Tumors are characterized by abnormal cell growth, which leads to uncontrolled cell proliferation, invasion of surrounding tissues, and potential metastasis to other parts of the body. Current treatments for tumors inc...
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Tumors are characterized by abnormal cell growth, which leads to uncontrolled cell proliferation, invasion of surrounding tissues, and potential metastasis to other parts of the body. Current treatments for tumors include surgery, chemotherapy, radiotherapy, targeted therapy, and biological therapy. However, many tumors are detected at advanced stages, limiting the effectiveness of these treatments. Therefore, there is a need to develop more sensitive and efficient techniques to improve patient survival rates. Surface-enhanced Raman scattering (SERS) technology is a highly sensitive molecular detection and characterization technique. It can be combined with histomorphology, immunolabeling assays, and molecular hybridization to achieve quantitative diagnosis at morphological, molecular, and genetic levels. SERS offers advantages such as objectivity, reproducibility, and comparability. However, there are challenges to address, including complex substrate or probe preparation, cumbersome data analysis, and signal reproducibility and reliability. To fully utilize the potential of SERS in the biomedical field and for clinical translation, it is important to optimize the technology and develop more sensitive, accurate, and reliable substrate or probe preparation and analysis methods. This review aims to comprehensively explain the mechanism and unique advantages of SERS technology and discuss its current status, challenges, and prospects in tumor-related research. The clinical applications of surface-enhanced Raman spectroscopy (SERS) for cancer diagnosis and treatment are illustrated. Key elements depicted include using SERS for noninvasive liquid biopsy, detecting tumor microenvironments, identifying precancerous lesions, guiding precision drug therapy, assisting in surgery and ablation, and monitoring prognosis. The image emphasizes SERS technology's potential and the need for optimization to enhance cancer patient ***
Wire arc additive manufacturing (WAAM) of Al-Zn-Mg-Cu alloys often leads to poor strength and ductility due to microstructure defects, significantly limiting its application. This study employed friction stir processi...
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Wire arc additive manufacturing (WAAM) of Al-Zn-Mg-Cu alloys often leads to poor strength and ductility due to microstructure defects, significantly limiting its application. This study employed friction stir processing (FSP) to reduce porosity and break up continuous coarse second phases along the grain boundaries. By adding high-entropy alloy (heA) particles with good wettability during FSP, Al-Zn-Mg-Cu alloy components with simultaneously improved strength and ductility were produced. The results indicated that the grain structure transformed from columnar to equiaxed and was refined to 2.3 mu m owing to the dynamic recrystallization of FSP and the particle-stimulated nucleation of heA particles, while the continuous second phase was fragmented into nanoscale precipitates uniformly distributed in the matrix, acting as dislocation movement barriers. The newly formed Ni3Al precipitates second phases ensure good ductility due to its low lattice mismatch with Al matrix. Additionally, theheA particles maintained strong interfacial bonding with Al matrix, with an interfacial layer thickness of similar to 400 nm. The FSP-heA treated components showed increased hardness (151.8 HV), ultimate tensile strength (374.3 +/- 20.4 MPa), and elongation (10.6 % +/- 1.6 %) compared to the WAAM as-deposited state. This study provides guidance for the improvement of microstructural defects and the simultaneous enhancement of the strength and ductility of high-strength Al alloy WAAM components.
Nutritional status is an essential factor in the occurrence of complications in patients with esophageal cancer. We sought to assess the relationship between malnutrition and complications using various nutritional as...
Nutritional status is an essential factor in the occurrence of complications in patients with esophageal cancer. We sought to assess the relationship between malnutrition and complications using various nutritional assessment indicators. We conducted a comprehensive literature search of medical databases for articles published up to July 2023. The primary outcome indicator is the occurrence of complications, for which we combined 95% confidence intervals (CIs) and odds ratios (ORs) for postoperative complications and analyzed them using a random effects model. The analysis was carried out using STATA15.0 software. A total of 33 study groups from 22 publications with 5,675 subjects were included. Pooled results show that nutritional indicators are strongly correlated with the occurrence of postoperative complications (OR = 1.45, 95% CI: 1.30-1.62). In the subgroup analyses, comprehensive indicators and the skeletal muscle index were significantly associated with complications, whereas laboratory indicators were not associated with complications (comprehensive indicators OR = 2.68, 95% CI: 1.80-4.00;skeletal muscle index OR = 2.93, 95% CI: 1.44-5.99;laboratory indicators OR = 1.05, 95% CI: 0.96-1.16). Patients with normal body mass index and hospitalized patients were more likely to develop complications. Malnutrition is strongly associated with the development of complications. Nutritional indicators and patient characteristics influenced this relationship.
Direct discharge of aquaculture wastewater in coastal areas could increase concentrations of antibiotics in coastal mangrove forests. This study focused on the Gaoqiao Mangrove Nature Reserve in Zhanjiang City, Guangd...
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Disclosed herein are compounds of formula (I) which are inhibitors of an IDO enzyme: (I). Also disclosed herein are uses of the compounds in the potential treatment or prevention of an IDO-associated disease or disord...
标准号:
EP3810117(A1)
Disclosed herein are compounds of formula (I) which are inhibitors of an IDO enzyme: (I). Also disclosed herein are uses of the compounds in the potential treatment or prevention of an IDO-associated disease or disorder. Also disclosed herein are compositions comprising these compounds. Further disclosed herein are uses of the compositions in the potential treatment or prevention of an DO-associated disease or disorder.
The community structure of Rotifera was investigated in four areas, namely the water inlet area (Ia, 1#), Sonneratia apetala area ((Sa, 2# and 3#), Phragmites australis area (Pa, 4# and 5#), and water outlet area (Oa,...
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Depth completion aims at reconstructing a dense depth from sparse depth input, frequently using color images as guidance. The sparse depth map lacks sufficient contexts for reconstructing focal contexts such as the sh...
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Depth completion aims at reconstructing a dense depth from sparse depth input, frequently using color images as guidance. The sparse depth map lacks sufficient contexts for reconstructing focal contexts such as the shape of objects. The RGB images contain redundant contexts including details useless for reconstruction, which reduces the efficiency of focal context extraction. The unaligned contextual information from these two modalities poses a challenge to focal context extraction and further fusion, as well as the accuracy of depth completion. To optimize the utilization of multimodal contextual information, we explore a novel framework: Context Aligned Fusion Network (CAFNet). CAFNet comprises two stages: the context-aligned stage and the full-scale stage. In the context-aligned stage, CAFNet downsamples input RGB-D pairs to the scale, at which multimodal contextual information is adequately aligned for feature extraction in two encoders and fusion in CF modules. In the full-scale stage, feature maps with fused multimodal context from the previous stage are upsampled to the original scale and subsequentially fused with full-scale depth features by the GF module utilizing a dynamic masked fusion strategy. Ultimately, accurate dense depth maps are reconstructed, leveraging the GF module's resultant features. Experiments conducted on indoor and outdoor benchmark datasets show that the CAFNet produces results comparable to state-of-the-art methods while effectively reducing computational costs.
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