Sarcopenia as a predictor of nutritional status and comorbidities: a cross-sectional and mendelian randomization study.

BACKGROUND: With the advancement of world population aging, age-related sarcopenia (SP) imposes enormous clinical burden on hospital. Clinical research of SP in non-geriatric wards has not been appreciated, necessitating further investigation. However, observational studies are susceptible to confounders. Mendelian randomization (MR) can effectively mitigate bias to assess causality. OBJECTIVE: To investigate the correlation between SP and comorbidities in orthopedic wards, and subsequently infer the causality, providing a theoretical basis for developing strategies in SP prevention and treatment. METHODS: Logistic regression models were employed to assess the correlation between SP and comorbidities. The MR analysis was mainly conducted with inverse variance weighted, utilizing data extracted from the UK and FinnGen biobank (Round 9). RESULTS: In the cross-sectional analysis, SP exhibited significant associations with malnutrition (P = 0.013) and some comorbidities, including osteoporosis (P = 0.014), body mass index (BMI) (P = 0.021), Charlson Comorbidity Index (CCI) (P = 0.006). The MR result also provided supporting evidence for the causality between SP and hypertension, osteoporosis and BMI. These results also withstood multiple sensitivity analyses assessing the validity of MR assumptions. CONCLUSION: The result indicated a significant association between SP and BMI, CCI, malnutrition, and osteoporosis. We highlighted that SP and comorbidities deserved more attention in non-geriatric wards, urging further comprehensive investigation.

Dynamics of Kramers-Henneberger atoms in focused laser beams of circular polarization.

In intense laser fields, electrons of atoms will follow the laser field and undergo quiver motion just like free electrons but still weakly bound to the atomic core, thus forming a set of specific dressed states named Kramers-Henneberger (KH) states, which comprise the KH atoms. In a focused laser beam, in addition to Ponderomotive (PM) force, KH atoms will experience KH force, which is unique to KH atoms. We examine both PM and KH forces as well as corresponding velocity gain of hydrogen and helium atoms in a focused laser field with circular polarization. We work out laser parameters which can be used in experimental confirmation of circularly polarized KH atoms.

Effects of walking on epigenetic age acceleration: a Mendelian randomization study.

INTRODUCTION: Walking stands as the most prevalent physical activity in the daily lives of individuals and is closely associated with physical functioning and the aging process. Nonetheless, the precise cause-and-effect connection between walking and aging remains unexplored. The epigenetic clock emerges as the most promising biological indicator of aging, capable of mirroring the biological age of the human body and facilitating an investigation into the association between walking and aging. Our primary objective is to investigate the causal impact of walking with epigenetic age acceleration (EAA). METHODS: We conducted a two-sample two-way Mendelian randomization (MR) study to investigate the causal relationship between walking and EAA. Walking and Leisure sedentary behavior data were sourced from UK Biobank, while EAA data were gathered from a total of 28 cohorts. The MR analysis was carried out using several methods, including the inverse variance weighted (IVW), weighted median, MR-Egger, and robust adjusted profile score (RAPS). To ensure the robustness of our findings, we conducted sensitivity analyses, which involved the MR-Egger intercept test, Cochran's Q test, and MR-PRESSO, to account for and mitigate potential pleiotropy. RESULTS: The IVW MR results indicate a significant impact of usual walking pace on GrimAge (BETA = - 1.84, 95% CI (- 2.94, - 0.75)), PhenoAge (BETA = - 1.57, 95% CI (- 3.05, - 0.08)), Horvath (BETA = - 1.09 (- 2.14, - 0.04)), and Hannum (BETA = - 1.63, 95% CI (- 2.70, - 0.56)). Usual walking pace is significantly associated with a delay in epigenetic aging acceleration (EAA) (P < 0.05). Moreover, the direction of effect predicted by the gene remained consistent across RAPS outcomes and sensitivity MR analyses. There is a lack of robust causal relationships between other walking conditions, such as walking duration and walking frequency, on EAA (P > 0.05). CONCLUSION: Our evidence demonstrates that a higher usual walking pace is associated with a deceleration of the acceleration of all four classical epigenetic clocks acceleration.

COVID-19 and sarcopenia-related traits: a bidirectional Mendelian randomization study.

Background: Emerging evidence suggested that coronavirus disease 2019 (COVID-19) patients were more prone to acute skeletal muscle loss and suffer sequelae, including weakness, arthromyalgia, depression and anxiety. Meanwhile, it was observed that sarcopenia (SP) was associated with susceptibility, hospitalization and severity of COVID-19. However, it is not known whether there is causal relationship between COVID-19 and SP-related traits. Mendelian randomization (MR) was a valid method for inferring causality. Methods: Data was extracted from the COVID-19 Host Genetic Initiative and the UK Biobank without sample overlapping. The MR analysis was performed with inverse variance weighted, weighted median, MR-Egger, RAPS and CAUSE, MR-APSS. Sensitivity analysis was conducted with MR-Egger intercept test, Cochran's Q test, MR-PRESSO to eliminate pleiotropy. Results: There was insufficient result in the MR-APSS method to support a direct causal relationship after the Bonferroni correction. Most other MR results were also nominally consistent with the MR-APSS result. Conclusions: Our study first explored the causal relationship between COVID-19 and SP-related traits, but the result indicated that they may indirectly interact with each other. We highlighted that older people had better absorb enough nutrition and strengthen exercise to directly cope with SP during the COVID-19 pandemic.

CD24-associated ceRNA network reveals prognostic biomarkers in breast carcinoma.

Breast cancer is one of the most common cancer types which is described as the leading cause of cancer death in women. After competitive endogenous RNA (ceRNA) hypothesis was proposed, this triple regulatory network has been observed in various cancers, and increasing evidences reveal that ceRNA network plays a significant role in the migration, invasion, proliferation of cancer cells. In the current study, our target is to construct a CD24-associated ceRNA network, and to further identify key prognostic biomarkers in breast cancer. Using the transcriptom profiles from TCGA database, we performed a comprehensive analysis between CD24high tumor samples and CD24low tumor samples, and identified 132 DElncRNAs, 602 DEmRNAs and 26 DEmiRNAs. Through comprehensive analysis, RP1-228H13.5/miR-135a-5p/BEND3 and SIM2 were identified as key CD24-associated biomarkers, which exhibited highly significance with overall survival, immune microenvironment as well as clinical features. To sum up the above, the current study constructed a CD24-associated ceRNA network, and RP1-228H13.5/miR-135a-5p/BEND3 and SIM2 axis worked as a potential therapeutic target and a predictor for BRCA diagnosis and prognosis.

Prognostic and immune implications of a novel 7-methylguanosine-related microRNA signature in breast invasive carcinoma: from exploration to validation.

OBJECTIVES: This study aims to develop and validate a prognostic signature based on 7-methylguanosine-related (M7G-related) miRNAs for predicting prognosis and immune implications in breast invasive carcinoma (BRCA). MATERIALS AND METHODS: M7G-related miRNA data of BRCA were obtained from The Cancer Genome Atlas (TCGA). Least absolute shrinkage and selection operator (LASSO)-penalized, univariate, and multivariate Cox regression analyses were used to construct the prognostic signature. Furthermore, the predictive validity was verified using Kaplan-Meier (KM) survival risk and receiver operating characteristic (ROC) plots. Internal random sampling verification was used to simplify and validate the signature. RT-qPCR was used to quantify the expression level of transcriptional profiles. The independent prognostic role of the risk score was validated using univariate and multivariate regression. Single-sample Gene Set Enrichment Analysis (ssGSEA) was used for functional and immune enrichment analysis. RESULTS: A total of 18 M7G-related miRNAs were identified to construct the prognostic signature in BRCA. The low-risk group exhibited significantly higher overall survival than the high-risk group in the KM survival plot (P < 0.001). The area under the curve (AUC) for 1-, 3-, and 5-year survivals in the ROC curve were 0.737, 0.724, and 0.702, respectively. The survival significance in the training and testing cohorts was confirmed by random sampling verification. The most prominent miRNAs in the signature were the miR-7, miR-139, miR-10b, and miR-4728. Furthermore, immune scores for B, mast, and Th1 cells varied between risk groups. Our research demonstrated that CD52 was the most positively correlated gene with immune cells and functions in BRCA. CONCLUSION: Our study presents a comprehensive and systematic analysis of M7G-related miRNAs to construct a prognostic signature in BRCA. The signature demonstrated excellent prognostic validity, with the risk score as an independent prognostic factor. These results provide critical evidence for further investigation of M7G miRNAs and offer new insights for BRCA patients in the context of effective immunotherapy.

Network Pharmacology and Molecular Docking to Elucidate the Potential Mechanism of Ligusticum Chuanxiong Against Osteoarthritis.

Background: Osteoarthritis (OA) is a degenerative disease which serious affects patients. Ligusticum chuanxiong (CX) has been shown to have a certain curative effect on osteoarthritis in traditional Chinese medicine therapy. This study is based on network pharmacology and molecular docking technology to explore the potential mechanism of CX. Methods: Components of CX to treat osteoarthritis were screened in the TCMSP database and targets were predicted by the PharmMapper database, the osteoarthritis targets were collected from the GeneCards database, and intersection genes were found to be the possible targets of CX anti-OA. The STRING database and Cytoscape software were utilized for protein-protein interaction analysis and further screening of core targets. The Metascape database was used for KEGG and GO enrichment analyses. Then, the top 10 pathways were selected to construct "drug-compound-target-pathway-disease" network analysis. Finally, molecular docking was used to analyze the binding affinity of seven compounds with core targets and TNF-α. Results: Seven compounds with 253 non-repetitive targets of CX were screened from the TCMSP database and 60 potential intersection targets of CX anti-OA were found. PPI network analysis showed that the core targets were ALB, AKT1, IGF1, CASP3, MAPK1, ANXA5, and MAPK14, while GO and KEGG pathway enrichment analyses showed that the relevant biological processes involved in the treatment of osteoarthritis by CX might include the MAPK cascade and reactive oxygen species metabolic process. The KEGG pathway analysis result was mainly associated with the MAPK signaling pathway and PI3K-AKT signaling pathway. We further docked seven ingredients with MAPK1 and MAPK14 enriched in the MAPK pathway, and TNF-α as the typical inflammatory cytokine. The results also showed good binding affinity, especially FA, which may be the most important component of CX anti-OA. Conclusion: Our research revealed the potential mechanism of CX in the treatment of OA, and our findings can also pave the way for subsequent basic experimental verification and a new research direction.

The role of AGEs in pathogenesis of cartilage destruction in osteoarthritis.

Osteoarthritis (OA) is a degenerative disease resulting from progressive joint destruction caused by many factors. Its pathogenesis is complex and has not been elucidated to date. Advanced glycation end products (AGEs) are a series of irreversible and stable macromolecular complexes formed by reducing sugar with protein, lipid, and nucleic acid through a non-enzymatic glycosylation reaction (Maillard reaction). They are an important indicator of the degree of ageing. Currently, it is considered that AGEs accumulation in vivo is a molecular basis of age-induced OA, and AGEs production and accumulation in vivo is one of the important reasons for the induction and acceleration of the pathological changes of OA. In recent years, it has been found that AGEs are involved in a variety of pathological processes of OA, including extracellular matrix degradation, chondrocyte apoptosis, and autophagy. Clearly, AGEs play an important role in regulating the expression of OA-related genes and maintaining the chondrocyte phenotype and the stability of the intra-articular environment. This article reviews the latest research results of AGEs in a variety of pathological processes of OA, to provide a new direction for the study of OA pathogenesis and a new target for prevention and treatment. Cite this article: Bone Joint Res 2022;11(5):292-300.

Characterization of the immune cell infiltration landscape in myxofibrosarcoma to aid immunotherapy.

Myxofibrosarcoma (MFS) is a highly malignant subtype of soft tissue sarcoma, accounting for 5% of cases. Immunotherapy guided by immune cell infiltration (ICI) is reportedly a promising treatment strategy. Here, MFS samples (n = 104) from two independent databases were classified as ICI clusters A/B/C and gene clusters A/B/C. Then, a close relationship between ICI and gene clusters was established. We found that the features of these clusters were consistent with the characteristics of immune-inflamed tumors (cluster C), immune-desert tumors (cluster B), and immune-excluded tumors (cluster A). Moreover, cluster C was sensitive to immunotherapy. Finally, an independent ICI score was established to predict the therapeutic effect, which has prospects for application in guiding immunotherapy during clinical practice.

Identification and validation of immune-related lncRNA prognostic signatures for melanoma.

INTRODUCTION: Melanoma is a highly aggressive malignant skin tumor as well as the primary reason for skin cancer-specific deaths. We first identified immune-related long noncoding RNA (lncRNA) prognostic signature and found potential immunotherapeutic targets for melanoma cancer. METHODS: RNA-seq data and clinical features of melanoma samples were obtained from The Cancer Genome Atlas. Samples of melanoma were randomly assigned to the training and testing cohort. The immune-related lncRNA signature was then obtained via using univariate, LASSO, and multivariate Cox analysis of patients in the training cohort. Eight significant immune-related lncRNA signature was then subsequently obtained through correlation analysis between immune-related genes and lncRNAs. The association between risk score and immune cell infiltration was finally assessed using TIMER and CIBERSORT. RESULTS: Three hundred and fifty-six immune-related lncRNAs were obtained. Among them, eight immune-related lncRNAs were identified to build a prognostic risk signature model. The model's performance was then confirmed using the Kaplan-Meier curves, risk plots, and time-dependent receiver-operating characteristic curves in the training cohort. The risk score was identified and confirmed as an independent prognostic factor through univariate and multivariate Cox regression analyses. These results were further verified in the testing and whole cohorts. CIBERSORT algorithm showed that the infiltration levels of T cells CD8, M1 macrophages, plasma cells, T cells CD4 memory activated, T cells gamma delta, and mast cells activated were significantly lower in the high-risk group while the infiltration level of macrophages M0 was significantly lower in the low-risk group. CONCLUSION: The immune-related lncRNA signature offers prognostic markers and potential immunotherapeutic targets for melanoma.

A Novel Overall Survival Prediction Signature Based on Cancer Stem Cell-Related Genes in Osteosarcoma.

Background: Osteosarcoma is the most general bone malignancy that mostly affects children and adolescents. Numerous stem cell-related genes have been founded in distinct forms of cancer. This study aimed at identifying a stem cell-related gene model for the expected assessment of the prognosis of osteosarcoma patients. Methods: We obtained the genes expression data and relevant clinical materials from Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and Gene Expression Omnibus (GEO) databases. We identified differentially expressed genes (DEGs) from the GEO dataset, whereas prognostic stem cell-related genes were obtained from the TARGET database. Subsequently, univariate, LASSO and multivariate Cox regression analyses were applied to establish the stem cell-related signature. Finally, the prognostic value of the signature was validated in the GEO dataset. Results: Twenty-five genes were prognostic ferroptosis-related DEGs. Consequently, we identified eight stem cell-related genes as a signature of prognosis of osteosarcoma patients. Then, the Kaplan-Meier (K-M) curve, the AUC value of ROC, and Cox regression analysis verified that the eight stem cell-related gene model were a new and substantial prognostic marker independent of other clinical traits. Moreover, the nomogram on the foundation of risk score and other clinical traits was established for predicting the survival rate of osteosarcoma patients. Biological function analyses displayed that tumor related pathways were affluent. Conclusion: The expression level of stem cell-related genes offers novel prognostic markers as well as underlying therapeutic targets for the therapy and prevention of osteosarcoma.

Identification of Epithelial-Mesenchymal Transition-Related Prognostic lncRNAs Biomarkers Associated With Melanoma Microenvironment.

Melanoma is the most common cancer of the skin, associated with a worse prognosis and distant metastasis. Epithelial-mesenchymal transition (EMT) is a reversible cellular biological process that plays significant roles in diverse tumor functions, and it is modulated by specific genes and transcription factors. The relevance of EMT-related lncRNAs in melanoma has not been determined. Therefore, RNA expression data and clinical features were collected from the TCGA database (N = 447). Melanoma samples were randomly assigned into the training (315) and testing sets (132). An EMT-related lncRNA signature was constructed via comprehensive analyses of lncRNA expression level and corresponding clinical data. The Kaplan-Meier analysis showed significant differences in overall survival in patients with melanoma in the low and high-risk groups in two sets. Receiver operating characteristic (ROC) curves were used to measure the performance of the model. Cox regression analysis indicated that the risk score was an independent prognostic factor in two sets. Besides, a nomogram was constructed based on the independent variables. Gene Set Enrichment Analysis (GSEA) was applied to evaluate the potential biological functions in the two risk groups. Furthermore, the melanoma microenvironment was evaluated using ESTIMATE and CIBERSORT algorithms in the risk groups. This study indicates that EMT-related lncRNAs can function as potential independent prognostic biomarkers for melanoma survival.

Identification and Verification of Immune-Related Gene Prognostic Signature Based on ssGSEA for Osteosarcoma.

Osteosarcoma is the most common malignant bone tumor in children and adolescence. Multiple immune-related genes have been reported in different cancers. The aim is to identify an immune-related gene signature for the prospective evaluation of prognosis for osteosarcoma patients. In this study, we evaluated the infiltration of immune cells in 101 osteosarcoma patients downloaded from TARGET using the ssGSEA to the RNA-sequencing of these patients, thus, high immune cell infiltration cluster, middle immune cell infiltration cluster and low immune cell infiltration cluster were generated. On the foundation of high immune cell infiltration cluster vs. low immune cell infiltration cluster and normal vs. osteosarcoma, we found 108 common differentially expressed genes which were sequentially submitted to univariate Cox and LASSO regression analysis. Furthermore, GSEA indicated some pathways with notable enrichment in the high- and low-immune cell infiltration cluster that may be helpful in understanding the potential mechanisms. Finally, we identified seven immune-related genes as prognostic signature for osteosarcoma. Kaplan-Meier analysis, ROC curve, univariate and multivariate Cox regression further confirmed that the seven immune-related genes signature was an innovative and significant prognostic factor independent of clinical features. These results of this study offer a means to predict the prognosis and survival of osteosarcoma patients with uncovered seven-gene signature as potential biomarkers.

Decoding English Alphabet Letters Using EEG Phase Information.

Increasing evidence indicates that the phase pattern and power of the low frequency oscillations of brain electroencephalograms (EEG) contain significant information during the human cognition of sensory signals such as auditory and visual stimuli. Here, we investigate whether and how the letters of the alphabet can be directly decoded from EEG phase and power data. In addition, we investigate how different band oscillations contribute to the classification and determine the critical time periods. An English letter recognition task was assigned, and statistical analyses were conducted to decode the EEG signal corresponding to each letter visualized on a computer screen. We applied support vector machine (SVM) with gradient descent method to learn the potential features for classification. It was observed that the EEG phase signals have a higher decoding accuracy than the oscillation power information. Low-frequency theta and alpha oscillations have phase information with higher accuracy than do other bands. The decoding performance was best when the analysis period began from 180 to 380 ms after stimulus presentation, especially in the lateral occipital and posterior temporal scalp regions (PO7 and PO8). These results may provide a new approach for brain-computer interface techniques (BCI) and may deepen our understanding of EEG oscillations in cognition.