Electroencephalography (EEG) is a non-invasive neurophysiological monitoring technique. It records the electrical activity of the cerebral cortex using electrodes placed on the scalp surface. Owing to its high safety, portability, and millisecond-level temporal resolution, EEG has been widely utilized in a variety of fields, including clinical diagnosis, brain-computer interfaces (BCIs), and cognitive neuroscience research. However, due to its microvolt-level amplitude, EEG is highly susceptible to various artifacts, including electrooculographic (EOG), electrocardiographic (ECG), electromyographic (EMG), and power line interference (PLI). These artifacts can obscure genuine neural activity and introduce spurious electrophysiological features. Consequently, they may compromise EEG signal quality, thereby reducing the reliability of downstream analyses. To address this issue, numerous EEG artifact removal methods have been developed, including both traditional denoising techniques and deep learning-based approaches. Traditional EEG denoising methods have long served as the primary solutions for artifact removal. Representative approaches include filtering, regression, and blind source separation. Although these methods have demonstrated effectiveness in specific scenarios, they suffer from several inherent limitations. Filtering assumes that artifacts and EEG signals can be separated in the frequency domain, but many artifacts, such as EOG and EMG, overlap with EEG spectra, which may lead to the loss of valuable neural information. Regression methods require high-quality artifact references to estimate and subtract contaminations, limiting their effectiveness in reference-free scenarios. Blind source separation can remove artifacts without external references, but it typically requires the number of EEG channels to exceed the number of sources, restricting its application in single- or low-channel EEG recordings. Deep learning-based EEG denoising methods address these limitations effectively. First, they learn the nonlinear mapping between contaminated and clean EEG directly from data in an end-to-end manner. This approach does not rely on assumptions about spectral separability, thereby preserving neural activity more completely. Second, the reference information is incorporated during the training phase, allowing the trained model to perform artifact removal independently without external references. Third, deep learning models can be flexibly designed to accommodate various recording setups, achieving robust denoising for both high-density and single-channel EEG. Collectively, these advantages enable deep learning-based methods to overcome the main challenges of traditional approaches, providing more accurate and reliable EEG signal recovery. The superior denoising performance of deep learning-based EEG denoising methods has attracted increasing attention in EEG artifact removal research. As a result, many deep learning-based denoising methods have been developed and successfully applied in neural engineering areas. However, a systematic review of the techniques and applications in this field is still lacking. To address this gap, this paper reviews recent advances in deep learning-based EEG denoising from four perspectives: technical principle, benchmark dataset, denoising model, and evaluation method. Representative applications in neural signal analysis and BCI decoding are also summarized. Furthermore, the advantage, existing challenge, and future research direction of deep learning-based EEG denoising are discussed. This review aims to provide valuable theoretical insights and technical guidance for researchers. It is also expected to promote further advances and broader applications of deep learning-based EEG denoising techniques.

Consciousness, which underpins human perception, cognition, and behavioural responses, has long been a major focus of neuroscience and medicine. Alterations in consciousness occur not only during reversible processes such as sleep and anaesthesia, but also in pathological conditions, including disorders of consciousness (DOC) and brain injury. With recent advances in the synchronized acquisition and analysis of multimodal physiological signals, such as electroencephalography (EEG) and electrocardiography (ECG), the dynamic interactions between the heart and brain have become increasingly apparent. A growing body of evidence indicates that heart-brain interactions are closely associated with the maintenance and transition of consciousness states, offering a novel perspective for elucidating their underlying mechanisms and supporting relevant clinical assessments. Current approaches to heart-brain interaction analysis for consciousness-state assessment can be broadly grouped into three categories: ECG-based methods, EEG-based methods, and heart-brain coupling analysis. These approaches characterize heart-brain interactions during changes in consciousness from the complementary perspectives of autonomic regulation, cortical interoceptive processing, and cross-system information transfer, respectively. Nevertheless, this field remains at an early stage. Most studies have focused on a single experimental context or a fixed analytical method, with insufficient systematic consideration of the applicability and limitations of different approaches. Moreover, the mechanisms underlying the emergence and alteration of consciousness remain incompletely understood. Accordingly, this review examines heart-brain interaction methods employed in recent consciousness-state assessment studies in accordance with progressively deeper analytical perspectives, and systematically evaluates the advantages and limitations of these methods across distinct consciousness-related conditions. Heart rate variability (HRV) is one of the classical ECG-based indices used to investigate heart-brain interactions. It refers to fluctuations in consecutive interbeat intervals over a specified time period and is regarded as a physiological measure of autonomic nervous system regulation that may indirectly reflect the strength of heart-brain interactions. Depending on the analytical dimension, HRV analysis is commonly divided into time-domain, frequency-domain, and complexity-index analyses. These approaches characterize autonomic activity in terms of temporal fluctuations, the distribution of frequency components, and nonlinear dynamical properties, respectively. Although extensive research supports their physiological interpretation, they cannot characterize directional interactions from the heart to the brain. Among EEG-based methods for heart-brain interaction analysis, EEG activity directly related to cardiac fluctuations has received particular attention. One of the most widely used measures is the heartbeat-evoked potential (HEP). HEP-based approaches are considered to directly reflect the capacity of the cerebral cortex to process cardiac afferent signals and may be especially advantageous for characterizing rapidly changing consciousness levels, such as those observed during sleep. However, they do not comprehensively capture dynamic bidirectional information exchange between the brain and heart and are susceptible to cardiac artefacts, respiration, and variation in preprocessing procedures. Heart-brain coupling analysis quantifies functional associations between ECG and EEG through cross-system interaction measures, thereby assessing dynamic interactions between the heart and brain. It emphasizes information transfer, feedback regulation, and cross-timescale coordination between the two systems, offering a more comprehensive perspective for investigating changes in consciousness during sleep, anaesthesia, and DOC. However, the neurophysiological mechanisms and physiological interpretation of individual computational indices have not yet been fully established, and the robustness of their results and their potential for clinical generalization require further validation. Finally, this review summarizes the commonalities and differences among the three classes of methods across research domains involving different consciousness states. It further argues that heart-brain interaction research requires deeper investigation at both the mechanistic level of consciousness and the level of clinical application. Current challenges and key issues are identified, and future directions and feasible pathways for heart-brain interaction research are discussed, with the aim of providing new perspectives for researchers in related fields.

Objective To verify the efficacy of a domestic human DNA quantification kit based on real-time fluorescence quantitative PCR in detecting the total human DNA concentration,male DNA concen-tration in mixed male/female DNA samples,the degree of DNA degradation and inhibitor tolerance.Methods Samples with different concentrations,different male/female ratios,different concentrations of inhibitors,and different degradation degrees were tested using the domestic human DNA quantification kit based on real-time fluorescence quantitative PCR.This kit was compared with a similar product on the market and was applied to the detection of DNA from real cases.Results This human DNA quan-tification kit can effectively detect human DNA as low as 0.001 65 ng/μL,and 6.25 pg/μL of male DNA in mixed samples with a male-to-female ratio of 1∶15 000.Even when the sample contains as high as 400 ng/μL of humic acid or 1 000 μmol/L of hemin alone,the DNA concentration can still be accurately detected.The degradation index can effectively characterize the degradation degree of the sample.This kit has been successfully applied in forensic practice.Conclusion This human DNA quan-tification kit is accurate and reliable in detection.It can accurately reflect the degradation of DNA and inhibitor tolerance.It has good performance in quantitative accuracy,determination of the male/female ratio in mixed samples,and inhibitor tolerance.It has application potential in forensic case examination.

Analyzing the spatial distribution pattern and formation factors of medicinal plant resources can provide a scientific basis for the protection and development of traditional Chinese medicine(TCM) resources. This study is based on the survey data of medicinal plant resources in 104 county-level administrative regions of Anhui province in the Fourth National Survey of TCM Resources. The global spatial autocorrelation analysis, trend surface analysis, local spatial autocorrelation analysis, hotspot analysis, and a geodetector were employed to analyze the spatial distribution pattern of medicinal plant richness, and its relationship with natural factors was explored. The results can provide a basis for the formulation of development strategies such as the protection and utilization of TCM resources, as well as offer a scientific foundation for the establishment of regional planning schemes for TCM resources in Anhui province. The results indicated that the richness of medicinal plant resources in Anhui province had significant spatial heterogeneity, exhibiting highly clustered distribution characteristics. Cold spots and hot spots presented clustered distribution patterns, with cold spots mostly located north of the Huaihe River and hot spots south of the Yangtze River. Overall, the distribution of medicinal plant resources in Anhui province showed an overall trend of high in the south and low in the north, which was consistent with the overall geomorphic trend of this province. In addition, natural factors such as altitude, precipitation, and vegetation type played an important role in the diversity and spatial distribution pattern formation of medicinal plant resources. The extraction and analysis of the spatial distribution characteristics of natural factors in cold and hot spot regions discovered that the heterogeneity of eco-environments constituted a fundamental condition for the formation of species diversity.
Plants, Medicinal/classification* ; China ; Spatial Analysis ; Conservation of Natural Resources ; Biodiversity

Objective To investigate the clinical efficacy and safety of facilitated percutaneous coronary intervention(PCI)with half-dose recombinant staphylokinase(r-SAK)in patients with ST-segment elevation myocardial infarction(STEMI)who are expected to undergo PCI within 120 minutes.Methods From October 2021 to August 2022,a total of 200 STEMI patients in eight centers were included and randomly assigned in a 1﹕1 ratio to either r-SAK group or control group.Patients received loading doses of aspirin and ticagrelor and intravenous heparin and were randomized to receive an intravenous bolus of either 5 mg r-SAK or normal saline prior to PCI.The outcomes were set as ST-segment resolution(STR)at 60-90 minutes after PCI,the proportion and transition of pathological Q waves on the 5th day after PCI,and the proportion of high-sensitivity cardiac troponin T(hs-cTnT)peaking within 12 hours of onset.The safety outcome was major bleeding events defined as Bleeding Academic Research Consortium(BARC)≥type 3 bleeding during hospitalization.Results Compared with the control group,the r-SAK group had a higher proportion of STR≥70%within 60-90 minutes after PCI(58.3%vs.40.3%,P=0.009);a lower proportion of pathological Q waves(59.1%vs.74.1%,P=0.040);a lower rate of Q wave progression(14.8%vs.43.2%,P＜0.001);a higher rate of Q wave disappearance(12.5%vs.3.7%,P=0.027);and a higher proportion of hs-cTnT peaking within 12 hours of symptom onset[31/40(77.5%)vs.17/33(51.5%),P=0.027].Regarding the safety outcome,no significant difference in BARC≥type 3 bleeding was found between the two groups during hospitalization(P＞0.05).Conclusions For STEMI patients who were expected to undergo primary PCI within 120 minutes of symptom onset,the facilitated PCI with half-dose r-SAK significantly increased the proportion of STR≥70%at 60-90 minutes after PCI,reduced the formation of pathological Q waves,and shortened the time to peak hs-cTnT,without increasing the risk of bleeding,which should be an alternative reperfusion strategy worthy of further study.

ObjectiveIn nature, objects cast shadows due to illumination, forming the basis for stereoscopic perception. Birds need to adapt to changes in lighting (meaning they can recognize stereoscopic shapes even when shadows look different) to accurately perceive different three-dimensional forms. However, how neurons in the key visual brain area in birds handle these lighting changes remains largely unreported. In this study, pigeons (Columba livia) were used as subjects to investigate how neurons in pigeon’s ventrolateral mesopallium (MVL) represent stereoscopic shapes consistently, regardless of changes in lighting. MethodsVisual cognitive training combined with neuronal recording was employed. Pigeons were first trained to discriminate different stereoscopic shapes (concave/convex). We then tested whether and how light luminance angle and surface appearance of the stereoscopic shapes affect their recognition accuracy, and further verify whether the results rely on specify luminance color. Simultaneously, neuronal firing activity of neurons was recorded with multiple electrode array implanted from the MVL during the presentation of difference shapes. The response was finally analyzed how selectively they responded to different stereoscopic shapes and whether their selectivity was affected by the changes of luminance condition (like lighting angle) or surface look. Support vector machine (SVM) models were trained on neuronal population responses recorded under one condition (light luminance angle of 45°) and used to decode responses under other conditions (light luminance angle of 135°, 225°, 315°) to verify the invariance of responses to different luminance conditions. ResultsBehavioral results from 6 pigeons consistently showed that the pigeons could reliably identify the core 3D shape (over 80% accuracy), and this ability wasn’t affected by changes in light angle or surface appearance. Statistical analysis of 88 recorded neurons from 6 pigeons revealed that 83% (73/88) showed strong selectivity for specific 3D shapes (selectivity index>0.3), and responses to convex shapes were consistently stronger than to concave shapes. These shape-selective responses remained stable across changes in light angle and surface appearance. Neural patterns were consistent under both blue and orange lighting. The decoding accuracy achieves above 70%, suggesting stable responses under different conditions (e.g., different lighting angles or surface appearance). ConclusionNeurons in the pigeon MVL maintain a consistent neural encoding pattern for different stereoscopic shapes, unaffected by illumination or surface appearance. This ensures stable object recognition by pigeons in changing visual environments. Our findings provide new physiological evidence for understanding how birds achieve stable perception (“invariant neural representations”) while coping with variations in the visual field.

OBJECTIVE: To investigate the clinical efficacy and safety of single Kirschner wire assisted poking and closed reduction in the treatment of Gartland type Ⅲ supracondylar humeral fractures in children. METHODS: A retrospective analysis was performed on patients diagnosed with Gravland type Ⅲ supracondylar humeral fractures between January 2022 and June 2023. A total of 46 patients were treated with closed reduction assisted by Kirschner wires and percutaneous Kirschner wire internal fixation.There were 25 males and 21 females. The age ranged from 5 to 10 years old, with an average of (5.8±1.8) years old. The left side was involved in 28 patients and the right side in 18 patients. Record the operative duration for patients, the number of fluoroscopic exposures, fracture healing time, postoperative carrying angle, Baumann angle, elbow joint function score at three months post-operation, and any associated complications. RESULTS: All 46 patients were followed up for a period of 12 to 16 weeks, with an average of (13.74±1.44 )weeks. The operation duration was (30.7±5.1) minutes, the fluoroscopy count was (10.2±2.7) times, the postoperative carrying angle of the elbow joint was (8.7±2.2) degrees, and the Baumann angle was (71.5±2.9) degrees. All fractures achieved successful union in all patients, with a mean healing time of (25.5±1.7) days.At the final follow-up, elbow joint function was assessed using the Flynn criteria, with 43 patients rated as excellent and 3 patients rated as good. No complications were observed, including cubitus varus, nerve injury, or local infection. CONCLUSION The use of a single Kirschner wire assisted prying reduction for treating Gartland type Ⅲ supracondylar humeral fractures in children demonstrates excellent clinical efficacy and safety.
Humans ; Male ; Female ; Child ; Bone Wires ; Child, Preschool ; Humeral Fractures/physiopathology* ; Retrospective Studies ; Fracture Fixation, Internal/instrumentation* ; Treatment Outcome ; Fracture Healing

OBJECTIVE: To compare the biomechanical properties of four internal fixation methods in a lower cervical spine injury model using the finite element method. METHODS: Cervical CT data of a 28-year-old healthy adult male were utilized to establish a finite element model of the normal cervical spine and a lower cervical spine three-column injury model. Four internal fixation methods were then applied to the three-column injury model, resulting in four groups:Group A, anterior cervical locked-plate(ACLP) fixation system model(anterior approach);Group B, posterior cervical pedicle screw fixation model (posterior approach);Group C, combined anterior and posterior cervical pedicle screw fixation model; Group D, Novel composite anterior cervical internal fixation model. A 75 N axial compressive load and a 1.0 N·m pure moment were applied to the upper surface of the cervical spine model to simulate flexion, extension, rotation, and lateral bending movements. The intervertebral range of motion(ROM) and stress distribution of the internal fixators under different motion conditions were compared across all models. RESULTS: Compared with the normal model, the reductions in overall intervertebral ROM for each group under flexion, extension, rotation, and lateral bending were as follows:Group A, 24.04°, 23.12°, 6.24°, and 9.06°;Group B, 24.42°, 24.34°, 6.48°, and 9.20°;Group C, 25.43°, 25.29°, 7.17°, and 9.57°;Group D, 24.75°, 25.5°, 6.71°, and 9.12°. The peak stress values of the internal fixators in each group were:Group A, 53.9 MPa, 79.9 MPa, 61.4 MPa, and 80.3 MPa;Group B, 218.3 MPa, 105.4 MPa, 206.6 MPa, and 186.8 MPa;Group C, 40.8 MPa, 97.2 MPa, 47.1 MPa, and 39.4 MPa;Group D, 93.0 MPa, 144.0 MPa, 64.8 MPa, and 106.3 MPa. CONCLUSION The biomechanical properties of the novel composite anterior cervical internal fixation method are similar to those of the combined anterior-posterior fixation method, and superior to both the anterior cervical ACLP plate-screw fixation and posterior cervical pedicle screw fixation methods.
Humans ; Finite Element Analysis ; Cervical Vertebrae/physiopathology* ; Male ; Biomechanical Phenomena ; Adult ; Fracture Fixation, Internal/methods* ; Range of Motion, Articular

Nitrogen narcosis is a neurological syndrome that manifests when humans or animals encounter hyperbaric nitrogen, resulting in a range of motor, emotional, and cognitive abnormalities. The anterior cingulate cortex (ACC) is known for its significant involvement in regulating motivation, cognition, and action. However, its specific contribution to nitrogen narcosis-induced hyperlocomotion and the underlying mechanisms remain poorly understood. Here we report that exposure to hyperbaric nitrogen notably increased the locomotor activity of mice in a pressure-dependent manner. Concurrently, this exposure induced heightened activation among neurons in both the ACC and dorsal medial striatum (DMS). Notably, chemogenetic inhibition of ACC neurons effectively suppressed hyperlocomotion. Conversely, chemogenetic excitation lowered the hyperbaric pressure threshold required to induce hyperlocomotion. Moreover, both chemogenetic inhibition and genetic ablation of activity-dependent neurons within the ACC reduced the hyperlocomotion. Further investigation revealed that ACC neurons project to the DMS, and chemogenetic inhibition of ACC-DMS projections resulted in a reduction in hyperlocomotion. Finally, nitrogen narcosis led to an increase in local field potentials in the theta frequency band and a decrease in the alpha frequency band in both the ACC and DMS. These results collectively suggest that excitatory neurons within the ACC, along with their projections to the DMS, play a pivotal role in regulating the hyperlocomotion induced by exposure to hyperbaric nitrogen.
Animals ; Gyrus Cinguli/drug effects* ; Male ; Mice, Inbred C57BL ; Locomotion/drug effects* ; Neurons/drug effects* ; Mice ; Nitrogen/toxicity* ; Inert Gas Narcosis/physiopathology* ; Corpus Striatum/physiopathology*