Analisis Perbedaan Citra Mri Brain Pada Sekuent1se Dan T1flair

MRI adalah bagian dari ilmu kedokteran untuk mediagnosa kelainan organ dengan memanfaatkan medan magnet dan pergerakan proton atom hidrogen. Salah satu pemeriksaan MRI adalah pemeriksaan brain. Pemeriksaan MRI brain dapat dilakukan T1 weighted image Spin Echo (T1 SE) atau T1 Fluid Attenuated Inversion Recovery (T1 FLAIR). Kajian dilakukan untuk menentukan perbedaan T1 SE dan T1 FLAIR dari segi citra berdasarkan nilai Rasio Signal terhadap Noise (SNR) dengan MRI GE Type Signa HD xt 1.5 Tesla. Penelitian menggunakan pendekatan kuantitatif. 20 pasien telah diambil pada pemeriksaan MRI brain pada potongan axial, dengan parameter T1 SE potongan axial dengan parameter Time Repetition (TR) 700 ms, Time Echo (TE) 20 ms, Field of View (FOV) 240 mm, Slice Thickness 5,0 mm, Spacing 1,0 mm, Number of Excitations (NEX) 1, Phase 224, dan total slice 20. T1 FLAIR parameter TR 3000 ms, TE 13,9 ms, TI 920 ms, FOV 240 mm, slice thickness 5,0 mm, spacing 1,0 mm, NEX 1, phase 224, dan total slice 20. SNR dihitung pada anatomi brain meliputi CSF (Cerebro Spinal Fluid), White Matter dan Gray Matter. Hasil penelitian kedua sequence tersebut menunjukkan bahwa sequence T1 SE lebih baik daripada sequence T1 FLAIR

Network Inference Algorithms Elucidate Nrf2 Regulation of Mouse Lung Oxidative Stress

A variety of cardiovascular, neurological, and neoplastic conditions have been associated with oxidative stress, i.e., conditions under which levels of reactive oxygen species (ROS) are elevated over significant periods. Nuclear factor erythroid 2-related factor (Nrf2) regulates the transcription of several gene products involved in the protective response to oxidative stress. The transcriptional regulatory and signaling relationships linking gene products involved in the response to oxidative stress are, currently, only partially resolved. Microarray data constitute RNA abundance measures representing gene expression patterns. In some cases, these patterns can identify the molecular interactions of gene products. They can be, in effect, proxies for protein–protein and protein–DNA interactions. Traditional techniques used for clustering coregulated genes on high-throughput gene arrays are rarely capable of distinguishing between direct transcriptional regulatory interactions and indirect ones. In this study, newly developed information-theoretic algorithms that employ the concept of mutual information were used: the Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNE), and Context Likelihood of Relatedness (CLR). These algorithms captured dependencies in the gene expression profiles of the mouse lung, allowing the regulatory effect of Nrf2 in response to oxidative stress to be determined more precisely. In addition, a characterization of promoter sequences of Nrf2 regulatory targets was conducted using a Support Vector Machine classification algorithm to corroborate ARACNE and CLR predictions. Inferred networks were analyzed, compared, and integrated using the Collective Analysis of Biological Interaction Networks (CABIN) plug-in of Cytoscape. Using the two network inference algorithms and one machine learning algorithm, a number of both previously known and novel targets of Nrf2 transcriptional activation were identified. Genes predicted as novel Nrf2 targets include Atf1, Srxn1, Prnp, Sod2, Als2, Nfkbib, and Ppp1r15b. Furthermore, microarray and quantitative RT-PCR experiments following cigarette-smoke-induced oxidative stress in Nrf2+/+ and Nrf2−/− mouse lung affirmed many of the predictions made. Several new potential feed-forward regulatory loops involving Nrf2, Nqo1, Srxn1, Prdx1, Als2, Atf1, Sod1, and Park7 were predicted. This work shows the promise of network inference algorithms operating on high-throughput gene expression data in identifying transcriptional regulatory and other signaling relationships implicated in mammalian disease