Subarachnoid aneurysmal hemorrhage (SAH) due to cerebral aneurysm rupture is a very serious disease resulting in high mortality rate. It has been known that genetic factors are involved in the risk of SAH. A recent breakthrough in genomic variation called copy number variation (CNV) has been revealed to be involved in risks of human diseases. In this study, we hypothesized that CNVs can predict the risk of SAH. We used the Illumina HumanHap300 BeadChip (317 503 markers) to genotype 497 individuals in a Japanese population. Furthermore, individual CNVs were identified using signal and allelic intensities. The genetic effect of CNV on the risk of SAH was evaluated using multivariate logistic regression controlling for age and gender in 187 common CNV regions (frequency >1%). From a total of 4574 individual CNVs identified in this study (9.7 CNVs per individual), we were able to discover 1644 unique CNV regions containing 1232 genes. The identified variations were validated using visual examination of the genoplot image, overlapping analysis with the Database of Genomic Variants (73.2%), CNVpartition (72.4%) and quantitative PCR. Interestingly, two CNV regions, chr4:153210505-153212191 (deletion, 4q31.3, P=0.0005, P(corr) (corrected P-value)=0.04) and chr10:6265006-6267388 (duplication, 10p15.1, P=0.0006, P(corr)=0.05), were significantly associated with the risk of SAH after multiple testing corrections. Our results suggest that the newly identified CNV regions may contribute to SAH disease susceptibility.
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http://dx.doi.org/10.1038/jhg.2010.97 | DOI Listing |
Brain Behav Immun Health
February 2025
University Center for Research and Development, Chandigarh University, Mohali, Punjab, India.
Background And Objective: Lyme disease, caused by , presents major health challenges worldwide, leading to serious neurological and musculoskeletal issues that impact patients' lives and healthcare systems. This systematic review and meta-analysis aim to determine the prevalence and link between Lyme disease and these complications, aiming to enhance clinical and public health approaches.
Methods: We systematically searched PubMed, EMBASE, and Web of Science up until April 01, 2024, to find studies reporting the prevalence and severity of neurological and musculoskeletal complications associated with Lyme disease.
J Clin Med
January 2025
Neurosurgery, San Giovanni Bosco Hospital, 10154 Turin, Italy.
Aneurysmal subarachnoid hemorrhage (aSAH) carries significant mortality and disability rates, with rebleeding posing a grave risk, particularly in anterior communicating artery (AcoA) aneurysms. This retrospective study aims to analyze preoperative and intraoperative variables of patients with ruptured AcoA aneurysms, evaluating the association of these variables with patient outcomes using machine learning techniques, proposing a prognostic score. : A retrospective study was conducted on 50 patients who underwent microsurgical clipping for a ruptured AcoA aneurysm at San Giovanni Bosco Hospital, Turin, Italy.
View Article and Find Full Text PDFMedicina (Kaunas)
January 2025
Department of Biomedical Sciences, Dubai Medical College for Girls, Dubai 20170, United Arab Emirates.
Eclampsia is a multisystem disorder of pregnancy and the puerperium. Posterior reversible encephalopathy syndrome (PRES), a neurotoxic condition characterized by various neurological symptoms, can arise from multiple causes including eclampsia. Although hemorrhage is a possible complication of PRES, subarachnoid hemorrhage (SAH) is a rare occurrence in eclamptic patients with this condition.
View Article and Find Full Text PDFDiagnostics (Basel)
January 2025
Institute of Mechatronic Engineering, National Taipei University of Technology, Taipei 106, Taiwan.
This study aimed to evaluate the effect of sample size on the development of a three-dimensional convolutional neural network (3DCNN) model for predicting the binary classification of three types of intracranial hemorrhage (ICH): intraparenchymal, subarachnoid, and subdural (IPH, SAH, SDH, respectively). During the training, we compiled all images of each brain computed tomography scan into a single 3D image, which was then fed into the model to classify the presence of ICH. We divided the non-hemorrhage quantities into 20, 30, 40, 50, 100, and 150 and the ICH quantities into 20, 30, 40, and 50.
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