Fast and robust hybrid framework for infant brain classification from structural MRI : a case study for early diagnosis of autism.

The ultimate goal of this work is to develop a computer-aided diagnosis (CAD) system for early autism diagnosis from infant structural magnetic resonance imaging (MRI). The vital step to achieve this goal is to get accurate segmentation of the different brain structures: whitematter, graymatter, and cerebrospinal fluid, which will be the main focus of this thesis. The proposed brain classification approach consists of two major steps. First, the brain is extracted based on the integration of a stochastic model that serves to learn the visual appearance of the brain texture, and a geometric model that preserves the brain geometry during the extraction process. Secondly, the brain tissues are segmented based on shape priors, built using a subset of co-aligned training images, that is adapted during the segmentation process using first- and second-order visual appearance features of infant MRIs. The accuracy of the presented segmentation approach has been tested on 300 infant subjects and evaluated blindly on 15 adult subjects. The experimental results have been evaluated by the MICCAI MR Brain Image Segmentation (MRBrainS13) challenge organizers using three metrics: Dice coefficient, 95-percentile Hausdorff distance, and absolute volume difference. The proposed method has been ranked the first in terms of performance and speed

Lung Ultrasound Versus Dynamic Lung Compliance to Detect the Optimum Positive End-Expiratory Pressure After Alveolar Recruitment for Patients Undergoing Laparoscopic Gastric Sleeve Surgery: A Randomized Trial

Background: Various maneuvers are used to maintain oxygenation and prevent atelectasis and desaturation during general anesthesia in bariatric surgery. These maneuvers include alveolar recruitment (ARM) and positive end-expiratory pressure (PEEP). The current study aimed to illustrate the role of transthoracic lung ultrasound (LUS) as a clinical tool in comparison to dynamic lung compliance Cdyn for the detection of optimum PEEP after ARM for obese patients undergoing laparoscopic gastric sleeve surgery. Materials and Methods: Sixty patients who were scheduled for laparoscopic gastric sleeve surgery, 18-60 years old, of both sex, American Society of Anesthesiologists physical status ASA II, and body mass index BMI>30kg/m2 were enrolled in the study. They were randomly allocated into two groups to detect the optimum PEEP after ARM, group I lung Cdyn (n=30) and group II LUS (n=30). In both groups, hemodynamic parameters HR and MAP, SpO2, PaO2, and PaO2/FiO2 were recorded. A lung ultrasound score (LUSS) was used in the US group. Results:  Both techniques effectively detected optimum PEEP after ARM without significant differences. Hemodynamics (HR, MBP) significantly changed within groups without significant differences between the groups regarding such changes. Regarding SpO2, PaO2, and PaO2/FiO2, there was a considerable increase within groups, especially after ARM, without substantial differences regarding such changes. Postoperative pulmonary complications (PPCS); PaO2<80mmHg, and SpO2<94% were non-significantly more frequent in the Cdyn group. Conclusion: Both lung Cdyn and LUS were effective methods to detect the optimum PEEP needed after ARM in laparoscopic gastric sleeve surgery

Learning under Distributed Weak Supervision

The availability of training data for supervision is a frequently encountered bottleneck of medical image analysis methods. While typically established by a clinical expert rater, the increase in acquired imaging data renders traditional pixel-wise segmentations less feasible. In this paper, we examine the use of a crowdsourcing platform for the distribution of super-pixel weak annotation tasks and collect such annotations from a crowd of non-expert raters. The crowd annotations are subsequently used for training a fully convolutional neural network to address the problem of fetal brain segmentation in T2-weighted MR images. Using this approach we report encouraging results compared to highly targeted, fully supervised methods and potentially address a frequent problem impeding image analysis research

Plasma Membrane Calcium ATPase Regulates Stoichiometry of CD4+ T-Cell Compartments

Immune responses involve mobilization of T cells within naïve and memory compartments. Tightly regulated Ca2+ levels are essential for balanced immune outcomes. How Ca2+ contributes to regulating compartment stoichiometry is unknown. Here, we show that plasma membrane Ca2+ ATPase 4 (PMCA4) is differentially expressed in human CD4+ T compartments yielding distinct store operated Ca2+ entry (SOCE) profiles. Modulation of PMCA4 yielded a more prominent increase of SOCE in memory than in naïve CD4+ T cell. Interestingly, downregulation of PMCA4 reduced the effector compartment fraction and led to accumulation of cells in the naïve compartment. In silico analysis and chromatin immunoprecipitation point towards Ying Yang 1 (YY1) as a transcription factor regulating PMCA4 expression. Analyses of PMCA and YY1 expression patterns following activation and of PMCA promoter activity following downregulation of YY1 highlight repressive role of YY1 on PMCA expression. Our findings show that PMCA4 adapts Ca2+ levels to cellular requirements during effector and quiescent phases and thereby represent a potential target to intervene with the outcome of the immune response

Ultrasound-Guided Erector Spinae Plane Block Versus Paravertebral Block for Perioperative Analgesia in Patients Undergoing Open Splenectomy: A Randomized Controlled Trial

Background: The preferred anesthetic technique for upper abdominal surgeries, including splenectomy, is general anesthesia (GA). However, these procedures frequently result in severe postoperative pain, necessitating a greater need for efficient pain management. Regional analgesic blocks like the paravertebral block (PVB) and erector spinae plane blocks (ESPB) offer better postoperative pain management. Materials and Methods Following the induction of GA, 99 patients were randomly assigned to receive bilateral ESPB (n = 33), or bilateral PVB  (n = 33), and a control group (n = 33) received traditional analgesia. Time to 1st  analgesic request was the primary outcome, and total morphine consumption and pain scores over the 1st 24 hours were the secondary outcomes. Postoperative side effects related to the block technique as pneumothorax, or drug side effects, including postoperative nausea and vomiting (PONV), hypotension, bradycardia, pruritus, shivering, and respiratory depression were recorded. Results Patients in the ESPB  and PVB groups experienced a significantly prolonged time of postoperative analgesia, lower total intra and postoperative opioid consumption, and lower pain scores (NRS) than patients in the control group (P < 0.001). Patients in both ESPB  and PVB   groups showed a significantly lower intraoperative heart rate and mean blood pressure (P < 0.001) after 10 minutes of block. Regarding adverse events,  Pruritus, Shivering, Nausea& vomiting, Urine retention, and Respiratory depression  (P < 0.001) were more frequent in the control group. Conclusion After an open splenectomy, ultrasound-guided ESPB, and PVB provided comparable postoperative analgesia, reduced the need for overall opioid intake, and lessened the side effects of opioid use. However, ESPB was technically easier. Keywords Erector spinae plane block; paravertebral block; Bupivacaine; splenectomy           &nbsp

Predicting Slice-to-Volume Transformation in Presence of Arbitrary Subject Motion

This paper aims to solve a fundamental problem in intensity-based 2D/3D registration, which concerns the limited capture range and need for very good initialization of state-of-the-art image registration methods. We propose a regression approach that learns to predict rotation and translations of arbitrary 2D image slices from 3D volumes, with respect to a learned canonical atlas co-ordinate system. To this end, we utilize Convolutional Neural Networks (CNNs) to learn the highly complex regression function that maps 2D image slices into their correct position and orientation in 3D space. Our approach is attractive in challenging imaging scenarios, where significant subject motion complicates reconstruction performance of 3D volumes from 2D slice data. We extensively evaluate the effectiveness of our approach quantitatively on simulated MRI brain data with extreme random motion. We further demonstrate qualitative results on fetal MRI where our method is integrated into a full reconstruction and motion compensation pipeline. With our CNN regression approach we obtain an average prediction error of 7mm on simulated data, and convincing reconstruction quality of images of very young fetuses where previous methods fail. We further discuss applications to Computed Tomography and X-ray projections. Our approach is a general solution to the 2D/3D initialization problem. It is computationally efficient, with prediction times per slice of a few milliseconds, making it suitable for real-time scenarios.Comment: 8 pages, 4 figures, 6 pages supplemental material, currently under review for MICCAI 201

Molecular characterization of chromosome 7 in AML and MDS patients

Myelodysplastic syndromes (MDS) share many features with acute myeloid leukemias (AML) and in fact 20 - 40% of the patients eventually develop a picture of full blown AML. Chromosome 7 has been a focus of attention as a site harboring tumor suppressor genes whose loss of function contributes to leukemia transformation or tumor progression. Abnormalities of chromosome 7 are frequently encountered in AML and MDS. The aim of the present study was to detect the molecular abnormalities of chromosome 7 in Egyptian AML and MDS patients using the FISH technique and whether the abnormality has an implication on the prognosis of the disease after a period of one year follow up. Fluorescence in-situ hybridization (FISH) was performed for chromosome 7 using a locus specific probe for 7q31 and a centromeric probe from 7p11.1-q11.1 in a series of 30 patients diagnosed as: AML (20 patients) and MDS (10 patients) according to the FAB criteria. Aberrations of Chromosome 7 were found in 36.6% of AML patients: 3 cases showing monosomy with a mean positivity of 17.3%, 2 cases showing 7q deletion with a mean positivity of 11%. While both monosomy and deletion were detected in 3 cases. However, in MDS patients; monosomy for chromosome 7 was the only abnormality detected and was found in 30% of cases. Genetic abnormality of chromosome 7 showed a significant association with poor prognostic criteria. Patients who had normal FISH results showed a higher percentage (31.6%) of complete remission (CR) versus 0% in patients with monosomy or deletion who showed a higher percentage (100%) of death or poor response to therapy (NR). Although AML patients had a worse prognosis when compared to MDS patients, patients with genetic abnormalities showed the worst outcome. African Journal of Health Sciences Vol. 13 (3-4) 2006: pp. 33-4

PialNN: A fast deep learning framework for cortical pial surface reconstruction

Traditional cortical surface reconstruction is time consuming and limited by the resolution of brain Magnetic Resonance Imaging (MRI). In this work, we introduce Pial Neural Network (PialNN), a 3D deep learning framework for pial surface reconstruction. PialNN is trained end-to-end to deform an initial white matter surface to a target pial surface by a sequence of learned deformation blocks. A local convolutional operation is incorporated in each block to capture the multi-scale MRI information of each vertex and its neighborhood. This is fast and memory-efficient, which allows reconstructing a pial surface mesh with 150k vertices in one second. The performance is evaluated on the Human Connectome Project (HCP) dataset including T1-weighted MRI scans of 300 subjects. The experimental results demonstrate that PialNN reduces the geometric error of the predicted pial surface by 30% compared to state-of-the-art deep learning approaches. The codes are publicly available at https://github.com/m-qiang/PialNN