 1.7% compared to the standard GA. Furthermore, compared to the GA–SA algorithm, its solving time is up to 60 times lower. This paper provides a new idea for future large-scale UAV task assignment problems.

HER2 is a well-established oncogenic driver in breast, gastric, and other solid tumors. While HER2-targeted therapies such as trastuzumab and pertuzumab have improved clinical outcomes, resistance, particularly to trastuzumab, remains a major therapeutic challenge. Here, we engineered two IgG-VHH biparatopic antibodies (bpAbs), A9B5-Bs-5 and A9B5-Bs-7, incorporating an ECD I-binding nanobody A9B5 with the IgG scaffolds. These bpAbs target non-overlapping epitopes on the HER2 extracellular domain, promoting rapid receptor internalization and demonstrating superior antitumor activity compared to the trastuzumab and pertuzumab combination in trastuzumab-resistant tumor cells. Structural modeling suggests that both bpAbs engage HER2 in a trans -binding mode, leading to receptor clustering and interference with ligand-driven HER2 heterodimerization. These findings demonstrate that epitope-guided biparatopic antibody design can enhance HER2 downregulation and restore sensitivity to HER2-targeted therapy in vitro , providing a strategy for the development of next-generation receptor-targeted biologics.

Human epithelial growth factor receptor-2 (HER2) plays an oncogenic role in numerous tumors, including breast, gastric, and various other solid tumors. While anti-HER2 therapies are approved for the treatment of HER2-positive tumors, a necessity persists for creating novel HER2-targeted agents to resolve therapeutic resistance. Utilizing a synthetic nanobody library and affinity maturation, our study identified four anti-HER2 nanobodies that exhibited high affinity and specificity. These nanobodies recognized three distinct epitopes of HER2-ECD. Additionally, we constructed VHH-Fc and discovered that they facilitated superior internalization and showed moderate growth inhibition. Compared to the combination of trastuzumab and pertuzumab, the VHH-Fc combos or their combination with trastuzumab demonstrated greater or comparable antitumor activity in both ligand-independent and ligand-driven tumors. Most remarkably, A9B5-Fc, which targeted domain I of HER2-ECD, displayed significantly enhanced trastuzumab-synergistic antitumor efficacy compared to pertuzumab under trastuzumab-resistant conditions. Our findings offer anti-HER2 nanobodies with high affinity and non-overlapping epitope recognition. The novel nanobody-based HER2-targeted antibody, A9B5-Fc, binding to HER2-ECD I, mediates promising receptor internalization. It possesses the potential to serve as a potent synergistic partner with trastuzumab, contributing to overcoming acquired resistance.

Despite the emergence of molecular targeted therapy and immune checkpoint inhibitors as standard first-line treatments for non-small cell lung cancer (NSCLC), their efficacy in some patients is limited by intrinsic and acquired resistance. Antibody-drug conjugates (ADCs), a revolutionary class of antitumor drugs, have displayed promising clinical outcomes in cancer treatment. In 2022, trastuzumab deruxtecan (Enhertu) was approved for treating HER2-mutated NSCLC, thereby underscoring the clinical value of ADCs in NSCLC treatment strategies. An increasing number of ADCs, focusing on NSCLC, are undergoing clinical trials, potentially positioning them as future treatment options. In this review, we encapsulate recent advancements in the clinical research of novel ADCs for treating NSCLC. Subsequently, we discuss the mechanisms of action, clinical efficacy, and associated limitations of these ADCs.