Disorders of consciousness (DOC) are pathological conditions characterized by severely suppressed brain function and the persistent interruption or loss of consciousness. Accurate diagnosis and evaluation of DOC are prerequisites for precise treatment. Traditional assessment methods are primarily based on behavioral scales, which are inherently subjective and rely on observable behaviors. Moreover, traditional methods have a high misdiagnosis rate, particularly in distinguishing minimally conscious state (MCS) from vegetative state/unresponsive wakefulness syndrome (VS/UWS). This diagnostic uncertainty has driven the exploration of objective, reliable, and efficient assessment tools. Among these tools, electroencephalography (EEG) has garnered significant attention for its non-invasive nature, portability, and ability to capture real-time neurodynamics. This paper systematically reviews the application of EEG biomarkers in DOC assessment. These biomarkers are categorized into 3 main types: resting-state EEG features, task-related EEG features, and features derived from transcranial magnetic stimulation-EEG (TMS-EEG). Resting-state EEG biomarkers include features based on spectrum, microstates, nonlinear dynamics, and brain network metrics. These biomarkers provide baseline representations of brain activity in DOC patients. Studies have shown their ability to distinguish different levels of consciousness and predict clinical outcomes. However, because they are not task-specific, they are challenging to directly associate with specific brain functions or cognitive processes. Strengthening the correlation between resting-state EEG features and consciousness-related networks could offer more direct evidence for the pathophysiological mechanisms of DOC. Task-related EEG features include event-related potentials, event-related spectral modulations, and phase-related features. These features reveal the brain’s responses to external stimuli and provide dynamic information about residual cognitive functions, reflecting neurophysiological changes associated with specific cognitive, sensory, or behavioral tasks. Although these biomarkers demonstrate substantial value, their effectiveness rely on patient cooperation and task design. Developing experimental paradigms that are more effective at eliciting specific EEG features or creating composite paradigms capable of simultaneously inducing multiple features may more effectively capture the brain activity characteristics of DOC patients, thereby supporting clinical applications. TMS-EEG is a technique for probing the neurodynamics within thalamocortical networks without involving sensory, motor, or cognitive functions. Parameters such as the perturbational complexity index (PCI) have been proposed as reliable indicators of consciousness, providing objective quantification of cortical dynamics. However, despite its high sensitivity and objectivity compared to traditional EEG methods, TMS-EEG is constrained by physiological artifacts, operational complexity, and variability in stimulation parameters and targets across individuals. Future research should aim to standardize experimental protocols, optimize stimulation parameters, and develop automated analysis techniques to improve the feasibility of TMS-EEG in clinical applications. Our analysis suggests that no single EEG biomarker currently achieves an ideal balance between accuracy, robustness, and generalizability. Progress is constrained by inconsistencies in analysis methods, parameter settings, and experimental conditions. Additionally, the heterogeneity of DOC etiologies and dynamic changes in brain function add to the complexity of assessment. Future research should focus on the standardization of EEG biomarker research, integrating features from resting-state, task-related, and TMS-EEG paradigms to construct multimodal diagnostic models that enhance evaluation efficiency and accuracy. Multimodal data integration (e.g., combining EEG with functional near-infrared spectroscopy) and advancements in source localization algorithms can further improve the spatial precision of biomarkers. Leveraging machine learning and artificial intelligence technologies to develop intelligent diagnostic tools will accelerate the clinical adoption of EEG biomarkers in DOC diagnosis and prognosis, allowing for more precise evaluations of consciousness states and personalized treatment strategies.

OBJECTIVES: To summarize the clinical manifestations and genetic characteristics of creatine deficiency syndrome (CDS) caused by GAMT gene mutations. METHODS: A retrospective analysis was conducted on the clinical and genetic data of two children diagnosed with GAMT deficiency-type CDS at the Children's Medical Center of Xiangya Hospital, Central South University, from December 2020 to December 2024. RESULTS: The two patients presented with symptoms in infancy, and both had compound heterozygous mutations in the GAMT gene. Case 1 exhibited seizures and intellectual disability, while Case 2 had intellectual disability and attention-deficit hyperactivity disorder. Magnetic resonance spectroscopy of cranial MRI in both patients indicated reduced creatine peaks. After creatine treatment, seizures in Case 1 were controlled, but both patients continued to experience intellectual disabilities and behavioral issues. As of December 2024, a total of 21 cases have been reported in China (including this study), and 115 cases have been reported abroad. All patients exhibited developmental delay or intellectual disabilities, with 66.9% (91/136) experiencing seizures, 33.8% (46/136) presenting with motor disorders, and 36.8% (50/136) having behavioral problems. Seventy-five percent (102/136) of patients received creatine treatment, leading to significant improvements in seizures and motor disorders, although cognitive improvement was not substantial. CONCLUSIONS GAMT deficiency-type CDS is rare and presents with nonspecific clinical features. Timely diagnosis facilitates targeted treatment, which can partially improve prognosis.
Child ; Female ; Humans ; Male ; Creatine/deficiency* ; Guanidinoacetate N-Methyltransferase/deficiency* ; Intellectual Disability/genetics* ; Mutation ; Retrospective Studies ; Rhabdomyolysis/genetics* ; Language Development Disorders ; Movement Disorders/congenital*

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective To investigate the feasibility of magnetic tracer technique for locating pulmonary nodules.Methods A tracer magnet and a matching puncture instrument were designed by ourselves for locating pulmonary nodules.After preliminarily verifying the feasibility of the operation in the isolated lung,four beagle dogs were used as animal models to perform puncture localization of the assumed lesions in the upper lobe of the right lung under the guidance of X-ray by using self-designed tracer magnets and puncture instruments,and the positioning effect was observed and evaluated after thorax opening.The operation time required for the tracer magnet implantation,whether there is bleeding at the puncture site,whether the tracer magnet is displaced,and the positioning time of pulmonary nodules after thorax opening were recorded.Results Two tracer magnets were successfully inserted into the upper lobe of the right lung under X-ray guidance in four beagle dogs,and the magnets were successfully attracted and fixed.The median insertion time of the tracer magnet was 5 minutes(4 to 7 minutes),and the insertion process was smooth without bleeding at the puncture site.After thorax opening,oval forceps were used to conveniently locate the location of the tracer magnet,achieving accurate positioning of pulmonary nodules with a median positioning time of 13 seconds(10 to 17 seconds),and the tracer magnet did not shift during the whole process.Conclusion The magnetic tracer technique is simple to operate and pricise for localization of pulmonary nodules.With further optimization of the operation process,this technique is expected to be applied in clinic.

Objective To explore the coupling mechanism between medical surge response resources and the spread of secondary risks during public health emergencies,as well as the effectiveness of relevant interventions.Methods Based on complex network theory,a dual-layer network model of medical resources and secondary events was constructed.The interactive feedback between medical resource status and secondary event risk,as well as the effects of network structure,were analyzed through MATLAB simulations,REPAST agent-based modeling,and mean-field analysis.Results Simulation and prediction results show that an increase in first-layer resource-deficient nodes significantly raises the activation rate and transmission speed of secondary events,while the clustering and spread of secondary events in the second layer,in turn,intensify resource depletion,creating a negative feedback loop.Mean-field analysis indicates a nonlinear positive correlation between the adequacy of medical resources and the likelihood of secondary events.Network structure analysis reveals that when the average node degree exceeds 8,resource allocation efficiency improves markedly.Conclusion There exists a dynamic coupling and bidirectional feedback relationship between medical resource status and secondary event risks.Enhancing the flexible allocation and responsiveness of medical resources,improving multi-sectoral collaborative monitoring and coordinated regulation,optimizing network connectivity and coordination mechanisms for resource distribution,and establishing dynamic monitoring and tiered early warning systems are key strategies for strengthening the resilience of healthcare systems and effectively containing the spread of secondary events.

Objective To investigate the feasibility of magnetic tracer technique for locating pulmonary nodules.Methods A tracer magnet and a matching puncture instrument were designed by ourselves for locating pulmonary nodules.After preliminarily verifying the feasibility of the operation in the isolated lung,four beagle dogs were used as animal models to perform puncture localization of the assumed lesions in the upper lobe of the right lung under the guidance of X-ray by using self-designed tracer magnets and puncture instruments,and the positioning effect was observed and evaluated after thorax opening.The operation time required for the tracer magnet implantation,whether there is bleeding at the puncture site,whether the tracer magnet is displaced,and the positioning time of pulmonary nodules after thorax opening were recorded.Results Two tracer magnets were successfully inserted into the upper lobe of the right lung under X-ray guidance in four beagle dogs,and the magnets were successfully attracted and fixed.The median insertion time of the tracer magnet was 5 minutes(4 to 7 minutes),and the insertion process was smooth without bleeding at the puncture site.After thorax opening,oval forceps were used to conveniently locate the location of the tracer magnet,achieving accurate positioning of pulmonary nodules with a median positioning time of 13 seconds(10 to 17 seconds),and the tracer magnet did not shift during the whole process.Conclusion The magnetic tracer technique is simple to operate and pricise for localization of pulmonary nodules.With further optimization of the operation process,this technique is expected to be applied in clinic.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective To explore the coupling mechanism between medical surge response resources and the spread of secondary risks during public health emergencies,as well as the effectiveness of relevant interventions.Methods Based on complex network theory,a dual-layer network model of medical resources and secondary events was constructed.The interactive feedback between medical resource status and secondary event risk,as well as the effects of network structure,were analyzed through MATLAB simulations,REPAST agent-based modeling,and mean-field analysis.Results Simulation and prediction results show that an increase in first-layer resource-deficient nodes significantly raises the activation rate and transmission speed of secondary events,while the clustering and spread of secondary events in the second layer,in turn,intensify resource depletion,creating a negative feedback loop.Mean-field analysis indicates a nonlinear positive correlation between the adequacy of medical resources and the likelihood of secondary events.Network structure analysis reveals that when the average node degree exceeds 8,resource allocation efficiency improves markedly.Conclusion There exists a dynamic coupling and bidirectional feedback relationship between medical resource status and secondary event risks.Enhancing the flexible allocation and responsiveness of medical resources,improving multi-sectoral collaborative monitoring and coordinated regulation,optimizing network connectivity and coordination mechanisms for resource distribution,and establishing dynamic monitoring and tiered early warning systems are key strategies for strengthening the resilience of healthcare systems and effectively containing the spread of secondary events.

This study was aimed at understanding the relationships among the drug resistance and genome characteristics of group A Streptococcus in Jiangsu Province.A total of 149 group A Streptococcus strains were collected from hospitals between 2016 and 2023.Thirteen antimicrobial minimal inhibitory concentrations were detected with the micro-dilution broth method.The GAS strains were typed with emm genotyping analysis and whole genome sequencing,to determine the carriage rates of drug resistance genes and the evolutionary relationships among strains.The resistance rates of 149 GAS strains to erythromy-cin,tetracycline,and clindamycin exceeded 90％,whereas the strains showed sensitivity to 8 different antibiotics,including penicillin.Notably,the resistance rates to erythromycin,tetracycline,and clindamycin consistently increased over time.All strains were classified into 9 emm types,among which emm12 accounted for the highest proportion(77/149;51.68％).Signifi-cant statistical differences were observed among emm types,in terms of the drug resistance rate,number of resistant species,and prevalence of drug resistance genes.Furthermore,SNP evolutionary tree analysis revealed 3 distinct clusters within the GAS strains:emm12,emm1,and other emm types.emm 12 and emm1 were the dominant GAS strains in Jiangsu Province.Most isolates were resistant to erythromycin,tetracycline,and clindamycin.Differences in phenotypes and genomic characteris-tics were observed among emm types.

This study was aimed at understanding the relationships among the drug resistance and genome characteristics of group A Streptococcus in Jiangsu Province.A total of 149 group A Streptococcus strains were collected from hospitals between 2016 and 2023.Thirteen antimicrobial minimal inhibitory concentrations were detected with the micro-dilution broth method.The GAS strains were typed with emm genotyping analysis and whole genome sequencing,to determine the carriage rates of drug resistance genes and the evolutionary relationships among strains.The resistance rates of 149 GAS strains to erythromy-cin,tetracycline,and clindamycin exceeded 90％,whereas the strains showed sensitivity to 8 different antibiotics,including penicillin.Notably,the resistance rates to erythromycin,tetracycline,and clindamycin consistently increased over time.All strains were classified into 9 emm types,among which emm12 accounted for the highest proportion(77/149;51.68％).Signifi-cant statistical differences were observed among emm types,in terms of the drug resistance rate,number of resistant species,and prevalence of drug resistance genes.Furthermore,SNP evolutionary tree analysis revealed 3 distinct clusters within the GAS strains:emm12,emm1,and other emm types.emm 12 and emm1 were the dominant GAS strains in Jiangsu Province.Most isolates were resistant to erythromycin,tetracycline,and clindamycin.Differences in phenotypes and genomic characteris-tics were observed among emm types.