Abstract Background Deep learning (DL) CT denoising models have the potential to improve image quality for lower radiation dose exams. These models are generally trained with large quantities of adult patient image data. However, CT, and increasingly DL denoising methods, are used in both adult and pediatric populations. Pediatric body habitus and size can differ significantly from adults and vary dramatically from newborns to adolescents. Ensuring that pediatric subgroups of different body sizes are not disadvantaged by DL methods requires evaluations capable of assessing performance in each subgroup. Purpose To assess DL CT denoising in pediatric and adult‐sized patients, we built a framework of computer simulated image quality (IQ) control phantoms and evaluation methodology. Methods The computer simulated IQ phantoms in the framework featured pediatric‐sized versions of standard CatPhan 600 and MITA‐LCD phantoms with a range of diameters matching the mean effective diameters of pediatric patients ranging from newborns to 18 years old. These phantoms were used in simulating CT images that were then inputs for a DL denoiser to evaluate performance in different sized patients. Adult CT test images were simulated using standard‐sized phantoms scanned with adult scan protocols. Pediatric CT test images were simulated with pediatric‐sized phantoms and adjusted pediatric protocols. The framework's evaluation methodology consisted of denoising both adult and pediatric test images then assessing changes in image quality, including noise, image sharpness, CT number accuracy, and low contrast detectability. To demonstrate the use of the framework, a REDCNN denoising model trained on adult patient images was evaluated. To validate that the DL model performance measured with the proposed pediatric IQ phantoms was representative of performance in more realistic patient anatomy, anthropomorphic pediatric XCAT phantoms of the same age range were also used to compare noise reduction performance. Results Using the proposed pediatric‐sized IQ phantom framework, size differences between adult and pediatric‐sized phantoms were observed to substantially influence the adult trained DL denoising model's performance. When applied to adult images, the DL model achieved a 60% reduction in noise standard deviation without substantial loss in sharpness in mid or high spatial frequencies. However, in smaller phantoms the denoising performance dropped due to different image noise textures resulting from the smaller field of view (FOV) between adult and pediatric protocols. In the validation study, noise reduction trends in the pediatric‐sized IQ phantoms were found to be consistent with those found in anthropomorphic phantoms. Conclusion We developed a framework of using pediatric‐sized IQ phantoms for pediatric subgroup evaluation of DL denoising models. Using the framework, we found the performance of an adult trained DL denoiser did not generalize well in the smaller diameter phantoms corresponding to younger pediatric patient sizes. Our work suggests noise texture differences from FOV changes between adult and pediatric protocols can contribute to poor generalizability in DL denoising and that the proposed framework is an effective means to identify these performance disparities for a given model.
Abstract The mammalian small heat shock protein (sHSPs) family is comprised of 10 members and includes HSPB1, which is proposed to play an essential role in cellular physiology, acting as a molecular chaperone to regulate diverse cellular processes. Whilst differential roles for sHSPs are suggested for specific tissues, the relative contribution of individual sHSP family members in cellular and organ physiology remains unclear. To address the function of HSPB1 in vivo and determine its tissue‐specific expression during development and in the adult, we generated knock‐in mice where the coding sequence of hspb 1 is replaced by a lacZ reporter gene. Hspb1 expression marks myogenic differentiation with specific expression first confined to developing cardiac muscles and the vascular system, and later in skeletal muscles with specific expression at advanced stages of myoblast differentiation. In the adult, hspb1 expression was observed in other tissues, such as stratified squamous epithelium of skin, oronasal cavity, tongue, esophagus, and uterine cervix but its expression was most prominent in the musculature. Interestingly, in cardiac muscle hsbp1 expression was down‐regulated during the neonatal period and maintained to a relatively low steady‐level throughout adulthood. Despite this widespread expression, hspb1 −/− mice were viable and fertile with no apparent morphological abnormalities in tissues under physiological conditions. However, at the cellular level and under stress conditions (heat challenge), HSPB1 act synergistically with the stress‐induced HSPA1 (HSP70) in thermotolerance development, protecting cells from apoptosis. Our data thus indicate a nonessential role for HSPB1 in embryonic development and for maintenance of tissues under physiological conditions, but also shows that it plays an important role by acting synergistically with other HSPs during stress conditions to exert cytoprotection and anti‐apoptotic effects. genesis 45:487–501, 2007. Published 2007 Wiley‐Liss, Inc.
Exoenzyme S (ExoS) is a mono-ADP-ribosyltransferase secreted by the opportunistic pathogen Pseudomonas aeruginosa. ExoS requires a eukaryotic factor, the 14-3-3 protein, for enzymatic activity. Here, two aspects of the activation of the ADP-ribosyltransferase activity of ExoS by 14-3-3 proteins are examined. Initial studies showed that several isoforms of 14-3-3, including β, ζ, η, σ, and τ, activated ExoS with similar efficiency. This implicates a conserved structure in 14-3-3 that contributes to the interaction between 14-3-3 and ExoS. One candidate structure is the conserved amphipathic groove that mediates the 14-3-3/Raf-1 interaction. The next series of experiments examined the role of individual amino acids of the amphipathic groove of 14-3-3ζ in ExoS activation and showed that ExoS activation required the basic residues lining the amphipathic groove of 14-3-3ζ without extensive involvement of the hydrophobic residues. Strikingly, mutations of Val-176 of 14-3-3ζ that disrupted its interaction with Raf-1 did not affect the binding and activation of ExoS by 14-3-3. Thus, ExoS selectively employs residues in the Raf-binding groove for its association with 14-3-3 proteins.
A common analytical approach in positron emission tomography (PET) imaging is to statistically compare a patient's PET images on a voxel-by voxel basis to a database of healthy individuals matched for age and gender (normative database).Voxels in which the patient differs by more than 2.5 standard deviations (Z-scores less than -2.5 or more than12.5)indicate areas of significantly altered function. 1Specificity and sensitivity of this approach can be affected by the size and patient population in the normative database, age adjustment, statistical methods, and the classification of the scan as normal or abnormal.Z score results are often displayed using a three-dimensional (3D) stereotactic surface projection system (3D-SSP).In this example, the Z-score results for fluorodeoxyglucose (FDG) PET imaging demonstrate the patterns of reduced metabolism characteristic of Alzheimer's disease (AD, yellow), frontotemporal dementia (FTD, pink) and dementia with Lewy bodies (DLB, blue) overlaid onto the lateral, medial and inferior surfaces of the brain.Note the predominately posterior pattern of hypometabolism in AD (asymmetric in this case, which is not unusual), involving the parietal and posterior temporal regions as well as the posterior cingulate gyrus.DLB has a similar pattern to AD.However, there is also involvement of the visual cortex.FTD has a predominantly anterior pattern involving the frontal lobes, anterior temporal lobes, and anterior cingulate gyrus.
The 14-3-3 proteins mediate phosphorylation-dependent protein-protein interactions. Through binding to numerous client proteins, 14-3-3 controls a wide range of physiological processes and has been implicated in a variety of diseases, including cancer and neurodegenerative disorders. To better understand the structure and function of 14-3-3 proteins and to develop small-molecule modulators of 14-3-3 proteins for physiological studies and potential therapeutic interventions, the authors have designed and optimized a highly sensitive fluorescence polarization (FP)-based 14-3-3 assay. Using the interaction of 14-3-3 with a fluorescently labeled phosphopeptide from Raf-1 as a model system, they have achieved a simple 1-step "mix-and-measure" method for analyzing 14-3-3 proteins. This is a solution-based, versatile method that can be used to monitor the binding of 14-3-3 with a variety of client proteins. The 14-3-3 FP assay is highly stable and has achieved a robust performance in a 384-well format with a demonstrated signal-to-noise ratio greater than 10 and a Z' factor greater than 0.7. Because of its simplicity and high sensitivity, this assay is generally applicable to studying 14-3-3/client-protein interactions and especially valuable for high-throughput screening of 14-3-3 modulators.
14-3-3 proteins bind a variety of molecules involved in signal transduction, cell cycle regulation and apoptosis. 14-3-3 binds ligands such as Raf-1 kinase and Bad by recognizing the phosphorylated consensus motif, RSXpSXP, but must bind unphosphorylated ligands, such as glycoprotein Ib and Pseudomonas aeruginosa exoenzyme S, via a different motif. Here we report the crystal structures of the zeta isoform of 14-3-3 in complex with two peptide ligands: a Raf-derived phosphopeptide (pS-Raf-259, LSQRQRSTpSTPNVHMV) and an unphosphorylated peptide derived from phage display (R18, PHCVPRDLSWLDLEANMCLP) that inhibits binding of exoenzyme S and Raf-1. The two peptides bind within a conserved amphipathic groove on the surface of 14-3-3 at overlapping but distinct sites. The phosphoserine of pS-Raf-259 engages a cluster of basic residues (Lys49, Arg56, Arg60, and Arg127), whereas R18 binds via the amphipathic sequence, WLDLE, with its two acidic groups coordinating the same basic cluster. 14-3-3 is dimeric, and its two peptide-binding grooves are arranged in an antiparallel fashion, 30 A apart. The ability of each groove to bind different peptide motifs suggests how 14-3-3 can act in signal transduction by inducing either homodimer or heterodimer formation in its target proteins.
