Renal Fanconi syndrome (FS) is a rare renal manifestation of primary Sjögren's syndrome (pSS). This study aims to analyze the clinical and prognostic characteristics of patients with pSS-associated renal FS (pSS-FS) and provide insights for clinical management.

ObjectiveThe frequency difference limen (FDL) serves as a fundamental metric utilized for effectively quantifying the precise perceptual capabilities of the central auditory system. However, traditional measurement methods rely heavily on the active behavioral responses of subjects and are consequently highly susceptible to the negative influence of confounding subjective factors. Furthermore, existing research paradigms frequently employ uniform stimulus configurations that overlook critical individual perceptual differences. Based on brain-computer interface (BCI) technology, this comprehensive study aims to establish an objective and quantitative evaluation method for auditory frequency discrimination by systematically analyzing and decoding the specific neural responses elicited at the exact threshold state. MethodsWe designed a personalized rapid serial auditory presentation (RSAP) paradigm customized based on each individual’s precise FDL. A cohort of eleven healthy participants was recruited to evaluate the paradigm using pure-tone sequences at a baseline frequency of 4 000 Hz. This experimental paradigm simulates a realistic auditory perception environment through the continuous presentation of acoustic stimuli, thereby allowing for an in-depth investigation into the specific neural representations evoked by weak frequency deviations at the threshold state. Given that auditory stimulus-evoked response features exhibit complex and differentiated spatiotemporal distribution patterns across multiple frequency domains, this study further deeply integrates the cross-scale feature interaction module with the dynamic spatiotemporal attention allocation strategy, innovatively proposing the Multi-Scale Spatial-Temporal Dual Attention Network (MS-STAMNet). Specifically, the network constructs parallel processing branches with multiple receptive fields and introduces a dynamic adaptive weighting strategy to precisely localize core neural activity signals, further deeply integrating multi-scale information through cross-branch feature information interaction to achieve robust single-trial decoding of weak auditory evoked responses. ResultsThe comprehensive electrophysiological data analysis demonstrated that subtle auditory frequency deviation stimuli presented at the threshold level successfully elicited pronounced N2 and P3 event-related potential features, reflecting pre-attentive mismatch detection and subsequent cognitive evaluation, which were prominently distributed over the frontal, central, and temporal regions of the scalp. In the complex time-frequency domain, the extracted neural response characteristics exhibited distinct, statistically significant event-related synchronization within both the low-frequency δ and θ frequency bands, which was simultaneously accompanied by a widespread, prominent event-related desynchronization within the higher α band. A comparative analysis of model performance demonstrated that MS-STAMNet achieved an average unweighted average recall (UAR) of (69.67±6.12)% and area under the curve (AUC) of 0.761 8±0.07, significantly outperforming the established baseline models such as EEGNet and PLNet. Furthermore, a distinct dissociation phenomenon was verified between neural decoding and behavioral performance through regression analysis (R²=0.016, P=0.709), indicating that this model can effectively capture the implicit features of subtle frequency deviations, even when they fail to trigger explicit conscious responses. Additionally, attention weight visualization analysis further reveals the highly accurate focus of the network on key features concentrated over the bilateral temporal and fronto-parietal regions. ConclusionThis study systematically and comprehensively uncovers the multi-dimensional spatiotemporal evolutionary patterns of complex neural responses processing subtle acoustic variations under long-sequence threshold auditory stimulation. Concurrently, it verifies the efficacy and robustness of the proposed MS-STAMNet architecture in accurately deciphering weak, single-trial electroencephalogram signals amidst complex background noise. Ultimately, these neurophysiological and algorithmic findings lay a solid theoretical and methodological foundation for the objective and quantitative evaluation of individual auditory cognitive capabilities in clinical applications, transcending the fundamental limitations of traditional behavioral paradigms and providing robust technical support for future auditory research and related clinical assessments.

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.

ObjectiveTo observe the clinical efficacy of tonifying kidney and replenishing essence on asthenozoospermia patients with the syndrome of kidney essence deficiency and the effects of this method on the adenosine 5′-monophosphate-activated protein kinase （AMPK）/mammalian target of rapamycin complex 1 （mTORC1） signaling pathway. MethodsSeventy-two eligible asthenozoospermia patients with the syndrome of kidney essence deficiency treated in the Affiliated Hospital of Chengdu University of Traditional Chinese Medicine from February 2023 to January 2024 were selected and randomly assigned into an observation group and a control group， with 36 patients in each group. The observation group received oral administration of Guilu Tianjing capsules， while the control group received oral administration of L-carnitine oral solution. The treatment course lasted for 4 weeks in both groups. The observed indicators included sperm progressive motility rate （PR）， total sperm motility （PR+NP）， percentage of normal mitochondrial membrane potential （MMP）， and traditional Chinese medicine （TCM） symptom scores before and after treatment in both groups. A three-month follow-up was instituted to record the conception status of the patients’ spouses. Additionally， eight patients were randomly selected from the eligible patients in the observation group， and four healthy males with normal semen routine examination results were included as the control group for the determination of protein expression. Western blotting was conducted to assess the expression of AMPK， phosphorylated （p）-AMPK， regulatory-associated protein of mTOR （RAPTOR） and p-RAPTOR， and PTEN-induced putative kinase 1 （PINK1） in sperms from the observation group before and after treatment， as well as in the sperms of the control group. ResultsThe pregnancy rate of spouses in the observation group was 9.09% （3/33）， which was higher than that （3.33%， 1/30） in the control group. The total response rate was 84.8% （28/33） in the observation group and 66.7% （20/30） in the control group， with no statistically significant difference. After treatment， both groups were improved considering PR， PR+NP， MMP， and TCM symptom scores （P<0.01）. Moreover， the observation group exhibited more pronounced decreases in TCM symptom scores than the control group （P<0.05）， while the changes in PR， PR+NP， and MMP showed no statistical significance between groups. Compared with the control group， the asthenozoospermia group exhibited upregulations in phosphorylation levels of AMPK and RAPTOR and protein level of PINK （P<0.01）. The administration of Guilu Tianjing Capsules led to downregulations in the phosphorylation levels of AMPK and RAPTOR and protein level of PINK1 （P<0.01）. However， the protein levels of AMPK and RAPTOR demonstrated no significant difference between before and after treatment. During the study period， neither group of patients exhibited any notable adverse reactions. ConclusionGuilu Tianjing capsules can enhance the sperm motility and percentage of normal mitochondrial membrane potential in asthenozoospermia patients with the syndrome of kidney essence deficiency by downregulating the AMPK/mTORC1 signaling pathway， lowering the protein level of PINK1， and inhibiting excessive activation of mitophagy.

ObjectiveTo observe the clinical efficacy of tonifying kidney and replenishing essence on asthenozoospermia patients with the syndrome of kidney essence deficiency and the effects of this method on the adenosine 5′-monophosphate-activated protein kinase （AMPK）/mammalian target of rapamycin complex 1 （mTORC1） signaling pathway. MethodsSeventy-two eligible asthenozoospermia patients with the syndrome of kidney essence deficiency treated in the Affiliated Hospital of Chengdu University of Traditional Chinese Medicine from February 2023 to January 2024 were selected and randomly assigned into an observation group and a control group， with 36 patients in each group. The observation group received oral administration of Guilu Tianjing capsules， while the control group received oral administration of L-carnitine oral solution. The treatment course lasted for 4 weeks in both groups. The observed indicators included sperm progressive motility rate （PR）， total sperm motility （PR+NP）， percentage of normal mitochondrial membrane potential （MMP）， and traditional Chinese medicine （TCM） symptom scores before and after treatment in both groups. A three-month follow-up was instituted to record the conception status of the patients’ spouses. Additionally， eight patients were randomly selected from the eligible patients in the observation group， and four healthy males with normal semen routine examination results were included as the control group for the determination of protein expression. Western blotting was conducted to assess the expression of AMPK， phosphorylated （p）-AMPK， regulatory-associated protein of mTOR （RAPTOR） and p-RAPTOR， and PTEN-induced putative kinase 1 （PINK1） in sperms from the observation group before and after treatment， as well as in the sperms of the control group. ResultsThe pregnancy rate of spouses in the observation group was 9.09% （3/33）， which was higher than that （3.33%， 1/30） in the control group. The total response rate was 84.8% （28/33） in the observation group and 66.7% （20/30） in the control group， with no statistically significant difference. After treatment， both groups were improved considering PR， PR+NP， MMP， and TCM symptom scores （P<0.01）. Moreover， the observation group exhibited more pronounced decreases in TCM symptom scores than the control group （P<0.05）， while the changes in PR， PR+NP， and MMP showed no statistical significance between groups. Compared with the control group， the asthenozoospermia group exhibited upregulations in phosphorylation levels of AMPK and RAPTOR and protein level of PINK （P<0.01）. The administration of Guilu Tianjing Capsules led to downregulations in the phosphorylation levels of AMPK and RAPTOR and protein level of PINK1 （P<0.01）. However， the protein levels of AMPK and RAPTOR demonstrated no significant difference between before and after treatment. During the study period， neither group of patients exhibited any notable adverse reactions. ConclusionGuilu Tianjing capsules can enhance the sperm motility and percentage of normal mitochondrial membrane potential in asthenozoospermia patients with the syndrome of kidney essence deficiency by downregulating the AMPK/mTORC1 signaling pathway， lowering the protein level of PINK1， and inhibiting excessive activation of mitophagy.

Electroencephalogram (EEG) is a non-invasive, high temporal-resolution technique for monitoring brain activity. However, affected by the volume conduction effect, EEG has a low spatial resolution and is difficult to locate brain neuronal activity precisely. The surface Laplacian (SL) technique obtains the Laplacian EEG (LEEG) by estimating the second-order spatial derivative of the scalp potential. LEEG can reflect the radial current activity under the scalp, with positive values indicating current flow from the brain to the scalp (“source”) and negative values indicating current flow from the scalp to the brain (“sink”). It attenuates signals from volume conduction, effectively improving the spatial resolution of EEG, and is expected to contribute to breakthroughs in neural engineering. This paper provides a systematic overview of the principles and development of SL technology. Currently, there are two implementation paths for SL technology: current source density algorithms (CSD) and concentric ring electrodes (CRE). CSD performs the Laplace transform of the EEG signals acquired by conventional disc electrodes to indirectly estimate the LEEG. It can be mainly classified into local methods, global methods, and realistic Laplacian methods. The global method is the most commonly used approach in CSD, which can achieve more accurate estimation compared with the local method, and it does not require additional imaging equipment compared with the realistic Laplacian method. CRE employs new concentric ring electrodes instead of the traditional disc electrodes, and measures the LEEG directly by differential acquisition of the multi-ring signals. Depending on the structure, it can be divided into bipolar CRE, quasi-bipolar CRE, tripolar CRE, and multi-pole CRE. The tripolar CRE is widely used due to its optimal detection performance. While ensuring the quality of signal acquisition, the complexity of its preamplifier is relatively acceptable. Here, this paper introduces the study of the SL technique in resting rhythms, visual-related potentials, movement-related potentials, and sensorimotor rhythms. These studies demonstrate that SL technology can improve signal quality and enhance signal characteristics, confirming its potential applications in neuroscientific research, disease diagnosis, visual pathway detection, and brain-computer interfaces. CSD is frequently utilized in applications such as neuroscientific research and disease detection, where high-precision estimation of LEEG is required. And CRE tends to be used in brain-computer interfaces, that have stringent requirements for real-time data processing. Finally, this paper summarizes the strengths and weaknesses of SL technology and envisages its future development. SL technology boasts advantages such as reference independence, high spatial resolution, high temporal resolution, enhanced source connectivity analysis, and noise suppression. However, it also has shortcomings that can be further improved. Theoretically, simulation experiments should be conducted to investigate the theoretical characteristics of SL technology. For CSD methods, the algorithm needs to be optimized to improve the precision of LEEG estimation, reduce dependence on the number of channels, and decrease computational complexity and time consumption. For CRE methods, the electrodes need to be designed with appropriate structures and sizes, and the low-noise, high common-mode rejection ratio preamplifier should be developed. We hope that this paper can promote the in-depth research and wide application of SL technology.

Disruption of the intestinal mucosal barrier caused by gut dysbiosis and metabolic imbalance is the underlying pathology of inflammatory bowel disease (IBD). Traditional Chinese medicine Wuji Wan (WJW) is commonly used to treat digestive system disorders and showed therapeutic potential for IBD. In this interdisciplinary study, we aim to investigate the pharmacological effects of WJW against experimental colitis by combining functional metabolomics and gut-microbiota sequencing techniques. Treatment with WJW altered the profile of the intestinal microbiota and notably increased the abundance of Lactobacillus, thereby facilitating the conversion of tryptophan into indole-3-acetic acid (IAA) and indoleacrylic acid (IA). These indole derivatives activated the aryl hydrocarbon receptor (AhR) pathway, which reduced colonic inflammation and restored the expression of intestinal barrier proteins. Interestingly, the beneficial effects of WJW on gut barrier function improvement and tryptophan metabolism were disappeared in the absence of gut microbiota. Finally, pre-treatment with the AhR antagonist CH-223191 confirmed the essential role of IAA-mediated AhR activation in the therapeutic effects of WJW. Overall, WJW enhanced intestinal barrier function and reduced colonic inflammation in a murine colitis model by modulating Lactobacillus-IAA-AhR signaling pathway. This study provides novel insights into colitis pathogenesis and presents an effective therapeutic and preventive approach against IBD.

Host-derived small RNAs are emerging as critical regulators in the dynamic interactions between host tissues and the microbiome, with implications for microbial pathogenesis and host defense. Among these, transfer RNA-derived small RNAs (tsRNAs) have garnered attention for their roles in modulating microbial behavior. However, the bacterial factors mediating tsRNA interaction and functionality remain poorly understood. In this study, using RNA affinity pull-down assay in combination with mass spectrometry, we identified a putative membrane-bound protein, annotated as P-type ATPase transporter (PtaT) in Fusobacterium nucleatum (Fn), which binds Fn-targeting tsRNAs in a sequence-specific manner. Through targeted mutagenesis and phenotypic characterization, we showed that in both the Fn type strain and a clinical tumor isolate, deletion of ptaT led to reduced tsRNA intake and enhanced resistance to tsRNA-induced growth inhibition. Global RNA sequencing and label-free Raman spectroscopy revealed the phenotypic differences between Fn wild type and PtaT-deficient mutant, highlighting the functional significance of PtaT in purine and pyrimidine metabolism. Furthermore, AlphaFold 3 prediction provides evidence supporting the specific binding between PtaT and Fn-targeting tsRNA. By uncovering the first RNA-binding protein in Fn implicated in growth modulation through interactions with host-derived small RNAs (sRNAs), our study offers new insights into sRNA-mediated host-pathogen interplay within the context of microbiome-host interactions.
Fusobacterium nucleatum/growth & development* ; RNA-Binding Proteins/genetics* ; Bacterial Proteins/genetics* ; RNA, Bacterial/metabolism* ; Humans ; RNA, Transfer/metabolism*