Loading rate effect on tradeoff of fractures from pelvis to lumbar spine under axial impact loading

Objectives: The transmission of impact loading from the seat-to-pelvis-to-lumbar spine in a seated occupant in automotive and military events is a mechanism for fractures to these body regions. While postmortem human subject (PMHS) studies have replicated fractures to the pelvis or lumbar spine using isolated/component models, the role of the time factor that manifests as a loading rate issue on injuries has not been fully investigated in literature. The objective of this study was to explore the hypothesis that short duration pulses fracture the pelvis while longer pulses fracture the spine, and intermediate pulses involve both components. Methods: Unembalmed PMHS thoracolumbar spine-pelvis specimens were fixed at the superior end, and a six-axis load cell was attached. The specimens were mounted on a vertical accelerator, and noninjury and injury tests were conducted by applying short, medium, or long pulses with 5, 15, or 35 ms durations, respectively. Peak axial, shear and resultant forces were obtained. Injuries were documented using posttest x-ray and computed tomography images and scaled using the AIS (2015). Results: The mean age, stature, weight, body mass index, and BMD of twelve specimens were 64.8 ± 11.4 years, 1.8 ± 0.01 m, 83 ± 13 kg, 26.7 ± 5.0 kg/m2, and 114.5 ± 21.3 mg/cc, respectively. For the short, long, and medium duration pulses, the mean resultant forces were 5.6 ± 0.9 kN, 5.9 ± 0.94 kN, and 5.4 ± 1.8 kN, and time durations were 4.8 ± 0.5 ms, 16.3 ± 7.3 ms, and 34.5 ± 7.5 ms, respectively. For the short pulse, pelvis injuries were more severe in 3 out 4 specimens, for the medium pulse, they were distributed between the pelvis and spine, and for the long pulse, spine injuries were more severe in 3 out of 4 specimens. Conclusions: While acknowledging the limitations of the sample size, the results of this study support the hypothesis of the time variable in the tradeoff between pelvis and spine injuries with pulse duration. The tradeoff pattern is attributed to mass recruitment: short pulse biases injuries to pelvis while limiting spinal injuries, and the opposite is true for the longer pulse, thus supporting the hypothesis. It is important to account for the time variable in injury analysis.</p

Table_1_Artificial intelligence in liver cancer research: a scientometrics analysis of trends and topics.docx

Background and aimsWith the rapid growth of artificial intelligence (AI) applications in various fields, understanding its impact on liver cancer research is paramount. This scientometrics project aims to investigate publication trends and topics in AI-related publications in liver cancer.Materials and MethodsWe employed a search strategy to identify AI-related publications in liver cancer using Scopus database. We analyzed the number of publications, author affiliations, and journals that publish AI-related publications in liver cancer. Finally, the publications were grouped based on intended application.ResultsWe identified 3950 eligible publications (2695 articles, 366 reviews, and 889 other document types) from 1968 to August 3, 2023. There was a 12.7-fold increase in AI-related publications from 2013 to 2022. By comparison, the number of total publications on liver cancer increased by 1.7-fold. Our analysis revealed a significant shift in trends of AI-related publications on liver cancer in 2019. We also found a statistically significant consistent increase in numbers of AI-related publications over time (tau = 0.756, p ConclusionOur study reveals increasing interest in AI for liver cancer research, evidenced by a 12.7-fold growth in related publications over the past decade. A common application of AI is in medical imaging analysis for various purposes. China, the US, and Germany are leading contributors.</p

Image_1_Artificial intelligence in liver cancer research: a scientometrics analysis of trends and topics.pdf

Background and aimsWith the rapid growth of artificial intelligence (AI) applications in various fields, understanding its impact on liver cancer research is paramount. This scientometrics project aims to investigate publication trends and topics in AI-related publications in liver cancer.Materials and MethodsWe employed a search strategy to identify AI-related publications in liver cancer using Scopus database. We analyzed the number of publications, author affiliations, and journals that publish AI-related publications in liver cancer. Finally, the publications were grouped based on intended application.ResultsWe identified 3950 eligible publications (2695 articles, 366 reviews, and 889 other document types) from 1968 to August 3, 2023. There was a 12.7-fold increase in AI-related publications from 2013 to 2022. By comparison, the number of total publications on liver cancer increased by 1.7-fold. Our analysis revealed a significant shift in trends of AI-related publications on liver cancer in 2019. We also found a statistically significant consistent increase in numbers of AI-related publications over time (tau = 0.756, p ConclusionOur study reveals increasing interest in AI for liver cancer research, evidenced by a 12.7-fold growth in related publications over the past decade. A common application of AI is in medical imaging analysis for various purposes. China, the US, and Germany are leading contributors.</p