In nature,cavitation bubbles typically appear in clusters,engaging in interactions that create a variety of dynamicmotion patterns.To better understand the behavior ofmultiple bubble collapses and the mechanisms of in...In nature,cavitation bubbles typically appear in clusters,engaging in interactions that create a variety of dynamicmotion patterns.To better understand the behavior ofmultiple bubble collapses and the mechanisms of interbubble interaction,this study employs molecular dynamics simulation combined with a coarse-grained force field.By focusing on collapsemorphology,local density,and pressure,it elucidates how the number and arrangement of bubbles influence the collapse process.The mechanisms behind inter-bubble interactions are also considered.The findings indicate that the collapse speed of unbounded bubbles located in lateral regions is greater than that of the bubbles in the center.Moreover,it is shown that asymmetrical bubble distributions lead to a shorter collapse time overall.展开更多
OBJECTIVE At present there are no serological indicators with high sensitivity and specificity to diagnose colorectal cancer (CRC). This study was designed to establish a serum protein fingerprinting technique coupled...OBJECTIVE At present there are no serological indicators with high sensitivity and specificity to diagnose colorectal cancer (CRC). This study was designed to establish a serum protein fingerprinting technique coupled with a pattern-matching algorithm to distinguish patients of colorectal cancer from that of benign colorectal diseases (BCD) and healthy people(HP).METHODS Proteomic patterns were procured by surface enhanced laser desorption/ionization-time of flight-mass spectrometry (SELDI-TOF-MS).Sera randomly selected from 73 CRC patients, 31 HP and 16 BCD patients were analyzed to develop a classification tree, which is a standard configuration to distinguish the sera of CRC patients'and noncancer cohorts.The classifiction tree proved to be valid by using 120 double-blind sera samples in the test group, including 73 CRC, 31 HP and 16 BCD.RESULTS At the protein masses of 4,467Da; 8,131Da; 8,939Da; 9,192Da;9,134Da; 8,221Da; 5,928Da; 8,324Da; and 11,732Da, protein levels from the CRC, HP and BCD patients in the preliminary group were significantly different based on software analysis. Correct ratio, sensitivity and specificity of the method were up to 98.33%, 97.26% (71/73) and 100% (47/47),respectively. Results of double-blind detection for the test group indicated that the correct ratio, sensitivity and specificity of the method were 96.77%(116/20), 95.89% (70/73) and 97.87% (46/47), respectively.CONCLUSION Via comparative proteomics analysis of the serum from CRC,HP and BCD patients using the SELDI-TOF-MS method, CRC can be diagnosed rapidly and correctly with high sensitivity and specificity.展开更多
基金funded by the Natural Science Foundation of China[U20A20292]Shandong Province Science andTechnology SMES InnovationAbility Improvement Project[2023TSGC0005]China Postdoctoral Science Foundation[2024M752697].
文摘In nature,cavitation bubbles typically appear in clusters,engaging in interactions that create a variety of dynamicmotion patterns.To better understand the behavior ofmultiple bubble collapses and the mechanisms of interbubble interaction,this study employs molecular dynamics simulation combined with a coarse-grained force field.By focusing on collapsemorphology,local density,and pressure,it elucidates how the number and arrangement of bubbles influence the collapse process.The mechanisms behind inter-bubble interactions are also considered.The findings indicate that the collapse speed of unbounded bubbles located in lateral regions is greater than that of the bubbles in the center.Moreover,it is shown that asymmetrical bubble distributions lead to a shorter collapse time overall.
文摘OBJECTIVE At present there are no serological indicators with high sensitivity and specificity to diagnose colorectal cancer (CRC). This study was designed to establish a serum protein fingerprinting technique coupled with a pattern-matching algorithm to distinguish patients of colorectal cancer from that of benign colorectal diseases (BCD) and healthy people(HP).METHODS Proteomic patterns were procured by surface enhanced laser desorption/ionization-time of flight-mass spectrometry (SELDI-TOF-MS).Sera randomly selected from 73 CRC patients, 31 HP and 16 BCD patients were analyzed to develop a classification tree, which is a standard configuration to distinguish the sera of CRC patients'and noncancer cohorts.The classifiction tree proved to be valid by using 120 double-blind sera samples in the test group, including 73 CRC, 31 HP and 16 BCD.RESULTS At the protein masses of 4,467Da; 8,131Da; 8,939Da; 9,192Da;9,134Da; 8,221Da; 5,928Da; 8,324Da; and 11,732Da, protein levels from the CRC, HP and BCD patients in the preliminary group were significantly different based on software analysis. Correct ratio, sensitivity and specificity of the method were up to 98.33%, 97.26% (71/73) and 100% (47/47),respectively. Results of double-blind detection for the test group indicated that the correct ratio, sensitivity and specificity of the method were 96.77%(116/20), 95.89% (70/73) and 97.87% (46/47), respectively.CONCLUSION Via comparative proteomics analysis of the serum from CRC,HP and BCD patients using the SELDI-TOF-MS method, CRC can be diagnosed rapidly and correctly with high sensitivity and specificity.
