Butylphthalide mitigates traumatic brain injury by activating anti-ferroptotic AHR-CYP1B1 pathway.

ETHNOPHARMACOLOGICAL RELEVANCE: Increasing evidence suggests that ferroptosis, an iron-dependent form of cell death characterized by lipid peroxidation, may play a substantial role in the traumatic brain injury (TBI) pathophysiology. 3-n-butylphthalide (NBP), a compound extracted from the seeds of Apium graveolens Linn (Chinese celery) and used in China to treat ischemic stroke, has demonstrated encouraging anti-reactive oxygen species (ROS) effects. Ascertaining whether NBP can inhibit ferroptosis and its mechanism could potentially expand its use in models of neurological injury and neurodegenerative diseases. METHODS AND RESULTS: In this study, we used erastin-induced in vitro ferroptosis models (HT22 cells, hippocampal slices, and primary neurons) and an in vivo controlled cortical impact mouse model. Our study revealed that NBP administration mitigated erastin-induced death in HT-22 cells and decreased ROS levels, lipid peroxidation, and mitochondrial superoxide indicators, resulting in mitochondrial protection. Moreover, the ability of NBP to inhibit ferroptosis was confirmed in organotypic hippocampal slice cultures and a TBI mouse model. NBP rescued neurons, inhibited microglial activation, and reduced iron levels in the brain tissue. The protective effect of NBP can be partly attributed to the inhibition of the AHR-CYP1B1 axis, as evidenced by RNA-seq and CYP1B1 overexpression/inhibition experiments in HT22 cells and primary neurons. CONCLUSIONS: Our study underscores that NBP inhibition of the AHR-CYP1B1 axis reduces ferroptosis in neuronal damage and ameliorates brain injury.

Ginsenoside CK cooperates with bone mesenchymal stem cells to enhance angiogenesis post-stroke via GLUT1 and HIF-1α/VEGF pathway.

The transplantation of bone marrow mesenchymal stem cells (MSCs) in stroke is hindered by the restricted rates of survival and differentiation. Ginsenoside compound K (CK), is reported to have a neuroprotective effect and regulate energy metabolism. We applied CK to investigate if CK could promote the survival of MSCs and differentiation into brain microvascular endothelial-like cells (BMECs), thereby alleviating stroke symptoms. Therefore, transwell and middle cerebral artery occlusion (MCAO) models were used to mimic oxygen and glucose deprivation (OGD) in vitro and in vivo, respectively. Our results demonstrated that CK had a good affinity for GLUT1, which increased the expression of GLUT1 and the production of ATP, facilitated the proliferation and migration of MSCs, and activated the HIF-1α/VEGF signaling pathway to promote MSC differentiation. Moreover, CK cooperated with MSCs to protect BMECs, promote angiogenesis and vascular density, enhance neuronal and astrocytic proliferation, thereby reducing infarct volume and consequently improving neurobehavioral outcomes. These results suggest that the synergistic effects of CK and MSCs could potentially be a promising strategy for stroke.

Synthetic minority over-sampling technique-enhanced machine learning models for predicting recurrence of postoperative chronic subdural hematoma.

Objective: Chronic subdural hematoma (CSDH) is a neurological condition with high recurrence rates, primarily observed in the elderly population. Although several risk factors have been identified, predicting CSDH recurrence remains a challenge. Given the potential of machine learning (ML) to extract meaningful insights from complex data sets, our study aims to develop and validate ML models capable of accurately predicting postoperative CSDH recurrence. Methods: Data from 447 CSDH patients treated with consecutive burr-hole irrigations at Wenzhou Medical University's First Affiliated Hospital (December 2014-April 2019) were studied. 312 patients formed the development cohort, while 135 comprised the test cohort. The Least Absolute Shrinkage and Selection Operator (LASSO) method was employed to select crucial features associated with recurrence. Eight machine learning algorithms were used to construct prediction models for hematoma recurrence, using demographic, laboratory, and radiological features. The Border-line Synthetic Minority Over-sampling Technique (SMOTE) was applied to address data imbalance, and Shapley Additive Explanation (SHAP) analysis was utilized to improve model visualization and interpretability. Model performance was assessed using metrics such as AUROC, sensitivity, specificity, F1 score, calibration plots, and decision curve analysis (DCA). Results: Our optimized ML models exhibited prediction accuracies ranging from 61.0% to 86.2% for hematoma recurrence in the validation set. Notably, the Random Forest (RF) model surpassed other algorithms, achieving an accuracy of 86.2%. SHAP analysis confirmed these results, highlighting key clinical predictors for CSDH recurrence risk, including age, alanine aminotransferase level, fibrinogen level, thrombin time, and maximum hematoma diameter. The RF model yielded an accuracy of 92.6% with an AUC value of 0.834 in the test dataset. Conclusion: Our findings underscore the efficacy of machine learning algorithms, notably the integration of the RF model with SMOTE, in forecasting the recurrence of postoperative chronic subdural hematoma. Leveraging the RF model, we devised an online calculator that may serve as a pivotal instrument in tailoring therapeutic strategies and implementing timely preventive interventions for high-risk patients.

Regulating Lars2 in mitochondria: A potential Alzheimer's therapy by inhibiting tau phosphorylation.

Driven by the scarcity of effective treatment options in clinical settings, the present study aimed to identify a new potential target for Alzheimer's disease (AD) treatment. We focused on Lars2, an enzyme synthesizing mitochondrial leucyl-tRNA, and its role in maintaining mitochondrial function. Bioinformatics analysis of human brain transcriptome data revealed downregulation of Lars2 in AD patients compared to healthy controls. During in vitro experiments, the knockdown of Lars2 in mouse neuroblastoma cells (neuro-2a cells) and primary cortical neurons led to morphological changes and decreased density in mouse hippocampal neurons. To explore the underlying mechanisms, we investigated how downregulated Lars2 expression could impede the phosphatidylinositol 3-kinase/protein kinase B (PI3K-AKT) pathway, thereby mitigating AKT's inhibitory effect on glycogen synthase kinase 3 beta (GSK3ß). This led to the activation of GSK3ß, causing excessive phosphorylation of Tau protein and subsequent neuronal degeneration. During in vivo experiments, knockout of lars2 in hippocampal neurons confirmed cognitive impairment through the Barnes maze test, the novel object recognition test, and nest-building experiments. Additionally, immunofluorescence assays indicated an increase in p-tau, atrophy in the hippocampal region, and a decrease in neurons following Lars2 knockout. Taken together, our findings indicate that Lars2 represents a promising therapeutic target for AD.

Development and performance assessment of novel machine learning models for predicting postoperative pneumonia in aneurysmal subarachnoid hemorrhage patients: external validation in MIMIC-IV.

Background: Postoperative pneumonia (POP) is one of the primary complications after aneurysmal subarachnoid hemorrhage (aSAH) and is associated with postoperative mortality, extended hospital stay, and increased medical fee. Early identification of pneumonia and more aggressive treatment can improve patient outcomes. We aimed to develop a model to predict POP in aSAH patients using machine learning (ML) methods. Methods: This internal cohort study included 706 patients with aSAH undergoing intracranial aneurysm embolization or aneurysm clipping. The cohort was randomly split into a train set (80%) and a testing set (20%). Perioperative information was collected from participants to establish 6 machine learning models for predicting POP after surgical treatment. The area under the receiver operating characteristic curve (AUC), precision-recall curve were used to assess the accuracy, discriminative power, and clinical validity of the predictions. The final model was validated using an external validation set of 97 samples from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Results: In this study, 15.01% of patients in the training set and 12.06% in the testing set with POP after underwent surgery. Multivariate logistic regression analysis showed that mechanical ventilation time (MVT), Glasgow Coma Scale (GCS), Smoking history, albumin level, neutrophil-to-albumin Ratio (NAR), c-reactive protein (CRP)-to-albumin ratio (CAR) were independent predictors of POP. The logistic regression (LR) model presented significantly better predictive performance (AUC: 0.91) than other models and also performed well in the external validation set (AUC: 0.89). Conclusion: A machine learning model for predicting POP in aSAH patients was successfully developed using a machine learning algorithm based on six perioperative variables, which could guide high-risk POP patients to take appropriate preventive measures.

Development of a deep learning model for early gastric cancer diagnosis using preoperative computed tomography images.

Background: Gastric cancer is a highly prevalent and fatal disease. Accurate differentiation between early gastric cancer (EGC) and advanced gastric cancer (AGC) is essential for personalized treatment. Currently, the diagnostic accuracy of computerized tomography (CT) for gastric cancer staging is insufficient to meet clinical requirements. Many studies rely on manual marking of lesion areas, which is not suitable for clinical diagnosis. Methods: In this study, we retrospectively collected data from 341 patients with gastric cancer at the First Affiliated Hospital of Wenzhou Medical University. The dataset was randomly divided into a training set (n=273) and a validation set (n=68) using an 8:2 ratio. We developed a two-stage deep learning model that enables fully automated EGC screening based on CT images. In the first stage, an unsupervised domain adaptive segmentation model was employed to automatically segment the stomach on unlabeled portal phase CT images. Subsequently, based on the results of the stomach segmentation model, the image was cropped out of the stomach area and scaled to a uniform size, and then the EGC and AGC classification models were built based on these images. The segmentation accuracy of the model was evaluated using the dice index, while the classification performance was assessed using metrics such as the area under the curve (AUC) of the receiver operating characteristic (ROC), accuracy, sensitivity, specificity, and F1 score. Results: The segmentation model achieved an average dice accuracy of 0.94 on the hand-segmented validation set. On the training set, the EGC screening model demonstrated an AUC, accuracy, sensitivity, specificity, and F1 score of 0.98, 0.93, 0.92, 0.92, and 0.93, respectively. On the validation set, these metrics were 0.96, 0.92, 0.90, 0.89, and 0.93, respectively. After three rounds of data regrouping, the model consistently achieved an AUC above 0.9 on both the validation set and the validation set. Conclusion: The results of this study demonstrate that the proposed method can effectively screen for EGC in portal venous CT images. Furthermore, the model exhibits stability and holds promise for future clinical applications.

Uncertainty-guided transformer for brain tumor segmentation.

Multi-model data can enhance brain tumor segmentation for the rich information it provides. However, it also introduces some redundant information that interferes with the segmentation estimation, as some modalities may catch features irrelevant to the tissue of interest. Besides, the ambiguous boundaries and irregulate shapes of different grade tumors lead to a non-confidence estimate of segmentation quality. Given these concerns, we exploit an uncertainty-guided U-shaped transformer with multiple heads to construct drop-out format masks for robust training. Specifically, our drop-out masks are composed of boundary mask, prior probability mask, and conditional probability mask, which can help our approach focus more on uncertainty regions. Extensive experimental results show that our method achieves comparable or higher results than previous state-of-the-art brain tumor segmentation methods, achieving average dice coefficients of [Formula: see text] and Hausdorff distance of 4.91 on the BraTS2021 dataset. Our code is freely available at https://github.com/chaineypung/BTS-UGT.

NeuroD4 converts glioblastoma cells into neuron-like cells through the SLC7A11-GSH-GPX4 antioxidant axis.

Cell fate and proliferation ability can be transformed through reprogramming technology. Reprogramming glioblastoma cells into neuron-like cells holds great promise for glioblastoma treatment, as it induces their terminal differentiation. NeuroD4 (Neuronal Differentiation 4) is a crucial transcription factor in neuronal development and has the potential to convert astrocytes into functional neurons. In this study, we exclusively employed NeuroD4 to reprogram glioblastoma cells into neuron-like cells. In vivo, the reprogrammed glioblastoma cells demonstrated terminal differentiation, inhibited proliferation, and exited the cell cycle. Additionally, NeuroD4 virus-infected xenografts exhibited smaller sizes compared to the GFP group, and tumor-bearing mice in the GFP+NeuroD4 group experienced prolonged survival. Mechanistically, NeuroD4 overexpression significantly reduced the expression of SLC7A11 and Glutathione peroxidase 4 (GPX4). The ferroptosis inhibitor ferrostatin-1 effectively blocked the NeuroD4-mediated process of neuron reprogramming in glioblastoma. To summarize, our study demonstrates that NeuroD4 overexpression can reprogram glioblastoma cells into neuron-like cells through the SLC7A11-GSH-GPX4 signaling pathway, thus offering a potential novel therapeutic approach for glioblastoma.

GBE1 Promotes Glioma Progression by Enhancing Aerobic Glycolysis through Inhibition of FBP1.

Tumor metabolism characterized by aerobic glycolysis makes the Warburg effect a unique target for tumor therapy. Recent studies have found that glycogen branching enzyme 1 (GBE1) is involved in cancer progression. However, the study of GBE1 in gliomas is limited. We determined by bioinformatics analysis that GBE1 expression is elevated in gliomas and correlates with poor prognoses. In vitro experiments showed that GBE1 knockdown slows glioma cell proliferation, inhibits multiple biological behaviors, and alters glioma cell glycolytic capacity. Furthermore, GBE1 knockdown resulted in the inhibition of the NF-κB pathway as well as elevated expression of fructose-bisphosphatase 1 (FBP1). Further knockdown of elevated FBP1 reversed the inhibitory effect of GBE1 knockdown, restoring glycolytic reserve capacity. Furthermore, GBE1 knockdown suppressed xenograft tumor formation in vivo and conferred a significant survival benefit. Collectively, GBE1 reduces FBP1 expression through the NF-κB pathway, shifting the glucose metabolism pattern of glioma cells to glycolysis and enhancing the Warburg effect to drive glioma progression. These results suggest that GBE1 can be a novel target for glioma in metabolic therapy.

A Reliable Nonhuman Primate Model of Ischemic Stroke with Reproducible Infarct Size and Long-term Sensorimotor Deficits.

A nonhuman primate model of ischemic stroke is considered as an ideal preclinical model to replicate various aspects of human stroke because of their similarity to humans in genetics, neuroanatomy, physiology, and immunology. However, it remains challenging to produce a reliable and reproducible stroke model in nonhuman primates due to high mortality and variable outcomes. Here, we developed a focal cerebral ischemic model induced by topical application of 50% ferric chloride (FeCl3) onto the MCA-M1 segment through a cranial window in the cynomolgus monkeys. We found that FeCl3 rapidly produced a stable intraarterial thrombus that caused complete occlusion of the MCA, leading to the quick decrease of the regional cerebral blood flow in 10 min. A typical cortical infarct was detected 24 hours by magnetic resonance imaging (MRI) and was stable at least for 1 month after surgery. The sensorimotor deficit assessed by nonhuman primate stroke scale was observed at 1 day and up to 3 months after ischemic stroke. No spontaneous revascularization or autolysis of thrombus was observed, and vital signs were not affected. All operated cynomolgus monkeys survived. Our data suggested that FeCl3-induced stroke in nonhuman primates was a replicable and reliable model that is necessary for the correct prediction of the relevance of experimental therapeutic approaches in human beings.