This paper proposes a two-stage method integrating visual foundation models （VFM） and diffusion models. The segment anything model （SAM） as VFM is combined with the SegRefiner diffusion model to construct the SAM-SegRefiner framework for automated segmentation of edema， inflammation， and thrombus regions in histopathological images of hemorrhoidal tissue， providing a reproducible technical tool for the objective quantification of pathological morphology and its application in traditional Chinese medicine （TCM） syndrome research. Trained and validated on multi-center retrospective data， the SAM-SegRefiner model achieved an average pixel accuracy of 95.32% and a mean intersection over union （mIoU） of 66.81% on an independent test set， significantly outperfor-ming comparative models such as U-Net， MixU-Net， and SAM-Med2D， and also demonstrating robust cross-center generalization capability. Furthermore， by correlating the quantitatively segmented results from the model with the patients' TCM syndrome types， the potential associations between pathomorphological features and TCM syndrome differentiation have been explored. The analysis revealed no statistically significant differences in the degree of inflammatory infiltration and thrombus formation among different syndrome types， suggesting a complex relationship between local pathological changes and systemic syndrome manifestations.

The initiation of adaptive immune responses relies on the precise recognition and interpretation of antigenic information. In this process, the specific binding of T cell receptors (TCRs) to peptide-major histocompatibility complex (pMHC) molecules represents one of the key molecular events in the initiation of adaptive immune responses. Accordingly, the structural features of TCR-pMHC complexes provide a fundamental basis for dissecting antigen recognition mechanisms and support rational vaccine design, therapeutic target discovery in TCR-based immunotherapy, and TCR identification and optimization. However, experimental determination of TCR-pMHC structures remains costly, time-consuming, and limited in coverage, making computational approaches essential for rapidly obtaining reliable structural information. Computational methods for predicting the structures of TCR-pMHC complexes have advanced rapidly in recent years, driven by progress in deep learning-based modeling frameworks and the increasing availability of structural and sequence resources. Despite these developments, most existing tools do not adequately distinguish the key structural and biophysical differences between MHC class I (MHC-I) and MHC class II (MHC-II) complexes during model construction. As a consequence, their predictive performance differs substantially between class I and class II complexes. In general, structural predictions for class I complexes outperform those for class II complexes. This discrepancy may be related to several fundamental differences between the two systems, including the architecture of the peptide-binding groove, the distribution of peptide lengths, and the properties of peptide flanking residues (PFRs). Compared with MHC-I molecules, MHC-II molecules usually bind longer antigenic peptides, which typically range from 13 to 25 amino acids in length. PFRs at both termini of these peptides participate in regulating the overall conformation of TCR-pMHC class II complexes and exert a pronounced effect on the geometric and physicochemical characteristics of the TCR-pMHC binding interface. Furthermore, within the TCR recognition interface, the complementarity-determining regions (CDRs) consist of segments that differ markedly in conformational behavior. They commonly include regions that are relatively rigid and structurally stable, together with highly flexible segments exhibiting substantial conformational plasticity. These rigidity-flexibility features constitute an essential structural basis enabling TCRs to recognize diverse peptide-MHC ligands and to accommodate conformational heterogeneity at the interface. However, many current modeling tools, in an effort to enforce global conformational stability or reduce structural noise, tend to over-constrain intrinsically flexible regions. Such oversimplification may lead to inappropriate rigidification of flexible CDR loops, resulting in local structural distortions, compromised interface geometry, or even complete modeling failure for specific complexes. Against this background, the review approaches the field from the perspective of computational differences between MHC-I and MHC-II complexes. We first systematically organize and summarize available resources related to TCRs and pMHCs, including structural datasets, sequence databases, prediction tools, and benchmarking studies. We then focus on five representative tools capable of predicting both class I and class II complexes—AlphaFold2, AlphaFold3, TCRmodel2, tFold-TCR, and TCR-pHLA_ModellerS. After excluding structures present in the training sets of these tools, we constructed a benchmark dataset comprising 25 class I and 10 class II TCR-pMHC complexes in the bound state and conducted a systematic evaluation using this dataset. We first employ widely used general evaluation metrics, including All-Atom Root Mean Square Deviation (All-Atom RMSD), Backbone RMSD, Template Modeling score (TM-score), and DockQ, to assess the global conformational accuracy and interface modeling quality of class I and class II complexes. For class II complexes, we propose for the first time a peptide flanking residue deviation index, including the PFRs-Deviation Index (PFRs-DI), N-PFR-Deviation Index (N-PFR-DI), and C-PFR-Deviation Index (C-PFR-DI), to quantitatively characterize conformational deviations in PFRs. In addition, we propose the CDR conformational consistency index (CCC) designed to qualitatively evaluate the ability of prediction tools to capture TCR CDR conformational flexibility. These metrics collectively assess a tool’s ability to model both overall conformation and critical functional regions, thereby addressing the limitations of existing evaluation criteria that overemphasize global structure while inadequately capturing modeling quality in key functional areas. This establishes a unified analytical framework for MHC-I and MHC-II complexes to guide data resource selection, modeling strategy formulation, and evaluation system development. The framework further advances computational modeling and provides crucial support for multi-scale analysis of TCR-pMHC recognition mechanisms and their biological functions.

The initiation of adaptive immune responses relies on the precise recognition and interpretation of antigenic information. In this process, the specific binding of T cell receptors (TCRs) to peptide-major histocompatibility complex (pMHC) molecules represents one of the key molecular events in the initiation of adaptive immune responses. Accordingly, the structural features of TCR-pMHC complexes provide a fundamental basis for dissecting antigen recognition mechanisms and support rational vaccine design, therapeutic target discovery in TCR-based immunotherapy, and TCR identification and optimization. However, experimental determination of TCR-pMHC structures remains costly, time-consuming, and limited in coverage, making computational approaches essential for rapidly obtaining reliable structural information. Computational methods for predicting the structures of TCR-pMHC complexes have advanced rapidly in recent years, driven by progress in deep learning-based modeling frameworks and the increasing availability of structural and sequence resources. Despite these developments, most existing tools do not adequately distinguish the key structural and biophysical differences between MHC class I (MHC-I) and MHC class II (MHC-II) complexes during model construction. As a consequence, their predictive performance differs substantially between class I and class II complexes. In general, structural predictions for class I complexes outperform those for class II complexes. This discrepancy may be related to several fundamental differences between the two systems, including the architecture of the peptide-binding groove, the distribution of peptide lengths, and the properties of peptide flanking residues (PFRs). Compared with MHC-I molecules, MHC-II molecules usually bind longer antigenic peptides, which typically range from 13 to 25 amino acids in length. PFRs at both termini of these peptides participate in regulating the overall conformation of TCR-pMHC class II complexes and exert a pronounced effect on the geometric and physicochemical characteristics of the TCR-pMHC binding interface. Furthermore, within the TCR recognition interface, the complementarity-determining regions (CDRs) consist of segments that differ markedly in conformational behavior. They commonly include regions that are relatively rigid and structurally stable, together with highly flexible segments exhibiting substantial conformational plasticity. These rigidity-flexibility features constitute an essential structural basis enabling TCRs to recognize diverse peptide-MHC ligands and to accommodate conformational heterogeneity at the interface. However, many current modeling tools, in an effort to enforce global conformational stability or reduce structural noise, tend to over-constrain intrinsically flexible regions. Such oversimplification may lead to inappropriate rigidification of flexible CDR loops, resulting in local structural distortions, compromised interface geometry, or even complete modeling failure for specific complexes. Against this background, the review approaches the field from the perspective of computational differences between MHC-I and MHC-II complexes. We first systematically organize and summarize available resources related to TCRs and pMHCs, including structural datasets, sequence databases, prediction tools, and benchmarking studies. We then focus on five representative tools capable of predicting both class I and class II complexes—AlphaFold2, AlphaFold3, TCRmodel2, tFold-TCR, and TCR-pHLA_ModellerS. After excluding structures present in the training sets of these tools, we constructed a benchmark dataset comprising 25 class I and 10 class II TCR-pMHC complexes in the bound state and conducted a systematic evaluation using this dataset. We first employ widely used general evaluation metrics, including All-Atom Root Mean Square Deviation (All-Atom RMSD), Backbone RMSD, Template Modeling score (TM-score), and DockQ, to assess the global conformational accuracy and interface modeling quality of class I and class II complexes. For class II complexes, we propose for the first time a peptide flanking residue deviation index, including the PFRs-Deviation Index (PFRs-DI), N-PFR-Deviation Index (N-PFR-DI), and C-PFR-Deviation Index (C-PFR-DI), to quantitatively characterize conformational deviations in PFRs. In addition, we propose the CDR conformational consistency index (CCC) designed to qualitatively evaluate the ability of prediction tools to capture TCR CDR conformational flexibility. These metrics collectively assess a tool’s ability to model both overall conformation and critical functional regions, thereby addressing the limitations of existing evaluation criteria that overemphasize global structure while inadequately capturing modeling quality in key functional areas. This establishes a unified analytical framework for MHC-I and MHC-II complexes to guide data resource selection, modeling strategy formulation, and evaluation system development. The framework further advances computational modeling and provides crucial support for multi-scale analysis of TCR-pMHC recognition mechanisms and their biological functions.

ObjectiveTo develop and validate a nomogram based on the albumin-bilirubin （ALBI） score for predicting the risk of severe perioperative complications in patients undergoing hepatectomy for hepatolithiasis. MethodsA retrospective analysis was conducted on the clinical data of 163 hepatolithiasis patients who underwent hepatectomy. Univariate and multivariate logistic regression analyses were used to identify independent risk factors for severe perioperative complications. A nomogram prediction model was constructed and its performance was evaluated. ResultsAmong the 163 patients， 66 and 97 were classified into the low-grade and high-grade ALBI groups， respectively. Significant intergroup differences were observed in gender， total bilirubin， albumin levels， and the incidence of severe complications （P0.05）. Severe complications occurred in 40 patients. Independent risk factors included age 60 years （OR=5.49， P0.001）， high-grade ALBI （OR=8.30， P0.001）， history of biliary surgery （OR=2.60， P=0.035）， hepatectomy （segmentectomy）≥3 （OR=2.75， P=0.028）， and open surgical approach （OR=4.00， P=0.009）. A nomogram for predicting severe perioperative complications was successfully established. Internal validation showed that the model had an area under the ROC curve （AUC） of 0.865， which outperformed traditional single predictors. The calibration curve closely aligned with the ideal curve， with a mean absolute error （MAE） of 0.027. Decision curve analysis （DCA） demonstrated a net clinical benefit when the threshold probability exceeded 10%， superior to that of traditional predictors. ConclusionThe ALBI score-based nomogram is successfully developed and validated to predict the risk of severe perioperative complications in hepatolithiasis patients undergoing hepatectomy. The model demonstrated favorable predictive performance and high clinical utility， serving as an effective tool for both preoperative risk assessment and postoperative risk stratification.

ObjectiveTo investigate the effect of sorafenib and donafenib on the pharmacokinetics of ertugliflozin in rats， and to provide a theoretical basis for drug combination in clinical practice. MethodsA total of 24 male Sprague-Dawley rats were randomly divided into groups A， B， C， and D， with 6 rats in each group. The rats in groups A and B were given sorafenib control solvent and sorafenib （100 mg/kg）， respectively， by gavage for 7 consecutive days， followed by ertugliflozin （1.5 mg/kg） by gavage on day 7. Blood samples were collected from the angular vein plexus at different time points， and ultra-performance liquid chromatography-tandem mass spectrometry was used to determine the mass concentration of ertugliflozin and plot the plasma concentration-time curves， while the non-compartment model in DAS 2.1.1 software was used to calculate related pharmacokinetic parameters. The independent-samples t test was used for comparison of normally distributed continuous data between two groups， and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between two groups. ResultsCompared with group A， group B had significant increases in the AUC_0-t and AUC_0-∞ of the plasma concentration-time curve of ertugliflozin （both P<0.05）， significant prolongation of t_1/2， MRT_0-t， and MRT_0-∞ （all P<0.05）， and a significant reduction in CL_Z/F （P<0.05）. Compared with group C， group D had significant increases in the AUC_0-t and AUC_0-∞ of ertugliflozin （both P<0.05）， significant prolongation of T_max， t_1/2， MRT_0-t， and MRT_0-∞ （all P<0.01）， and significant reductions in V_Z/F and CL_Z/F （both P<0.05）. ConclusionBoth sorafenib and donafenib can affect the pharmacokinetics of ertugliflozin in rats and significantly increase the plasma exposure of ertugliflozin. The efficacy and adverse drug reactions of ertugliflozin should be closely monitored during combined use in clinical practice and the dose should be adjusted when necessary to avoid the potential risk of drug interaction.

Objective: This study aims to investigate the therapeutic effects and underlying mechanisms of Xuanfei Baidu Decoction (XFBD) in a mouse model of dampness-heat toxin pneumonia. By exploring how XFBD exerts its effects, we seek to deepen our understanding of its role in treating pulmonary diseases and to address the current knowledge gap regarding its mechanisms of action, thereby supporting its clinical application. Methods: Ultra-high-performance liquid chromatography and high-resolution mass spectrometry (HRMS) were employed to analyze the chemical constituents of XFBD. The protective effects of XFBD were evaluated using a dampness-heat toxin-induced mouse model, established through dampness-heat exposure and HCoV-229E infection. XFBD was administered orally, followed by assessments including lung index measurement, micro-CT imaging, viral load quantification, cytokine analysis, and histological evaluation via hematoxylin-eosin staining. Proteomics and single-cell transcriptomic analyses were conducted to explore the potential mechanisms underlying XFBD’s pharmacological effects. A cellular model of HCoV-229E infection was developed to investigate changes in the cAMP/PKA signaling pathway. Molecular docking and surface plasmon resonance (SPR) experiments confirmed the strong binding affinity between key XFBD components and PKA. Finally, PKA activators and inhibitors were applied in vitro to validate these mechanistic findings. Results: In vivo studies demonstrated that XFBD significantly reduced the lung index, improved the structural integrity of lung and tongue tissues, and decreased levels of proinflammatory mediators, including IL-6, IL-8, and TNF-α. Proteomic and single-cell transcriptomic analyses showed that the differentially expressed proteins after XFBD treatment were primarily associated with inflammatory responses and immune regulation. The cAMP/PKA signaling pathway was identified as a key mechanism underlying these therapeutic effects. Notably, Western blot, ELISA, molecular docking, and SPR analyses confirmed that XFBD elevated cAMP levels and p-PKA expression, thereby activating the cAMP/PKA signaling pathway in vitro. Conclusion: This study demonstrated that XFBD significantly alleviates symptoms in mice with dampness-heat toxin pneumonia. Its therapeutic effects are mediated, at least in part, through activation of the cAMP/PKA signaling pathway. These findings provide compelling evidence that XFBD is an effective herbal remedy against HCoV-229E infection.

Objective:To summarize and analyze the clinical efficacy and experience of ultrasound-guided radiofrequency ablation (RFA) in the treatment of Kasabach-Merritt syndrome (KMS).Methods:A retrospective analysis was conducted on the data of pediatric patients with KMS who underwent ultrasound-guided RFA in Department of Hemangioma Surgery, the Third Affiliated Hospital of Zhengzhou University, between March 2018 and March 2024. Preoperative laboratory tests and imageological examination were performed. Under general anesthesia, the working tip of the RFA electrode needle was precisely reached the bottom of the lesion under ultrasound guidance. The electrode needle was then gradually withdrawn until the entire lesion area was covered by hyperechoic signals, indicating complete ablation. Postoperative symptomatic and supportive treatments, such as ice pack application and dressing changes, were administered to the surgical area. Platelet detection was performed immediately after the operation. Complications were closely monitored and regular follow-ups were carried out.Results:A total of 30 pediatric patients were included, comprising 14 males and 16 females, from 10 min to 5 months and 29 d after birth, with a median time of 6 d. Lesions were located in the limbs and trunk in 27 cases, and head and neck region in 3 cases, with lesion volumes ranged from 2.4 cm×2.3 cm×1.2 cm to 14.4 cm×9.3 cm×3.3 cm. The mean preoperative platelet count was 43×10 9/L, among them, the platelet values of 11 cases were (10-30) ×10 9/L, and those of 6 cases were lower than 10×10 9/L, other 13 cases with progressive thrombocytopenia. All patients successfully underwent RFA, achieving complete lesion ablation and normalization of platelet counts postoperatively. Platelet counts recovered to above 300×10 9/L in 15 patients, with no severe complications observed. The RFA area became slightly hardened within 7 d postoperatively but gradually returned to normal after consistent dressing changes for 2 weeks. During the follow-up period of 6 months to 2 years, complete lesion ablation was confirmed, with disappearance of the mass, no recurrence, good local function, mild local scar formation, and satisfactory cosmetic appearance. Conclusion:Ultrasound-guided RFA for KMS has advantages of favorable therapeutic outcomes, minimal tissue damage, no significant complications, and satisfactory cosmetic result.

Objective A scoping review of studies on pulmonary prehabilitation in cardiac surgery patients was conducted to provide evidence support for the construction of a preoperative pulmonary rehabilitation program for cardiac surgery patients that suitable for China's national conditions.Methods In accordance with the scope review's research methodologies,databases including PubMed,Embase,Web of Science,CINAHL,CNKI and Wanfang were searched by the computer for relevant studies.The deadline for retrieval is from the establishment of databases to June,2024.The included literature was systematically analyzed.Results 26 articles were finally included.Among them,4 were quasi-experiment studies,while the other 22 were randomized controlled trials.Forms of the intervention included comprehensive breathing exercises,inspiratory muscle training,positive expiratory pressure,incentive spirometer training and balloon blowing training.The intervention initiation ranged from 10 weeks to 1 day preoperatively;the outcome measures included postoperative pulmonary complications,lung function metrics,the 6-Minute Walk Test,duration of mechanical ventilation,length of hospital stay,patient-reported outcomes and so on.Conclusion There remains a deficiency in standardized protocols for preoperative pulmonary rehabilitation training among patients undergoing cardiac surgery.High-quality studies should be conducted,and intervention strategies for pulmonary prehabilitation in cardiac surgery patients should be optimized and a unified evaluation standard system should be established.

Objective:In order to meet the needs of building Internet of Things(IoT)of medical equipment for mobile deployment,rapid deployment,high-speed and stable data transmission,a wireless IoT acquisition terminal for medical equipment on the basis of Wi-Fi 6 was developed.Methods:Wi-Fi 6 technique was adopted to construct IoT of medical equipment,and the data acquisition terminal included Wi-Fi 6-based customer premises equipment(CPE)and intelligent wireless access point(AP).The CPE adopted a domestic main control chip and Wi-Fi chips,which included two 2.4G and 5G antennas,and was compatible with multiple interfaces such as RS232 and RJ45.The data of medical equipment were converted into wireless transmission through wired communication interfaces.The security access and data traceability of medical equipment were supported through secure secondary authentication with security control enhanced by"white list plus certificate".The intelligent wireless AP was compatible with various RF devices such as Wi-Fi,bluetooth,radio frequency identification,etc.(included 2.4G and 5G antennas).CPE and AP jointly apply dual-transmitter selection technique to ensure stable data transmission.Results:The key performance of wireless IoT acquisition terminals has been tested,and the results indicated that the integrity of acquisition data of intelligent acquisition terminal was consistent with that of output data,with a maximum latency of 9 ms and an average latency of 2 ms.The tested results can meet the expected requirements.Conclusion:The wireless IoT data of medical equipment that based on the acquisition terminal can stably and quickly collect data of equipment to IoT platform,providing paradigm for the construction of wireless IoT of medical equipment.

This article reported a case of generalized arterial calcification in infancy (GACI), which was suspected prenatally and confirmed postnatally. At 24 weeks of gestation, a systematic ultrasound revealed aortic wall thickening with hyperechogenicity, a thickened mitral valve accompanied by hyperechoic changes, thickened and calcified tricuspid valves, and multiple hyperechoic foci in both ventricles. A follow-up echocardiography at 28 weeks showed no significant progression. Emergency cesarean section was performed at 31 weeks due to fetal distress. Postnatal echocardiography confirmed generalized arterial calcification. Whole-exome trio sequencing identified two variants in the ENPP1 gene: c.1274-2A>G and c.1437+3(IVS14)_c.1437+6(IVS14)delGAGT. Based on imaging, laboratory results, and genetic testing, the infant was diagnosed with GACI type 1, concomitant with autosomal recessive hypophosphatemic rickets type 2. Given the poor prognosis, the family withdrew from further medical intervention.