BACKGROUND:Maxillofacial bone three-dimensional(3D)printing technology has been widely used in clinical diagnosis and treatment,but the data source before performing maxillofacial bone 3D printing mainly comes from the CT scanning data.The lens,thyroid and other parts of the human body are extremely sensitive to X-rays;therefore,it is particularly important to effectively reduce the dose of CT radiation when acquiring the data source.OBJECTIVE:To explore the feasibility of low-dose CT technology in optimizing radiation dose for maxillofacial bone 3D printing.METHODS:The medical records of 65 patients who underwent maxillofacial bone 3D printing in the Department of Stomatology at the General Hospital of Northern Theater Command from March 2021 to December 2023 were retrospectively collected and categorized into a conventional CT-dose 3D printing group(conventional CT-dose,120 kVp,automated tube current modulation,n=32)and a low-CT-dose 3D printing group(low-CT-dose group,80 kVp,automated tube current modulation,n=33).The effective dose of radiation was calculated and compared between the two groups.A Likert scale was used to evaluate the quality of 3D printing in the two groups,and the measurement bias and consistency between evaluators were measured using the Bland-Altman method.RESULTS AND CONCLUSION:(1)There was no significant difference in the general demographic characteristics(age,height,weight,body mass,sex,and body mass index)between the two groups(all P＞0.05).(2)The effective dose value of the low CT-dose 3D printing group was(0.3±0.1)mSv,which was about 62.5％lower than that in the conventional CT-dose 3D printing group[(0.8±0.1)mSv].(3)There was no significant difference in the subjective scoring of 3D printing quality between the two groups(all P＞0.05).The subjective consistency among evaluators was good,with Kappa values of 0.85,0.80,and 0.76.The scatter points in the Bland-Altman for both protocols were uniformly distributed within the standard deviation line,indicating good consistency between the two groups.To conclude,low-dose CT technology can be effectively applied in maxillofacial bone 3D printing,reducing radiation dose without affecting the quality of 3D printing.

BACKGROUND:The Guilu Erxian Glue consists of Testudinis Plastrum,Cornu Cervi,Lycii Fructus,and Ginseng Radix.In earlier clinical observations,it is discovered that using Guilu Erxian Glue to treat patients with liver-kidney deficiency type knee osteoarthritis effectively alleviated knee pain,increased the range of motion,and improved walking ability.However,the exact mechanism by which oral administration of Guilu Erxian Glue can produce local therapeutic effects on the knee joint is still unclear.OBJECTIVE:To investigate the effects of Guilu Erxian Glue on gut microbiota in rats with knee osteoarthritis and to evaluate its mechanism using 16S rDNA sequencing and machine learning analysis.METHODS:Totally 18 female SD rats were randomly divided into three groups:blank group,model group,and Guilu Erxian Glue group,with 6 rats in each group.A knee osteoarthritis model was prepared using the destabilization of the medial meniscus surgical method.After successful modeling,the Guilu Erxian Glue group was given a decoction of Guilu Erxian Glue by gavage,while the blank and model groups were given an equal amount of distilled water.After 28 days of continuous intervention,high performance liquid chromatography was used to detect the active ingredients of Guilu Erxian Glue.MRI imaging was used to observe the condition of rat knee articular cartilage.Fecal samples were collected;DNA was extracted using a kit,amplified and purified by PCR,and an Illumina sequencing library was constructed.The Illumina MiSeq platform was used for high-throughput sequencing to generate raw sequence data.After obtaining the raw data,QIIME2 software was used to process the data.Linear Discriminant Analysis Effect Size analysis and random forest algorithm were used to screen for differential species in microbial data.KEGG and MetaCyc functional pathway analyses were used to explore the association between key microbial communities and experimental groups.Linear discriminant analysis effect values and random forest algorithm were used to screen for differential species.Association networks were used to analyze the interactions between microbial communities,and machine learning methods were used to analyze the composition and changes of gut microbiota.RESULTS AND CONCLUSION:(1)LC-MS component identification was conducted on the traditional Chinese medicine formula of Guilu Erxian Glue,and a total of 7 effective ingredients were identified.(2)MRI imaging showed that synovitis scope of high-density shadows in rats of the Guilu Erxian Glue group was reduced,and the degeneration of medial femoral condyle cartilage was less than that in the model group.(3)16S rDNA sequencing showed that the model group rats exhibited significant microbial imbalance,with a significant decrease in the abundance of Firmicutes and Bacteroidetes at the phylum level,while the proportion of Proteobacteria increased significantly(P＜0.05).The gut microbiota structure of rats in the Guilu Erxian Glue group was significantly improved,and the proportion of Firmicutes and Bacteroidetes increased,restoring a more diverse microbiota composition,approaching that of the blank group(P＜0.05).(4)KEGG and MetaCyc functional pathway analysis showed that the Guilu Erxian Glue group significantly activated multiple metabolic pathways,including amino acid metabolism,lipid metabolism,and biotin synthesis pathways(P＜0.05).(5)The results indicate that Guilu Erxian Glue contains seven active ingredients,and the changes in gut microbiota of knee osteoarthritis rats were analyzed using 16S rDNA sequencing.Guilu Erxian Glue can significantly improve the imbalance of gut microbiota,restore the abundance of beneficial bacteria,and have a significant impact on the composition of gut microbiota,providing scientific basis for the efficacy and mechanism of Guilu Erxian Glue.

BACKGROUND:Maxillofacial bone three-dimensional(3D)printing technology has been widely used in clinical diagnosis and treatment,but the data source before performing maxillofacial bone 3D printing mainly comes from the CT scanning data.The lens,thyroid and other parts of the human body are extremely sensitive to X-rays;therefore,it is particularly important to effectively reduce the dose of CT radiation when acquiring the data source.OBJECTIVE:To explore the feasibility of low-dose CT technology in optimizing radiation dose for maxillofacial bone 3D printing.METHODS:The medical records of 65 patients who underwent maxillofacial bone 3D printing in the Department of Stomatology at the General Hospital of Northern Theater Command from March 2021 to December 2023 were retrospectively collected and categorized into a conventional CT-dose 3D printing group(conventional CT-dose,120 kVp,automated tube current modulation,n=32)and a low-CT-dose 3D printing group(low-CT-dose group,80 kVp,automated tube current modulation,n=33).The effective dose of radiation was calculated and compared between the two groups.A Likert scale was used to evaluate the quality of 3D printing in the two groups,and the measurement bias and consistency between evaluators were measured using the Bland-Altman method.RESULTS AND CONCLUSION:(1)There was no significant difference in the general demographic characteristics(age,height,weight,body mass,sex,and body mass index)between the two groups(all P＞0.05).(2)The effective dose value of the low CT-dose 3D printing group was(0.3±0.1)mSv,which was about 62.5％lower than that in the conventional CT-dose 3D printing group[(0.8±0.1)mSv].(3)There was no significant difference in the subjective scoring of 3D printing quality between the two groups(all P＞0.05).The subjective consistency among evaluators was good,with Kappa values of 0.85,0.80,and 0.76.The scatter points in the Bland-Altman for both protocols were uniformly distributed within the standard deviation line,indicating good consistency between the two groups.To conclude,low-dose CT technology can be effectively applied in maxillofacial bone 3D printing,reducing radiation dose without affecting the quality of 3D printing.

BACKGROUND:The Guilu Erxian Glue consists of Testudinis Plastrum,Cornu Cervi,Lycii Fructus,and Ginseng Radix.In earlier clinical observations,it is discovered that using Guilu Erxian Glue to treat patients with liver-kidney deficiency type knee osteoarthritis effectively alleviated knee pain,increased the range of motion,and improved walking ability.However,the exact mechanism by which oral administration of Guilu Erxian Glue can produce local therapeutic effects on the knee joint is still unclear.OBJECTIVE:To investigate the effects of Guilu Erxian Glue on gut microbiota in rats with knee osteoarthritis and to evaluate its mechanism using 16S rDNA sequencing and machine learning analysis.METHODS:Totally 18 female SD rats were randomly divided into three groups:blank group,model group,and Guilu Erxian Glue group,with 6 rats in each group.A knee osteoarthritis model was prepared using the destabilization of the medial meniscus surgical method.After successful modeling,the Guilu Erxian Glue group was given a decoction of Guilu Erxian Glue by gavage,while the blank and model groups were given an equal amount of distilled water.After 28 days of continuous intervention,high performance liquid chromatography was used to detect the active ingredients of Guilu Erxian Glue.MRI imaging was used to observe the condition of rat knee articular cartilage.Fecal samples were collected;DNA was extracted using a kit,amplified and purified by PCR,and an Illumina sequencing library was constructed.The Illumina MiSeq platform was used for high-throughput sequencing to generate raw sequence data.After obtaining the raw data,QIIME2 software was used to process the data.Linear Discriminant Analysis Effect Size analysis and random forest algorithm were used to screen for differential species in microbial data.KEGG and MetaCyc functional pathway analyses were used to explore the association between key microbial communities and experimental groups.Linear discriminant analysis effect values and random forest algorithm were used to screen for differential species.Association networks were used to analyze the interactions between microbial communities,and machine learning methods were used to analyze the composition and changes of gut microbiota.RESULTS AND CONCLUSION:(1)LC-MS component identification was conducted on the traditional Chinese medicine formula of Guilu Erxian Glue,and a total of 7 effective ingredients were identified.(2)MRI imaging showed that synovitis scope of high-density shadows in rats of the Guilu Erxian Glue group was reduced,and the degeneration of medial femoral condyle cartilage was less than that in the model group.(3)16S rDNA sequencing showed that the model group rats exhibited significant microbial imbalance,with a significant decrease in the abundance of Firmicutes and Bacteroidetes at the phylum level,while the proportion of Proteobacteria increased significantly(P＜0.05).The gut microbiota structure of rats in the Guilu Erxian Glue group was significantly improved,and the proportion of Firmicutes and Bacteroidetes increased,restoring a more diverse microbiota composition,approaching that of the blank group(P＜0.05).(4)KEGG and MetaCyc functional pathway analysis showed that the Guilu Erxian Glue group significantly activated multiple metabolic pathways,including amino acid metabolism,lipid metabolism,and biotin synthesis pathways(P＜0.05).(5)The results indicate that Guilu Erxian Glue contains seven active ingredients,and the changes in gut microbiota of knee osteoarthritis rats were analyzed using 16S rDNA sequencing.Guilu Erxian Glue can significantly improve the imbalance of gut microbiota,restore the abundance of beneficial bacteria,and have a significant impact on the composition of gut microbiota,providing scientific basis for the efficacy and mechanism of Guilu Erxian Glue.

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.

Background: and Purpose Live cell-based assays (LCBA) are increasingly used for serological antibody detection due to their ability to preserve antigen conformation, offering moderately higher sensitivity than fixed cell-based assays. However, the clinical necessity of prioritizing LCBA for the detection of aquaporin-4 immunoglobulin G (AQP4-IgG) in neuromyelitis optica spectrum disorder (NMOSD) remains unclear, especially when compared to its established role in diagnosing myelin oligodendrocyte glycoprotein antibody-associated disease. Methods: We compared the performance of live cell-based assays using immunofluorescence (LCBA-IF) and fixed cell-based assays using immunofluorescence (FCBA-IF) in detecting AQP4-IgG in 90 cases of NMOSD meeting 2015 International Panel for Neuromyelitis Optica Diagnosis criteria, alongside 40 controls. Additionally, we further investigated the relationship between AQP4-IgG titers as measured by LCBA-IF and FCBA-IF and clinical parameters in NMOSD patients. Results: Results showed 96.9% agreement between LCBA-IF and FCBA-IF (Cohen’s κ=0.935), with a strong Spearman correlation (0.977, p<0.001). Both methods demonstrated 100% specificity, with LCBA-IF showing slightly higher sensitivity compared to FCBA-IF. Within LCBAIF-tested groups, statistically significant differences in annualized relapse rates were observed across all pairwise comparisons (low-titer vs. moderate-titer, low-titer vs. high-titer, and moderate-titer vs. high-titer; all p<0.050). However, this association reached statistical significance in some FCBA-IF-tested groups. Conclusions Overall, there is a strong concordance between LCBA-IF and FCBA-IF in detecting AQP4-IgG, where LCBA-IF shows slightly higher sensitivity. Furthermore, there is a potential link between elevated AQP4-IgG titers and an increased risk of relapse, and this correlation may appear more pronounced when using LCBA-IF.

ObjectiveTo investigate the migration and distribution characteristics of chemical constituents in blood and hippocampal tissues before and after vinegar processing of Citri Reticulatae Pericarpium Viride（CRPV）， and to explore the potential material basis and mechanisms underlying their regulatory effects on emotional disorders by comparing the effects of raw and vinegar-processed products of CRPV. MethodsUltra-performance liquid chromatography-quadrupole-time-of-flight mass spectrometry（UPLC-Q-TOF-MS/MS） was employed to characterize and identify the chemical constituents of raw and vinegar-processed products of CRPV extracts， as well as their migrating components in blood and hippocampal tissues after oral administration. Reference standards， databases， and relevant literature were utilized for compound annotation， with data processing performed using PeakView 1.2 software. Seventy male C57BL/6 mice were randomly divided into seven groups， including the blank group， model group， diazepam group（2.5 mg·kg^-1）， raw CRPV low/high dose groups（0.6， 1.2 g·kg^-1）， and vinegar-processed CRPV low/high dose groups（0.6， 1.2 g·kg^-1）， with 10 mice per group. Except for the blank group， all other groups underwent chronic restraint stress（2 h·d^-1） for 20 d. Each drug-treated group received oral administration at the predetermined dose starting 10 d after modeling， with a total treatment duration of 10 d. Following model-based drug administration， mice underwent open-field， forced swimming， and elevated plus maze tests. After anesthesia with isoflurane， whole brains were collected from each group of mice， and hippocampi were dissected. Reactive oxygen species（ROS） level in hippocampal tissues was quantified by enzyme-linked immunosorbent assay（ELISA）. Hematoxylin-eosin（HE） staining was used to observe hippocampal tissue morphology. Immunofluorescence was performed to detect neuronal nuclei（NeuN） and peroxisome proliferator-activated receptor alpha（PPARα） expressions in hippocampal tissue. Then， pharmacodynamic evaluations were conducted to assess the effects of raw and vinegar-processed CRPV on mood disorders， exploring the potential mechanisms. ResultsVinegar processing caused significant changes in the chemical composition of CRPV， with 18 components showing increased relative content and 35 components showing decreased relative content. The primary changes occurred in flavonoid compounds， including 20 flavonoids， 20 flavonoid glycosides， 3 triterpenes， 3 phenolic acids， 1 alkaloid， and 6 other compounds. Twenty-one components were detected in blood（15 methoxyflavones， 4 flavonoid glycosides， and 2 phenolic acids）， with 17 shared between raw and vinegar-processed CRPV. Seven components reached hippocampal tissues（all common to both forms）. In regulating emotional disorders， Vinegar-processed CRPV exhibited superior antidepressant-like effects compared to raw products. HE staining revealed that both treatments improved hippocampal neuronal morphology， particularly in the damaged CA1 and CA3 regions. Immunofluorescence and ELISA analyses demonstrated that both raw and vinegar-processed CRPV significantly modulated NeuN and PPARα expressions in hippocampal tissue while alleviating oxidative stress induced by excessive ROS（P<0.05）. ConclusionThe chemical composition of CRPV undergoes changes after vinegar processing， but the migrating components in blood and hippocampus are primarily methoxyflavonoids. These components may serve as the potential material basis for activating the PPARα pathway， thereby negatively regulating ROS generation in the hippocampus， reducing oxidative stress， and promoting the development of NeuN-positive neurons. These findings provide experimental evidence for enhancing quality standards， pharmacodynamic material research， and active drug development of raw and vinegar-processed CRPV.