From the perspective of transfusion medicine and based on the vision and framework of patient blood management, this article combines the advances in basic science, blood transfusion, laboratory, and clinical medicine. It aims to systematically review the key elements and characteristics of patient fibrinogen management by maintaining and optimizing patients' hemostatic function while reducing blood transfusions. This review enriches the connotation of transfusion medicine, especially patient blood management, and provides valuable insights for clinical practice.

Objective To establish and verify a pseudovirus detection method for respiratory syncytial virus(RSV) neutralizing antibodies, in order to provide a rapid, high-throughput and safe method for RSV vaccine development and antiviral drug screening.MethodsThe RSV A2 pseudovirus was prepared, and the pseudovirus detection method for RSV neutralizing antibody was established by optimizing the pseudovirus concentration factor(0, 2, 4, 8 and 16 times), infected cell type(HEK293T, Hep-2, Huh7.5.1, HeLa and Vero cells), cell inoculation density(0. 5 × 10~4, 1. 0 × 10~4, 1. 5 × 10~4, 2. 0 × 10~4 and2. 5 × 10~4 cells/well), cell generation(1, 5, 10, 15, 20 and 25 generations), detection time(6, 12, 24, 36, 48, 60 and 72 h) and virus inoculation amount(5. 0 × 10~3, 2. 5 × 10~3, 1. 25 × 10~3, 6. 25 × 10~2, 3. 125 × 10~2, 1. 562 5 × 10~2 TCID50/mL). In addition, the precision, specificity, accuracy and edge effects of the method were verified. Finally, the established method and plaque-reduction neutralization test(PRNT) were used to detect 30 mouse positive sera simultaneously, and the correlation between the detection results was analyzed.ResultsThe optimal pseudovirus concentration factor was 8, the infected cells were HEK293T cells of 1-10 passages after resuscitation with the inoculation density of 2. 0 × 10~4 cells/well, the detection duration was 48 h, and the virus inoculation amount was 3. 125 × 10~2-2. 5 × 10~3 TCID50/mL. The coefficients of variation(CVs) of repeatability and intermediate precision verification were both less than 15%. No RSV neutralizing activity was detected in normal mouse serum, and high RSV neutralizing activity was detected in positive mouse serum and Nirsevimab monoclonal antibody. The recovery rates of accuracy verification were within the range of 80% to 120%. The ratios of the detection values of edge holes to non-edge holes were 0. 5-0. 7, indicating that there was edge hole effects, and non-edge holes should be used for detection. The results of 30 mouse positive sera detected by the two methods showed a good positive correlation(r = 0. 914 5, P < 0. 000 1).ConclusionThe established RSV neutralizing antibody titer pseudovirus detection method has good precision, specificity, accuracy, with simple and fast operation, which can be used for the evaluation of RSV vaccine immunogenicity and the high-throughput detection of serum neutralizing antibody potency.

ObjectiveThis study aimed to investigate the anti-Mycoplasma pneumoniae (MP) activity of luteolin and elucidate its underlying mechanisms. MethodsLuteolin was identified as the primary active compound from the polyphenol extract ofF. diotrys using network pharmacology. Its efficacy was evaluated against two MP strains: the standard strain M129 and the multidrug-resistant strain M19. A modified culture medium with visual characteristics was employed to determine the minimum inhibitory concentration (MIC) of luteolin. The expression of key proteins involved in MP growth and pathogenicity was assessed by qRT-PCR following luteolin treatment. Additionally, the viability of A549 cells infected with MP was compared between luteolin-treated and untreated groups. In vivo anti-MP activity was evaluated using a mouse model, and the expression of inflammatory cytokines in lung tissues was analyzed. ResultsLuteolin effectively inhibited both MP strains, with MIC₉₀ values of 100 mg/L for M19 and M129. Treatment with luteolin significantly downregulated the expression of adhesion proteins P1 and P30 in both strains. However, the expression of P65, HMW3, TrmB, and CARDS TX was reduced only in the M19 strain following luteolin intervention. Luteolin also enhanced the growth and viability of A549 cells infected with MP. In the mouse model, luteolin treatment resulted in steady weight gain and was well tolerated. The bacteriostatic rate of luteolin in lung tissues was 50.7%, significantly higher than the 25.2% observed in the roxithromycin group. Furthermore, luteolin reduced the expression of inflammatory factors, including IL-6, TNF-α, and HMGB1, in MP-infected mice. ConclusionLuteolin effectively and safely inhibits the proliferation and pathogenicity of MP, particularly the drug-resistant M19 strain, by downregulating the expression of toxicity-associated proteins (P1, P30, P65, HMW3, TrmB, CARDS TX) and modulating host inflammatory responses. These findings suggest that luteolin may offer a novel therapeutic strategy for treating MP infections, especially those caused by drug-resistant strains.

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.

ObjectiveThis study aimed to investigate the anti-Mycoplasma pneumoniae (MP) activity of luteolin and elucidate its underlying mechanisms. MethodsLuteolin was identified as the primary active compound from the polyphenol extract ofF. diotrys using network pharmacology. Its efficacy was evaluated against two MP strains: the standard strain M129 and the multidrug-resistant strain M19. A modified culture medium with visual characteristics was employed to determine the minimum inhibitory concentration (MIC) of luteolin. The expression of key proteins involved in MP growth and pathogenicity was assessed by qRT-PCR following luteolin treatment. Additionally, the viability of A549 cells infected with MP was compared between luteolin-treated and untreated groups. In vivo anti-MP activity was evaluated using a mouse model, and the expression of inflammatory cytokines in lung tissues was analyzed. ResultsLuteolin effectively inhibited both MP strains, with MIC₉₀ values of 100 mg/L for M19 and M129. Treatment with luteolin significantly downregulated the expression of adhesion proteins P1 and P30 in both strains. However, the expression of P65, HMW3, TrmB, and CARDS TX was reduced only in the M19 strain following luteolin intervention. Luteolin also enhanced the growth and viability of A549 cells infected with MP. In the mouse model, luteolin treatment resulted in steady weight gain and was well tolerated. The bacteriostatic rate of luteolin in lung tissues was 50.7%, significantly higher than the 25.2% observed in the roxithromycin group. Furthermore, luteolin reduced the expression of inflammatory factors, including IL-6, TNF-α, and HMGB1, in MP-infected mice. ConclusionLuteolin effectively and safely inhibits the proliferation and pathogenicity of MP, particularly the drug-resistant M19 strain, by downregulating the expression of toxicity-associated proteins (P1, P30, P65, HMW3, TrmB, CARDS TX) and modulating host inflammatory responses. These findings suggest that luteolin may offer a novel therapeutic strategy for treating MP infections, especially those caused by drug-resistant strains.

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.

Objective To establish a fluorescent recombinase-aided amplification (RAA) assay for detection of Strongyloides stercoralis nucleic acid and to preliminarily evaluate its performance. Methods Six sets of specific primers targeting S. stercoralis 18S ribosomal RNA (18S rRNA) gene and one fluorescent probe were designed and synthesized. The optimal primer-probe set was determined through systematic screening and optimization to establish the fluorescent RAA assay. The assay was evaluated using S. stercoralis genomic DNA at concentrations of 100, 10, and 1 pg/μL, and 100, 10, and 1 fg/μL, as well as recombinant pUC57 plasmids containing the target gene fragments at 1 × 10⁵, 1 × 10⁴, 1 × 10³, 1 × 10², 1 × 10¹, 1 × 10⁰ copies/reaction, to determine the analytical sensitivity. Genomic DNA from Ascaris lumbricoides, Ancylostoma duodenale, Enterobius vermicularis, Angiostrongylus cantonensis, Trichinella spiralis, Clonorchis sinensis, Schistosoma japonicum, and Taenia saginata was used to assess assay specificity. A total of 25 stool samples from patients suspected of S. stercoralis infection were tested by the modified Baermann funnel technique, PCR, and the established fluorescent RAA assay. The sensitivity, specificity, concordance rate and their 95% confidence intervals (CI) of these three techniques were estimated, and agreement between methods was evaluated using the Kappa coefficient. Results Exo-4 was identified as the optimal primer set screened from the six primer sets, and the best amplification performance was achieved when the final concentrations of the forward and reverse primers were 0.44 μmol/L and a probe concentration was 0.20 μmol/L. The limit of detection of the fluorescent RAA assay was 100 fg/μL for genomic DNA of S. stercoralis and 1 × 10⁰ copies/reaction for recombinant plasmids. Specific fluorescence signals were detected within 5 min, with no cross-reactivity observed with A. lumbricoides, A. duodenale, E. vermicularis, A. cantonensis, T. spiralis, C. sinensis, S. japonicum, or T. saginata. Among the 25 clinical stool samples from patients suspected of S. stercoralis infections, the modified Baermann funnel technique and fluorescent RAA assay detected 19 positives and 6 negatives, whereas PCR detected 18 positives and 7 negatives. The fluorescent RAA assay showed a sensitivity of 100.00% [95% CI: (82.35%, 100.00%)], specificity of 100.00% [95% CI: (54.07%, 100.00%)], concordance rate of 100.00% [95% CI: (86.28%, 100.00%)], and a Kappa coefficient of 1.00 [95% CI: (1.00, 1.00)] (P < 0.001) relative to the modified Baermann funnel technique, and a sensitivity of 100.00% [95% CI: (81.47%, 100.00%)], specificity of 85.71% [95% CI: (42.13%, 99.64%)], concordance rate of 96.00% [95% CI: (79.65%, 99.90%)], and a Kappa coefficient of 0.90 [95% CI: (0.70, 1.00)] (P < 0.001). Positive amplification products emitted green fluorescence under a portable blue-light device, enabling visual interpretation of results. Conclusions The fluorescent RAA assay established in this study is rapid, highly sensitive, and highly specific. It enables detection of S. stercoralis nucleic acid under isothermal conditions and allows visual interpretation of results, providing a novel tool for rapid clinical diagnosis and field screening of S. stercoralis infections.

This study investigates the pharmacokinetics and metabolic characteristics of three marine-derived piericidins as potential drug leads for kidney disease: piericidin A (PA) and its two glycosides (GPAs), glucopiericidin A (GPA) and 13-hydroxyglucopiericidin A (13-OH-GPA). The research aims to facilitate lead selection and optimization for developing a viable preclinical candidate. Rapid absorption of PA and GPAs in mice was observed, characterized by short half-lives and low bioavailability. Glycosides and hydroxyl groups significantly enhanced the absorption rate (13-OH-GPA > GPA > PA). PA and GPAs exhibited metabolic instability in liver microsomes due to Cytochrome P450 enzymes (CYPs) and uridine diphosphoglucuronosyl transferases (UGTs). Glucuronidation emerged as the primary metabolic pathway, with UGT1A7, UGT1A8, UGT1A9, and UGT1A10 demonstrating high elimination rates (30%-70%) for PA and GPAs. This rapid glucuronidation may contribute to the low bioavailability of GPAs. Despite its low bioavailability (2.69%), 13-OH-GPA showed higher kidney distribution (19.8%) compared to PA (10.0%) and GPA (7.3%), suggesting enhanced biological efficacy in kidney diseases. Modifying the C-13 hydroxyl group appears to be a promising approach to improve bioavailability. In conclusion, this study provides valuable metabolic insights for the development and optimization of marine-derived piericidins as potential drug leads for kidney disease.
Animals ; Male ; Mice ; Aquatic Organisms/chemistry* ; Biological Availability ; Cytochrome P-450 Enzyme System/metabolism* ; Glucuronosyltransferase/metabolism* ; Microsomes, Liver/metabolism* ; Molecular Structure ; Biological Products/pharmacokinetics* ; Pyridines/pharmacokinetics*