Spontaneous Intracerebral hemorrhage (sICH) is a disease with high mortality and disability. Non-contrast computed tomography (NCCT) is the most commonly used imaging method in the diagnosis and treatment of sICH. This study aimed to develop a clinically useful prediction model for the short-term prognosis of sICH patients based on NCCT features using a machine learning model. We retrospectively collected data from sICH patients from four centers in China between January 2021 and June 2024, used data from three centers as training cohort to build the model, and another single center data for external validation. The NCCT imaging features were combined with the basic clinical characteristics of sICH patients as training features for machine learning. We developed and verified the effectiveness of five models: support vector machine (SVM), logistic regression (LR), random forest (RF), eXtreme Gradient Boosting (XGboost) and Light Gradient Boosting Machine (LightGBM). The clinical feature set, NCCT imaging feature set and fusion feature set were modeled separately and externally validated. The performance of machine learning models with different features was comprehensively evaluated using ROC curves, accuracy and other related indicators. The SHapley Additive exPlanations (SHAP) diagram was used to illustrate the importance of variables in the model, and the Sequential Forward Selection (SFS) was used to screen out the core features. Finally, a convenient and practical prognosis prediction platform was built based on the core features. This study is registered with ClinicalTrials.gov (NCT06535438). A total of 1091 sICH patients from three centers were included as the training cohort, and 102 patients from a single center were included as the external validation cohort. The LightGBM model showed the best performance in predicting the short-term prognosis of sICH patients, with an area under the receiver operating characteristic curve (AUROC) of 0.813 ± 0.012. The clinical feature cohort model (AUC: 0.822, 95% CI (0.763-0.881)), the NCCT imaging feature model (AUC: 0.770, 95% CI (0.704-0.835)) and the fusion model (AUC: 0.852, 95% CI (0.797-0.906)) were developed respectively. The external validation cohort were the clinical feature model (AUC: 0.792, 95% CI (0.689-0.894)), the NCCT imaging feature model (AUC: 0.746, 95% CI (0.637-0.855)), and the fusion feature (AUC: 0.796, 95% CI (0.694-0.897). Finally, the core factors obtained through screening, including Glasgow Coma Scale (GCS) score at admission, intraventricular hemorrhage (IVH), National Institutes of Health Stroke Scale (NIHSS) score at admission, hematoma volume, mean CT value, and black hole sign were incorporated into the model to generate a publicly accessible online platform (https://surge-ustc.shinyapps.io/multi_para_sih_prognosis/). The prediction model based on NCCT features established by the LightGBM model has a reliable predictive effect on the short-term prognosis of sICH patients and is of great clinical convenience and practicality. Funding provided by National Natural Science Foundation of China (82427808, 82171382, U23A20424, 82090034 and 82371201), the Anhui Province Clinical Medical Research Transformation Special Project (202204295107020006 and 202204295107020028) and Research Fund of Anhui Institute of translational medicine (2022zhyx-B11).

Background: Mild traumatic brain injury (mTBI) frequently results in persistent cognitive, emotional, and functional impairments, closely linked to disruptions in the default mode network (DMN). Understanding the mechanisms driving these network abnormalities is critical for advancing diagnostic and therapeutic strategies.Methods: This study adopted a multimodal approach, combining functional connectivity (FC) analysis, diffusion tensor imaging (DTI), and gene expression profiling to investigate DMN disruptions in mTBI. A primary focus was placed on the middle cingulate cortex (MCC), a region consistently identified with increased connectivity. We explored the structural and molecular changes underlying this phenomenon. Receiver operating characteristic (ROC) curve analysis was utilized to assess the diagnostic potential of DTI‐derived metrics, while white matter tractography was employed to explore structural connectivity between the MCC and the dorsolateral prefrontal cortex (DLPFC).Results: Our findings revealed significant disruptions in DMN connectivity, with the MCC prominently involved in mTBI pathology. DTI analyses identified pronounced axonal injury in the MCC, characterized by decreased fractional anisotropy (FA) and axial diffusivity (AD), alongside increased isotropy (ISO), indicating compromised white matter integrity and diffuse axonal injury. Gene expression profiling revealed the upregulation of pathways related to synaptic transmission, ion channel regulation, and axonal injury response. ROC analysis demonstrated that ISO serves as a particularly effective biomarker for mTBI, showing high diagnostic accuracy (AUC = 0.871). White matter tractography further confirmed strong structural connectivity between the MCC and the DLPFC, identifying potential therapeutic targets for neuromodulation.Conclusion: This study provides robust evidence that diffuse axonal injury plays a pivotal role in DMN abnormalities observed in mTBI. The integration of FC, DTI, and gene expression profiling offers a comprehensive framework for understanding mTBI's impact on brain networks. Our findings also highlight the DLPFC as a promising target for therapeutic interventions aimed at addressing cognitive and emotional deficits associated with mTBI.

Background: Intracerebral hemorrhage (ICH) is a devastating subtype of stroke, in which neuroinflammation and blood–brain barrier (BBB) disruption are secondary pathophysiological events that drive progressive brain injury. Histone lysine demethylase JMJD3 (Jumonji C domain-containing protein 3) is a master epigenetic switch governing inflammatory signaling; however, its participation in ICH-induced vascular disruption and its possible mechanism remain elusive. Objective: To examine the expression patterns of JMJD3 in the context of ICH and to evaluate the therapeutic potential of its specific inhibitor, GSK-J4, in attenuating neuroinflammation and BBB disruption in a murine ICH model. Methods: Hemin treatment of a mouse C8-D1A astrocytic cell line was used to develop an in vitro ICH model. The transcript level of the Jmjd3 gene and its correlation with pro-inflammatory signaling were analyzed with or without GSK-J4 pretreatment. ICH in vivo was created experimentally in adult male C57BL/6 mice through stereotactic striatal injection of collagenase IV, and the mice were randomly assigned to sham, ICH + vehicle, and ICH + GSK-J4 (30 mg/kg intraperitoneally (i.p.), every other day starting three days before ICH) groups. At three days post-ICH, ipsilateral brain tissues were collected to detect JMJD3 cellular localization, pro-inflammatory mediator levels, tight junction protein expression, BBB ultrastructure, and hematoma volume. White matter integrity and neuronal recovery were assessed on day 7, and sensorimotor function was assessed longitudinally on days 1, 3, 5, 7, and 14. Results: Jmjd3 gene transcription was upregulated in hemin-treated astrocytes and correlated positively with IL-6 pro-inflammatory signaling activation. In vivo, the co-localization of JMJD3 with the astrocytic identifier glial fibrillary acidic protein (GFAP) was markedly increased in the area adjacent to the hematoma at three days post-ICH. GSK-J4 administration significantly suppressed the pro-inflammatory signaling cascade by decreasing the levels of inducible nitric oxide synthase (iNOS), tumor necrosis factor-α (TNF-α), and matrix metalloproteinase-9 (MMP-9), enhanced brain vascular structural and functional integrity by upregulating tight junction proteins zonula occludens protein-1 (ZO-1) and claudin-5, improved BBB ultrastructural integrity, and decreased hematoma volume at three days post-ICH. Furthermore, GSK-J4 administration promoted white matter integrity (increased myelin basic protein [MBP] expression) and neuronal recovery (increased neuron-specific nuclear protein [NeuN] expression) at seven days post-ICH and significantly improved the performance of ICH mice in sensorimotor behavioral tests. Conclusions: Astrocytic JMJD3 is upregulated following ICH and promotes neuroinflammation, which in turn mediates BBB disruption. Pharmacological inhibition of JMJD3 by GSK-J4 attenuates neuroinflammation and subsequent BBB damage, accelerates hematoma resolution, and promotes histological and functional recovery after ICH, likely by downregulating MMP-9 expression. These findings identify astrocytic JMJD3 as a novel epigenetic therapeutic target for acute ICH.