BACKGROUND: To explore the risk factors for preoperative massive cerebral infarction (MCI) in pediatric patients with moyamoya disease (MMD).
METHODS: Pediatric patients with MMD treated between 2017 and 2022 were enrolled. Logistic regression analysis was performed to identify risk factors for MCI among the patients, and a nomogram was constructed to identify potential predictors of MCI. Receiver operating characteristic (ROC) curves and areas under the curves were calculated to determine the effects of different risk factors.
RESULTS: This study included 308 pediatric patients with MMD, including 36 with MCI. The MCI group exhibited an earlier age of onset than the non-MCI group. Significant intergroup differences were observed in familial MMD history, postcirculation involvement, duration from diagnosis to initiation of treatment, Suzuki stage, magnetic resonance angiography (MRA) score, collateral circulation score, and RNF213 p.R4810K variations. Family history, higher MRA score, lower collateral circulation score, and RNF213 p.R4810K variations were substantial risk factors for MCI in pediatric patients with MMD. The nomogram demonstrated excellent discrimination and calibration capabilities. The integrated ROC model, which included all the abovementioned four variables, showed superior diagnostic precision with a sensitivity of 67.86%, specificity of 87.01%, and accuracy of 85.11%.
CONCLUSIONS: This study showed that family history, elevated MRA score, reduced collateral circulation score, and RNF213 p.R4810K variations are risk factors for MCI in pediatric patients with MMD. The synthesized model including these variables demonstrated superior predictive efficacy; thus, it can facilitate early identification of at-risk patients and timely initiation of appropriate interventions.

UNASSIGNED: Massive cerebral infarction (MCI) causes severe neurological deficits, coma and can even result in death. Here, we identified hub genes and pathways after MCI by analyzing microarray data from a murine model of ischemic stroke and identified potential therapeutic agents for the treatment of MCI.
UNASSIGNED: Microarray expression profiling was performed using the GSE28731 and GSE32529 datasets from the Gene Expression Omnibus (GEO) database. Data from a sham group (n = 6 mice) and a middle cerebral artery occlusion (MCAO) group (n = 7 mice) were extracted to identify common differentially expressed genes (DEGs). After identifying gene interactions, we generated a protein-protein interaction (PPI) network with Cytoscape software. Then, the MCODE plug-in in Cytoscape was used to determine key sub-modules according to MCODE scores. Enrichment analyses were then conducted on DEGs in the key sub-modules to evaluate their biological functions. Furthermore, hub genes were identified by generating the intersections of several algorithms in the cytohubba plug-in; these genes were then verified in other datasets. Finally, we used Connectivity MAP (CMap) to identify potential agents for MCI therapy.
UNASSIGNED: A total of 215 common DEGs were identified and a PPI network was generated with 154 nodes and 947 edges. The most significant key sub-module had 24 nodes and 221 edges. Gene ontology (GO) analysis showed that the DEGs in this sub-module showed enrichment in inflammatory response, extracellular space and cytokine activity in terms of biological process, cellular component and molecular function, respectively. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that TNF signaling was the most enriched pathway. Myd88 and Ccl3 were identified as hub genes and TWS-119 was identified as the most potential therapeutic agent by CMap.
UNASSIGNED: Bioinformatic analysis identified two hub genes (Myd88 and Ccl3) for ischemic injury. Further analysis identified TWS-119 as the best potential candidate for MCI therapy and that this target may be associated with TLR/MyD88 signaling.

UNASSIGNED: To summarize and analyze the early clinical manifestations, risk factors, treatment and prognosis of myocardial noncompaction in children, and to provide scientific basis for early and effective intervention.
UNASSIGNED: Combined with a case of myocardial noncompaction with massive cerebral infarction in a child, the related research reports of myocardial noncompaction in children were analyzed retrospectively.
UNASSIGNED: Myocardial noncompaction in children is cardiomyopathy caused by abnormal myocardial compaction during embryonic development. Feeding intolerance, dyspnea, chest tightness, fatigue, eyelid edema and other non-specific manifestations may occur in the early stage. It is easy to miss the diagnosis and misdiagnosis in clinical diagnosis and treatment, leading to intractable heart failure, nausea and arrhythmia, thromboembolism and even sudden death and other serious complications. Early diagnosis, symptomatic treatment, control of complications and regular follow-up can prevent the occurrence of serious complications and reduce mortality.
UNASSIGNED: There is no specific clinical manifestation in the early stage of myocardial noncompaction in children. If it is not detected early and treated symptomatically, the prognosis is poor and the mortality is high. Therefore, clinicians should fully improve the understanding of the early clinical manifestations of this disease, give early diagnosis and early intervention to children, reduce the occurrence of serious complications and improve the survival rate.

UNASSIGNED: This study investigated the diagnostic performance of the thrombin-antithrombin complex (TAT), plasmin-α2 plasmin inhibitor complex (PIC), tissue plasminogen activator-plasminogen activator inhibitor complex (t-PAIC), and thrombomodulin (TM) in the early identification of massive cerebral infarction.
UNASSIGNED: A total of 423 patients with cerebral infarction confirmed by imaging examination were divided into the massive cerebral infarction (MCI) group and the non-massive cerebral infarction (NMCI) group. TAT, PIC, t-PAIC, and TM were measured immediately after admission. The diagnostic performance was analyzed by the receiver characteristic operating curve (ROC).
UNASSIGNED: The median plasma concentrations of TAT, PIC, and t-PAIC in patients with MCI at early onset were 5.10 ng/ml, 1.11 μg/ml, and 8.80 ng/ml, respectively, which were higher than those in patients with NMCI (2.20 ng/ml, 0.59 μg/ml, and 7.35 ng/ml), and the difference was statistically significant (P < 0.001). TAT was shown to be an independent risk factor for the development of massive cerebral infarction by a multivariate logistic regression analysis (OR = 1.138). A ROC curve analysis showed that PIC had the best performance in identifying MCI at an early stage (AUC = 82.8%), with a sensitivity of 80.7% and a specificity of 76.2% when the PIC concentration was ≥0.8 μg/ml; TAT had the highest specificity in identifying MCI, with a specificity of 80.6% when the TAT concentration was ≥3.97 ng/ml.
UNASSIGNED: The detection of PIC, TAT, t-PAIC, and TM is a comprehensive assessment of vascular endothelial damage and activation of the coagulation and fibrinolytic systems and has diagnostic value for early identification of patients with MCI, which, together with its ease of detection, can be used as a plasma marker for early identification of large vessel occlusion.

To evaluate the prognostic value of the buffer coefficient, calculated as the ratio of the buffer volume (volume of intracranial cerebrospinal fluid) at the peak of brain edema to the baseline brain volume, and some other parameters in patients with massive cerebral infarction (MCI).
The cohort comprised 161 patients with MCI who were divided into good and poor prognosis groups according to modified Rankin Scale score at 90 days after onset. Differences in clinical and imaging parameters between these groups were analyzed by univariate analysis, and multifactorial binary logistic regression analysis was used to further identify influencing factors that were significantly different. Receiver operating characteristic curve was used to evaluate the diagnostic performance between the buffer volume and the buffer coefficient.
The findings showed that a history of atrial fibrillation, intravenous tissue-type plasminogen activator administration, successful reperfusion, successful craniectomy, low-density lesion volume, brain volume, buffer volume, and buffer coefficient were significantly different between the poor and good prognosis groups (P < 0.05 for all comparisons). Multifactorial binary logistic regression analyses revealed that patients who had large low-density lesion volume and patients who had not achieved successful reperfusion or received intravenous tissue-type plasminogen activator were likely to have a poor prognosis (P < 0.05). The buffer coefficient was identified as an independent predictive factor for MCI (P < 0.001). The area under the receiver operating characteristic curve for the buffer coefficient was 0.862. When the cutoff value was 9.3%, sensitivity of predicting poor prognosis of patients with MCI was 94.7%.
The buffer coefficient has potential benefits as a prognostic indicator for MCI that can be used to detect even subtle changes in brain edema.

UNASSIGNED: The purpose of this study is to establish and evaluate an early biomarker prediction model of massive cerebral infarction caused by anterior circulation occlusion.
UNASSIGNED: One hundred thirty-four patients with acute cerebral infarction from January 2018 to October 2020 were selected to establish the development cohort for the internal test of the nomogram. Ninety-one patients with acute cerebral infarction hospitalized in our hospital from December 2020 to December 2021 were constituted the validation cohort for the external validation. All patients underwent baseline computed tomography (CT) scans within 12 h of onset and early imaging signs (hyperdense middle cerebral artery sign, obscuration of the lentiform nucleus, insular ribbon sign) of acute cerebral infarction were identified on CT by two neurologists. Based on follow-up CT images, patients were then divided into a massive cerebral infarction group and a non-massive cerebral infarction group. The nomogram model was constructed based on logistic regression analysis with R language. The nomogram was subsequently validated in an independent external validation cohort. Accuracy and discrimination of the prediction model were evaluated by a calibration chart, receiver operating characteristic (ROC) curve, and decision curve.
UNASSIGNED: The indicators, including insular ribbon sign, reperfusion therapy, National Institutes of Health Stroke Scale (NHISS) score, previous cerebral infarction, and atrial fibrillation, were entered into the prediction model through binary logistic regression analysis. The prediction model showed good predictive ability. The area under the ROC curve of the prediction model was 0.848. The specificity, sensitivity, and Youden index were 0.864, 0.733, and 0.597, respectively. This nomogram to the validation cohort also showed good discrimination (AUC = 0.940, 95% CI 0.894-0.985) and calibration.
UNASSIGNED: Demonstrating favorable predictive efficacy and reproducibility, this study successfully established a prediction model of CT imaging signs and clinical data as early biomarkers of massive cerebral infarction caused by anterior circulation occlusion.

Massive cerebral infarction (MCI) is a devastating condition and associated with high rate of morbidity and mortality. Hemorrhagic transformation (HT) is a common complication after acute MCI, and often results in poor outcomes. Although several predictors of HT have been identified in acute ischemic stroke (AIS), the association between the predictors and HT remains controversial. Therefore, we aim to explore the value of texture analysis on magnetic resonance image (MRI) for predicting HT after acute MCI. This retrospective study included a total of 98 consecutive patients who were admitted for acute MCI between January 2019 and October 2020. Patients were divided into the HT group (n = 44) and non-HT group (n = 54) according to the follow-up computed tomography (CT) images. A total of 11 quantitative texture features derived from images of diffusion-weighted image (DWI) or T2-weighted-Fluid-Attenuated Inversion Recovery (T2/FLAIR) were extracted for each patient. Receiver operating characteristic (ROC) analysis were performed to determine the predictive performance of textural features, with HT as the outcome measurement. There was no significant difference in the baseline demographic and clinical characteristics between the two groups. The distribution of atrial fibrillation and National Institutes of Health Stroke Scale (NIHSS) were significantly higher in patients with HT than those without HT. Among the textural parameters extracted from DWI images, six parameters, f2 (contrast), f3 (correlation), f4 (sum of squares), f5 (inverse difference moment), f10 (difference variance), and f11 (difference entropy), differs significantly between the two groups (p < 0.05). Moreover, five of six parameters (f2, f3, f5, f10, and f11) have good predictive performances of HT with the area under the ROC curve (AUC) values of 0.795, 0.779, 0.791, 0.780, and 0.797, respectively. However, the texture features f2, f3, and f10 in T2/FLAIR images were the only three significant predictors of HT in patients with acute MCI, but with a relatively low AUC values of 0.652, 0.652, and 0.670, respectively. In summary, our preliminary results showed DWI-based texture analysis has a good predictive validity for HT in patients with acute MCI. Multiparametric MRI texture analysis model should be developed to improve the prediction performance of HT following acute MCI.

OBJECTIVE: To evaluate the efficacy and prognosis of decompressive craniectomy combined with temporal pole resection in the treatment of massive cerebral infarction, in order to provide basis for treatment selection.
METHODS: The clinical data of the patient with massive cerebral infarction treated in our hospital from January 2015 to December 2018 were analyzed retrospectively. According to the surgical methods, the patients were divided into control group (decompressive craniectomy) and study group (decompressive craniectomy + temporal pole resection). Intracranial pressure monitoring devices were placed in both groups. The NIHSS scores of the two groups before and 14 days after operation, the changes of intracranial pressure, length of hospital stay, length of NICU, mortality and modified Rankin scale before and after treatment were compared between the two groups.
RESULTS: The NIHSS score of the two groups after operation was lower than that before operation, and the NIHSS score of the study group was significantly lower than that of the control group (P < 0.05); The intracranial pressure in the study group was significantly lower than that in the control group (P < 0.05); One month after operation, the mortality of the study group (13.0%) was lower than that of the control group (27.8%). After one year of follow-up, the mortality of the study group (21.7%) was significantly lower than that of the control group (38.8%) (P < 0.05); The scores of mRS in the two groups were significantly improved compared with those before treatment (P < 0.05), and the scores of mRS in the study group were better than those in the control group (P < 0.05).
CONCLUSIONS: Decompressive craniectomy combined with temporal pole resection has a better effect in the treatment of patients with massive cerebral infarction. It has good decompression effect, the postoperative intracranial pressure is well controlled, and significantly reduced the mortality. So it has better clinical application value.

Massive cerebral infarction (MCI) is a life-threatening disease and may lead to cerebral herniation. Neutrophil degranulation contributes to ischemic injury in the early stage. To investigate whether neutrophil degranulating factors can predict cerebral herniation and the long-term prognosis of patients with MCI and to investigate the relationship between neutrophil degranulation and blood brain barrier (BBB) damage. In this case-control study of 14 MCI patients, we divided the patients into a cerebral hernia group and no cerebral hernia group according to whether they developed cerebral herniation within 5 days. The prognosis of MCI patients was assessed using the Modified Rankin Scale (mRS) score at 6 months, which was the primary end point. The composition of white blood cells (WBC) and degranulating factors for neutrophils in the plasma of MCI patients was determined on days 2 and 4. Baseline characteristics were comparable in both groups. The neurological functional scores and long-term prognosis showed no difference between patients with or without cerebral herniation, while the mortality rate of the cerebral hernia group in the short term was higher (P < 0.05). The WBC count, neutrophil to lymphocyte ratio (NLR) and plasma myeloperoxidase (MPO) levels of patients with cerebral hernia were significantly higher than those of patients without cerebral hernia (all P < 0.05). MPO is a better predictor of cerebral herniation, and the NLR showed superior predictive value in the prognosis of MCI patients. neutrophil degranulation may play an important role in malignant cerebral hernia during MCI. These data suggest that, MPO and the NLR might be predictive factors for cerebral herniation and the prognosis of MCI patients.

We proposed the concept of the cerebral infarction coefficient, which is cerebral infarction volume/brain volume. This study aimed to evaluate the prognostic value of the cerebral infarction coefficient in patients with massive cerebral infarction (MCI).
According to the modified Rankin score, 71 patients with acute MCI were divided into good prognosis and poor prognosis groups. Clinical and imaging data of the two groups were collected and univariate analysis was carried out. If there were significant differences in the data between the two groups, binary logistic regression analysis was performed.
The poor prognosis group had a significantly higher cerebral infarction volume, cerebral infarction coefficient, and D-dimer levels, older age, the highest body temperature, a higher rate of a history of atrial fibrillation, and a lower rate of a history of hypertension compared with the good prognosis group (all P < 0.05). Binary logistic regression analysis showed that the cerebral infarction coefficient was an independent risk factor for a poor prognosis of patients with MCI (P < 0.05, 95 % confidence interval, 2.091, 42.562), and the odds ratio was 8.506. The area under the receiver operating characteristic curve for the cerebral infarction coefficient was 0.753. When the cut-off value was 7.8 %, the sensitivity of predicting a poor prognosis of patients with MCI was 92.5 %.
The cerebral infarction coefficient may have predictive value in determining the prognosis of patients with MCI.