In Silico Study on a Binding Mechanism of ssDNA Aptamers Targeting Glycosidic Bond-Containing Small Molecules.

Aptamer-based detection targeting glycoconjugates has attracted significant attention for its remarkable potential in identifying structural changes in saccharides in different stages of various diseases. However, the challenges in screening aptamers for small carbohydrates or glycoconjugates, which contain highly flexible and diverse glycosidic bonds, have hindered their application and commercialization. In this study, we investigated the binding conformations between three glycosidic bond-containing small molecules (GlySMs; glucose, N-acetylneuraminic acid, and neomycin) and their corresponding aptamers in silico, and analyzed factors contributing to their binding affinities. Based on the findings, a novel binding mechanism was proposed, highlighting the central role of the stem structure of the aptamer in binding and recognizing GlySMs and the auxiliary role of the mismatched bases in the adjacent loop. Guided by this binding mechanism, an aptamer with a higher 6'-sialyllactose binding affinity was designed, achieving a KD value of 4.54 ± 0.64 µM in vitro through a single shear and one mutation. The binding mechanism offers crucial guidance for designing high-affinity aptamers, enhancing the virtual screening efficiency for GlySMs. This streamlined workflow filters out ineffective binding sites, accelerating aptamer development and providing novel insights into glycan-nucleic acid interactions.

Design and heterologous expression of a novel dimeric LL37 variant in Pichia pastoris.

BACKGROUND: The antimicrobial peptide LL37 is produced by white blood cells (mainly neutrophils) and various epithelial cells, and has the outstanding advantages of participating in immune regulation, causing chemotaxis of immune cells and promoting wound healing. However, the central domain of LL37 needs to be improved in terms of antimicrobial activity. RESULTS: In this study, the amino acid substitution method was used to improve the antimicrobial activity of the LL37 active center, and a dimeric design with a better selection index was selected. A flexible linker was selected and combined with the 6 × His-SUMO tag and LG was successfully expressed using Pichia pastoris as a host. Recombinant LG displayed strong antimicrobial activity by destroying the cell membrane of bacteria but had low hemolytic activity. In addition, compared with monomeric peptide FR, rLG had improved ability to tolerate salt ions. CONCLUSION: This research provides new ideas for the production of modified AMPs in microbial systems and their application in industrial production.

Correlation between ITGB2 expression and clinical characterization of glioma and the prognostic significance of its methylation in low-grade glioma(LGG).

Introduction: Glioma is the most common primary tumor in the brain.Integrin beta 2(ITGB2) is a member of the leukocyte integrin family (leukocyte integrin), participating in lymphocyte recycling and homing, cell adhesion, and cell surface-mediated signal transduction. However, few studies on ITGB2 in gliomas have been reported yet.This study first discussed the relationship between ITGB2 expression and clinical characterization of glioma and the prognostic significance of its methylation in low-grade glioma. Methods: We collected Clinical data and transcription of glioma patients from TCGA, CGGA, and Rembrant datasets to analyze the differential expression of ITGB2 mRNA in glioma tissues and normal tissues. The box polts to evaluated the expression patterns of ITGB2 in different molecular subtypes. Receiver operating characteristic curve (ROC) were used to evaluate and verify the reliability of the model. Kaplan-Meier survival curves to evaluated the relationship between the level of ITGB2 mRNA expression and overall survival (OS). Using cox regression analysis to verify the ability of ITGB2 as an independent predictor of OS in glioma patients. We use TIMER to analyze and visualize the association between immune infiltration levels and a range of variables. The methylation of GBMLGG patients were obtained from the TCGA database through the biological portal. Results: ITGB2 can be a potential marker for mesenchymal molecular subtype gliomas. COX regression analysis shows that ITGB2 is an independent predictive marker of OS in malignant glioma patients. Biological processes show that ITGB2 has involved glioma immune-related activities, especially closely related to B cells, CD4+Tcells, macrophages, neutrophils, and dendritic cells. ITGB2 is negatively regulated by ITGB2 methylation, resulting in low expression in LGG tissues. Low expression of ITGB2 and high methylation indicate good OS in patients with LGG. The ITGB2 methylation risk score (ITMRS) obtained from the ITGB2 methylation CpG site can better predict the OS of LGG patients. We used univariate and multivariate cox regression analysis of methylationsites, used the R language predict function to obtain the risk score of these ITGB2 methylation sites(ITMRS). Discussion: ITGB2 can be used as a potential marker of mesenchymal molecular subtypes of gliomas and as an independent predictive marker of OS in patients with malignant gliomas. The ITMRS we established can be used as an independent prognostic factor for LGG and provide a new idea for the diagnosis and treatment of LGG.

Analysis of Knee Joint Injury Caused by Physical Training of Freshmen Students Based on 3T MRI and Automatic Cartilage Segmentation Technology: A Prospective Study.

Background: The differential effects of various exercises on knee joint injury have not been well documented. Improper physical training can cause irreversible damage to the knee joint. MRI is generally used to precisely analyze morphological and biochemical changes in the knee cartilage. We compared the effects of long-walking and regular daily physical training on acute and chronic knee joint injuries as well as cartilage structure in freshmen students. Methods: A total of 23 young male college freshmen were recruited to participate in an 8-day 240 km long distance walk and a one-year daily training. 3D-DESSwe, 2D T2 mapping, DIXON, and T1WI of the right knee joint were performed using the MAGNETOM Spectra 3T MR scanner. The injury of meniscus, bone marrow edema, ligaments and joint effusion is graded. Cartilage volume, thickness and T2 values of 21 sub-regions of the knee cartilage were estimated using automatic cartilage segmentation prototype software. Friedman's test and Wilcoxon paired rank-sum test were used to compare quantitative indices of knee cartilage in three groups. Results: The injury to the medial meniscus and anterior cruciate ligament of the knee joint, joint effusion, and bone marrow edema was significantly higher in the long-walking group compared to the baseline and daily groups. Furthermore, injury to the lateral meniscus was significantly worse in the long-walking group compared to the baseline group but was significantly better in the daily group compared to the baseline group. No significant changes to the posterior cruciate ligament were observed among the three groups. Knee cartilage volume was significantly increased, mainly in the stress surface of the femur, patella, and the lateral area of the tibial plateau. Regular daily training did not significantly change the thickness of the knee cartilage. Conversely, knee cartilage thickness decreased in the long-walking group, especially in the medial and lateral areas of the femur and tibial plateau. Moreover, no significant changes were observed in the knee cartilage volume of the long-walking group. Both long-walking and daily groups showed reduced T2 values of the knee joint compared to the baseline. Conclusion: Among freshmen students and the training of this experimental intensity, our results show that regular daily training does not cause high-level injury to the knee joint, but improve the knee joint function adaptability by increasing cartilage volume. Moreover, knee injury caused by short-term long walking can be reversible.

CT imaging features of different clinical types of COVID-19 calculated by AI system: a Chinese multicenter study.

BACKGROUND: The study is designed to explore the chest CT features of different clinical types of coronavirus disease 2019 (COVID-19) pneumonia based on a Chinese multicenter dataset using an artificial intelligence (AI) system. METHODS: A total of 164 patients confirmed COVID-19 were retrospectively enrolled from 6 hospitals. All patients were divided into the mild type (136 cases) and the severe type (28 cases) according to their clinical manifestations. The total CT severity score and quantitative CT features were calculated by AI pneumonia detection and evaluation system with correction by radiologists. The clinical and CT imaging features of different types were analyzed. RESULTS: It was observed that patients in the severe type group were older than the mild type group. Round lesions, Fan-shaped lesions, crazy-paving pattern, fibrosis, "white lung", pleural thickening, pleural indentation, mediastinal lymphadenectasis were more common in the CT images of severe patients than in the mild ones. A higher total lung severity score and scores of each lobe were observed in the severe group, with higher scores in bilateral lower lobes of both groups. Further analysis showed that the volume and number of pneumonia lesions and consolidation lesions in overall lung were higher in the severe group, and showed a wider distribution in the lower lobes of bilateral lung in both groups. CONCLUSIONS: Chest CT of patients with severe COVID-19 pneumonia showed more consolidative and progressive lesions. With the assistance of AI, CT could evaluate the clinical severity of COVID-19 pneumonia more precisely and help the early diagnosis and surveillance of the patients.