BACKGROUND:The inflammatory response of microglia is closely related to neuronal survival,regeneration,and functional recovery after spinal cord injury.Peroxiredoxin 1 is not only involved in the regulation of oxidative stress,but also has an important effect on cell proliferation,apoptosis,and inflammatory response.OBJECTIVE:To investigate the role and mechanism of peroxiredoxin 1 in the inflammatory response of microglia following spinal cord injury.METHODS:(1)Twelve female C57BL/6 mice were randomly divided into sham-operated(n=6)and spinal cord injury(n=6)groups.The sham-operated group was not modeled and acute spinal cord injury models were constructed in the spinal cord injury group using the modified Allen's method.Spinal cord tissue at the injured site was taken at 7 days after modeling and transcriptome sequencing was performed to identify differentially expressed genes.The expression of peroxiredoxin 1 in spinal cord tissues was verified using western blot and RT-qPCR.(2)Mouse microglia BV2 were divided into two groups:the control group was stimulated with lipopolysaccharide for 6 hours,and in the knockout group,lipopolysaccharide stimulation was applied for 6 hours at 24 hours after peroxiredoxin 1 was knocked down in the cells.RT-qPCR was performed to detect mRNA expression of peroxiredoxin 1,inflammatory factors(interleukin 1β,interleukin 6,inducible nitric oxide synthase,tumor necrosis factor α,C-C motif chemokine ligand 2,and C-X-C motif chemokine ligand 2),and western blot was performed to detect the expression of peroxiredoxin 1,inducible nitric oxide synthase,and reactive oxygen/mitogen-activated protein kinase signaling pathway proteins.Mouse microglia BV2 were treated in two groups:the control group was stimulated by hydrogen peroxide for 4 hours,and the knockout group was stimulated by hydrogen peroxide for 4 hours at 24 hours after knockdown of peroxiredoxin 1.The level of reactive oxygen species was detected by 2,7-dichlorodihydrofluorescein diacetate probe.RESULTS AND CONCLUSION:(1)Results from transcriptome sequencing,western blot and RT-qPCR confirmed that peroxiredoxin 1 expression levels in mouse spinal cord tissues were significantly higher in the spinal cord injury group than the sham-operated group(P＜0.05).(2)Peroxiredoxin 1 knockdown in microglial cells led to decreased expression of peroxiredoxin 1 mRNA and protein(P＜0.05),increased mRNA expression of interleukin 1β,interleukin 6,inducible nitric oxide synthase,tumor necrosis factor α,C-C motif chemokine ligand 2,and C-X-C motif chemokine ligand 2(P＜0.05),increased protein expression of inducible nitric oxide synthase,P-P38,P-JNK and P-ERK proteins(P＜0.05),and increased level of reactive oxygen species(P＜0.05).To conclude,peroxiredoxin 1 regulates microglial inflammation by targeting the reactive oxygen species/mitogen-activated protein kinase signaling pathway.

ObjectiveTo construct an automatic measurement model for palmar tilt and radial inclination suitable for traditional Chinese medicine （TCM） clinical scenarios， and to validate its accuracy and efficiency in TCM manipulative reduction settings. MethodsData on anteroposterior （AP） and lateral X-rays of distal radius fractures were collected from patients admitted to 18 TCM/ integrated TCM and western medicine hospitals in Jiangsu province between September 1st， 2023， and September 1st， 2024， via the Jiangsu Diagnosis and Treatment Big Data Platform for TCM Dominant Diseases. A medical image segmentation framework based on multi-scale feature fusion and edge-awareness was employed， combined with anatomical knowledge specific to TCM orthopedics， to optimize the feature extraction strategy of an artificial intelligence （AI） model. This framework enabled automatic segmentation of fracture regions and measurement of distal radius palmar tilt and radial inclination. The accuracy of the AI model in measuring radial inclination and volar tilt was validated， and the measurement time and average time gain rate of the AI model were compared to those of manual measurement. ResultsA total of 15,444 AP and lateral X-ray images of distal radius fractures were collected， and were divided into a training set （11,144 images， 5066 AP and 6078 lateral）， a validation set （3700 images， 1840 AP and 1860 lateral）， and an independent test set （600 images， 300 AP and 300 lateral） after preprocessing. In the measurement of 300 AP X-rays in the independent test set for radial inclination， when the degree error between AI measurement and manual measurement was ＜3° and ＜5°， AI measurement accuracy was 83% and 93%， respectively. In 300 lateral X-rays in the test set for palmar tilt， when AI measurements had an error of ＜3° and ＜5° compared to manual measurements， corresponding accuracy rate was 78% and 90%， respectively. For 50 X-ray images， AI measurement time was （1.37±0.05） min for radial inclination while manual measurement time was （22.57±2.52） min （P＜0.001）； in terms of palmar tilt， the AI measurement time was （1.33±0.14） min， shorter than （23.70±2.80） min for manual measurement time （P＜0.001）. Average time gain rates for manual and AI measurements were 93.93% and 94.39% respectively. ConclusionAn automatic measurement model for palmar tilt and radial inclination in distal radius fractures has been established， enabling more accurate and efficient assessment as well as providing a tool to support the quantitative evaluation of the efficacy of TCM manipulative reduction and large-sample clinical research.

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 expression of prefrontal cortical neurons， methyltransferase-like 3 （METTL3）， fat mass and obesity-associated gene （FTO）， and AlkB homolog 5 （ALKBH5） proteins in a mouse model of post-traumatic stress disorder （PTSD）. MethodsA PTSD mouse model was established using a single prolonged stress and foot shock stimulation （SPSS） method. The despair， anxiety， and learning and memory functions of PTSD mice were assessed through the open field test， Y-maze test， and forced swimming test. Neuronal damage was detected via HE and Nissl staining. The expression levels of METTL3， FTO， ALKBH5， and neuronal nuclear protein （NEUN） were assessed by Western blot and immunofluorescence staining. ResultsCompared to control group， PTSD mice subjected to SPSS exhibited signs of despair， anxiety， and impaired learning and memory. HE and Nissl staining results showed neuronal damage in the prefrontal cortex of PTSD mice. Western blot and immunofluorescence staining results showed that the expression of the m6A-related proteins METTL3 and FTO decreased， while the expression of ALKBH5 increased in the prefrontal cortex. Additionally， NEUN protein levels showed a declining trend. ConclusionThe pathogenesis of PTSD may be associated with neuronal damage in the prefrontal cortex and alterations in m6A methylation proteins.

SMYD5 is a ribosomal methyltransferase with SET and MYND structural domains， which is a member of the SMYD family and is expressed in a variety of tissues， including ovary and testis. This enzyme participates in biological processes such as gene expression regulation， cell development and differentiation， and maintenance of genomic stability through ribosomal protein methylation modification. In recent years， research on SMYD5 has increased in cancers including hepatocellular carcinoma， gastric adenocarcinoma， and lung cancer. Studies have revealed that SMYD5 exhibits high expression levels in various diseases including hepatocellular carcinoma， gastric adenocarcinoma， lung cancer， and inflammatory bowel disease， influencing the progression of these conditions. This review summarizes the role of SMYD5 in hepatocellular carcinoma， inflammatory bowel disease， and other biological functions， aiming to provide a reference for related disease research.

Background Microparticles (MPs) are one of the main medium of inflammatory reaction with an important role in atherosclerotic progression. Studies on association of air pollution exposure and levels of peripheral blood MPs are limited among human. Objective To evaluate the effects of short-term exposure to air pollution on levels of peripheral blood MPs. Method A panel of 73 healthy adults was followed with 4 repeated follow-ups in Beijing, China, from November 2014 to January 2016. During each visit, we collected questionnaire information, fasting venous blood, urine, and exposures to fine particulate matter (PM_2.5), black carbon, nitric oxide, nitrogen dioxide, nitrogen oxide, sulfur dioxide, carbon monoxide, and ozone. We used linear mixed-effect models to analyze associations of air pollution exposure with levels of total MPs (TMPs) and MPs derived from various cells. Stratified analysis was conducted by levels of C-reactive protein (CRP) and malondialdehyde (MDA). Results The results showed significant associations between air pollution exposure and peripheral blood TMPs at 2 h-6 d prior to the follow-ups (P<0.05), while no statistical associations were found for MPs derived from different cell types. Significant increases in TMPs of 7.8% (95%CI: 0.7%, 15.3%) and 14.3% (95%CI: 2.8%, 27.2%) were observed with each interquartile range (IQR) increase in PM_2.5 (IQR=64.9 μg·m⁻³) at prior 18 h and NO (IQR=40.5 μg·m⁻³) at prior 48 h. Among participants with low levels of CRP and MDA, significantly positive associations were observed between air pollution exposure and levels of TMPs (P<0.05). Conclusion Short-term exposure to air pollution is significantly associated with increased levels of circulating MPs in healthy adults, and in people with lower systemic inflammation, peripheral blood MPs levels are more easily affected after exposure to air pollutants.

ObjectiveTo evaluate the measurement uncertainty of human immune serum IgG antibody content against type1 pneumococcal capsular polysaccharide detecting by ELISA through control charting techniques, so as to establish a method for monitoring the performance of laboratory measurement system and evaluating the detection ability.MethodsAccording to GB/T 27411-2012“Routine methods for evaluation and expression of measurement uncertainty in testing laboratory”, GB/T 27407-2010“Quality control in laboratories——Applying statistical quality assurance and control charting techniques to evaluate analytical measurement system performance” and GB/T 27420-2018“Conformity assessment——Application guide for evaluation and expression of uncertainty in biological sample measurement”, the measurement uncertainty of type 1 IgG antibody detection results in quality control serum 09 CS by ELISA was assessed under intermediate precision conditions using the control charting techniques.ResultsThe quality control data conformed to the assumptions of normality and independence. The bias of the measurement process was negligible. The overlay plot of measured values and exponentially weighted moving average(EWMA), along with the moving range(MR) chart, indicated that both the measurement system shift and the measurement process were under control. The measurement uncertainty(u) of IgG antibody content was equivalent to the intermediate precision(s_(R')), with the type 1 IgG antibody detection result reported as(18. 53 ± 2. 16) ??g/mL, k = 2.ConclusionThe control charting techniques, combined with quality control serum testing results, can be used to evaluate the measurement uncertainty of IgG antibody content against pneumococcal capsular polysaccharide in human immune serum in laboratory settings.

A unified consensus statement on medication-related osteonecrosis of the jaw (MRONJ) has not yet been established among the Asian member countries or regions of the Asian Federation of Osteoporosis Societies (AFOS). This study aimed to develop a consensus on MRONJ in patients with osteoporosis across these countries and regions. In this study, the term “Asia-Pacific” refers specifically to the Asian member countries and regions of AFOS. A structured survey consisting of nine MRONJ-related questions was distributed across 10 countries and regions to assess the level of agreement and summarize regional perspectives. In addition, a manual literature review and voting were conducted to evaluate the current evidence on MRONJ. The key aspects of MRONJ, including definition, staging, diagnosis, pathogenesis, risk factors, management, and prevention, were generally consistent among the AFOS countries and regions. The annual incidence and incidence rate of MRONJ associated with low-dose antiresorptive therapy in patients with osteoporosis ranged from 0.025% to 0.136% and 21 to 283 cases per 100,000 person-years, respectively. However, evidence regarding the benefits of drug discontinuation before dental surgery, such as tooth extraction, remains insufficient. Large-scale, multinational studies across AFOS countries and regions are warranted to determine the incidence of MRONJ better and evaluate the impact of antiresorptive drug discontinuation before dental procedures. These findings may contribute to the devel opment of effective evidence-based strategies for preventing MRONJ in patients with osteoporosis.

BACKGROUND:The inflammatory response of microglia is closely related to neuronal survival,regeneration,and functional recovery after spinal cord injury.Peroxiredoxin 1 is not only involved in the regulation of oxidative stress,but also has an important effect on cell proliferation,apoptosis,and inflammatory response.OBJECTIVE:To investigate the role and mechanism of peroxiredoxin 1 in the inflammatory response of microglia following spinal cord injury.METHODS:(1)Twelve female C57BL/6 mice were randomly divided into sham-operated(n=6)and spinal cord injury(n=6)groups.The sham-operated group was not modeled and acute spinal cord injury models were constructed in the spinal cord injury group using the modified Allen's method.Spinal cord tissue at the injured site was taken at 7 days after modeling and transcriptome sequencing was performed to identify differentially expressed genes.The expression of peroxiredoxin 1 in spinal cord tissues was verified using western blot and RT-qPCR.(2)Mouse microglia BV2 were divided into two groups:the control group was stimulated with lipopolysaccharide for 6 hours,and in the knockout group,lipopolysaccharide stimulation was applied for 6 hours at 24 hours after peroxiredoxin 1 was knocked down in the cells.RT-qPCR was performed to detect mRNA expression of peroxiredoxin 1,inflammatory factors(interleukin 1β,interleukin 6,inducible nitric oxide synthase,tumor necrosis factor α,C-C motif chemokine ligand 2,and C-X-C motif chemokine ligand 2),and western blot was performed to detect the expression of peroxiredoxin 1,inducible nitric oxide synthase,and reactive oxygen/mitogen-activated protein kinase signaling pathway proteins.Mouse microglia BV2 were treated in two groups:the control group was stimulated by hydrogen peroxide for 4 hours,and the knockout group was stimulated by hydrogen peroxide for 4 hours at 24 hours after knockdown of peroxiredoxin 1.The level of reactive oxygen species was detected by 2,7-dichlorodihydrofluorescein diacetate probe.RESULTS AND CONCLUSION:(1)Results from transcriptome sequencing,western blot and RT-qPCR confirmed that peroxiredoxin 1 expression levels in mouse spinal cord tissues were significantly higher in the spinal cord injury group than the sham-operated group(P＜0.05).(2)Peroxiredoxin 1 knockdown in microglial cells led to decreased expression of peroxiredoxin 1 mRNA and protein(P＜0.05),increased mRNA expression of interleukin 1β,interleukin 6,inducible nitric oxide synthase,tumor necrosis factor α,C-C motif chemokine ligand 2,and C-X-C motif chemokine ligand 2(P＜0.05),increased protein expression of inducible nitric oxide synthase,P-P38,P-JNK and P-ERK proteins(P＜0.05),and increased level of reactive oxygen species(P＜0.05).To conclude,peroxiredoxin 1 regulates microglial inflammation by targeting the reactive oxygen species/mitogen-activated protein kinase signaling pathway.