Conventional oligonucleotide based drug delivery systems suffer from lengthy synthetic protocols, high cost, and poor chemical or enzymatic stability under certain circumstances. Canonical free individual nucleosides cannot form stable nanostructures in aqueous solution as drug vehicles. Here, we report the development of a monomeric self-assembled nucleoside nanoparticle (SNNP) into an efficient drug delivery system which has currently no parallel in such field. This was achieved using a l-configurational pyrimido[4,5-d]pyrimidine nucleoside building block that can form robust discrete nanoparticles in just one step with water as the sole solvent. Its high biocompatibility and low toxicity was demonstrated in vitro and in vivo. In mouse xenograft model of oral squamous cell carcinoma (OSCC), SNNP loaded with 5-fluoro-uracile (5-FU-SNNP) remarkably retarded the tumor growth compared with free 5-FU, albeit SNNP alone showed no antitumor effect. The stability in blood circulation and the effective concentration of 5-FU in tumor tissue were increased upon the loading with SNNP. TUNEL and immunohistochemistry analyses further indicated that the superior in vivo antitumor efficacy of 5-FU-SNNP compared to free 5-FU was associated with an enhanced degree of inhibition of cell proliferation and stimulation of cell apoptosis. Furthermore, SNNP alleviated the toxic side effects of 5-FU. These findings suggested that when loaded with SNNP, 5-FU has better antitumor efficacy and lower side effects, indicating that SNNP can efficiently act as a readily accessible, robust, biocompatible and low-toxic nanobiomaterial which may find wide therapeutic applications clinically in the future.
Metastasis, a powerful prognostic indicator of oral squamous cell carcinoma (OSCC), is chiefly responsible for poor cancer outcomes. Despite an increasing number of studies examining the mechanisms underlying poor outcomes, the development of potent strategies is hindered by insufficient characterization of the crucial regulators. Long noncoding RNAs (lncRNAs) have recently been gaining interest as significant modulators of OSCC metastasis; however, the detailed mechanisms underlying lncRNA-mediated OSCC metastasis remain relatively uncharacterized. Here, we identified a novel alternative splice variant of oral cancer overexpressed 1 ( ORAOV1), named as ORAOV1-B, which was subsequently validated as an lncRNA and correlated with OSCC lymph node metastasis; significantly increased invasion and migration were observed in ORAOV1-B–overexpressing OSCC cells. RNA pulldown and mass spectrometry identified Hsp90 as a direct target of ORAOV1-B, and cDNA microarrays suggested TNFα as a potential downstream target of ORAOV1-B. ORAOV1-B was shown to directly bind to and stabilize Hsp90, which maintains the function of client proteins, receptor-interaction protein, and IκB kinase beta, thus activating the NF-κB pathway and inducing TNFα. Additionally, TNFα reciprocally enhanced p-NF-κB-p65 and the downstream epithelial-mesenchymal transition. ORAOV1-B effects were reversed by a TNFα inhibitor, demonstrating that TNFα is essential for ORAOV1-B–regulated metastatic ability. Consistent epithelial-mesenchymal transition in the ORAOV1-B group was demonstrated via an orthotopic model. In the metastatic model, ORAOV1-B significantly contributed to OSCC-related lung metastasis. In summary, the novel splice variant ORAOV1-B is an lncRNA, which significantly potentiates OSCC invasion and metastasis by binding to Hsp90 and activating the NF-κB-TNFα loop. These findings demonstrate the versatile role of ORAOV1 family members and the significance of genes located within 11q13 in promoting OSCC. ORAOV1-B might serve as an attractive OSCC metastasis intervention target.
Due to the challenges of small detection targets, dense target distribution, and complex backgrounds in aerial images, existing object detection algorithms perform poorly in aerial image detection tasks. To address these issues, this paper proposes an improved algorithm called YOLOv5s-DSD based on YOLOv5s. Specifically, the SPDA-C3 structure is proposed and used to reduce information loss while focusing on useful features, effectively tackling the challenges of small detection targets and complex backgrounds. The novel decoupled head structure, Res-DHead, is introduced, along with an additional small object detection head, further improving the network’s performance in detecting small objects. The original NMS is replaced by Soft-NMS-CIOU to address the issue of neighboring box suppression caused by dense object distribution. Finally, extensive ablation experiments and comparative tests are conducted on the VisDrone2019 dataset, and the results demonstrate that YOLOv5s-DSD outperforms current state-of-the-art object detection models in aerial image detection tasks. The proposed improved algorithm achieves a significant improvement compared with the original algorithm, with an increase of 17.4% in mAP@0.5 and 16.4% in mAP@0.5:0.95, validating the superiority of the proposed improvements.
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