Modeling, Control, and Clinical Validation of an Upper-Limb Medical Education Task Trainer for Elbow Spasticity and Rigidity Assessment.

The goal of this study was to validate a series elastic actuator (SEA)-based robotic arm that can mimic three abnormal muscle behaviors, namely lead-pipe rigidity, cogwheel rigidity, and spasticity for medical education training purposes. Key characteristics of each muscle behavior were first modeled mathematically based on clinically-observed data across severity levels. A controller that incorporated feedback, feedforward, and disturbance observer schemes was implemented to deliver haptic target muscle resistive torques to the trainee during passive stretch assessments of the robotic arm. A series of benchtop tests across all behaviors and severity levels were conducted to validate the torque estimation accuracy of the custom SEA (RMSE: ~ 0.16 Nm) and the torque tracking performance of the controller (torque error percentage: < 2.8 %). A clinical validation study was performed with seven experienced clinicians to collect feedback on the task trainer's simulation realism via a Classification Test and a Disclosed Test. In the Classification Test, subjects were able to classify different muscle behaviors with a mean accuracy > 87 % and could further distinguish severity level within each behavior satisfactorily. In the Disclosed Test, subjects generally agreed with the simulation realism and provided suggestions on haptic behaviors for future iterations. Overall, subjects scored 4.9 out of 5 for the potential usefulness of this device as a medical education tool for students to learn spasticity and rigidity assessment.

Genomic Profiling of Lower-Grade Gliomas Subtype with Distinct Molecular and Clinicopathologic Characteristics via Altered DNA-Damage Repair Features.

Lower WHO grade II and III gliomas (LGGs) exhibit significant genetic and transcriptional heterogeneity, and the heterogeneity of DNA damage repair (DDR) and its relationship to tumor biology, transcriptome, and tumor microenvironment (TME) remains poorly understood. In this study, we conducted multi-omics data integration to investigate DDR alterations in LGG. Based on clinical parameters and molecular characteristics, LGG patients were categorized into distinct DDR subtypes, namely, DDR-activated and DDR-suppressed subtypes. We compared gene mutation, immune spectrum, and immune cell infiltration between the two subtypes. DDR scores were generated to classify LGG patients based on DDR subtype features, and the results were validated using a multi-layer data cohort. We found that DDR activation was associated with poorer overall survival and that clinicopathological features of advanced age and higher grade were more common in the DDR-activated subtype. DDR-suppressed subtypes exhibited more frequent mutations in IDH1. In addition, we observed significant upregulation of activated immune cells in the DDR-activated subgroup, which suggests that immune cell infiltration significantly influences tumor progression and immunotherapeutic responses. Furthermore, we constructed a DDR signature for LGG using six DDR genes, which allowed for the division of patients into low- and high-risk groups. Quantitative real-time PCR results showed that CDK1, CDK2, TYMS, SMC4, and WEE1 were significantly upregulated in LGG samples compared to normal brain tissue samples. Overall, our study sheds light on DDR heterogeneity in LGG and provides insight into the molecular pathways of DDR involved in LGG development.

N7-methylguanosin regulators-mediated methylation modification patterns and characterization of the immune microenvironment in lower-grade glioma.

N7-methylguanosine (m7G) modification signature has recently emerged as a crucial regulator of tumor progression and treatment in cancer. However, there is limited information available on the genomic profile of lower-grade gliomas (LGGs) related to m7G methylation modification genes' function in tumorigenesis and progression. In this study, we employed bioinformatics methods to characterize m7G modifications in individuals with LGG from The Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA). We used gene set enrichment analysis (GSEA), single sample GSEA (ssGSEA), CIBERSORT algorithm, ESTIMATE algorithm, and TIDE to evaluate the association between m7G modification patterns, tumor microenvironment (TME) cell infiltration properties, and immune infiltration markers. The m7G scoring scheme using principal component analysis (PCA) was employed to investigate the m7G modification patterns quantitatively. We examined the m7G modification hub genes' expression levels in normal samples, refractory epilepsy samples, and LGG samples using immunohistochemistry, western-blotting, and qRT-PCR. Our findings revealed that individuals with LGG could be categorized into two groups based on m7G scores (high and low) according to the properties of m7G. Moreover, we observed that high m7G score was associated with significant clinical benefit and prolonged survival duration in the anti-PD-1 cohort, while low m7G score was associated with improved prognostic outcomes and increased likelihood of complete or partial response in the anti-PD-L1 cohort. Different m7G subtypes also showed varying Tumor Mutational Burden (TMB) and immune profiles and might have distinct responses to immunotherapy. Furthermore, we identified five potential genetic markers that were highly correlated with the m7G score signature index. These findings provide insight into the features and classification associated with m7G methylation modifications and may aid in improving the clinical outcome of LGG.

N6-methyladenosine RNA methylation regulator-related alternative splicing gene signature as prognostic predictor and in immune microenvironment characterization of patients with low-grade glioma.

Background: N6-methyladenosine (m6A) RNA methylation is an important epigenetic modification affecting alternative splicing (AS) patterns of genes to regulate gene expression. AS drives protein diversity and its imbalance may be an important factor in tumorigenesis. However, the clinical significance of m6A RNA methylation regulator-related AS in the tumor microenvironment has not been investigated in low-grade glioma (LGG). Methods: We used 12 m6A methylation modulatory genes (WTAP, FTO, HNRNPC, YTHDF2, YTHDF1, YTHDC2, ALKBH5, YTHDC1, ZC3H13, RBM15, METTL14, and METTL3) from The Cancer Genome Atlas (TCGA) database as well as the TCGA-LGG (n = 502) dataset of AS events and transcriptome data. These data were downloaded and subjected to machine learning, bioinformatics, and statistical analyses, including gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Univariate Cox, the Least Absolute Shrinkage and Selection Operator (LASSO), and multivariable Cox regression were used to develop prognostic characteristics. Prognostic values were validated using Kaplan-Maier survival analysis, proportional risk models, ROC curves, and nomograms. The ESTIMATE package, TIMER database, CIBERSORT method, and ssGSEA algorithm in the R package were utilized to explore the role of the immune microenvironment in LGG. Lastly, an AS-splicing factor (SF) regulatory network was examined in the case of considering the role of SFs in regulating AS events. Results: An aggregate of 3,272 m6A regulator-related AS events in patients with LGG were screened using six machine learning algorithms. We developed eight AS prognostic characteristics based on splice subtypes, which showed an excellent prognostic prediction performance. Furthermore, quantitative prognostic nomograms were developed and showed strong validity in prognostic prediction. In addition, prognostic signatures were substantially associated with tumor immune microenvironment diversity, ICB-related genes, and infiltration status of immune cell subtypes. Specifically, UGP2 has better promise as a prognostic factor for LGG. Finally, splicing regulatory networks revealed the potential functions of SFs. Conclusion: The present research offers a novel perspective on the role of AS in m6A methylation. We reveal that m6A methylation regulator-related AS events can mediate tumor progression through the immune-microenvironment, which could serve as a viable biological marker for clinical stratification of patients with LGG so as to optimize treatment regimens.

Identification and validation of a novel eight mutant-derived long non-coding RNAs signature as a prognostic biomarker for genome instability in low-grade glioma.

Long non-coding RNAs (lncRNAs) comprise an integral part of the eukaryotic transcriptome. Alongside proteins, lncRNAs modulate lncRNA-based gene signatures of unstable transcripts, play a crucial role as antisense lncRNAs to control intracellular homeostasis and are implicated in tumorigenesis. However, the role of genomic instability-associated lncRNAs in low-grade gliomas (LGG) has not been fully explored. In this study, lncRNAs expression and somatic mutation profiles in low-grade glioma genome were used to identify eight novel mutant-derived genomic instability-associated lncRNAs including H19, FLG-AS1, AC091932.1, AC064875.1, AL138767.3, AC010273.2, AC131097.4 and ISX-AS1. Patients from the LGG gene mutagenome atlas were grouped into training and validation sets to test the performance of the signature. The genomic instability-associated lncRNAs signature (GILncSig) was then validated using multiple external cohorts. A total of 59 novel genomic instability-associated lncRNAs in LGG were used for least absolute shrinkage and selection operator (Lasso), single and multifactor Cox regression analysis using the training set. Furthermore, the independent predictive role of risk features in the training and validation sets were evaluated through survival analysis, receiver operating feature analysis and construction of a nomogram. Patients with IDH1 mutation status were grouped into two different risk groups based on the GILncSig score. The low-risk group showed a relatively higher rate of IDH1 mutations compared with patients in the high-risk group. Furthermore, patients in the low-risk group had better prognosis compared with patients in the high-risk group. In summary, this study reports a reliable prognostic prediction signature and provides a basis for further investigation of the role of lncRNAs on genomic instability. In addition, lncRNAs in the signature can be used as new targets for treatment of LGG.

Integrated Gene Expression and Methylation Analyses Identify DLL3 as a Biomarker for Prognosis of Malignant Glioma.

Glioma is one of the most common neurological malignancies worldwide. Delta-like ligand 3 (DLL3), an inhibitory ligand-driven activation of the Notch pathway, has been shown to be significantly associated with overall survival in patients with glioma. Therefore, the purpose of this study was to determine whether DLL3 as a biomarker in glioma is associated with patients' clinicopathological features and prognosis. We identified differences in transcriptome and promoter methylation in the Chinese Glioma Genome Atlas (CGGA) in patients with malignant glioma with shorter (less than 1 year) and longer (greater than 3 years) survival time. Further analysis of The Cancer Genome Atlas (TCGA) revealed that four genes (DLL3, TSPAN15, RTN1, PAK7) are highly associated with patient prognosis and play an indispensable role in evolution. We chose the expression level of DLL3 in glioma patients for our study. Patients were divided into groups with low and high expression of DLL3 according to the cutoff values obtained, and Kaplan-Meier and Cox analysis were used to examine the correlation between DLL3 gene expression and patient survival. We then performed a gene set enrichment analysis (GSEA) to identify significantly enriched signaling pathways. Our results confirmed that the overall survival of patients with low DLL3 expression was significantly shorter than that of patients with high DLL3 expression. GSEA showed that the signaling pathways of the immune process and immune response, among others, were enhanced with the DLL3 low-expression phenotype. Collectively, our findings signify that DLL3 is a potent prognostic factor for glioma, which can provide a viable approach for glioma prognostic assessment and valuable insights for anti-tumor immune-targeted therapies.

Identification and validation of an individualized prognostic signature of lower-grade glioma based on nine immune related long non-coding RNA.

BACKGROUND: Low-grade glioma (LGG)is one of the most common and aggressive neurological malignant tumors of the central nervous system. Mounting evidence indicates that aberrantly expressed long non-coding RNA (lncRNAs) and immune cell infiltration influence low-grade glioma development. Despite the increasing amount of research on lncRNA, there are very few immune-related lncRNA for LGG studies. METHODS: We evaluated immune cell infiltration in 529 low-grade glioma patient specimens from TCGA and 1152 normal brain tissue samples from GTEx. ssGSEA was used to generate high, medium, and low immune cell infiltration groups and to examine the heterogeneity of the low-grade glioma immune microenvironment. A risk model of immune-related lncRNAs based on immune gene sets was developed. Sequential single-factor Cox regression, Lasso regression, and stepwise multiple Cox regression analyses uncovered immune-related lncRNAs with low-grade glioma prognostic value. Kaplan-Meier analysis, ROC analysis, and nomograms were used to predict low-grade glioma OS. At length, We performed GO term and KEGG enrichment analyses and used standardized enrichment scores (NES) to identify signaling pathways that were significantly enriched. RESULTS: We identified nine immune-associated lncRNAs with low-grade glioma prognostic value (AC009283.1, AC009227.1, AL121899.1, LINC00174, LINC02166, AC018647.1, AC061961.1, NRAV, and LINC00320).These prognostic lncRNAs were used to establish prognostic markers. Kaplan-Meier Survival analysis revealed a 10-year survival rate of 22.68 % (95 % CI: 13.54-38 %] in high-risk LGG vs. 54 % (95 % CI: 39.04-74.8 %] in low-risk LGG patients. Univariate Cox regression analysis showed that the HR of risk score and 95 % CI were 1.081 and (1.060-1.102) (p < 0.001), respectively. In contrast, those from multivariate Cox regression analysis were 1.066 and (1.046-1.087) (p < 0.001). This indicated that nine LncRNAs are independent prognostic factors for patients with low-grade glioma. GSEA suggests that the identified lncRNAs influence low-grade glioma tumorigenesis and prognosis by modulating immune responses and cancer pathways. CONCLUSIONS: Our data highlight the potential prognostic value of the nine immune-related lncRNA in low-grade glioma and may open new research lines and guide low-grade glioma management.