Motif-Aware miRNA-Disease Association Prediction Via Hierarchical Attention Network.

As post-transcriptional regulators of gene expression, micro-ribonucleic acids (miRNAs) are regarded as potential biomarkers for a variety of diseases. Hence, the prediction of miRNA-disease associations (MDAs) is of great significance for an in-depth understanding of disease pathogenesis and progression. Existing prediction models are mainly concentrated on incorporating different sources of biological information to perform the MDA prediction task while failing to consider the fully potential utility of MDA network information at the motif-level. To overcome this problem, we propose a novel motif-aware MDA prediction model, namely MotifMDA, by fusing a variety of high- and low-order structural information. In particular, we first design several motifs of interest considering their ability to characterize how miRNAs are associated with diseases through different network structural patterns. Then, MotifMDA adopts a two-layer hierarchical attention to identify novel MDAs. Specifically, the first attention layer learns high-order motif preferences based on their occurrences in the given MDA network, while the second one learns the final embeddings of miRNAs and diseases through coupling high- and low-order preferences. Experimental results on two benchmark datasets have demonstrated the superior performance of MotifMDA over several state-of-the-art prediction models. This strongly indicates that accurate MDA prediction can be achieved by relying solely on MDA network information. Furthermore, our case studies indicate that the incorporation of motif-level structure information allows MotifMDA to discover novel MDAs from different perspectives. The data and codes are available at https://github.com/stevejobws/MotifMDA.

Unraveling the efficacy network: A network meta-analysis of adjuvant external beam radiation therapy methods after hepatectomy.

BACKGROUND: Primary liver cancer is a malignant tumor with a high recurrence rate that significantly affects patient prognosis. Postoperative adjuvant external radiation therapy (RT) has been shown to effectively prevent recurrence after liver cancer resection. However, there are multiple RT techniques available, and the differential effects of these techniques in preventing postoperative liver cancer recurrence require further investigation. AIM: To assess the advantages and disadvantages of various adjuvant external RT methods after liver resection based on overall survival (OS) and disease-free survival (DFS) and to determine the optimal strategy. METHODS: This study involved network meta-analyses and followed the PRISMA guidelines. The data of qualified studies published before July 10, 2023, were collected from PubMed, Embase, the Web of Science, and the Cochrane Library. We included relevant studies on postoperative external beam RT after liver resection that had OS and DFS as the primary endpoints. The magnitudes of the effects were determined using risk ratios with 95% confidential intervals. The results were analyzed using R software and STATA software. RESULTS: A total of 12 studies, including 1265 patients with hepatocellular carcinoma (HCC) after liver resection, were included in this study. There was no significant heterogeneity in the direct paired comparisons, and there were no significant differences in the inclusion or exclusion criteria, intervention measures, or outcome indicators, meeting the assumptions of heterogeneity and transitivity. OS analysis revealed that patients who underwent stereotactic body radiotherapy (SBRT) after resection had longer OS than those who underwent intensity modulated radiotherapy (IMRT) or 3-dimensional conformal RT (3D-CRT). DFS analysis revealed that patients who underwent 3D-CRT after resection had the longest DFS. Patients who underwent IMRT after resection had longer OS than those who underwent 3D-CRT and longer DFS than those who underwent SBRT. CONCLUSION: HCC patients who undergo liver cancer resection must consider distinct advantages and disadvantages when choosing between SBRT and 3D-CRT. IMRT, a RT technique that is associated with longer OS than 3D-CRT and longer DFS than SBRT, may be a preferred option.

Screening and Stability Evaluation of Freeze-Dried Protective Agents for a Live Recombinant Pseudorabies Virus Vaccine.

Infection of pigs with the pseudorabies virus (PRV) causes significant economic losses in the pig industry. Immunization with live vaccines is a crucial aspect in the prevention of pseudorabies in swine. The TK/gE/gI/11k/28k deleted pseudorabies vaccine is a promising alternative for the eradication of epidemic pseudorabies mutant strains. This study optimized the lyophilization of a heat-resistant PRV vaccine to enhance the quality of a live vaccine against the recombinant PRV rHN1201TK-/gE-/gI-/11k-/28k-. The A4 freeze-dried protective formulation against PRV was developed by comparing the reduction in virus titer after lyophilization and after seven days of storage at 37 °C. The formulation contains 1% gelatin, 5% trehalose, 0.5% poly-vinylpyrimidine (PVP), 0.5% thiourea, and 1% sorbitol. The A4 freeze-dried vaccine demonstrated superior protection and thermal stability. It experienced a freeze-dried loss of 0.31 Lg post-freeze-drying and a heat loss of 0.42 Lg after being stored at a temperature of 37 °C for 7 consecutive days. The A4 freeze-dried vaccine was characterized through XRD, FTIR, and SEM analyses, which showed that it possessed an amorphous structure with a consistent porous interior. The trehalose component of the vaccine formed stable hydrogen bonds with the virus. Long-term and accelerated stability studies were also conducted. The A4 vaccine maintained viral titer losses of less than 1.0 Lg when exposed to 25 °C for 90 days, 37 °C for 28 days, and 45 °C for 7 days. The A4 vaccine had a titer loss of 0.3 Lg after storage at 2-8 °C for 24 months, and a predicted shelf life of 6.61 years at 2-8 °C using the Arrhenius equation. The A4 freeze-dried vaccine elicited no side effects when used to immunize piglets and produced specific antibodies. This study provides theoretical references and technical support to improve the thermal stability of recombinant PRV rHN1201TK-/gE-/gI-/11k-/28k- vaccines.

Discovering Consensus Regions for Interpretable Identification of RNA N6-Methyladenosine Modification Sites via Graph Contrastive Clustering.

As a pivotal post-transcriptional modification of RNA, N6-methyladenosine (m6A) has a substantial influence on gene expression modulation and cellular fate determination. Although a variety of computational models have been developed to accurately identify potential m6A modification sites, few of them are capable of interpreting the identification process with insights gained from consensus knowledge. To overcome this problem, we propose a deep learning model, namely M6A-DCR, by discovering consensus regions for interpretable identification of m6A modification sites. In particular, M6A-DCR first constructs an instance graph for each RNA sequence by integrating specific positions and types of nucleotides. The discovery of consensus regions is then formulated as a graph clustering problem in light of aggregating all instance graphs. After that, M6A-DCR adopts a motif-aware graph reconstruction optimization process to learn high-quality embeddings of input RNA sequences, thus achieving the identification of m6A modification sites in an end-to-end manner. Experimental results demonstrate the superior performance of M6A-DCR by comparing it with several state-of-the-art identification models. The consideration of consensus regions empowers our model to make interpretable predictions at the motif level. The analysis of cross validation through different species and tissues further verifies the consistency between the identification results of M6A-DCR and the evolutionary relationships among species.

Evaluation of Key Molecular Markers in Adult Diffuse Gliomas Based on a Novel Combination of Diffusion and Perfusion MRI and MR Spectroscopy.

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.

Development and External Validation of an MRI-based Radiomics Nomogram to Distinguish Circumscribed Astrocytic Gliomas and Diffuse Gliomas: A Multicenter Study.

RATIONALE AND OBJECTIVES: The 5th edition of the World Health Organization classification of tumors of the Central Nervous System (WHO CNS) has introduced the term "diffuse" and its counterpart "circumscribed" to the category of gliomas. This study aimed to develop and validate models for distinguishing circumscribed astrocytic gliomas (CAGs) from diffuse gliomas (DGs). MATERIALS AND METHODS: We retrospectively analyzed magnetic resonance imaging (MRI) data from patients with CAGs and DGs across three institutions. After tumor segmentation, three volume of interest (VOI) types were obtained: VOItumor and peritumor, VOIwhole, and VOIinterface. Clinical and combined models (incorporating radiomics and clinical features) were also established. To address imbalances in training dataset, Synthetic Minority Oversampling Technique was employed. RESULTS: A total of 475 patients (DGs: n = 338, CAGs: n = 137) were analyzed. The VOIinterface model demonstrated the best performance for differentiating CAGs from DGs, achieving an area under the curve (AUC) of 0.806 and area under the precision-recall curve (PRAUC)of 0.894 in the cross-validation set. Using analysis of variance (ANOVA) feature selector and Support Vector Machine (SVM) classifier, seven features were selected. The model achieved an AUC and AUPRC of 0.912 and 0.972 in the internal validation dataset, and 0.897 and 0.930 in the external validation dataset. The combined model, incorporating interface radiomics and clinical features, showed improved performance in the external validation set, with an AUC of 0.94 and PRAUC of 0.959. CONCLUSION: Radiomics models incorporating the peritumoral area demonstrate greater potential for distinguishing CAGs from DGs compared to intratumoral models. These findings may hold promise for evaluating tumor nature before surgery and improving clinical management of glioma patients.

MRI characteristics of H3 G34-mutant diffuse hemispheric gliomas and possible differentiation from IDH-wild-type glioblastomas in adolescents and young adults.

OBJECTIVE: H3 G34-mutant diffuse hemispheric gliomas (G34m-DHGs) are rare and constitute a new infiltrating brain tumor entity whose characteristics require elucidation, and their difference from isocitrate dehydrogenase-wild-type glioblastomas (IDH-WT-GBMs) needs to be clarified. In this study, the authors report the demographic, clinical, and neuroradiological features of G34m-DHG and investigate the capability of quantitative MRI features in differentiating them. METHODS: Twenty-three patients with G34m-DHG and 30 patients with IDH-WT-GBM were included in this retrospective study. The authors reviewed the clinical, radiological, and molecular data of G34m-DHGs and compared their neuroimaging features with those of IDH-WT-GBMs in adolescents and young adults. Visually Accessible Rembrandt Images (VASARI) features were extracted, and the Kruskal-Wallis test was performed. A logistic regression model was constructed to evaluate the diagnostic performance for differentiating between G34m-DHG and IDH-WT-GBM. Subsequently, FeAture Explorer (FAE) was used to generate the machine learning pipeline and select important radiomics features that had been extracted with PyRadiomics. Estimates of the performance were supplied by metrics such as sensitivity, specificity, accuracy, and area under the curve (AUC). RESULTS: The mean age of the 23 patients with G34m-DHG was 23.7 years (range 11-45 years), younger than the mean age of patients with IDH-WT-GBM (30.96 years, range 5-43 years). All tumors were hemispheric. Most cases were immunonegative for ATRX (95%) and Olig2 (100%), were immunopositive for p53 (95%), and exhibited MGMT promoter methylation (81%). The radiological presentations of G34m-DHG were different from those of IDH-WT-GBM. The majority of the G34m-DHGs were in the frontal, parietal, and temporal lobes and demonstrated no or only faint contrast enhancement (74%), while IDH-WT-GBMs were mostly seen in the frontal lobe and showed marked contrast enhancement in 83% of cases. The FAE-generated model, based on radiomics features (AUC 0.925) of conventional MR images, had better discriminatory performance between G34m-DHG and IDH-WT-GBM than VASARI feature analysis (AUC 0.843). CONCLUSIONS: G34m-DHGs most frequently occur in the frontal, parietal, and temporal lobes in adolescent and young adults and are associated with radiological characteristics distinct from those of IDH-WT-GBMs. Successful identification can be achieved by using either VASARI features or radiomics signatures, which may contribute to prognostic evaluation and assist in clinical settings.

Metabolic heterogeneity in different subtypes of malformations of cortical development causing epilepsy: a proton magnetic resonance spectroscopy study.

Background: The most common subtypes of malformations of cortical development (MCDs) are gray matter heterotopia (GMH), focal cortical dysplasia (FCD), and polymicrogyria (PMG). This study aimed to characterize the possible neurometabolic abnormalities and heterogeneity in different MCDs subtypes using proton magnetic resonance spectroscopy (1H-MRS). Methods: In this prospective cross-sectional study, we recruited 29 patients with MCDs and epilepsy, including ten with GMH, ten with FCD, and nine with PMG, as well as 25 age- and sex-matched healthy controls (HC) from the Epilepsy Center of West China Hospital of Sichuan University between August 2018 and November 2021. Inclusion criteria for the patients were based upon typical magnetic resonance imaging (MRI) findings of MCDs and full clinical assessment for epilepsy. Single-voxel point-resolved spectroscopy was used to acquire data from both the lesion and the normal-appearing contralateral side (NACS) in patients and from the frontal lobe in HC. Metabolite measures, including N-acetyl aspartate (NAA), myoinositol (Ins), choline (Cho), creatine (Cr), and glutamate + glutamine (Glx) concentrations, were quantitatively estimated with linear combination model (LCModel) software and corrected for the partial volume effect of cerebrospinal fluid (CSF). Results: The NAA concentration was lower and the Ins concentration was higher in the MCDs lesions than in the NACS and in HC (P=0.002-0.007), and the Cho and Cr concentrations were higher in MCDs lesions than in HC (P=0.001-0.016). Moreover, the Cho concentration was higher in NACS than in HC (P=0.015). In the GMH lesions, the only metabolic alteration was an NAA reduction (GMH_lesion vs. HC: P=0.001). In the FCD lesions, there were more metabolite abnormalities than in the other two subtypes, particularly a lower NAA and a higher Ins than in HC and NACS (P=0.012-0.042). In the PMG lesions, Cr (lesion vs. HC or NACS: P=0.017-0.021) and Glx (lesion vs. NACS: P=0.043) were increased, while NAA was normal. Correlation analysis revealed that the Cr concentration in MCDs lesions was positively correlated with seizure frequency (r=0.411; P=0.027). Conclusions: Based upon 1H-MRS, our study demonstrated that different MCDs subtypes exhibited variable metabolic features, which may be associated with distinct functional and cytoarchitectural properties.

Drug-disease association prediction using semantic graph and function similarity representation learning over heterogeneous information networks.

Discovering new indications for existing drugs is a promising development strategy at various stages of drug research and development. However, most of them complete their tasks by constructing a variety of heterogeneous networks without considering available higher-order connectivity patterns in heterogeneous biological information networks, which are believed to be useful for improving the accuracy of new drug discovering. To this end, we propose a computational-based model, called SFRLDDA, for drug-disease association prediction by using semantic graph and function similarity representation learning. Specifically, SFRLDDA first integrates a heterogeneous information network (HIN) by drug-disease, drug-protein, protein-disease associations, and their biological knowledge. Second, different representation learning strategies are applied to obtain the feature representations of drugs and diseases from different perspectives over semantic graph and function similarity graphs constructed, respectively. At last, a Random Forest classifier is incorporated by SFRLDDA to discover potential drug-disease associations (DDAs). Experimental results demonstrate that SFRLDDA yields a best performance when compared with other state-of-the-art models on three benchmark datasets. Moreover, case studies also indicate that the simultaneous consideration of semantic graph and function similarity of drugs and diseases in the HIN allows SFRLDDA to precisely predict DDAs in a more comprehensive manner.

Biocaiv: an integrative webserver for motif-based clustering analysis and interactive visualization of biological networks.

BACKGROUND: As an important task in bioinformatics, clustering analysis plays a critical role in understanding the functional mechanisms of many complex biological systems, which can be modeled as biological networks. The purpose of clustering analysis in biological networks is to identify functional modules of interest, but there is a lack of online clustering tools that visualize biological networks and provide in-depth biological analysis for discovered clusters. RESULTS: Here we present BioCAIV, a novel webserver dedicated to maximize its accessibility and applicability on the clustering analysis of biological networks. This, together with its user-friendly interface, assists biological researchers to perform an accurate clustering analysis for biological networks and identify functionally significant modules for further assessment. CONCLUSIONS: BioCAIV is an efficient clustering analysis webserver designed for a variety of biological networks. BioCAIV is freely available without registration requirements at http://bioinformatics.tianshanzw.cn:8888/BioCAIV/ .

The amino acid metabolomics signature of differentiating myocardial infarction from strangulation death in mice models.

This study differentiates myocardial infarction (MI) and strangulation death (STR) from the perspective of amino acid metabolism. In this study, MI mice model via subcutaneous injection of isoproterenol and STR mice model by neck strangulation were constructed, and were randomly divided into control (CON), STR, mild MI (MMI), and severe MI (SMI) groups. The metabolomics profiles were obtained by liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics. Principal component analysis, partial least squares-discriminant analysis, volcano plots, and heatmap were used for discrepancy metabolomics analysis. Pathway enrichment analysis was performed and the expression of proteins related to metabolomics was detected using immunohistochemical and western blot methods. Differential metabolites and metabolite pathways were screened. In addition, we found the expression of PPM1K was significantly reduced in the MI group, but the expression of p-mTOR and p-S6K1 were significantly increased (all P < 0.05), especially in the SMI group (P < 0.01). The expression of Cyt-C was significantly increased in each group compared with the CON group, especially in the STR group (all P < 0.01), and the expression of AMPKα1 was significantly increased in the STR group (all P < 0.01). Our study for the first time revealed significant differences in amino acid metabolism between STR and MI.

CCK1R2R-/- ameliorates myocardial damage caused by unpredictable stress via altering fatty acid metabolism.

The heart is the main organ of the circulatory system and requires fatty acids to maintain its activity. Stress is a contributor to aggravating cardiovascular diseases and even death, and exacerbates the abnormal lipid metabolism. The cardiac metabolism may be disturbed by stress. Cholecystokinin (CCK), which is a classical peptide hormone, and its receptor (CCKR) are expressed in myocardial cells and affect cardiovascular function. Nevertheless, under stress, the exact role of CCKR on cardiac function and cardiac metabolism is unknown and the mechanism is worth exploring. After unpredictable stress, a common stress-inducing model that induces the development of mood disorders such as anxiety and reduces motivated behavior, we found that the abnormal contraction and diastole of the heart, myocardial injury, oxidative stress and inflammation of mice were aggravated. Cholecystokinin A receptor and cholecystokinin B receptor knockout (CCK1R2R-/-) significantly reversed these changes. Mechanistically, fatty acid metabolism was found to be altered in CCK1R2R-/- mice. Differential metabolites, especially L-tryptophan, L-aspartic acid, cholesterol, taurocholic acid, ADP, oxoglutaric acid, arachidonic acid and 17-Hydroxyprogesterone, influenced cardiac function after CCK1R2R knockout and unpredictable stress. We conclude that CCK1R2R-/- ameliorated myocardial damage caused by unpredictable stress via altering fatty acid metabolism.

Lnc-ZEB2-19 Inhibits the Progression and Lenvatinib Resistance of Hepatocellular Carcinoma by Attenuating the NF-κB Signaling Pathway through the TRA2A/RSPH14 Axis.

Long non-coding RNAs have been reported to play a crucial role in tumor progression in hepatocellular carcinoma (HCC). Lnc-ZEB2-19 has been validated to be deficiently expressed in HCC. However, the capabilities and underlying mechanisms of lnc-ZEB2-19 remain uncertain. In this study, we verified that the downregulation of lnc-ZEB2-19 was prevalent in HCC and significantly correlated with the unfavorable prognosis. Further in vitro and in vivo verified that lnc-ZEB2-19 notably inhibited the proliferation, metastasis, stemness, and lenvatinib resistance (LR) of HCC cells. Mechanistically, lnc-ZEB2-19 inhibited HCC progression and LR by specifically binding to transformer 2α (TRA2A) and promoting its degradation, which resulted in the instability of RSPH14 mRNA, leading to the downregulation of Rela(p65) and p-Rela(p-p65). Furthermore, rescue assays showed that silencing RSPH14 partially restrained the effect of knockdown expression of lnc-ZEB2-19 on HCC cell metastatic ability and stemness. The findings describe a novel regulatory axis, lnc-ZEB2-19/TRA2A/RSPH14, downregulating the nuclear factor kappa B to inhibit HCC progression and LR.

iGRLDTI: an improved graph representation learning method for predicting drug-target interactions over heterogeneous biological information network.

MOTIVATION: The task of predicting drug-target interactions (DTIs) plays a significant role in facilitating the development of novel drug discovery. Compared with laboratory-based approaches, computational methods proposed for DTI prediction are preferred due to their high-efficiency and low-cost advantages. Recently, much attention has been attracted to apply different graph neural network (GNN) models to discover underlying DTIs from heterogeneous biological information network (HBIN). Although GNN-based prediction methods achieve better performance, they are prone to encounter the over-smoothing simulation when learning the latent representations of drugs and targets with their rich neighborhood information in HBIN, and thereby reduce the discriminative ability in DTI prediction. RESULTS: In this work, an improved graph representation learning method, namely iGRLDTI, is proposed to address the above issue by better capturing more discriminative representations of drugs and targets in a latent feature space. Specifically, iGRLDTI first constructs an HBIN by integrating the biological knowledge of drugs and targets with their interactions. After that, it adopts a node-dependent local smoothing strategy to adaptively decide the propagation depth of each biomolecule in HBIN, thus significantly alleviating over-smoothing by enhancing the discriminative ability of feature representations of drugs and targets. Finally, a Gradient Boosting Decision Tree classifier is used by iGRLDTI to predict novel DTIs. Experimental results demonstrate that iGRLDTI yields better performance that several state-of-the-art computational methods on the benchmark dataset. Besides, our case study indicates that iGRLDTI can successfully identify novel DTIs with more distinguishable features of drugs and targets. AVAILABILITY AND IMPLEMENTATION: Python codes and dataset are available at https://github.com/stevejobws/iGRLDTI/.

Advancements in understanding mechanisms of hepatocellular carcinoma radiosensitivity: A comprehensive review.

Primary liver cancer is a significant health problem worldwide. Hepatocellular carcinoma (HCC) is the main pathological type of primary liver cancer, accounting for 75%-85% of cases. In recent years, radiotherapy has become an emerging treatment for HCC and is effective for various stages of HCC. However, radiosensitivity of liver cancer cells has a significant effect on the efficacy of radiotherapy and is regulated by various factors. How to increase radiosensitivity and improve the therapeutic effects of radiotherapy require further exploration. This review summarizes the recent research progress on the mechanisms affecting sensitivity to radiotherapy, including epigenetics, transportation and metabolism, regulated cell death pathways, the microenvironment, and redox status, as well as the effect of nanoparticles on the radiosensitivity of liver cancer. It is expected to provide more effective strategies and methods for clinical treatment of liver cancer by radiotherapy.

Multiparametric mapping of white matter reorganizations in patients with frontal glioma-related epilepsy.

AIMS: Epilepsy is a common symptom in diffuse lower-grade glioma (DLGG). The specific role of white matter (WM) alteration in patients with glioma-related epilepsy (GRE) is largely unknown. This study aims to investigate the reorganization of WM tracts and changes in structural networks related to GRE. METHODS: Diffusion-weighted images were collected from 70 patients with left frontal DLGG (GRE = 33, non-GRE = 37) and 41 healthy controls (HC). Tractometry with TractSeg was applied to segment tracts and quantify fractional anisotropy (FA) along each tract. Structural network was constructed using constrained spherical deconvolution and probabilistic tractography. FA and network properties were compared among three groups. RESULTS: Compared with HC, both GRE and non-GRE showed decreased FA in contralateral inferior fronto-occipital fasciculus, superior longitudinal fasciculus II and arcuate fasciculus, increased nodal efficiency in contralateral nodes of frontal-parietal and limbic networks, whereas decreased degree centrality and betweenness centrality in nodes of dorsal temporal lobe and rostral middle frontal gyrus (rMFG). Additionally, when compared GRE with non-GRE, increased FA in contralateral corticospinal tract (CST) and lower betweenness centrality in paracentral lobule (PCL) in GRE (all p < 0.05 after Bonferroni correction). CONCLUSION: This study indicates that patients with left frontal DLGG exhibit complex WM reorganization, and the altered regions mainly concentrated in the language, frontal-parietal and limbic networks. Moreover, the preserved integrity in contralateral CST and server decreased nodal betweenness in PCL may be potential neuroimaging markers underlying the occurrence of presurgical seizures of GRE.

Progressive structural damage in sleep-related hypermotor epilepsy.

This study aimed to explore the alterations in gray matter volume (GMV) based on high-resolution structural data and the temporal precedence of structural alterations in patients with sleep-related hypermotor epilepsy (SHE). After preprocessing of T1 structural images, the voxel-based morphometry and source-based morphometry (SBM) methods were applied in 60 SHE patients and 56 healthy controls to analyze the gray matter volumetric alterations. Furthermore, a causal network of structural covariance (CaSCN) was constructed using Granger causality analysis based on structural data of illness duration ordering to assess the causal impact of structural changes in abnormal gray matter regions. The GMVs of SHE patients were widely reduced, mainly in the bilateral cerebellums, fusiform gyri, the right angular gyrus, the right postcentral gyrus, and the left parahippocampal gyrus. In addition to those regions, the results of the SBM analysis also found decreased GMV in the bilateral frontal lobes, precuneus, and supramarginal gyri. The analysis of CaSCN showed that along with disease progression, the cerebellum was the prominent node that tended to affect other brain regions in SHE patients, while the frontal lobe was the transition node and the supramarginal gyrus was the prominent node that may be easily affected by other brain regions. Our study found widely affected regions of decreased GMVs in SHE patients; these regions underlie the morphological basis of epileptic networks, and there is a temporal precedence relationship between them.

Magnetic Resonance Imaging Features of Zinc Finger Translocation Associated-RELA Fusion Ependymoma Compared to Its Wild-Type Counterpart.

OBJECTIVE: To explore the predictive value of quantitative features extracted from conventional magnetic resonance imaging (MRI) in distinguishing Zinc Finger Translocation Associated (ZFTA)-RELA fusion-positive and wild-type ependymomas. METHODS: Twenty-seven patients with pathologically confirmed ependymomas (17 patients with ZFTA-RELA fusions and 10 ZFTA-RELA fusion-negative patients) who underwent conventional MRI were enrolled in this retrospective study. Two experienced neuroradiologists who were blinded to the histopathological subtypes independently extracted imaging features using Visually Accessible Rembrandt Images annotations. The consistency between the readers was evaluated with the Kappa test. The imaging features with significant differences between the 2 groups were obtained using the least absolute shrinkage and selection operator regression model. Logistic regression analysis and receiver operating characteristic analysis were performed to analyze the diagnostic performance of the imaging features in predicting the ZFTA-RELA fusion status in ependymoma. RESULTS: There was a good interevaluator agreement on the imaging features (kappa value range 0.601-1.000). Enhancement quality, thickness of the enhancing margin, and edema crossing the midline have high predictive performance in identifying ZFTA-RELA fusion-positive and ZFTA-RELA fusion-negative ependymomas (C-index = 0.862 and area under the curve= 0.8618). CONCLUSIONS: Quantitative features extracted from preoperative conventional MRI by Visually Accessible Rembrandt Images provide high discriminatory accuracy in predicting the ZFTA-RELA fusion status of ependymoma.

CT based intratumor and peritumoral radiomics for differentiating complete from incomplete capsular characteristics of parotid pleomorphic adenoma: a two-center study.

OBJECTIVE: Capsular characteristics of pleomorphic adenoma (PA) has various forms. Patients without complete capsule has a higher risk of recurrence than patients with complete capsule. We aimed to develop and validate CT-based intratumoral and peritumoral radiomics models to make a differential diagnosis between parotid PA with and without complete capsule. METHODS: Data of 260 patients (166 patients with PA from institution 1 (training set) and 94 patients (test set) from institution 2) were retrospectively analyzed. Three Volume of interest (VOIs) were defined in the CT images of each patient: tumor volume of interest (VOItumor), VOIperitumor, and VOIintra-plus peritumor. Radiomics features were extracted from each VOI and used to train nine different machine learning algorithms. Model performance was evaluated using receiver operating characteristic (ROC) curves and the area under the curve (AUC). RESULTS: The results showed that the radiomics models based on features from VOIintra-plus peritumor achieved higher AUCs compared to models based on features from VOItumor. The best performing model was Linear discriminant analysis, which achieved an AUC of 0.86 in the tenfold cross-validation and 0.869 in the test set. The model was based on 15 features, including shape-based features and texture features. CONCLUSIONS: We demonstrated the feasibility of combining artificial intelligence with CT-based peritumoral radiomics features can be used to accurately predict capsular characteristics of parotid PA. This may assist in clinical decision-making by preoperative identification of capsular characteristics of parotid PA.