Objective To investigate the temporal changes in pathological damage and macrophage polarization in liver and spleen tissues of mice infected with Angiostrongylus cantonensis, and to preliminarily unravel the peripheral immune responses during the early stage of A. cantonensis infection. Methods Forty female BALB/c mice at ages of 6 to 8 weeks were randomly divided into four groups, including the control group and 7-, 14-, and 21-day infection groups, with 10 mice in each group. Each mouse in the infection groups was inoculated with 30 third-stage (L3) larvae of A. cantonensis by oral gavage, and five mice were randomly selected from each infection group on days 7, 14, and 21 post-infection, while mice in the control group were given the same volume of physiological saline and five mice were randomly selected from the control group on the day of oral gavage. Mouse liver and spleen tissues were sampled. The histopathological changes of mouse liver and spleen tissues were observed using hematoxylin and eosin (HE) staining, and the percentage of positive staining area and the co-localization positive rates of the macrophage surface antigens F4/80, CD86, and CD206 were quantified in mouse liver and spleen tissues using immunohistochemical and immunofluorescence staining. In addition, five mice were collected from each infection group on days 7, 14, and 21 post-infection, and five mice were collected from the control group on the day of oral gavage. Mouse liver and spleen tissues were sampled for detection of macrophage markers CD86 and CD206 and macrophage phenotyping using flow cytometry, and the expression of M1 macrophage markers, including inducible nitric oxide synthase (Nos2), tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β) and M2 markers, including arginase 1 (Arg1), mannose receptor C-type 1 (Mrc1) and chitinase-like protein 3 (Chil3) was quantified in mouse liver and spleen tissues using real-time quantitative PCR (RT-qPCR) assay. Results Proliferative lesions of the hepatocyte were observed in mouse liver tissues and the follicular structures of the mouse spleen white pulp were disrupted 21 days post-infection with A. cantonensis. Immunohistochemical staining showed that there were significant differences in the percentages of F4/80, CD86 and CD206 positive staining areas in the liver and spleen tissues among the four groups of mice (F = 242.40, 197.14, 183.19, 157.65, 242.35 and 146.24; all P values < 0.001), and the percentages of positive staining in the liver and spleen tissues of mice in the 14-day infection group [(4.45 ± 0.51)%, (3.74 ± 0.67)%, (8.32 ± 0.72)%, (16.56 ± 1.14)%, (11.62 ± 0.52)%, and (8.29 ± 0.72)%, respectively] and the 21-day infection group [(3.70 ± 0.11)%, (3.22 ± 0.43)%, (11.53 ± 1.03)%, (12.59 ± 1.05)%, (9.02 ± 0.83)%, and (11.67 ± 1.10)%, respectively] were higher than in the control group [(0.35 ± 0.16)%, (0.40 ± 0.02)%, (0.93 ± 0.05)%, (2.78 ± 0.26)%, (2.33 ± 0.20)%, and (1.85 ± 0.20)%, respectively] (all P values < 0.05). Immunofluorescence staining showed significant differences in the positive rates of F4/80 co-localization with CD86 and CD206 in mouse liver and spleen tissues among the four groups (F = 24.42, 25.28, 54.51 and 130.55; all P values < 0.001). Flow cytometry detected significant differences in the proportions of CD86⁺ and CD206⁺ macrophages in mouse liver and spleen tissues among the four groups (F = 67.98, 18.41, 29.77, 172.80; all P values < 0.001), and the proportions of CD206⁺ macrophages in the liver and spleen of the 21-day infection group were significantly higher than those in the control group [(9.25 ± 2.55)% vs (3.83 ± 0.72)%, and (4.22 ± 0.56)% vs (0.47 ± 0.18)%, respectively] (both P values < 0.05). In addition, RT-qPCR assay quantified significant differences in the relative mRNA expression of M1 macrophage markers (IL-1β, TNF-α and Nos2) and M2 macrophage markers (Arg1, Chil3 and Mrc1) in mouse liver and spleen tissues among the four groups (F = 41.30, 31.82, 199.33, 19.96, 62.01, 119.76, 23.67, 95.90, 72.27, 82.59, 123.41 and 29.75; all P values < 0.05). Conclusions A. cantonensis infection may cause progressive pathological damage in mouse liver and spleen tissues, accompanied by dynamic temporal changes in macrophage polarization. M1 macrophage polarization predominates at the early stage of A. cantonensis infection and shifts towards M2 polarization at the later stages, suggesting that M2 polarization may participate in immune regulation at late stages of A. cantonensis infection by suppressing excessive inflammatory responses and promoting tissue repair.

Background/Aims: Liver transplantation (LT) following total hepatectomy is a life-saving treatment for hepatocellular carcinoma (HCC). The HCC recurrence after LT hinders the effectiveness of the procedure. The objective of this study is to develop a pre-operative risk stratification model based on a liquid biopsy. Methods: We conducted a comprehensive multi-omics study of 260 HCC patients from three centers, including clinical data, low-coverage whole-genome sequencing of cell-free DNA (cfDNA) from plasma, as well as whole-exome, single-nucleus RNA, and spatial transcriptomics from matched tumor and non-tumor tissues. Results: We identified cfDNA-derived copy number alteration (CNA) signatures associated with post-transplant recurrence. By integrating cfDNA-derived CNA profiles with single-cell transcriptomic data, we traced recurrence-associated cfDNA to a distinct subpopulation of malignant cells within the primary tumor. These cells were embedded in a pro-metastatic microenvironment of specialized endothelial subtypes and cancer-associated fibroblasts. Notably, most recurrence-associated lesions were detectable in cfDNA prior to liver transplantation (LT). Building on these insights, we developed the ZJU Criteria based on CNA fragments and tumor markers, a pre-LT risk prediction tool that integrates conventional clinical factors with cfDNA-derived CNA signatures, and validated it using internal and independent external cohorts. Conclusion Our findings suggest that post-transplant recurrence commonly originates from advanced subclones that emerge late during tumor evolution. The ZJU Criteria provides an accurate, non-invasive strategy that significantly improves pre-LT risk stratification and clinical decision-making for patients with HCC.

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.

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.

ObjectiveTo investigate the mechanism underlying the weight loss effect of electrothermal acupuncture （ETA） in diet-induced obese （DIO） model mice based on the hypoxia-inducible factor-1α （HIF-1α）/sphingosine-1-phosphate （S1P）sphingosine-1-phosphate receptor 3 （S1PR3） signaling pathway-mediated macrophage polarization. MethodsAn obesity mouse model was established by continuously feeding at a high-fat diet （HFD） for 8 weeks. Twenty successfully modeled obese mice were randomly divided into a model group and an electrothermal acupuncture （ETA） group with 10 mice per group， both of which continued to receive the HFD till week 12. Another 10 mice were assigned to the normal group and fed a standard chow diet continuously for 12 weeks. Starting from week 9 of the experiment， mice in the ETA group received electrothermal acupuncture stimulation at Zhongwan （CV12）， bilateral Tianshu （ST25）， and bilateral Zusanli （ST36） for 30 minutes per session， with the instrument current set at （40±0.5）mA and a constant temperature of （40±0.5）℃. Mice in the normal group and the model group were restrained in the same manner for 30 minutes without acupuncture. All interventions were performed once every other day for a total of 4 weeks. Ten hours after the final intervention， the body weight， waist circumference， and body length of mice in each group were measured， and Lee's index was calculated. Serum biochemical analysis was performed to assess fasting blood glucose （FBG）， total cholesterol （TC）， triglycerides （TG）， high-density lipoprotein cholesterol （HDL-C）， low-density lipoprotein cholesterol （LDL-C）， alanine aminotransferase （ALT）， aspartate aminotransferase （AST）， blood urea nitrogen （BUN）， and creatinine （Cr）. Serum leptin （LEP） and adiponectin （ADPN） levels， as well as sphingosine-1-phosphate （S1P） levels in epididymal white adipose tissue （eWAT）， were detected by ELISA. Histomorphological changes in eWAT were monitored via HE staining. Flow cytometry was used to analyze macrophage subtype-related indexes in eWAT， including the total proportion of macrophages， the proportion of M1 macrophages， the proportion of M2 macrophages， and the M1/M2 ratio. Antibody array technology was used to determine the levels of differentially expressed inflammatory factors in eWAT between the model and blank groups， as well as between the ETA and model groups. Western blot and qPCR were used to detect the protein and mRNA expression levels of HIF-1α and S1PR3 in the eWAT， respectively. ResultsCompared to the normal group， the model group showed significantly increased body weight， Lee's index， waist circumference， and FBG， TC， TG， HDL-C， LDL-C， AST， and ALT levels， upgraded LEP and S1P levels， decreased ADPN level and ADPN/LEP ratio， elevated proportion of M1 macrophages and the M1/M2 ratio， reduced proportion of M2 macrophages， and upregulated protein and mRNA expression levels of HIF-1α and S1PR3 （P＜0.05 or P＜0.01）. With the exception of HDL-C， all the abovementioned indicators in the ETA group were significantly improved compared to the model group （P＜0.05 or P＜0.01）. Analysis of differentially expressed inflammatory cytokines revealed that， compared to the normal group， the levels of interleukin-1β （IL-1β）， interferon-γ （IFN-γ）， interleukin-6 （IL-6）， and interleukin-12p70 （IL-12p70） in the eWAT of the model group were elevated； conversely， the levels of interleukin-17 （IL-17）， interleukin-5 （IL-5）， IL-6， and interleukin-23 （IL-23） in the eWAT of the ETA group decreased compared to the model group （P＜0.05 or P＜0.01）. ConclusionETA exerts a weight-reducing effect in obese model mice， and the mechanism may involve inhibition of the HIF-1α/S1P/S1PR3 signaling pathway activation， which shifts macrophage polarization from the M1 to M2 phenotype， thereby alleviating chronic inflammation.

Background Pulmonary heart disease (PHD) is a critical cardiopulmonary disease in high-altitude areas, with pulmonary hypertension (PH) as its central pathological basis. Cold and hypoxia are vital environmental characteristics of high-altitude regions; however, their joint effect on the hospitalization risk for PHD and the underlying biological mechanisms remain unclear. Objective To evaluate the associations of cold, hypoxia, and their joint exposure with PHD hospitalization risk, and to investigate PH-related pathological changes induced by combined cold and hypoxic exposure and the intervention effects of γ-aminobutyric acid (GABA) in rats. Methods Based on hospitalization data for 92986 PHD cases from 333 hospitals in Gansu Province (2018–2022), a generalized additive model (GAM) with a quasi-Poisson distribution was used to assess the associations between daily mean temperature, atmospheric oxygen content (AOC) and the daily number of PHD hospitalizations. Four joint exposure categories were constructed using the fifth percentile of temperature (P₅, −7.90℃) and AOC grades (high or low) to assess the associations between different exposure combinations and daily PHD hospital admissions. To further investigate PH-related pathological changes and the intervention effects of GABA, 24 specific-pathogen-free male Sprague–Dawley rats were randomly assigned to four groups (n=6): normal temperature and normoxia control group (NC), combined cold and hypoxia exposure group (CH), combined exposure plus low-dose GABA intervention group (CHL), and combined exposure plus high-dose GABA intervention group (CHH). After 5 weeks, pulmonary hemodynamics and vascular remodeling were evaluated. Gut microbiota and inflammatory cytokines in lung tissue were assessed using 16S rRNA sequencing and enzyme-linked immunosorbent assay (ELISA), respectively. Results Each 1 g·m⁻³ decrease in AOC and each 1 ℃ decrease in daily mean temperature were associated with 2.6% (RR=1.026, 95%CI: 1.024, 1.027) and 1.8% (RR=1.018, 95%CI: 1.015, 1.020) increases in PHD hospitalization risk, respectively. Compared with the non-low-temperature and high-AOC group, the low-temperature and low-AOC group had the highest risk estimate (RR=1.673, 95%CI: 1.567, 1.785). Animal experiments demonstrated that, compared with the NC group, rats in the CH group showed shortened pulmonary artery acceleration time (PAT) and increased mean pulmonary artery pressure (MPAP), right ventricular hypertrophy index (RVHI), pulmonary arterial wall thickness, and α-smooth muscle actin (α-SMA) expression (P<0.05). The Observed Species and Chao1 indices of the gut microbiota were significantly lower in the CH group than in the NC group (P<0.05), while levels of tumor necrosis factor-alpha (TNF-α) and interleukin-1beta (IL-1β) in lung tissue were elevated (P<0.05). GABA intervention improved selected hemodynamic, vascular remodeling, and inflammatory indices. Conclusion Decreases in daily mean temperature and AOC are associated with an increased risk of PHD hospitalization, with the highest risk estimate observed in the low-temperature and low-AOC group. Animal experiments suggest that combined cold and hypoxic exposure induce PH-related pathological changes accompanied by alterations in the gut microbiota and pulmonary inflammation. GABA may partially ameliorate these changes.

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.

AIM To study the chemical constituents from Tetrastigma hemsleyanum Diels et Gilg and their antitumor activity in vitro.METHODS Silica gel,ODS,Sephadex LH-20 and semi-preparative HPLC were used for isolation and purification,then the structures of obtained compounds were identified by physicochemical properties and spectral data.The antitumor activity in vitro was determined by MTT mothod.RESULTS Twenty-eight compounds were isolated and identified as triphyllin A(1),eruberin B(2),(2S,4R)-5,7-dihydroxy-4,4'-dimethyl-6,8-dimethyl-flavan-5-O-β-D-6-acetylglucopyranoside-7-O-β-D-glucopyranoside(3),eruberin A(4),abacopterin Ⅰ(5),matteucinol(6),homoerodictyol(7),(2S)-5,3',4'-trihydroxy-7-methoxy-flavanone(8),(2S)-5,2',5'-trihydroxy-7-methoxyflavanone(9),galinsonside B(10),quercetin-3-O-β-D-glucopyranoside(11),kaempferol 3-O-robinobioside(12),rutin(13),geniposide(14),jasminoside A(15),β-sitostenone(16),sitosterol palmitate(17),β-sitosterol(18),ursolic acid(19),hyptadienic acid(20),3,4-dihydroxybenzoic acid(21),3,4-dimethoxybenzoic acid(22),gallic acid(23),dibutylphthalate(24),bis-(2-ethylhexyl)phthalate(25),9-nonadecenoic acid(26),triacylglycerol(27),crocin Ⅰ(28).The IC50 values of compound 1 for human gastric adenocarcinoma cells BGC-823 and human colon cancer cells HCT-116 were(22.07±0.38),(20.67±0.11)μmol/L,respectively.The IC50 value of compound 9 for BGC-823 cells was(21.58±0.05)μmol/L,and the IC50 value of compound 4 for HCT-116 cells was(16.67±0.36)μmol/L.CONCLUSION Compounds 1-10,14-15 and 28 are first isolated from Tetrastigma genus.Compounds 1,4,9 have weak antitumor activity in vitro.

Objective:To investigate microstructural alterations in the optic chiasm and optic radiations of multiple sclerosis (MS) and neuromyelitis optica spectrum disorders (NMOSD) based on diffusion kurtosis imaging (DKI).Methods:This study was a cross-sectional study. Retrospective analyses were conducted on the clinical and imaging data of 63 patients with relapsing-remitting MS (RRMS) and 62 patients with NMOSD diagnosed at First Affiliated Hospital of Chongqing Medical University from January 2019 to December 2023. According to the occurrence of optic neuritis (ON), they were categorized into ON-positive MS (ON+MS) group (40 cases), ON-negative MS (ON-MS) group (23 cases), ON-positive NMOSD (ON+NMOSD) group (40 cases) and ON-negative NMOSD (ON-NMOSD) group (22 cases). In addition, 40 healthy controls were enrolled during the same period. DKI data of all subjects were collected, and DKI post-processing was performed to obtain fractional anisotropy (FA), mean kurtosis (MK), axial kurtosis (AK), and radial kurtosis (RK) values of the optic chiasm and bilateral optic radiations. The scores of the mini-mental state examination (MMSE), montreal cognitive assessment (MoCA), and expanded disability status scale (EDSS) were obtained. The Kruskal-Wallis test was used to analyze the differences in DKI parameters of the optic chiasm and bilateral optic radiation among the 5 groups, and the Holm-Bonferroni method was employed for multiple comparison correction in pairwise comparisons.Results:There were statistically significant overall differences in the DKI parameters of the optic chiasm and bilateral optic radiations among healthy control group, ON+MS group, ON-MS group, ON+NMOSD group, and ON-NMOSD group (all P0.05). The FA value of the optic chiasm in ON+NMOSD group was significantly lower than that of healthy control group and ON-MS group, as well as ON-NMOSD group ( P0.05). The FA value of the left optic radiation in ON+NMOSD group was lower than that in healthy control group and the ON-MS group. The RK value of the optic chiasm in ON+MS group was lower than that in the healthy control group and ON-NMOSD group ( P0.05). The MK and RK values of the left optic radiation in ON-MS group were significantly lower than those in the ON+NMOSD group and ON-NMOSD group ( P0.05). Conclusions:NMOSD and RRMS patients demonstrate varying degrees of microstructural damage in the optic chiasm and optic radiations. Differences of DKI parameters suggest different pathological mechanisms of visual pathway damage between NMOSD and MS, which may be helpful for early detection of occult visual pathway lesions.

Guidelines for Digital Ancient Books of TCM Indexing(T/CIATCM 119-2024)is based on the theoretical knowledge,disciplinary methods,and practical applications of TCM classical cataloging.Taking digital ancient books of TCM as the object,it systematically reveals the content of TCM knowledge,which is an essential indexing processing standard for building an intelligent retrieval system for TCM ancient books,and can provide support for the deep development and innovative utilization of TCM knowledge.It can not only promote the co-construction and sharing of ancient book resources in the TCM industry,but also promote the standardization construction and application of TCM information.This standard specifies the principles,methods,and examples of free indexing of digital ancient books of TCM based on their original content.It is applicable to the indexing and processing of digital ancient books of TCM for TCM professional libraries and related institutions,and to the data processing and construction of various types of TCM ancient book databases.