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Compartmentalization of cellular components and their associated biological processes is crucial for cellular function. Protein glycosylation provides a basis for diversity of protein functions. Diversity of glycan composition in animal cells remains poorly understood. We used differential centrifugation techniques to isolate four subcellular protein fractions from homogenate of metastatic bladder YTS1 cells, low grade nonmuscle invasive bladder cancer KK47 cells and normal bladder epithelia HCV29 cells: microsomal (Mic), mitochondrial (Mito), nuclear (Nuc), and cytosolic (Cyto). An integrated strategy combining lectin microarray and mass spectrometry (MS) analysis was then applied to evaluate protein glycosylation of the four fractions. Lectin microarray analysis revealed significant differences among the four fractions in terms of glycan binding to the lectins LCA, AAL, MPL, WGA and PWM in YTS1 cell, STL, Jacalin, VVA, LCA and WGA in KK47, and ConA, GNA, VVA and ACA in HCV29 cell. Among a total of 40, 32 and 15 N-glycans in four fractions of three cells detected by MS analysis, high-mannose and fucosylated structures were predominant, 10 N-glycans in YTS1, 5 N-glycans in KK47 and 7 N-glycans in HCV29 were present in all four fractions; and 10 N-glycans in YTS1, 16 N-glycans in KK47, and 3 N-glycans in HCV29 were present in only one fraction. Glycans in the latter category are considered potential markers for the corresponding organelles. The integrated strategy described here allows detailed examination of glycomes subcellular fraction with high resolution and sensitivity, and will be useful for elucidation of the functional roles of glycans and corresponding glycosylated proteins in distinct organelles.

e21044 Background: The purpose of this study was to investigate whether the combined radiomic model based on tumor-associated and margin-related (5mm) radiomic features can effectively improve prediction performance of distinguishing precancerous lesions from early stage lung adenocarcinoma. Methods: 264 patients underwent preoperative chest CT in Guangdong Provincial People’s hospital from March 1, 2015 to December 31,2019 were sorted by three cohorts. All lesions were pathologically confirmed as precancerous lesions or Stage I lung adenocarcinoma and a total of 861 analyzable radiomic features were extracted from two segmented lesions including pulmonary lesions and margins, using PyRadiomics by two senior radiologists. In training cohort, 145 patients (70%) are selected randomly from the single-nodular patients (N = 207). As for the validation cohorts, the models were validated using the resting 62 patients from single-nodular cohort and multi-nodular cohort (n = 57) respectively. Least Absolute Shrinkage and Selector Operation and Support Vector Machine-Recursive Feature Elimination were used for feature selection. ROC analysis and AUC were used to evaluate the performance of three models which were developed by multiple logistic regression on distinguishing the precancerous lesions from early stage lung adenocarcinoma. Results: Selected features from pulmonary lesions and pericarcinous tissue were developed into two independent radiomic models and a combined model. Margin-related radiomic model performs well in three validation cohorts. The AUC Brock of single-nodular cohort in training cohort was 0.912 (95% CI: 0.876-0.948), while in single-nodular validation cohort was 0.93 (95% CI: 0.862-0.966). Multi-nodular validation cohort in this model shows an AUC of 0.891 (95% CI = 0.824–0.943). Comparing combined model and tumor-associated radiomic model, it is found that the AUC of combined model was improved from 0.865 (95% CI: 0.767-0.963) to 0.94 (95% CI: 0.767-0.963) for single-nodular validation cohort. Respectively, this combined model also performs well in multi-nodular validation cohort. Conclusions: This study demonstrated the potential of margin-related radiomic features based on preoperative CT scans to distinguish precancerous lesions from early stage lung adenocarcinoma. The constructed radiomic model provided an easy-to-use, preoperative tool for surgeons to develop accurate therapeutic strategies for multi-nodular patients.

BACKGROUND Congenital clubfoot is a common pediatric orthopedic deformity that can be corrected by Ponseti method, and pedobarographic analysis has been used to assess the outcomes. However, the relationship between the plantar pressure distribution of the right and left foot in children with bilateral clubfoot has not been studied. In this study, the pedobarographic data of patients with bilateral clubfoot who were treated by the Ponseti method were reviewed, and a correlation analysis was conducted to clarify the relationship between the right and left foot. MATERIAL AND METHODS A retrospective cross-sectional study of children with bilateral clubfoot who were treated by the Ponseti method in infancy was performed, in which all the patients were available for clinical evaluation, and pedobarographic analysis was conducted on each patient after treatment. The Pearson's correlation coefficient (r) were calculated for all the measurements of the left and right foot. RESULTS A total of 20 children (mean age 6.9±1.07 years, range 4-8 years) with bilateral clubfoot who were treated by the Ponseti method were included. The Dimeglio and Pirani scores before and after treatment between the right and left foot were significantly correlated. All the pedobarographic measurements between the left and right foot were correlated, indicating different degrees of positive correlation. CONCLUSIONS The plantar pressure measurements between the 2 feet in patients with bilateral clubfoot were highly correlated before treatment, and a correlation was also observed after those patients were treated by the Ponseti method. We should take these correlations into consideration during study design and analysis of clubfoot cases.

The data aggregation technology can save resources of wireless sensor networks, but it can also add extra delays. To this end, specific to the special scenario where data transmission must be completed under specified delay constraints, the Markov model-based low delay data aggregation tree (MLDGT) algorithm is proposed. Firstly, the formal expression of the problem of constructing data aggregation tree under delay constraints is given. This problem has been con-firmed as a NP problem. Then, the Markov approximate model is used to find a suboptimal solution, and further obtain the low delay data aggregation tree. Finally, the effectiveness of the MLDGT algorithm is analysed by simulation and comparison. The experimental results show that the MLDGT algorithm can reduce the data aggregation delay.

Recent trends have led to an interest in Ponseti treatment for correcting neglected congenital clubfoot. Although good clinical and functional outcomes have been reported, the plantar pressure distribution after the treatment of neglected clubfoot has not been explored yet. The present study aimed to investigate whether plantar pressures normalized following Ponseti treatment in patients with neglected congenital clubfoot. Pedobarographic, clinical, and functional examinations were performed in 22 children (aged, 91.0 ± 40.3 months) with unilateral neglected congenital clubfeet, treated using Ponseti method at 27.8 ± 12.1 months of age. Plantar pressure parameters were recorded using a Footscan pressure plate. The contact time, contact area, peak pressure, and pressure-time integral were determined. The data of the affected feet were compared with those of the unaffected feet and healthy controls. Although clinical and functional examinations showed satisfactory results according to the Dimeglio and Pirani scores, considerable differences in plantar pressure parameters were identified among the affected feet, unaffected feet, and healthy controls. Internal foot progression angle and a load transfer from the medial forefoot and hindfoot to the lateral forefoot and midfoot were observed in the affected feet. Future studies should attempt to investigate the factors accounting for plantar pressure deviations and the possible effect of these deviations on the lower limb musculoskeletal development of children.

The main etiopathogenesis of rheumatoid arthritis (RA) is overexpressed inflammatory cytokines and tissue injury mediated by persistent NF-κB activation. MicroRNAs widely participate in the regulation of target gene expression and play important roles in various diseases. Here, we explored the mechanisms of microRNAs in RA. We found that microRNA (miR)-10a was downregulated in the fibroblast-like synoviocytes (FLSs) of RA patients compared with osteoarthritis (OA) controls and this downregulation could be triggered by TNF-α and IL-1β in an NF-κB-dependent manner through promoting the expression of the YingYang 1 (YY1) transcription factor. Downregulated miR-10a could accelerate IκB degradation and NF-κB activation by targeting IRAK4, TAK1 and BTRC. This miR-10a-mediated NF-κB activation then significantly promoted the production of various inflammatory cytokines, including TNF-α, IL-1β, IL-6, IL-8 and MCP-1 and matrix metalloproteinase (MMP)-1 and MMP-13. In addition, transfection of a miR-10a inhibitor accelerated the proliferation and migration of FLSs. Collectively, our data demonstrates the existence of a novel NF-κB/YY1/miR-10a/NF-κB regulatory circuit that promotes the excessive secretion of NF-κB-mediated inflammatory cytokines and the proliferation and migration of RA FLSs. Thus, miR-10a acts as a switch to control this regulatory circuit and may serve as a diagnostic and therapeutic target for RA treatment.