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.

ObjectiveTo explore the data standard system of electronic medical records in the field of rehabilitation, focusing on the terminology and coding standards, data structure, and key content categories of rehabilitation electronic medical records. MethodsBased on the Administrative Norms for the Application of Electronic Medical Records issued by the National Health Commission of China, the electronic medical record standard architecture issued by the International Organization for Standardization and Health Level Seven (HL7), the framework of the World Health Organization Family of International Classifications (WHO-FICs), Basic Architecture and Data Standards of Electronic Medical Records, Basic Data Set of Electronic Medical Records, and Specifications for Sharing Documents of Electronic Medical Records, the study constructed and organized the data structure, content, and data standards of rehabilitation electronic medical records. ResultsThe data structure of rehabilitation electronic medical records should strictly follow the structure of electronic medical records, including four levels (clinical document, document section, data set and data element) and four major content areas (basic information, diagnostic information, intervention information and cost information). Rehabilitation electronic medical records further integrated information related to rehabilitation needs and characteristics, emphasizing rehabilitation treatment, into clinical information. By fully applying the WHO-FICs reference classifications, rehabilitation electronic medical records could establish a standardized framework, diagnostic criteria, functional description tools, coding tools and terminology index tools for the coding, indexing, functional description, and analysis and interpretation of diseases and health problems. The study elaborated on the data structure and content categories of rehabilitation electronic medical records in four major categories, refined the granularity of reporting rehabilitation content in electronic medical records, and provided detailed data reporting guidance for rehabilitation electronic medical records. ConclusionThe standardization of rehabilitation electronic medical records is significant for improving the quality of rehabilitation medical services and promoting the rehabilitation process of patients. The development of rehabilitation electronic medical records must be based on the national and international standards. Under the general electronic medical records data structure and standards, a rehabilitation electronic medical records data system should be constructed which incorporates core data such as disease diagnosis, functional description and assessment, and rehabilitation interventions. The standardized rehabilitation electronic medical records scheme constructed in this study can support the improvement of standardization of rehabilitation electronic medical records data information.

ObjectiveTo analyze the data structure and standards of rehabilitation outpatient medical records, to provide data support for improving the quality of rehabilitation outpatient care and developing medical insurance payment policies. MethodsBased on the normative documents issued by the National Health Commission, Basic Standards for Medical Record Writing and Standards for Electronic Medical Record Sharing Documents, in accordance with the Quality Management Regulations for Outpatient (Emergency) Diagnosis and Treatment Information Pages (Trial), reference to the framework of the World Health Organization Family of International Classifications (WHO-FICs), the data framework and content of rehabilitation outpatient medical records were determined, and the data standards were discussed. ResultsThis study constructed a data framework for rehabilitation outpatient medical records, including four main components: patient basic information, visit process information, diagnosis and treatment information, and cost information. Three major reference classifications of WHO-FICs, International Classification of Diseases, International Classification of Functioning, Disability and Health, and International Classification of Health Interventions,were used to establish diagnostic standards and standardized terminology, as well as coding disease diagnosis, functional description, functional assessment, and rehabilitation interventions, to improve the quality of data reporting, and level of quality control in rehabilitation. ConclusionThe structuring and standardization of rehabilitation outpatient medical records are the foundation for sharing of rehabilitation data. The using of the three major classifications of WHO-FICs is valuable for the terminology and coding of disease diagnosis, functional description and assessment, and intervention in rehabilitation outpatient medical records, which is significant for sharing and interconnectivity of rehabilitation outpatient data, as well as for optimizing the quality and safety of rehabilitation medical services.

ObjectiveTo explore the standardization of inpatient rehabilitation medical record summary sheet, encompassing its structure, content and data standards, to enhance the standardization level of inpatient rehabilitation medical record summary sheet, improve data reporting quality, and provide accurate data support for medical insurance payment, hospital performance evaluation, and rehabilitation discipline evaluation. MethodsBased on the relevant specifications of the National Health Commission's Basic Norms for Medical Record Writing, Specifications for Sharing Documents of Electronic Medical Records, and Quality Management and Control Indicators for Inpatient Medical Record Summary Sheet (2016 Edition), this study analyzed the structure and content of the inpatient rehabilitation medical record summary sheet. The study systematically applied the three major reference classifications of the World Health Organization Family of International Classifications, International Classification of Diseases (ICD-10/ICD-11, ICD-9-CM-3), International Classification of Functioning, Disability and Health (ICF), and International Classification of Health Interventions (ICHI Beta-3), for disease diagnosis, functional description and assessment, and rehabilitation intervention, forming a standardized terminology system and coding methods. ResultsThe inpatient rehabilitation medical record summary sheet covered four major sections: inpatient information, hospitalization information, diagnosis and treatment information, and cost information. ICD-10/ICD-11 were the standards and coding tools for admission and discharge diagnoses in the inpatient rehabilitation medical record summary sheet. The three functional assessment tools recommended by ICD-11, the 36-item version of World Health Organization Disability Assessment Schedule 2.0, Brief Model Disability Survey and Generic Functioning domains, as well as ICF, were used for rehabilitation functioning assessment and the coding of outcomes. ICHI Beta-3 and ICD-9-CM-3 were used for coding surgical procedures and operations in the medical record summary sheet, and also for coding rehabilitation intervention items. ConclusionThe inpatient rehabilitation medical record summary sheet is a summary of the relevant content of the rehabilitation medical record and a tool for reporting inpatient rehabilitation data. It needs to be refined and optimized according to the characteristics of rehabilitation, with necessary data supplemented. The application of ICD-11/ICD-10, ICF and ICHI Beta-3/ICD-9-CM-3 classification standards would comprehensively promote the accuracy of inpatient diagnosis of diseases and functions. Based on ICD-11 and ICF, relevant functional assessment result data would be added, and ICHI Beta-3/ICD-9-CM-3 should be used to code rehabilitation interventions. Improving the quality of rehabilitation medical records and inpatient rehabilitation medical record summary sheet is an important part of rehabilitation quality control, and also lays an evidence-based data foundation for the analysis and application of inpatient rehabilitation medical record summary sheet.