Search

Colorectal cancer (CRC) is the third most common cancer worldwide. In the United States alone, CRC was responsible for approximately 52,550 deaths in 2023, with an estimated 153,020 new cases. CRC presents with synchronous peritoneal spread in 5–10% of patients, and up to 20–50% of patients with recurrent disease will develop metachronous colorectal cancer peritoneal metastatic (CRC-PM) disease. Eradication of the tumor, tumor margins and microscopic residual disease is paramount, as microscopic residual disease is associated with local recurrences, with 5-year survival rates of less than 35%. The success of resection and reduction of residual disease depends on the accuracy with which cancer cells and normal tissue can be intra-operatively distinguished. Fluorescence Molecular Imaging (IFMI) and tumor-targeted contrast agents represent a promising approach for intraoperative detection and surgical intervention. Proper target selection, the development of scalable imaging agents and enhanced real-time tumor and tumor microenvironment imaging are critical to enabling enhanced surgical resection. LGR5 (leucine-rich repeat-containing G-protein-coupled receptor 5), a colonic crypt stem cell marker and the receptor for the R-spondins (RSPO) in the Wnt signaling pathway, is also expressed on colorectal cancer stem cells (CSC) and on CRC tumors and metastases, suggesting it could be a useful target for imaging of CRC. However, there are numerous diverging reports on the role of LGR5 in CRC therapy and outcomes. Herein, we report on the synthesis and validation of a 37 amino acid RSPO1-mimetic peptide, termed RC18, that was specifically designed to access the R-spondin binding site of LGR5 to potentially be used for interoperative imaging of CRC-PM. The receptor-binding capabilities of the RC18 indicate that direct interactions with LGR5 neither significantly increased LGR5 signaling nor blocked RSPO1 binding and signal transduction, suggesting that the RSPO1-mimetic is functionally inert, making it an attractive contrast agent for intraoperative CRC-PM imaging. [ABSTRACT FROM AUTHOR]

AbstractThis research investigates the strategy for developing large-scale solid aluminum structures with multi-bead multi-layer (MBML) overlapping using wire-arc additive manufacturing (WAAM). The study addresses challenges, i.e. material accumulation, surface irregularities, and inter-bead and inter-layer gaps, by examining deposits’ geometrical characteristics, process parameters, and deposition methods. Initial test coupon solid blocks are printed and analyzed using optical microscopy, and subsequent statistical optimization leads to the successful printing of a defect-free 140 mm × 150 mm × 28 mm solid block. The flat surface achieved in the MBML structure is attributed to maintaining an optimal overlap coefficient and a parabolic profile with the maximum width at the base. Microstructural analysis reveals the absence of fusion gaps, with an average porosity of 11.7 ± 2.45%, due to hydrogen evolution and reheating effects, with few larger pores near fusion lines. The microstructural and mechanical analyses confirm the homogeneity of properties along the overlapping direction (OD), with microhardness averaging 85.80 ± 6.63 HV0.1 and tensile strength of 161.55 ± 1.19 MPa with 2.18 ± 0.08% elongation. Microhardness fluctuations along the OD are attributed to fusion boundaries, partially melted and heat-affected zones. The research revealed that careful consideration of bead geometry and process parameters is crucial for achieving defect-free aluminum solid structures in WAAM through MBML overlapping. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks have been used for sensing and gathering data about an environment from a remote location for many years in a variety of engineering applications. In WSN, nodes must overcome energy consumption to function efficiently. To resolve these issues and extend the usefulness of the network, clustering and routing algorithms are promising. One of the main issues with WSNs is that they lack restricted energy sources. To overcome this issue, a Hybrid Optimized Energy-Efficient Adaptive Clustered Routing approach (HOEEACR) has been proposed for WSNs. This paper presents the Genetic Bee Colony (GBC) Algorithm for Cluster Head Selection by considering distances to neighbors, residual energy, node degrees, and node centralities. Furthermore, an optimal routing path for the cluster heads is found by utilizing the Aquila with African Vulture Optimization (AAVO) algorithm. The AAVO optimizes network performance using residual energy, node degree, and distance. The proposed HOEEACR method has been extensively tested to ensure network lifetime and energy efficiency. According to experimental results, the proposed HOEEACR method consistently outperforms existing techniques on a variety of performance metrics. [ABSTRACT FROM AUTHOR]