Radiogenomics, an extension of radiomics, explores the relationship between imaging features and underlying gene expression patterns. This field is instrumental in providing reliable imaging surrogates, thus potentially representing an alternative to genetic testing. The rapidly growing area of radiogenomics that utilizes ultrasound (US) imaging seeks to elucidate the connections between US image characteristics and genomic data. In this review, the authors outline the radiogenomics workflow and summarize the applications of US-based radiogenomics. These include the prediction of gene variations, molecular subtypes, and other biological characteristics, as well as the exploration of the relationships between US phenotypes and cancer gene profiles. Although the field faces various challenges, US-based radiogenomics offers promising prospects and avenues for future research.

ObjectiveTo clearly identify the difference between rescue injuries and agonal injuries and to avoid duplicate identifications and misidentifications. MethodsBased on the forensic pathological data of 5 923 cases of death cause identification from 2013 to 2022 in Sun Yat-sen University Forensic Identification Center and Guangzhou Tianhe District Branch of Guangzhou Public Security Bureau， this study retrospectively studied the characteristics of rescue injuries and agonal injuries seen in cause of death identification and their influence on cause of death identification. ResultsAmong all the 5 923 cases， 640 cases were found to have rescue injuries or agonal injuries， and 624 cases received treatment， of which 609 cases were found to have rescue injuries （97.60%）， 44 cases were found to have agonal injuries， and 13 cases were found to have both types of injuries. Among the 640 cases， 441 were male and 199 were female. The age of death was discontinuously distributed from 0 to 95 years old. The leading cause of death was disease， followed by mechanical injury and asphyxia. The main manifestations of rescue injuries were rib and sternum fractures， soft tissue injuries in the prechest area or face， and pericardial rupture. The most common injuries in agonal stage were falling after unconsciousness， inhalation of foreign body in respiratory tract or multiple violent injuries. Among the 640 cases， 19 cases were repeatedly identified， including 15 cases of rescue injuries， 6 cases of agonal injuries， and 2 cases of both types of injuries. Compared with the cases where neither type of injuries was detected， the repeated identification rate of treatment injuries and agonal injuries was significantly increased （χ²=4.04， P=0.044； χ²=43.49， P<0.001）. Among the 640 cases， 11 cases （1.72%） were misidentified as the initial injuries in the first identification， and 13 cases had combined rescue injuries or agonal injuries that were involved in death. ConclusionsBy elucidating the epidemiological characteristics of the two types of injuries， this study proved that the two types of injuries were associated with higher rates of repeated identification and misidentification， which provided a reference for reducing repeated identification and misidentification and improving the accuracy of cause of death identification.

Radiogenomics, an extension of radiomics, explores the relationship between imaging features and underlying gene expression patterns. This field is instrumental in providing reliable imaging surrogates, thus potentially representing an alternative to genetic testing. The rapidly growing area of radiogenomics that utilizes ultrasound (US) imaging seeks to elucidate the connections between US image characteristics and genomic data. In this review, the authors outline the radiogenomics workflow and summarize the applications of US-based radiogenomics. These include the prediction of gene variations, molecular subtypes, and other biological characteristics, as well as the exploration of the relationships between US phenotypes and cancer gene profiles. Although the field faces various challenges, US-based radiogenomics offers promising prospects and avenues for future research.

Radiogenomics, an extension of radiomics, explores the relationship between imaging features and underlying gene expression patterns. This field is instrumental in providing reliable imaging surrogates, thus potentially representing an alternative to genetic testing. The rapidly growing area of radiogenomics that utilizes ultrasound (US) imaging seeks to elucidate the connections between US image characteristics and genomic data. In this review, the authors outline the radiogenomics workflow and summarize the applications of US-based radiogenomics. These include the prediction of gene variations, molecular subtypes, and other biological characteristics, as well as the exploration of the relationships between US phenotypes and cancer gene profiles. Although the field faces various challenges, US-based radiogenomics offers promising prospects and avenues for future research.

Radiogenomics, an extension of radiomics, explores the relationship between imaging features and underlying gene expression patterns. This field is instrumental in providing reliable imaging surrogates, thus potentially representing an alternative to genetic testing. The rapidly growing area of radiogenomics that utilizes ultrasound (US) imaging seeks to elucidate the connections between US image characteristics and genomic data. In this review, the authors outline the radiogenomics workflow and summarize the applications of US-based radiogenomics. These include the prediction of gene variations, molecular subtypes, and other biological characteristics, as well as the exploration of the relationships between US phenotypes and cancer gene profiles. Although the field faces various challenges, US-based radiogenomics offers promising prospects and avenues for future research.

BACKGROUND:Multiple observational studies have suggested a potential association between telomere length and musculoskeletal diseases.However,the underlying mechanisms remain unclear. OBJECTIVE:To investigate the genetic causal relationship between telomere length and musculoskeletal diseases using two-sample Mendelian randomization analysis. METHODS:Genome-wide association study summary data of telomere length were obtained from the UK Biobank.Genome-wide association study summary data of 10 common musculoskeletal diseases(osteonecrosis,osteomyelitis,osteoporosis,rheumatoid arthritis,low back pain,spinal stenosis,gout,scapulohumeral periarthritis,ankylosing spondylitis and deep venous thrombosis of lower limbs)were obtained from the FinnGen consortium.Inverse variance weighting,Mendelian randomization-Egger and weighted median methods were used to evaluate the causal relationship between telomere length and 10 musculoskeletal diseases.Inverse variance weighting was the primary Mendelian randomization analysis method,and sensitivity analysis was performed to explore the robustness of the results. RESULTS AND CONCLUSION:(1)Inverse variance-weighted results indicated a negative causal relationship between genetically predicted telomere length and rheumatoid arthritis(odds ratio=0.78,95%confidence interval:0.64-0.95,P=0.015)and osteonecrosis(odds ratio=0.56,95%confidence interval:0.36-0.90,P=0.016).No causal relationship was found between telomere length and the other eight musculoskeletal diseases(all P>0.05).(2)Sensitivity analysis affirmed the robustness of these causal relationships,and Mendelian randomization-Egger intercept analysis found no evidence of potential horizontal pleiotropy(all P>0.05).(3)This Mendelian randomized study supports that telomere length has protective effects against rheumatoid arthritis and osteonecrosis.However,more basic and clinical research will be needed to support our findings in the future.

BACKGROUND:In recent years,artificial intelligence has gradually emerged and has been applied in various fields such as spinal cord nerve injury and repair,which has a positive impact on clinical treatment. OBJECTIVE:To study the application progress of artificial intelligence in the diagnosis,treatment,and rehabilitation of spinal cord nerve injury and repair,clarify the research hotspots and shortcomings in this field,and provide suggestions for future research work. METHODS:Relevant literature on artificial intelligence in the field of spinal cord nerve injury and repair was retrieved on the Web of Science core collection database until 2023.CiteSpace 6.1.R6 and VOSviewer 1.6.19 software was used to perform general literature analysis,co-citation of literature,co-citation of journals,double image overlay of journals,keyword clustering,and other visual analysis on the literature data. RESULTS AND CONCLUSION:(1)A total of 1 713 articles were selected,and the annual publication volume in this field showed a fluctuating upward trend,with the United States taking the lead,and Kadone and Hideki being the authors with the highest publication volume.ARCH PHYS MED REHAB was the journal with the highest number of citations.(2)Keyword co-occurrence and cluster analysis showed that after removing keywords similar to the search terms,the main keywords were divided into three main clusters:Exoskeleton and exercise rehabilitation(the largest core hotspot);machine learning and neural plasticity;robotics and rehabilitation training.(3)Keyword burst analysis showed that deep learning and artificial intelligence had become burst terms in the past five years.(4)The results of in-depth analysis of co cited and highly cited literature showed that the hotspots of artificial intelligence in the field of spinal cord nerve injury and repair were mainly focused on powered exoskeletons,gaits,electrical nerve stimulation,intracortical brain-computer interface(IBCI),robots,and polymer biomaterials,and neural stem cell.(5)The research on artificial intelligence in the field of spinal cord nerve injury and repair has shown an upward trend in recent years.The focus of this field had gradually shifted from single treatment methods such as exoskeletons and electrical stimulation to intelligent,precise,and personalized directions.(6)There were some limitations in this field,such as the consequences of missing or imbalanced data,low data accuracy and reproducibility,and ethical issues(such as privacy,research transparency,and clinical reliability).Future research should address the issue of data collection,requiring large sample,high-quality clinical datasets to establish effective artificial intelligence models.At the same time,the research on genomics and other mechanisms in this field is very weak.In the future,various machine learning technologies such as brain chips can be used,and gene editing therapy,single-cell spatial transcriptome and other methods can be used to study the basic mechanisms of regeneration-related gene upregulation and axon growth structural protein production.