Chronic post-surgical pain (CPSP) is a common long-term complication following lung surgery. Its high incidence significantly impacts patients’ quality of life and functional recovery, and imposes a substantial socioeconomic burden. This consensus aims to systematically establish a standardized integrated Chinese and Western medicine diagnostic and treatment framework for chronic post-lung surgery pain (CPLSP). Based on the latest domestic and international evidence-based medical research and multidisciplinary clinical experience, the working group comprehensively elaborates on core issues regarding CPLSP, including its definition, epidemiology, pathogenesis, clinical assessment, Western medical treatment, traditional Chinese medicine (TCM) treatment, and integrated strategies. The consensus emphasizes a patient-centered approach, adhering to the principles of multimodality, individualization, and stepwise management, highlighting the synergistic advantages of integrating Chinese and Western medicine throughout the entire perioperative management cycle encompassing "perioperative anti-inflammation, acute analgesia, and chronic rehabilitation." Through systematic literature retrieval and evidence integration, a total of 9 core recommendations were established to provide scientifically sound and clinically practical guidance.

OBJECTIVE To investigate the effects and mechanism of the Yishen paidu formula on renal fibrosis in rats with chronic renal failure （CRF） through the reactive oxygen species （ROS）/thioredoxin-interacting protein （TXNIP）/NOD-like receptor thermal protein domain associated protein 3 （NLRP3） pathway. METHODS Rats were randomly divided into control group， model group， Yishen paidu formula low-dose （Yishen paidu formula-L） group， Yishen paidu formula high-dose （Yishen paidu formula- H） group， Yishen paidu formula-H+pcDNA-NC group， and Yishen paidu formula-H+ pcDNA-TXNIP group， with 10 rats in each group. Except for control group， all other rats were fed a diet containing 0.5% adenine to establish a CRF model； the rats were then administered corresponding drugs or normal saline intragastrically or via tail vein， once daily， for 8 consecutive weeks. After the last administration， the levels of serum creatinine （Scr）， blood urea nitrogen （BUN）， ROS， superoxide dismutase （SOD）， malondialdehyde （MDA）， tumor necrosis factor-α （TNF-α）， interleukin （IL）-6， and IL-1β were measured in each group. Pathological changes in renal tissue were observed， and the protein expression levels of Collagen Ⅲ， α-smooth muscle actin （α-SMA）， transforming growth factor-β1 （TGF-β1）， TXNIP and NLRP3 in renal tissue were detected. RESULTS Compared with model group， the renal histopathological damage and fibrosis of rats in Yishen paidu formula-L group and Yishen paidu formula-H group were significantly alleviated. The levels of Scr， BUN， ROS， MDA， TNF- α， IL-6 and IL-1β， and the protein expressions of Collagen Ⅲ， α-SMA， TGF-β1， TXNIP and NLRP3 were significantly decreased， while SOD levels were significantly increased （P＜0.05）. Moreover， the changes were more pronounced in the Yishen paidu formula-H group （P＜0.05）. Compared with Yishen paidu formula-H+pcDNA-NC group， above indexes of rats in Yishen paidu formula-H+pcDNA-TXNIP group were reversed significantly （P＜0.05）. CONCLUSIONS Yishen paidu formula can inhibit renal fibrosis in CRF rats by suppressing the ROS/TXNIP/NLRP3 pathway.

Objective: To establish an accurate and rapid typing method for Rh typing of samples from patients who have received recent blood transfusions by utilizing the difference in osmotic fragility between fresh and old red blood cells. Methods: A lysing solution suitable for destroying old RBCs was prepared. Sixty-one samples collected in our hospital in 2024 with Rh typing of double groups were treated with the lysing solution to remove the old allogeneic red blood cells while preserving the patient's own fresh red blood cells, followed by repeat Rh typing tests. Results: For 61 samples with Rh typing in double groups, 41 were accurately detected identified through the red blood cell lysis method, yielding an identification rate of 67.21%. No significant difference was observed compared to the detection rate of the commonly used capillary centrifugation modified method (χ =0.103, P>0.05). Conclusion: The red blood cell lysis method provides a novel and rapid experimental approach for clinical use in processing Rh-typed samples that are of double groups, thereby offering a basis for Rh compatibility blood transfusion.

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.

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.

The development of traditional Chinese medicine (TCM) diagnosis and treatment has undergone multiple paradigms, evolving from sporadic experiential practices to systematic approaches in syndrome differentiation and treatment and further integration of disease and syndrome frameworks. TCM is a vital component of the medical system, valued alongside Western medicine. Treatment based on syndrome differentiation embodies both personalized treatment and holistic approaches; however, the inconsistency and lack of stability in syndrome differentiation limit clinical efficacy. The existing integration of diseases and syndromes primarily relies on patchwork and embedded systems, where the full advantages of synergy between Chinese and Western medicine are not fully realized. Recently, driven by the development of diagnosis and treatment concepts and advances in analytical technology, Western medicine has been rapidly transforming from a traditional biological model to a precision medicine model. TCM faces a similar need to progress beyond traditional syndrome differentiation and disease-syndrome integration toward a more precise diagnosis and treatment paradigm. Unlike the micro-level precision trend of Western medicine, precision diagnosis and treatment in TCM is primarily reflected in data-driven applications that incorporate information at various levels, including precise syndrome differentiation, medication, disease management, and efficacy evaluation. The current priority is to accelerate the development of TCM precision diagnosis and treatment technology platforms and advance discipline construction in this area.

The pathological changes of myocardial infarction （MI） are mainly characterized by progressive myocardial ischemic necrosis， decline in cardiac diastolic function， thinning of the ventricular wall， and enlargement of the ventricles. The clinical manifestations include myocardial ischemia， heart failure， arrhythmia， shock， and even sudden cardiac death， rendering MI one of the most perilous cardiovascular diseases. Currently， the clinical treatment for MI primarily involves interventional procedures and drug therapy. However， due to their significant side effects and high complication rates associated with these treatments， they fail to ensure a satisfactory quality of life and long-term prognosis for patients. On the other hand， traditional Chinese medicine has demonstrated remarkable potential in improving patient prognosis while reducing side effects. Research has elucidated that various signaling pathways such as nuclear transcription factor-κB （NF-κB）， adenosine 5̒-monophosphate-activated protein kinase （AMPK）， transforming growth factor-β （TGF-β）/Smads， mitogen-activated protein kinase （MAPK）， Wnt/β-catenin （β-catenin）， and phosphatidylinositol 3-kinase （PI3K）/protein kinase B（Akt） play crucial roles in regulating the occurrence and development of MI. Effectively modulating these signaling pathways through its therapeutic interventions， traditional Chinese medicine can enhance MI management by inhibiting apoptosis， providing anti-inflammatory properties， alleviating oxidative stress levels， and resisting myocardial ischemia. Due to its notable efficacy and favorable safety， it has become an area of focus in clinical practice.

The concept of holism is the core idea of traditional Chinese medicine （TCM）. Various organs and tissues coordinate with each other to maintain the body's life activities， with a close and mutual influence between the spleen， stomach， and the central nervous system （brain）. The gut-brain axis plays an important bridging role between the digestive system and the central nervous system， achieving bidirectional information exchange between the brain and the gastrointestinal tract through complex neuroendocrine and immune mechanisms. The theory of cross-organ interaction involves the mutual influence， coordination， and integration between different organs and systems； multimodality， on the other hand， utilizes multiple sensory modalities， such as vision， hearing， and touch， to convey information. By combining TCM theory with the gut-brain axis theory， a cross-organ multimodal characterization system is established to explore its mechanism and application value in refractory diseases such as functional gastrointestinal disorders， precancerous gastrointestinal diseases， Alzheimer's disease， Parkinson's syndrome， type 2 diabetes， and depression.

BACKGROUND:Carnosic acid,a bioactive compound found in rosemary,has been shown to reduce inflammation and reactive oxygen species(ROS).However,its mechanism of action in osteoclast differentiation remains unclear. OBJECTIVE:To investigate the effects of carnosic acid on osteoclast activation,ROS production,and mitochondrial function. METHODS:Primary bone marrow-derived macrophages from mice were extracted and cultured in vitro.Different concentrations of carnosic acid(0,10,15,20,25 and 30 μmol/L)were tested for their effects on bone marrow-derived macrophage proliferation and toxicity using the cell counting kit-8 cell viability assay to determine a safe concentration.Bone marrow-derived macrophages were cultured in graded concentrations and induced by receptor activator of nuclear factor-κB ligand for osteoclast differentiation for 5-7 days.The effects of carnosic acid on osteoclast differentiation and function were then observed through tartrate-resistant acid phosphatase staining,F-actin staining,H2DCFDA probe and mitochondrial ROS,and Mito-Tracker fluorescence detection.Western blot and RT-PCR assays were subsequently conducted to examine the effects of carnosic acid on the upstream and downstream proteins of the receptor activator of nuclear factor-κB ligand-induced MAPK signaling pathway. RESULTS AND CONCLUSION:Tartrate-resistant acid phosphatase staining and F-actin staining showed that carnosic acid dose-dependently inhibited in vitro osteoclast differentiation and actin ring formation in the cell cytoskeleton,with the highest inhibitory effect observed in the high concentration group(30 μmol/L).Carnosic acid exhibited the most significant inhibitory effect during the early stages(days 1-3)of osteoclast differentiation compared to other intervention periods.Fluorescence imaging using the H2DCFDA probe,mitochondrial ROS,and Mito-Tracker demonstrated that carnosic acid inhibited cellular and mitochondrial ROS production while reducing mitochondrial membrane potential,thereby influencing mitochondrial function.The results of western blot and RT-PCR revealed that carnosic acid could suppress the expression of NFATc1,CTSK,MMP9,and C-fos proteins associated with osteoclast differentiation,and downregulate the expression of NFATc1,Atp6vod2,ACP5,CTSK,and C-fos genes related to osteoclast differentiation.Furthermore,carnosic acid enhanced the expression of antioxidant enzyme proteins and reduced the generation of ROS during the process of osteoclast differentiation.Overall,carnosic acid exerts its inhibitory effects on osteoclast differentiation by inhibiting the phosphorylation modification of the P38/ERK/JNK protein and activating the MAPK signaling pathway in bone marrow-derived macrophages.