Antibodies play a critical role in adaptive immune responses and serve as key components in disease diagnosis and treatment. These molecules exhibit dynamic post-translational modifications (PTMs), such as glycosylation and phosphorylation, which regulate their effector functions. To date, nearly all of our knowledge about antibody repertoires has come from B cell receptor (BCR) sequencing (BCR-seq), which facilitates the profiling of clonal composition and the tracing of maturation trajectories within B-cell repertoires. However, circulating antibodies found in bodily fluids—such as serum, saliva, milk, mucosal secretions, and cerebrospinal fluid—exhibit diversities and specificities beyond what BCR-seq alone can predict. Therefore, identifying and quantifying antibody clonotypes at the protein level could enhance diagnosis, prognosis, and treatment strategies in personalized medicine. The critical gap between genotype and phenotype necessitates complementary methodologies that enable the direct characterization of antibody proteins in their native functional states. Mass spectrometry (MS)-based antibody repertoire sequencing (Ab-seq) is currently the only feasible approach for this task and primarily includes database-dependent methods—such as bottom-up, middle-down, and top-down approaches—as well as database-independent de novo sequencing technology. These strategies enable multi-level, high-precision characterization ranging from peptides and domains to intact antibody molecules. Unlike the shotgun strategy commonly used in routine proteomics, obtaining full sequences of all antibodies presents unique challenges. It requires specialized methodological adaptations to address issues related to dynamic range, sequence variation, and sample complexity. This review introduces the technical principles, methodological workflows, and recent applications of various mass spectrometry-based antibody repertoire sequencing (Ab-seq) strategies, with a focus on approaches designed to improve sequence coverage and identification accuracy. These include multi-enzyme digestion, hybrid fragmentation methods, and artificial intelligence-assisted de novo sequencing. By systematically comparing database-dependent techniques—such as bottom-up, middle-down, and top-down approaches—with database-independent de novo sequencing, this review outlines their respective advantages and limitations in terms of sample throughput, sequence coverage, post-translational modification characterization, and data analysis complexity. In addition, this review discusses emerging technological trends, including the integration of ion mobility separation, native mass spectrometry, and artificial intelligence-driven data interpretation, which are expected to enhance the depth and accuracy of antibody characterization. Although current methods continue to face challenges related to sample complexity, dynamic range, and unambiguous sequence variant assignment, we emphasize the importance of integrating BCR-seq and Ab-seq data to construct gene-protein association maps. These maps help validate sequence accuracy and facilitate epitope discovery. This dual-platform strategy helps bridge the gap between genotype and phenotype, thereby enhancing both the resolution and scope of antibody repertoire studies. Such an integrative approach also offers a valuable tool for therapeutic antibody development, structure-function analysis, and precise evaluation of vaccine efficacy.

ObjectiveThe present study aimed to investigate alterations in white matter microstructure and spontaneous neural activity in male college smokers, and to further explore their associations with nicotine dependence. Given that adolescence and early adulthood represent critical periods for brain maturation, particularly for white matter development, understanding the neural correlates of smoking behavior during this stage is of substantial importance for both neuroscience and public health. MethodsA total of 115 male undergraduate students were initially recruited for this study. After quality control and exclusion procedures, 52 male college smokers and 42 demographically matched healthy non-smokers were included in the final analysis. All participants underwent multimodal magnetic resonance imaging (MRI), including diffusion tensor imaging (DTI) and resting-state functional MRI (rs-fMRI). White matter fiber tracts were reconstructed using the automated fiber quantification (AFQ) method, which enables precise identification and quantification of major fiber bundles. Eighteen major white matter tracts were segmented for each participant. Along the core trajectory of each tract, 100 equidistant nodes were sampled. Fractional anisotropy (FA) was calculated at each node to assess white matter microstructural integrity, while amplitude of low-frequency fluctuation (ALFF) was computed to evaluate spontaneous neural activity within white matter tracts. Between-group differences in FA and ALFF were assessed using two-sample t-tests, with appropriate corrections applied for multiple comparisons. Furthermore, Pearson correlation analyses were conducted to examine the relationships between imaging-derived metrics (FA and ALFF values in regions showing significant group differences) and nicotine dependence severity, as measured by the Fagerström test for nicotine dependence (FTND). ResultsCompared with healthy non-smokers, male college smokers exhibited significantly increased FA values in several white matter tracts, including the left thalamic radiation, right corticospinal tract, forceps major of the corpus callosum, left uncinate fasciculus, and right arcuate fasciculus. These findings suggest altered microstructural organization or increased directional coherence within these pathways. In addition, smokers demonstrated significantly elevated ALFF values in the forceps major, right uncinate fasciculus, and left arcuate fasciculus, indicating enhanced spontaneous neural activity in these white matter regions. Correlation analyses revealed that FA values in the left thalamic radiation and right corticospinal tract were negatively correlated with FTND scores, suggesting that higher levels of nicotine dependence were associated with reduced microstructural integrity or altered fiber organization in these regions. In contrast, ALFF values in the forceps major and right uncinate fasciculus were positively correlated with FTND scores, indicating that greater nicotine dependence was associated with increased spontaneous neural activity in specific white matter pathways. ConclusionThe present study provides evidence that male college smokers exhibit distinct alterations in both white matter microstructure and functional activity. These abnormalities are not uniformly distributed but rather localized to specific fiber tracts implicated in sensorimotor processing, interhemispheric communication, and higher-order cognitive and emotional regulation. Importantly, the observed associations between imaging metrics and nicotine dependence severity suggest that these structural and functional alterations may reflect neurobiological mechanisms underlying addiction. The combination of AFQ-based tract profiling and multimodal MRI offers a sensitive approach for detecting subtle changes along white matter pathways, highlighting its potential utility in identifying neuroimaging biomarkers of nicotine dependence. Overall, these findings indicate that smoking during early adulthood may disrupt ongoing white matter maturation, potentially leading to long-term consequences for brain function. This study provides novel insights into the neural basis of nicotine dependence and underscores the importance of early intervention and prevention strategies targeting young smokers.

OBJECTIVE To analyze the incidence of cyclosporine （CsA）-associated acute kidney injury （AKI） in hospitalized patients， identify influencing factors， and construct a risk prediction model. METHODS A single-center retrospective study was conducted， enrolling clinical data from hospitalized patients treated with CsA at the First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital from January 2018 to July 2024. The patients were classified into AKI group and non-AKI group based on the occurrence of CsA-related AKI. Univariate analysis and multivariate Logistic regression analysis were used to identify independent risk factors for CsA-related AKI， and a risk prediction model was constructed and its performance was evaluated. RESULTS A total of 439 patients were included， of whom 54 developed CsA-related AKI， with an incidence rate of 12.30%. The occurrence of CsA-associated AKI was positively correlated with concurrent bacterial pulmonary infection， cytomegalovirus viremia， respiratory failure， renal insufficiency， gastrointestinal bleeding， and peripheral central venous catheterization （odds ratios of 763.750， 16.944， 41.933， 236.806， 17.537 and 212.789， respectively； P ＜0.05）； while uric acid， prealbumin， and calcium levels were negatively associated with it （odds ratios of 0.983， 0.967 and 0.058， respectively； P ＜0.05）. The prediction model constructed based on the above factors yielded a χ 2 value of 10.254 （ P ＞0.05） in the Hosmer-Lemeshow test. The average area under the curve （AUC） from 10-fold cross-validation was 0.885. The AUC of the receiver operating characteristic curve was 0.883， with a sensitivity of 84.3% and a specificity of 80.4%， respectively， at the optimal cutoff value of 0.1. CONCLUSIONS Six factors， including concurrent bacterial pulmonary infection and cytomegalovirus viremia， are positively associated with the occurrence of CsA-related AKI； while uric acid， prealbumin， and calcium levels are negatively associated. The Logistic regression model constructed based on these factors demonstrates good predictive performance and can assist clinic in conducting early risk assessment and personalized interventions.

Objective This study reports on the obligatory parasitism of water mites Arrenurus sp. on Anopheles messeae at the Manzhouli Port, Inner Mongolia, China. Methods Duing July 2024, a survey on the mosquito diversity was conducted at the Manzhouli Port. Captured mosquitoes and their ectoparasites were identified to species level. Results A total of 1840 adult mosquitoes were collected, representing species from three genera: Culex(Cx. modestus, Cx. pipiens pallens), Aedes(Ae. dorsalis, Ae. flavidorsalis, Ae. flavescens), and Anopheles (An. messeae). Among all the mosqutioes specimens,3 out of 150 captured An. messeae were found to carry ectoparasitic mites, with number of 2,4,27 mites separately. Morphological and molecular identification reached the same result as water mites(Hydrachnidiae, Hydracrina). COI gene sequence showed 94% similarity with the closest species Arrenurus truncatellus. Conlusions Literature review suggests water mites are host-specific parasitism of mosquito species and herein with the first record of Arrenurus sp. parasiting on An. Messeae in the most high-latitude region globally.

Autism spectrum disorder (ASD) is a neurodevelopmental condition with a steadily rising global prevalence, yet effective pharmacological interventions remain notably limited, highlighting an urgent need for safe, accessible, and mechanism-based therapeutic strategies. Physical exercise has emerged as a promising non-pharmacological intervention that ameliorates both core symptoms—social communication deficits and restricted repetitive behaviors—and associated features including cognitive dysfunction and motor impairments, in children and adolescents with ASD. However, the molecular mechanisms mediating these beneficial effects remain incompletely defined, impeding the development of evidence-based exercise prescriptions and biomarker-driven rehabilitation protocols. MicroRNAs (miRNAs) are evolutionarily conserved small non-coding RNAs that post-transcriptionally regulate approximately 60% of protein-coding genes. Within the central nervous system, miRNAs orchestrate diverse neurobiological processes including neural progenitor proliferation, neuronal differentiation, dendritic spine morphogenesis, synaptic plasticity, and neuroinflammatory homeostasis. Notably, miRNAs are remarkably stable in biological fluids and can be packaged into extracellular vesicles, rendering them attractive candidates as both mechanistic mediators and non-invasive peripheral biomarkers. Among the hundreds of miRNAs expressed in the brain, three—miR-132, miR-34a, and miR-146a—have emerged as particularly relevant to ASD pathophysiology. This review focuses on these three miRNAs for the following reasons: miR-132 is a master regulator of activity-dependent synaptic plasticity through its modulation of BDNF/MeCP2/PTEN signaling and has been consistently downregulated in ASD prefrontal cortex; miR-34a functions as a pro-apoptotic factor that suppresses Bcl-2-mediated neuronal survival pathways and is upregulated in ASD cerebellum; and miR-146a serves as a key brake on neuroinflammation via TLR7/IRAK1 signaling and shows region-specific dysregulation in ASD temporal lobe. We first summarize evidence from human post-mortem brain tissues and ASD animal models demonstrating the consistent dysregulation of these three miRNAs. Notably, the pathological consequences of these miRNA alterations—impaired synaptic plasticity, excessive neuronal apoptosis, and sustained neuroinflammation—are interconnected and collectively contribute to the heterogeneous symptomatology of ASD. We then present a synthesis of emerging evidence demonstrating that various exercise modalities, including swimming, treadmill running, and voluntary wheel running, can concurrently reverse these ASD-like behavioral phenotypes and normalize the expression of the three key miRNAs. These data provide the first direct experimental evidence linking exercise-induced miRNA modulation to ASD symptom improvement. On the basis of these findings, we propose an integrative “exercise-miRNA-ASD” framework wherein exercise functions as a multi-targeted modulator—simultaneously enhancing synaptic plasticity, promoting neuronal survival, and attenuating neuroinflammation—through coordinated regulation of the three miRNAs. Importantly, this framework is not merely descriptive but offers testable predictions: exercise-induced miRNA changes should be dose-dependent, show temporal correlation with behavioral improvements, and be blunted by miRNA-specific antagonists or CRISPR/Cas9-mediated knockout. Beyond its specific application to ASD, this framework has broader implications. The miR-132/BDNF, miR-34a/Bcl-2, and miR-146a/TLR7 pathways are not ASD-specific but represent fundamental neural stress and repair mechanisms that are dysregulated across Alzheimer’s disease (AD), traumatic brain injury, Parkinson’s disease (PD), and major depressive disorder. Exercise has been shown to modulate these same miRNAs in several of these conditions, suggesting that the “exercise-miRNA-neural function” axis may represent a conserved neuroprotective mechanism that transcends diagnostic boundaries. Thus, we propose that ASD serves as an ideal model disease for elucidating this universal mechanism, with findings potentially generalizable to other neurological disorders. We also critically evaluate current translational barriers: the near-absence of human clinical trials with serial miRNA profiling; the undefined dose-response relationships between exercise parameters and miRNA expression; the unresolved causality issue (current evidence demonstrates association, not causation); and the uncertain correlation between peripheral exosomal miRNA levels and brain miRNA dynamics. We argue that future research must prioritize CRISPR/Cas9-based miRNA manipulation in animal models combined with longitudinal exercise interventions to establish causality, and that cross-disease validation studies are essential to determine whether exercise-induced miRNA changes represent a shared neuroprotective signature or disease-specific responses. Ultimately, we envision a paradigm where a simple blood test measuring exosomal miR-132, miR-34a, and miR-146a levels could guide personalized exercise prescriptions, enabling precision rehabilitation for individuals with ASD and potentially other neurological conditions.

ObjectiveThis study aimed to provide a scientific basis for the safety management of drinking water by assessing the contamination levels of 21 per- and polyfluoroalkyl substances (PFASs) in water samples collected from the Yangtze River Delta region. MethodsSolid-phase extraction coupled with ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) was employed to determine 21 PFASs in 6 source water samples, 20 treated water samples, and 60 tap water samples collected from 5 administrative districts in the Yangtze River Delta region across four seasons in 2023. The Kruskal-Wallis H test was used to compare differences in PFAS concentrations among the three types of water samples, followed by post hoc pairwise comparisons with Bonferroni correction. Removal efficiencies of target compounds were also calculated. ResultsAmong the 86 samples, 14 PFASs were detected, with 7 compounds detected in 100% of samples. The total concentrations of PFASs in source water, treated water, and tap water ranged from 63.85 to 190.54 ng·L^-¹, 55.75 to 774.36 ng·L^-¹, and 40.72 to 689.80 ng·L^-¹, respectively. The compounds with the highest median concentrations were perfluorobutanoic acid (PFBA) (40.19, 15.20, and 14.05 ng·L^-¹), perfluorohexanoic acid (PFHxA) (29.69, 32.62, and 34.24 ng·L^-¹), perfluorooctanoic acid (PFOA) (17.65, 39.45, and 36.83 ng·L^-¹), and potassium perfluorobutanesulfonic acid (PFBS) (16.99, 24.38, and 23.89 ng·L^-¹). Notably, concentrations of PFOA (7.71‒71.63 ng·L^-¹) and PFOS (-¹) in tap water were below the regulatory limits specified in GB 5749‒2022 Standards for Drinking Water Quality (PFOA: 0.000 08 mg·L^-1, PFOS: 0.000 04 mg·L^-1). Significant seasonal variations in PFAS concentrations were observed, with total PFAS levels in March being higher than those in September and November (adjusted P<0.001, and P=0.006), and levels in May being higher than those in September (adjusted P=0.037). The removal efficiencies of PFASs in the water treatment plants ranged from -14% to 34%. ConclusionPFAS contamination is widespread in source water, treated water, and tap water in the Yangtze River Delta region, and concentrations of PFOA and PFOS in all tested water samples comply with the Chinese national standards for drinking water quality (GB 5749‒2022). The use of PFASs should be continuously regulated to ensure water safety.

ObjectiveTobacco-related diseases remain one of the leading preventable public health challenges worldwide and are among the primary causes of premature death. In recent years, accumulating evidence has supported the classification of nicotine addiction as a chronic brain disease, profoundly affecting both brain structure and function. Despite the urgency, effective diagnostic methods for smoking addiction remain lacking, posing significant challenges for early intervention and treatment. To address this issue and gain deeper insights into the neural mechanisms underlying nicotine dependence, this study proposes a novel graph neural network framework, termed TI-GNN. This model leverages functional magnetic resonance imaging (fMRI) data to identify complex and subtle abnormalities in brain connectivity patterns associated with smoking addiction. MethodsThe study utilizes fMRI data to construct functional connectivity matrices that represent interaction patterns among brain regions. These matrices are interpreted as graphs, where brain regions are nodes and the strength of functional connectivity between them serves as edges. The proposed TI-GNN model integrates a Transformer module to effectively capture global interactions across the entire brain network, enabling a comprehensive understanding of high-level connectivity patterns. Additionally, a spatial attention mechanism is employed to selectively focus on informative inter-regional connections while filtering out irrelevant or noisy features. This design enhances the model’s ability to learn meaningful neural representations crucial for classification tasks. A key innovation of TI-GNN lies in its built-in causal interpretation module, which aims to infer directional and potentially causal relationships among brain regions. This not only improves predictive performance but also enhances model interpretability—an essential attribute for clinical applications. The identification of causal links provides valuable insights into the neuropathological basis of addiction and contributes to the development of biologically plausible and trustworthy diagnostic tools. ResultsExperimental results demonstrate that the TI-GNN model achieves superior classification performance on the smoking addiction dataset, outperforming several state-of-the-art baseline models. Specifically, TI-GNN attains an accuracy of 0.91, an F1-score of 0.91, and a Matthews correlation coefficient (MCC) of 0.83, indicating strong robustness and reliability. Beyond performance metrics, TI-GNN identifies critical abnormal connectivity patterns in several brain regions implicated in addiction. Notably, it highlights dysregulations in the amygdala and the anterior cingulate cortex, consistent with prior clinical and neuroimaging findings. These regions are well known for their roles in emotional regulation, reward processing, and impulse control—functions that are frequently disrupted in nicotine dependence. ConclusionThe TI-GNN framework offers a powerful and interpretable tool for the objective diagnosis of smoking addiction. By integrating advanced graph learning techniques with causal inference capabilities, the model not only achieves high diagnostic accuracy but also elucidates the neurobiological underpinnings of addiction. The identification of specific abnormal brain networks and their causal interactions deepens our understanding of addiction pathophysiology and lays the groundwork for developing targeted intervention strategies and personalized treatment approaches in the future.

Objective: This study aimed to construct an animal model of heart failure with preserved ejection fraction (HFpEF) that integrates disease and syndrome based on the "deficiency-blood stasis-toxin" pathogenesis and to evaluate it comprehensively. Methods: The HFpEF mouse model was constructed using a combination of Nω-nitro-L-arginine methyl ester (L-NAME) and a high-fat diet. According to the random number table method, SPF-grade male C57BL/6J mice were randomly assigned to the control, L-NAME, high-fat diet, and model groups, 10 in each group. Comprehensive observations and data collection on macroscopic signs (e.g., fur condition, mental state, stool and urine, oral and nasal condition, paw and body condition, etc.) and cardiac function were performed after 10 and 16 weeks of model induction. Additionally, the syndrome evolution was elucidated based on diagnostic criteria for clinical syndromes of heart failure. Furthermore, pathological and molecular biological examinations of myocardial tissue were performed to assess the stability and reliability of the model. Results: Mice in the model group showed typical characteristics of syndrome of qi deficiency and blood stasis, as well as syndrome of internal heat accumulation, including lethargy, slow response, dull paw color and oral/nasal color, exercise intolerance, abnormal platelet activation, dry feces, and dark yellow urine. The time window for these syndromes was between 10 and 16 weeks post-modeling. Cardiac function assessments revealed severe diastolic dysfunction, concentric myocardial hypertrophy, and myocardial fibrosis in the model group. Pathological examinations showed a significantly increased collagen deposition in the myocardial interstitium, enlarged cross-sectional area of cardiomyocytes, and sparse coronary microvasculature in the model group. Molecular biological analyses indicated marked activation of the inducible nitric oxide synthase/nuclear factor kappa-light-chain-enhancer of activated B cells/NOD-like receptor family pyrin domain containing 3 inflammatory pathway and significantly elevated inflammation levels in the myocardial tissue of the model group. Although mice in the L-NAME and high-fat diet groups also showed certain manifestations of qi deficiency syndrome, the substantial cardiac damage was relatively limited compared to the control group. Conclusion This study has constructed an animal model of HFpEF that integrates disease and syndrome based on the "deficiency-blood stasis-toxin" pathogenesis. The macroscopic and microscopic characteristics of this model are consistent with the manifestations of syndrome of qi deficiency and blood stasis, toxin syndrome, and syndrome of internal heat accumulation. Moreover, it can stably simulate the HFpEF state and reflect phenotypic changes in human disease. This model provides a suitable experimental platform to explore the pathogenesis of HFpEF, evaluate the effectiveness of traditional Chinese medicine (TCM) treatment regimens, and promote in-depth research on TCM syndromes of heart failure.

Objective To evaluate the influence of the wearing position of dosimeters outside lead aprons on effective dose estimation for interventional radiology workers, analyze the differences between single and double dosimeter methods in effective dose estimation, and provide a reference for the personal dose monitoring of interventional radiology workers. Methods This study employed a combined approach of on-site monitoring and Monte Carlo simulation to evaluate the impact of the wearing position of dosimeters outside lead aprons on effective dose estimation, as well as the differences between effective doses measured using single and double dosimeters. Interventional radiology workers wore dosimeters at three positions: the neck outside the lead collar, the left chest outside the lead apron, and inside the lead apron. Effective doses were estimated using the single and double dosimeter methods specified in GBZ 128-2019 Specifications for individual monitoring of occupational external exposure, and the impact of different wearing positions on the estimation results was compared. Geant4 Monte Carlo simulations were used to model dose distributions at the neck outside the lead collar and at the left chest outside the lead apron for operators performing cardiovascular interventions under tube voltages of 70, 80, 90, and 100 kVp and exposure angles of posteroanterior (PA), anteroposterior (AP), and left anterior oblique 45° (LAO45°) positions. The study assessed the impact of dosimeter wearing position on effective dose estimation. Results Monte Carlo simulations demonstrated that neck doses consistently exceeded left chest doses across different tube voltages and exposure angles, with neck-to-chest dose ratios of 0.80-0.90. Under identical tube voltage conditions, AP showed the highest doses, followed by LAO45°, and PA demonstrated the lowest doses. The single and double dosimeter methods exhibited consistent patterns in effective dose estimation. Single dosimeter method generally yielded higher effective doses with relative deviations of 9.9% to 83%, though these deviations decreased under high tube voltages. Field monitoring data indicated that most interventional radiology workers maintained relative deviations between single and double dosimeter calculations below 6%, with neck-to-chest dose ratios of 0.95-1.1. The estimation patterns remained consistent across both methods, though single dosimeter method showed slightly higher results. Conclusion Under PA, AP, or LAO45°, the doses at the neck consistently exceeded those at the left chest. Therefore, when wearing lead protective equipment, the dosimeter should be properly positioned at the neck outside the lead collar to accurately reflect the radiation doses of surgeons. Some interventional radiology workers improperly positioned the dosimeter (intended at the neck outside the lead collar) at the left chest outside the lead apron, and this may result in an underestimation of the effective dose.

OBJECTIVE: To explore the effect mechanism of electroacupuncture (EA) for ameliorating diabetic peripheral neuropathy (DPN) based on the analysis of gut microbiota and metabolomics. METHODS: Thirty SPF-grade male SD rats were randomly divided into a normal group, a model group, and an EA group, with 10 rats in each one. Except in the normal group, the intraperitoneally injection with streptozotocin was used to induce diabetes mellitus model in the rest groups. In the EA group, acupuncture was delivered at bilateral "Zusanli" (ST36), "Sanyinjiao" (SP6), "Pishu" (BL20) and "Shenshu" (BL23), and electric stimulation was attached to "Zusanli" (ST36)-"Sanyinjiao" (SP6) and "Pishu" (BL20)-"Shenshu" (BL23), on the same side, with continuous wave and a frequency of 2 Hz, for 10 min in each intervention. The intervention measure of each group was delivered once every 2 days, 3 times a week, for 8 consecutive weeks. Body weight, random blood glucose (RBG), thermal withdrawal latency (TWL), and mechanical withdrawal threshold (MWT) before intervention, and in 4 and 8 weeks of intervention, separately, as well as sensory nerve conduction velocity (SCV) and motor nerve conduction velocity (MCV) of the sciatic nerve after intervention were measured. Metagenomic sequencing (MS) was used to analyze gut microbiota and screen for differential species. Liquid chromatography-mass spectrometry (LC-MS) was employed to detect the differential metabolites in plasma, and the metabolic pathway enrichment analysis was performed on the differential metabolites. Spearman correlation analysis was adopted to assess the relationship between gut microbiota and metabolomics. RESULTS: After 4 and 8 weeks of intervention, when compared with the model group, the EA group showed the increase in body weight, TWL, MWT (P<0.01), and the decrease in RBG (P<0.01). Compared with the normal group, SCV and MCV, as well as Chao1 index were dropped in the model group (P<0.01), and those were elevated in the EA group when compared with those in the model group (P<0.01). The dominant bacterial phyla of each group were Firmicutes (F) and Bacteroidota (B), the ratio of them (F/B) in the model group was lower than that of the normal group (P<0.05), and F/B in the EA group was higher when compared with that in the model group (P<0.05). In comparison with the normal group, the relative abundance increased in Prevotella, Segatella, Prevotella-hominis and Segatella-copri (P<0.05); and it decreased in Ligilactobacillus, Eubacterium, Pseudoflavonifractor, Ligilactobacillus-murinus (P<0.05) in the model group. Compared with the model group, the relevant abundance of the above mentioned gut bacteria was all ameliorated in the EA group (P<0.05, P<0.01). Among the three groups, 120 differential metabolites were identified and enriched in 28 key metabolic pathways, such as glycerophospholipid and linoleic acid, of which, glycerophospholipid was the most significantly affected pathway in EA intervention. Spearman correlation analysis showed that 6 phosphatidylcholine metabolites were significantly positively correlated with Pseudoflavonifractor and were negatively with Prevotella, Segatella, Prevotella-hominis, Segatella-copri; 5 phosphatidylethanolamine metabolites were significantly negatively correlated with Pseudoflavonifractor and positively correlated with Prevotella, Segatella, Prevotella-hominis, Segatella-copri. CONCLUSION EA may regulate metabolic pathways such as glycerophospholipid, modulate specific gut microbiota such as Pseudoflavonifractor, Prevotella, and Segatella, and the co-expressed differential metabolites like phosphatidylcholine and phosphatidylethanolamine, thereby reducing blood glucose and protecting nerve function, so as to relieve the symptoms of DPN of rats.
Animals ; Electroacupuncture ; Male ; Gastrointestinal Microbiome ; Diabetic Neuropathies/microbiology* ; Rats, Sprague-Dawley ; Rats ; Metabolomics ; Humans ; Acupuncture Points