[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.

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.

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.

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.

An efficient circRNA-miRNA interaction prediction model by combining biological text mining and wavelet diffusion-based sparse network structure embedding.

MOTIVATION: Accumulating clinical evidence shows that circular RNA (circRNA) plays an important regulatory role in the occurrence and development of human diseases, which is expected to provide a new perspective for the diagnosis and treatment of related diseases. Using computational methods can provide high probability preselection for wet experiments to save resources. However, due to the lack of neighborhood structure in sparse biological networks, the model based on network embedding and graph embedding is difficult to achieve ideal results. RESULTS: In this paper, we propose BioDGW-CMI, which combines biological text mining and wavelet diffusion-based sparse network structure embedding to predict circRNA-miRNA interaction (CMI). In detail, BioDGW-CMI first uses the Bidirectional Encoder Representations from Transformers (BERT) for biological text mining to mine hidden features in RNA sequences, then constructs a CMI network, obtains the topological structure embedding of nodes in the network through heat wavelet diffusion patterns. Next, the Denoising autoencoder organically combines the structural features and Gaussian kernel similarity, finally, the feature is sent to lightGBM for training and prediction. BioDGW-CMI achieves the highest prediction performance in all three datasets in the field of CMI prediction. In the case study, all the 8 pairs of CMI based on circ-ITCH were successfully predicted. AVAILABILITY: The data and source code can be found at https://github.com/1axin/BioDGW-CMI-model.

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.

Experimental Realization of Weyl Exceptional Rings in a Synthetic Three-Dimensional Non-Hermitian Phononic Crystal.

Weyl points-topological monopoles of quantized Berry flux-are predicted to spread to Weyl exceptional rings in the presence of non-Hermiticity. Here, we use a one-dimensional Aubry-Andre-Harper model to construct a Weyl semimetal in a three-dimensional parameter space comprising one reciprocal dimension and two synthetic dimensions. The inclusion of non-Hermiticity in the form of gain and loss produces a synthetic Weyl exceptional ring (SWER). The topology of the SWER is characterized by both its topological charge and non-Hermitian winding numbers. We experimentally observe the SWER and synthetic Fermi arc in a one-dimensional phononic crystal with the non-Hermiticity introduced by active acoustic components. Our findings pave the way for studying the high-dimensional non-Hermitian topological physics in acoustics.

iGRLCDA: identifying circRNA-disease association based on graph representation learning.

While the technologies of ribonucleic acid-sequence (RNA-seq) and transcript assembly analysis have continued to improve, a novel topology of RNA transcript was uncovered in the last decade and is called circular RNA (circRNA). Recently, researchers have revealed that they compete with messenger RNA (mRNA) and long noncoding for combining with microRNA in gene regulation. Therefore, circRNA was assumed to be associated with complex disease and discovering the relationship between them would contribute to medical research. However, the work of identifying the association between circRNA and disease in vitro takes a long time and usually without direction. During these years, more and more associations were verified by experiments. Hence, we proposed a computational method named identifying circRNA-disease association based on graph representation learning (iGRLCDA) for the prediction of the potential association of circRNA and disease, which utilized a deep learning model of graph convolution network (GCN) and graph factorization (GF). In detail, iGRLCDA first derived the hidden feature of known associations between circRNA and disease using the Gaussian interaction profile (GIP) kernel combined with disease semantic information to form a numeric descriptor. After that, it further used the deep learning model of GCN and GF to extract hidden features from the descriptor. Finally, the random forest classifier is introduced to identify the potential circRNA-disease association. The five-fold cross-validation of iGRLCDA shows strong competitiveness in comparison with other excellent prediction models at the gold standard data and achieved an average area under the receiver operating characteristic curve of 0.9289 and an area under the precision-recall curve of 0.9377. On reviewing the prediction results from the relevant literature, 22 of the top 30 predicted circRNA-disease associations were noted in recent published papers. These exceptional results make us believe that iGRLCDA can provide reliable circRNA-disease associations for medical research and reduce the blindness of wet-lab experiments.

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.

Treatment for subtrochanteric fracture and subsequent nonunion in an adult patient with osteopetrosis: A case report and review of the literature.

BACKGROUND: As a congenital metabolic bone disease caused by defective osteoclastic resorption of immature bone, osteopetrosis is characterized by diffused sclerosis of bones, brittle bones, easy fracturing, narrow medullary canals, and a weak fracture healing ability. At present, clear standards and principles for the treatment of fractures in patients with osteopetrosis are lacking. Non-operative treatment can prevent fracture hematoma and preserve the blood supply to the bone fragments, while being associated with frequent failures and higher mortality rates. Meanwhile, closed reduction and internal fixation with intramedullary nail (CRIF + IMN) approaches can also protect blood supply to the fracture site. However, IMN cannot be used for the vast majority of patients with osteopetrosis due to the narrowing of medullary canals. Thus, open reduction and internal fixation with plate remains the most appropriate surgical method for treating fractures in patients with osteopetrosis, but this approach is complicated by the lack of intramedullary hematopoiesis in such patients. Fracture healing primarily depends on the blood supply to the external periosteum. Open reduction can also easily destroy the periosteum and cause delayed fracture healing or even nonunion; however, CRIF may be the most practical approach. As a result, it would be prudent to solve the difficulty of drilling during the operation and the problem of postoperative nonunion. CASE SUMMARY: In 2018, we treated an adult patient with osteopetrosis presenting with a subtrochanteric fracture. The fracture was fixed using a femoral locking compression plate. Because of delayed consolidation, at 12 mo postoperatively the patient was further treated with platelet-rich plasma (PRP) combined with radial extracorporeal shock wave therapy (rESWT). Antero-posterior and lateral radiographs obtained at the latest follow-up (10 mo) showed that the callus had grown at the original fracture site, and the medial fracture line almost disappeared. CONCLUSION: Osteosynthesis remains the first choice of treatment approach for fractures in patients with osteopetrosis, especially peritrochanteric fractures. Preoperative preparation is necessary to avoid risks such as drill bit breakage and iatrogenic fracture during the operation. Moreover, fractures in a patient with osteopetrosis present with a high risk of delayed union and nonunion, which can be potentially cured with PRP + rESWT.

Robust table recognition for printed document images.

The recognition and analysis of tables on printed document images is a popular research field of the pattern recognition and image processing. Existing table recognition methods usually require high degree of regularity, and the robustness still needs significant improvement. This paper focuses on a robust table recognition system that mainly consists of three parts: Image preprocessing, cell location based on contour mutual exclusion, and recognition of printed Chinese characters based on deep learning network. A table recognition app has been developed based on these proposed algorithms, which can transform the captured images to editable text in real time. The effectiveness of the table recognition app has been verified by testing a dataset of 105 images. The corresponding test results show that it could well identify high-quality tables, and the recognition rate of low-quality tables with distortion and blur reaches 81%, which is considerably higher than those of the existing methods. The work in this paper could give insights into the application of the table recognition and analysis algorithms.

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.