Congenital spinal tumor in a patient with encephalocele and hydrocephalus: a case report

<p>Abstract</p> <p>Introduction</p> <p>Encephalocele is a rare congenital abnormality of the central nervous system, where brain tissue protrudes from a defect in the skull. Some anomalies are associated with encephalocele. However, the association of spinal teratoma and encephalocele has not been reported in the English literature.</p> <p>Case presentation</p> <p>We report the case of an Iranian girl with a history of encephalocele surgery, who, at the age of four years, developed an intramedullary spinal teratoma, and discuss the pathogenesis of this association.</p> <p>Conclusion</p> <p>To the best of our knowledge, this is the first report of an association between encephalocele and spinal teratoma.</p

Epidemiological aspects and clinical outcome of patients with rhinocerebral zygomycosis: A survey in a referral hospital in Iran

Introduction: No comprehensive reports have been published on epidemiological status of Rhinocerebral zygomycosis infections and its outcome in our population, Hence, the current study came to address epidemiological characteristics as well as clinical outcome of patients with Rhinocerebral zygomycosis infection referred to a referral hospital in Iran. Methods: This retrospective study was performed at the Rasoul-e-Akram hospital, an 800-bed tertiary care teaching hospital in Tehran, Iran. The pathology recorded charts were reviewed to identify all cases of Rhinocerebral zygomycosis from patients admitted between April 2007 and March 2014. A diagnosis of Rhinocerebral zygomycosis was based on histopathological assessments. Results: Sixty four patients with Rhinocerebral zygomycosis were assessed. The mean age of the patients was 46.07 ± 22.59 years and 51.6 were female. Among those, 67.2 were diabetic, 26.6 were hypertensive and 29.7 had history of cancer. Different sinuses were infected in 73.4 of the patients. Out of all the patients 26.6 underwent surgical procedures and 17.2 were controlled medically. Extensive debridement was carried out in 40.6. Neutropenia ( 14 days) was found in 60.9. According to the Multivariable logistic regression analysis, the main predictors of in-hospital mortality included female gender, advanced age, the presence of sinus infection, and neutropenia, while higher dosages of amphotericin administered had a protective role in preventing early mortality. In a similar Multivariate model, history of cancer could predict prolonged hospital stay, whereas using higher dose of amphotericin could lead to shortening length of hospital stay. Conclusion: There is no difference in demographic characteristics between our patients with Rhinocerebral zygomycosis and other nations. The presence of diabetes mellitus is closely associated with the presence of this infection. Sinus involvement is very common in those with Rhinocerebral zygomycosis leading to high mortality and morbidity. Besides female gender, advanced age, and presence of neutropenia was a major risk factor for increasing early mortality. The use of higher doses of antifungal treatment such as amphotericin can prevent both mortality and prolonged hospital stay. The cancer patients may need longer hospital stay because of needing comprehensive in-hospital treatment. © Vida Bozorgiet al

MRI-based radiomics for prognosis of pediatric diffuse intrinsic pontine glioma: an international study.

Background: Diffuse intrinsic pontine gliomas (DIPGs) are lethal pediatric brain tumors. Presently, MRI is the mainstay of disease diagnosis and surveillance. We identify clinically significant computational features from MRI and create a prognostic machine learning model. Methods: We isolated tumor volumes of T1-post-contrast (T1) and T2-weighted (T2) MRIs from 177 treatment-naïve DIPG patients from an international cohort for model training and testing. The Quantitative Image Feature Pipeline and PyRadiomics was used for feature extraction. Ten-fold cross-validation of least absolute shrinkage and selection operator Cox regression selected optimal features to predict overall survival in the training dataset and tested in the independent testing dataset. We analyzed model performance using clinical variables (age at diagnosis and sex) only, radiomics only, and radiomics plus clinical variables. Results: All selected features were intensity and texture-based on the wavelet-filtered images (3 T1 gray-level co-occurrence matrix (GLCM) texture features, T2 GLCM texture feature, and T2 first-order mean). This multivariable Cox model demonstrated a concordance of 0.68 (95% CI: 0.61-0.74) in the training dataset, significantly outperforming the clinical-only model (C = 0.57 [95% CI: 0.49-0.64]). Adding clinical features to radiomics slightly improved performance (C = 0.70 [95% CI: 0.64-0.77]). The combined radiomics and clinical model was validated in the independent testing dataset (C = 0.59 [95% CI: 0.51-0.67], Noether's test P = .02). Conclusions: In this international study, we demonstrate the use of radiomic signatures to create a machine learning model for DIPG prognostication. Standardized, quantitative approaches that objectively measure DIPG changes, including computational MRI evaluation, could offer new approaches to assessing tumor phenotype and serve a future role for optimizing clinical trial eligibility and tumor surveillance

Genetic architecture of white matter hyperintensities differs in hypertensive and nonhypertensive ischemic stroke.

BACKGROUND AND PURPOSE: Epidemiological studies suggest that white matter hyperintensities (WMH) are extremely heritable, but the underlying genetic variants are largely unknown. Pathophysiological heterogeneity is known to reduce the power of genome-wide association studies (GWAS). Hypertensive and nonhypertensive individuals with WMH might have different underlying pathologies. We used GWAS data to calculate the variance in WMH volume (WMHV) explained by common single nucleotide polymorphisms (SNPs) as a measure of heritability (SNP heritability [HSNP]) and tested the hypothesis that WMH heritability differs between hypertensive and nonhypertensive individuals. METHODS: WMHV was measured on MRI in the stroke-free cerebral hemisphere of 2336 ischemic stroke cases with GWAS data. After adjustment for age and intracranial volume, we determined which cardiovascular risk factors were independent predictors of WMHV. Using the genome-wide complex trait analysis tool to estimate HSNP for WMHV overall and within subgroups stratified by risk factors found to be significant in multivariate analyses. RESULTS: A significant proportion of the variance of WMHV was attributable to common SNPs after adjustment for significant risk factors (HSNP=0.23; P=0.0026). HSNP estimates were higher among hypertensive individuals (HSNP=0.45; P=7.99×10(-5)); this increase was greater than expected by chance (P=0.012). In contrast, estimates were lower, and nonsignificant, in nonhypertensive individuals (HSNP=0.13; P=0.13). CONCLUSIONS: A quarter of variance is attributable to common SNPs, but this estimate was greater in hypertensive individuals. These findings suggest that the genetic architecture of WMH in ischemic stroke differs between hypertensives and nonhypertensives. Future WMHV GWAS studies may gain power by accounting for this interaction

Radiomic signatures of posterior fossa ependymoma: Molecular subgroups and risk profiles

BACKGROUND: The risk profile for posterior fossa ependymoma (EP) depends on surgical and molecular status [Group A (PFA) versus Group B (PFB)]. While subtotal tumor resection is known to confer worse prognosis, MRI-based EP risk-profiling is unexplored. We aimed to apply machine learning strategies to link MRI-based biomarkers of high-risk EP and also to distinguish PFA from PFB. METHODS: We extracted 1800 quantitative features from presurgical T2-weighted (T2-MRI) and gadolinium-enhanced T1-weighted (T1-MRI) imaging of 157 EP patients. We implemented nested cross-validation to identify features for risk score calculations and apply a Cox model for survival analysis. We conducted additional feature selection for PFA versus PFB and examined performance across three candidate classifiers. RESULTS: For all EP patients with GTR, we identified four T2-MRI-based features and stratified patients into high- and low-risk groups, with 5-year overall survival rates of 62% and 100%, respectively (p < 0.0001). Among presumed PFA patients with GTR, four T1-MRI and five T2-MRI features predicted divergence of high- and low-risk groups, with 5-year overall survival rates of 62.7% and 96.7%, respectively (p = 0.002). T1-MRI-based features showed the best performance distinguishing PFA from PFB with an AUC of 0.86. CONCLUSIONS: We present machine learning strategies to identify MRI phenotypes that distinguish PFA from PFB, as well as high- and low-risk PFA. We also describe quantitative image predictors of aggressive EP tumors that might assist risk-profiling after surgery. Future studies could examine translating radiomics as an adjunct to EP risk assessment when considering therapy strategies or trial candidacy

A magnetic topological semimetal Sr1-y Mn1-zSb2 (y, z \u3c 0.1)

Weyl (WSMs) evolve from Dirac semimetals in the presence of broken time-reversal symmetry (TRS) or space-inversion symmetry. The WSM phases in TaAs-class materials and photonic crystals are due to the loss of space-inversion symmetry. For TRS-breaking WSMs, despite numerous theoretical and experimental efforts, few examples have been reported. In this Article, we report a new type of magnetic semimetal Sr1-y Mn1-z Sb2 (y, z \u3c 0.1) with nearly massless relativistic fermion behaviour (m-=0.04-0.05m0, where m 0 is the free-electron mass). This material exhibits a ferromagnetic order for 304

Genome-wide meta-analysis of cerebral white matter hyperintensities in patients with stroke.

OBJECTIVE: For 3,670 stroke patients from the United Kingdom, United States, Australia, Belgium, and Italy, we performed a genome-wide meta-analysis of white matter hyperintensity volumes (WMHV) on data imputed to the 1000 Genomes reference dataset to provide insights into disease mechanisms. METHODS: We first sought to identify genetic associations with white matter hyperintensities in a stroke population, and then examined whether genetic loci previously linked to WMHV in community populations are also associated in stroke patients. Having established that genetic associations are shared between the 2 populations, we performed a meta-analysis testing which associations with WMHV in stroke-free populations are associated overall when combined with stroke populations. RESULTS: There were no associations at genome-wide significance with WMHV in stroke patients. All previously reported genome-wide significant associations with WMHV in community populations shared direction of effect in stroke patients. In a meta-analysis of the genome-wide significant and suggestive loci (p < 5 × 10(-6)) from community populations (15 single nucleotide polymorphisms in total) and from stroke patients, 6 independent loci were associated with WMHV in both populations. Four of these are novel associations at the genome-wide level (rs72934505 [NBEAL1], p = 2.2 × 10(-8); rs941898 [EVL], p = 4.0 × 10(-8); rs962888 [C1QL1], p = 1.1 × 10(-8); rs9515201 [COL4A2], p = 6.9 × 10(-9)). CONCLUSIONS: Genetic associations with WMHV are shared in otherwise healthy individuals and patients with stroke, indicating common genetic susceptibility in cerebral small vessel disease