Erratum: Optical parameters estimation in inhomogeneous turbid media using backscattered light: for transcutaneous scattering measurement of intravascular blood: erratum.

[This corrects the article on p. 237 in vol. 15, PMID: 38223194.].

Optical parameters estimation in inhomogeneous turbid media using backscattered light: for transcutaneous scattering measurement of intravascular blood.

In our earlier research, a technique was developed to estimate the effective attenuation coefficient of subcutaneous blood vessels from the skin surface using the spatial distribution of backscattered near-infrared (NIR) light. The scattering effect in surrounding tissues was suppressed through the application of a differential principle, provided that the in vivo structure is known. In this study, a new method is proposed enabling the separate estimation of both scattering and absorption coefficients using NIR light of different wavelengths. The differential technique is newly innovated to make it applicable to the subcutaneous structure without requiring explicit geometrical information. Suppression of the scattering effect from surrounding tissue can be incorporated into the process of estimating the scattering and absorption coefficients. The validity of the proposed technique can be demonstrated through Monte Carlo simulations using both homogeneous and inhomogeneous tissue-simulating models. The estimated results exhibit good coherence with theoretical values (r2 = 0.988-0.999). Moreover, the vulnerability and robustness of the proposed technique against different measurement errors are verified. Optimal conditions for practical measurement are specified under various light-detection conditions. Separate estimation of scattering and absorption coefficients improves the accuracy of turbidity measurements and spectroscopy in biomedical applications considerably, particularly for noninvasive measurements and analysis of blood, lipids, and other components in subcutaneous blood vessels.

HMCDA: a novel method based on the heterogeneous graph neural network and metapath for circRNA-disease associations prediction.

Circular RNA (CircRNA) is a type of non-coding RNAs in which both ends are covalently linked. Researchers have demonstrated that many circRNAs can act as biomarkers of diseases. However, traditional experimental methods for circRNA-disease associations identification are labor-intensive. In this work, we propose a novel method based on the heterogeneous graph neural network and metapaths for circRNA-disease associations prediction termed as HMCDA. First, a heterogeneous graph consisting of circRNA-disease associations, circRNA-miRNA associations, miRNA-disease associations and disease-disease associations are constructed. Then, six metapaths are defined and generated according to the biomedical pathways. Afterwards, the entity content transformation, intra-metapath and inter-metapath aggregation are implemented to learn the embeddings of circRNA and disease entities. Finally, the learned embeddings are used to predict novel circRNA-disase associations. In particular, the result of extensive experiments demonstrates that HMCDA outperforms four state-of-the-art models in fivefold cross validation. In addition, our case study indicates that HMCDA has the ability to identify novel circRNA-disease associations.

Sequence pre-training-based graph neural network for predicting lncRNA-miRNA associations.

MicroRNAs (miRNAs) silence genes by binding to messenger RNAs, whereas long non-coding RNAs (lncRNAs) act as competitive endogenous RNAs (ceRNAs) that can relieve miRNA silencing effects and upregulate target gene expression. The ceRNA association between lncRNAs and miRNAs has been a research hotspot due to its medical importance, but it is challenging to verify experimentally. In this paper, we propose a novel deep learning scheme, i.e. sequence pre-training-based graph neural network (SPGNN), that combines pre-training and fine-tuning stages to predict lncRNA-miRNA associations from RNA sequences and the existing interactions represented as a graph. First, we utilize a sequence-to-vector technique to generate pre-trained embeddings based on the sequences of all RNAs during the pre-training stage. In the fine-tuning stage, we use Graph Neural Network to learn node representations from the heterogeneous graph constructed using lncRNA-miRNA association information. We evaluate our proposed scheme SPGNN on our newly collected animal lncRNA-miRNA association dataset and demonstrate that combining the $k$-mer technique and Doc2vec model for pre-training with the Simple Graph Convolution Network for fine-tuning is effective in predicting lncRNA-miRNA associations. Our approach outperforms state-of-the-art baselines across various evaluation metrics. We also conduct an ablation study and hyperparameter analysis to verify the effectiveness of each component and parameter of our scheme. The complete code and dataset are available on GitHub: https://github.com/zixwang/SPGNN.