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.

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 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:How to repair bone defect has been a clinical problem for a long time.The effective ingredients of traditional Chinese medicine have good biological activity and therapeutic effect,and the combination of effective ingredients of traditional Chinese medicine and tissue engineering materials has a broad prospect in the field of bone repair.The combination of different effective ingredients of traditional Chinese medicine and scaffolds has similarities in their functional relationships. OBJECTIVE:To collect the cases of the combinations of effective ingredients of traditional Chinese medicine and scaffolds,then analogize tissue engineering scaffolds and effective ingredients of traditional Chinese medicine into two types of traditional Chinese medicine that generate compatibility relationships based on the inspiration of the compatibility of seven emotions and summarize the relationship between the two based on their functional relationships. METHODS:Relevant articles from January 1998 to January 2024 were searched in PubMed and China National Knowledge Infrastructure(CNKI),using English search terms"traditional Chinese medicine,Chinese medicine,traditional Chinese medicine monomers,bone defect,bone repair,bone tissue engineering,tissue engineering,scaffold"and Chinese search terms"traditional Chinese medicine,effective ingredients of traditional Chinese medicine,traditional Chinese medicine monomers,bone tissue engineering,bone tissue engineering scaffold,scaffold,tissue engineering,bone defect,bone repair."A total of 88 articles were included for review and analysis. RESULTS AND CONCLUSION:(1)Both tissue engineering scaffold materials and active ingredients of traditional Chinese medicine have been widely used in the field of bone repair.Although they have obvious advantages in osteogenesis,there are still many shortcomings.Many studies are dedicated to preparing composite materials from the two,hoping to exert a detoxification and synergism through the interaction between the two.(2)Some drugs and materials can promote each other in osteogenesis,antibacterial,and promoting angiogenesis,enhancing their original effects.Inspired by the traditional concept of prescription compatibility,this article summarized it as a"Mutual promotion"relationship and provided examples to support it.(3)Some drugs can enhance the strength of materials,while some materials can achieve sustained release and controlled release effects,increase drug loading and stability,or achieve targeted delivery of drugs loaded on them.The article summarized this unilateral enhancement effect as a"Mutual assistance"relationship.(4)The combination of some traditional Chinese medicine and materials can reduce the toxic side effects of the other party.The article summarizes this detoxification relationship as"Mutual restraint and detoxification."(5)The article provided a new perspective on traditional Chinese medicine composite scaffolds,inspired by the seven emotions compatibility relationship and based on the classification of action relationships.It introduced traditional Chinese medicine concepts into the field of tissue engineering,providing new research ideas for subsequent researchers of composite scaffolds,and providing certain convenience in material selection and matching.

ObjectiveTo find out whether there is any difference in the monitoring results of mosq-ovitraps placed in different orientations in multi-storey residential areas, so as to provide a scientific basis for routine and emergency monitoring of Aedes albopictus with mosq-ovitraps in residential areas. MethodsFrom July 6th to October 26th 2023, one mosquito ovitrap was set up in each of the 4 orientations of east, south, west and north around the buildings in a multi-storey residential area in Jinhui Town, Fengxian District, Shanghai. Data was collected and recorded 72 hours after placement. The chi-square test was used to compare the mosquito ovitrap indices (MOIs) of two independent samples, and the Kruskal⁃Wallis H test was used to compare the MOIs of multiple independent samples. ResultsAfter 16 weeks of surveillance, 997 mosquito ovitraps were recovered, of which 211 were positive, with the mosquito ovitrap index (MOI) of 21.16% and the Aedes albopictus density index of 1.03 mosquitoes·ovitrap^-1. The MOIs were higher in September (24.22%) and October (23.96%), and the MOIs in the west, south and north within the two months were all above 20.00%. From July to October, the MOIs in the east, west, south and north were 20.70%, 22.20%, 25.50% and 16.20%, respectively, and the difference in MOIs among the 4 orientations was not statistically significant (χ²=6.647, P=0.084). Stratified analysis by month showed that in August, the south side of the multi-storey residential areas had the highest MOI (31.30%), the north side had the lowest MOI (1.30%), and there was a statistically significant difference in MOI in the east, west, south and north (χ²=25.986, P<0.001). In October, the MOI in the west was the highest (33.30%) and the MOI in the east was the lowest (6.30%), the difference in MOIs of the 4 orientations was statistically significant (χ²=12.007, P=0.007). The MOIs in the south side of the building in the outskirts of the residential area from the 1st week in July to the 4th week in October was lower (19.20%) than that in the south side of the inner building (31.70%), and the difference in MOI was statistically significant (χ²=5.118, P=0.024). ConclusionThe study of MOI in different orientations in a multi-storey residential area is a preliminary exploration based on field work, and the results show that there is a difference in MOIs in different orientations during the peak breeding period of mosquitoes. Further indicators such as temperature, humidity and wind speed in different orientations can be collected to explore the influencing factors of MOIs.

Purpose To evaluate the value of preoperative MRI-based radiomic models for assessing tumor-infiltrating CD8+T-cell expression in glioblastoma patients,and to identify the most stable and efficient radiomic feature region for predicting prognosis following immunotherapy.Materials and Methods This retrospective study included 150 patients with histopathologically confirmed glioblastoma from Lanzhou University Second Hospital(January 2018 to April 2022).Tumor-infiltrating CD8+T-cell expression was quantitatively assessed using immunohistochemical staining,with patients stratified into CD8-high and CD8-low expression groups based on overall survival.A total of 1 185 radiomic features were extracted from each patient's contrast-enhanced T1C and T2WI images,covering the original tumor region and sequentially expanded peritumoral regions(2.5 mm,5.0 mm,7.5 mm,10.0 mm,12.5 mm,15.0 mm morphological dilation of tumor core+peritumoral area).Feature selection was performed using variance threshold,minimum redundancy maximum relevance,and least absolute shrinkage and selection operator methods.XGBoost classifier was employed to construct clinical,radiomic,and clinical-radiomic multimodal combined prediction models.Diagnostic performance was evaluated using receiver operating characteristic curve analysis.Results The radiomic model based on tumor expansion of 7.5 mm(tumor+peritumoral region)demonstrated optimal predictive performance.The clinical-radiomic multimodal combined model showed superior predictive capability compared to clinical and radiomic models alone,achieving an area under the curve of 0.991 and accuracy of 99.0%in the training set,and area under the curve of 0.840 with accuracy of 80.0%in the validation set.Conclusion MRI radiomics provides a feasible approach for evaluating tumor-infiltrating CD8+T-cell expression in glioblastoma patients,offering potential for preoperative prognosis prediction.

Objective To conduct comprehensive clinical evaluation of injectable anti-inflammatory and hepatoprotective drugs with different mechanisms of action,and to provide a basis for drug selection and rational drug use in medical institutions.Methods Twenty-two experts in clinical and pharmacological fields were organized to construct a quantitative rating scale for the comprehensive clinical evaluation of drugs by applying the literature research method,expert interview method,and Delphi method,through seminars and interviews,and by referring to the real-world clinical data and evidence-based medical evidence such as the Guidelines for the Management of Comprehensive Clinical Evaluation of Drugs,so as to conduct a comprehensive evaluation of eight injectable anti-inflammatory and hepatoprotective drugs in terms of six dimensions:effectiveness,safety,economy,appropriateness,accessibility and maturity.Results A comprehensive clinical evaluation index system of injectable anti-inflammatory and hepatoprotective drugs for the treatment of drug-induced liver injury was constructed,including 6 first-level indexes,14 second-level indexes,and 27 third-level indexes,with a total of 100 points.The scoring results showed that among the evaluated varieties,the scores were,in descending order,magnesium isoglycyrrhizinate injection,compound glycyrrhizin injection,polyene phosphatidylcholine injection,reduced glutathione for injection,thiopronin injection,compound ammonium glycyrrhizinate injection,acetylcysteine injection and diammonium glycyrrhizinate injection.Conclusion The constructed quantitative rating scale for comprehensive clinical evaluation of drugs is operable,and the evaluation process can provide academic guidance for exploring the standardized path of comprehensive clinical evaluation of drugs,which needs to be applied in combination with the actual drug varieties of the medical institutions as well as the specific conditions of the patients to make individualized therapeutic choices.