ObjectiveSpinal cord injury (SCI) directly impairs the regulatory function of the autonomic nervous system, induces intestinal dysfunction, and significantly reduces patients’ quality of life. Preclinical studies have shown that electroacupuncture (EA) therapy can regulate the brain-gut axis and is used to treat central nervous system diseases such as major depressive disorder, Alzheimer’s disease and Parkinson’s disease. Recent research has established that fecal microbiota transplantation (FMT) from EA-treated SCI rats restored intestinal motility and colonic morphology. However, it remains unclear whether the regulation of gut microbiota by EA therapy directly contributes to neural repair after SCI. This study aims to explore whether gut microbiota mediates the neuroprotective effect of EA in the treatment of SCI and its possible mechanism. MethodsThe study employed RNA transcriptome analysis of spinal cord tissue to characterize gene expression profiles and to identify key signaling pathways following EA treatment for SCI. Hematoxylin-Eosin (HE) staining and Nissl staining were used to observe the morphological changes in spinal cord tissue. Western blot (WB) and enzyme-linked immunosorbent assay (ELISA) were applied to detect the effects of EA on the expression of proteins related to nucleotide-binding domain leucine-rich repeat and pyrin domain-containing receptor 3 (NLRP3) -dependent pyroptosis. Using 16S rDNA sequencing, the study observed alterations in gut microbiota diversity and community composition in SCI rats. Prior to establishing SCI models, rats were pretreated with an antibiotic cocktail to induce gut dysbiosis, and the effects on intestinal function and spinal cord neural repair were evaluated. FMT was performed to investigate the regulatory effects of post-EA FMT on motor function, general status, liver and spleen indices, and NLRP3-mediated pyroptosis in SCI rats. ResultsEA improved motor function and reduced regulated neuronal cell death in SCI rats. Transcriptomic analysis demonstrated the activation of immune- and inflammation-related pathways post-SCI, including NOD-like receptors, nuclear factor-kappa B(NF-κB), and Toll-like receptor (TLR) pathways. EA primarily influenced intestinal inflammation and autoimmune functions. 16S rDNA sequencing illustrated that EA did not alter the diversity of gut microbiota. However, EA altered the gut microbiota composition in SCI rats, increasing Lactobacillus and Akkermansia genera while rebalancing the Firmicutes/Bacteroidetes ratio. Furthermore, depletion of gut microbiota by antibiotics disrupted the intestinal barrier, reduced the expression of intestinal barrier proteins Zonula Occludens-1 (ZO-1) and Occludin, elevated serum lipopolysaccharide-binding protein (LBP) levels, exacerbated spinal cord tissue damage, and hindered motor function recovery in SCI rats. FMT from donors treated with EA reduced LBP levels in the intestine, blood, and spinal cord of rats, inhibited the TLR4 myeloid differentiation primary response protein 88 (MyD88)-NF‑κB pathway and NLRP3-dependent pyroptosis, and improved motor function. On the other hand, FMT treatment resulted in decreased body weight and food intake, whereas FMT using EA-treated donors effectively alleviated these alterations. ConclusionEA effectively alleviated neuroinflammatory responses in rats with SCI, primarily through regulating the gut microbiota and suppressing the NLRP3-dependent pyroptosis signaling pathway.

The initiation of adaptive immune responses relies on the precise recognition and interpretation of antigenic information. In this process, the specific binding of T cell receptors (TCRs) to peptide-major histocompatibility complex (pMHC) molecules represents one of the key molecular events in the initiation of adaptive immune responses. Accordingly, the structural features of TCR-pMHC complexes provide a fundamental basis for dissecting antigen recognition mechanisms and support rational vaccine design, therapeutic target discovery in TCR-based immunotherapy, and TCR identification and optimization. However, experimental determination of TCR-pMHC structures remains costly, time-consuming, and limited in coverage, making computational approaches essential for rapidly obtaining reliable structural information. Computational methods for predicting the structures of TCR-pMHC complexes have advanced rapidly in recent years, driven by progress in deep learning-based modeling frameworks and the increasing availability of structural and sequence resources. Despite these developments, most existing tools do not adequately distinguish the key structural and biophysical differences between MHC class I (MHC-I) and MHC class II (MHC-II) complexes during model construction. As a consequence, their predictive performance differs substantially between class I and class II complexes. In general, structural predictions for class I complexes outperform those for class II complexes. This discrepancy may be related to several fundamental differences between the two systems, including the architecture of the peptide-binding groove, the distribution of peptide lengths, and the properties of peptide flanking residues (PFRs). Compared with MHC-I molecules, MHC-II molecules usually bind longer antigenic peptides, which typically range from 13 to 25 amino acids in length. PFRs at both termini of these peptides participate in regulating the overall conformation of TCR-pMHC class II complexes and exert a pronounced effect on the geometric and physicochemical characteristics of the TCR-pMHC binding interface. Furthermore, within the TCR recognition interface, the complementarity-determining regions (CDRs) consist of segments that differ markedly in conformational behavior. They commonly include regions that are relatively rigid and structurally stable, together with highly flexible segments exhibiting substantial conformational plasticity. These rigidity-flexibility features constitute an essential structural basis enabling TCRs to recognize diverse peptide-MHC ligands and to accommodate conformational heterogeneity at the interface. However, many current modeling tools, in an effort to enforce global conformational stability or reduce structural noise, tend to over-constrain intrinsically flexible regions. Such oversimplification may lead to inappropriate rigidification of flexible CDR loops, resulting in local structural distortions, compromised interface geometry, or even complete modeling failure for specific complexes. Against this background, the review approaches the field from the perspective of computational differences between MHC-I and MHC-II complexes. We first systematically organize and summarize available resources related to TCRs and pMHCs, including structural datasets, sequence databases, prediction tools, and benchmarking studies. We then focus on five representative tools capable of predicting both class I and class II complexes—AlphaFold2, AlphaFold3, TCRmodel2, tFold-TCR, and TCR-pHLA_ModellerS. After excluding structures present in the training sets of these tools, we constructed a benchmark dataset comprising 25 class I and 10 class II TCR-pMHC complexes in the bound state and conducted a systematic evaluation using this dataset. We first employ widely used general evaluation metrics, including All-Atom Root Mean Square Deviation (All-Atom RMSD), Backbone RMSD, Template Modeling score (TM-score), and DockQ, to assess the global conformational accuracy and interface modeling quality of class I and class II complexes. For class II complexes, we propose for the first time a peptide flanking residue deviation index, including the PFRs-Deviation Index (PFRs-DI), N-PFR-Deviation Index (N-PFR-DI), and C-PFR-Deviation Index (C-PFR-DI), to quantitatively characterize conformational deviations in PFRs. In addition, we propose the CDR conformational consistency index (CCC) designed to qualitatively evaluate the ability of prediction tools to capture TCR CDR conformational flexibility. These metrics collectively assess a tool’s ability to model both overall conformation and critical functional regions, thereby addressing the limitations of existing evaluation criteria that overemphasize global structure while inadequately capturing modeling quality in key functional areas. This establishes a unified analytical framework for MHC-I and MHC-II complexes to guide data resource selection, modeling strategy formulation, and evaluation system development. The framework further advances computational modeling and provides crucial support for multi-scale analysis of TCR-pMHC recognition mechanisms and their biological functions.

ObjectiveSpinal cord injury (SCI) directly impairs the regulatory function of the autonomic nervous system, induces intestinal dysfunction, and significantly reduces patients’ quality of life. Preclinical studies have shown that electroacupuncture (EA) therapy can regulate the brain-gut axis and is used to treat central nervous system diseases such as major depressive disorder, Alzheimer’s disease and Parkinson’s disease. Recent research has established that fecal microbiota transplantation (FMT) from EA-treated SCI rats restored intestinal motility and colonic morphology. However, it remains unclear whether the regulation of gut microbiota by EA therapy directly contributes to neural repair after SCI. This study aims to explore whether gut microbiota mediates the neuroprotective effect of EA in the treatment of SCI and its possible mechanism. MethodsThe study employed RNA transcriptome analysis of spinal cord tissue to characterize gene expression profiles and to identify key signaling pathways following EA treatment for SCI. Hematoxylin-Eosin (HE) staining and Nissl staining were used to observe the morphological changes in spinal cord tissue. Western blot (WB) and enzyme-linked immunosorbent assay (ELISA) were applied to detect the effects of EA on the expression of proteins related to nucleotide-binding domain leucine-rich repeat and pyrin domain-containing receptor 3 (NLRP3) -dependent pyroptosis. Using 16S rDNA sequencing, the study observed alterations in gut microbiota diversity and community composition in SCI rats. Prior to establishing SCI models, rats were pretreated with an antibiotic cocktail to induce gut dysbiosis, and the effects on intestinal function and spinal cord neural repair were evaluated. FMT was performed to investigate the regulatory effects of post-EA FMT on motor function, general status, liver and spleen indices, and NLRP3-mediated pyroptosis in SCI rats. ResultsEA improved motor function and reduced regulated neuronal cell death in SCI rats. Transcriptomic analysis demonstrated the activation of immune- and inflammation-related pathways post-SCI, including NOD-like receptors, nuclear factor-kappa B(NF-κB), and Toll-like receptor (TLR) pathways. EA primarily influenced intestinal inflammation and autoimmune functions. 16S rDNA sequencing illustrated that EA did not alter the diversity of gut microbiota. However, EA altered the gut microbiota composition in SCI rats, increasing Lactobacillus and Akkermansia genera while rebalancing the Firmicutes/Bacteroidetes ratio. Furthermore, depletion of gut microbiota by antibiotics disrupted the intestinal barrier, reduced the expression of intestinal barrier proteins Zonula Occludens-1 (ZO-1) and Occludin, elevated serum lipopolysaccharide-binding protein (LBP) levels, exacerbated spinal cord tissue damage, and hindered motor function recovery in SCI rats. FMT from donors treated with EA reduced LBP levels in the intestine, blood, and spinal cord of rats, inhibited the TLR4 myeloid differentiation primary response protein 88 (MyD88)-NF‑κB pathway and NLRP3-dependent pyroptosis, and improved motor function. On the other hand, FMT treatment resulted in decreased body weight and food intake, whereas FMT using EA-treated donors effectively alleviated these alterations. ConclusionEA effectively alleviated neuroinflammatory responses in rats with SCI, primarily through regulating the gut microbiota and suppressing the NLRP3-dependent pyroptosis signaling pathway.

The initiation of adaptive immune responses relies on the precise recognition and interpretation of antigenic information. In this process, the specific binding of T cell receptors (TCRs) to peptide-major histocompatibility complex (pMHC) molecules represents one of the key molecular events in the initiation of adaptive immune responses. Accordingly, the structural features of TCR-pMHC complexes provide a fundamental basis for dissecting antigen recognition mechanisms and support rational vaccine design, therapeutic target discovery in TCR-based immunotherapy, and TCR identification and optimization. However, experimental determination of TCR-pMHC structures remains costly, time-consuming, and limited in coverage, making computational approaches essential for rapidly obtaining reliable structural information. Computational methods for predicting the structures of TCR-pMHC complexes have advanced rapidly in recent years, driven by progress in deep learning-based modeling frameworks and the increasing availability of structural and sequence resources. Despite these developments, most existing tools do not adequately distinguish the key structural and biophysical differences between MHC class I (MHC-I) and MHC class II (MHC-II) complexes during model construction. As a consequence, their predictive performance differs substantially between class I and class II complexes. In general, structural predictions for class I complexes outperform those for class II complexes. This discrepancy may be related to several fundamental differences between the two systems, including the architecture of the peptide-binding groove, the distribution of peptide lengths, and the properties of peptide flanking residues (PFRs). Compared with MHC-I molecules, MHC-II molecules usually bind longer antigenic peptides, which typically range from 13 to 25 amino acids in length. PFRs at both termini of these peptides participate in regulating the overall conformation of TCR-pMHC class II complexes and exert a pronounced effect on the geometric and physicochemical characteristics of the TCR-pMHC binding interface. Furthermore, within the TCR recognition interface, the complementarity-determining regions (CDRs) consist of segments that differ markedly in conformational behavior. They commonly include regions that are relatively rigid and structurally stable, together with highly flexible segments exhibiting substantial conformational plasticity. These rigidity-flexibility features constitute an essential structural basis enabling TCRs to recognize diverse peptide-MHC ligands and to accommodate conformational heterogeneity at the interface. However, many current modeling tools, in an effort to enforce global conformational stability or reduce structural noise, tend to over-constrain intrinsically flexible regions. Such oversimplification may lead to inappropriate rigidification of flexible CDR loops, resulting in local structural distortions, compromised interface geometry, or even complete modeling failure for specific complexes. Against this background, the review approaches the field from the perspective of computational differences between MHC-I and MHC-II complexes. We first systematically organize and summarize available resources related to TCRs and pMHCs, including structural datasets, sequence databases, prediction tools, and benchmarking studies. We then focus on five representative tools capable of predicting both class I and class II complexes—AlphaFold2, AlphaFold3, TCRmodel2, tFold-TCR, and TCR-pHLA_ModellerS. After excluding structures present in the training sets of these tools, we constructed a benchmark dataset comprising 25 class I and 10 class II TCR-pMHC complexes in the bound state and conducted a systematic evaluation using this dataset. We first employ widely used general evaluation metrics, including All-Atom Root Mean Square Deviation (All-Atom RMSD), Backbone RMSD, Template Modeling score (TM-score), and DockQ, to assess the global conformational accuracy and interface modeling quality of class I and class II complexes. For class II complexes, we propose for the first time a peptide flanking residue deviation index, including the PFRs-Deviation Index (PFRs-DI), N-PFR-Deviation Index (N-PFR-DI), and C-PFR-Deviation Index (C-PFR-DI), to quantitatively characterize conformational deviations in PFRs. In addition, we propose the CDR conformational consistency index (CCC) designed to qualitatively evaluate the ability of prediction tools to capture TCR CDR conformational flexibility. These metrics collectively assess a tool’s ability to model both overall conformation and critical functional regions, thereby addressing the limitations of existing evaluation criteria that overemphasize global structure while inadequately capturing modeling quality in key functional areas. This establishes a unified analytical framework for MHC-I and MHC-II complexes to guide data resource selection, modeling strategy formulation, and evaluation system development. The framework further advances computational modeling and provides crucial support for multi-scale analysis of TCR-pMHC recognition mechanisms and their biological functions.

Purpose: The aim of this study was to investigate the feasibility of an intelligent handheld ultrasound (US) device for assisting non-expert general practitioners (GPs) in detecting carotid plaques (CPs) in community populations. Methods: This prospective parallel controlled trial recruited 111 consecutive community residents. All of them underwent examinations by non-expert GPs and specialist doctors using handheld US devices (setting A, setting B, and setting C). The results of setting C with specialist doctors were considered the gold standard. Carotid intima-media thickness (CIMT) and the features of CPs were measured and recorded. The diagnostic performance of GPs in distinguishing CPs was evaluated using a receiver operating characteristic curve. Inter-observer agreement was compared using the intragroup correlation coefficient (ICC). Questionnaires were completed to evaluate clinical benefits. Results: Among the 111 community residents, 80, 96, and 112 CPs were detected in settings A, B, and C, respectively. Setting B exhibited better diagnostic performance than setting A for detecting CPs (area under the curve, 0.856 vs. 0.749; P<0.01). Setting B had better consistency with setting C than setting A in CIMT measurement and the assessment of CPs (ICC, 0.731 to 0.923). Moreover, measurements in setting B required less time than the other two settings (44.59 seconds vs. 108.87 seconds vs. 126.13 seconds, both P<0.01). Conclusion Using an intelligent handheld US device, GPs can perform CP screening and achieve a diagnostic capability comparable to that of specialist doctors.

Objective To analyze the research status and hotspots in the field of astragalus polysaccharides;To provide references for further research.Methods Research literature about astragalus polysaccharides was retrieved from CNKI,Wanfang Data,VIP,PubMed,and Web of Science databases from 1st,Jan.2013 to 1st,July 2023.NoteExpress 3.7 software was used to manage the literature and ultimately establish a database.Excel 2019,CiteSpace 6.2.2R and VOSviewer 1.6.18 were used to visually analyze the publication volume,authors,institutions,and keywords of the included literature.Results A total of 2 462 articles were included,with 1 284 Chinese articles and 1 178 English articles.The main research institutions were Gansu University of Chinese Medicine,Shandong University of Traditional Chinese Medicine,and Beijing University of Chinese Medicine.The core authors of Chinese literature were Liu Yongqi,Wang Hongxin,Lu Meili,etc.The core authors of English literature included Zhang Wei,Li Ke,Yang Xiaojun,etc.High-frequency keywords of Chinese literature included Astragali Radix,rats,polysaccharides,cell apoptosis,and oxidative stress,etc.High frequency keywords in English literature included expression,in vitro,oxidative stress,apoptosis,etc.Conclusion The research on astragalus polysaccharides focuses on their pharmacological effects and mechanisms.Intestinal flora,immune regulation,autophagy and apoptosis are the hot action mechanisms in this field.The focus of disease research involves tumor and diabetes,and antiviral,anti infection and other pharmacological effects are the research trend.

Elderly patients are a crucial population for medical treatment and referral.The establishment of standardized and efficient referral channels is essential for enhancing the referral process, improving treatment outcomes for the elderly, and optimizing the allocation of medical resources.Referral network analysis examines the integrity, structure, and dynamics of referrals to infer the characteristics of the network.This can offer insights for enhancing referral policies and elevating medical service standards.While existing research predominantly concentrates on referral networks within the general population, there is a noticeable gap in studies focusing on elderly patients.This review article assesses domestic and international research on networks formed between medical institutions or physicians through patient referrals, aiming to inform and enhance referral policies in our country.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

OBJECTIVE: To explore the early clinical outcomes of 3D printed individualised customised prostheses for in hip revision in patients with combined severe bone defects. METHODS: Twenty-two patients from January 2021 to May 2023 underwent hip revision using 3D printed personalised customised prostheses were retrospective analyzed, including 10 males and 12 females, age 28 to 78 with a mean of (58.9±12.8) years old. All of patients were combined with severe bone defects (Parprosky type Ⅲ). Among of them, 9 patients had periprosthetic infections and 13 patients had aseptic prosthesis loosening. All patients were treated with a 3D printed personalised prosthesis protocol, patients with the periprosthetic infection received a second stage revision after infection control. The operation time, preoperative waiting time, intraoperative and postoperative complications were recorded, and the clinical efficacy were evaluated at the final follow-up using the visual analogue scale (VAS) for pain, the Harris hip score. RESULTS: One patient was lost to follow-up and the remaining 21 patients were followed up for 10 to 15 with a mean of (12.91±1.44) months after surgery. All patients completed surgery as planned, with an operative time of 135 to 390 with a mean of (165.4±39.3) minutes and a preoperative waiting time of 7 to 16 with a mean of (10.5±3.3) days. Regarding patient complications:one patient had a severe intraoperative periprosthetic femoral fracture due to the combination of severe osteoporosis; one patient had an intraoperative greater trochanteric femur fracture. At the latest follow-up, all patients had good position of the custom-made prosthesis and no loosening of the prosthesis;all patients had good wound healing and no local redness or swelling. The total Harris score at the final follow-up (85.86±7.04) was significantly improved compared to the preoperative (44.86±2.36), P<0.001. The VAS at the last follow-up (2.19±0.87) was significantly improved compared with preoperative (7.41±0.96), P<0.001. CONCLUSION The clinical efficacy of 3D-printed personalised customised prosthesis in combined severe bone defect hip revision is satisfactory, but due to the increased preoperative waiting time of the patients and certain risks, certain indications should be mastered when applying in the clinic.
Humans ; Male ; Female ; Printing, Three-Dimensional ; Middle Aged ; Aged ; Adult ; Retrospective Studies ; Hip Prosthesis ; Arthroplasty, Replacement, Hip ; Reoperation ; Prosthesis Design ; Treatment Outcome

OBJECTIVE: To preliminarily investigate the effects and mechanism of action of Yishen Yangsui Formula (, YSYSF)on the recovery of neurological function in rats with degenerative cervical myelopathy. METHODS: Fifty adult SD female rats were randomly divided into control group, sham group, model group, YSYSF group and positive drug group by using randomized numerical table method. In the model group, YSYSF group and positive drug group, polyvinyl alcohol acrylamide interpenetrating network hydrogel(water-absorbent swelling material) was used to construct a rat spinal cord chronic compression model. The sham group was implanted with the water-absorbent swelling material and then removed without causing spinal cord compression. The control group, the sham group and the model group were given equal amounts of saline by gavage, the group of YSYSF was given Chinese herbal medicine soup by gavage 9.1 g·kg^-1 once a day, and the positive drug group was given tetrahexylsalicylglucoside sodium monosialate ganglioside by intraperitoneal injection 4.2 mg·kg^-1 once a day. The motor function of the rats was assessed by the BBB method after 1, 3, 7, and 14 d of drug administration. The spinal cord tissues were taken from rats executed 14 d after drug administration, and the morphological changes of the spinal cord compression site were observed by HE staining, and the expression levels of Caspase-1, GSDMD, NLRP3, PYCARD, IL-1β, and IL-18 were detected in the area of spinal cord injury by Western blot method. RESULTS: The BBB scores of the control group and the sham group were normal at all time points after modeling, which were higher than the BBB scores of the model group, the YSYSF, and the positive drug group (P<0.05). From the 3rd day after gavage, at all time points, the BBB scores of rats in the YSYSF group and the positive drug group were higher than those of rats in the model group (P<0.05). The staining pattern of HE spinal cord tissue was normal in the control group and the sham group, and the HE spinal cord in the model group was severely damaged with a large number of neuron deaths, whereas the damage to the spinal cord and neuron cells was reduced in the YSYSF group and the positive drug group. The expression levels of caspase-1, GSDMD, NLRP3, PYCARD, IL-1β and IL-18 in the spinal cord of the model group were significantly higher than those of the sham group (P<0.0001), and the expression levels of caspase-1, GSDMD, NLRP3, PYCARD, IL-1β, and IL-18 in the YSYSF group and the drug group were significantly lower than those in the model group (P<0.05). CONCLUSION YSYSF can improve the motor function of rats with degenerative cervical spinal cord disease, alleviate the pathological changes, and promote the recovery of spinal cord neurological function. The specific mechanism may be related to the inhibition of the activation of inflammatory vesicles NLRP3 and PYCARD, the reduction of the release of inflammatory factors IL-1β and IL-18, the reduction of the expression of caspase-1 and GSDMD, the reduction of cellular death, and the inhibition of inflammatory response.
Animals ; Female ; Drugs, Chinese Herbal/administration & dosage* ; Rats ; Rats, Sprague-Dawley ; Pyroptosis/drug effects* ; Spinal Cord/pathology* ; NLR Family, Pyrin Domain-Containing 3 Protein ; Spinal Cord Diseases/drug therapy* ; Interleukin-1beta/metabolism*