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.

ObjectiveTo explore the characteristics of traditional Chinese medicine （TCM） syndromes in the patients with concurrent knee osteoarthritis （KOA） and postmenopausal osteoporosis （PMOP） and provide a scientific basis for precise TCM syndrome differentiation， diagnosis， and treatment of such concurrent diseases. MethodsA prospective， multicenter， cross-sectional clinical survey was conducted to analyze the characteristics of TCM syndromes in the patients with concurrent PMOP and KOA. Excel 2021 was used to statistically analyze the general characteristics of the included patients. Continuous variables were reported as x¯±s， and binary variables were reported as percentages. UCINET and Gephi were used to construct a complex "isease-syndrome-symptom" network for in-depth mining. Topological structure indicators， edge connection information between nodes， and weighted adjacency matrices were calculated by UCINET and Python. Gephi was used for complex network visualization. ResultsA total of 157 patients with concurrent PMOP and KOA from 12 TCM hospitals or community health service centers were selected for the collection of TCM syndrome and symptom information. A total of 4 main TCM syndromes were obtained， which were kidney-Yang deficiency （47.13%）， liver-kidney Yin deficiency （36.94%）， spleen-kidney Yang deficiency （8.92%）， and kidney Yin-Yang deficiency （7.01%）. In addition， 10 concurrent syndromes （mainly Qi stagnation and blood stasis， Qi and blood deficiency， spleen Yang deficiency， and cold-dampness） were identified. Based on the results of comprehensive frequency statistics and complex network analysis， as well as TCM theoretical knowledge and relevant guidelines， the general characteristics of concurrent PMOP and KOA and the main TCM syndromes in the dimensions of physical symptoms， pain， tongue and pulse manifestations， and defecation and urination were obtained. ConclusionThe main TCM syndromes in the patients with concurrent PMOP and KOA may be kidney Yang deficiency， liver-kidney Yin deficiency， spleen-kidney Yang deficiency， and kidney Yin-Yang deficiency， among which kidney Yang deficiency and liver-kidney Yin deficiency are the core TCM syndromes.

ObjectiveTo explore the characteristics of traditional Chinese medicine （TCM） syndromes in the patients with concurrent knee osteoarthritis （KOA） and postmenopausal osteoporosis （PMOP） and provide a scientific basis for precise TCM syndrome differentiation， diagnosis， and treatment of such concurrent diseases. MethodsA prospective， multicenter， cross-sectional clinical survey was conducted to analyze the characteristics of TCM syndromes in the patients with concurrent PMOP and KOA. Excel 2021 was used to statistically analyze the general characteristics of the included patients. Continuous variables were reported as x¯±s， and binary variables were reported as percentages. UCINET and Gephi were used to construct a complex "isease-syndrome-symptom" network for in-depth mining. Topological structure indicators， edge connection information between nodes， and weighted adjacency matrices were calculated by UCINET and Python. Gephi was used for complex network visualization. ResultsA total of 157 patients with concurrent PMOP and KOA from 12 TCM hospitals or community health service centers were selected for the collection of TCM syndrome and symptom information. A total of 4 main TCM syndromes were obtained， which were kidney-Yang deficiency （47.13%）， liver-kidney Yin deficiency （36.94%）， spleen-kidney Yang deficiency （8.92%）， and kidney Yin-Yang deficiency （7.01%）. In addition， 10 concurrent syndromes （mainly Qi stagnation and blood stasis， Qi and blood deficiency， spleen Yang deficiency， and cold-dampness） were identified. Based on the results of comprehensive frequency statistics and complex network analysis， as well as TCM theoretical knowledge and relevant guidelines， the general characteristics of concurrent PMOP and KOA and the main TCM syndromes in the dimensions of physical symptoms， pain， tongue and pulse manifestations， and defecation and urination were obtained. ConclusionThe main TCM syndromes in the patients with concurrent PMOP and KOA may be kidney Yang deficiency， liver-kidney Yin deficiency， spleen-kidney Yang deficiency， and kidney Yin-Yang deficiency， among which kidney Yang deficiency and liver-kidney Yin deficiency are the core TCM syndromes.

Aim To investigate the effect of quercetin on the aging model of bone marrow mesenchymal stem cells established under microgravity. Methods Using 3D gyroscope, a aging model of bone marrow mesenchymal stem cells was constructed, and after receiving quercetin and microgravity treatment, the anti-aging effect of the quercetin was evaluated by detecting related proteins and oxidation indexes. Results Compared to the control group, the expressions of age-related proteins p21, pi6, p53 and RB in the microgravity group significantly increased, while the expressions of cyclin D1 and lamin B1 significantly decreased, with statistical significance (P<0.05). In the microgravity group, mitochondrial membrane potential significantly decreased (P<0.05), ROS accumulation significantly increased (P <0.05), SOD content significantly decreased and MDA content significantly increased (P<0.05). Compared to the microgravity group, the expressions of age-related proteins p21, pi6, p53 and RB in the quercetin group significantly decreased, while the expressions of cyclin D1 and lamin B1 significantly increased, with statistical significance (P<0.05). In the quercetin group, mitochondrial membrane potential significantly increased (P<0.05), ROS accumulation significantly decreased (P<0.05), SOD content significantly increased and MDA content significantly decreased (P<0.05). Conclusions Quercetin can resist oxidation, protect mitochondrial function and normal cell cycle, thus delaying the aging of bone marrow mesenchymal stem cells induced by microgravity.

Objective To investigate the epidemic features and pathogen spectrum distribution of diarrhea cases in Minhang District of Shanghai City so as to provide scientific evidence for developing prevention and control measures. Methods Surveillance on diarrhea was conducted in sentinel hospitals in Minghang District from 2018 to 2020. According to the quantity of outpatients in the monitoring hospital, the stool samples were collected by systematic sampling method according to the fixed interval proportion in the case queue which met the requirements of the monitored cases, and the pathogenic composition and epidemiological characteristics were analyzed. Results Among the 721 samples detected , 307(42.58%) were pathogen positive, The main positive bacteria was Vibrio parahaemolyticus, which accounted for 36.11%(39/108) among all positive bacteria.The main positive virus was norovirus GII, which accounted for 24.43%(75/307) among all positive virus. Positive cases were detected among all age groups. 81 positive cases (26.38%) were detected among 31-40 years old, with the highest detection rate. There was no difference in the positive detection rate between genders(χ²= 1.95, P = 0.16). The positive cases showed two peaks during the season of winter and spring. The positive rate of bacteria was highest in the third quarter and positive rate of viruses was highest in the first quarter. The mixed infection rate of bacteria and viruses was highest in the second quarter. Conclusions Diarrhea cases in Minhang District of Shanghai from 2018 to 2020 is caused by a variety of pathogens and related seasonality is obvious in Minghang District, Shanghai City in 2018-2020. It is necessary to take specific prevention based on various pathogens to reduce the incidence of diarrhea.

Based on the strategy of metabolomics combined with bioinformatics, this study analyzed the potential allergens and mechanism of pseudo-allergic reactions (PARs) induced by the combined use of Reduning injection and penicillin G injection. All animal experiments and welfare are in accordance with the requirements of the First Affiliated Experimental Animal Ethics and Animal Welfare Committee of Henan University of Chinese Medicine (approval number: YFYDW2020002). Based on UPLC-Q-TOF/MS technology combined with UNIFI software, a total of 21 compounds were identified in Reduning and penicillin G mixed injection. Based on molecular docking technology, 10 potential allergens with strong binding activity to MrgprX2 agonist sites were further screened. Metabolomics analysis using UPLC-Q-TOF/MS technology revealed that 34 differential metabolites such as arachidonic acid, phosphatidylcholine, phosphatidylserine, prostaglandins, and leukotrienes were endogenous differential metabolites of PARs caused by combined use of Reduning injection and penicillin G injection. Through the analysis of the "potential allergen-target-endogenous differential metabolite" interaction network, the chlorogenic acids (such as chlorogenic acid, neochlorogenic acid, cryptochlorogenic acid, and isochlorogenic acid A) and β-lactam allergens in the combination of the two may be mainly regulated by PLD1, PLA2G12A and CYP1A1. The three upstream signal target proteins mainly activate the arachidonic acid metabolic pathway, promote the degranulation of mast cells, release downstream endogenous inflammatory mediators, and induce PARs.

Objective: To investigate the prevalence of depressive symptoms among college freshmen and analyze their influencing factors, so as to provide insights into the prevention and intervention of depressive symptoms among college students. Methods: The freshmen enrolled in a college of Hunan Province from 2020 to 2022 were recruited, and demographic data, diet and sleep status were collected using questionnaire surveys. Depressive symptoms were evaluated using Patient Health Questionnaire-9. Factors affecting depressive symptoms were analyzed using a multivariable logistic regression model. Results: Totally 17 862 questionnaires were allocated, and 16 480 valid questionnaires were recovered, with an effective rate of 92.26%. There were 3 374 students of 2020 cohort, 7 038 students of 2021 cohort and 6 068 students of 2022 cohort, 6 029 boys (36.58%) and 10 451 girls (63.42%). The prevalence of depressive symptoms among freshmen was 41.37%. Multivariable logistic regression analysis showed that female (OR=1.482, 95%CI: 1.377-1.594), enrolling through the college entrance examination (OR=1.561, 95%CI: 1.384-1.809), depression history (OR=1.990, 95%CI: 1.513-2.618), abnormal marital status of parents (divorced, OR=1.197, 95%CI: 1.064-1.348; other problem, OR=1.401, 95%CI: 1.174-1.672), abnormal diet (mild, OR=2.883, 95%CI: 2.585-3.105; moderate, OR=6.755, 95%CI: 4.653-9.808; severe, OR=38.897, 95%CI: 12.200-124.012) and abnormal sleep (mild, OR=2.785, 95%CI: 2.593-2.992; moderate, OR=9.009, 95%CI: 7.011-11.578; severe, OR=29.281, 95%CI: 14.163-60.536) were associated with increased risk of depressive symptoms among college freshmen. Conclusion The prevalence of depressive symptoms among college freshmen is relatively high, and is influenced by gender, mode of admission, history of depression, parental marital status, diet and sleep.

Exploring the risk "time interval window" of sequential medication of Reduning injection (RDN) and penicillin G injection (PG) by detecting the correlation between serum biochemical indexes and plasma metabonomic characteristics, in order to reduce the risk of adverse reactions caused by the combination of RDN and PG. All animal experiments and welfare are in accordance with the requirements of the First Affiliated Experimental Animal Ethics and Animal Welfare Committee of Henan University of Chinese Medicine (approval number: YFYDW2020002). The changes of biochemical indexes in serum of rats were detected by enzyme-linked immunosorbent assay. It was determined that RDN combined with PG could cause pseudo-allergic reactions (PARs) activated by complement pathway. Further investigation was carried out at different time intervals (1.5, 2, 3.5, 4, 6, and 8 h PG+RDN). It was found that sequential administration within 3.5 h could cause significant PARs. However, PARs were significantly reduced after administration interval of more than 4 h. LC-MS was used for plasma metabolomics analysis, and the levels of serum biochemical indicators and plasma metabolic profile characteristics were compared in parallel. 22 differential metabolites showed similar or opposite trends to biochemical indicators before and after 3.5 h. And enriched to 10 PARs-related pathways such as arachidonic acid metabolism, steroid hormone biosynthesis, linoleic acid metabolism, glycerophospholipid metabolism, and tryptophan metabolism. In conclusion, there is a risk "time interval window" phenomenon in the adverse drug reactions caused by the sequential use of RDN and PG, and the interval medication after the "time interval window" can significantly reduce the risk of adverse reactions.

This study aimed to investigate the effect of iron overload on the transplant outcomes of pediatric patients with severe aplastic anemia (SAA) undergoing allogeneic hematopoietic stem cell transplantation (allo-HSCT). A retrospective analysis was conducted on the clinical data of 74 children with SAA who received allo-HSCT at the Hematology Department of Wuhan Children’s Hospital between January 2018 and August 2022. Children with iron overload (serum ferritin >1 000 μg/L) before transplantation had a longer disease course, received more red blood cell transfusions, and had a higher number of CD34 + cells infused. Moreover, iron overload significantly delayed the reconstitution of regulatory T cells after transplantation, increasing the incidence of hemorrhagic cystitis and grade Ⅲ-Ⅳ acute graft-versus-host disease after transplantation. However, iron overload did not significantly affect the overall survival and failure-free survival rates of the children.

Objective To propose an evaluation and screening indicator for the reliability of emergency ventilators.Methods Firstly,a regression model was used to clean the data and remove noise to ensure the accuracy of regression analysis.Then,four groups of highly correlated data combinations,including inspiratory tidal volume-minute expiratory volume,peak airway pressure-minute expiratory volume,peak airway pressure-inspiratory tidal volume and positive end-expiratory pressure(PEEP)-mean airway pressure,were determined with the methods of curve fitting and transfer function,and the difference between the theoretical output and the actual output of the data combinations was regarded as an indicator to judge whether the ventilator functioned well or not;finally,the indicator proposed was applied to single and multiple ventilators,and the feasibility of the indicator was determined by the proportion of the ventilators functioning well.Results The evaluation results with a single ventilator showed the four groups of data combinations had the actual output fitted well with the theoretical output,and all the differences between the theoretical output and the actual output were lower than the threshold;the results with multiple ventilators indicated there were 71.49％ventilators functioning well,which was very close to the actual result that 72％ventilators behaved well.Conclusion The evaluation and screening indicator for emergency ventilators has high feasibility,and theoretical support is provided for reliability assessment and selection of series of emergency treatment equipment.[Chinese Medical Equipment Journal,2024,45(7):8-16]