Lifelike PixelPrint phantoms for assessing clinical image quality and dose reduction capabilities of a deep learning CT reconstruction algorithm.

Deep learning CT reconstruction (DLR) has become increasingly popular as a method for improving image quality and reducing radiation exposure. Due to their nonlinear nature, these algorithms result in resolution and noise performance which are object-dependent. Therefore, traditional CT phantoms, which lack realistic tissue morphology, have become inadequate for assessing clinical imaging performance. We propose to utilize 3D-printed PixelPrint phantoms, which exhibit lifelike attenuation profiles, textures, and structures, as a better tool for evaluating DLR performance. In this study, we evaluate a DLR algorithm (Precise Image (PI), Philips Healthcare) using a custom PixelPrint lung phantom and perform head-to-head comparisons between DLR, iterative reconstruction, and filtered back projection (FBP) with scans acquired at a broad range of radiation exposures (CTDIvol: 0.5, 1, 2, 4, 6, 9, 12, 15, 19, and 20 mGy). We compared the performance of each resultant image using noise, peak signal to noise ratio (PSNR), structural similarity index (SSIM), feature-based similarity index (FSIM), information theoretic-based statistic similarity measure (ISSM) and universal image quality index (UIQ). Iterative reconstruction at 9 mGy matches the image quality of FBP at 12 mGy (diagnostic reference level) for all metrics, demonstrating a dose reduction capability of 25%. Meanwhile, DLR matches the image quality of diagnostic reference level FBP images at doses between 4 - 9 mGy, demonstrating dose reduction capabilities between 25% and 67%. This study shows that DLR allows for reduced radiation dose compared to both FBP and iterative reconstruction without compromising image quality. Furthermore, PixelPrint phantoms offer more realistic testing conditions compared to traditional phantoms in the evaluation of novel CT technologies. This, in turn, promotes the translation of new technologies, such as DLR, into clinical practice.

Hybrid spectral CT system with clinical rapid kVp-switching x-ray tube and dual-layer detector for improved iodine quantification.

Spectral computed tomography (CT) is a powerful diagnostic tool offering quantitative material decomposition results that enhance clinical imaging by providing physiologic and functional insights. Iodine, a widely used contrast agent, improves visualization in various clinical contexts. However, accurately detecting low-concentration iodine presents challenges in spectral CT systems, particularly crucial for conditions like pancreatic cancer assessment. In this study, we present preliminary results from our hybrid spectral CT instrumentation which includes clinical-grade hardware (rapid kVp-switching x-ray tube, dual-layer detector). This combination expands spectral datasets from two to four channels, wherein we hypothesize improved quantification accuracy for low-dose and low-iodine concentration cases. We modulate the system duty cycle to evaluate its impact on quantification noise and bias. We evaluate iodine quantification performance by comparing two hybrid weighting strategies alongside rapid kVp-switching. This evaluation is performed with a polyamide phantom containing seven iodine inserts ranging from 0.5 to 20 mg/mL. In comparison to alternative methodologies, the maximum separation configuration, incorporating data from both the 80 kVp, low photon energy detector layer and the 140 kVp, high photon energy detector layer produces spectral images containing low quantitative noise and bias. This study presents initial evaluations on a hybrid spectral CT system, leveraging clinical hardware to demonstrate the potential for enhanced precision and sensitivity in spectral imaging. This research holds promise for advancing spectral CT imaging performance across diverse clinical scenarios.

Impact of scatter radiation on spectral quantification performance of first- and second-generation dual-layer spectral computed tomography.

OBJECTIVE: To assess the impact of scatter radiation on quantitative performance of first and second-generation dual-layer spectral computed tomography (DLCT) systems. METHOD: A phantom with two iodine inserts (1 and 2 mg/mL) configured to intentionally introduce high scattering conditions was scanned with a first- and second-generation DLCT. Collimation widths (maximum of 4 cm for first generation and 8 cm for second generation) and radiation dose levels were varied. To evaluate the performance of both systems, the mean CT numbers of virtual monoenergetic images (MonoEs) at different energies were calculated and compared to expected values. MonoEs at 50  versus 150 keV were plotted to assess material characterization of both DLCTs. Additionally, iodine concentrations were determined, plotted, and compared against expected values. For each experimental scenario, absolute errors were reported. RESULTS: An experimental setup, including a phantom design, was successfully implemented to simulate high scatter radiation imaging conditions. Both CT scanners illustrated high spectral accuracy for small collimation widths (1 and 2 cm). With increased collimation (4 cm), the second-generation DLCT outperformed the earlier DLCT system. Further, the spectral performance of the second-generation DLCT at an 8 cm collimation width was comparable to a 4 cm collimation on the first-generation DLCT. A comparison of the absolute errors between both systems at lower energy MonoEs illustrates that, for the same acquisition parameters, the second-generation DLCT generated results with decreased errors. Similarly, the maximum error in iodine quantification was less with second-generation DLCT (0.45  and 0.33 mg/mL for the first and second-generation DLCT, respectively). CONCLUSION: The implementation of a two-dimensional anti-scatter grid in the second-generation DLCT improves the spectral quantification performance. In the clinical routine, this improvement may enable additional clinical benefits, for example, in lung imaging.

Patient-derived PixelPrint phantoms for evaluating clinical imaging performance of a deep learning CT reconstruction algorithm.

Objective. Deep learning reconstruction (DLR) algorithms exhibit object-dependent resolution and noise performance. Thus, traditional geometric CT phantoms cannot fully capture the clinical imaging performance of DLR. This study uses a patient-derived 3D-printed PixelPrint lung phantom to evaluate a commercial DLR algorithm across a wide range of radiation dose levels.Method. The lung phantom used in this study is based on a patient chest CT scan containing ground glass opacities and was fabricated using PixelPrint 3D-printing technology. The phantom was placed inside two different size extension rings to mimic a small- and medium-sized patient and was scanned on a conventional CT scanner at exposures between 0.5 and 20 mGy. Each scan was reconstructed using filtered back projection (FBP), iterative reconstruction, and DLR at five levels of denoising. Image noise, contrast to noise ratio (CNR), root mean squared error, structural similarity index (SSIM), and multi-scale SSIM (MS SSIM) were calculated for each image.Results.DLR demonstrated superior performance compared to FBP and iterative reconstruction for all measured metrics in both phantom sizes, with better performance for more aggressive denoising levels. DLR was estimated to reduce dose by 25%-83% in the small phantom and by 50%-83% in the medium phantom without decreasing image quality for any of the metrics measured in this study. These dose reduction estimates are more conservative compared to the estimates obtained when only considering noise and CNR.Conclusion. DLR has the capability of producing diagnostic image quality at up to 83% lower radiation dose, which can improve the clinical utility and viability of lower dose CT scans. Furthermore, the PixelPrint phantom used in this study offers an improved testing environment with more realistic tissue structures compared to traditional CT phantoms, allowing for structure-based image quality evaluation beyond noise and contrast-based assessments.

Age-related decline in social interaction is associated with decreased c-Fos induction in select brain regions independent of oxytocin receptor expression profiles.

Social behavior decreases with aging, and we have previously found a substantial decline in social investigative behavior of old female rats. In this study we examined the neural activation pattern (c-Fos mRNA) of young (3 month) and old (18 month) female rats after brief 10 min exposure to a novel female rat in order to identify forebrain regions that show selective age-related alterations in their neural response to social investigation. We also measured relative oxytocin receptor expression (Oxtr mRNA) as a possible factor in age-related declines in c-Fos induction after social interaction. Young rats exposed to a social partner had a greater c-Fos mRNA response than those exposed to novel context alone in the lateral septum and septohypothalamic area, with blunted increases evident in old rats. In addition, c-Fos mRNA levels in the lateral septum were positively correlated with social investigative behavior. Interestingly, age-related differences in c-Fos gene induction were unrelated to the local amount of Oxtr expression within specific brain regions, although we found an age-related decline in Oxtr expression in the ventromedial hypothalamus. This functional neuroanatomical characterization may point to certain brain regions that are especially sensitive to age-related declines associated with social interaction behavior.

Spectral performance evaluation of a second-generation spectral detector CT.

PURPOSE: The aim of this study was to characterize a second-generation wide-detector dual-layer spectral computed tomography (CT) system for material quantification accuracy, acquisition parameter and patient size dependencies, and tissue characterization capabilities. METHODS: A phantom with multiple tissue-mimicking and material-specific inserts was scanned with a dual-layer spectral detector CT using different tube voltages, collimation widths, radiation dose levels, and size configurations. Accuracy of iodine density maps and virtual monoenergetic images (MonoE) were investigated. Additionally, differences between conventional and MonoE 70 keV images were calculated to evaluate acquisition parameter and patient size dependencies. To demonstrate material quantification and differentiation, liver-mimicking inserts with adipose and iron were analyzed with a two-base decomposition utilizing MonoE 50 and 150 keV, and root mean square error (RMSE) for adipose and iron content was reported. RESULTS: Measured inserts exhibited quantitative accuracy across a wide range of MonoE levels. MonoE 70 keV images demonstrated reduced dependence compared to conventional images for phantom size (1 vs. 27 HU) and acquisition parameters, particularly tube voltage (4 vs. 37 HU). Iodine density quantification was successful with errors ranging from -0.58 to 0.44 mg/mL. Similarly, inserts with different amounts of adipose and iron were differentiated, and the small deviation in values within inserts corresponded to a RMSE of 3.49 ± 1.76% and 1.67 ± 0.84 mg/mL for adipose and iron content, respectively. CONCLUSION: The second-generation dual-layer CT enables acquisition of quantitatively accurate spectral data without compromises from differences in patient size and acquisition parameters.

Prenatal alcohol exposure alters expression of genes involved in cell adhesion, immune response, and toxin metabolism in adolescent rat hippocampus.

Prenatal alcohol exposure (PAE) can result in mild to severe consequences for children throughout their lives, with this range of symptoms referred to as Fetal Alcohol Spectrum Disorders (FASD). These consequences are thought to be linked to changes in gene expression and transcriptional programming in the brain, but the identity of those changes, and how they persist into adolescence are unclear. In this study, we isolated RNA from the hippocampus of adolescent rats exposed to ethanol during prenatal development and compared gene expression to controls. Briefly, dams were either given free access to standard chow ad libitum (AD), pair-fed a liquid diet (PF) or were given a liquid diet with ethanol (6.7% ethanol, ET) throughout gestation (gestational day (GD) 0-20). All dams were given control diet ad libitum beginning on GD 20 and throughout parturition and lactation. Hippocampal tissue was collected from adolescent male and female offspring (postnatal day (PD) 35-36). Exposure to ethanol caused widespread downregulation of many genes as compared to control rats. Gene ontology analysis demonstrated that affected pathways included cell adhesion, toxin metabolism, and immune responses. Interestingly, these differences were not strongly affected by sex. Furthermore, these changes were consistent when comparing ethanol-exposed rats to pair-fed controls provided with a liquid diet and those fed ad libitum on a standard chow diet. We conclude from this study that changes in genetic architecture and the resulting neuronal connectivity after prenatal exposure to alcohol continue through adolescent development. Further research into the consequences of specific gene expression changes on neural and behavioral changes will be vital to our understanding of the FASD spectrum of diseases.

Reproducible spectral CT thermometry with liver-mimicking phantoms for image-guided thermal ablation.

Objectives. Evaluate the reproducibility, temperature tolerance, and radiation dose requirements of spectral CT thermometry in tissue-mimicking phantoms to establish its utility for non-invasive temperature monitoring of thermal ablations.Methods. Three liver mimicking phantoms embedded with temperature sensors were individually scanned with a dual-layer spectral CT at different radiation dose levels during heating (35 °C-80 °C). Physical density maps were reconstructed from spectral results using varying reconstruction parameters. Thermal volumetric expansion was then measured at each temperature sensor every 5 °C in order to establish a correlation between physical density and temperature. Linear regressions were applied based on thermal volumetric expansion for each phantom, and coefficient of variation for fit parameters was calculated to characterize reproducibility of spectral CT thermometry. Additionally, temperature tolerance was determined to evaluate effects of acquisition and reconstruction parameters. The resulting minimum radiation dose to meet the clinical temperature accuracy requirement was determined for each slice thickness with and without additional denoising.Results. Thermal volumetric expansion was robustly replicated in all three phantoms, with a correlation coefficient variation of only 0.43%. Similarly, the coefficient of variation for the slope and intercept were 9.6% and 0.08%, respectively, indicating reproducibility of the spectral CT thermometry. Temperature tolerance ranged from 2 °C to 23 °C, decreasing with increased radiation dose, slice thickness, and iterative reconstruction level. To meet the clinical requirement for temperature tolerance, the minimum required radiation dose ranged from 20, 30, and 57 mGy for slice thickness of 2, 3, and 5 mm, respectively, but was reduced to 2 mGy with additional denoising.Conclusions. Spectral CT thermometry demonstrated reproducibility across three liver-mimicking phantoms and illustrated the clinical requirement for temperature tolerance can be met for different slice thicknesses. The reproducibility and temperature accuracy of spectral CT thermometry enable its clinical application for non-invasive temperature monitoring of thermal ablation.

Patient-derived PixelPrint phantoms for evaluating clinical imaging performance of a deep learning CT reconstruction algorithm.

Objective: Deep learning reconstruction (DLR) algorithms exhibit object-dependent resolution and noise performance. Thus, traditional geometric CT phantoms cannot fully capture the clinical imaging performance of DLR. This study uses a patient-derived 3D-printed PixelPrint lung phantom to evaluate a commercial DLR algorithm across a wide range of radiation dose levels. Approach: The lung phantom used in this study is based on a patient chest CT scan containing ground glass opacities and was fabricated using PixelPrint 3D-printing technology. The phantom was placed inside two different sized extension rings to mimic a small and medium sized patient and was scanned on a conventional CT scanner at exposures between 0.5 and 20 mGy. Each scan was reconstructed using filtered back projection (FBP), iterative reconstruction, and DLR at five levels of denoising. Image noise, contrast to noise ratio (CNR), root mean squared error (RMSE), structural similarity index (SSIM), and multi-scale SSIM (MS SSIM) were calculated for each image. Main Results: DLR demonstrated superior performance compared to FBP and iterative reconstruction for all measured metrics in both phantom sizes, with better performance for more aggressive denoising levels. DLR was estimated to reduce dose by 25-83% in the small phantom and by 50-83% in the medium phantom without decreasing image quality for any of the metrics measured in this study. These dose reduction estimates are more conservative compared to the estimates obtained when only considering noise and CNR with a non-anatomical physics phantom. Significance: DLR has the capability of producing diagnostic image quality at up to 83% lower radiation dose which can improve the clinical utility and viability of lower dose CT scans. Furthermore, the PixelPrint phantom used in this study offers an improved testing environment with more realistic tissue structures compared to traditional CT phantoms, allowing for structure-based image quality evaluation beyond noise and contrast-based assessments.

Reproducible spectral CT thermometry with liver-mimicking phantoms for image-guided thermal ablation.

Objectives: Evaluate the reproducibility, temperature sensitivity, and radiation dose requirements of spectral CT thermometry in tissue-mimicking phantoms to establish its utility for non-invasive temperature monitoring of thermal ablations. Materials and Methods: Three liver mimicking phantoms embedded with temperature sensors were individually scanned with a dual-layer spectral CT at different radiation dose levels during heating and cooling (35 to 80 °C). Physical density maps were reconstructed from spectral results using a range of reconstruction parameters. Thermal volumetric expansion was then measured at each temperature sensor every 5°C in order to establish a correlation between physical density and temperature. Linear regressions were applied based on thermal volumetric expansion for each phantom, and coefficient of variation for fit parameters was calculated to characterize reproducibility of spectral CT thermometry. Additionally, temperature sensitivity was determined to evaluate the effect of acquisition parameters, reconstruction parameters, and image denoising. The resulting minimum radiation dose to meet the clinical temperature sensitivity requirement was determined for each slice thickness, both with and without additional denoising. Results: Thermal volumetric expansion was robustly replicated in all three phantoms, with a correlation coefficient variation of only 0.43%. Similarly, the coefficient of variation for the slope and intercept were 9.6% and 0.08%, respectively, indicating reproducibility of the spectral CT thermometry. Temperature sensitivity ranged from 2 to 23 °C, decreasing with increased radiation dose, slice thickness, and iterative reconstruction level. To meet the clinical requirement for temperature sensitivity, the minimum required radiation dose ranged from 20, 30, and 57 mGy for slice thickness of 2, 3, and 5 mm, respectively, but was reduced to 2 mGy with additional denoising. Conclusions: Spectral CT thermometry demonstrated reproducibility across three liver-mimicking phantoms and illustrated the clinical requirement for temperature sensitivity can be met for different slice thicknesses. Moreover, additional denoising enables the use of more clinically relevant radiation doses, facilitating the clinical translation of spectral CT thermometry. The reproducibility and temperature accuracy of spectral CT thermometry enable its clinical application for non-invasive temperature monitoring of thermal ablation.

Penile diameter during puberty in boys: a retrospective analysis of longitudinally obtained data.

OBJECTIVES: Tanner staging is the standard for rating sexual maturation (SMR) in boys (pubic hair (PH) and genital (G) development). G staging is tripartite in nature and is prone to ambiguity because it is based upon somewhat vague visual cues that may lead to erroneous assessments and medical errors. Measurement of penile growth (penile girth or diameter) may provide an additional tool (in addition to the orchidometer) to make G staging more valid. Although studies on penile growth (either circumference of width) have been reported, none were longitudinal. Therefore, our objective was to compare penile development in boys - measured as penile diameter (PD) - to PH stage and testicular volume (TV) and secondarily to G stage; moreover, to do so on a longitudinal basis. METHODS: Charts of 61 boys, ages 6-21 years of age, who were seen longitudinally, were reviewed. Each boy had his PD and TV measured along with his PH and G stage assessed on a quarterly to semi-annual basis. RESULTS: PD increased significantly among PH stages II, III, and IV only. PD increased significantly among G stages I, II, III and IV only. PD correlated well with TV. There were significant correlations between PD and TV in all PH stages. However, for G stage correlations were not significant for stages II, III, and IV. PH stage was a better predictor of PD than G stage. CONCLUSIONS: Measuring PD may be another tool to help in objectifying male SMR during puberty and overcome the vagueness encountered with the visual SMR G stage scales.

Information Extraction From Electronic Health Records to Predict Readmission Following Acute Myocardial Infarction: Does Natural Language Processing Using Clinical Notes Improve Prediction of Readmission?

Background Social risk factors influence rehospitalization rates yet are challenging to incorporate into prediction models. Integration of social risk factors using natural language processing (NLP) and machine learning could improve risk prediction of 30-day readmission following an acute myocardial infarction. Methods and Results Patients were enrolled into derivation and validation cohorts. The derivation cohort included inpatient discharges from Vanderbilt University Medical Center between January 1, 2007, and December 31, 2016, with a primary diagnosis of acute myocardial infarction, who were discharged alive, and not transferred from another facility. The validation cohort included patients from Dartmouth-Hitchcock Health Center between April 2, 2011, and December 31, 2016, meeting the same eligibility criteria described above. Data from both sites were linked to Centers for Medicare & Medicaid Services administrative data to supplement 30-day hospital readmissions. Clinical notes from each cohort were extracted, and an NLP model was deployed, counting mentions of 7 social risk factors. Five machine learning models were run using clinical and NLP-derived variables. Model discrimination and calibration were assessed, and receiver operating characteristic comparison analyses were performed. The 30-day rehospitalization rates among the derivation (n=6165) and validation (n=4024) cohorts were 15.1% (n=934) and 10.2% (n=412), respectively. The derivation models demonstrated no statistical improvement in model performance with the addition of the selected NLP-derived social risk factors. Conclusions Social risk factors extracted using NLP did not significantly improve 30-day readmission prediction among hospitalized patients with acute myocardial infarction. Alternative methods are needed to capture social risk factors.

A real-world assessment of mycophenolate mofetil for remission induction in eosinophilic granulomatosis with polyangiitis.

Eosinophilic granulomatosis with polyangiitis (EGPA) is a form of ANCA-associated vasculitis (AAV). Clinical trials demonstrating the efficacy of mycophenolate mofetil (MMF) for remission induction in AAV excluded patients with EGPA. Despite this, MMF is commonly used in these patients. The objective of this study was to evaluate, for the first time, the effectiveness and tolerance of MMF in EGPA remission induction. A retrospective, two-center, real-world study was conducted in patients with EGPA who received MMF in addition to prednisolone for newly diagnosed or relapsing disease between 2009 and 2019. Baseline, 3-, 6- and 12-month outcome data were extracted from electronic health records. The primary outcome was disease remission, defined as a Birmingham Vasculitis Activity Score of 0 at 6 months. Secondary outcomes included disease relapse, median prednisolone dose at 12 months and drug tolerance. In total, 15 patients (73% male, median age 57) with EGPA (11 newly diagnosed/4 relapsing) were identified. At 6 months, 67% had achieved disease remission. At 12 months, this was maintained (66.7%) and 4 patients had relapsed. All but one patient remained on MMF at study completion and all patients tolerated MMF. Our real-world data suggest that MMF is an effective and well-tolerated agent for achieving disease remission in EGPA. A future randomized controlled trial of MMF in this neglected orphan disease is now warranted.

Comparison of COVID-19 versus influenza on the incidence, features, and recovery from acute kidney injury in hospitalized United States Veterans.

Acute kidney injury is a common complication in patients hospitalized with SARSCoV-2 (COVID-19), with prior studies implicating multiple potential mechanisms of injury. Although COVID-19 is often compared to other respiratory viral illnesses, few formal comparisons of these viruses on kidney health exist. In this retrospective cohort study, we compared the incidence, features, and outcomes of acute kidney injury among Veterans hospitalized with COVID-19 or influenza and adjusted for baseline conditions using weighted comparisons. A total of 3402 hospitalizations for COVID-19 and 3680 hospitalizations for influenza admitted between October 1, 2019 and May 31, 2020 across 127 Veterans Administration hospitals nationally were studied using the electronic medical record. Acute kidney injury occurred more frequently among those with COVID-19 compared to those with influenza (40.9% versus 29.4%, weighted analysis) and was more severe. Patients with COVID-19 were more likely to require mechanical ventilation and vasopressors and experienced higher mortality. Proteinuria and hematuria were frequent in both groups but more common in COVID-19. Recovery of kidney function was less common in patients with COVID-19 and acute kidney injury but was similar among survivors. Thus, findings from this study confirm that acute kidney injury is more common and severe among patients hospitalized with COVID-19 compared to influenza, a finding that may be driven largely by illness severity. Hence, the combined impact of these two illnesses on kidney health may be significant and have important implications for resource allocation.

Development of Electronic Health Record-Based Prediction Models for 30-Day Readmission Risk Among Patients Hospitalized for Acute Myocardial Infarction.

Importance: In the US, more than 600 000 adults will experience an acute myocardial infarction (AMI) each year, and up to 20% of the patients will be rehospitalized within 30 days. This study highlights the need for consideration of calibration in these risk models. Objective: To compare multiple machine learning risk prediction models using an electronic health record (EHR)-derived data set standardized to a common data model. Design, Setting, and Participants: This was a retrospective cohort study that developed risk prediction models for 30-day readmission among all inpatients discharged from Vanderbilt University Medical Center between January 1, 2007, and December 31, 2016, with a primary diagnosis of AMI who were not transferred from another facility. The model was externally validated at Dartmouth-Hitchcock Medical Center from April 2, 2011, to December 31, 2016. Data analysis occurred between January 4, 2019, and November 15, 2020. Exposures: Acute myocardial infarction that required hospital admission. Main Outcomes and Measures: The main outcome was thirty-day hospital readmission. A total of 141 candidate variables were considered from administrative codes, medication orders, and laboratory tests. Multiple risk prediction models were developed using parametric models (elastic net, least absolute shrinkage and selection operator, and ridge regression) and nonparametric models (random forest and gradient boosting). The models were assessed using holdout data with area under the receiver operating characteristic curve (AUROC), percentage of calibration, and calibration curve belts. Results: The final Vanderbilt University Medical Center cohort included 6163 unique patients, among whom the mean (SD) age was 67 (13) years, 4137 were male (67.1%), 1019 (16.5%) were Black or other race, and 933 (15.1%) were rehospitalized within 30 days. The final Dartmouth-Hitchcock Medical Center cohort included 4024 unique patients, with mean (SD) age of 68 (12) years; 2584 (64.2%) were male, 412 (10.2%) were rehospitalized within 30 days, and most of the cohort were non-Hispanic and White. The final test set AUROC performance was between 0.686 to 0.695 for the parametric models and 0.686 to 0.704 for the nonparametric models. In the validation cohort, AUROC performance was between 0.558 to 0.655 for parametric models and 0.606 to 0.608 for nonparametric models. Conclusions and Relevance: In this study, 5 machine learning models were developed and externally validated to predict 30-day readmission AMI hospitalization. These models can be deployed within an EHR using routinely collected data.

Renin-angiotensin aldosterone inhibitor use at hospital discharge among patients with moderate to severe acute kidney injury and its association with recurrent acute kidney injury and mortality.

Recurrent episodes of acute kidney injury (AKI) are common among AKI survivors. Renin-angiotensin aldosterone inhibitors (RAASi) are often indicated for these patients but may increase the risk for recurrent AKI. Here, we examined whether RAASi associates with a higher risk for recurrent AKI and mortality among survivors of moderate to severe AKI in a retrospective cohort of Veterans who survived Stage II or III AKI. The primary exposure was RAASi at hospital discharge and the primary endpoint was recurrent AKI within 12 months. Cox proportional hazards models were fit on a propensity score-weighted cohort to compare time to recurrent AKI and mortality by RAASi exposure. Among 96,983 patients, 40% were on RAASi at discharge. Compared to patients who continued RAASi use, those discontinuing use experienced no difference in risk for recurrent AKI but had a significantly higher risk of mortality [hazard ratio 1.33 (95% confidence interval1.26-1.41)]. No differences in recurrent AKI risk was observed for non-users started or not on RAASi compared to prevalent users who continued RAASi. Subgroup analyses among those with diabetes, chronic kidney disease, heart failure, and malignancy were similar with exception of a modest reduction in recurrent AKI risk among RAASi discontinuers with chronic kidney disease. Thus, RAASi use among survivors of moderate to severe AKI was associated with little to no difference in risk for recurrent AKI but was associated with improved survival. Reinitiating or starting RAASi among patients with strong indications is warranted but should be balanced with individual overall risk for recurrent AKI and with adequate monitoring.

Assessment of neuroinflammation in the aging hippocampus using large-molecule microdialysis: Sex differences and role of purinergic receptors.

Aging is associated with an enhanced neuroinflammatory response to acute immune challenge, often termed "inflammaging." However, there are conflicting reports about whether baseline levels of inflammatory markers are elevated under ambient conditions in the aging brain, or whether such changes are observed predominantly in response to acute challenge. The present studies utilized two distinct approaches to assess inflammatory markers in young and aging Fischer 344 rats. Experiment 1 examined total tissue content of inflammatory markers from hippocampus of adult (3 month), middle-aged (12 month), and aging (18 month) male Fischer (F) 344 rats using multiplex analysis (23-plex). Though trends emerged for several cytokines, no significant differences in basal tissue content were observed across the 3 ages examined. Experiment 2 measured extracellular concentrations of inflammatory factors in the hippocampus from adult (3 month) and aging (18 month) males and females using large-molecule in vivo microdialysis. Although few significant aging-related changes were observed, robust sex differences were observed in extracellular concentrations of CCL3, CCL20, and IL-1α. Experiment 2 also evaluated the involvement of the P2X7 purinergic receptor in neuroinflammation using reverse dialysis of the selective agonist BzATP. BzATP produced an increase in IL-1α and IL-1ß release and rapidly suppressed the release of CXCL1, CCL2, CCL3, CCL20, and IL-6. Other noteworthy sex by aging trends were observed in CCL3, IL-1ß, and IL-6. Together, these findings provide important new insight into late-aging and sex differences in neuroinflammation, and their regulation by the P2X7 receptor.

Quantitative positron emission tomography imaging in the presence of iodinated contrast media using electron density quantifications from dual-energy computed tomography.

PURPOSE: As preparation for future positron emission tomography (PET)/dual-energy computed tomography (DECT)T imaging modality and new possible clinical applications, the study aimed to evaluate the utility of clinically available spectral results from a DECT system for improving attenuation corrections of PET acquisitions in the presence of iodinated contrast media. The dependence of the accuracy of PET quantification values, reconstructed with conventional and spectral-based attenuation corrections, was examined as a function of the amount of iodine content and x-ray radiation exposure. METHODS: Measurements were performed on commercial PET/CT and DECT systems, using a semi-anthropomorphic phantom with seven centrifuge tubes in its bore. Five different configurations of tube contents were scanned by both PET/CT and DECT. With the aim of mimicking clinically observed concentrations, in all phantom configurations the center tube contained a high concentration of radionuclide while the peripheral tubes contained a lower concentration of radionuclide. Iodine content was incrementally increased between phantom configurations by replacing iodine-free tubes with tubes that contained the original radionuclide concentration within a 10 mg/ml iodine dilution. DECT-based attenuation correction maps were generated by scaling electron density spectral results into corresponding 511 keV photon linear attenuation coefficients. RESULTS: Mean SUV values obtained from the nominal PET reconstruction, using conventional CT images as input for the attenuation correction, demonstrate a monotonic increase of 8.6% when the water and radionuclide mixtures were replaced by iodine, water, and radionuclide (same level of activity) mixture. Mean SUV values obtained from the DECT-based reconstruction, in which the attenuation correction utilizes electron density values as input, demonstrate different, more stable behavior across all iodine insert configurations, with a standard deviation to mean ratio of less than 1%. This observed behavior was independent of the area size used for measurement. A minor radiation dose dependency of the electron density values (below 0.5%) was observed. This resulted in consistent (iodine independent) PET quantification behavior, which persisted even at the lowest radiation dose levels tested in our experiment, that is, 25% of the radiation dose utilized for CT acquisition in the clinical PET/CT protocol. CONCLUSIONS: Utilization of DECT-generated electron density estimations for attenuation correction benefit PET quantification consistency in the presence of iodine and at nominal and low DECT radiation exposure levels. The ability to correctly account for iodinated contrast media in PET acquisitions will allow the development of new clinical applications that rely on the quantitative capabilities of spectral CT technologies and modern PET systems.

Fibrillization of 40-Residue ß-Amyloid Peptides in Membrane-Like Environments Leads to Different Fibril Structures and Reduced Molecular Polymorphisms.

The molecular-level polymorphism in ß-Amyloid (Aß) fibrils have recently been considered as a pathologically relevant factor in Alzheimer's disease (AD). Studies showed that the structural deviations in human-brain-seeded Aß fibrils potentially correlated with the clinical histories of AD patients. For the 40-residue Aß (Aß40) fibrils derived from human brain tissues, a predominant molecular structure was proposed based on solid-state nuclear magnetic resonance (ssNMR) spectroscopy. However, previous studies have shown that the molecular structures of Aß 40 fibrils were sensitive to their growth conditions in aqueous environments. We show in this work that biological membranes and their phospholipid bilayer mimics serve as environmental factors to reduce the structural heterogeneity in Aß40 fibrils. Fibrillization in the presence of membranes leads to fibril structures that are significantly different to the Aß40 fibrils grown in aqueous solutions. Fibrils grown from multiple types of membranes, including the biological membranes extracted from the rats' synaptosomes, shared similar ssNMR spectral features. Our studies emphasize the biological relevance of membranes in Aß40 fibril structures and fibrillization processes.