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.

The importance of gait assessment in the rehabilitation of cancer patients is gradually being recognized. However, quantitative analysis of balance ability in cancer patients is still limited. A total of 102 cancer patients meeting the inclusion criteria were recruited from Hefei Cancer Hospital, Chinese Academy of Sciences. Their balance ability was evaluated using the Berg Balance Scale (BBS). Gait data were collected by an electronic walkway and an IMU sensor system, including spatial-temporal and kinematic gait features such as step length, cadence, support time, and range of motion. Recursive feature elimination was used for feature selection. Ridge, Elastic Net, SVR, RF, and AdaBoost models were used to predict balance ability scores. Five-fold cross-validation was used to evaluate the performance of these models. Results show that the SVR model achieves the best performance with fifteen features (RMSE=3.22, R ²=0.91), followed by Ridge (RMSE=3.63, R ²=0.89). A method for evaluating balance ability based on gait features is proposed, providing a quantitative tool for personalized rehabilitation interventions in cancer patients.
Humans ; Postural Balance ; Neoplasms/rehabilitation* ; Gait ; Gait Analysis ; Biomechanical Phenomena ; Female

Prostate cancer is the second most common cancer in men, accounting for 14.1% of new cancer cases in 2020. The aggressiveness of prostate cancer is highly variable, depending on its grade and stage at the time of diagnosis. Despite recent advances in prostate cancer treatment, some patients still experience recurrence or even progression after undergoing radical treatment. Accurate initial staging and monitoring for recurrence determine patient management, which in turn affect patient prognosis and survival. Classical imaging has limitations in the diagnosis and treatment of prostate cancer, but the use of novel molecular probes has improved the detection rate, specificity, and accuracy of prostate cancer detection. Molecular probe-based imaging modalities allow the visualization and quantitative measurement of biological processes at the molecular and cellular levels in living systems. An increased understanding of tumor biology of prostate cancer and the discovery of new tumor biomarkers have allowed the exploration of additional molecular probe targets. The development of novel ligands and advances in nano-based delivery technologies have accelerated the research and development of molecular probes. Here, we summarize the use of molecular probes in positron emission tomography (PET), single-photon emission computed tomography (SPECT), magnetic resonance imaging (MRI), optical imaging, and ultrasound imaging, and provide a brief overview of important target molecules in prostate cancer.
Humans ; Male ; Prostatic Neoplasms/diagnosis* ; Molecular Probes ; Molecular Imaging/methods* ; Magnetic Resonance Imaging ; Positron-Emission Tomography ; Tomography, Emission-Computed, Single-Photon ; Ultrasonography ; Optical Imaging ; Biomarkers, Tumor ; Precision Medicine/methods*

Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder worldwide, causing dementia and affecting millions of individuals. One prominent characteristic in the brains of AD patients is glucose hypometabolism. In the context of galactose metabolism, intracellular glucose levels are heightened. Galactose mutarotase (GALM) plays a crucial role in maintaining normal galactose metabolism by catalyzing the conversion of β-D-galactose into α-D-galactose (α-D-G). The latter is then converted into glucose-6-phosphate, improving glucose metabolism levels. However, the involvement of GALM in AD progression is still unclear. In the present study, we found that the expression of GALM was significantly increased in AD patients and model mice. Genetic knockdown of GALM using adeno-associated virus did not change the expression of amyloid precursor protein (APP) and APP-cleaving enzymes including a disintegrin and metalloprotease 10 (ADAM10), β-site APP-cleaving enzyme 1 (BACE1), and presenilin-1 (PS1). Interestingly, genetic overexpression of GALM reduced APP and Aβ deposition by increasing the maturation of ADAM10, although it did not alter the expression of BACE1 and PS1. Further electrophysiological and behavioral experiments showed that GALM overexpression significantly ameliorated the deficits in hippocampal CA1 long-term potentiation (LTP) and spatial learning and memory in AD model mice. Importantly, direct α-D-G (20 mg/kg, i.p.) also inhibited Aβ deposition by increasing the maturation of ADAM10, thereby improving hippocampal CA1 LTP and spatial learning and memory in AD model mice. Taken together, our results indicate that GALM shifts APP processing towards α-cleavage, preventing Aβ generation by increasing the level of mature ADAM10. These findings indicate that GALM may be a potential therapeutic target for AD, and α-D-G has the potential to be used as a dietary supplement for the prevention and treatment of AD.
Animals ; ADAM10 Protein/metabolism* ; Alzheimer Disease/pathology* ; Amyloid Precursor Protein Secretases/metabolism* ; Disease Models, Animal ; Humans ; Mice ; Amyloid beta-Peptides/metabolism* ; Male ; Mice, Transgenic ; Membrane Proteins/metabolism* ; Cognitive Dysfunction/pathology* ; Mice, Inbred C57BL ; Amyloid beta-Protein Precursor/metabolism* ; Female ; Hippocampus/metabolism* ; Long-Term Potentiation/physiology*

The incomplete degradation of tumour cells by macrophages (Mϕ) is a contributing factor to tumour progression and metastasis, and the degradation function of Mϕ is mediated through phagosomes and lysosomes. In our preliminary experiments, we found that overactivation of NADPH oxidase 2 (NOX2) reduced the ability of Mϕ to degrade engulfed tumour cells. Above this, we screened out liquiritin from Glycyrrhiza uralensis Fisch, which can significantly inhibit NOX2 activity and inhibit tumours, to elucidate that suppressing NOX2 can enhance the ability of Mϕ to degrade tumour cells. We found that the tumour environment could activate the NOX2 activity in Mϕ phagosomes, causing Mϕ to produce excessive reactive oxygen species (ROS), thus prohibiting the formation of phagolysosomes before degradation. Conversely, inhibiting NOX2 in Mϕ by liquiritin can reduce ROS and promote phagosome-lysosome fusion, therefore improving the enzymatic degradation of tumour cells after phagocytosis, and subsequently promote T cell activity by presenting antigens. We further confirmed that liquiritin down-regulated the expression of the NOX2 specific membrane component protein gp91 phox, blocking its binding to the NOX2 cytoplasmic component proteins p67 phox and p47 phox, thereby inhibiting the activity of NOX2. This study elucidates the specific mechanism by which Mϕ cannot degrade tumour cells after phagocytosis, and indicates that liquiritin can promote the ability of Mϕ to degrade tumour cells by suppressing NOX2.

Small intestinal bacterial overgrowth（SIBO）is a clinically common but poorly recognized disease with clinical symptoms that overlap with those of functional gastrointestinal disorders（FGID）. FGID is a common risk factor for the occurrence of SIBO，and its SIBO incidence rate is significantly higher than that of healthy people，and has a certain correlation. Intestinal microbiota dysbiosis（including SIBO）plays an important role in the pathophysiology of FGID. At present，the diagnosis and treatment of SIBO still face challenges，and the treatment plan of FGID is gradually introduced into the treatment of SIBO. SIBO and FGID are closely related in clinical manifestations，incidence rate，pathophysiology，treatment and other aspects，but there is little research on the relationship between them. Prospective and large-scale clinical research is still needed to improve understanding and diagnostic accuracy.This article reviews the research progress on the relationship between SIBO and FGID.

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 investigate the diagnostic value and clinical significance of hypersensitive C-reactive protein(hs-CRP),procalcitonin(PCT)combined with interleukin-6(IL-6)in children with severe hand-foot-mouth disease.Methods A total of 62 children hospitalized in our hospital from January 2022 to December 2022 were collected as research objects.According to the severity of infection,they were divided into observation group(severe infection group)with 29 cases and control group(mild infection group)with 33 cases.The differences of general data,total leukocyte count,neutrophil count,lymphocyte count,platelet count,hs-CRP,PCT,IL-6 and creatine kinase isoenzyme(CK-MB)between the two groups and their clinical applications were analyzed and compared.Results The total white blood cell count,neutrophil count,lymphocyte count,hs-CRP,PCT and IL-6 in the observation group were higher than those in the control group,and the difference has statistically significant.Receiver operator characteristic(ROC)curve analysis of hs-CRP predicted the sensitivity and specificity of severe infection of hand-foot-mouth disease were 79.3%and 93.9%(95%CI:0.852-10.985,P<0.05);The sensitivity and specificity of PCT were 93.1%and 84.8%(95%CI:0.907-1,P<0.05);The sensitivity and specificity of IL-6 were 96.6%and 87.9%(95%CI:0.945-1,P<0.05).Conclusions In hand-foot-mouth classification,PCT and IL-6 are highly sensitive.Although hs-CRP is less sensitive than the former,its specificity is higher than the former.Therefore,the combination of hs-CRP,PCT and IL-6 has higher value for hand-foot-mouth classification.