Prediction of Progression to Severe Stroke in Initially Diagnosed Anterior Circulation Ischemic Cerebral Infarction

Purpose: Accurate prediction of the progression to severe stroke in initially diagnosed nonsevere patients with acute–subacute anterior circulation nonlacuna ischemic infarction (ASACNLII) is important in making clinical decision. This study aimed to apply a machine learning method to predict if the initially diagnosed nonsevere patients with ASACNLII would progress to severe stroke by using diffusion-weighted images and clinical information on admission.Methods: This retrospective study enrolled 344 patients with ASACNLII from June 2017 to August 2020 on admission, and 108 cases progressed to severe stroke during hospitalization within 3–21 days. The entire data were randomized into a training set (n = 271) and an independent test set (n = 73). A U-Net neural network was employed for automatic segmentation and volume measurement of the ischemic lesions. Predictive models were developed and used for evaluating the progression to severe stroke using different feature sets (the volume data, the clinical data, and the combination) and machine learning methods (random forest, support vector machine, and logistic regression).Results: The U-Net showed high correlation with manual segmentation in terms of Dice coefficient of 0.806 and R2 value of the volume measurements of 0.960 in the test set. The random forest classifier of the volume + clinical combination achieved the best area under the receiver operating characteristic curve of 0.8358 (95% CI 0.7321–0.9269), and the accuracy, sensitivity, and specificity were 0.7780 (0.7397–0.7945), 0.7695 (0.6102–0.9074), and 0.8686 (0.6923–1.0), respectively. The Shapley additive explanation diagram showed the volume variable as the most important predictor.Conclusion: The U-Net was fully automatic and showed a high correlation with manual segmentation. An integrated approach combining clinical variables and stroke lesion volumes that were derived from the advanced machine learning algorithms had high accuracy in predicting the progression to severe stroke in ASACNLII patients

Mesoscopic Failure Behavior of Strip Footing on Geosynthetic-Reinforced Granular Soil Foundations Using PIV Technology

Two-dimensional model tests combined with PIV technology were conducted to study the failure behavior of strip footing on geosynthetic-reinforced granular soil foundations on a mesoscale. The results showed that geosynthetic reinforcements improve the bearing capacity of granular soil foundations; however, the effectiveness of the reinforcement was affected by the position, length, and number of geosynthetics. The mesoscale factor affecting the reinforcement effectiveness was the size of the sliding wedge in the foundation, which was changed by the embedded geosynthetics. As the depth, length, number, and vertical spacing of the reinforcements varied, three possible failure modes occurred in the reinforced foundations: failure above the top reinforcement layer, failure between reinforcement layers, and failure similar to footings on the unreinforced foundation

Degenerated nucleus pulposus cells derived exosome carrying miR-27a-3p aggravates intervertebral disc degeneration by inducing M1 polarization of macrophages

Abstract Background Intervertebral disc degeneration (IVDD) is a major contributor to spinal disorders. Previous studies have indicated that the infiltration of immunocytes, specifically macrophages, plays a crucial role in the advancement of IVDD. Exosomes (exo) are believed to play a significant role in intercellular communication. This study aims to investigate the role of exosomes derived from degenerated nucleus pulposus (dNPc) in the process of macrophages M1 polarization. Methods Nucleus pulposus (NP) tissue and nucleus pulposus cells (NPc) were collected from patients with intervertebral disc degeneration (IVDD) and idiopathic scoliosis. Immunohistochemistry analysis was performed to determine the number of M1 macrophages in NP tissue. Subsequently, exosomes derived from degenerated NP cells (dNPc-exo) and non-degenerated NP cells (nNPc-exo) were collected and co-cultured with M0 macrophages, which were induced from THP-1 cells. The M1 phenotype was assessed using western blot, flow cytometry, immunofluorescence staining, and qRT-PCR. RNA-sequencing analysis was conducted to examine the expression levels of microRNAs in the dNPc-exo and nNPc-exo groups, and qRT-PCR was performed to investigate the effect pf different microRNA to induce macrophage polarization. Furthermore, western blot and qRT-PCR were employed to demonstrate the regulatory effect of microRNAs carried by dNPc-exo on downstream target signaling pathways in macrophages. Finally, an animal model of IVDD was utilized to investigate the impact of dNPc-exo on inducing M1 polarization of macrophages and its role in the IVDD process. Results In this study, we observed an increase in the number of M1 macrophages as the intervertebral disc (IVD) degraded. Additionally, we discovered that dNPc releases exosomes (dNPc-exo) could promote the polarization of macrophages towards the M1 phenotype. Notably, through RNA-sequencing analysis of dNPc-exo and nNPc-exo groups, we identified miR-27a-3p as a highly expressed miRNA in the dNPc-exo group, which significantly influences the induction of M1 polarization of macrophages. And then, we discovered that dNPc-exo has the ability to transport miR-27a-3p and target the PPARγ/NFκB/PI3K/AKT signaling pathway, thereby influencing the M1 polarization of macrophages. We conducted experiments using rat model of IVDD and observed that the exosomes carrying miR-27a-3p actually induced the M1 polarization of macrophages and exacerbated the degradation of IVD. Conclusion In conclusion, our findings highlight the significant role of dNPc-exo in IVDD process and provide a basis for further investigation into the mechanism of IVDD and the potential of exosome-based therapy. Graphic abstrac

Outcome Prediction of Spontaneous Supratentorial Intracerebral Hemorrhage after Surgical Treatment Based on Non-Contrast Computed Tomography: A Multicenter Study

This study aims to explore the value of a machine learning (ML) model based on radiomics features and clinical features in predicting the outcome of spontaneous supratentorial intracerebral hemorrhage (sICH) 90 days after surgery. A total of 348 patients with sICH underwent craniotomy evacuation of hematoma from three medical centers. One hundred and eight radiomics features were extracted from sICH lesions on baseline CT. Radiomics features were screened using 12 feature selection algorithms. Clinical features included age, gender, admission Glasgow Coma Scale (GCS), intraventricular hemorrhage (IVH), midline shift (MLS), and deep ICH. Nine ML models were constructed based on clinical feature, and clinical features + radiomics features, respectively. Grid search was performed on different combinations of feature selection and ML model for parameter tuning. The averaged receiver operating characteristics (ROC) area under curve (AUC) was calculated and the model with the largest AUC was selected. It was then tested using multicenter data. The combination of lasso regression feature selection and logistic regression model based on clinical features + radiomics features had the best performance (AUC: 0.87). The best model predicted an AUC of 0.85 (95%CI, 0.75–0.94) on the internal test set and 0.81 (95%CI, 0.64–0.99) and 0.83 (95%CI, 0.68–0.97) on the two external test sets, respectively. Twenty-two radiomics features were selected by lasso regression. The second-order feature gray level non-uniformity normalized was the most important radiomics feature. Age is the feature with the greatest contribution to prediction. The combination of clinical features and radiomics features using logistic regression models can improve the outcome prediction of patients with sICH 90 days after surgery