Additional file 2: of A novel fibrillin-1 gene missense mutation associated with neonatal Marfan syndrome: a case report and review of the mutation spectrum

CARE Checklist (2013) of information to include when writing a case report. (DOCX 1484 kb

Additional file 1: of A novel fibrillin-1 gene missense mutation associated with neonatal Marfan syndrome: a case report and review of the mutation spectrum

Clinical features of the patient showing facial appearance, dolichocephaly, the pectus deformity, arachnodactyly, the thumb sign, and pes planus. (JPEG 896 kb

Image_1_Development and Validation of a Prognostic Signature Associated With Tumor Microenvironment Based on Autophagy-Related lncRNA Analysis in Hepatocellular Carcinoma.TIF

Objective: The present study aimed to establish a prognostic signature based on the autophagy-related long non-coding RNAs (lncRNAs) analysis in patients with hepatocellular carcinoma (HCC).Methods: Patients with HCC from The Cancer Genome Atlas (TCGA) were taken as the training cohort, and patients from the International Cancer Genome Consortium (ICGC) were treated as the validation cohort. Autophagy-related lncRNAs were obtained via a co-expression network analysis. According to univariate and multivariate analyses, a multigene prognostic signature was constructed in the training cohort. The predictive power of the signature was confirmed in both cohorts. The detailed functions were investigated using functional analysis. The single-sample gene set enrichment analysis (ssGSEA) score was used to evaluate the tumor microenvironment. The expression levels of immunotherapy and targeted therapy targets between the two risk groups were compared. Finally, a nomogram was constructed by integrating clinicopathological parameters with independently predictive value and the risk score.Results: Four autophagy-related lncRNAs were identified to establish a prognostic signature, which separated patients into high- and low-risk groups. Survival analysis showed that patients in the high-risk group had a shorter survival time in both cohorts. A time-independent receiver-operating characteristic (ROC) curve and principal component analysis (PCA) confirmed that the prognostic signature had a robust predictive power and reliability in both cohorts. Functional analysis indicated that the expressed genes in the high-risk group are mainly enriched in autophagy- and cancer-related pathways. ssGSEA revealed that the different risk groups were associated with the tumor microenvironment. Moreover, the different risk groups had positive correlations with the expressions of specific mutant genes. Multivariate analysis showed that the risk score also exhibited excellent predictive power irrespective of clinicopathological characteristics in both cohorts. A nomogram was established. The nomogram showed good discrimination, with Harrell's concordance index (C-index) of 0.739 and good calibration.Conclusion: The four autophagy-related lncRNAs could be used as biological biomarkers and therapeutic targets. The prognostic signature and nomogram might aid clinicians in individual treatment optimization and clinical decision-making for patients with HCC.</p

Data_Sheet_1_Development and Validation of a Prognostic Signature Associated With Tumor Microenvironment Based on Autophagy-Related lncRNA Analysis in Hepatocellular Carcinoma.DOCX

Objective: The present study aimed to establish a prognostic signature based on the autophagy-related long non-coding RNAs (lncRNAs) analysis in patients with hepatocellular carcinoma (HCC).Methods: Patients with HCC from The Cancer Genome Atlas (TCGA) were taken as the training cohort, and patients from the International Cancer Genome Consortium (ICGC) were treated as the validation cohort. Autophagy-related lncRNAs were obtained via a co-expression network analysis. According to univariate and multivariate analyses, a multigene prognostic signature was constructed in the training cohort. The predictive power of the signature was confirmed in both cohorts. The detailed functions were investigated using functional analysis. The single-sample gene set enrichment analysis (ssGSEA) score was used to evaluate the tumor microenvironment. The expression levels of immunotherapy and targeted therapy targets between the two risk groups were compared. Finally, a nomogram was constructed by integrating clinicopathological parameters with independently predictive value and the risk score.Results: Four autophagy-related lncRNAs were identified to establish a prognostic signature, which separated patients into high- and low-risk groups. Survival analysis showed that patients in the high-risk group had a shorter survival time in both cohorts. A time-independent receiver-operating characteristic (ROC) curve and principal component analysis (PCA) confirmed that the prognostic signature had a robust predictive power and reliability in both cohorts. Functional analysis indicated that the expressed genes in the high-risk group are mainly enriched in autophagy- and cancer-related pathways. ssGSEA revealed that the different risk groups were associated with the tumor microenvironment. Moreover, the different risk groups had positive correlations with the expressions of specific mutant genes. Multivariate analysis showed that the risk score also exhibited excellent predictive power irrespective of clinicopathological characteristics in both cohorts. A nomogram was established. The nomogram showed good discrimination, with Harrell's concordance index (C-index) of 0.739 and good calibration.Conclusion: The four autophagy-related lncRNAs could be used as biological biomarkers and therapeutic targets. The prognostic signature and nomogram might aid clinicians in individual treatment optimization and clinical decision-making for patients with HCC.</p

Data_Sheet_2_Development and Validation of a Prognostic Signature Associated With Tumor Microenvironment Based on Autophagy-Related lncRNA Analysis in Hepatocellular Carcinoma.XLSX

Objective: The present study aimed to establish a prognostic signature based on the autophagy-related long non-coding RNAs (lncRNAs) analysis in patients with hepatocellular carcinoma (HCC).Methods: Patients with HCC from The Cancer Genome Atlas (TCGA) were taken as the training cohort, and patients from the International Cancer Genome Consortium (ICGC) were treated as the validation cohort. Autophagy-related lncRNAs were obtained via a co-expression network analysis. According to univariate and multivariate analyses, a multigene prognostic signature was constructed in the training cohort. The predictive power of the signature was confirmed in both cohorts. The detailed functions were investigated using functional analysis. The single-sample gene set enrichment analysis (ssGSEA) score was used to evaluate the tumor microenvironment. The expression levels of immunotherapy and targeted therapy targets between the two risk groups were compared. Finally, a nomogram was constructed by integrating clinicopathological parameters with independently predictive value and the risk score.Results: Four autophagy-related lncRNAs were identified to establish a prognostic signature, which separated patients into high- and low-risk groups. Survival analysis showed that patients in the high-risk group had a shorter survival time in both cohorts. A time-independent receiver-operating characteristic (ROC) curve and principal component analysis (PCA) confirmed that the prognostic signature had a robust predictive power and reliability in both cohorts. Functional analysis indicated that the expressed genes in the high-risk group are mainly enriched in autophagy- and cancer-related pathways. ssGSEA revealed that the different risk groups were associated with the tumor microenvironment. Moreover, the different risk groups had positive correlations with the expressions of specific mutant genes. Multivariate analysis showed that the risk score also exhibited excellent predictive power irrespective of clinicopathological characteristics in both cohorts. A nomogram was established. The nomogram showed good discrimination, with Harrell's concordance index (C-index) of 0.739 and good calibration.Conclusion: The four autophagy-related lncRNAs could be used as biological biomarkers and therapeutic targets. The prognostic signature and nomogram might aid clinicians in individual treatment optimization and clinical decision-making for patients with HCC.</p

Data_Sheet_3_Development and Validation of a Prognostic Signature Associated With Tumor Microenvironment Based on Autophagy-Related lncRNA Analysis in Hepatocellular Carcinoma.XLSX

Objective: The present study aimed to establish a prognostic signature based on the autophagy-related long non-coding RNAs (lncRNAs) analysis in patients with hepatocellular carcinoma (HCC).Methods: Patients with HCC from The Cancer Genome Atlas (TCGA) were taken as the training cohort, and patients from the International Cancer Genome Consortium (ICGC) were treated as the validation cohort. Autophagy-related lncRNAs were obtained via a co-expression network analysis. According to univariate and multivariate analyses, a multigene prognostic signature was constructed in the training cohort. The predictive power of the signature was confirmed in both cohorts. The detailed functions were investigated using functional analysis. The single-sample gene set enrichment analysis (ssGSEA) score was used to evaluate the tumor microenvironment. The expression levels of immunotherapy and targeted therapy targets between the two risk groups were compared. Finally, a nomogram was constructed by integrating clinicopathological parameters with independently predictive value and the risk score.Results: Four autophagy-related lncRNAs were identified to establish a prognostic signature, which separated patients into high- and low-risk groups. Survival analysis showed that patients in the high-risk group had a shorter survival time in both cohorts. A time-independent receiver-operating characteristic (ROC) curve and principal component analysis (PCA) confirmed that the prognostic signature had a robust predictive power and reliability in both cohorts. Functional analysis indicated that the expressed genes in the high-risk group are mainly enriched in autophagy- and cancer-related pathways. ssGSEA revealed that the different risk groups were associated with the tumor microenvironment. Moreover, the different risk groups had positive correlations with the expressions of specific mutant genes. Multivariate analysis showed that the risk score also exhibited excellent predictive power irrespective of clinicopathological characteristics in both cohorts. A nomogram was established. The nomogram showed good discrimination, with Harrell's concordance index (C-index) of 0.739 and good calibration.Conclusion: The four autophagy-related lncRNAs could be used as biological biomarkers and therapeutic targets. The prognostic signature and nomogram might aid clinicians in individual treatment optimization and clinical decision-making for patients with HCC.</p

Additional file 1 of Regeneration of meniscal avascular zone using autogenous meniscal fragments in a rabbit model

Additional file 1: Figure S1. The details of the defect model

Identification of PHB2 as a Potential Biomarker of Luminal A Breast Cancer Cells Using a Cell-Specific Aptamer

Precise diagnosis of breast cancer molecular subtypes remains a great challenge in clinics. The present molecular biomarkers are not specific enough to classify breast cancer subtypes precisely, which requests for more accurate and specific molecular biomarkers to be discovered. Aptamers evolved by the cell-systematic evolution of ligands by exponential enrichment (SELEX) method show great potential in the discovery and identification of cell membrane targets via aptamer-based cell membrane protein pull-down, which has been regarded as a novel and powerful weapon for the discovery and identification of new molecular biomarkers. Herein, a cell membrane protein PHB2 was identified as a potential molecular biomarker specifically expressed in the cell membranes of MCF-7 breast cancer cells using a DNA aptamer MF3Ec. Further experiments demonstrated that the PHB2 protein is differentially expressed in the cell membranes of MCF-7, SK-BR-3, and MDA-MB-231 breast cancer cells and MCF-10A cells, and the binding molecular domains of aptamer MF3Ec and anti-PHB2 antibodies to the PHB2 protein are different due to there being no obvious competitions between aptamer MF3Ec and anti-PHB2 antibodies in the binding to the cell membranes of target MCF-7 cells. Due to those four cells belonging to luminal A, HER2-positive, and triple-negative breast cancer cell subtypes and human normal mammary epithelial cells, respectively, the PHB2 protein in the cell membrane may be a potential biomarker for precise diagnosis of the luminal A breast cancer cell subtype, which is endowed with the ability to differentiate the luminal A breast cancer cell subtype from HER2-positive and triple-negative breast cancer cell subtypes and human normal mammary epithelial cells, providing a new molecular biomarker and therapeutic target for the accurate and precise classification and diagnostics and personalized therapy of breast cancer

Tough, Long-Term, Water-Resistant, and Underwater Adhesion of Low-Molecular-Weight Supramolecular Adhesives

Modern functional adhesives have attracted considerable attention due to their reversible adhesion capacities and stimuli-responsive adhesion behavior. However, for modern functional adhesives, polymeric structures were highly necessary to realize adhesion behaviors. Supramolecular adhesives from low-molecular-weight monomers were rarely recognized. Compared with polymeric adhesive materials, it remains challenging for supramolecualr adhesive materials to realize tough adhesion on wet surfaces or even under water. In this study, a new supramolecular adhesive consisting of low-molecular-weight monomers was successfully designed and prepared. Strong and long-term adhesion performance was realized on various surfaces, with a maximum adhesion strength of 4.174 MPa. This supramolecular adhesive exhibits tough and stable adhesion properties in high-moisture and underwater environments (including seawater). Long-term underwater adhesion tests display the potential application of low-molecular-weight adhesive as a marine adhesive

Tough, Long-Term, Water-Resistant, and Underwater Adhesion of Low-Molecular-Weight Supramolecular Adhesives

Modern functional adhesives have attracted considerable attention due to their reversible adhesion capacities and stimuli-responsive adhesion behavior. However, for modern functional adhesives, polymeric structures were highly necessary to realize adhesion behaviors. Supramolecular adhesives from low-molecular-weight monomers were rarely recognized. Compared with polymeric adhesive materials, it remains challenging for supramolecualr adhesive materials to realize tough adhesion on wet surfaces or even under water. In this study, a new supramolecular adhesive consisting of low-molecular-weight monomers was successfully designed and prepared. Strong and long-term adhesion performance was realized on various surfaces, with a maximum adhesion strength of 4.174 MPa. This supramolecular adhesive exhibits tough and stable adhesion properties in high-moisture and underwater environments (including seawater). Long-term underwater adhesion tests display the potential application of low-molecular-weight adhesive as a marine adhesive