Traumatic brain injury (TBI) is the major cause of high mortality and disability rates worldwide. Pioglitazone is an activator of peroxisome proliferator-activated receptor-gamma (PPARγ) that can reduce inflammation following TBI. Clinically, neuroinflammation after TBI lacks effective treatment. Although there are many studies on PPARγ in TBI animals, only few could be converted into clinical, since TBI mechanisms in humans and animals are not completely consistent. The present study, provided a potential theoretical basis and therapeutic target for neuroinflammation treatment after TBI. First, we detected interleukin-6 (IL-6), nitric oxide (NO) and Caspase-3 in TBI clinical specimens, confirming a presence of a high expression of inflammatory factors. Western blot (WB), quantitative real-time PCR (qRT-PCR) and immunohistochemistry (IHC) were used to detect PPARγ, IL-6, and p-NF-κB to identify the mechanisms of neuroinflammation. Then, in the rat TBI model, neurobehavioral and cerebral edema levels were investigated after intervention with pioglitazone (PPARγ activator) or T0070907 (PPARγ inhibitor), and PPARγ, IL-6 and p-NF-κB were detected again by qRT-PCR, WB and immunofluorescence (IF). The obtained results revealed that: 1) increased expression of IL-6, NO and Caspase-3 in serum and cerebrospinal fluid in patients after TBI, and decreased PPARγ in brain tissue; 2) pioglitazone could improve neurobehavioral and reduce brain edema in rats after TBI; 3) the protective effect of pioglitazone was achieved by activating PPARγ and reducing NF-κB and IL-6. The neuroprotective effect of pioglitazone on TBI was mediated through the PPARγ/NF-κB/IL-6 pathway.

Hemorrhagic progression of contusion (HPC) often occurs early in cerebral contusions (CC) patients, significantly impacting their prognosis. It is vital to promptly assess HPC and predict outcomes for effective tailored interventions, thereby enhancing prognosis in CC patients. We utilized the Attention-3DUNet neural network to semi-automatically segment hematomas from computed tomography (CT) images of 452 CC patients, incorporating 695 hematomas. Subsequently, 1502 radiomic features were extracted from 358 hematomas in 261 patients. After a selection process, these features were used to calculate the radiomic signature (Radscore). The Radscore, along with clinical features such as medical history, physical examinations, laboratory results, and radiological findings, was employed to develop predictive models. For prognosis (discharge Glasgow Outcome Scale score), radiomic features of each hematoma were augmented and fused for correlation. We employed various machine learning methodologies to create both a combined model, integrating radiomics and clinical features, and a clinical-only model. Nomograms based on logistic regression were constructed to visually represent the predictive procedure, and external validation was performed on 170 patients from three additional centers. The results showed that for HPC, the combined model, incorporating hemoglobin levels, Rotterdam CT score of 3, multi-hematoma fuzzy sign, concurrent subdural hemorrhage, international normalized ratio, and Radscore, achieved area under the receiver operating characteristic curve (AUC) values of 0.848 and 0.836 in the test and external validation cohorts, respectively. The clinical model predicting prognosis, utilizing age, Abbreviated Injury Scale for the head, Glasgow Coma Scale Motor component, Glasgow Coma Scale Verbal component, albumin, and Radscore, attained AUC values of 0.846 and 0.803 in the test and external validation cohorts, respectively. Selected radiomic features indicated that irregularly shaped and highly heterogeneous hematomas increased the likelihood of HPC, while larger weighted axial lengths and lower densities of hematomas were associated with a higher risk of poor prognosis. Predictive models that combine radiomic and clinical features exhibit robust performance in forecasting HPC and the risk of poor prognosis in CC patients. Radiomic features complement clinical features in predicting HPC, although their ability to enhance the predictive accuracy of the clinical model for adverse prognosis is limited.

Cerebrovascular dysfunction resulting from traumatic brain injury (TBI) significantly contributes to poor patient outcomes. Recent studies revealed the involvement of iron metabolism in neuronal survival, yet its effect on vasculature remains unclear. This study aims to explore the impact of endothelial ferroptosis on cerebrovascular function in TBI. A Controlled Cortical Impact (CCI) model was established in mice, resulting in a significant increase in iron-related proteins such as TfR1, FPN1, and FTH, as well as oxidative stress biomarker 4HNE. This was accompanied by a decline in expression of the ferroptosis inhibitor GPX4. Moreover, Perls' staining and nonhemin iron content assay showed iron overload in brain microvascular endothelial cells (BMECs) and the ipsilateral cortex. Immunofluorescence staining revealed more FTH-positive cerebral endothelial cells, consistent with impaired perfusion vessel density and cerebral blood flow. As a specific iron chelator, deferoxamine (DFO) treatment inhibited such ferroptotic proteins expression and the accumulation of lipid-reactive oxygen species following CCI, enhancing glutathione peroxidase(GPx) activity. DFO treatment significantly reduced iron deposition in BMECs and brain tissue, and increased density of the cerebral capillaries as well. Consequently, DFO treatment led to improvements in cerebral blood flow (as measured by laser speckle imaging) and behavioral performance (as measured by the neurological severity scores, rotarod test, and Morris water maze test). Taken together, our results indicated that TBI induces remarkable iron disorder and endothelial ferroptosis, and DFO treatment may help maintain iron homeostasis and protect vascular function. This may provide a novel therapeutic strategy to prevent cerebrovascular dysfunction following TBI.

Early brain injury (EBI) is a major contributor to the high mortality and morbidity after subarachnoid hemorrhage (SAH). Inflammatory responses and neuronal apoptosis are important causes of EBI. Because 5- lipoxygenase (5-LOX) is known to be involved various central nervous system diseases, we investigated the effects of 5-LOX inhibition during EBI after SAH. Zileuton and LY294002 were used to inhibit expression of 5-LOX and Akt, respectively. We found that 5-LOX expression was significantly increased in the cytoplasm of cortical neurons after SAH and was accompanied by upregulated expression of the inflammatory factors LTB4, TNF-α, IL-1β and IL-6; upregulation of the pro-apoptotic factor Bax; downregulation of the anti-apoptotic factor Bcl-2; and an increased apoptosis rate. Gastric Zileuton administration significantly suppressed all of those effects and improved neurological function. Zileuton also upregulated activated (phosphorylated) AKT levels, and these beneficial effects of Zileuton were abolished by intracerebroventricular infusion of the PI3K inhibitor LY294002. Taken together, these findings indicate that 5-LOX mediates pro-inflammatory and pro-apoptotic effects that contribute to EBI after SAH and that those effects are suppressed by activation of PI3K/Akt signaling. This suggests targeting 5-LOX may be an effective approach to treating EBI after SAH.

Objective: This study endeavors to construct a machine learning model to forecast in-hospital mortality and dissect associated risk factors, utilizing a vast dataset from multiple hospitals in Chongqing. Methods: We amassed detailed baseline data encompassing demographics, medical histories, laboratory tests, and imaging indicators from 23,307 ischemic stroke patients. The NIHSS score was derived from admission records, and both in-hospital survival status and causes of death were meticulously documented. Employing the missForest method, we imputed missing values, addressing data imbalance through random oversampling, validated via five-fold cross-validation. The SHAPRFECV technique was instrumental in identifying the most impactful features, steering clear of multicollinearity. A suite of machine learning models, including LR, RF, and KNN, were meticulously tuned using three-fold cross-validation and grid search to optimize hyperparameters. Results: Our cohort had an average age of 67.347 ± 12.822 years, a baseline NIHSS score of 8.430 ± 3.162, and a 51.186% male predominance, with an in-hospital mortality rate of 6.183%. The Random Forest model excelled with an AUC of 0.940 in the test set, trailed closely by CatBoost at 0.937, LightGBM at 0.930, and XGBoost at 0.929. Notably, CatBoost boasted the highest F1 score of 0.595420 on the test set, with no significant predictive performance disparity between it and the Random Forest model (p = 0.500). Conclusion: Grounded in data from four hospitals in Chongqing, our machine learning model, predicated on baseline features, not only streamlines clinical application but also ensures robust predictive efficacy. It provides an in-depth analysis of mortality risk factors, serving as a pivotal reference for clinical decision-making. Future endeavors will concentrate on validating the model within larger-scale, geographically diverse samples, thereby amplifying its applicability and value in clinical practice.

Objective: Minimally invasive puncture and drainage (MIPD) of hematomas was the preferred option for appropriate patients with hypertensive intracerebral hemorrhage (HICH). The goal of our research was to introduce the MIPD surgery using mixed reality holographic navigation technology (MRHNT). Method: We provided the complete workflow for hematoma puncture using MRHNT included three-dimensional model reconstruction by preoperative CT examination, puncture trajectory design, immersive presentation of model, and real environment and hematoma puncture using dual-plane navigation by wearing special equipment. We collected clinical data on eight patients with HICH who underwent MIPD using MRHNT from March 2021 to August 2021, including the hematoma evacuation rate, operation time, deviation in drainage tube target, postoperative complications, and 2-week postoperative GCS. Result: The workflow for hematoma puncture using MRHNT were performed in all eight cases, in which the average hematoma evacuation rate was 47.36±9.16%, the average operation time was 82.14±15.74 min, and the average deviation of the drainage tube target was 5.76±0.80 mm. There was no delayed bleeding, acute ischemic stroke, intracranial infection, or epilepsy 2 weeks after surgery. The 2-week postoperative GCS was improved compared with the preoperative GCS. Conclusion: The research concluded it was feasible to perform the MIPD by MRHNT on patients with HICH. The risk of general anesthesia and highly professional holographic information processing restricted the promotion of the technology, it was necessary for technical innovation and the accumulation of more case experience and verification of its superiority.