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Abstract Myodural bridge (MDB) is a dense connective tissue between suboccipital muscle and dura mater. However, there are few reports on the development and maturation of the human MDB. This study aims to explore the developmental relationship between suboccipital muscle and MDB. 30 head and neck specimens from human fetuses (F) ranging from the 12th to 41st week (W) were made into histological sections. The F12W sections showed evidence that the dura mater dominated by fibroblasts, attached to the posterior atlanto-axial membrane (PAAM) which completely sealed the atlanto-axial space. In the F13W stage, myofibrils of the suboccipital muscle fibers increased significantly in number. At the F14W stage, a gap was observed at the caudal end of the PAAM. Numerous myodural bridge-like structures were observed blending into the dura mater through the gap. At the F19W stage, muscle cells mature. Starting at the F21W stage, the MDB were observed as fibroblasts that cross the atlanto-axial interspace and attach to the dura mater. Therefore, the traction generated by the suboccipital muscles seems to promote the maturity of MDB. This study will provide new morphological knowledge to support future research on the function of the human MDB and regulating the development mechanism of MDB.

Abstract State-of-the-art medical studies proved that long noncoding ribonucleic acids (lncRNAs) are closely related to various diseases. However, their large-scale detection in biological experiments is problematic and expensive. To aid screening and improve the efficiency of biological experiments, this study introduced a prediction model based on the nearest neighbor concept for lncRNA–disease association prediction. We used a new similarity algorithm in the model that fused potential associations. The experimental validation of the proposed algorithm proved its superiority over the available Cosine, Pearson, and Jaccard similarity algorithms. Satisfactory results in the comparative leave-one-out cross-validation test (with AUC = 0.96) confirmed its excellent predictive performance. Finally, the proposed model’s reliability was confirmed by performing predictions using a new dataset, yielding AUC = 0.92.

Abstract Computer aided research of lncRNA-disease association is an important way to study the development of lncRNA-disease. The correlation analysis of existing data, the establishment of prediction model, prediction of unknown lncRNA-disease association, can make the biological experiment targeted, improve the accuracy of biological experiment. In this paper, a lncRNA-disease association prediction model based on latent factor model and projection is proposed (LFMP). This method uses lncRNA-miRNA association data and miRNA-disease association data to predict the unknown lncRNA-disease association, so this method does not need lncRNA-disease association data. The simulation results show that under the LOOCV framework, the AUC of LFMP can reach 0.8964. Better than the latest results. Through the case study of lung and colorectal tumors, LFMP can effectively infer the undetected lncRNA-disease association.