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Background: Preoperative identification of isocitrate dehydrogenase (IDH) mutation and 1p/19q codeletion status could help clinicians select the optimal therapy in patients with diffuse glioma. Although, the value of multimodal intersection was underutilized. Purpose: To evaluate the value of quantitative MRI biomarkers for the identification of IDH mutation and 1p/19q codeletion in adult patients with diffuse glioma. Study Type: Retrospective. Population: Two hundred sixteen adult diffuse gliomas with known genetic test results, divided into training (N = 130), test (N = 43), and validation (N = 43) groups. Sequence/Field Strength: Diffusion/perfusion‐weighted‐imaging sequences and multivoxel MR spectroscopy (MRS), all 3.0 T using three different scanners. Assessment: The apparent diffusion coefficient (ADC) and cerebral blood volume (CBV) of the core tumor were calculated to identify IDH‐mutant and 1p/19q‐codeleted statuses and to determine cut‐off values. ADC models were built based on the 30th percentile and lower, CBV models were built based on the 75th centile and higher (both in five centile steps). The optimal tumor region was defined and the metabolite concentrations of MRS voxels that overlapped with the ADC/CBV optimal region were calculated and added to the best‐performing diagnostic models. Statistical Tests: DeLong's test, diagnostic test, and decision curve analysis were performed. A P value <0.05 was considered to be statistically significant. Results: Almost all ADC models achieved good performance in identifying IDH mutation status, among which ADC_15th was the most valuable parameter (threshold = 1.186; Youden index = 0.734; AUC_train = 0.896). The differential power of CBV histogram metrics for predicting 1p/19q codeletion outperformed ADC histogram metrics, and the CBV_80th‐related model performed best (threshold = 1.435; Youden index = 0.458; AUC_train = 0.724). The AUCs of ADC_15th and CBV_80th models in the validation set were 0.857 and 0.733. These models tended to improve after incorporation of N‐acetylaspartate/total_creatine and glutamate‐plus‐glutamine/total_creatine, respectively. Data Conclusion: The intersection of ADC‐, CBV‐based histogram and MRS provide a reliable paradigm for identifying the key molecular markers in adult diffuse gliomas. Evidence Level: 3 Technical Efficacy: Stage 3 [ABSTRACT FROM AUTHOR]

Drug–drug interaction (DDI) prediction is one of the essential tasks in drug development to ensure public health and patient safety. Drug combinations with potentially severe DDIs have been verified to threaten the safety of patients critically, and it is therefore of great significance to develop effective computational algorithms for identifying potential DDIs in clinical trials. By modeling DDIs with a graph structure, recent attempts have been made to solve the prediction problem of DDIs by using advanced graph representation learning techniques. Still, their representational capacity is limited by isomorphic structures that are frequently observed in DDI networks. To address this problem, we propose a novel algorithm called DDIGIN to predict DDIs by incorporating a graph isomorphism network (GIN) such that more discriminative representations of drugs can thus be learned for improved performance. Given a DDI network, DDIGIN first initializes the representations of drugs with Node2Vec according to the topological structure and then optimizes these representations by propagating and aggregating the first-order neighboring information in an injective way. By doing so, more powerful representations can thus be learned for drugs with isomorphic structures. Last, DDIGIN estimates the interaction probability for pairwise drugs by multiplying their representations in an end-to-end manner. Experimental results demonstrate that DDIGIN outperforms several state-of-the-art algorithms on the ogbl-ddi (Acc = 0.8518, AUC = 0.8594, and AUPR = 0.9402) and DDInter datasets (Acc = 0.9763, AUC = 0.9772, and AUPR = 0.9868). In addition, our case study indicates that incorporating GIN enhances the expressive power of drug representations for improved performance of DDI prediction. [ABSTRACT FROM AUTHOR]

Background: The human brain has ability to reorganize itself in response to glioma. However, the mechanism of cortical reorganization remains unclear. Purpose: To investigate alterations in cortical thickness and local gyration index (LGI) in patients with unilateral frontal lobe diffuse low‐grade glioma (DLGG). Study Type: Retrospective. Subjects: Ninety‐nine patients with histopathologically proven DLGG invading the left frontal lobe (LF; N = 56) or the right frontal lobe (RF; N = 43), and healthy controls (HC; N = 53). Field Strength/Sequence: 3.0 T, 3D T1‐weighted images and gadolinium enhanced T1‐weighted images using magnetization‐prepared rapid gradient echo sequence, T2‐weighted images, and fluid‐attenuated inversion recovery using turbo spin echo sequence. Assessment: In patients with DLGG, virtual brain grafting combined with Freesurfer was utilized to enable automated cortical thickness and LGI calculation. In HC, standard FreeSurfer pipeline was applied to calculate these measures. Radiomic features were extracted from glioma using Pyradiomic software. Statistical Tests: General linear model and Pearson's correlation analysis. A P value <0.05 was considered statistically significant. Results: For LF patients, there was significantly increased cortical thickness in the rostral middle frontal gyrus, significantly reduced cortical thickness in the precentral gyrus and hypogyrification in the lingual and medial orbitofrontal (MOF) gyrus in contralateral hemisphere. For RF patients, there was significantly increased cortical thickness in the middle temporal, lateral occipital extending to isthmus cingulate gyrus, significantly reduced cortical thickness in the precentral gyrus and hypogyrification in the lingual gyrus in the contralateral hemisphere. A negative association between four textural features of DLGG and LGI in the right MOF gyrus of LF group was found (r = −0.609, −0.442, −0.545, and −0.417, respectively). Data Conclusion: Cortical thickness compensation was shown in contralateral homotopic location and some distant contralateral regions. Additionally, there was decreased cortical thickness in the contralateral precentral gyrus and hypogyrification in contralateral lingual gyrus. Evidence Level: 4 Technical Efficacy: Stage 2 [ABSTRACT FROM AUTHOR]

Aims: This study aimed to use resting‐state functional magnetic resonance imaging (rs‐fMRI) to determine the temporal features of functional connectivity states and changes in connectivity strength in sleep‐related hypermotor epilepsy (SHE). Methods: High‐resolution T1 and rs‐fMRI scanning were performed on all the subjects. We used a sliding‐window approach to construct a dynamic functional connectivity (dFC) network. The k‐means clustering method was performed to analyze specific FC states and related temporal properties. Finally, the connectivity strength between the components was analyzed using network‐based statistics (NBS) analysis. The correlations between the abovementioned measures and disease duration were analyzed. Results: After k‐means clustering, the SHE patients mainly exhibited two dFC states. The frequency of state 1 was higher, which was characterized by stronger connections within the networks; state 2 occurred at a relatively low frequency, characterized by stronger connections between networks. SHE patients had greater fractional time and a mean dwell time in state 2 and had a larger number of state transitions. The NBS results showed that SHE patients had increased connectivity strength between networks. None of the properties was correlated with illness duration among patients with SHE. Conclusion: The patterns of dFC patterns may represent an adaptive and protective mode of the brain to deal with epileptic seizures. [ABSTRACT FROM AUTHOR]

As microRNAs (miRNAs) are involved in many essential biological processes, their abnormal expressions can serve as biomarkers and prognostic indicators to prevent the development of complex diseases, thus providing accurate early detection and prognostic evaluation. Although a number of computational methods have been proposed to predict miRNA–disease associations (MDAs) for further experimental verification, their performance is limited primarily by the inadequacy of exploiting lower order patterns characterizing known MDAs to identify missing ones from MDA networks. Hence, in this work, we present a novel prediction model, namely HiSCMDA, by incorporating higher order network structures for improved performance of MDA prediction. To this end, HiSCMDA first integrates miRNA similarity network, disease similarity network and MDA network to preserve the advantages of all these networks. After that, it identifies overlapping functional modules from the integrated network by predefining several higher order connectivity patterns of interest. Last, a path-based scoring function is designed to infer potential MDAs based on network paths across related functional modules. HiSCMDA yields the best performance across all datasets and evaluation metrics in the cross-validation and independent validation experiments. Furthermore, in the case studies, 49 and 50 out of the top 50 miRNAs, respectively, predicted for colon neoplasms and lung neoplasms have been validated by well-established databases. Experimental results show that rich higher order organizational structures exposed in the MDA network gain new insight into the MDA prediction based on higher order connectivity patterns. [ABSTRACT FROM AUTHOR]