[Expression and significance of hypoxia-inducible factor 1α and Bcl-2/adenovirus E1B19kDa-interacting protein 3 in children with traumatic brain injury]. / ç¼ºæ°§è¯±å¯¼å› å­1α、Bcl-2/è ºç— æ¯’E1B19kDa相互作用蛋白3在儿童创伤性脑损伤中的表达及意义.

OBJECTIVES: To dynamically observe the changes in hypoxia-inducible factor 1α (HIF-1α) and Bcl-2/adenovirus E1B19kDa-interacting protein 3 (BNIP3) in children with traumatic brain injury (TBI) and evaluate their clinical value in predicting the severity and prognosis of pediatric TBI. METHODS: A prospective study included 47 children with moderate to severe TBI from January 2021 to July 2023, categorized into moderate (scores 9-12) and severe (scores 3-8) subgroups based on the Glasgow Coma Scale. A control group consisted of 30 children diagnosed and treated for inguinal hernia during the same period, with no underlying diseases. The levels of HIF-1α, BNIP3, autophagy-related protein Beclin-1, and S100B were compared among groups. The predictive value of HIF-1α, BNIP3, Beclin-1, and S100B for the severity and prognosis of TBI was assessed using receiver operating characteristic (ROC) curves. RESULTS: Serum levels of HIF-1α, BNIP3, Beclin-1, and S100B in the TBI group were higher than those in the control group (P<0.05). Among the TBI patients, the severe subgroup had higher levels of HIF-1α, BNIP3, Beclin-1, and S100B than the moderate subgroup (P<0.05). Correlation analysis showed that the serum levels of HIF-1α, BNIP3, Beclin-1, and S100B were negatively correlated with the Glasgow Coma Scale scores (P<0.05). After 7 days of treatment, serum levels of HIF-1α, BNIP3, Beclin-1, and S100B in both non-surgical and surgical TBI patients decreased compared to before treatment (P<0.05). ROC curve analysis indicated that the areas under the curve for predicting severe TBI based on serum levels of HIF-1α, BNIP3, Beclin-1, and S100B were 0.782, 0.835, 0.872, and 0.880, respectively (P<0.05), and for predicting poor prognosis of TBI were 0.749, 0.775, 0.814, and 0.751, respectively (P<0.05). CONCLUSIONS: Serum levels of HIF-1α, BNIP3, and Beclin-1 are significantly elevated in children with TBI, and their measurement can aid in the clinical assessment of the severity and prognosis of pediatric TBI.

SiSGC: A Drug Repositioning Prediction Model Based on Heterogeneous Simplifying Graph Convolution.

Drug repositioning plays a key role in disease treatment. With the large-scale chemical data increasing, many computational methods are utilized for drug-disease association prediction. However, most of the existing models neglect the positive influence of non-Euclidean data and multisource information, and there is still a critical issue for graph neural networks regarding how to set the feature diffuse distance. To solve the problems, we proposed SiSGC, which makes full use of the biological knowledge information as initial features and learns the structure information from the constructed heterogeneous graph with the adaptive selection of the information diffuse distance. Then, the structural features are fused with the denoised similarity information and fed to the advanced classifier of CatBoost to make predictions. Three different data sets are used to confirm the robustness and generalization of SiSGC under two splitting strategies. Experiment results demonstrate that the proposed model achieves superior performance compared with the six leading methods and four variants. Our case study on breast neoplasms further indicates that SiSGC is trustworthy and robust yet simple. We also present four drugs for breast cancer treatment with high confidence and further give an explanation for demonstrating the rationality. There is no doubt that SiSGC can be used as a beneficial supplement for drug repositioning.

GSLCDA: An Unsupervised Deep Graph Structure Learning Method for Predicting CircRNA-Disease Association.

Growing studies reveal that Circular RNAs (circRNAs) are broadly engaged in physiological processes of cell proliferation, differentiation, aging, apoptosis, and are closely associated with the pathogenesis of numerous diseases. Clarification of the correlation among diseases and circRNAs is of great clinical importance to provide new therapeutic strategies for complex diseases. However, previous circRNA-disease association prediction methods rely excessively on the graph network, and the model performance is dramatically reduced when noisy connections occur in the graph structure. To address this problem, this paper proposes an unsupervised deep graph structure learning method GSLCDA to predict potential CDAs. Concretely, we first integrate circRNA and disease multi-source data to constitute the CDA heterogeneous network. Then the network topology is learned using the graph structure, and the original graph is enhanced in an unsupervised manner by maximize the inter information of the learned and original graphs to uncover their essential features. Finally, graph space sensitive k-nearest neighbor (KNN) algorithm is employed to search for latent CDAs. In the benchmark dataset, GSLCDA obtained 92.67% accuracy with 0.9279 AUC. GSLCDA also exhibits exceptional performance on independent datasets. Furthermore, 14, 12 and 14 of the top 16 circRNAs with the most points GSLCDA prediction scores were confirmed in the relevant literature in the breast cancer, colorectal cancer and lung cancer case studies, respectively. Such results demonstrated that GSLCDA can validly reveal underlying CDA and offer new perspectives for the diagnosis and therapy of complex human diseases.

MAGCDA: A Multi-Hop Attention Graph Neural Networks Method for CircRNA-Disease Association Prediction.

With a growing body of evidence establishing circular RNAs (circRNAs) are widely exploited in eukaryotic cells and have a significant contribution in the occurrence and development of many complex human diseases. Disease-associated circRNAs can serve as clinical diagnostic biomarkers and therapeutic targets, providing novel ideas for biopharmaceutical research. However, available computation methods for predicting circRNA-disease associations (CDAs) do not sufficiently consider the contextual information of biological network nodes, making their performance limited. In this work, we propose a multi-hop attention graph neural network-based approach MAGCDA to infer potential CDAs. Specifically, we first construct a multi-source attribute heterogeneous network of circRNAs and diseases, then use a multi-hop strategy of graph nodes to deeply aggregate node context information through attention diffusion, thus enhancing topological structure information and mining data hidden features, and finally use random forest to accurately infer potential CDAs. In the four gold standard data sets, MAGCDA achieved prediction accuracy of 92.58%, 91.42%, 83.46% and 91.12%, respectively. MAGCDA has also presented prominent achievements in ablation experiments and in comparisons with other models. Additionally, 18 and 17 potential circRNAs in top 20 predicted scores for MAGCDA prediction scores were confirmed in case studies of the complex diseases breast cancer and Almozheimer's disease, respectively. These results suggest that MAGCDA can be a practical tool to explore potential disease-associated circRNAs and provide a theoretical basis for disease diagnosis and treatment.

Anterior column realignment via a minimally invasive hybrid approach in adult spinal deformity surgery: a short-term retrospective study.

BACKGROUND: Anterior column realignment (ACR) is a novel surgical method for correcting spinal sagittal balance. meanwhile, oblique lumbar interbody fusion (OLIF) and anterior lumbar interbody fusion (ALIF) are considered minimally invasive surgical methods through natural anatomical space. This study aimed to explore the corrective effects and clinical outcomes of OLIF or ALIF combined with ACR technology in patients with adult spinal deformity (ASD). METHODS: We retrospectively analyzed patients with sagittal imbalance who received OLIF and/or ALIF and ACR treatment from 2018 to 2021. Surgical time and intraoperative bleeding volume are recorded, the corrective effect is determined by the intervertebral space angle (IVA), lumbar lordosis (LL), the sagittal vertical axis (SVA), clinical outcome is evaluated by preoperative and final follow-up visual analog pain score (VAS), Japanese orthopedic association scores (JOA) and complications. RESULTS: Sixty-four patients were enrolled in the study, average age of 65.1(range, 47-82) years. All patients completed 173 fusion segments, for 150 segments of ACR surgery. The operation time of ALIF-ACR was 50.4 ± 22.1 min; The intraoperative bleeding volume was 50.2 ± 23.6 ml. The operation time and intraoperative bleeding volume of single-segment OLIF-ACR was 66.2 ± 19.4 min and 70.2 ± 31.6 ml. At the follow-up of 6 months after surgery, the intervertebral space angle correction for OLIF-ACR and ALIF-ACR is 9.2° and 12.2°, the preoperative and postoperative lumbar lordosis were 16.7° ± 6.4°and 47.1° ± 3.6° (p < 0.001), VAS and JOA scores were improved from 6.8 to 1.8 and 7.8 to 22.1 respectively, statistically significant differences were observed in these parameters. The incidence of surgical related complications is 29.69%, but without serious complications. CONCLUSION: ACR via a minimally invasive hybrid approach for ASD has significant advantages in restoring local intervertebral space angulation and correcting the overall sagittal balance. Simultaneously, it can achieve good clinical outcomes and fewer surgical complications.

KS-CMI: A circRNA-miRNA interaction prediction method based on the signed graph neural network and denoising autoencoder.

Circular RNA (circRNA) plays an important role in the diagnosis, treatment, and prognosis of human diseases. The discovery of potential circRNA-miRNA interactions (CMI) is of guiding significance for subsequent biological experiments. Limited by the small amount of experimentally supported data and high randomness, existing models are difficult to accomplish the CMI prediction task based on real cases. In this paper, we propose KS-CMI, a novel method for effectively accomplishing CMI prediction in real cases. KS-CMI enriches the 'behavior relationships' of molecules by constructing circRNA-miRNA-cancer (CMCI) networks and extracts the behavior relationship attribute of molecules based on balance theory. Next, the denoising autoencoder (DAE) is used to enhance the feature representation of molecules. Finally, the CatBoost classifier was used for prediction. KS-CMI achieved the most reliable prediction results in real cases and achieved competitive performance in all datasets in the CMI prediction.

SPRDA: a link prediction approach based on the structural perturbation to infer disease-associated Piwi-interacting RNAs.

piRNA and PIWI proteins have been confirmed for disease diagnosis and treatment as novel biomarkers due to its abnormal expression in various cancers. However, the current research is not strong enough to further clarify the functions of piRNA in cancer and its underlying mechanism. Therefore, how to provide large-scale and serious piRNA candidates for biological research has grown up to be a pressing issue. In this study, a novel computational model based on the structural perturbation method is proposed to predict potential disease-associated piRNAs, called SPRDA. Notably, SPRDA belongs to positive-unlabeled learning, which is unaffected by negative examples in contrast to previous approaches. In the 5-fold cross-validation, SPRDA shows high performance on the benchmark dataset piRDisease, with an AUC of 0.9529. Furthermore, the predictive performance of SPRDA for 10 diseases shows the robustness of the proposed method. Overall, the proposed approach can provide unique insights into the pathogenesis of the disease and will advance the field of oncology diagnosis and treatment.

Predicting MiRNA-Disease Associations by Graph Representation Learning Based on Jumping Knowledge Networks.

Growing studies have shown that miRNAs are inextricably linked with many human diseases, and a great deal of effort has been spent on identifying their potential associations. Compared with traditional experimental methods, computational approaches have achieved promising results. In this article, we propose a graph representation learning method to predict miRNA-disease associations. Specifically, we first integrate the verified miRNA-disease associations with the similarity information of miRNA and disease to construct a miRNA-disease heterogeneous graph. Then, we apply a graph attention network to aggregate the neighbor information of nodes in each layer, and then feed the representation of the hidden layer into the structure-aware jumping knowledge network to obtain the global features of nodes. The output features of miRNAs and diseases are then concatenated and fed into a fully connected layer to score the potential associations. Through five-fold cross-validation, the average AUC, accuracy and precision values of our model are 93.30%, 85.18% and 88.90%, respectively. In addition, for three case studies of the esophageal tumor, lymphoma and prostate tumor, 46, 45 and 45 of the top 50 miRNAs predicted by our model were confirmed by relevant databases. Overall, our method could provide a reliable alternative for miRNA-disease association prediction.

Hidden blood loss of minimally invasive hybrid lumbar interbody fusion: an analysis of influencing factors.

BACKGROUND: Lumbar interbody fusion(LIF) is the leading way to treat Lumbar Degenerative Diseases(LDD). At present, there is a lack of research on the influencing factors of hidden blood loss in minimally invasive hybrid lumbar interbody fusion. This study comprehensively explores the definite factors affecting the hidden blood loss in minimally invasive hybrid lumbar interbody fusion. MATERIALS AND METHODS: One hundred patients with Lumbar degenerative diseases who underwent minimally invasive hybrid lumbar interbody fusion in our center were included. Demographics, laboratory data, surgical data, and radiographic data were collected. The Gross equation and Sehat equation were used to calculate the estimated value of hidden blood loss. Multi-factor linear regression analysis was used to determine the influencing factors of hidden blood loss. RESULT: We reviewed and collected 100 patients who underwent minimally invasive hybrid approach, mean age 65 ± 10 years, male: female 37:63; 17 patients of diabetes and 83 patients of non-diabetes; Total blood loss was 645.59 ± 376.37 ml, hidden blood loss was 421.39 ± 337.45 ml, the hidden blood loss percentage was 57 ± 26%. Results from the multi-factorial linear regression model: Diabetes (p < 0.05), hypertension (p < 0.05), psoas thickness (p < 0.05) and dorsal extensor group thickness (p < 0.05) were potential risk factors for postoperative hidden blood loss. CONCLUSION: Although minimally invasive hybrid approach is minimally invasive surgery, there is still a significant amount of hidden blood loss. There is a greater risk of blood loss in diabetes, hypertension and preoperative MRI assessment of thickness of the psoas, thickness of the dorsal extensor group.

Combined Endovascular and Surgical Treatment for Brain Arteriovenous Malformations in Biplanar Hybrid Operating Room.

OBJECTIVE: Combined surgical and endovascular treatment for vascular disorders has become prevalent in recent years. However, reports on one-session hybrid surgery for arteriovenous malformations (AVMs) are relatively rare. The safety and efficiency of combined treatment for brain AVMs were analyzed in biplanar hybrid operating room (OR) at one stage. METHODS: We retrospectively analyzed 20 patients with AVMs undergoing combined surgical and endovascular treatment from October 2015 to June 2018. The data for resection rate, microcatheter adhesion, surgical position and postoperative outcomes were analyzed. Total resection or near-total resection was achieved in all cases. RESULTS: A total of 13 patients were under combined endovascular and surgical procedures, and 7 experienced surgery with intraoperative digital subtraction angiography. Sitting position was applied in 3 of them; 2 niduses in cerebellum, and 1 in parietal lobe. Compared with admission modified Rankin Scale (mRS) in all patients, postoperative 12-month mRS showed a significant decline. Besides, 3 patients experienced microcatheter adhesion after endovascular embolization, thereafter underwent surgical adhesion removal while nidus resection was done. CONCLUSION: Combined endovascular and surgical modality in a hybrid OR at one stage provides a safe strategy for the treatment of AVMs. The biplanar hybrid neurointerventional suite is endowed with unconstrained operating angle which enables combined endovascular and surgical treatment in sitting position. It also reduces the risk of microcatheter adhesion, which enables interventional radiologists to perform aggressively.

DANE-MDA: Predicting microRNA-disease associations via deep attributed network embedding.

Predicting the microRNA-disease associations by using computational methods is conductive to the efficiency of costly and laborious traditional bio-experiments. In this study, we propose a computational machine learning-based method (DANE-MDA) that preserves integrated structure and attribute features via deep attributed network embedding to predict potential miRNA-disease associations. Specifically, the integrated features are extracted by using deep stacked auto-encoder on the diverse orders of matrixes containing structure and attribute information and are then trained by using random forest classifier. Under 5-fold cross-validation experiments, DANE-MDA yielded average accuracy, sensitivity, and AUC at 85.59%, 84.23%, and 0.9264 in term of HMDD v3.0 dataset, and 83.21%, 80.39%, and 0.9113 in term of HMDD v2.0 dataset, respectively. Additionally, case studies on breast, colon, and lung neoplasms related disease show that 47, 47, and 46 of the top 50 miRNAs can be predicted and retrieved in the other database.

DF-MDA: An effective diffusion-based computational model for predicting miRNA-disease association.

It is reported that microRNAs (miRNAs) play an important role in various human diseases. However, the mechanisms of miRNA in these diseases have not been fully understood. Therefore, detecting potential miRNA-disease associations has far-reaching significance for pathological development and the diagnosis and treatment of complex diseases. In this study, we propose a novel diffusion-based computational method, DF-MDA, for predicting miRNA-disease association based on the assumption that molecules are related to each other in human physiological processes. Specifically, we first construct a heterogeneous network by integrating various known associations among miRNAs, diseases, proteins, long non-coding RNAs (lncRNAs), and drugs. Then, more representative features are extracted through a diffusion-based machine-learning method. Finally, the Random Forest classifier is adopted to classify miRNA-disease associations. In the 5-fold cross-validation experiment, the proposed model obtained the average area under the curve (AUC) of 0.9321 on the HMDD v3.0 dataset. To further verify the prediction performance of the proposed model, DF-MDA was applied in three significant human diseases, including lymphoma, lung neoplasms, and colon neoplasms. As a result, 47, 46, and 47 out of top 50 predictions were validated by independent databases. These experimental results demonstrated that DF-MDA is a reliable and efficient method for predicting potential miRNA-disease associations.

LncRNA MALAT1 facilitates lung metastasis of osteosarcomas through miR-202 sponging.

Lungs are the primary metastatic sites for osteosarcomas responsible for associated mortality. Recent data has documented role of long non-coding RNAs (lncRNAs) in proliferation and growth of osteosarcoma cells. We evaluated a role of lncRNAs in the lung metastasis of osteosarcoma with the goal of identifying a unique signature. Comparison of different lncRNAs in tumor samples from osteosarcoma with and without lung metastasis led to identification of MALAT1 as the most differentially upregulated lncRNA in the osteosarcoma patients with lung metastasis. MALAT1 was also high in osteosarcoma cells KRIB and MALAT1's targeted downregulation in these cells led to decreased invasive potential and identification of miR-202 as the miRNA that is sponged by MALAT1. In the lung metastasis in vivo model, parental KRIB cells metastasized to lungs and such metastasis was significantly inhibited in KRIB cells with downregulated MALAT1. Ectopic miR-202 expression attenuated KRIB downregulation-mediated effects on lung metastasis. In yet another in vivo model involving parental SAOS-2 and lung-metastatic derivatives SAOS-2-LM, MALAT1 expression was found to be elevated in lung metastatic cells, which also correlated with reduced miR-202. In conclusion, MALAT1-miR-202 represents a potential lncRNA-miRNA signature that affects lung metastasis of osteosarcomas and could potentially be targeted for therapy.

DBMDA: A Unified Embedding for Sequence-Based miRNA Similarity Measure with Applications to Predict and Validate miRNA-Disease Associations.

MicroRNAs (miRNAs) play a critical role in human diseases. Determining the association between miRNAs and disease contributes to elucidating the pathogenesis of liver diseases and seeking the effective treatment method. Despite great recent advances in the field of the associations between miRNAs and diseases, implementing association verification and recognition efficiently at scale presents serious challenges to biological experimental approaches. Thus, computational methods for predicting miRNA-disease association have become a research hotspot. In this paper, we present a new computational method, named distance-based sequence similarity for miRNA-disease association prediction (DBMDA), that directly learns a mapping from miRNA sequence to a Euclidean space. The notable feature of our approach consists of inferring global similarity from region distances that can be figured by chaos game representation algorithm based on the miRNA sequences. In the 5-fold cross-validation experiment, the area under the curve (AUC) obtained by DBMDA in predicting potential miRNA-disease associations reached 0.9129. To assess the effectiveness of DBMDA more effectively, we compared it with different classifiers and former prediction models. Besides, we constructed two case studies for prostate neoplasms and colon neoplasms. Results show that 39 and 39 out of the top 40 predicted miRNAs were confirmed by other databases, respectively. BDMDA has made new attempts in sequence similarity and achieved excellent results, while at the same time providing a new perspective for predicting the relationship between diseases and miRNAs. The source code and datasets explored in this work are available online from the University of Chinese Academy of Sciences (http://220.171.34.3:81/).

Obturator dislocation of the femoral head combined with ipsilateral femoral neck and pubic fractures: A rare case report.

RATIONALE: Obturator dislocation of the femoral head combined with ipsilateral femoral neck and pubic fracture is a rare injury. We used cannulated screws combined with a femoral neck medial plate for the first time to treat this type of injury and achieved good follow-up results. PATIENT CONCERNS: The patient was hospitalized due to an accident resulting in swelling and deformity accompanied by limited mobility of the right hip and left knee. DIAGNOSES: X-ray examination and computed tomography confirmed that the patient suffered from right hip obturator dislocation, femoral head and neck fracture, pubic fracture, as well as open fracture of the left femoral shaft. INTERVENTIONS: Open reduction and internal fixation with cannulated screws and a medial support plate of the femoral neck were performed for the right hip obturator dislocation, femoral head and neck fracture, and the pubic fracture. Artificial bone grafting was performed to support the femoral head defect. Debridement and the second phase of internal fixation were executed to cure the open fracture of the left femoral shaft. OUTCOMES: The patient was followed-up for 6 months and showed good hip function. X-ray examination and computed tomography indicated that the fractures healed well without fracture displacement or loosening of the implants. Meanwhile, there were no signs of femoral neck valgus and femoral head necrosis observed. LESSONS: The combination of cannulated screws and medial support plate was suggested for the treatment of hip obturator dislocation and femoral head and neck fracture. Furthermore, partial weight loading exercise should be performed promptly to reduce the risk of muscular atrophy and myasthenia.

MLMDA: a machine learning approach to predict and validate MicroRNA-disease associations by integrating of heterogenous information sources.

BACKGROUND: Emerging evidences show that microRNA (miRNA) plays an important role in many human complex diseases. However, considering the inherent time-consuming and expensive of traditional in vitro experiments, more and more attention has been paid to the development of efficient and feasible computational methods to predict the potential associations between miRNA and disease. METHODS: In this work, we present a machine learning-based model called MLMDA for predicting the association of miRNAs and diseases. More specifically, we first use the k-mer sparse matrix to extract miRNA sequence information, and combine it with miRNA functional similarity, disease semantic similarity and Gaussian interaction profile kernel similarity information. Then, more representative features are extracted from them through deep auto-encoder neural network (AE). Finally, the random forest classifier is used to effectively predict potential miRNA-disease associations. RESULTS: The experimental results show that the MLMDA model achieves promising performance under fivefold cross validations with AUC values of 0.9172, which is higher than the methods using different classifiers or different feature combination methods mentioned in this paper. In addition, to further evaluate the prediction performance of MLMDA model, case studies are carried out with three Human complex diseases including Lymphoma, Lung Neoplasm, and Esophageal Neoplasms. As a result, 39, 37 and 36 out of the top 40 predicted miRNAs are confirmed by other miRNA-disease association databases. CONCLUSIONS: These prominent experimental results suggest that the MLMDA model could serve as a useful tool guiding the future experimental validation for those promising miRNA biomarker candidates. The source code and datasets explored in this work are available at http://220.171.34.3:81/ .

microRNA-4500 inhibits human glioma cell progression by targeting IGF2BP1.

Insulin-like growth factor 2 mRNA-binding protein 1 (IGF2BP1) overexpression promotes glioma cell progression. The aim of the current study is to silence IGF2BP1 in glioma cells by the microRNA (miRNA) strategy. The bio-informatic analyses identified that microRNA-4500 (miR-4500) putatively targets 3'-UTR (3'-untranslated region) of IGF2BP1. In A172 cells and primary human glioma cells ectopic overexpression of the wild-type miR-4500 (but not the mutant form) downregulated IGF2BP1 and its target genes (Gli1, IGF2 and c-Myc). Functional studies show that ectopic miR-4500 overexpression inhibited glioma cell growth, survival, proliferation, migration and invasion. Conversely, in A172 cells miR-4500 inhibition, by a lentiviral construct, increased expression of IGF2BP1 and its targets, promoting cell survival, proliferation and migration. Furthermore, IGF2BP1 knockout by the CRISPR/Cas9 method inhibited A172 cell progression. Significantly, miR-4500 overexpression or miR-4500 inhibition was ineffective in IGF2BP1 knockout A172 cells. At last, we show that miR-4500 levels are downregulated in human glioma tissues, correlating with IGF2BP1 upregulation. Together, we conclude that miR-4500 inhibits human glioma cell progression by targeting IGF2BP1.

DRMDA: deep representations-based miRNA-disease association prediction.

Recently, microRNAs (miRNAs) are confirmed to be important molecules within many crucial biological processes and therefore related to various complex human diseases. However, previous methods of predicting miRNA-disease associations have their own deficiencies. Under this circumstance, we developed a prediction method called deep representations-based miRNA-disease association (DRMDA) prediction. The original miRNA-disease association data were extracted from HDMM database. Meanwhile, stacked auto-encoder, greedy layer-wise unsupervised pre-training algorithm and support vector machine were implemented to predict potential associations. We compared DRMDA with five previous classical prediction models (HGIMDA, RLSMDA, HDMP, WBSMDA and RWRMDA) in global leave-one-out cross-validation (LOOCV), local LOOCV and fivefold cross-validation, respectively. The AUCs achieved by DRMDA were 0.9177, 08339 and 0.9156 ± 0.0006 in the three tests above, respectively. In further case studies, we predicted the top 50 potential miRNAs for colon neoplasms, lymphoma and prostate neoplasms, and 88%, 90% and 86% of the predicted miRNA can be verified by experimental evidence, respectively. In conclusion, DRMDA is a promising prediction method which could identify potential and novel miRNA-disease associations.

PRMDA: personalized recommendation-based MiRNA-disease association prediction.

Recently, researchers have been increasingly focusing on microRNAs (miRNAs) with accumulating evidence indicating that miRNAs serve as a vital role in various biological processes and dysfunctions of miRNAs are closely related with human complex diseases. Predicting potential associations between miRNAs and diseases is attached considerable significance in the domains of biology, medicine, and bioinformatics. In this study, we developed a computational model of Personalized Recommendation-based MiRNA-Disease Association prediction (PRMDA) to predict potential related miRNA for all diseases by implementing personalized recommendation-based algorithm based on integrated similarity for diseases and miRNAs. PRMDA is a global method capable of prioritizing candidate miRNAs for all diseases simultaneously. Moreover, the model could be applied to diseases without any known associated miRNAs. PRMDA obtained AUC of 0.8315 based on leave-one-out cross validation, which demonstrated that PRMDA could be regarded as a reliable tool for miRNA-disease association prediction. Besides, we implemented PRMDA on the HMDD V1.0 and HMDD V2.0 databases for three kinds of case studies about five important human cancers in order to test the performance of the model from different perspectives. As a result, 92%, 94%, 88%, 96% and 88% out of the top 50 candidate miRNAs predicted by PRMDA for Colon Neoplasms, Esophageal Neoplasms, Lymphoma, Lung Neoplasms and Breast Neoplasms, respectively, were confirmed by experimental reports.

microRNA-135b expression silences Ppm1e to provoke AMPK activation and inhibit osteoblastoma cell proliferation.

Forced-activation of AMP-activated protein kinase (AMPK) can possibly inhibit osteoblastoma cells. Here, we aim to provoke AMPK activation via microRNA silencing its phosphatase Ppm1e (protein phosphatase Mg2+/Mn2+-dependent 1e). We showed that microRNA-135b-5p ("miR-135b-5p"), the anti-Ppm1e microRNA, was significantly downregulated in human osteoblastoma tissues. It was correlated with Ppm1e upregulation and AMPKα1 de-phosphorylation. Forced-expression of miR-135b-5p in human osteoblastoma cells (MG-63 and U2OS lines) silenced Ppm1e, and induced a profound AMPKα1 phosphorylation (at Thr-172). Osteoblastoma cell proliferation was inhibited after miR-135b-5p expression. Intriguingly, Ppm1e shRNA knockdown similarly induced AMPKα1 phosphorylation, causing osteoblastoma cell proliferation. Reversely, AMPKα1 shRNA knockdown or dominant negative mutation almost abolished miR-135b-5p's actions in osteoblastoma cells. Further in vivo studies demonstrated that U2OS tumor growth in mice was dramatically inhibited after expressing miR-135b-5p or Ppm1e shRNA. Together, our results suggest that miR-135b-induced Ppm1e silence induces AMPK activation to inhibit osteoblastoma cell proliferation.