Objectives: . This study aimed to investigate the regulatory functions of gamma-aminobutyric acid (GABA)ergic neural circuits from the inferior colliculus (IC) to the medial geniculate body (MGB) in salicylate-induced tinnitus. Methods: . Mice were treated with salicylate to induce tinnitus, and tinnitus-like behaviors were evaluated via gap prepulse inhibition of acoustic startle. Using combined viral tracing methodologies, we identified and mapped the pathways and connections from the IC to the MGB. Furthermore, we employed Gq-coupled human M3 designer receptors exclusively activated by designer drugs (DREADDs) and Gi-coupled human M4 DREADDs to achieve targeted excitation or suppression of GABAergic neurons in the IC and MGB. Following the administration of clozapine N-oxide, which binds to these receptors, we modulated these neural circuits to assess their impact on tinnitus severity in a mouse model. Results: . Our findings demonstrated that mice exposed to salicylate exhibited tinnitus-like behaviors. GABAergic neurons projecting retrogradely from the MGB to the IC were primarily concentrated in the external nucleus of the IC. After clozapine N-oxide administration, chemogenetic activation of IC-MGB GABAergic neurons aggravated salicylate-induced tinnitus. Additionally, activation of GABAergic neurons between the IC and MGB induced the perception of tinnitus even without salicylate. However, chemogenetic inhibition of the IC-MGB GABAergic circuit did not reverse salicylate-induced tinnitus. Conclusion . These findings suggest that activation of the IC-MGB GABAergic neural circuit may contribute to tinnitus generation through a mechanism distinct from that of salicylate-induced tinnitus. This study provides novel insights into the mechanisms underlying tinnitus.

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.

With the widespread use of antibiotics, drug-resistant bacterial infections have become a significant threat to human health. Finding new antibacterial strategies that can effectively control drug-resistant bacterial infections has become an urgent task. Unlike small molecule drugs that target bacterial proteins, antisense oligonucleotide (ASO) can target genes related to bacterial resistance, pathogenesis, growth, reproduction and biofilm formation. By regulating the expression of these genes, ASO can inhibit or kill bacteria, providing a novel approach for the development of antibacterial drugs. To overcome the challenge of delivering antisense oligonucleotide into bacterial cells, various drug delivery systems have been applied in this field, including cell-penetrating peptides, lipid nanoparticles and inorganic nanoparticles, which have injected new momentum into the development of antisense oligonucleotide in the antibacterial realm. This review summarizes the current development of small nucleic acid drugs, the antibacterial mechanisms, targets, sequences and delivery vectors of antisense oligonucleotide, providing a reference for the research and development of antisense oligonucleotide in the treatment of bacterial infections.

Objective:To explore the safety and efficacy of intraosseous regional administration (IORA) of vancomycin for preventing infection in primary total knee arthroplasty (TKA).Methods:A total of 124 patients with knee osteoarthritis undergoing TKA between February 2024 and May 2024 at nine hospitals were enrolled. Preoperative infection prophylaxis involved either IORA (0.5 g vancomycin administered via intraosseous regional infusion before incision) or intravenous infusion (1 g vancomycin via peripheral vein). The IORA group included 15 males and 47 females with a median age of 66.5 years (range, 60.0-70.0 years), while the intravenous group included 14 males and 48 females with a median age of 66.0 years (range, 61.8-70.3 years) years. Intraoperative samples were collected including fat and synovium tissues after incision, before prosthesis placement, and after tourniquet release; distal femoral cancellous bone during femoral osteotomy; proximal tibial cancellous bone during tibial osteotomy; proximal intercondylar cancellous bone before prosthesis placement; and peripheral blood from non-infused arms at surgery initiation and after tourniquet release. Vancomycin concentrations were measured using liquid chromatography-tandem mass spectrometry. Vital sign changes were recorded from admission to 5~10 minutes post-IORA (IORA group) or post-incision (intravenous group). Follow-ups were conducted on postoperative day 1 and 3, and at 1 and 3 months, to document complications including IORA-related adverse events, periprosthetic joint infections, surgical site infections, red man syndrome, acute kidney injury, deep vein thrombosis and so on.Results:Vancomycin concentrations in bone, fat, and synovial tissue samples were significantly higher in the IORA group than in the intravenous group ( P<0.05), while vancomycin concentrations in blood samples were significantly lower in the IORA group than in the intravenous group ( P<0.05). Only 7.3%(41/558) of tissue samples in the IORA group had vancomycin concentrations below 2.0 μg/g (the minimum inhibitory concentration of vancomycin against coagulase-negative staphylococcus), compared to 59.3%(331/558) in the intravenous group (χ 2=11.285, P<0.001). In the intravenous group, 16.9%(21/124) of blood samples had vancomycin concentrations exceeding 15.0 mg/L (the threshold associated with a significantly increased risk of nephrotoxicity), while all concentrations in the IORA group were below this threshold, the difference was statistically significant (χ 2=22.943, P<0.001). There were no statistically significant difference ( P>0.05) in vital signs changes before and after vancomycin administration between the two groups. Two patients in the intravenous group experienced incision exudate, while no other related complications occurred in either group. Conclusions:Compared to the traditional intravenous infusion of 1 g vancomycin, intraosseous injection of a low dose (0.5 g) of vancomycin achieves higher local tissue concentrations in the knee joint with a lower incidence of adverse reactions and is safe for infection prophylaxis. Despite guidelines not recommending the routine use of vancomycin for preventing infection after primary TKA, intraosseous injection of 0.5 g vancomycin may be considered intraoperatively for primary TKA in the following scenarios: patients in medical institutions with a high prevalence of methicillin-resistant staphylococcus aureus (MRSA) infections, patients with potential preoperative MRSA colonization, or patients with cephalosporin allergy.

AIM To investigate the effects of Jisuishang Formula on neurological function and ferroptosis in a rat model of cervical spondylotic myelopathy(CSM).METHODS The CSM rat models were established and randomly assigned to the model group,the Fer-1 group(2 g/kg Ferrostatin-1 via intraperitoneal injection),the low-dose(9.7 g/kg,intragastrically),medium-dose(19.4 g/kg,intragastrically)and high-dose(38.8 g/kg,intragastrically)Jisuishang Formula groups,and the sham operation group,with 6 rats in each group.Following 4 weeks of treatment administration,BBB locomotor scores and oblique plate test result were recorded to assess their neurological function in rats.Histopathological evaluation utilized HE staining for spinal cord tissue pathology,Nissl staining for Nissl body visualization,and Prussian blue staining for iron ion deposition analysis.Protein expressions of Nrf2,SLC7A11,GPX4,HO-1,TFRC and Cox2 in spinal cord tissues was detected by immunofluorescence and Western blot,while mRNA expressions were quantified using RT-qPCR.RESULTS Compared to the sham group,the CSM model group exhibited significantly reduced BBB locomotor scores and inclined plane test performance at 1,2 and 4 weeks post-operation(P＜0.05);obvious tissue cavitation,cellular edema and Prussian blue positive iron deposition in spinal cord tissues;downregulated protein and mRNA expressions of Nrf2,SLC7A11,GPX4,HO-1(P＜0.05);and upregulated protein and mRNA expressions of TFRC and Cox2(P＜0.05).Compared to the model group,the Jisuishang Formula and Fer-1 intervention groups showed significantly improved BBB scores and inclined plane test result at 1,2 and 4 weeks post-operation(P＜0.05);reduced tissue cavitation,attenuated cellular edema and decreased Prussian blue positive iron deposition in spinal cord tissues;upregulated protein and mRNA expression of Nrf2,SLC7A11,GPX4 and HO-1 in spinal cord tissues(P＜0.05);and downregulated protein and mRNA expressions of TFRC and Cox2(P＜0.05).CONCLUSION Targeting the Nrf2/SLC7A11/GPX4 signaling pathway,Jisuishang Formula potentially suppresses ferroptosis and alleviates iron accumulation in spinal cord neurons,thereby improving neurological recovery in CSM rats.

Objective To determine the knee joint cartilage thickness using different methods and explore the feasibility of mathematical statistical models of dataset for the prediction of cartilage thickness.Methods A total of 304 patients diagnosed as knee osteoarthritis(OA)combined with varus deformity and undergoing unilateral total knee arthroplasty at the Second Affiliated Hospital of Army Medical University from March 2023 to March 2024 were selected for the study.All patients had complete preoperative and postoperative clinical data.The healthy cartilage at four anatomical sites of patients,including the distal femur lateral condyle,lateral tibial plateau,posterior medial femoral condyle,and posterior lateral femoral condyle were selected,and the knee joint cartilage thickness was determined based on preoperative MRI analysis,robotic navigation system tracing,tissue section of surgical specimen and digital vernier caliper.The baseline indicators of demographics,disease and imaging ffor patients were collected to construct a dataset,and four models of linear regression analysis,principal component analysis,Least Absolute Shrinkage and Selection Operator(LASSO)regression analysis,and K-nearest neighbors(KNN)analysis were established for predicting the accuracy,determination coefficient(R2)and root mean square error(RMSE),and the regression equation for predicting cartilage thickness was established.Results The knee joint cartilage thicknesses determined by preoperative MRI analysis,robotic navigation system tracing,tissue section of surgical specimen had no statistically significant difference with that by digital vernier caliper(P>0.05).The predictive efficiencies of models of linear regression analysis,principal component analysis,and LASSO regression analysis for the knee joint cartilage thickness all failed to meet the expectations(R2<0.3,RMSE>0.03).The predictive effect of KNN model on the cartilage thickness of the distal femur lateral condyle and lateral tibial plateau was not ideal(R2=0.23,RMSE=0.29),while it had potential predictive value(accuracy=0.21,accuracy=0.15).Conclusion The prediction model of knee joint cartilage thickness based on individual parameters has certain scientificity,and the feasibility of KNN model is relatively high.However,due to insufficient sample size and unclear individual parameter weight,the efficiencies of the four established prediction models are not ideal,which fails to provide definite prediction equations.Therefore,the construction scheme of the prediction model still needs to be further optimized.

BACKGROUND: Postoperative pulmonary complications (PPCs) are major adverse events in neurosurgical patients. This study aimed to develop and validate machine learning models predicting PPCs after neurosurgery. METHODS: PPCs were defined according to the European Perioperative Clinical Outcome standards as occurring within 7 postoperative days. Data of cases meeting inclusion/exclusion criteria were extracted from the anesthesia information management system to create three datasets: The development (data of Huashan Hospital, Fudan University from 2018 to 2020), temporal validation (data of Huashan Hospital, Fudan University in 2021) and external validation (data of other three hospitals in 2023) datasets. Machine learning models of six algorithms were trained using either 35 retrievable and plausible features or the 11 features selected by Lasso regression. Temporal validation was conducted for all models and the 11-feature models were also externally validated. Independent risk factors were identified and feature importance in top models was analyzed. RESULTS: PPCs occurred in 712 of 7533 (9.5%), 258 of 2824 (9.1%), and 207 of 2300 (9.0%) patients in the development, temporal validation and external validation datasets, respectively. During cross-validation training, all models except Bayes demonstrated good discrimination with an area under the receiver operating characteristic curve (AUC) of 0.840. In temporal validation of full-feature models, deep neural network (DNN) performed the best with an AUC of 0.835 (95% confidence interval [CI]: 0.805-0.858) and a Brier score of 0.069, followed by Logistic regression (LR), random forest and XGBoost. The 11-feature models performed comparable to full-feature models with very close but statistically significantly lower AUCs, with the top models of DNN and LR in temporal and external validations. An 11-feature nomogram was drawn based on the LR algorithm and it outperformed the minimally modified Assess respiratory RIsk in Surgical patients in CATalonia (ARISCAT) and Laparoscopic Surgery Video Educational Guidelines (LAS VEGAS) scores with a higher AUC (LR: 0.824, ARISCAT: 0.672, LAS: 0.663). Independent risk factors based on multivariate LR mostly overlapped with Lasso-selected features, but lacked consistency with the important features using the Shapley additive explanation (SHAP) method of the LR model. CONCLUSIONS: The developed models, especially the DNN model and the nomogram, had good discrimination and calibration, and could be used for predicting PPCs in neurosurgical patients. The establishment of machine learning models and the ascertainment of risk factors might assist clinical decision support for improving surgical outcomes. TRIAL REGISTRATION ChiCTR 2100047474; https://www.chictr.org.cn/showproj.html?proj=128279 .
Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; Algorithms ; Lung Diseases/etiology* ; Machine Learning ; Neurosurgical Procedures/adverse effects* ; Postoperative Complications/diagnosis* ; Risk Factors ; ROC Curve

With Glycyrrhizae Radix et Rhizoma-Gypsum Fibrosum drug pair as the research object, supramolecular chemistry of traditional Chinese medicine(TCM) was used to study differences between the compatibility of herbal medicine Glycyrrhizae Radix et Rhizoma with mineral medicine Gypsum Fibrosum and its main component CaSO_4·2H_2O, so as to preliminarily discuss the scientific connotation of compatibility of Gypsum Fibrosum in clinical application. A Malvern particle sizer, a scanning electron microscope(SEM), and a conductivity meter were used to observe and determine the physical properties such as microscopic morphology, particle size, and conductivity of Gypsum Fibrosum, CaSO_4·2H_2O, and water decoctions of them with Glycyrrhizae Radix et Rhizoma. An inductively coupled plasma optical emission spectrometer(ICP-OES) was employed to detect the inorganic metal elements in Glycyrrhizae Radix et Rhizoma-Gypsum Fibrosum and Glycyrrhizae Radix et Rhizoma-CaSO_4·2H_2O. Isothermal titration calorimetry(ITC) was conducted to quantify the interactions of Gypsum Fibrosum and CaSO_4·2H_2O with Glycyrrhizae Radix et Rhizoma. A Fourier transform infrared spectrometer(FTIR) was used to analyze the characteristic absorption peak change of Glycyrrhizae Radix et Rhizoma-Gypsum Fibrosum and Glycyrrhizae Radix et Rhizoma-CaSO_4·2H_2O. X-ray diffraction(XRD) was performed to determine the crystal structure and phase composition of Glycyrrhizae Radix et Rhizoma-Gypsum Fibrosum and Glycyrrhizae Radix et Rhizoma-CaSO_4·2H_2O. Further, glycyrrhizic acid(GA) was substituted for Glycyrrhizae Radix et Rhizoma to co-decoct with Gypsum Fibrosum, CaSO_4·2H_2O, and freeze-dried powder of their respective water decoctions. The results of XRD were used for verification analysis. The results showed that although CaSO_4·2H_2O is the main component of Gypsum Fibrosum, there were significant differences between their decoctions and between the decoctions of them with Glycyrrhizae Radix et Rhizoma. Specifically,(1) Both CaSO_4·2H_2O and Gypsum Fibrosum were amorphous fibrous. However, the particle size and conductivity were significantly different between the decoctions of CaSO_4·2H_2O and Gypsum Fibrosum alone.(2) Under SEM, Glycyrrhizae Radix et Rhizoma-CaSO_4·2H_2O was a hybrid system with various morphologies, while Glycyrrhizae Radix et Rhizoma-Gypsum Fibrosum presented uniform nanoparticles.(3) The particle sizes and conductivities of Glycyrrhizae Radix et Rhizoma-CaSO_4·2H_2O and Glycyrrhizae Radix et Rhizoma-Gypsum Fibrosum were significantly different and did not follow the same tendency as those of the decoctions of CaSO_4·2H_2O and Gypsum Fibrosum alone.(4) Compared with Glycyrrhizae Radix et Rhizoma-CaSO_4·2H_2O, Glycyrrhizae Radix et Rhizoma-Gypsum Fibrosum had stronger molecular binding ability and functional group structure change.(5) The crystal form was largely different between the freeze-dried powder of CaSO_4·2H_2O decoction and Gypsum Fibrosum decoction, and their crystal forms were also significantly different from those of the freeze-dried powder of Glycyrrhizae Radix et Rhizoma-CaSO_4·2H_2O and Glycyrrhizae Radix et Rhizoma-Gypsum Fibrosum decoctions. The reason for the series of differences is that Gypsum Fibrosum is richer in trace elements than CaSO_4·2H_2O. The XRD results of GA-Gypsum Fibrosum and GA-CaSO_4·2H_2O decoctions further prove the importance of trace elements in Gypsum Fibrosum for supramolecule formation. This research preliminarily reveals the influence of compatibility of Gypsum Fibrosum or CaSO_4·2H_2O on decoction phase state, material form, and crystal form, providing a basis for the rational clinical application of Gypsum Fibrosum.
Drugs, Chinese Herbal/chemistry* ; Calcium Sulfate/chemistry* ; Glycyrrhiza/chemistry* ; Crystallization ; Particle Size ; Medicine, Chinese Traditional ; Rhizome/chemistry*

Aromatic immunity in traditional Chinese medicine(TCM) is the medical knowledge accumulated in the process of people's struggling with diseases. It plays an important role in plague prevention, disease treatment, health preservation, and rehabilitation, and has profound TCM basic theoretical support and abundant modern scientific evidence. With the in-depth promotion of the Healthy China initiative and the succession of health needs in the post-COVID-19 era, how to practice the health concept of aromatic immunity in TCM and develop its health service resources with high quality has become an important proposition to be discussed urgently. This paper summarizes the cognitive process, puts forward the basic concept, discusses the scientific connotation and clinical application value, and looks forward to the future development trend of aromatic immunity in TCM, aiming to provide guidance for the development of great health products and promote the application of aromatic immunity in TCM in serving people's health.
Medicine, Chinese Traditional/methods* ; Humans ; COVID-19/immunology* ; China ; Drugs, Chinese Herbal/therapeutic use* ; SARS-CoV-2