Biomolecular condensates are formed through phase separation of biomacromolecules such as proteins and RNAs. These condensates exhibit liquid-like properties that can futher transition into more stable material states. They form complex internal structures via multivalent weak interactions, enabling precise spatiotemporal regulations. However, the use of inconsistent and non-standardized terminology has become increasingly problematic, hindering academic exchange and the dissemination of scientific knowledge. Therefore, it is necessary to discuss the terminology related to biomolecular condensates in order to clarify concepts, promote interdisciplinary cooperation, enhance research efficiency, and support the healthy development of this field.

Immobilized enzyme-based enzyme electrode biosensors, characterized by high sensitivity and efficiency, strong specificity, and compact size, demonstrate broad application prospects in life science research, disease diagnosis and monitoring, etc. Immobilization of enzyme is a critical step in determining the performance (stability, sensitivity, and reproducibility) of the biosensors. Random immobilization (physical adsorption, covalent cross-linking, etc.) can easily bring about problems, such as decreased enzyme activity and relatively unstable immobilization. Whereas, directional immobilization utilizing amino acid residue mutation, affinity peptide fusion, or nucleotide-specific binding to restrict the orientation of the enzymes provides new possibilities to solve the problems caused by random immobilization. In this paper, the principles, advantages and disadvantages and the application progress of enzyme electrode biosensors of different directional immobilization strategies for enzyme molecular sensing elements by specific amino acids (lysine, histidine, cysteine, unnatural amino acid) with functional groups introduced based on site-specific mutation, affinity peptides (gold binding peptides, carbon binding peptides, carbohydrate binding domains) fused through genetic engineering, and specific binding between nucleotides and target enzymes (proteins) were reviewed, and the application fields, advantages and limitations of various immobilized enzyme interface characterization techniques were discussed, hoping to provide theoretical and technical guidance for the creation of high-performance enzyme sensing elements and the manufacture of enzyme electrode sensors.

ObjectiveThe nucleolar protein PES1 (Pescadillo homolog 1) plays critical roles in ribosome biogenesis and cell cycle regulation, yet its involvement in cellular senescence remains poorly understood. This study aimed to comprehensively investigate the functional consequences of PES1 suppression in cellular senescence and elucidate the molecular mechanisms underlying its regulatory role. MethodsInitially, we assessed PES1 expression patterns in two distinct senescence models: replicative senescent mouse embryonic fibroblasts (MEFs) and doxorubicin-induced senescent human hepatocellular carcinoma HepG2 cells. Subsequently, PES1 expression was specifically downregulated using siRNA-mediated knockdown in these cell lines as well as additional relevant cell types. Cellular proliferation and senescence were assessed by EdU incorporation and SA-β-gal staining assays, respectively. The expression of senescence-associated proteins (p53, p21, and Rb) and SASP factors (IL-6, IL-1β, and IL-8) were analyzed by Western blot or qPCR. Furthermore, Northern blot and immunofluorescence were employed to evaluate pre-rRNA processing and nucleolar morphology. ResultsPES1 expression was significantly downregulated in senescent MEFs and HepG2 cells. PES1 knockdown resulted in decreased EdU-positive cells and increased SA‑β‑gal-positive cells, indicating proliferation inhibition and senescence induction. Mechanistically, PES1 suppression activated the p53-p21 pathway without affecting Rb expression, while upregulating IL-6, IL-1β, and IL-8 production. Notably, PES1 depletion impaired pre-rRNA maturation and induced nucleolar stress, as evidenced by aberrant nucleolar morphology. ConclusionOur findings demonstrate that PES1 deficiency triggers nucleolar stress and promotes p53-dependent (but Rb-independent) cellular senescence, highlighting its crucial role in maintaining nucleolar homeostasis and regulating senescence-associated pathways.

Objective To evaluate cardiac involvement in patients with anti-neutrophil cytoplasmic antibody-associated vasculitis (AAV) using echocardiography combined with electrocardiography. Methods A retrospective analysis was performed on the detailed medical records of AAV patients treated in Jining First People’s Hospital between January 2020 and December 2024. Eighty patients were enrolled in the AAV group, and the risk of heart disease was compared between the AAV group and a control group with 80 subjects matched for age, sex, and cardiovascular disease risk factors. Results Electrocardiographic abnormalities were observed in 78.75% of patients in the AAV group, while significant electrocardiographic abnormalities only occurred in symptomatic patients in the control group. There were no differences in left atrial enlargement or interventricular septal thickening between the AAV group and the control group. The overall left ventricular systolic function in the AAV group was lower than that in the control group (8.75% vs. 0). The incidence of reduced diastolic function in the AAV group was significantly higher than that in the control group (37.5% vs. 15%). The incidence rates of tricuspid regurgitation, mitral regurgitation, aortic regurgitation, and pericardial effusion in the AAV group were significantly higher than those in the control group. Pericardial thickening, aortic stenosis, pulmonary hypertension, and rare periaortic granulomas were found in the AAV group, but not in the control group. Conclusion Echocardiography and electrocardiography are important examination methods for evaluating cardiac involvement in AAV. These methods have key roles in disease screening, diagnosis and treatment, follow-up, and prognosis judgment.

Background::Gastric cancer (GC), a malignant tumor with poor prognosis, is one of the leading causes of cancer-related deaths worldwide; consequently, identifying novel therapeutic targets is crucial for its corresponding treatment. NUF2, a component of the NDC80 kinetochore complex, promotes cancer progression in multiple malignancies. Therefore, this study aimed to explore the potential of NUF2 as a therapeutic target to inhibit GC progression. Methods::Clinical samples were obtained from patients who underwent radical resection of GC at Lanzhou University Second Hospital from 2016 to 2021. Cell count assays, colony formation assays, and cell-derived xenotransplantation (CDX) models were used to determine the effects of NUF2 on GC progression. Flow cytometry was used to detect the effect of NUF2 or quercetin on cell cycle progression and apoptosis. A live-cell time-lapse imaging assay was performed to determine the effect of NUF2 on the regulation of mitotic progression. Transcriptomics was used to investigate the NUF2-associated molecular mechanisms. Virtual docking and microscale thermophoresis were used to identify NUF2 inhibitors. Finally, CDX, organoid, and patient-derived xenograft (PDX) models were used to examine the efficacy of the NUF2 inhibitor in GC. Results::NUF2 expression was significantly increased in GC and was negatively correlated with prognosis. The deletion of NUF2 suppressed GC progression both in vivo and in vitro. NUF2 significantly regulated the mitogen-activated protein kinase (MAPK) pathway, promoted G2/M phase transition, and inhibited apoptosis in GC cells. Additionally, quercetin was identified as a selective NUF2 inhibitor with low toxicity that significantly suppressed tumor growth in GC cells, organoids, CDX, and PDX models. Conclusions::Collectively, NUF2-mediated G2/M phase transition and apoptosis inhibition promoted GC progression; additionally, NUF2 inhibitors exhibited potent anti-GC activity. This study provides a new strategy for targeting NUF2 to suppress GC progression in clinical settings.

Phosphatidylinositol 4 kinases are the initial and key molecules of the phosphatidyl inositol signaling pathway.Among them,the phosphatidylinositol 4-kinaseⅢ β(PI4KⅢβ)is in-volved in the synthesis of the Golgi PI4P pool,playing a vital role in numerous physiological processes.Meanwhile,the en-zyme is an important host factor mediating the replication of some pathogenic RNA viruses,and participating in other patho-logical processes such as bacterial infection and malaria.In ad-dition,studies have shown that the function of PI4KⅢβ is regu-lated by numerous factors,including host and viral protein bind-ing partners.This review will discuss the structure and the phys-iopathology regulatory mechanism of PI4KⅢβ.

Objective To explore the efficiency of artificial intelligence and radiomics-assisted X-ray in diagnosis of lumbar osteoporotic vertebral compression fractures(OVCF).Methods The clinical data of 455 patients diagnosed as lumbar OVCF by MRI in our hospital were selected.The patients were divided into the training group(n=364)and the validation group(n=91),X-ray films were extracted,the image delineation,feature extraction and data analysis were carried out,and the artificial intelligence radiomics deep learning was applied to establish a diagnostic model for OVCF.After verifying the effectiveness of the model by receiver operating characteristic(ROC)curve,area under the curve(AUC),calibration curve,and decision curve analysis(DCA),the efficiencies of manual reading,model reading,and model-assisted manual reading of X-ray in the early diagnosis of OVCF were compared.Results The ROC curve,AUC and calibration curve proved that the model had good discrimination and calibration,and excellent diagnostic performance.DCA demonstrated that the model had a higher clinical net benefit.The diagnostic efficiency of the manual reading group:the accuracy rate was 0.89,the recall rate was 0.62.The diagnostic efficiency of the model reading group:the accuracy rate was 0.93,the recall rate was 0.86,the model diagnosis showed good predictive performance,which was significantly better than the manual reading group.The diagnostic efficiency of the model-assisted manual reading group:the accuracy rate was 0.92,the recall rate was 0.72,and the recall rate of the model-assisted manual reading group was higher than that of the manual reading group,but lower than that of the model reading group,indicating the superiority of the model diagnosis.Conclusion The diagnostic model established based on artificial intelligence and radiomics in this study has reached an ideal level of efficacy,with better diagnostic efficacy compared with manual reading,and can be used to assist X-ray in the early diagnosis of OVCF.

Objective To develop a clinical prediction model for predicting risk factors for prolonged hospital stay after anterior cervical discectomy and fusion(ACDF).Methods The clinical data of 914 patients underwent ACDF treatment for cervical spondylotic myelopathy(CSM)were retrospectively analyzed.According to the screening criteria,800 eligible patients were eventually included,and the patients were divided into the development cohort(n=560)and the validation cohort(n=240).LASSO regression was used to screen variables,and multivariate Logistic regression analysis was used to establish a prediction model.The prediction model was evaluated from three aspects:differentiation,calibration and clinical effectiveness.The performance of the model was evaluated by area under the curve(AUC)and Hosmer-Lemeshow test.Decision curve analysis(DCA)was used to evaluate the clinical effectiveness of the model.Results In this study,the five factors that were significantly associated with prolonged hospital stay were male,abnormal BMI,mild-to-moderate anemia,stage of surgery(morning,afternoon,evening),and alcohol consumption history.The AUC of the development cohort was 0.778(95%CI:0.740 to 0.816),with a cutoff value of 0.337,and that of the validation cohort was 0.748(95%CI:0.687 to 0.809),with a cutoff value of 0.169,indicating that the prediction model had good differentiation.At the same time,the Hosmer-Lemeshow test showed that the model had a good calibration degree,and the DCA proved that it was effective in clinical application.Conclusion The prediction model established in this study has excellent comprehensive performance,which can better predict the risk of prolonged hospital stay,and can guide clinical intervention as soon as possible,so as to minimize the postoperative hospital stay and reduce the cost of hospitalization.

Objective To explore the risk factors for surgical site infection(SSI)after transforaminal lumbar interbody fusion(TLIF)for the treatment of lumbar degenerative diseases.Methods A total of 1 000 patients who underwent TLIF for lumbar degenerative diseases in our hospital were included and divided into the infection group(n=23)and the non-infection group(n=977)according to whether the surgical incision was infected.General data,surgical and laboratory indicators of patients were collected,and potential risk factors of SSI were screened by univariate analysis and multivariate regression analysis,a nomogram model was established,and its predictive efficiency was validated by the receive operating characteristic(ROC)curve.Results The incidence of SSI in patients after TLIF was 2.3％.The results of univariate analysis showed that age,operative time,intraoperative blood loss,preoperative C-reactive protein(CRP),smoking,and diabetes mellitus were the significant risk factors for the occurrence of SSI.Multivariate regression analysis showed that older age,longer operation time,more intraoperative blood loss,smoking and diabetes mellitus were the independent risk factors for postoperative SSI.ROC curve showed that the nomogram model established in this study has good predictive efficiency.Conclusion Older age,longer operation time,more intraoperative blood loss,smoking,and diabetes mellitus were independent risk factors for postoperative SSI.For patients with these high risk factors,corresponding intervention measures should be taken before operation to reduce the incidence of SSI.

This study was aimed at characterizing the variations in hemagglutinin(HA)and neuraminidase(NA)genes of influenza virus subtype A(H1N1)pdm09 isolated during the 2022-2023 influenza monitoring year in Shandong Province,to provide a scientific basis for influenza prevention and control.A total of 14 A(H1N1)pdm09 subtype influenza strains were se-lected randomly by city by the influenza monitoring network laboratory.The vaccine strains recommended by the WHO served as references for whole gene sequencing analysis.A fluorescence method was used to conduct neuraminidase inhibition experi-ments to evaluate drug sensitivity.The A(H1N1)pdm09 influenza virus in Shandong Province,2022-2023 belonged to the 5a.2a evolutionary cluster in the 6B.1A branch.Nucleotide sequence analysis indicated that the HA and NA genes were closely re-lated to the Northern Hemisphere vaccine strain A/Victoria/2570/2019 in the years 2021-2023,and showed homology of 98.5％to 98.7％and 98.8％to 99.1％,respectively.Amino acid sequence analysis revealed 20 amino acid sequence mutations in the HA protein,but only one virus strain was found to have antigen drift,and three virus strains showed loss of HA protein glycosylation sites.No mutations were found at important sites affecting NA enzymes.The neuraminidase inhibition experiment indicated viral sensitivity to anti-influenza drugs.In conclusion,the monitored virus strains had high overall homology with vac-cine strains but showed some amino acid variation.In the future,continued monitoring of the genetic variation characteristics of influenza viruses will be necessary to understand the risk of influenza epidemics,and the effectiveness of influenza vaccines and therapeutic drugs.