The coexistence of meningioma and pituitary adenoma is very rare, especially in the same location after meningioma surgery. Here, we reported a case of coexisting meningioma and pituitary adenoma secondary to postoperative meningioma in the sellar region in a patient who had not received radiation therapy before the second surgery. A 61-year-old woman underwent craniotomy for tumor resection for sellar meningioma in 2017, and postoperative imaging showed no residual in the surgical area. In 2022, the patient had a history of decreased vision again. MRI showed the possibility of postoperative pituitary adenoma in the sellar region. The patient underwent endoscopic resection of the skull base lesion again. After surgery, the patient's visual symptoms improved. Histology of the sellar tumor showed both meningioma (meningeal epithelial type and WHO grade I) and pituitary adenoma in the same section. The coexistence of meningioma and pituitary adenoma is a very rare surgical entity. This report provides a theoretical basis for the selection of intracerebral tumor surgery and provides a diagnostic basis and treatment reference for patients diagnosed with meningioma and pituitary adenoma at the same time.

Objective: To investigate the risk factors for early neurological deterioration (END) in patients with spontaneous intracerebral hemorrhage (ICH), construct a predictive model, and evaluate its predictive efficacy.Methods: We retrospectively selected 450 ICH patients admitted to the First People’s Hospital of Lianyungang from June 2023 to September 2024. The patients were randomly divided into a training set (315 patients) and a validation set (135 patients) at a 7:3 ratio. In the training set, patients were categorized into END group (n = 66) and non-END group (n = 249) based on the criteria of a decrease in GCS score by ≥2 points or an increase in NIHSS score by ≥4 points within 72 h of admission. We compared the general data, laboratory test results, and imaging features between the two groups. We used LASSO regression and multivariate logistic regression analysis to identify the independent risk factors for END in ICH patients. A nomogram model for predicting END in ICH patients was constructed using the R language rms package and applied to the validation set to assess the model’s predictive ability and accuracy by drawing ROC curves, calibration curves, and decision curve analysis (DCA) curves.Results: In the training set, there were significant differences between the END and non-END groups in terms of age, admission systolic blood pressure, admission GCS score, admission NIHSS score, serum potassium, serum calcium, blood glucose, homocysteine (Hcy), white blood cell count (WBC), C-reactive protein (CRP), neutrophil-to-lymphocyte ratio (NLR), intraventricular hemorrhage (IVH), blend sign, midline shift, hematoma expansion (HE), and initial hematoma volume (p < 0.05). The results of the LASSO regression and multivariate logistic regression analysis showed that the independent risk factors for END in ICH patients included age, WBC, Hcy, HE, blend sign, and admission systolic blood pressure. The area under the ROC curve (AUC) for predicting END in the training and validation sets were 0.909 and 0.831, respectively. The Hosmer-Lemeshow goodness-of-fit test showed that the model had good calibration (p = 0.550 for the training set and p = 0.368 for the validation set). The DCA curves in the training and validation sets indicated that the model had good clinical utility.Conclusion: Age, WBC, Hcy, HE, blend sign, and admission systolic blood pressure are independent risk factors for END in ICH patients. The nomogram model established based on these parameters can effectively predict END and provide a reference for clinical decision-making.

Plasma homocysteine (Hcy) has been globally recognized as an independent risk factor for various neurovascular diseases. In this study, the authors investigated the relationship between critical Hcy concentration and the risk of rupture in intracranial aneurysms (IAs). This study collected data from 423 patients with both ruptured and unruptured IAs. We compared demographic data, vascular rupture risk factors, and laboratory test results between the two groups. Multivariable logistic regression analysis was employed to determine the correlation between critical plasma Hcy levels and the risk of rupture in small to medium-sized IAs. A total of 330 cases of ruptured intracranial aneurysms (RIA) and 93 cases of unruptured intracranial aneurysms (UIA) were included. Univariate analysis revealed statistically significant differences between the ruptured and unruptured groups in terms of hypertension, hyperlipidemia, plasma Hcy levels, and IA morphology (all P < 0.05). Multivariable logistic regression analysis indicated that hypertension (odds ratio [OR] 0.504; 95% confidence interval [CI] 0.279–0.911; P = 0.023), hyperlipidemia (OR 1.924; 95% CI 1.079–3.429; P = 0.027), and plasma Hcy levels (OR 1.420; 95% CI 1.277–1.578; P < 0.001) were independently associated with the rupture of small to medium-sized IAs, all with statistical significance (P < 0.05). Our study suggests that critical plasma Hcy levels are an independent risk factor for increased rupture risk in small to medium-sized intracranial aneurysms. Therefore, reducing plasma Hcy levels may be considered a valuable strategy to mitigate the risk of intracranial vascular abnormalities rupture and improve patient prognosis.

Background: Early hematoma expansion (HE) reflects interval progression of traumatic intracranial hemorrhage, but its association with long-term outcome and its prognostic contribution beyond admission characteristics remain incompletely defined. We evaluated whether HE was independently associated with 6-month functional outcome and provided incremental prognostic information beyond admission characteristics. Methods: This single-center retrospective study included 640 adults with traumatic brain injury, including 285 with HE. HE was determined from the first two in-hospital head computed tomography scans obtained within 48 h after injury and was defined as a new intracranial hemorrhagic lesion or an increase of >3 ml in total hemorrhage volume. Six-month outcome was assessed using the Glasgow Outcome Scale (1–3 unfavorable; 4–5 favorable). HE was added to a study-specific baseline model comprising age, admission Glasgow Coma Scale score, D-dimer, baseline hematoma volume, cerebral contusion, diffuse axonal injury, frontal lobe involvement, and long-bone fracture. Results: At 6 months, 145 patients (22.7%) had an unfavorable outcome. HE was independently associated with a lower likelihood of favorable outcome (OR, 0.20; 95% CI, 0.10–0.42). Adding HE reduced the Akaike information criterion from 392.767 to 365.908 (likelihood-ratio P < 0.001) and increased the area under the curve from 0.920 to 0.929 (ΔAUC = 0.009; DeLong P = 0.0289). Prognostic heterogeneity remained among HE-positive patients. Conclusions: Early HE is independently associated with 6-month functional outcome in adult patients with TBI. As a readily obtainable finding on routine serial CT, HE may help refine early prognostic assessment in patients with TBI.

Background: Glioma is the most common primary malignant tumor of the central nervous system and is characterized by aggressive progression, high recurrence rates, and limited therapeutic options. Apigenin, a naturally occurring flavonoid, exhibits promising anti-tumor properties; however, its molecular mechanisms in glioma remain insufficiently understood. To systematically identify the therapeutic targets and molecular mechanisms underlying apigenin-mediated anti-glioma activity through an integrated computational and experimental approach. Methods: An integrated bioinformatics framework combining network pharmacology, transcriptomic analysis, and machine learning algorithms was applied to identify potential therapeutic targets of apigenin in glioma. Differentially expressed genes were screened using TCGA and GTEx datasets, and prognostic genes were identified through LASSO Cox regression, ElasticNet, and stepwise Cox regression analyses, followed by construction and validation of a multigene prognostic model. Weighted gene co-expression network analysis (WGCNA), over-representation analysis (ORA), and gene set enrichment analysis (GSEA) were performed to explore functional mechanisms. Molecular docking, molecular dynamics simulations, and ADMET prediction were conducted to evaluate the interaction stability and drug-like properties of apigenin with candidate targets. Furthermore, the predicted regulatory axis was examined in LN229 and U87 glioma cell models through cell viability assays, colony formation assays, and Western blotting. Results: Six prognostic genes, including EGFR, MMP9, HIF1A, STAT3, RELA , and CASP3 , were identified and used to construct a prognostic model with robust predictive performance. The model achieved favorable discrimination, with 1-, 3-, and 5-year AUC values of 0.840, 0.876, and 0.817, respectively, and a cross-validated C-index of 0.78. Functional enrichment analyses indicated that these genes were primarily associated with cancer-related signaling pathways. Molecular docking and molecular dynamics simulations predicted potential stable interactions between apigenin and key targets, particularly EGFR and STAT3. Cellular experiments showed reduced glioma cell viability following apigenin treatment and reduced colony formation after EGFR or MMP9 knockdown, accompanied by suppression of the EGFR/STAT3 signaling pathway and downstream MMP9 expression. Conclusion: This study established an integrated computational and experimental framework to investigate potential mechanisms associated with the anti-glioma activity of apigenin. Our findings suggest that apigenin-associated growth inhibition in glioma cells involves changes in EGFR/STAT3 signaling and MMP9 expression, providing new insights into its therapeutic potential and supporting further pre-clinical investigation of apigenin as a potential candidate for glioma treatment based on computational and in vitro cellular evidence.