lncRNA DLEU2 acts as a miR-181a sponge to regulate SEPP1 and inhibit skeletal muscle differentiation and regeneration.

Sarcopenia is a serious public health problem associated with the loss of muscle mass and function. The purpose of this study was to identify molecular markers and construct a ceRNA pathway as a significant predictor of sarcopenia. We designed a prediction model to select important differentially expressed mRNAs (DEMs), and constructed a sarcopenia associated ceRNA network. After correlation analysis of each element in the ceRNA network based on clinical samples and GTEX database, C2C12 mouse myoblasts were used as a model to verify the identified ceRNA pathways. A new model for predicting sarcopenia based on four molecular markers SEPP1, SV2A, GOT1, and GFOD1 was developed. The model was used to construct a ceRNA network and showed high accuracy. Correlation analysis showed that the expression levels of lncDLEU2, SEPP1, and miR-181a were closely associated with a high risk of sarcopenia. lncDLEU2 inhibits muscle differentiation and regeneration by acting as a miR-181a sponge regulating SEPP1 expression. In this study, a highly accurate prediction tool was developed to improve the prediction outcomes of sarcopenia. These findings suggest that the lncDLEU2-miR-181a-SEPP1 pathway inhibits muscle differentiation and regeneration. This pathway may be a new therapeutic target for the treatment of sarcopenia.

A prediction modeling based on SNOT-22 score for endoscopic nasal septoplasty: a retrospective study.

BACKGROUND: To create a nomogram prediction model for the efficacy of endoscopic nasal septoplasty, and the likelihood of patient benefiting from the operation. METHODS: A retrospective analysis of 155 patients with nasal septum deviation (NSD) was performed to develop a predictive model for the efficacy of endoscopic nasal septoplasty. Quality of life (QoL) data was collected before and after surgery using Sinonasal Outcome Test-22 (SNOT-22) scores to evaluate the surgical outcome. An effective surgical outcome was defined as a SNOT-22 score change ≥ 9 points after surgery. Multivariate logistic regression analysis was then used to establish a predictive model for the NSD treatment. The predictive quality and clinical utility of the predictive model were assessed by C-index, calibration plots, and decision curve analysis. RESULTS: The identified risk factors for inclusion in the predictive model were included. The model had a good predictive power, with a AUC of 0.920 in the training group and a C index of 0.911 in the overall sample. Decision curve analysis revealed that the prediction model had a good clinical applicability. CONCLUSIONS: Our prediction model is efficient in predicting the efficacy of endoscopic surgery for NSD through evaluation of factors including: history of nasal surgery, preoperative SNOT-22 score, sinusitis, middle turbinate plasty, BMI, smoking, follow-up time, seasonal allergies, and advanced age. Therefore, it can be cost-effective for individualized preoperative assessment.

MiR-1908/EXO1 and MiR-203a/FOS, regulated by scd1, are associated with fracture risk and bone health in postmenopausal diabetic women.

BACKGROUND: Stearoyl-coenzyme A desaturase-1 (SCD1) can inhibit the development of diabetic bone disease by promoting osteogenesis. In this study, we examined whether this regulation by SCD1 is achieved by regulating the expression of related miRNAs. METHODS: SCD1 expression levels were observed in human bone-marrow mesenchymal stem cells (BM-MSCs) of patients with type 2 diabetes mellitus (T2DM), and the effect of SCD1 on osteogenesis was observed in human adipose-derived MSCs transfected with the SCD1 lentiviral system. We designed a bioinformatics prediction model to select important differentially expressed miRNAs, and established protein-protein interaction and miRNA-mRNA networks. miRNAs and mRNAs were extracted and their differential expression was detected. The SCD1-miRNA-mRNA network was validated. FINDINGS: SCD1 expression in bone marrow was downregulated in patients with T2DM and low-energy fracture, and SCD1 expression promotes BM-MSC osteogenic differentiation. The predictors in the nomogram were seven microRNAs, including hsa-miR-1908 and hsa-miR-203a. SCD1 inhibited the expression of CDKN1A and FOS, but promoted the expression of EXO1 and PLS1. miR-1908 was a regulator of EXO1 expression, and miR-203a was a regulator of FOS expression. INTERPRETATION: The regulation of BM-MSCs by SCD1 is a necessary condition for osteogenesis through the miR-203a/FOS and miR-1908/EXO1 regulatory pathways.

Predicting the risk of sarcopenia in elderly patients with patellar fracture: development and assessment of a new predictive nomogram.

PURPOSE: To develop a risk prediction model for postoperative sarcopenia in elderly patients with patellar fractures in China. PATIENTS AND METHODS: We conducted a community survey of patients aged ≥55 years who underwent surgery for patellar fractures between January 2013 and October 2018, through telephone interviews, community visits, and outpatient follow-up. We established a predictive model for assessing the risk of sarcopenia after patellar fractures. We developed the prediction model by combining multivariate logistic regression analysis with the least absolute shrinkage model and selection operator regression (lasso analysis) as well as the Support Vector Machine (SVM) algorithm. The predictive quality and clinical utility of the predictive model were determined using C-index, calibration plots, and decision curve analysis. We also conducted internal sampling methods for qualitative assessment. RESULT: We recruited 137 participants (53 male; mean age, 65.7 years). Various risk factors were assessed, and low body mass index and advanced age were identified as the most important risk factor (P < 0.05). The prediction rate of the model was good (C-index: 0.88; 95% CI [0.80552-0.95448]), with a satisfactory correction effect. The C index is 0.97 in the validation queue and 0.894 in the entire cohort. Decision curve analysis suggested good clinical practicability. CONCLUSION: Our prediction model shows promise as a cost-effective tool for predicting the risk of postoperative sarcopenia in elderly patients based on the following: advanced age, low body mass index, diabetes, less outdoor exercise, no postoperative rehabilitation, different surgical methods, diabetes, open fracture, and removal of internal fixation.