Objective To explore the construction of heart preservation model of empty beating donor based on extracorporeal membrane oxygenation (ECMO). Methods From January 2022 to August 2023, 20 Guangxi Bama miniature pigs weighing 25-30 kg were selected, half male and half female. Under general anesthesia and heparinization, a midline thoracotomy was performed. The pericardium was cut after freeing the anterior and posterior vena cavae, and a perfusion needle was inserted near the brachiocephalic artery in the ascending aorta, connected to a blood collection bag to collect 500-600 mL of blood. The anterior and posterior vena cavae were ligated, the aorta was blocked and perfused with HTK solution to stop the heart beating. The superior and inferior vena cavae were cut off, the right pulmonary vein was decompressed, the aorta and left and right pulmonary arteries and veins were cut off, and the whole heart was removed. An ECMO device was used to continuously perfuse a cardioprotective solution mainly composed of oxygenated warm blood, maintaining the isolated pig heart beating for 8 hours, monitoring (once/hour) ECMO perfusion parameters, blood gas indicators, perfusate electrolytes, inflammatory factors, myocardial enzymes, myoglobin, and troponin levels. Myocardial tissue was taken for hematoxylin-eosin (HE) staining to observe myocardial cell damage and evaluate the quality of heart preservation. Results Among the 20 isolated beating pig hearts, 17 successfully resumed beating, 3 experienced ventricular fibrillation, resuscitated after intracardiac electrical defibrillation, and all 20 pig hearts successfully beat for 8 hours. There was no statistical difference in ECMO perfusion parameters, blood gas indicators, perfusate electrolytes, and inflammatory factors at each time point (P>0.05). There were statistical increases in myocardial enzymes, myoglobin, and troponin levels (P<0.05). HE staining results suggested that there was no severe myocardial damage. Conclusion ECMO technology can be used for pig heart preservation with good results, and this study provides experimental evidence for improving heart preservation research in clinical heart transplantation.

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: Adrenocortical hormones, particularly 11-oxygenated androgens, are pivotal in female reproductive health and fertility. Standardized detection kits and population-specific reference intervals are lacking in China, hindering related clinical applications. Methods: A HPLC-tandem mass spectrometry (HPLC-MS/MS) pipeline was developed, rigorously validated, and applied to simultaneously quantify corticosterone, cortisone, cortisol, 18-OH cortisol, androstenedione (A4), 11β-hydroxyandrostenedione (11-OH A4), dehydroepiandrosterone, and dehydroepiandrosterone sulfate in serum samples from 455 reproductive-aged women (18–45 yrs) in Guangxi, China. Age-dependent concentration trends were analyzed, and reference intervals stratified by age (2.5th to 97.5th percentiles) were established. Correlations with body-composition metrics, ethnicity, and the menstrual cycle were investigated. Results: The HPLC-MS/MS method demonstrated high precision (intra- and inter-assay CVs < 15%), accuracy, and sensitivity. All eight hormones exhibited significant age-related declines (P < 0.001 for seven hormones; P = 0.001 for 11-OH A4). Notably, 11-OH A4 levels were significantly lower in the 35–45-yr (3.05 nmol/L) and 25–34-yr (3.09 nmol/L) age groups than in the 18–24-yr (3.57 nmol/L) age group, whereas no significant difference was observed between the 35–45-yr and 25–34-yr age groups. Weak negative correlations were observed between the body mass index and corticosterone and cortisone levels, whereas ethnicity and the menstrual cycle showed no significant associations with hormone levels. Conclusions We developed an HPLC-MS/MS-based method for simultaneously quantifying eight adrenocortical hormones, including 11-OH A4, and defined age-specific reference intervals for reproductive-aged Chinese women. These findings advance the clinical utility of adrenocortical hormones in diagnosing and managing reproductive disorders.

ObjectiveTo evaluate the therapeutic effects of modified Buwangsan on cognitive dysfunction in the rat model of type 2 diabetes mellitus with mild cognitive impairment （T2DM-MCI） and explore the underlying mechanism. MethodsThirty-six 5-week-old SPF-grade SD rats were randomly assigned into 6 groups： Normal （Con， fed with a normal diet）， model （DM， fed with a high-sugar and high-fat diet）， low-dose modified Buwangsan （L-BWS， 1.86 g·kg^-1）， medium-dose modified Buwangsan （M-BWS， 3.72 g·kg^-1）， high-dose modified Buwangsan （H-BWS，7.44 g·kg^-1）， and huperzine A （SSJJ， 0.018 mg·kg^-1）. The rats were treated by gavage once a day for 12 weeks. The body weight and blood glucose level were monitored dynamically. Morris water maze was employed to test the cognitive function of rats. Hematoxylin-eosin and Nissl staining were employed to observe the pathological changes of the hippocampus. The levels of tumor necrosis factor-α （TNF-α）， interleukin-1β （IL-1β）， and interleukin-6 （IL-6） in the serum and hippocampus were assessed by enzyme-linked immunosorbent assay. Western blotting was employed to determine the expression levels of key autophagy-related proteins including microtubule-associated protein 1 light chain 3 （LC3）， type Ⅲ phosphatidylinositol 3-kinase complex regulatory subunit （Beclin1）， and phosphorylated UNC-51-like kinase （p-ULK） 1/2 in the hippocampus. Immunofluorescence staining was employed to observe the regulation of p-PI3K/PI3K， p-mTOR/mTOR， and p-Akt/Akt ratios. ResultsCompared with the DM group， the L-BWS， M-BWS， H-BWS， and SSJJ groups showed increases in body weight at the end of the experiment （P<0.05）， and the M-BWS， H-BWS and SSJJ groups showed declines in fasting blood glucose level （P<0.05）. In the water maze test， compared with the DM group， the M-BWS， H-BWS， and SSJJ groups presented shortened escape latency （P<0.001）. The L-BWS， M-BWS， H-BWS， and SSJJ group showcased regularly arranged cells in the hippocampus and cortex， markedly increased number of neurons， and significantly recovered Nissl bodies. Compared with the DM group， the L-BWS， M-BWS， H-BWS， and SSJJ groups had reductions in the levels of IL-1β and IL-6 in the serum and hippocampus （P<0.05）， increases in the LC3-II/LC3-I ratio and expression level of beclin1 in the hippocampus （P<0.05） and the p-ULK level （P<0.05）. The p-PI3K/PI3K， p-Akt/Akt， and p-mTOR/mTOR ratios in the hippocampus decreased in the M-BWS， H-BWS， and SSJJ groups （P<0.01）. ConclusionModified Buwangsan significantly ameliorates cognitive dysfunction and neurological damage in the rat model of T2DM through multiple mechanisms. It regulates metabolic disorders， lowers the blood glucose level， improves lipid metabolism， and alleviates oxidative stress. It promotes the protection and repair of neurons by inhibiting inflammatory responses and activating the autophagy pathway in the hippocampus. At the same time， modified Buwangsan relieves autophagy inhibition by regulating the PI3K/Akt/mTOR signaling pathway to alleviate the brain tissue injury.

Background The Inner Mongolia Autonomous Region is a vast area with a wide array of ecological environments, resulting in considerable regional variations in air pollution characteristics. Current research is limited by a scarcity of systematic, region-wide studies and risk assessments. Objective To assess the health risks associated with inhalation exposure to nine heavy metal and metalloid elements in atmospheric fine particulate matter (PM_2.5) for the population of the Inner Mongolia Autonomous Region. Methods From the 10th to the 16th of each month throughout 2023, atmospheric PM_2.5 samples were collected at designated monitoring sites in 12 leagues (cities) across the Inner Mongolia Autonomous Region to analyze the characteristics and trends in concentration. The health risk assessment model developed by the United States Environmental Protection Agency was employed to evaluate both the non-carcinogenic and carcinogenic risks associated with the heavy metal elements beryllium (Be), cadmium (Cd), chromium (Cr), hydrargyrum (Hg), plumbum (Pb), manganese (Mn), and nickel (Ni) and the metalloid elements stibium (Sb) and arsenic (As). Results In 2023, a total of 1206 valid PM_2.5 samples were analyzed from the Inner Mongolia Autonomous Region. The annual average PM_2.5 concentrations in Inner Mongolia, as well as in the cities of Ordos, Wuhai, and the Alxa League, all exceeded the national limit of 35 μg·m⁻³. The PM_2.5 concentrations across the leagues (cities) exhibited a clear seasonal variation, peaking in winter, followed by spring, and reaching a minimum in summer and autumn. The median PM_2.5 concentration in Ordos throughout the year was higher than that in the other leagues (cities) of the region. Concerning health risks, the non-carcinogenic risks associated with the 9 elements detected in each league (city) were all below 1, indicating acceptable levels. However, the non-carcinogenic risk values for Mn, Cr, and As were relatively high; specifically, Ordos City displayed the highest non-carcinogenic risk values for Mn (7.74×10⁻¹) and Cr (2.97×10⁻²), while Chifeng City recorded the highest value for As (2.34×10⁻³). The carcinogenic risk values for As, Cd, Cr, and Ni fell within the range of 1×10⁻⁹ to 1×10⁻⁵, representing an acceptable level. The health risks varied with age, and the elderly residents faced the highest non-carcinogenic and carcinogenic health risks. Conclusion While the carcinogenic and non-carcinogenic risks from heavy metals and metalloids within atmospheric PM_2.5 in the Inner Mongolia Autonomous Region are generally within acceptable limits, the potential risks that Mn, As, and Cr pose to the health of elderly people warrants particular attention, and Ordos and Chifeng cities exhibit notably higher health risks among other areas. It is recommended that regular, long-term monitoring be conducted, taking into account the specific conditions at monitoring sites across each league (city), and that targeted air quality management strategies be implemented to protect public health.

Objective To compare the application effects of plankton multiplex polymerase chain reac-tion-capillary electrophoresis(PCR-CE),SYBR Green Ⅰ real-time quantitative PCR(qPCR)and microwave digestion-vacuum filtration-automated scanning electron microscopy(MD-VF-Auto SEM)in the diagnosis of drowning.Methods Lung,liver and kidney tissues from 212 drowned corpses and 30 non-drowned corpses were examined respectively by the three drowning-related plankton testing methods,and the detection rates of plankton in each tissue by three methods were compared.Results In drowned corpses,the total detection rates of PCR-CE,qPCR,and MD-VF-Auto SEM were 93.9%,96.2%,and 95.3%,respectively,with no statistically significant difference(P>0.05).The detection rate of lung tissue by MD-VF-Auto SEM(100%)was higher than those of PCR-CE and qPCR(P<0.05),and there was no significant difference in the detection rates of the three methods in liver or kidney tissues(P>0.05).In non-drowning corpses,a small number of diatoms(less than 10 cells/10 g)were detected by MD-VF-Auto SEM method,only in liver and kidney tissues,while the other two methods yielded negative results for all tissues.Conclusion All three methods have good efficacy in the examination of drowned corpses.The MD-VF-Auto SEM method directly observes diatom morpho-logical characteristics through scanning electron microscopy,and the qualitative and quantitative analy-ses are intuitive and accurate.It has great advantages in the examination of difficult degradation samples.The PCR-CE method and qPCR method have a low sample demand(0.5 g),are easy to operate and have short detection time(4-7 h).They are easy to be applied in the grassroots depart-ments and are suitable for the rapid determination of drowned corpses in routin cases.The combina-tion of the two DNA methods with the MD-VF-Auto SEM method can increase the detection rate of plankton,ensuring the reliability of examination results.This combined use is of significant importance in the application of drowning diagnosis.

Corynebacterium pseudotuberculosis(C.pseudotuberculosis)is a group of intracellular Gram-positive bacteria that can cause zoonotic diseases.This study investigated the mechanisms of inflammatory mediator secretion and the phagocytic and bactericidal functions of mouse peritoneal macrophages following C.pseudotuberculosis infection.Initially,transcriptomic sequencing was em-ployed to identify genes critical for C.pseudotuberculosis infection in macrophages.Subsequently,gene knockout mice were utilized to assess the impact of these key genes on inflammatory media-tor secretion,activation of inflammatory signaling pathways,and the phagocytic and bactericidal functions of macrophages infected with C.pseudotuberculosis.Techniques such as ELISA,Western blot,and immunofluorescence were employed in this analysis.Further,transcriptomic sequencing was conducted to identify key downstream genes.Following C.pseudotuberculosis infection,GO enrichment analysis was performed,and TLR2 was identified as the focal point of the study.Perito-neal macrophages from C57BL/6J and TLR2 knockout(TLR2-/-)mice were infected with C.pseudotuberculosis.ELISA results revealed that the levels of TNF-α,IL-1β,and IL-10 were signifi-cantly downregulated in TLR2-/-macrophages compared to C57BL/6J macrophages post-infec-tion.Western blot demonstrated that the absence of TLR2 led to a marked decrease in M APK(p38 and ERK)signaling pathway phosphorylation following C.pseudotuberculosis infection.Immuno-fluorescence results indicated that the phagocytic rate of TLR2-/-macrophages was significantly higher than that of C57BL/6J macrophages after infection.Subsequently,transcriptomic analysis of C57BL/6J and TLR2-/-macrophages infected with C.pseudotuberculosis was performed,followed by GO enrichment analysis of differential genes.IL-36a,Cx3cr1,TLR1,and TLR2 were identified as key differential genes.TLR2 plays a crucial role in the inflammatory response induced by C.pseudotuberculosis infection in mice,influencing the progression of the inflammatory response and host outcomes through the secretion of inflammatory mediators,activation of signaling pathways,and modulation of phagocytic and bactericidal functions.IL-36a and Cx3cr1 were identified as key downstream factors in this process.

AIM:To evaluate the pharmacokinet-ics(PK)and pharmacodynamics(PD)of two recom-binant human insulin injection by euglycemic clamp technology in healthy male subjects after a single subcutaneous injection.METHODS:We con-ducted a randomized,open-label,single dose,two period,crossover study.A total of 24 healthy male subjects were enrolled and randomized to receive single subcutaneous doses(0.2 U/kg)of the investi-gational products every period.The PK and PD characteristics were assessed by euglycemic clamp up to 14 hours after dosing.RESULTS:Euglycemic clamp technique was successfully established.C-peptide levels detected at each time point before and after administration indicated that endoge-nous insulin secretion was inhibited in the two groups after administration.The geometric mean ratio of Cmax and AUC0-tand 90％confidence interval(CI)of test preparation and reference preparation under fasting condition were in the range of 80.00％-125.00％.CONCLUSION:The human insulin produced by KP Biotech demonstrated similarity to the reference preparation Humulin? in PK and PD characteristics in healthy Chinese subjects.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline and memory loss, with few effective treatments currently available. The multifactorial nature of AD, shaped by genetic, environmental, and biological factors, complicates both research and clinical management. Recent advances in artificial intelligence (AI) and multi-omics technologies provide new opportunities to elucidate the molecular mechanisms of AD and identify early biomarkers for diagnosis and prognosis. AI-driven approaches such as machine learning, deep learning, and network-based models have enabled the integration of large-scale genomic, transcriptomic, proteomic, metabolomic, and microbiomic datasets. These efforts have facilitated the discovery of novel molecular signatures and therapeutic targets. Methods including deep belief networks and joint deep semi-non-negative matrix factorization have contributed to improvements in disease classification and patient stratification. However, ongoing challenges remain. These include data heterogeneity, limited interpretability of complex models, a lack of large and diverse datasets, and insufficient clinical validation. The absence of standardized multi-omics data processing methods further restricts progress. This review systematically summarizes recent advances in AI-driven multi-omics research in AD, highlighting achievements in early diagnosis and biomarker discovery while discussing limitations and future directions needed to advance these approaches toward clinical application.