In recent years, significant progress has been made in the field of liver transplantation in terms of donor expansion, technological innovation and perioperative management. Machine perfusion technology, through dynamic repair and assessment of donor liver quality, can effectively reduce postoperative complications and increase the utilization rate of marginal donor livers. The optimization of split liver transplantation technology combined with normothermic perfusion further alleviates the shortage of donors, but its promotion is still limited by technical barriers. Xenotransplantation has achieved preclinical breakthroughs in the field of genetically modified pig livers, but ethical and immune barrier issues need to be urgently resolved. In the field of liver cancer liver transplantation, the focus is on neoadjuvant treatment with immune checkpoint inhibitors and the development of recurrence prediction models, which promotes precise treatment. For perioperative management, the optimization of individualized immunosuppressive regimens, artificial liver support, and strategies for the prevention and control of vascular complications has significantly improved patients’ survival rates. Personalized treatment for children, elderly recipients, and recipients with multiple comorbidities provides new ideas for liver transplantation in special populations. In the future, liver transplantation research may focus on the integration of multidisciplinary approaches, individualized treatment and emerging technologies to advance the global liver transplantation cause to new heights.

Autophagy, a highly conserved cellular degradation mechanism, maintains intracellular homeostasis by removing damaged organelles and abnormal proteins. Its dysregulation is closely associated with various diseases. Autophagy-related protein 12 (ATG12), a core member of the ubiquitin-like protein family, covalently binds to ATG5 through a ubiquitin-like conjugation system to form the ATG12-ATG5-ATG16L1 complex. This complex directly regulates the formation and maturation of autophagosomes, making ATG12 a key molecule in the initiation of autophagy. Recent studies have revealed that ATG12 functions extend far beyond the classical autophagy context. It promotes apoptosis by binding to anti-apoptotic proteins of the Bcl-2 family (e.g., Bcl-2 and Mcl-1) and enhances host antiviral immunity by regulating the NF-κB and interferon signaling pathways. Moreover, ATG12 deficiency can lead to mitochondrial biogenesis impairment, energy metabolism disorders, and substrate-dependent metabolic shifts, underscoring its pivotal role in cellular metabolic homeostasis. At the disease level, dysregulation of ATG12 expression is closely linked to tumorigenesis and cancer progression. By modulating the dynamic balance between autophagy and apoptosis, ATG12 influences cancer cell proliferation, metastasis, and chemoresistance. Notably, ATG12 is abnormally overexpressed in multiple cancers, including breast, liver, and gastric cancer, highlighting its potential as a therapeutic target. Furthermore, in neurodegenerative diseases such as Parkinson’s disease, ATG12 mitigates protein toxicity by enhancing mitochondrial autophagy. In cardiovascular diseases, it alleviates ischemia-reperfusion injury by regulating cardiomyocyte autophagy and apoptosis, demonstrating its broad regulatory role across various pathological conditions. Genetic studies further underscore the clinical significance of ATG12. Polymorphisms in the ATG12 gene (e.g., rs26537 and rs26538) have been significantly associated with the risk of head and neck squamous cell carcinoma, hepatocellular carcinoma, and atrophic gastritis. Notably, the risk allele of rs26537 enhances ATG12 promoter activity, leading to its overexpression and promoting tumorigenesis. These findings provide a molecular basis for individualized risk assessment and targeted interventions based on ATG12 genotype. Despite significant progress, many aspects of ATG12 biology remain unclear. The precise regulatory mechanisms of its post-translational modifications (e.g., ubiquitination and acetylation) are yet to be fully elucidated. Additionally, the molecular pathways underlying its non-canonical functions, such as metabolic regulation and immune modulation, require further investigation. Moreover, the functional heterogeneity of ATG12 in different tumor microenvironments and its role in drug resistance warrant in-depth exploration. Future research should integrate advanced technologies such as cryo-electron microscopy, single-cell sequencing, and organoid models to decipher the intricate regulatory network of ATG12. Additionally, developing small-molecule inhibitors or gene-editing tools targeting its protein interaction interfaces (e.g., the ATG12-ATG3 binding domain) may help overcome current therapeutic challenges. Through interdisciplinary collaboration and clinical translation, ATG12 holds promise as a next-generation molecular target for precision intervention in autophagy-related diseases. This review summarizes the structure and function of ATG12, its role in autophagy initiation, its physiological functions, and its involvement in disease pathogenesis. Furthermore, it discusses future research directions and potential challenges, emphasizing ATG12’s potential as a biomarker and therapeutic target in autophagy-related diseases.

Parkinson’s disease (PD) is a common neurodegenerative disorder that significantly impacts patients’ independence and quality of life, imposing a substantial burden on both individuals and society. Although dopaminergic replacement therapies provide temporary relief from various symptoms, their long-term use often leads to motor complications, limiting overall effectiveness. In recent years, non-invasive deep brain stimulation (DBS) techniques have emerged as promising therapeutic alternatives for PD, offering a means to modulate deep brain regions with high precision without invasive procedures. These techniques include temporal interference stimulation (TIs), low-intensity transcranial focused ultrasound stimulation (LITFUS), transcranial magneto-acoustic stimulation (TMAS), non-invasive optogenetic modulation, and non-invasive magnetoelectric stimulation. They have demonstrated significant potential in alleviating various PD symptoms by modulating neural activity within specific deep brain structures affected by the disease. Among these approaches, TIs and LITFUS have received considerable attention. TIs generate low-frequency interference by applying two slightly different high-frequency electric fields, targeting specific brain areas to alleviate symptoms such as tremors and bradykinesia. LITFUS, on the other hand, uses low-intensity focused ultrasound to non-invasively stimulate deep brain structures, showing promise in improving both motor function and cognition in PD patients. The other three techniques, while still in early research stages, also hold significant promise for deep brain modulation and broader clinical applications, potentially complementing existing treatment strategies. Despite these promising findings, significant challenges remain in translating these techniques into clinical practice. The heterogeneous nature of PD, characterized by variable disease progression and individualized treatment responses, necessitates flexible protocols tailored to each patient’s unique needs. Additionally, a comprehensive understanding of the mechanisms underlying these treatments is crucial for refining protocols and maximizing their therapeutic potential. Personalized medicine approaches, such as the integration of neuroimaging and biomarkers, will be pivotal in customizing stimulation parameters to optimize efficacy. Furthermore, while early-stage clinical trials have reported improvements in certain symptoms, long-term efficacy and safety data are limited. To validate these techniques, large-scale, multi-center, randomized controlled trials are essential. Parallel advancements in device design, including the development of portable and cost-effective systems, will improve patient access and adherence to treatment protocols. Combining non-invasive DBS with other interventions, such as pharmacological treatments and physical therapy, could also provide a more comprehensive and synergistic approach to managing PD. In conclusion, non-invasive deep brain stimulation techniques represent a promising frontier in the treatment of Parkinson’s disease. While they have demonstrated considerable potential in improving symptoms and restoring neural function, further research is needed to refine protocols, validate long-term outcomes, and optimize clinical applications. With ongoing technological and scientific advancements, these methods could offer PD patients safer, more effective, and personalized treatment options, ultimately improving their quality of life and reducing the societal burden of the disease.

ObjectiveThe rapid advancement of bioanalytical technologies has heightened the demand for high-throughput, label-free, and real-time cellular analysis. Electrochemical impedance spectroscopy (EIS) operating in the GHz frequency range (GHz-EIS) has emerged as a promising tool for characterizing cell suspensions due to its ability to rapidly and non-invasively capture the dielectric properties of cells and their microenvironment. Although GHz-EIS enables rapid and label-free detection of cell suspensions, significant challenges remain in interpreting GHz impedance data for complex samples, limiting the broader application of this technique in cellular research. To address these challenges, this study presents a novel method that integrates GHz-EIS with deep learning algorithms, aiming to improve the precision of cell suspension concentration identification and quantification. This method provides a more efficient and accurate solution for the analysis of GHz impedance data. MethodsThe proposed method comprises two key components: dielectric property dataset construction and backpropagation (BP) neural network modeling. Yeast cell suspensions at varying concentrations were prepared and separately introduced into a coaxial sensor for impedance measurement. The dielectric properties of these suspensions were extracted using a GHz-EIS dielectric property extraction method applied to the measured impedance data. A dielectric properties dataset incorporating concentration labels was subsequently established and divided into training and testing subsets. A BP neural network model employing specific activation functions (ReLU and Leaky ReLU) was then designed. The model was trained and tested using the constructed dataset, and optimal model parameters were obtained through this process. This BP neural network enables automated extraction and analytical processing of dielectric properties, facilitating precise recognition of cell suspension concentrations through data-driven training. ResultsThrough comparative analysis with conventional centrifugal methods, the recognized concentration values of cell suspensions showed high consistency, with relative errors consistently below 5%. Notably, high-concentration samples exhibited even smaller deviations, further validating the precision and reliability of the proposed methodology. To benchmark the recognition performance against different algorithms, two typical approaches—support vector machines (SVM) and K-nearest neighbor (KNN)—were selected for comparison. The proposed method demonstrated superior performance in quantifying cell concentrations. Specifically, the BP neural network achieved a mean absolute percentage error (MAPE) of 2.06% and an R² value of 0.997 across the entire concentration range, demonstrating both high predictive accuracy and excellent model fit. ConclusionThis study demonstrates that the proposed method enables accurate and rapid determination of unknown sample concentrations. By combining GHz-EIS with BP neural network algorithms, efficient identification of cell concentrations is achieved, laying the foundation for the development of a convenient online cell analysis platform and showing significant application prospects. Compared to typical recognition approaches, the proposed method exhibits superior capabilities in recognizing cell suspension concentrations. Furthermore, this methodology not only accelerates research in cell biology and precision medicine but also paves the way for future EIS biosensors capable of intelligent, adaptive analysis in dynamic biological research.

Purpose: This study aimed to assess the impact of aerobic exercise at different intensities over an eight-week period on the expression and activation of cortical synaptic proteins, with the potential to reduce anxiety and improve memory in young mice. Methods: Seven-week-old C57BL/6 mice were subjected to treadmill exercises at low (n = 10), moderate (n = 10), and high intensity (n = 10) for eight weeks. Behavioral assessments were conducted to evaluate anxiety and cognitive function. To explore the underlying mechanisms, we measured the phosphorylated levels of extracellular signal-regulated kinase (ERK), cyclic adenosine monophosphate response-binding protein (CREB), protein kinase (AKT), adenosine monophosphate activated protein kinase (AMPK), synapsin (S9, S549, S609), and PSD-95 in the cortex, as these are associated with synaptic strength. Additionally, the expression of doublecortin (DCX), a neurogenic factor, was analyzed in the hippocampus. Results: Exercise led to reductions in depressive and anxiety-related behaviors and elevated the levels of phosphorylated ERK, CREB, AKT, AMPK, synapsin (S9, S549, S609), and PSD-95 in the cortex of young mice. Furthermore, exercise increased DCX expression in the hippocampus. Moderate-intensity exercise yielded more pronounced effects than other intensities. Conclusion The findings of this research indicate that consistent moderate-intensity exercise increases synaptic strength and reduces depression and anxiety in young mice by activating multiple factors.

Background: Nasopharyngeal carcinoma (NPC) is characterized by high programmed death-ligand 1 (PD-L1) expression and abundant infiltration of non-malignant lymphocytes, which renders patients potentially suitable candidates for immune checkpoint blockade therapies. Palate, lung, and nasal epithelium clone (PLUNC) inhibit the growth of NPC cells and enhance cellular apoptosis and differentiation. Currently, the relationship between PLUNC (as a tumor-suppressor) and PD-L1 in NPC is unclear. Methods: We collected clinical samples of NPC to verify the relationship between PLUNC and PD-L1. PLUNC plasmid was transfected into NPC cells, and the variation of PD-L1 was verified by western blot and immunofluorescence. In NPC cells, we verified the relationship of PD-L1, activating transcription factor 3 (ATF3), and β-catenin by western blot and immunofluorescence. Later, we further verified that PLUNC regulates PD-L1 through β-catenin. Finally, the effect of PLUNC on β-catenin was verified by co-immunoprecipitation (Co-IP). Results: We found that PLUNC expression was lower in NPC tissues than in paracancer tissues. PD-L1 expression was opposite to that of PLUNC. Western blot and immunofluorescence showed that β-catenin could upregulate ATF3 and PD-L1, while PLUNC could downregulate ATF3/PD-L1 by inhibiting the expression of β-catenin. PLUNC inhibits the entry of β-catenin into the nucleus. Co-IP experiments demonstrated that PLUNC inhibited the interaction of DEAD-box helicase 17 (DDX17) and β-catenin. Conclusions PLUNC downregulates the expression of PD-L1 by inhibiting the interaction of DDX17/β-catenin in NPC.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

ObjectiveTo explore the therapeutic effect of Xuebijing injection （XBJ） on severe acute pancreatitis induced acute lung injury （SAP-ALI） by regulating formyl peptide receptors （FPRs）/nucleotide-binding oligomerization domain-like receptor 3 （NLRP3） inflammatory pathway. MethodsSixty rats were randomly divided into a sham group， a SAP-ALI model group， low-， medium-， and high-dose XBJ groups （4， 8， and 12 mL·kg^-1）， and a positive drug （BOC2， 0.2 mg·kg^-1） group. For the sham group， the pancreas of rats was only gently flipped after laparotomy， and then the abdomen was closed， while for the remaining five groups， SAP-ALI rat models were established by retrograde injection of 5% sodium taurocholate （Na-Tc） via the biliopancreatic duct. XBJ and BOC2 were administered via intraperitoneal injection once daily for 3 d prior to modeling and 0.5 h after modeling. Blood was collected from the abdominal aorta 6 h after the completion of modeling， and the expression of interleukin （IL）-1β， IL-6， and tumor necrosis factor-α （TNF-α） in plasma was measured by enzyme-linked immunosorbent assay （ELISA）. The amount of ascites was measured， and the dry-wet weight ratios of pancreatic and lung tissue were determined. Pancreatic and lung tissue was taken for hematoxylin-eosin （HE） staining to observe pathological changes and then scored. The protein expression levels of FPR1， FPR2， and NLRP3 in lung tissue were detected by the immunohistochemical method. Western blot was used to detect the expression of FPR1， FPR2， and NLRP3 in lung tissue. Real-time fluorescence quantitative polymerase chain reaction （Real-time PCR） was used to detect the mRNA expression of FPR1， FPR2， and NLRP3 in lung tissue. ResultsCompared with the sham group， the SAP-ALI model group showed significantly decreased dry-wet weight ratio of lung tissue （P<0.01）， serious pathological changes of lung tissue， a significantly increased pathological score （P<0.01）， and significantly increased protein and mRNA expression levels of FPR1， FPR2， and NLRP3 in lung tissue （P<0.01）. After BOC2 intervention， the above detection indicators were significantly reversed （P<0.01）. After treatment with XBJ， the groups of different XBJ doses achieved results consistent with BOC2 intervention. ConclusionXBJ can effectively improve the inflammatory response of the lungs in SAP-ALI rats and reduce damage. The mechanism may be related to inhibiting the expression of FPRs and NLRP3 in lung tissue， which thereby reduces IL-1β and simultaneously antagonize the release of inflammatory factors IL-6 and TNF-α.

ObjectiveThis paper aims to investigate the potential molecular biological mechanism of Buyang Huanwu Tongfeng decoction in treating hyperuricemia and gouty arthritis by network pharmacology and molecular docking technology and preliminarily verify the mechanism through animal experiments. MethodsThe active ingredients and targets in the Buyang Huanwu Tongfeng decoction were obtained by the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform （TCMSP） and ETCM databases. The DisGeNET and GeneCards databases were utilized to acquire disease targets associated with hyperuricemia and gouty arthritis. These disease targets were then intersected with drug targets to identify key targets. The R language ClusterProfiler package and Python were employed for conducting gene ontology（GO） enrichment analysis and Kyoto encyclopedia of genes and genomes（KEGG） enrichment analysis. The regulatory network diagram of the drug-key target-function-pathway was visualized using Cytoscape 3.9.1 software， and the protein-protein interaction （PPI） network for key targets was depicted. Finally， the hub gene was determined through topological analysis. Auto Dock， PyMOL， and other software were used for molecular docking to explore the possible therapeutic mechanism of Buyang Huanwu Tongfeng decoction for hyperuricemia and gouty arthritis. In animal experiments， a composite rat model of hyperuricemia induced by intraperitoneal injection of oteracil potassium combined with gouty arthritis induced by the modified Coderre method was established. Through hematoxylin-eosin（HE） staining， uric acid test， enzyme linked immunosorbent assay（ELISA）， Western blot， and real-time polymerase chain reaction（Real-time PCR）， the molecular mechanism and key targets of Buyang Huanwu Tongfeng decoction for treating hyperuricemia and gouty arthritis were observed. ResultsAfter screening and removing duplicate values， 76 active ingredients and 15 key targets were finally obtained. GO enrichment analysis yielded that the treatment of hyperuricemia and gouty arthritis with Buyang Huanwu Tongfeng decoction was significantly associated with acute inflammatory response， astrocyte activation， regulation of interleukin （IL）-8 production， nuclear receptor activity， and binding of growth factor receptor. KEGG pathway enrichment analysis obtained that the key target genes were significantly associated with the IL-17 signaling pathway， advanced glycosylation end/receptor of advanced glycation endproducts（AGE/RAGE） signaling pathway， anti-inflammatory， and other pathways. PPI network indicated that albumin（ALB）， peroxisome proliferator-activated receptor-γ （PPAR-γ）， IL-6， IL-1β， and C-reactive protein（CRP） were the key protein targets. The molecular docking results showed that ALB had the strongest binding force with beta-carotene （β-carotene）. Biochemical results showed that blood uric acid decreased in the Buyang Huanwu Tongfeng decoction groups. HE staining results showed that the low-dose （7.76 g·kg^-1·d^-1）， medium-dose （15.53 g·kg^-1·d^-1）， and high-dose （31.05 g·kg^-1·d^-1） groups of Buyang Huanwu Tongfeng decoction had different degrees of remission， and the remission of the high-dose group was the most obvious. Fibroblastic tissue hyperplasia in synovial joints accompanied with inflammatory cell infiltration， as well as inflammatory cell infiltration in renal tissue of the high-dose group was significantly reduced， followed by the medium-dose and low-dose groups， and the expression of ALB， PPAR-γ， IL-6， IL-1β， and CRP was down-regulated to different degrees. ConclusionBy regulating the targets such as ALB， PPAR-γ， IL-6， IL-1β， and CRP， inhibiting the PPAR-γ/nuclear transcription factor （NF）-κB pathway， and reducing AGEs/RAGE-mediated inflammation， Buyang Huanwu Tongfeng decoction exerts anti-inflammatory and analgesic effects and activates blood circulation and diuresis in the treatment of hyperuricemia and gouty arthritis.

Prescription optimization is a crucial aspect in the study of traditional Chinese medicine （TCM） compounds. In recent years， the introduction of mathematical methods， data mining techniques， and artificial neural networks has provided new tools for elucidating the compatibility rules of TCM compounds. The study of TCM compounds involves numerous variables， including the proportions of different herbs， the specific extraction parts of each ingredient， and the interactions among multiple components. These factors together create a complex nonlinear dose-effect relationship. In this context， it is essential to identify methods that suit the characteristics of TCM compounds and can leverage their advantages for effective application in new drug development. This paper provided a comprehensive review of the cutting-edge optimization experimental design methods applied in recent studies of TCM compound compatibilities. The key technical issues， such as the optimization of source material selection， dosage optimization of compatible herbs， and multi-objective optimization indicators， were discussed. Furthermore， the evaluation methods for component effects were summarized during the optimization process， so as to provide scientific and practical foundations for innovative research in TCM and the development of new drugs based on TCM compounds.