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.

Nonunion of osteoporotic vertebral fractures (OVF), predominantly affecting the elderly, can lead to intractable pain, vertebral collapse, progressive kyphotic deformity, and neurological impairment, significantly compromising patients′ quality of life. There exists considerable debate on diagnosis and management of OVF, encompassing key issues such as clinical diagnosis and staging criteria for nonunion, surgical indications and procedure selection, and postoperative rehabilitation planning. Currently, there lacks standardized clinical guideline and expert consensus on the diagnosis and management of OVF nonunion in China. To address this gap, Minimally Invasive Surgery Group of Chinese Orthopedic Association, Osteoporosis Committee of Chinese Association of Orthopedic Surgeons, Prevention and Rehabilitation Committee for Osteoporosis of Chinese Association of Rehabilitation Medicine and Minimally Invasive Orthopedic Surgery Branch of China Association for Geriatric Care jointly organized domestic experts in spinal surgery, endocrinology, and rehabilitation to formulate the Clinical guideline for the diagnosis and treatment for nonunion of osteoporotic vertebral fractures ( version 2025), based on existing literature and clinical experience and adhering to principles of scientific rigor and practicality. The guideline provided 13 evidence-based recommendations encompassing diagnosis and treatment of OVF nonunion, aiming to standardize its clinical management.

Acute symptomatic osteoporotic thoracolumbar compression fracture (ASOTLF) can lead to chronic low back pain, kyphosis deformity, pulmonary dysfunction, loss of mobility, and even life-threatening complications. Vertebral augmentation is currently the mainstream treatment method for this condition. In 2019, the Editorial Board of Chinese Journal of Trauma and the Spinal Trauma Group of Orthopedic Surgeons Branch of Chinese Medical Doctor Association collaboratively led the development of Clinical guideline for vertebral augmentation for acute symptomatic osteoporotic thoracolumbar compression fractures. Six years later, with advances in clinical diagnosis and treatment techniques as well as accumulating evidence in related fields, the 2019 guideline requires updating. To this end, the Spinal Trauma Group of Orthopedic Surgeons Branch of Chinese Medical Doctor Association, the Spinal Health Professional Committee of China Human Health Science and Technology Promotion Association, and the Minimally Invasive Orthopedics Professional Committee of Shaanxi Medical Doctor Association have organized experts in the field to develop the Clinical guideline for vertebral augmentation of acute symptomatic osteoporotic thoracolumbar compression fractures ( version 2025) , based on the latest evidence-based medical researches. This guideline incorporates 3 recommendations retained from the 2019 version with updated strength of evidence, along with 12 new recommendations. It provides recommendations from six aspects of diagnosis, pain management, treatment option selection, prevention of postoperative complications, anti-osteoporosis therapy, and postoperative rehabilitation, aiming to provide a reference for standard treatment of vertebral augmentation for ASOTLF in hospitals at all levels.

Global population aging has exacerbated the clinical management challenges of osteoporotic fractures, especially during the high-risk period of re-fracture within 2 years after fracture. Although significant progress has been made in the comprehensive management of osteoporotic fractures in China, which has improved patient prognosis. There are still many challenges in clinical practice, including the mismatch between traditional implants and osteoporotic bone structure, the clinical management bottlenecks from treatment to rehabilitation of osteoporotic fractures (such as low implementation rates of drug treatment, poor drug compliance, and inadequate rehabilitation awareness), as well as the underdevelopment and regional disparities of fracture liaison services in China. To address these challenges, we propose three strategic improvements: transitioning from a purely mechanical fixation to a combined "mechanical-biological" synergistic treatment model, optimizing a full-cycle management pathway for osteoporotic fractures based on the enhanced recovery after surgery, and developing a systematic and intelligent approach for re-fracture prevention (such as establishing exclusive management teams, constructing specialized databases, and developing intelligent data platforms). These strategies are expected to enable efficient screening, precise monitoring, and full lifecycle management of patients with osteoporotic fractures, thereby enhancing the effectiveness and precision of clinical management and offering new insights and practical pathways for improving the treatment and care of osteoporotic fractures in China.

Objective To conduct preliminary evaluation of the reliability and safety of pancreatic duct-to-jejunum stent-bridging internal drainage as a supplementary approach to pancreaticojejunostomy in central pancreatectomy.Methods The clinical data of 28 patients who underwent robotic central pancreatectomy performed by our team between January 2021 and November 2024 were retrospectively collected,and and follow-up of postoperative endocrine and exocrine functions was performed.Based on the methods of digestive tract reconstruction adopted,the patients were divided into a conventional pancreaticojejunostomy group and a pancreatic duct-to-jejunum stent-bridging internal drainage group(PancreaticoJejunal-Stent bridge group).The operative time,digestive tract reconstruction time,and short-term complications were compared between the two groups.Results Among patients undergoing robotic central pancreatectomy,the digestive tract reconstruction time was shorter(t=5.168,P<0.001)in the PancreaticoJejunal-Stent bridge group([31.1±6.3]min)than that in the conventional pancreaticojejunostomy group([49.7±8.9]min)(t=5.168,P<0.001).The total operative time was(172.7±64.6)min in the PancreaticoJejunal-Stent bridge group and(200.1±52.7)min in the conventional pancreaticojejunostomy group,showing no statistically significant difference(t=1.215,P=0.235).In the PancreaticoJejunal-Stent bridge group,one patient developed a postoperative biochemical fistula,and 14 patients developed grade B pancreatic fistulas.Among the 14 patients with grade B pancreatic fistulas,1 case was complicated by fistula-related intra-abdominal infection,and 13 cases had drainage tube retention time of more than 21 days.In the conventional pancreaticojejunostomy group,2 patients developed postoperative biochemical fistulas,and 11 patients developed grade B pancreatic fistulas.Among the 11 patients with grade B pancreatic fistulas,1 case was complicated by fistula-related intra-abdominal infection,and 1 case was complicated by fistula-related intra-abdominal bleeding and infection.No postoperative gastroparesis,pancreatitis,or grade C pancreatic fistulas occurred in either group.There were no statistically significant differences between the two groups in overall postoperative complication rate(P=0.522),postoperative pancreatic fistula rate(P=0.583),intra-abdominal infection rate(P=0.583),or bleeding rate(P=0.464).Conclusion Pancreatic duct-to-jejunum stent-bridging internal drainage optimizes the anastomosis between the distal end of the pancreas and the jejunum during central pancreatectomy,shortens digestive tract reconstruction time,and reduces surgical complexity without increasing the risk of short-term severe postoperative complications.This approach is safe and feasible.

Objective To construct a database for molecular mass spectra of toxins and drugs in order to facilitate the management and retrieval of mass spectra for nerve agents,metabolites and other small molecules.Methods Requirement analysis and functional design were performed using software engineering methods.The Spec2Vec algorithm was used for vector representation of mass spectra,while SMILES molecular structures were vectorized using the extended connectivity fingerprint(ECFP).A data storage model integrating structured information and vector representations was established using the Milvus database.Similarity search of mass spectra and molecular structures was conducted via vector similarity comparison and the FlashEntropySearch algorithm.Results The constructed database of mass spectra encompassed over 400,000 entries from such sources as OCAD,NIST,MASSBANK,metabolic products,and natural products of TCM,which was capable of searching for similarities in mass spectra and molecular structures.On a standard server,similarity search of mass spectra took no more than 5 seconds,while that of molecular structures took no more than 1 second.Conclusion The system enables efficient management of complex mass spectra and provides rapid retrieval and comparison of mass spectra-related information through advanced vector indexing technology,offering robust data support and research tools for toxicology and pharmacology.

With the growing complexity and higher risk of modern warfare,artificial intelligence(AI)technologies have been increasingly used in the field of military medicine.This study investigates the innovative applications of AI in military medicine,focusing on practices in such countries as the United States,Israel and the United Kingdom.The research reveals that AI technologies have been extensively applied in battlefield medical training,casualty status monitoring,medical decision support and unmanned rescue operations.Through virtual reality simulation,intelligent decision support and vital sign monitoring technologies,AI has significantly improved the efficiency and precision of battlefield medical care.Despite challenges related to technological implementation,environmental adaptability and ethical controversies,future battlefield medical care will increasingly rely on unmanned systems and intelligent equipment to deliver efficient medical treatment through human-machine collaboration.

Objective:To construct a long non-coding RNA(lncRNA)model based on ferroptosis and predict the prognosis of non-small cell lung cancer(NSCLC)patients after radiotherapy,to develop a comprehensive framework that integrates genomic data with clinical outcomes,and to identify lncRNA associated with ferroptosis and evaluate their predictive power for patient survival and progression-free survival following radiotherapy.Methods:This study commenced by acquiring standardized transcrip-tome data from primary tumors and normal tissues,along with corresponding clinical information,from the cancer genome atlas(TCGA)database.This dataset provided a robust foundation for identifying differentially expressed genes(DEGs)related to ferroptosis.These analyses helped pinpoint specific pathways and biological processes involved in ferroptosis,such as glutathione metabolism,lipid signa-ling,oxidative stress,and reactive oxygen species(ROS)metabolism.Subsequently,univariate and multivariate Cox regression analyses were conducted to construct a predictive model based on lncRNA as-sociated with ferroptosis.The goal was to differentiate between the high-risk and low-risk groups of NSCLC patients who had undergone radiotherapy.By incorporating these lncRNA into the model,we aimed to provide a more accurate prediction of patient outcomes.The performance of the model was vali-dated by comparing the survival rates and progression-free survival between the high-risk and low-risk groups.Additionally,differences in gene expression patterns and pathway activities between these two groups were examined to further validate the model's effectiveness.Results:Our analysis revealed that the differentially expressed genes related to ferroptosis were significantly enriched in several key path-ways,including ferroptosis itself,glutathione metabolism,lipid signaling,and processes involving oxida-tive stress and ROS metabolism.Based on these findings,we constructed a prognostic model using 14 lncRNA that showed strong associations with ferroptosis.Further data analysis demonstrated that these lncRNA could independently predict the prognosis of NSCLC patients after radiotherapy.Specifically,age,stage,and gender were used as clinical pathological variables,and the results indicated that the high-risk group of NSCLC patients had a poorer prognosis following radiotherapy.This finding underscores the po-tential of the model to serve as a valuable tool for predicting prognosis for NSCLC patients undergoing ra-diotherapy.Conclusion:The risk model developed in this study can independently predict the prognosis of NSCLC patients after radiotherapy.This model provides a solid basis for understanding the role of fer-roptosis-related lncRNA in the prognosis of NSCLC patients following radiotherapy.Furthermore,it offers clinical guidance for combining radiotherapy with ferroptosis-targeted treatments,potentially improving therapeutic outcomes for NSCLC patients.The integration of genomic and clinical data in this study high-lights the importance of personalized medicine approaches in oncology,paving the way for more precise and effective treatment strategies.

Hepatocellular carcinoma(HCC)is categorized into two major classes based on the heterogeneity of histological phenotypes:proliferative HCC and non-proliferative HCC.These classes exhibit distinct clinical,molecular and imaging characteristics.Within these two major categories,there exist multiple pathological subtypes,each demonstrating unique molecular and histological features that manifest differently in imaging findings.Additionally,the prognosis of these HCC pathological subtypes diverges from that of not-otherwise-specified HCC,and current clinical guidelines for their treatment remain unclear.This article aims to summarize the imaging manifestations and prognosis of the histopathological subtypes of HCC,so as to enhance identification and judgement of HCC subtypes,and to provide a theoretical basis for selecting appropriate therapeutic strategies.

In clinical practice,the diagnosis of hepatocellular carcinoma primarily relies on imaging findings.For cases with atypical imaging features or insufficient diagnostic specificity,pathological analysis remains essential.With the advancement of precision medicine,research focus has expanded from pure diagnostic evaluation to therapeutic efficacy prediction.Imaging-based prognostic biomarkers have emerged as a key research frontier in hepatocellular carcinoma management.Although accurate diagnosis remains paramount,current investigations increasingly prioritize the identification of biomarkers for treatment response prediction and outcome stratification.Recent studies demonstrate that radiological characteristics not only reflect tumor heterogeneity but also carry prognostic implications for hepatocellular carcinoma.These imaging biomarkers enable non-invasive outcome prediction and provide objective evidence to optimize therapeutic decision-making.This comprehensive review summarizes MRI-derived imaging features associated with hepatocellular carcinoma prognosis,aiming to guide personalized treatment strategies and ultimately improve survival outcomes for hepatocellular carcinoma patients.