MiR-6924-5p-rich exosomes derived from genetically modified Scleraxis-overexpressing PDGFRα(+) BMMSCs as novel nanotherapeutics for treating osteolysis during tendon-bone healing and improving healing strength.

Osteolysis at the tendon-bone interface can impair pullout strength during tendon-bone healing and lead to surgery failure, but the effects of clinical treatments are not satisfactory. Mesenchymal stem cell (MSC)-derived exosomes have been used as potent and feasible natural nanocarriers for drug delivery and have been proven to enhance tendon-bone healing strength, indicating that MSC-derived exosomes could be a promising therapeutic strategy. In this study, we explored Scleraxis (Scx) dynamically expressed in PDGFRα(+) bone marrow-derived mesenchymal stem cells (BMMSCs) during natural tendon-bone healing. Then, we investigated the role of PDGFRα(+) BMMSCs in tendon-bone healing after Scx overexpression as well as the underlying mechanisms. Our data demonstrated that Scx-overexpressing PDGFRα(+) BMMSCs (BMMSCScx) could efficiently inhibit peritunnel osteolysis and enhance tendon-bone healing strength by preventing osteoclastogenesis in an exosomes-dependent manner. Exosomal RNA-seq revealed that the abundance of a novel miRNA, miR-6924-5p, was highest among miRNAs. miR-6924-5p could directly inhibit osteoclast formation by binding to the 3'-untranslated regions (3'UTRs) of OCSTAMP and CXCL12. Inhibition of miR-6924-5p expression reversed the prevention of osteoclastogenic differentiation by BMMSCScx derived exosomes (BMMSCScx-exos). Local injection of BMMSCScx-exos or miR-6924-5p dramatically reduced osteoclast formation and improved tendon-bone healing strength. Furthermore, delivery of miR-6924-5p efficiently inhibited the osteoclastogenesis of human monocytes. In brief, our study demonstrates that BMMSCScx-exos or miR-6924-5p could serve as a potential therapy for the treatment of osteolysis during tendon-bone healing and improve the outcome.

Screening of key biomarkers of tendinopathy based on bioinformatics and machine learning algorithms.

Tendinopathy is a complex multifaceted tendinopathy often associated with overuse and with its high prevalence resulting in significant health care costs. At present, the pathogenesis and effective treatment of tendinopathy are still not sufficiently elucidated. The purpose of this research is to intensely explore the genes, functional pathways, and immune infiltration characteristics of the occurrence and development of tendinopathy. The gene expression profile of GSE106292, GSE26051 and GSE167226 are downloaded from GEO (NCBI comprehensive gene expression database) and analyzed by WGCNA software bag using R software, GSE26051, GSE167226 data set is combined to screen the differential gene analysis. We subsequently performed gene enrichment analysis of Gene Ontology (GO) and "Kyoto Encyclopedia of Genes and Genomes" (KEGG), and immune cell infiltration analysis. By constructing the LASSO regression model, Support vector machine (SVM-REF) and Gaussian mixture model (GMMs) algorithms are used to screen, to identify early diagnostic genes. We have obtained a total of 171 DEGs through WGCNA analysis and differentially expressed genes (DEGs) screening. By GO and KEGG enrichment analysis, it is found that these dysregulated genes were related to mTOR, HIF-1, MAPK, NF-κB and VEGF signaling pathways. Immune infiltration analysis showed that M1 macrophages, activated mast cells and activated NK cells had infiltration significance. After analysis of THE LASSO SVM-REF and GMMs algorithms, we found that the gene MACROD1 may be a gene for early diagnosis. We identified the potential of tendon disease early diagnosis way and immune gene regulation MACROD1 key infiltration characteristics based on comprehensive bioinformatics analysis. These hub genes and functional pathways may as early biomarkers of tendon injuries and molecular therapy level target is used to guide drug and basic research.

Application of machine learning models in predicting early stone-free rate after flexible ureteroscopic lithotripsy for renal stones / 北京大学学报(医学版)

OBJECTIVE@#To establish predictive models based on random forest and XGBoost machine learning algorithm and to investigate their value in predicting early stone-free rate (SFR) after flexible ureteroscopic lithotripsy (fURL) in patients with renal stones.@*METHODS@#The clinical data of 201 patients with renal stones who underwent fURL were retrospectively investigated. According to the stone-free standard, the patients were divided into stone-free group (SF group) and stone-residual group (SR group). We compared a number of factors including patient age, body mass index (BMI), stone number, stone volume, stone density and hydronephrosis between the two groups. For low calyceal calculi, renal anatomic parameters including infundibular angle (IPA), infundibular width (IW), infundibular length (IL) and pelvic calyceal height (PCH), would be measured. We brought above potential predictive factors into random forest and XGBoost machine learning algorithm respectively to develop two predictive models. The receiver operating characteristic curve (ROC curve) was established in order to test the predictive ability of the model. Clinical data of 71 patients were collected prospectively to validate the predictive models externally.@*RESULTS@#In this study, 201 fURL operations were successfully completed. The one-phase early SFR was 61.2%. We built two predictive models based on random forest and XGBoost machine learning algorithm. The predictive variables' importance scores were obtained. The area under the ROC curve (AUROC) of the two predictive models for early stone clearance status prediction was 0.77. In the study, 71 test samples were used for external validation. The results showed that the total predictive accuracy, predictive specificity and predictive sensitivity of the random forest and XGBoost models were 75.7%, 82.6%, 60.0%, and 81.4%, 87.0%, 68.0%, respectively. The first four predictive variables in importance were stone volume, mean stone density, maximal stone density and BMI in both random forest and XGBoost predictive models.@*CONCLUSION@#The predictive models based on random forest and XGBoost machine learning algorithm can predict postoperative early stone status after fURL for renal stones accurately, which will facilitate preoperative evaluation and clinical decision-making. Stone volume, mean stone density, maximal stone density and BMI may be the important predictive factors affecting early SFR after fURL for renal stones.

Outcomes after transanal endoscopic microsurgery for early rectal cancer and risk factors associated with recurrence / 中华胃肠外科杂志

<p><b>OBJECTIVE</b>To investigate outcomes after transanal endoscopic microsurgery (TEM) for early rectal cancer, identify risk factors associated with recurrence, and explore the indication of TEM for rectal cancer.</p><p><b>METHODS</b>Sixty patients with rectal cancer undergoing TEM between June 2006 and June 2009 in the Provincial Qianfoshan Hospital of Shandong University were included in this study and data were retrospectively analyzed.</p><p><b>RESULTS</b>There were 12 patients with pTis rectal cancer, 38 with pT1 and 10 with pT2. All the lesions were excised en bloc by full-thickness TEM. No positive resection margins were reported. The operative time was(65.0 ± 36.5) min. Estimated blood loss was (10.5 ± 5.8) ml and hospital stay was(4.5 ± 2.7) d. No perioperative mortality and complications occurred. The median follow-up was 28.5(range, 12-48) months. No recurrence developed in pTis lesions. There was significant difference in local recurrence rate between pT1 and pT2(2.6% vs. 40.0%, P<0.05). The recurrence rate in lesions larger than 3 cm in diameter(19.0%, 4/21) was significantly higher than that in lesions smaller than 3 cm in diameter (2.6%, 1/39) (P<0.05). Multivariate analysis showed that depth of tumour invasion(T stage) and tumour size were independently associated with recurrence after TEM.</p><p><b>CONCLUSION</b>Local excision by TEM is oncologically safe and effective for pTis and pT1 rectal cancers and early lesions smaller than 3 cm in diameter.</p>