G-TRACE: rapid Gal4-based cell lineage analysis in Drosophila.

We combined Gal4-UAS and the FLP recombinase-FRT and fluorescent reporters to generate cell clones that provide spatial, temporal and genetic information about the origins of individual cells in Drosophila melanogaster. We named this combination the Gal4 technique for real-time and clonal expression (G-TRACE). The approach should allow for screening and the identification of real-time and lineage-traced expression patterns on a genomic scale

Loss of MECP2 Leads to Activation of P53 and Neuronal Senescence.

To determine the role for mutations of MECP2 in Rett syndrome, we generated isogenic lines of human induced pluripotent stem cells, neural progenitor cells, and neurons from patient fibroblasts with and without MECP2 expression in an attempt to recapitulate disease phenotypes in vitro. Molecular profiling uncovered neuronal-specific gene expression changes, including induction of a senescence-associated secretory phenotype (SASP) program. Patient-derived neurons made without MECP2 showed signs of stress, including induction of P53, and senescence. The induction of P53 appeared to affect dendritic branching in Rett neurons, as P53 inhibition restored dendritic complexity. The induction of P53 targets was also detectable in analyses of human Rett patient brain, suggesting that this disease-in-a-dish model can provide relevant insights into the human disorder

Cardiovascular magnetic resonance imaging of myocardial oedema following acute myocardial infarction: Is whole heart coverage necessary?

© 2016 Hamshere et al. Background: AAR measurement is useful when assessing the efficacy of reperfusion therapy and novel cardioprotective agents after myocardial infarction. Multi-slice (Typically 10-12) T2-STIR has been used widely for its measurement, typically with a short axis stack (SAX) covering the entire left ventricle, which can result in long acquisition times and multiple breath holds. This study sought to compare 3-slice T2-short-tau inversion recovery (T2- STIR) technique against conventional multi-slice T2-STIR technique for the assessment of area at risk (AAR). Methods: CMR imaging was performed on 167 patients after successful primary percutaneous coronary intervention. 82 patients underwent a novel 3-slice SAX protocol and 85 patients underwent standard 10-slice SAX protocol. AAR was obtained by manual endocardial and epicardial contour mapping followed by a semi- automated selection of normal myocardium; the volume was expressed as mass (%) by two independent observers. Results: 85 patients underwent both 10-slice and 3-slice imaging assessment showing a significant and strong correlation (intraclass correlation coefficient = 0.92;p < 0.0001) and a low Bland-Altman limit (mean difference -0.03 ± 3.21 %, 95 % limit of agreement,- 6.3 to 6.3) between the 2 analysis techniques. A further 82 patients underwent 3-slice imaging alone, both the 3-slice and the 10-slice techniques showed statistically significant correlations with angiographic risk scores (3-slice to BARI r = 0.36, 3-slice to APPROACH r = 0.42, 10-slice to BARI r = 0.27, 10-slice to APPROACH r = 0.46). There was low inter-observer variability demonstrated in the 3-slice technique, which was comparable to the 10-slice method (z = 1.035, p = 0.15). Acquisition and analysis times were quicker in the 3-slice compared to the 10-slice method (3-slice median time: 100 seconds (IQR: 65-171 s) vs (10-slice time: 355 seconds (IQR: 275-603 s); p < 0.0001. Conclusions: AAR measured using 3-slice T2-STIR technique correlates well with standard 10-slice techniques, with no significant bias demonstrated in assessing the AAR. The 3-slice technique requires less time to perform and analyse and is therefore advantageous for both patients and clinicians

Evolution of Radiographic Changes of a Vascularized Pedicled Nasoseptal Flap after Endonasal Endoscopic Skull Base Surgery.

BACKGROUND AND PURPOSE: There is active research involving the radiographic appearance of the skull base following reconstruction. The purpose of this study was to describe the radiographic appearance of the vascularized pedicle nasoseptal flap after endoscopic skull base surgery across time. MATERIALS AND METHODS: We performed chart and imaging review of all patients with intraoperative nasoseptal flap placement during endoscopic skull base surgery at a tertiary academic skull base surgery program between July 2018 and March 2021. All patients underwent immediate and delayed (&gt;3 months) postoperative MR imaging. Primary outcome variables included flap and pedicle enhancement, flap thickness, and flap adherence to the skull base. RESULTS: Sixty-eight patients were included. Flap (P = .003) enhancement significantly increased with time. Mean nasoseptal flap thickness on immediate and delayed postoperative scans was 3.8 and 3.9 mm, respectively (P = .181). The nasoseptal flap adhered entirely to the skull base in 37 (54.4%) and 67 (98.5%) patients on immediate and delayed imaging, respectively (P &lt; .001). CONCLUSIONS: Our findings demonstrate heterogeneity of the nasoseptal flap appearance after skull base reconstruction. While it is important for surgeons and radiologists to evaluate variations in flap appearance, the absence of enhancement and lack of adherence to the skull base on immediate postoperative imaging do not appear to predict reconstructive success and healing, with many flaps self-adjusting with time

Hybrid 3D/2D Convolutional Neural Network for Hemorrhage Evaluation on Head CT.

Background and purposeConvolutional neural networks are a powerful technology for image recognition. This study evaluates a convolutional neural network optimized for the detection and quantification of intraparenchymal, epidural/subdural, and subarachnoid hemorrhages on noncontrast CT.Materials and methodsThis study was performed in 2 phases. First, a training cohort of all NCCTs acquired at a single institution between January 1, 2017, and July 31, 2017, was used to develop and cross-validate a custom hybrid 3D/2D mask ROI-based convolutional neural network architecture for hemorrhage evaluation. Second, the trained network was applied prospectively to all NCCTs ordered from the emergency department between February 1, 2018, and February 28, 2018, in an automated inference pipeline. Hemorrhage-detection accuracy, area under the curve, sensitivity, specificity, positive predictive value, and negative predictive value were assessed for full and balanced datasets and were further stratified by hemorrhage type and size. Quantification was assessed by the Dice score coefficient and the Pearson correlation.ResultsA 10,159-examination training cohort (512,598 images; 901/8.1% hemorrhages) and an 862-examination test cohort (23,668 images; 82/12% hemorrhages) were used in this study. Accuracy, area under the curve, sensitivity, specificity, positive predictive value, and negative-predictive value for hemorrhage detection were 0.975, 0.983, 0.971, 0.975, 0.793, and 0.997 on training cohort cross-validation and 0.970, 0.981, 0.951, 0.973, 0.829, and 0.993 for the prospective test set. Dice scores for intraparenchymal hemorrhage, epidural/subdural hemorrhage, and SAH were 0.931, 0.863, and 0.772, respectively.ConclusionsA customized deep learning tool is accurate in the detection and quantification of hemorrhage on NCCT. Demonstrated high performance on prospective NCCTs ordered from the emergency department suggests the clinical viability of the proposed deep learning tool

Hybrid 3D/2D Convolutional Neural Network for Hemorrhage Evaluation on Head CT.

Background and purposeConvolutional neural networks are a powerful technology for image recognition. This study evaluates a convolutional neural network optimized for the detection and quantification of intraparenchymal, epidural/subdural, and subarachnoid hemorrhages on noncontrast CT.Materials and methodsThis study was performed in 2 phases. First, a training cohort of all NCCTs acquired at a single institution between January 1, 2017, and July 31, 2017, was used to develop and cross-validate a custom hybrid 3D/2D mask ROI-based convolutional neural network architecture for hemorrhage evaluation. Second, the trained network was applied prospectively to all NCCTs ordered from the emergency department between February 1, 2018, and February 28, 2018, in an automated inference pipeline. Hemorrhage-detection accuracy, area under the curve, sensitivity, specificity, positive predictive value, and negative predictive value were assessed for full and balanced datasets and were further stratified by hemorrhage type and size. Quantification was assessed by the Dice score coefficient and the Pearson correlation.ResultsA 10,159-examination training cohort (512,598 images; 901/8.1% hemorrhages) and an 862-examination test cohort (23,668 images; 82/12% hemorrhages) were used in this study. Accuracy, area under the curve, sensitivity, specificity, positive predictive value, and negative-predictive value for hemorrhage detection were 0.975, 0.983, 0.971, 0.975, 0.793, and 0.997 on training cohort cross-validation and 0.970, 0.981, 0.951, 0.973, 0.829, and 0.993 for the prospective test set. Dice scores for intraparenchymal hemorrhage, epidural/subdural hemorrhage, and SAH were 0.931, 0.863, and 0.772, respectively.ConclusionsA customized deep learning tool is accurate in the detection and quantification of hemorrhage on NCCT. Demonstrated high performance on prospective NCCTs ordered from the emergency department suggests the clinical viability of the proposed deep learning tool

Loss of MECP2 Leads to Activation of P53 and Neuronal Senescence.

To determine the role for mutations of MECP2 in Rett syndrome, we generated isogenic lines of human induced pluripotent stem cells, neural progenitor cells, and neurons from patient fibroblasts with and without MECP2 expression in an attempt to recapitulate disease phenotypes in&nbsp;vitro. Molecular profiling uncovered neuronal-specific gene expression changes, including induction of a senescence-associated&nbsp;secretory phenotype (SASP) program. Patient-derived neurons made without MECP2 showed signs of stress, including induction of P53, and senescence. The induction of P53 appeared to affect dendritic branching in Rett neurons, as P53 inhibition restored dendritic complexity. The induction of P53 targets was also detectable in analyses of human Rett patient brain, suggesting that this disease-in-a-dish model can provide relevant insights into the human disorder

Impact of an automated large vessel occlusion detection tool on clinical workflow and patient outcomes.

PurposeAutomated large vessel occlusion (LVO) tools allow for prompt identification of positive LVO cases, but little is known about their role in acute stroke triage when implemented in a real-world setting. The purpose of this study was to evaluate the automated LVO detection tool's impact on acute stroke workflow and clinical outcomes.Materials and methodsConsecutive patients with a computed tomography angiography (CTA) presenting with suspected acute ischemic stroke were compared before and after the implementation of an AI tool, RAPID LVO (RAPID 4.9, iSchemaView, Menlo Park, CA). Radiology CTA report turnaround times (TAT), door-to-treatment times, and the NIH stroke scale (NIHSS) after treatment were evaluated.ResultsA total of 439 cases in the pre-AI group and 321 cases in the post-AI group were included, with 62 (14.12%) and 43 (13.40%) cases, respectively, receiving acute therapies. The AI tool demonstrated a sensitivity of 0.96, a specificity of 0.85, a negative predictive value of 0.99, and a positive predictive value of 0.53. Radiology CTA report TAT significantly improved post-AI (mean 30.58 min for pre-AI vs. 22 min for post-AI, p &lt; 0.0005), notably at the resident level (p &lt; 0.0003) but not at higher levels of expertise. There were no differences in door-to-treatment times, but the NIHSS at discharge was improved for the pre-AI group adjusted for confounders (parameter estimate = 3.97, p &lt; 0.01).ConclusionImplementation of an automated LVO detection tool improved radiology TAT but did not translate to improved stroke metrics and outcomes in a real-world setting

G-TRACE: rapid Gal4-based cell lineage analysis in Drosophila

We combine Gal4/UAS, FLP/FRT and fluorescent reporters to generate cell clones that provide spatial, temporal, and genetic information about the origins of individual cells in Drosophila. We name this combination the Gal4 Technique for Real-time and Clonal Expression (G-TRACE). The approach should allow for screening and the identification of real-time and lineage-traced expression patterns on a genomic scale