LDA-VGHB: identifying potential lncRNA-disease associations with singular value decomposition, variational graph auto-encoder and heterogeneous Newton boosting machine.

Long noncoding RNAs (lncRNAs) participate in various biological processes and have close linkages with diseases. In vivo and in vitro experiments have validated many associations between lncRNAs and diseases. However, biological experiments are time-consuming and expensive. Here, we introduce LDA-VGHB, an lncRNA-disease association (LDA) identification framework, by incorporating feature extraction based on singular value decomposition and variational graph autoencoder and LDA classification based on heterogeneous Newton boosting machine. LDA-VGHB was compared with four classical LDA prediction methods (i.e. SDLDA, LDNFSGB, IPCARF and LDASR) and four popular boosting models (XGBoost, AdaBoost, CatBoost and LightGBM) under 5-fold cross-validations on lncRNAs, diseases, lncRNA-disease pairs and independent lncRNAs and independent diseases, respectively. It greatly outperformed the other methods with its prominent performance under four different cross-validations on the lncRNADisease and MNDR databases. We further investigated potential lncRNAs for lung cancer, breast cancer, colorectal cancer and kidney neoplasms and inferred the top 20 lncRNAs associated with them among all their unobserved lncRNAs. The results showed that most of the predicted top 20 lncRNAs have been verified by biomedical experiments provided by the Lnc2Cancer 3.0, lncRNADisease v2.0 and RNADisease databases as well as publications. We found that HAR1A, KCNQ1DN, ZFAT-AS1 and HAR1B could associate with lung cancer, breast cancer, colorectal cancer and kidney neoplasms, respectively. The results need further biological experimental validation. We foresee that LDA-VGHB was capable of identifying possible lncRNAs for complex diseases. LDA-VGHB is publicly available at https://github.com/plhhnu/LDA-VGHB.

Longitudinal Imaging of Tumor Perfusion After Preoperative Endovascular Embolization in Meningiomas: Surgical Time Window Selecting, Clinical Consideration, and Outcomes.

OBJECTIVE: To quantitatively investigate the longitudinal computed tomography perfusion (CTP) imaging in meningiomas preoperatively embolized using microcatheters. METHODS: This retrospective monocentric study included 27 patients with symptomatic supratentorial meningiomas. Quantitative computed tomography perfusion (CTP) images before and postembolization were evaluated and correlated with angiographic, immunohistochemical, and clinical data. RESULTS: The mean age of the patients was 45 ± 18 years, with a female-to-male ratio of 1.45:1. After embolization, both the embolized (Eb) and unembolized (UEb) regions showed hypoperfusion. A steady state was achieved on days 4-6 postembolization, during which differences in regional cerebral blood volume (rCBV) (Eb 0.5 ± 0.3 ml/100 mg, UEb 3.3 ± 1.4 ml/100 mg; P < 0.05), and mean transit time (MTT) (Eb 3.5 ± 1.8 s, UEb 3.1 ± 0.4 s) were observed. The cerebral blood flow (rCBF) and time to the peak (TTP) exhibited opposite patterns between Eb and UEb. A steady state was reached in rCBF (Eb 1.7 ± 1.2 ml/100 g/min, UEb 30 ± 5.4 ml/100 g/min; P < 0.01), and TTP (Eb 5 ± 4.8 s, UEb 1.8 ± 1.5 s; P < 0.01) within 4 to 6 days. Estimated blood loss (EBL) showed significant association with the surgical time interval among the 3 groups (P < 0.05). Tissue necrosis predominated over 7 days postembolization, indicating a correlation with the devascularization process. The overall incidence of postembolized headache, seizures, extremity weakness/paralysis, and postoperational headache was 11.1%, 7.4%, 3.7%; and 7.4%, respectively. All symptoms resolved by the last follow-up (3 months). CONCLUSION: Preoperative embolization of meningiomas using N-butyl cyanoacrylate effectively induced significant and sustained tissue transformation and decreased estimated blood loss (EBL) over 7 days. Hemodynamic fluctuations tended to stabilize within 4 to 6 days.

Hessian Regularized [Formula: see text]-Nonnegative Matrix Factorization and Deep Learning for miRNA-Disease Associations Prediction.

Since the identification of microRNAs (miRNAs), empirical research has demonstrated their crucial involvement in the functioning of organisms. Investigating miRNAs significantly bolsters efforts related to averting, diagnosing, and treating intricate human maladies. Yet, exploring every conceivable miRNA-disease association consumes significant resources and time within conventional wet experiments. On the computational front, forecasting potential miRNA-disease connections serves as a valuable source of preliminary insights for medical investigators. As a result, we have developed a novel matrix factorization model known as Hessian-regularized [Formula: see text] nonnegative matrix factorization in combination with deep learning for predicting associations between miRNAs and diseases, denoted as [Formula: see text]-NMF-DF. In particular, we introduce a novel iterative fusion approach to integrate all similarities. This method effectively diminishes the sparsity of the initial miRNA-disease associations matrix. Additionally, we devise a mixed model framework that utilizes deep learning, matrix decomposition, and singular value decomposition to capture and depict the intricate nonlinear features of miRNA and disease. The prediction performance of the six matrix factorization methods is improved by comparison and analysis, similarity matrix fusion, data preprocessing, and parameter adjustment. The AUC and AUPR obtained by the new matrix factorization model under fivefold cross validation are comparative or better with other matrix factorization models. Finally, we select three diseases including lung tumor, bladder tumor and breast tumor for case analysis, and further extend the matrix factorization model based on deep learning. The results show that the hybrid algorithm combining matrix factorization with deep learning proposed in this paper can predict miRNAs related to different diseases with high accuracy.

Urolithin B Attenuates Cerebral Ischemia-reperfusion Injury by Modulating Nrf2-regulated Anti-oxidation in Rats.

Ischemia-reperfusion (IR) induces a wide range of irreversible injuries. Cerebral IR injury (IRI) refers to additional brain tissue damage that occurs after blood flow is restored following cerebral ischemia. Currently, no established methods exist for treating IRI. Oxidative stress is recognized as a primary mechanism initiating IRI and a crucial focal target for its treatment. Urolithin B, a metabolite derived from ellagitannins, antioxidant polyphenols, has demonstrated protective effects against oxidative stress in various disease conditions. However, the precise mechanism underlying UB's effect on IRI remains unclear. In our current investigation, we assessed UB's ability to mitigate neurological functional impairment induced by IR using a neurological deficit score. Additionally, we examined cerebral infarction following UB administration through TTC staining and neuron Nissl staining. UB's inhibition of neuronal apoptosis was demonstrated through the TUNEL assay and Caspase-3 measurement. Additionally, we examined UB's effect on oxidative stress levels by analyzing malondialdehyde (MDA) concentration, superoxide dismutase (SOD) activity, and immunohistochemistry analysis of inducible nitric oxide synthase (iNOS) and 8-hydroxyl-2'-deoxyguanosine (8-OHdG). Notably, UB demonstrated a reduction in oxidative stress levels. Mechanistically, UB was found to stimulate the Nrf2/HO-1 signaling pathway, as evidenced by the significant reduction in UB's neuroprotective effects upon administration of ATRA, an Nrf2 inhibitor. In summary, UB effectively inhibits oxidative stress induced by IR through the activation of the Nrf2/HO-1 signaling pathway. These findings suggest that UB holds promise as a therapeutic agent for the treatment of IRI.