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.

The expert consensus on the clinical treatment of herpes zoster with fire needling was developed, and the commonly used fire needling treatment scheme verified by clinical research was selected to form a standardized diagnosis and treatment scheme for acute herpes zoster and postherpetic neuralgia (PHN), so as to answer the core problems in clinical application. The consensus focuses on patients with herpes zoster, and forms recommendations for 9 key clinical issues, covering simple fire needling and TCM comprehensive therapy based on fire needling, including fire needling combined with cupping, fire needling combined with Chinese herb, fire needling combined with cupping and Chinese herb, fire needling combined with filiform needling, fire needling combined with moxibustion, and provides specific recommendations and operational guidelines for various therapies.
Humans ; Herpes Zoster/therapy* ; Acupuncture Therapy/instrumentation* ; Consensus ; Clinical Protocols

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 To investigate the effects of ertugliflozin on pharmacokinetic of sorafenib and donafenib in rats and explore the mechanism. METHODS Twenty-four male SD rats were randomly divided into four groups， with 6 rats in each group. Groups A and B were respectively gavaged with 0.5% sodium carboxymethyl cellulose solution and ertugliflozin （1.5 mg/kg） for 7 consecutive days， and both were given sorafenib （100 mg/kg） on the 7th day. Groups C and D were administered intragastrically in the same way as those in Groups A and B， respectively， for the first 7 days； after the drug administration on the 7th day， all rats in Groups C and D were further gavaged with donafenib （40 mg/kg）. Blood samples were collected at different time points before and after administration of sorafenib or donafenib， the concentrations of sorafenib in plasma of rats in groups A and B and donafenib in groups C and D were determined by UPLC-MS/MS method. The pharmacokinetic parameters were calculated by DAS 2.1.1 software. Six additional rats were randomly divided into blank control group and ertugliflozin group， with three rats in each group. Blank control group was given 0.5% sodium carboxymethyl cellulose intragastrically， while rats in ertugliflozin group were given ertugliflozin （1.5 mg/kg） once a day for 7 consecutive days. After the last administration， the mRNA expression levels of uridine diphosphate glucuronosyl transferase 1A7 （UGT1A7）， breast cancer resistance protein （BCRP）， and P-glycoprotein （P-gp） in the liver and small intestine tissues of the rats were detected. RESULTS Compared with group A， the AUC0-t， AUC0-∞， cmax， tmax， MRT0-t and MRT0-∞ of sorafenib in group B were decreased significantly， while CL and V were increased significantly. Compared with group C， the AUC0-t， AUC0-∞ ， tmax， cmax and MRT0-t of Δ donafenib in group D were decreased significantly， while V and CL were increased significantly （P＜0.05）. mRNA expression of UGT1A7， P-gp and BCRP in the liver tissue and small intestine of rats were not significantly affected after intragastric administration of ertugliflozin for 7 consecutive days. CONCLUSIONS Ertugliflozin can affect the pharmacokinetics of sorafenib and donafenib in rats and decrease the plasma exposure of them significantly. However， its mechanism of action may not be through the regulation of related metabolic enzymes and transporters. When using drugs in combination clinically， one should be vigilant about the potential for disease progression due to poor therapeutic effects.

Objective: To design and establish a new method for flow cytometry-based detection of commonly observed highly expressed antigens on red blood cells, and to further evaluate the differences and distribution characteristics of antigen expression levels between ABO blood type homozygotes and heterozygotes in healthy individuals. Methods: Residual blood samples after donor blood type identification by Shanghai Blood Center in April 2024 were collected. Among them, samples of 19 homozygous and 19 heterozygous individuals of type A and type B were selected. Then the expression level of ABO antigen on red blood cells were detected using the new method established in this study and the traditional aldehyde fixed red blood cell method. Both methods were tested independently three times and the results were compared. Results: The mean values of the three detection results of the new method was (×10 /RBC): AA homozygous 3.3±0.5, AO heterozygous 2.8±0.3, BB homozygous 3.6±0.3, BO heterozygous 3.1±2.8. The mean values of the three detection results of the aldehyde fixation method were AA homozygous 5.9±0.9, AO heterozygous 5.0±1.4, BB homozygous 3.8±0.6, and BO heterozygous 3.3±0.4. The average antigen distribution of each genotype followed a normal distribution. Comparing the average antigen expression levels of homozygotes and heterozygotes, both methods showed that A/B homozygotes had higher antigen levels than heterozygotes, with AA being 1.17 to 1.18 times that of AO and BB being 1.15 to 1.16 times that of BO. Comparing the inter batch differences in the three test results of two methods, the new method showed no significant difference in the three test results for four genotypes (P>0.05). The aldehyde fixation method showed significant differences in the test results for all three genotypes (P<0.01) except for BB homozygotes (P>0.05). The reliability and reproducibility of the new method were better than those of the traditional aldehyde fixation method. Conclusion: The antigen expression level of ABO homozygotes is higher than that of heterozygotes, and the difference in antigen level between type A homozygotes and heterozygotes is slightly higher than that of type B. The new method is superior to traditional aldolization fixation methods.

The chemical constituents of sesquiterpenes from 95% ethanol extract of Aquilariae Lignum Resinatum were isolated and purified by various column chromatography techniques, including silica gel, Sephadex LH-20, octadecylsilyl(ODS), and semi-preparative high performance liquid chromatography(HPLC). Their planar structures and absolute configurations were elucidated by ultraviolet(UV) spectrometry, infrared(IR) spectroscopy, mass spectrometry(MS), nuclear magnetic resonance(NMR), electronic circular dichroism(ECD), and other techniques. Eight sesquiterpenoids were isolated and identified as(+)-(7R,10R)-selina-4,11-dien-12-dimethoxy-15-al(1),(+)-(7R,10R)-selina-4,11-diene-12,15-dial(2), agalleudesmanol B(3), aquisinenoid C(4), 12,15-dioxo-α-selinen(5), agarospiranic aldehyde B(6), neopetasane(7), and eremophila-7(11),9-dien-8-one(8). Compound 1 was a new compound, and it was the first time to find a dimethoxy substitution on the side chain of eudesmane-type sesquiterpene skeleton.
Sesquiterpenes/isolation & purification* ; Thymelaeaceae/chemistry* ; Molecular Structure ; Drugs, Chinese Herbal/isolation & purification* ; Magnetic Resonance Spectroscopy

BACKGROUND AND OBJECTIVE: Patients with glioma experience a high symptom burden and have diverse palliative care needs. However, the assessment scales used in palliative care remain non-standardized and highly heterogeneous. To evaluate the application patterns of the current scales used in palliative care for glioma, we aim to identify gaps and assess the need for disease-specific scales in glioma palliative care. METHODS: We conducted a systematic search of five databases including PubMed, Web of Science, Medline, EMBASE, and CINAHL for quantitative studies that reported scale-based assessments in glioma palliative care. We extracted data on scale characteristics, domains, frequency, and psychometric properties. Quality assessments were performed using the Cochrane ROB 2.0 and ROBINS-I tools. RESULTS: Of the 3,405 records initially identified, 72 studies were included. These studies contained 75 distinct scales that were used 193 times. Mood (21.7%), quality of life (24.4%), and supportive care needs (5.2%) assessments were the most frequently assessed items, exceeding half of all scale applications. Among the various assessment dimensions, the Distress Thermometer (DT) was the most frequently used tool for assessing mood, while the Short Form-36 Health Survey Questionnaire (SF-36) was the most frequently used tool for assessing quality of life. The Mini Mental Status Examination (MMSE) was the most common tool for cognitive assessment. Performance status (5.2%) and social support (6.8%) were underrepresented. Only three brain tumor-specific scales were identified. Caregiver-focused scales were limited and predominantly burden-oriented. CONCLUSIONS: There are significant heterogeneity, domain imbalances, and validation gaps in the current use of assessment scales for patients with glioma receiving palliative care. The scale selected for use should be comprehensive and user-friendly.
Humans ; Glioma/psychology* ; Palliative Care/methods* ; Quality of Life ; Psychometrics ; Brain Neoplasms/psychology*