Rupture-Related Features of Cerebral Arteriovenous Malformations and Their Utility in Predicting Hemorrhage.

BACKGROUND: Evaluating rupture risk in cerebral arteriovenous malformations currently lacks quantitative hemodynamic and angioarchitectural features necessary for predicting subsequent hemorrhage. We aimed to derive rupture-related hemodynamic and angioarchitectural features of arteriovenous malformations and construct an ensemble model for predicting subsequent hemorrhage. METHODS: This retrospective study included 3 data sets, as follows: training and test data sets comprising consecutive patients with untreated cerebral arteriovenous malformations who were admitted from January 2015 to June 2022 and a validation data set comprising patients with unruptured arteriovenous malformations who received conservative treatment between January 2009 and December 2014. We extracted rupture-related features and developed logistic regression (clinical features), decision tree (hemodynamic features), and support vector machine (angioarchitectural features) models. These 3 models were combined into an ensemble model using a weighted soft-voting strategy. The performance of the models in discriminating ruptured arteriovenous malformations and predicting subsequent hemorrhage was evaluated with confusion matrix-related metrics in the test and validation data sets. RESULTS: A total of 896 patients (mean±SD age, 28±14 years; 404 women) were evaluated, with 632, 158, and 106 patients in the training, test, and validation data sets, respectively. From the training set, 9 clinical, 10 hemodynamic, and 2912 pixel-based angioarchitectural features were extracted. A logistic regression model was built using 4 selected clinical features (age, nidus size, location, and venous aneurysm), whereas a decision-tree model was constructed from 4 hemodynamic features (outflow time, stasis index, cerebral blood flow, and outflow volume ratio). A support vector machine model was designed using 5 pixel-based angioarchitectural features. In the validation data set, the accuracy, sensitivity, specificity, and area under the curve of the ensemble model for predicting subsequent hemorrhages were 0.840, 0.889, 0.823, and 0.911, respectively. CONCLUSIONS: The ensemble model incorporating clinical, hemodynamic, and angioarchitectural features showed favorable performance in predicting subsequent hemorrhage of cerebral arteriovenous malformations.

An MRI radiomics approach to discriminate hemorrhage prone intracranial tumors before stereotactic biopsy.

PURPOSE: To explore imaging biomarkers predictive of intratumoral hemorrhage for lesions intended for elective stereotactic biopsy. METHOD: This study included a retrospective cohort of 143 patients with 175 intracranial lesions intended for stereotactic biopsy. All the lesions were randomly split into a training dataset (n=121) and a test dataset (n=54) at a ratio of 7:3. 34 lesions were defined as "hemorrhage-prone tumors" as hemorrhage occurred between initial diagnostic MRI acquisition and the scheduled biopsy procedure. Radiomics features were extracted from the contrast-enhanced T1WI and T2WI images. Features informative of hemorrhage were then selected by the LASSO algorithm and an SVM model was built with selected features. The SVM model was further simplified by discarding features with low importance calculated using a "permutation importance" method. The model's performance was evaluated with confusion matrix-derived metrics and AUC value on the independent test dataset. RESULTS: Nine radiomics features were selected as hemorrhage related features of intracranial tumors by the LASSO algorithm. The simplified model's sensitivity, specificity, accuracy, and AUC reached 0.909, 0.930, 0.926, and 0.949 (95%CI: 0.865-1.000) on the test dataset in the discrimination of "hemorrhage-prone tumors". The permutation method rated feature "T2_gradient_firstorder_10Percentile" as the most important, the absence of which decreased the model's accuracy by 10.9%. CONCLUSION: Radiomics features extracted on contrast-enhanced T1WI and T2WI sequences were predictive of future hemorrhage of intracranial tumors with favorable accuracy. This model may assist in the arrangement of biopsy procedures and the selection of target lesions in patients with multiple lesions.

Puerarin reduces cell damage from cerebral ischemia-reperfusion by inhibiting ferroptosis.

This study explores the protective effects of Puerarin, a compound derived from the traditional Chinese herb Pueraria, against cellular damage induced by Oxygen-Glucose Deprivation/Reoxygenation (OGD/R) in PC12 cells. The research focuses on understanding how Puerarin influences the mechanisms of ferroptosis and oxidative stress, key factors in ischemia-reperfusion injury relevant to neurodegenerative diseases. In our in vitro model, we identified the optimal OGD duration to induce significant cell stress and confirmed the non-toxicity of Puerarin up to 100uM. The results reveal that Puerarin substantially mitigates the detrimental effects of OGD/R, including improvements in cell viability, mitochondrial integrity, and reductions in oxidative stress markers like ROS and lipid peroxidation. Notably, Puerarin modulates key proteins in the autophagy process and the Nrf2 pathway, crucial in cellular stress responses. Further, the use of 3-Methyladenine, an autophagy inhibitor, underscores the significance of autophagy in managing OGD/R-induced stress. These findings suggest Puerarin's potential as a therapeutic agent for conditions characterized by ischemic cellular damage, highlighting the need for further clinical exploration.

Estimation of venous sinus pressure drop in patients with idiopathic intracranial hypertension using 4D-flow MRI.

OBJECTIVE: We aimed to explore a non-invasive estimate of pressure drop in patients who undergo venous sinus stenting to treat idiopathic intracranial hypertension (IIH). METHODS: This prospective study included 28 IIH patients scheduled for venous stenting. 4D-flow MRI was acquired 24-48 h before venous manometry. Manometry-obtained pressure drop (Mp) was dichotomized into low (Lp: 0-8 mmHg) and high (Hp: 8-30 mmHg) groups. Hemodynamic indices were compared between Lp and Hp. Trans-stenotic pressure drop was estimated by work-energy equation, simplified Bernoulli equation, vorticity magnitude, and velocity difference between inlet and outlet and was compared with Mp. Measurement agreement, correlation, and accuracy were evaluated using the κ coefficient, Pearson's r, and confusion matrix-derived accuracy. RESULTS: Among 28 patients (mean age 38.8 ± 12.7), 19 (67.9%) were female. Work-energy equation-estimated pressure drop (WEp) had strong correlation (r = 0.91, 95% confidence interval [CI]: 0.81-0.96, p < 0.001) and high agreement (intraclass correlation coefficient = 0.90, 95% CI: 0.78-0.95, p < 0.001) with Mp. WEp classified Lp and Hp with an accuracy of 0.96. The κ value between WEp and Mp was 0.92 (95% CI: 0.78-1.00). In the work-energy equation, the viscosity energy term (Ve) had the largest weights, and the ratio of Ve to the summation of the three energy terms was 0.93 ± 0.07. Ve had strong correlation with mVort (r = 0.93, 95% CI: 0.85-0.97, p < 0.001), and mean vorticity magnitude was significantly elevated in Hp compared to that in Lp (259.8 vs. 174.9 mL/s, p < 0.001). CONCLUSION: Trans-stenotic pressure drop in IIH can be estimated using the work-energy equation with favorable accuracy. KEY POINTS: • Trans-stenotic pressure drop in patients with idiopathic intracranial hypertension can be estimated accurately with the work-energy equation using the 4D-flow MRI full velocity field. • Compared with traditional venous sinus manometry, the 4D-flow MRI-derived pressure drop is totally non-invasive and cost-saving. • 4D-flow MRI may help neurointerventionalist to select IIH patients suitable for venous sinus stenting.

Quantitative evaluation of the hemodynamic differences between ruptured and unruptured cerebral arteriovenous malformations using angiographic parametric imaging-derived radiomics features.

PURPOSE: Imaging features of cerebral arteriovenous malformations (AVMs) are mainly interpreted according to demographic and qualitative anatomical characteristics. This study aimed to use angiographic parametric imaging (API)-derived radiomics features to explore whether these features extracted from digital subtraction angiography (DSA) were associated with the hemorrhagic presentation of AVMs. METHODS: Patients with AVM were retrospectively evaluated. Among them, 80% were randomly assigned to a training dataset, and the remaining 20% were assigned to an independent test dataset. Radiomics features were extracted from DSA by API. Then, informative features were selected from radiomics features and clinical features using the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm. A model was constructed based on the selected features to classify the dichotomous hemorrhagic presentation in the training dataset. The model performance was evaluated in the test dataset with confusion matrix-related metrics. RESULTS: A total of 529 consecutive patients with AVMs between July 2011 and December 2020 were included in this study. After being selected by the LASSO algorithm and analyzed by multivariable logistic regression, three clinical features, namely, age (p = 0.01), nidus size (p < 0.001), and venous drainage patterns (p < 0.001), and four radiomics features were used to construct a model in the training dataset. On the independent test dataset, the model demonstrated a sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of 0.852, 0.844, 0.881, 0.809, and 0.849, respectively. CONCLUSION: The radiomics features extracted from DSA by API could be potential indicators for the hemorrhagic presentation of AVMs.

Dual Mechanisms of Coniferyl Alcohol in Phenylpropanoid Pathway Regulation.

Lignin is a complex phenolic polymer that imparts cell wall strength, facilitates water transport and functions as a physical barrier to pathogens in all vascular plants. Lignin biosynthesis is a carbon-consuming, non-reversible process, which requires tight regulation. Here, we report that a major monomer unit of the lignin polymer can function as a signal molecule to trigger proteolysis of the enzyme L-phenylalanine ammonia-lyase, the entry point into the lignin biosynthetic pathway, and feedback regulate the expression levels of lignin biosynthetic genes. These findings highlight the highly complex regulation of lignin biosynthesis and shed light on the biological importance of monolignols as signaling molecules.

Prediction of the trans-stenotic pressure gradient with arteriography-derived hemodynamic features in patients with idiopathic intracranial hypertension.

The pathogenesis of idiopathic intracranial hypertension (IIH) is attributed to segmental stenosis of the venous sinus. The current treatment paradigm requires a trans-stenotic pressure gradient of ≥8 mmHg or ≥6 mmHg threshold. This study aimed to develop a machine learning screening method to identify patients with IIH using hemodynamic features. A total of 204 venous manometry instances (n = 142, training and validation; n = 62, test) from 135 patients were included. Radiomic features extracted from five arteriography perfusion parameter maps were selected using least absolute shrinkage and selection operator and then entered into support vector machine (SVM) classifiers. The Thr8-23-SVM classifier was created with 23 radiomic features to predict if the pressure gradient was ≥8 mmHg. On an independent test dataset, prediction sensitivity, specificity, accuracy, and AUC were 0.972, 0.846, 0.919, and 0.980, respectively (95% confidence interval: 0.980-1.000). For the 6 mmHg threshold, thr6-28-SVM incorporated 28 features, and its sensitivity, specificity, accuracy, and AUC were 0.923, 0.956, 0.935, and 0.969, respectively (95% confidence interval: 0.927-1.000). The trans-stenotic pressure gradient result was associated with perfusion pattern changes, and SVM classifiers trained with arteriography perfusion map-derived radiomic features could predict the 8 mmHg and 6 mmHg dichotomized trans-stenotic pressure gradients with favorable accuracy.

Phase Separation as a Molecular Thermosensor.

How organisms sense temperature is a long-standing question. However, the identification of molecular thermosensors has been limited. Now, in a recent issue of Nature, Jung et al. demonstrate that phase separation of ELF3, a component of the circadian clock, acts as a thermosensor.