Objective To construct glucose oxidase（GOx）–loaded nanogels （GONGs）, optimize their formulation, and evaluate their capacity to inhibit the Warburg effect in glioma cells. Methods A responsive polymer （HAM） was synthesized and used to self-assemble GONGs, which were then characterized. Encapsulation efficiency and drug loading were determined using fluorescence spectrophotometry. Biocompatibility was tested by measuring cytotoxicity and hemolytic activity. Western blotting was used to evaluate the effects of GONGs on the expression of proteins associated with the Warburg phenotype and oxidative damage in glioma cells. Results GONGs prepared at a drug–to–polymer ratio of 1∶10 exhibited a particle size of 140.3 nm and a zeta potential of −27.2 mV. Compared with free GOx, GONGs markedly reduced cytotoxicity, increased the IC₅₀ in hUVEC cells from 2.150 nmol/L to 74.86 nmol/L, and significantly decreased hemolysis. At a GOx concentration of 2 nmol/L, GONGs effectively downregulated glycolysis-related proteins, such as HK2 and LDHA, and inhibited glutamine metabolism in glioma cells. Conclusion GONGs exhibited high GOx loading capacity, significantly reduced GOx-induced cytotoxicity, inhibited the Warburg effect in glioma cells and induced oxidative damage.

ObjectiveTo construct a multifunctional liposomal delivery system by replacing cholesterol（Chol） in conventional liposomes with saikosaponin D（SSD） and modifying with poloxamer 407（P407） for co-delivery of curcumin（Cur）. The system was evaluated for in vivo tumor targeting and inhibitory effects on mouse subcutaneous solid tumors. MethodsSingle-factor and orthogonal tests combined with information entropy weighting were used to optimize the formulation process of the liposome with encapsulation efficiency and absolute Zeta potential as indexes， and validation studies and liposomal characterization were performed. A subcutaneous solid tumor model was established by injecting H22 hepatocellular carcinoma cells subcutaneously into the dorsal surface of the right forelimb of mice. DiR-loaded traditional Chol liposomes（P407-DiR-Chol-LPs， PDCL） and novel SSD-based liposomes（P407-DiR-SSD-LPs， PDSL） were prepared by the optimized formulation process， and tail vein injection was performed to investigate the impact of SSD on liposome tumor targeting with small animal in vivo imaging. Mice were randomly divided into eight groups， including blank group， model group， free doxorubicin（DOX） group（2 mg·kg^-1）， free Cur group（8 mg·kg^-1）， free SSD group（10 mg·kg^-1）， P407-Cur-Chol-LPs（PCCL） group， P407-SSD-LPs（PSL） group， and P407-Cur-SSD-Lps（PCSL） group. Treatments were administered intraperitoneally every other day for seven doses. Antitumor efficacy and biocompatibility were evaluated by monitoring body weight change， organ indices， tumor volume and mass， relative tumor proliferation rate（T/C）， and tumor growth inhibition rate（TGI）. Histopathological analysis of liver， kidney， and tumor tissues was performed using hematoxylin-eosin（HE） staining. Serum levels of aspartate aminotransferase（AST）， alanine aminotransferase （ALT）， blood urea nitrogen（BUN）， and creatinine（Crea）in mice were quantified by fully automated biochemical analyzer. ResultsOrthogonal test yielded optimal ratios of Cur， SSD， and P407 to soybean phosphatidylcholine（SPC） as 1∶25， 1∶20， and 1∶4. The optimized PCSL exhibited spherical morphology with a particle size of 179.15 nm， a Zeta potential of -47.25 mV， and an encapsulation efficiency of 96.40%. Its in vitro release profile conformed to first-order kinetics， demonstrating excellent storage stability and hemocompatibility. In vivo imaging revealed that the fluorescence signal in tumor tissues and the fluorescence intensity ratio between tumors and organs were significantly higher in the PDSL group than in the PDCL group（P<0.05， P<0.01）. Among the treatment groups， PCSL group showed superior efficacy over free Cur group， free SSD group， PCCL group， and PSL group， with TGI>40% and T/C<60%， indicating pronounced anti-hepatocellular carcinoma effects（P<0.05， P<0.01）. Histopathology and serum biochemistry indicated minimal hepatorenal toxicity and improved hepatic and renal function in PCSL-treated mice. ConclusionReplacing Chol with SSD in preparing multifunctional drug delivery systems not only stabilizes liposomes but also yields superior anti-hepatocellular carcinoma efficacy， achieving the effect of drug-excipient integration. Co-delivery of Cur via this system can be used for treating subcutaneous solid tumors in hepatocellular carcinoma， providing new insights and technical approaches for anti-hepatocellular carcinoma research and the meridian-guiding and messenger-directing theory in traditional Chinese medicine.

Objective To analyze the main causes and risk factors of unplanned reoperation after liver transplantation. Methods The clinical data of 242 liver transplant recipients in the First Affiliated Hospital of Anhui Medical University from January 2015 to December 2024 were retrospectively analyzed. According to whether unplanned reoperation was performed during the same hospitalization after surgery, the recipients were divided into the reoperation group (n=36) and the non-reoperation group (n=206). The preoperative, intraoperative and postoperative data of the two groups, as well as donor and graft-related data, were compared to analyze the risk factors of unplanned reoperation after liver transplantation and the survival status of the two groups. Results Among the 242 liver transplant recipients, 36 underwent unplanned reoperations, with a total of 54 procedures including various laparotomies, endoscopic and interventional surgeries, among which there were 20 laparotomies, 18 endoscopic surgeries and 16 interventional surgeries. The most common cause of unplanned reoperation was biliary complications (20 times), followed by vascular complications (17 times). Compared with the non-reoperation group, the reoperation group had longer graft cold ischemia time, higher postoperative fatality rate of recipients, longer length of stay in the intensive care unit and postoperative hospital stay, and higher total hospitalization costs (all P<0.05). The incidence of unplanned reoperation was higher in recipients who underwent split liver transplantation (P<0.05). Multivariate analysis showed that intraoperative blood loss ≥1 000 mL, positive culture of graft perfusate and split liver transplantation were independent risk factors for unplanned reoperation (all P<0.05). The postoperative 7-day, 1-month, 3-month and 6-month survival rates of recipients in the reoperation group and the non-reoperation group were 100% vs. 98.1%, 88.9% vs. 94.2%, 69.4% vs. 90.8% and 66.7% vs. 90.8%, respectively, and the postoperative survival rate of recipients in the reoperation group was lower than that in the non-reoperation group (P<0.05). Conclusions The main causes of unplanned reoperation after liver transplantation are biliary complications, vascular complications, abdominal incision infection and intra-abdominal hemorrhage. Intraoperative massive blood loss, positive culture of graft perfusate and split liver transplantation are the risk factors associated with unplanned reoperation after liver transplantation.

BACKGROUND:Forskolin,a diterpenoid natural compound extracted from Coleus forskohlii,has a crucial regulatory role in skeletal muscle repair.However,the regulatory role of forskolin on myogenic differentiation of C2C12 skeletal muscle cells has not been fully explored.OBJECTIVE:To explore the effects of forskolin on the differentiation of C2C12 myoblast cell line and probe into the underlying molecular mechanisms.METHODS:C2C12 cells were treated with 0,0.1,0.25,0.5,1,5,10 and 20 μmol/L forskolin during growth,and cell proliferation was detected by cell counting kit-8 and qRT-PCR.C2C12 cells were treated with 0,0.25,0.5 and 1 μmol/L forskolin during the induction of myogenic differentiation.Immunofluorescence staining and qRT-PCR were used to detect C2C12 cells differentiation.Western blot was used to detect the expression level of myogenic differentiation-related signaling pathway proteins.RESULTS AND CONCLUSION:(1)The viability of C2C12 cells was decreased and cell proliferation was inhibited after treatment with high concentrations(＞1 μmol/L)of forskolin.(2)The qRT-PCR results showed that forskolin up-regulated the expression of Myh2,Myh4,Myomaker,but down-regulated the expression of Myh7 compared with the 0 μmol/L group,when C2C12 cells were differentiated for 4 days.Immunofluorescence staining results showed that the fusion index and myotube diameter of C2C12 cells were increased after forskolin treatment,and the number of myotubes was also increased.(3)Western blot results showed that the phosphorylated extracellular signal-regulated kinase 1/2 expression was inhibited;however,the phosphorylated protein kinase B was promoted after treatment with forskolin.The protein expression level of the myogenic differentiation transcription factor Myogenin was significantly up-regulated after treatment with forskolin.The above results demonstrate that forskolin may promote myogenic differentiation of C2C12 skeletal muscle cells through the extracellular signal-regulated kinase 1/2 and protein kinase B signaling pathway.

BACKGROUND:Forskolin,a diterpenoid natural compound extracted from Coleus forskohlii,has a crucial regulatory role in skeletal muscle repair.However,the regulatory role of forskolin on myogenic differentiation of C2C12 skeletal muscle cells has not been fully explored.OBJECTIVE:To explore the effects of forskolin on the differentiation of C2C12 myoblast cell line and probe into the underlying molecular mechanisms.METHODS:C2C12 cells were treated with 0,0.1,0.25,0.5,1,5,10 and 20 μmol/L forskolin during growth,and cell proliferation was detected by cell counting kit-8 and qRT-PCR.C2C12 cells were treated with 0,0.25,0.5 and 1 μmol/L forskolin during the induction of myogenic differentiation.Immunofluorescence staining and qRT-PCR were used to detect C2C12 cells differentiation.Western blot was used to detect the expression level of myogenic differentiation-related signaling pathway proteins.RESULTS AND CONCLUSION:(1)The viability of C2C12 cells was decreased and cell proliferation was inhibited after treatment with high concentrations(＞1 μmol/L)of forskolin.(2)The qRT-PCR results showed that forskolin up-regulated the expression of Myh2,Myh4,Myomaker,but down-regulated the expression of Myh7 compared with the 0 μmol/L group,when C2C12 cells were differentiated for 4 days.Immunofluorescence staining results showed that the fusion index and myotube diameter of C2C12 cells were increased after forskolin treatment,and the number of myotubes was also increased.(3)Western blot results showed that the phosphorylated extracellular signal-regulated kinase 1/2 expression was inhibited;however,the phosphorylated protein kinase B was promoted after treatment with forskolin.The protein expression level of the myogenic differentiation transcription factor Myogenin was significantly up-regulated after treatment with forskolin.The above results demonstrate that forskolin may promote myogenic differentiation of C2C12 skeletal muscle cells through the extracellular signal-regulated kinase 1/2 and protein kinase B signaling pathway.

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 analyze the epidemiological characteristics of a local dengue fever cluster outbreak in Qingpu District of Shanghai in 2024, and to provide a reference for subsequent dengue fever prevention and control. MethodsSeven confirmed local dengue fever cases reported through the National Notifiable Infectious Diseases Surveillance System in Qingpu District of Shanghai in 2024 were selected as the research subjects. Descriptive epidemiological methods were used to conduct investigation and analysis from the aspects of onset, medical treatment and reporting, clinical symptoms, travel and contact history within 15 days before onset, and activity trajectories. ResultsA total of 7 cases were identified in this outbreak. None of the cases had a travel history to dengue-endemic areas within 15 days prior to onset, while all had shared exposure environments and mosquito bite histories, indicating a local clustered transmission pattern. The main clinical manifestations included fever (100.00%) and myalgia (42.86%). All 7 cases were positive for dengue virus serotype 2 (DENV-2) by nucleic acid testing. Genetic sequencing showed that the virus strains belonged to the Cosmopolitan genotype and were most closely related to the epidemic DENV strains circulating in southern China in recent years. ConclusionThis outbreak might be a local secondary infection caused by the short-term stay of dengue fever-infected individuals, and the possible source of importation was dengue fever endemic areas in southern China.

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 investigate the influencing factors for stroke-associated pneumonia(SAP)in acute ischemic stroke(AIS)patients after endovascular treatment(EVT).Methods A retrospective case-control trial was conducted on 426 AIS patients with large vessel occlusion(LVO)in anterior circulation admitted in the neurological departments from First Affiliated Hospital of Army Medical University and Zigong Third People's Hospital during January 2017 and April 2021.Based on SAP occurrence or not,they were divided into an SAP group and a non-SAP group.Demographic information(gender and age),TOAST stroke subtypes(large artery atherosclerosis type,cardiac embolism type,others),vascular risk factors(hypertension,hyperlipidemia,diabetes,atrial fibrillation,smoking,prior stroke history,smoking),and post-onset clinical data[dysphagia,LDL cholesterol,white blood cells,neutrophils,baseline and postoperative NIHSS scores,endovascular outcomes(mTICI grade 2b or 3),90-day good prognosis(mRS 0-1)]were collected and compared between the 2 groups.Multivariate logistic regression analysis was performed using the parameters with P<0.1 in univariate analysis as independent variables to investigate factors influencing SAP occurrence after EVT in AIS patients.Results Among the 426 participants,SAP occurred in 194 cases(45.5%).Multivariate logistic regression analysis revealed that admission white blood cell count(OR=1.125,95%CI:1.043～1.213,P=0.000 2),postoperative NIHSS score(OR=1.019,95%CI:1.001～1.037,P=0.041),and male(OR=1.687,95%CI:1.078～2.638,P=0.022)were associated with SAP occurrence after EVT in AIS patients.Conclusion Higher admission white blood cell count,elevated postoperative NIHSS score,and male gender are risk factors for SAP in AIS patients after EVT.These risk factors should be focused on clinical practice to control SAP incidence.

Diabetes is one of the top ten fatal diseases globally,and effective diabetes management can significantly reduce the incidence and progression of diabetes-related complications.Traditional blood glucose monitoring relies on fingertip blood sampling to measure glucose concentration,which requires multiple finger pricks per day.However,the long intervals between tests often result in missed hyperglycemic or hypoglycemic events.Therefore,there is an urgent need for non-invasive,continuous,and accurate glucose monitoring technologies to improve patient compliance and provide timely alerts for abnormal glucose levels.Sensors based on electrochemical detection methods,which indirectly estimate glucose levels by analyzing interstitial fluid,sweat,or other bodily fluids,have emerged as a promising direction due to their high sensitivity and low cost.This review focused on recent advancements in non-invasive,continuous glucose monitoring sensors developed using various electrochemical detection methods,with an in-depth analysis of chronoamperometry,impedance spectroscopy,and voltammetry in sensor applications.Finally,the challenges faced by current detection methods in non-invasive continuous glucose monitoring was summarized,and the future directions,including the integration of enzyme-free sensors with deep learning algorithms to enhance accuracy and reliability were proposed.