Search

Coronavirus disease 2019 (COVID-19), is an ongoing issue in certain populations, presenting rapidly worsening pneumonia and persistent symptoms. This study aimed to test the predictability of rapid progression using radiographic scores and laboratory markers and present longitudinal changes. This retrospective study included 218 COVID-19 pneumonia patients admitted at the Chungnam National University Hospital. Rapid progression was defined as respiratory failure requiring mechanical ventilation within one week of hospitalization. Quantitative COVID (QCOVID) scores were derived from high-resolution computed tomography (CT) analyses: (1) ground glass opacity (QGGO), (2) mixed diseases (QMD), and (3) consolidation (QCON), and the sum, quantitative total lung diseases (QTLD). Laboratory data, including inflammatory markers, were obtained from electronic medical records. Rapid progression was observed in 9.6% of patients. All QCOVID scores predicted rapid progression, with QMD showing the best predictability (AUC = 0.813). In multivariate analyses, the QMD score and interleukin(IL)-6 level were important predictors for rapid progression (AUC = 0.864). With >2 months follow-up CT, remained lung lesions were observed in 21 subjects, even after several weeks of negative reverse transcription polymerase chain reaction test. AI-driven quantitative CT scores in conjugation with laboratory markers can be useful in predicting the rapid progression and monitoring of COVID-19.

PURPOSE: To integrate and evaluate an artificial intelligence (AI) system that assists in checking endotracheal tube (ETT) placement on chest x-rays (CXRs) in clinical practice. APPROACH: In clinical use over 17 months, 214 CXR images were ordered to check ETT placement with AI assistance by intensive care unit (ICU) physicians. The system was built on the SimpleMind Cognitive AI platform and integrated into a clinical workflow. It automatically identified the ETT and checked its placement relative to the trachea and carina. The ETT overlay and misplacement alert messages generated by the AI system were compared with radiology reports as the reference. A survey study was also conducted to evaluate usefulness of the AI system in clinical practice. RESULTS: The alert messages indicating that either the ETT was misplaced or not detected had a positive predictive value of 42% (21/50) and negative predictive value of 98% (161/164) based on the radiology reports. In the survey, radiologist and ICU physician users indicated that they agreed with the AI outputs and that they were useful. CONCLUSIONS: The AI system performance in real-world clinical use was comparable to that seen in previous experiments. Based on this and physician survey results, the system can be deployed more widely at our institution, using insights gained from this evaluation to make further algorithm improvements and quality assurance of the AI system.

In-situ marine cloud droplet number concentrations (CDNCs), cloud condensation nuclei (CCN), and CCN proxies, based on particle sizes and optical properties, are accumulated from seven field campaigns: ACTIVATE; NAAMES; CAMP2EX; ORACLES; SOCRATES; MARCUS; and CAPRICORN2. Each campaign involves aircraft measurements, ship-based measurements, or both. Measurements collected over the North and Central Atlantic, Indo-Pacific, and Southern Oceans, represent a range of clean to polluted conditions in various climate regimes. With the extensive range of environmental conditions sampled, this data collection is ideal for testing satellite remote detection methods of CDNC and CCN in marine environments. Remote measurement methods are vital to expanding the available data in these difficult-to-reach regions of the Earth and improving our understanding of aerosol-cloud interactions. The data collection includes particle composition and continental tracers to identify potential contributing CCN sources. Several of these campaigns include High Spectral Resolution Lidar (HSRL) and polarimetric imaging measurements and retrievals that will be the basis for the next generation of space-based remote sensors and, thus, can be utilized as satellite surrogates.

To translate artificial intelligence (AI) algorithms into clinical practice requires generalizability of models to real-world data. One of the main obstacles to generalizability is data shift, a data distribution mismatch between model training and real environments. Explainable AI techniques offer tools to detect and mitigate the data shift problem and develop reliable AI for clinical practice. Most medical AI is trained with datasets gathered from limited environments, such as restricted disease populations and center-dependent acquisition conditions. The data shift that commonly exists in the limited training set often causes a significant performance decrease in the deployment environment. To develop a medical application, it is important to detect potential data shift and its impact on clinical translation. During AI training stages, from premodel analysis to in-model and post hoc explanations, explainability can play a key role in detecting model susceptibility to data shift, which is otherwise hidden because the test data have the same biased distribution as the training data. Performance-based model assessments cannot effectively distinguish the model overfitting to training data bias without enriched test sets from external environments. In the absence of such external data, explainability techniques can aid in translating AI to clinical practice as a tool to detect and mitigate potential failures due to data shift. ©RSNA, 2023 Quiz questions for this article are available in the supplemental material.

BackgroundIdiopathic pulmonary fibrosis (IPF) is a progressive, irreversible, and usually fatal lung disease of unknown reasons, generally affecting the elderly population. Early diagnosis of IPF is crucial for triaging patients' treatment planning into anti-fibrotic treatment or treatments for other causes of pulmonary fibrosis. However, current IPF diagnosis workflow is complicated and time-consuming, which involves collaborative efforts from radiologists, pathologists, and clinicians and it is largely subject to inter-observer variability.PurposeThe purpose of this work is to develop a deep learning-based automated system that can diagnose subjects with IPF among subjects with interstitial lung disease (ILD) using an axial chest computed tomography (CT) scan. This work can potentially enable timely diagnosis decisions and reduce inter-observer variability.MethodsOur dataset contains CT scans from 349 IPF patients and 529 non-IPF ILD patients. We used 80% of the dataset for training and validation purposes and 20% as the holdout test set. We proposed a two-stage model: at stage one, we built a multi-scale, domain knowledge-guided attention model (MSGA) that encouraged the model to focus on specific areas of interest to enhance model explainability, including both high- and medium-resolution attentions; at stage two, we collected the output from MSGA and constructed a random forest (RF) classifier for patient-level diagnosis, to further boost model accuracy. RF classifier is utilized as a final decision stage since it is interpretable, computationally fast, and can handle correlated variables. Model utility was examined by (1) accuracy, represented by the area under the receiver operating characteristic curve (AUC) with standard deviation (SD), and (2) explainability, illustrated by the visual examination of the estimated attention maps which showed the important areas for model diagnostics.ResultsDuring the training and validation stage, we observe that when we provide no guidance from domain knowledge, the IPF diagnosis model reaches acceptable performance (AUC±SD = 0.93±0.07), but lacks explainability; when including only guided high- or medium-resolution attention, the learned attention maps are not satisfactory; when including both high- and medium-resolution attention, under certain hyperparameter settings, the model reaches the highest AUC among all experiments (AUC±SD = 0.99±0.01) and the estimated attention maps concentrate on the regions of interests for this task. Three best-performing hyperparameter selections according to MSGA were applied to the holdout test set and reached comparable model performance to that of the validation set.ConclusionsOur results suggest that, for a task with only scan-level labels available, MSGA+RF can utilize the population-level domain knowledge to guide the training of the network, which increases both model accuracy and explainability.

BackgroundTo evaluate the anatomic site(s) of initial disease progression in patients with castration resistant metastatic prostate cancer (mCRPC) in the presence or absence of pre-treatment visceral metastases while on systemic therapy.MethodsThis is a retrospective cohort study of mCRPC patients who have baseline and at least one follow up bone scan and CT chest, abdomen and pelvis (CAP). Disease progression was determined by RECIST and/or ≥ 30% increase in automated bone scan lesion area score. Kaplan-Meier plot was used to estimate the median progression free survival and log-rank tests were used to compare anatomic sites.ResultsOf 203 patients, 61 (30%) had pre-treatment visceral metastases. Patients with baseline visceral disease were 1.5 times more likely to develop disease progression (HR = 1.53; 95% CI, 1.03-2.26). Disease progression was a result of worsening bone scan disease (42% (16/38)) versus visceral (32% (12/38)) or lymph node disease (3% (1/38)) by CT or a combination thereof (23% (9/38)). Median time to progression (TTP) did not differ by anatomic location of initial progression (p = 0.86). Development of new lesions occurred in 50% of those visceral patients with soft tissue only progression and was associated with a significantly longer TTP (3.1 months (2.8-4.3 months) than those with worsening of pre-existing lesions (1.8 months (1.6-2.7 months); p = 0.04.ConclusionsPatients with pre-treatment visceral metastases in mCRPC are more likely to experience disease progression of bone disease with the initial anatomic site of progression similar to those without baseline visceral involvement.

Imaging response assessment is a cornerstone of patient care and drug development in oncology. Clinicians/clinical researchers rely on tumor imaging to estimate the impact of new treatments and guide decision making for patients and candidate therapies. This is important in brain cancer, where associations between tumor size/growth and emerging neurological deficits are strong. Accurately measuring the impact of a new therapy on tumor growth early in clinical development, where patient numbers are small, would be valuable for decision making regarding late-stage development activation. Current attempts to measure the impact of a new therapy have limited influence on clinical development, as determination of progression, stability or response does not currently account for individual tumor growth kinetics prior to the initiation of experimental therapies. Therefore, we posit that imaging-based response assessment, often used as a tool for estimating clinical effect, is incomplete as it does not adequately account for growth trajectories or biological characteristics of tumors prior to the introduction of an investigational agent. Here, we propose modifications to the existing framework for evaluating imaging assessment in primary brain tumors that will provide a more reliable understanding of treatment effects. Measuring tumor growth trajectories prior to a given intervention may allow us to more confidently conclude whether there is an anti-tumor effect. This updated approach to imaging-based tumor response assessment is intended to improve our ability to select candidate therapies for later-stage development, including those that may not meet currently sought thresholds for "response" and ultimately lead to identification of effective treatments.

RATIONALE: The long-acting β2-agonist/long-acting muscarinic antagonist combination indacaterol/glycopyrronium (IND/GLY) elicits bronchodilation, improves symptoms, and reduces exacerbations in COPD. Magnetic resonance imaging (MRI) of the lung with hyperpolarized gas and gadolinium contrast enhancement enables assessment of whole lung functional responses to IND/GLY. OBJECTIVES: The primary objective was assessment of effect of IND/GLY on global ventilated lung volume (%VV) versus placebo in COPD. Lung function, regional ventilation and perfusion in response to IND/GLY were also measured. METHODS: This double-blind, randomized, placebo-controlled, crossover study assessed %VV and pulmonary perfusion in patients with moderate-to-severe COPD after 8 days of once-daily IND/GLY treatment (110/50 µg) followed by 8 days of placebo, or vice versa, using inhaled hyperpolarized 3He gas and gadolinium contrast-enhanced MRI, respectively. Lung function measures including spirometry were performed for each treatment after 8 days. MEASUREMENTS AND MAIN RESULTS: Of 31 patients randomized, 29 completed both treatment periods. IND/GLY increased global %VV versus placebo (61.73% vs. 56.73%, respectively, least squares means treatment difference: 5.00% [90% CI 1.40 to 8.60]; P = 0.025). IND/GLY improved whole lung index of ventilation volume to perfusion volume (V/Q) ratio versus placebo; 94% (90% CI 83 to 105) versus 86% (90% CI 75 to 97; P = 0.047), respectively. IND/GLY showed a trend to improve diffusing capacity for carbon monoxide (DLCO) (+ 0.66 mL/min/mmHg; P = 0.082). By Day 8, forced expiratory volume in 1 s (FEV1) was increased by 0.32 L versus placebo (90% CI 0.26 to 0.38; P

Atmospheric marine particle concentrations impact cloud properties, which strongly impact the amount of solar radiation reflected back into space or absorbed by the ocean surface. While satellites can provide a snapshot of current conditions at the overpass time, models are necessary to simulate temporal variations in both particle and cloud properties. However, poor model accuracy limits the reliability with which these tools can be used to predict future climate. Here, we leverage the comprehensive ocean ecosystem and atmospheric aerosol–cloud dataset obtained during the third deployment of the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES3). Airborne and ship-based measurements were collected in and around a cold-air outbreak during a 3 d (where d stands for day) intensive operations period from 17–19 September 2017. Cold-air outbreaks are of keen interest for model validation because they are challenging to accurately simulate, which is due, in part, to the numerous feedbacks and sub-grid-scale processes that influence aerosol and cloud evolution. The NAAMES observations are particularly valuable because the flight plans were tailored to lie along Lagrangian trajectories, making it possible to spatiotemporally connect upwind and downwind measurements with the state-of-the-art FLEXible PARTicle (FLEXPART) Lagrangian particle dispersion model and then calculate a rate of change in particle properties. Initial aerosol conditions spanning an east–west, closed-cell-to-clear-air transition region of the cold-air outbreak indicate similar particle concentrations and properties. However, despite the similarities in the aerosol fields, the cloud properties downwind of each region evolved quite differently. One trajectory carried particles through a cold-air outbreak, resulting in a decrease in accumulation mode particle concentration (−42 %) and cloud droplet concentrations, while the other remained outside of the cold-air outbreak and experienced an increase in accumulation mode particle concentrations (+62 %). The variable meteorological conditions between these two adjacent trajectories result from differences in the local sea surface temperature in the Labrador Current and surrounding waters, altering the stability of the marine atmospheric boundary layer. Further comparisons of historical satellite observations indicate that the observed pattern occurs annually in the region, making it an ideal location for future airborne Lagrangian studies tracking the evolution of aerosols and clouds over time under cold-air outbreak conditions.

Circadian rhythm disruption (CD) is associated with impaired glucose homeostasis and type 2 diabetes mellitus (T2DM). While the link between CD and T2DM remains unclear, there is accumulating evidence that disruption of fasting/feeding cycles mediates metabolic dysfunction. Here, we used an approach encompassing analysis of behavioral, physiological, transcriptomic, and epigenomic effects of CD and consequences of restoring fasting/feeding cycles through time-restricted feeding (tRF) in mice. Results show that CD perturbs glucose homeostasis through disruption of pancreatic β cell function and loss of circadian transcriptional and epigenetic identity. In contrast, restoration of fasting/feeding cycle prevented CD-mediated dysfunction by reestablishing circadian regulation of glucose tolerance, β cell function, transcriptional profile, and reestablishment of proline and acidic amino acid–rich basic leucine zipper (PAR bZIP) transcription factor DBP expression/activity. This study provides mechanistic insights into circadian regulation of β cell function and corresponding beneficial effects of tRF in prevention of T2DM.

PurposeRecent studies have demonstrated a lack of reproducibility of radiomic features in response to variations in CT parameters. In addition, reproducibility of radiomic features has not been well established in clinical datasets. We aimed to investigate the effects of a wide range of CT acquisition and reconstruction parameters on radiomic features in a realistic setting using clinical low dose lung cancer screening cases. We performed univariable and multivariable explorations to consider the effects of individual parameters and the simultaneous interactions between three different acquisition/reconstruction parameters of radiation dose level, reconstructed slice thickness, and kernel.MethodA cohort of 89 lung cancer screening patients were collected that each had a solid lung nodule >4mm diameter. A computational pipeline was used to perform a simulation of dose reduction of the raw projection data, collected from patient scans. This was followed by reconstruction of raw data with weighted filter back projection (wFBP) algorithm and automatic lung nodule detection and segmentation using a computer-aided detection tool. For each patient, 36 different image datasets were created corresponding to dose levels of 100%, 50%, 25%, and 10% of the original dose level, three slice thicknesses of 0.6 mm, 1 mm, and 2 mm, as well as three reconstruction kernels of smooth, medium, and sharp. For each nodule, 226 well-known radiomic features were calculated at each image condition. The reproducibility of radiomic features was first evaluated by measuring the intercondition agreement of the feature values among the 36 image conditions. Then in a series of univariable analyses, the impact of individual CT parameters was assessed by selecting subsets of conditions with one varying and two constant CT parameters. In each subset, intraparameter agreements were assessed. Overall concordance correlation coefficient (OCCC) served as the measure of agreement. An OCCC ≥ 0.9 implied strong agreement and reproducibility of radiomic features in intercondition or intraparameter comparisons. Furthermore, the interaction of CT parameters in impacting radiomic feature values was investigated via ANOVA.ResultsAll included radiomic features lacked intercondition reproducibility (OCCC 50% of radiomic features.ConclusionWe systematically explored the multidimensional space of CT parameters in affecting lung nodule radiomic features. Univariable and multivariable analyses of this study not only showed the lack of reproducibility of the majority of radiomic features but also revealed existing interactions among CT parameters, meaning that the effect of individual CT parameters on radiomic features can be conditional upon other CT acquisition and reconstruction parameters. Our findings advise on careful radiomic feature selection and attention to the inclusion criteria for CT image acquisition protocols within the datasets of radiomic studies.

Determination of therapeutic benefit in intracranial tumors is intimately dependent on serial assessment of radiographic images. The Response Assessment in Neuro-Oncology (RANO) criteria were established in 2010 to provide an updated framework to better characterize tumor response to contemporary treatments. Since this initial update a number of RANO criteria have provided some basic principles for the interpretation of changes on MR images; however, the details of how to operationalize RANO and other criteria for use in clinical trials are ambiguous and not standardized. In this review article designed for the neuro-oncologist or treating clinician, we outline essential steps for performing radiographic assessments by highlighting primary features of the Imaging Charter (referred to as the Charter for the remainder of this article), a document that describes the clinical trial imaging methodology and methods to ensure operationalization of the Charter into the workings of a clinical trial. Lastly, we provide recommendations for specific changes to optimize this methodology for neuro-oncology, including image registration, requirement of growing tumor for eligibility in trials of recurrent tumor, standardized image acquisition guidelines, and hybrid reader paradigms that allow for both unbiased measurements and more comprehensive interpretation.

GABAergic neurons of the hypothalamus regulate many innate behaviors, but little is known about the mechanisms that control their development. We previously identified hypothalamic neurons that express the LIM homeodomain transcription factor Lhx6, a master regulator of cortical interneuron development, as sleep-promoting. In contrast to telencephalic interneurons, hypothalamic Lhx6 neurons do not undergo long-distance tangential migration and do not express cortical interneuronal markers such as Pvalb. Here, we show that Lhx6 is necessary for the survival of hypothalamic neurons. Dlx1/2, Nkx2-2, and Nkx2-1 are each required for specification of spatially distinct subsets of hypothalamic Lhx6 neurons, and that Nkx2-2+/Lhx6+ neurons of the zona incerta are responsive to sleep pressure. We further identify multiple neuropeptides that are enriched in spatially segregated subsets of hypothalamic Lhx6 neurons, and that are distinct from those seen in cortical neurons. These findings identify common and divergent molecular mechanisms by which Lhx6 controls the development of GABAergic neurons in the hypothalamus.

Marine biogenic particle contributions to atmospheric aerosol concentrations are not well understood though they are important for determining cloud optical and cloud-nucleating properties. Here we examine the relationship between marine aerosol measurements (with satellites and model fields of ocean biology) and meteorological variables during the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES). NAAMES consisted of four field campaigns between November 2015 and April 2018 that aligned with the four major phases of the annual phytoplankton bloom cycle. The FLEXible PARTicle (FLEXPART) Lagrangian particle dispersion model is used to spatiotemporally connect these variables to ship-based aerosol and dimethyl sulfide (DMS) observations. We find that correlations between some aerosol measurements with satellitemeasured and modeled variables increase with increasing trajectory length, indicating that biological and meteorological processes over the air mass history are influential for measured particle properties and that using only spatially coincident data would miss correlative connections that are lagged in time. In particular, the marine non-refractory organic aerosol mass correlates with modeled marine net primary production when weighted by 5 d air mass trajectory residence time (r = 0:62). This result indicates that nonrefractory organic aerosol mass is influenced by biogenic volatile organic compound (VOC) emissions that are typically produced through bacterial degradation of dissolved organic matter, zooplankton grazing on marine phytoplankton, and as a by-product of photosynthesis by phytoplankton stocks during advection into the region. This is further supported by the correlation of non-refractory organic mass with 2 d residence-time-weighted chlorophyll a (r = 0:39), a proxy for phytoplankton abundance, and 5 d residencetime-weighted downward shortwave forcing (r = 0:58), a requirement for photosynthesis. In contrast, DMS (formed through biological processes in the seawater) and primary marine aerosol (PMA) concentrations showed better correlations with explanatory biological and meteorological variables weighted with shorter air mass residence times, which reflects their localized origin as primary emissions. Aerosol submicron number and mass negatively correlate with sea surface wind speed. The negative correlation is attributed to enhanced PMA concentrations under higher wind speed conditions. We hypothesized that the elevated total particle surface area associated with high PMA concentrations leads to enhanced rates of condensation of VOC oxidation products onto PMA. Given the high deposition velocity of PMA relative to submicron aerosol, PMA can limit the accumulation of secondary aerosol mass. This study provides observational evidence for connections between marine aerosols and underlying ocean biology through complex secondary formation processes, emphasizing the need to consider air mass history in future analyses.

GABAergic neurons of the hypothalamus regulate many innate behaviors, but little is known about the mechanisms that control their development. We previously identified hypothalamic neurons that express the LIM homeodomain transcription factor Lhx6, a master regulator of cortical interneuron development, as sleep-promoting. In contrast to telencephalic interneurons, hypothalamic Lhx6 neurons do not undergo long-distance tangential migration and do not express cortical interneuronal markers such as Pvalb. Here, we show that Lhx6 is necessary for the survival of hypothalamic neurons. Dlx1/2, Nkx2-2, and Nkx2-1 are each required for specification of spatially distinct subsets of hypothalamic Lhx6 neurons, and that Nkx2-2+/Lhx6+ neurons of the zona incerta are responsive to sleep pressure. We further identify multiple neuropeptides that are enriched in spatially segregated subsets of hypothalamic Lhx6 neurons, and that are distinct from those seen in cortical neurons. These findings identify common and divergent molecular mechanisms by which Lhx6 controls the development of GABAergic neurons in the hypothalamus.

BackgroundThe COVID-19 pandemic has reached 40 million confirmed cases worldwide. Given its rapid progression, it is important to examine its origins to better understand how people's knowledge, attitudes, and reactions have evolved over time. One method is to use data mining of social media conversations related to information exposure and self-reported user experiences.ObjectiveThis study aims to characterize the knowledge, attitudes, and behaviors of social media users located at the initial epicenter of the outbreak by analyzing data from the Sina Weibo platform in Chinese.MethodsWe used web scraping to collect public Weibo posts from December 31, 2019, to January 20, 2020, from users located in Wuhan City that contained COVID-19-related keywords. We then manually annotated all posts using an inductive content coding approach to identify specific information sources and key themes including news and knowledge about the outbreak, public sentiment, and public reaction to control and response measures.ResultsWe identified 10,159 COVID-19 posts from 8703 unique Weibo users. Among our three parent classification areas, 67.22% (n=6829) included news and knowledge posts, 69.72% (n=7083) included public sentiment, and 47.87% (n=4863) included public reaction and self-reported behavior. Many of these themes were expressed concurrently in the same Weibo post. Subtopics for news and knowledge posts followed four distinct timelines and evidenced an escalation of the outbreak's seriousness as more information became available. Public sentiment primarily focused on expressions of anxiety, though some expressions of anger and even positive sentiment were also detected. Public reaction included both protective and elevated health risk behavior.ConclusionsBetween the announcement of pneumonia and respiratory illness of unknown origin in late December 2019 and the discovery of human-to-human transmission on January 20, 2020, we observed a high volume of public anxiety and confusion about COVID-19, including different reactions to the news by users, negative sentiment after being exposed to information, and public reaction that translated to self-reported behavior. These findings provide early insight into changing knowledge, attitudes, and behaviors about COVID-19, and have the potential to inform future outbreak communication, response, and policy making in China and beyond.

ObjectiveNonsteroidal antiinflammatory drugs (NSAIDs) increase blood pressure and potentially cardiovascular burden, which may limit their use in ankylosing spondylitis (AS). Our objective was to determine the association of NSAID use with incident hypertension in a longitudinal AS cohort.MethodsAdults with AS were enrolled in a prospective cohort study of patient outcomes and examined every 4-6 months. Hypertension was defined by patient-reported hypertension; antihypertensive medication use; or, on 2 consecutive visits, systolic blood pressure ≥140 mm Hg or diastolic blood pressure ≥90 mm Hg. Continuous NSAID use was dichotomized based on the validated NSAID index. We assessed the association of NSAID use as a time-varying exposure with the incidence of hypertension using Cox proportional hazards models.ResultsOf the 1,282 patients in the cohort, 628 patients without baseline hypertension had at least 1 year of follow-up and were included in the analysis. Of these, 72% were male, the mean age at baseline was 39 ± 13 years, and 200 patients used NSAIDs continuously. On follow-up, 129 developed incident hypertension. After controlling for other variables, continuous NSAID use was associated with a hazard ratio of 1.12 for incident hypertension (95% confidence interval 1.04-1.20), compared to noncontinuous or no use. The association did not differ in subgroups defined by age, body mass index, biologic use, or disease activity.ConclusionIn our prospective, longitudinal AS cohort, continuous NSAID use was associated with a 12% increased risk for the development of incident hypertension, as compared to noncontinuous or no NSAID use.

ObjectiveNonsteroidal antiinflammatory drugs (NSAIDs) increase blood pressure and potentially cardiovascular burden, which may limit their use in ankylosing spondylitis (AS). Our objective was to determine the association of NSAID use with incident hypertension in a longitudinal AS cohort.MethodsAdults with AS were enrolled in a prospective cohort study of patient outcomes and examined every 4-6 months. Hypertension was defined by patient-reported hypertension; antihypertensive medication use; or, on 2 consecutive visits, systolic blood pressure ≥140 mm Hg or diastolic blood pressure ≥90 mm Hg. Continuous NSAID use was dichotomized based on the validated NSAID index. We assessed the association of NSAID use as a time-varying exposure with the incidence of hypertension using Cox proportional hazards models.ResultsOf the 1,282 patients in the cohort, 628 patients without baseline hypertension had at least 1 year of follow-up and were included in the analysis. Of these, 72% were male, the mean age at baseline was 39 ± 13 years, and 200 patients used NSAIDs continuously. On follow-up, 129 developed incident hypertension. After controlling for other variables, continuous NSAID use was associated with a hazard ratio of 1.12 for incident hypertension (95% confidence interval 1.04-1.20), compared to noncontinuous or no use. The association did not differ in subgroups defined by age, body mass index, biologic use, or disease activity.ConclusionIn our prospective, longitudinal AS cohort, continuous NSAID use was associated with a 12% increased risk for the development of incident hypertension, as compared to noncontinuous or no NSAID use.

Many common illnesses, for reasons that have not been identified, differentially affect men and women. For instance, the autoimmune diseases systemic lupus erythematosus (SLE) and Sjögren's syndrome affect nine times more women than men1, whereas schizophrenia affects men with greater frequency and severity relative to women2. All three illnesses have their strongest common genetic associations in the major histocompatibility complex (MHC) locus, an association that in SLE and Sjögren's syndrome has long been thought to arise from alleles of the human leukocyte antigen (HLA) genes at that locus3-6. Here we show that variation of the complement component 4 (C4) genes C4A and C4B, which are also at the MHC locus and have been linked to increased risk for schizophrenia7, generates 7-fold variation in risk for SLE and 16-fold variation in risk for Sjögren's syndrome among individuals with common C4 genotypes, with C4A protecting more strongly than C4B in both illnesses. The same alleles that increase risk for schizophrenia greatly reduce risk for SLE and Sjögren's syndrome. In all three illnesses, C4 alleles act more strongly in men than in women: common combinations of C4A and C4B generated 14-fold variation in risk for SLE, 31-fold variation in risk for Sjögren's syndrome, and 1.7-fold variation in schizophrenia risk among men (versus 6-fold, 15-fold and 1.26-fold variation in risk among women, respectively). At a protein level, both C4 and its effector C3 were present at higher levels in cerebrospinal fluid and plasma8,9 in men than in women among adults aged between 20 and 50 years, corresponding to the ages of differential disease vulnerability. Sex differences in complement protein levels may help to explain the more potent effects of C4 alleles in men, women's greater risk of SLE and Sjögren's syndrome and men's greater vulnerability to schizophrenia. These results implicate the complement system as a source of sexual dimorphism in vulnerability to diverse illnesses.

When mining image data from PACs or clinical trials or processing large volumes of data without curation, the relevant scans must be identified among irrelevant or redundant data. Only images acquired with appropriate technical factors, patient positioning, and physiological conditions may be applicable to a particular image processing or machine learning task. Automatic labeling is important to make big data mining practical by replacing conventional manual review of every single-image series. Digital imaging and communications in medicine headers usually do not provide all the necessary labels and are sometimes incorrect. We propose an image-based high throughput labeling pipeline using deep learning, aimed at identifying scan direction, scan posture, lung coverage, contrast usage, and breath-hold types. They were posed as different classification problems and some of them involved further segmentation and identification of anatomic landmarks. Images of different view planes were used depending on the specific classification problem. All of our models achieved accuracy > 99 % on test set across different tasks using a research database from multicenter clinical trials.

ObjectiveTo examine changes in the extent of specific patterns of interstitial lung disease (ILD) as they transition from one pattern to another in response to immunosuppressive therapy in systemic sclerosis-related ILD (SSc-ILD).MethodsWe evaluated changes in the quantitative extent of specific lung patterns of ILD using volumetric high-resolution computed tomography (HRCT) scans obtained at baseline and after 2 years of therapy in patients treated with either cyclophosphamide (CYC) for 1 year or mycophenolate mofetil (MMF) for 2 years in Scleroderma Lung Study II. ILD patterns included lung fibrosis, ground glass, honeycombing, and normal lung. Net change was calculated as the difference in the probability of change from one ILD pattern to another. Wilcoxon's signed rank test was used to compare the changes.ResultsForty-seven and 50 patients had baseline and follow-up scans in the CYC and MMF groups, respectively. Mean net improvements reflecting favorable changes from one ILD pattern to another in the whole lung in the CYC and MMF groups, respectively, were as follows: from lung fibrosis to a normal lung pattern, 21% and 19%; from a ground-glass pattern to a normal lung pattern, 30% and 28%; and from lung fibrosis to a ground-glass pattern, 5% and 0.5%. The mean overall improvement in transitioning from a ground-glass pattern or lung fibrosis to a normal lung pattern was significant for both treatments (all P < 0.001).ConclusionSignificantly favorable transitions from both ground-glass and lung fibrosis ILD patterns to a normal lung pattern were observed in patients undergoing immunosuppressive treatment for SSc-ILD, suggesting the usefulness of examining these transitions for insights into the underlying pathobiology of treatment response.

Evaluating the association between diseases and the longitudinal pattern of pharmacological therapy has become increasingly important. However, in many longitudinal studies, self-reported medication usage data collected at patients' follow-up visits could be missing for various reasons. These pieces of missing or inaccurate/untenable information complicate determining the trajectory of medication use and its complete effects for patients. Although longitudinal models can deal with specific types of missing data, inappropriate handling of this issue can lead to a biased estimation of regression parameters especially when missing data mechanisms are complex and depend upon multiple sources of variation. We propose a latent class-based multiple imputation (MI) approach using a Bayesian quantile regression (BQR) that incorporates cluster of unobserved heterogeneity for medication usage data with intermittent missing values. Findings from our simulation study indicate that the proposed method performs better than traditional MI methods under certain scenarios of data distribution. We also demonstrate applications of the proposed method to data from the Prospective Study of Outcomes in Ankylosing Spondylitis (AS) cohort when assessing an association between longitudinal nonsteroidal anti-inflammatory drugs (NSAIDs) usage and radiographic damage in AS, while the longitudinal NSAID index data are intermittently missing.

Idiopathic pulmonary fibrosis (IPF) is a fatal lung disease characterized by an unpredictable progressive declinein lung function. Natural history of IPF is unknown and the prediction of disease progression at the time ofdiagnosis is notoriously difficult. High resolution computed tomography (HRCT) has been used for the diagnosisof IPF, but not generally for monitoring purpose. The objective of this work is to develop a novel predictivemodel for the radiological progression pattern at voxel-wise level using only baseline HRCT scans. Mainly, thereare two challenges: (a) obtaining a data set of features for region of interest (ROI) on baseline HRCT scans andtheir follow-up status; and (b) simultaneously selecting important features from high-dimensional space, andoptimizing the prediction performance. We resolved the first challenge by implementing a study design andhaving an expert radiologist contour ROIs at baseline scans, depending on its progression status in follow-upvisits. For the second challenge, we integrated the feature selection with prediction by developing an algorithmusing a wrapper method that combines quantum particle swarm optimization to select a small number of featureswith random forest to classify early patterns of progression. We applied our proposed algorithm to analyzeanonymized HRCT images from 50 IPF subjects from a multi-center clinical trial. We showed that it yields aparsimonious model with 81.8% sensitivity, 82.2% specificity and an overall accuracy rate of 82.1% at the ROIlevel. These results are superior to other popular feature selections and classification methods, in that ourmethod produces higher accuracy in prediction of progression and more balanced sensitivity and specificity witha smaller number of selected features. Our work is the first approach to show that it is possible to use onlybaseline HRCT scans to predict progressive ROIs at 6 months to 1year follow-ups using artificial intelligence.

Clinical trials for orphan diseases are critical for developing effective therapies. One such condition, fibrodysplasia ossificans progressiva (FOP; MIM#135100), is characterized by progressive heterotopic ossification (HO) that leads to severe disability. Individuals with FOP are extremely sensitive to even minor traumatic events. There has been substantial recent interest in clinical trials for novel and urgently-needed treatments for FOP. The International Clinical Council on FOP (ICC) was established in 2016 to provide consolidated and coordinated advice on the best practices for clinical care and clinical research for individuals who suffer from FOP. The Clinical Trials Committee of the ICC developed a focused list of key considerations that encompass the specific and unique needs of the FOP community - considerations that are endorsed by the entire ICC. These considerations complement established protocols for developing and executing robust clinical trials by providing a foundation for helping to ensure the safety of subjects with FOP in clinical research trials.

Clinical trials for orphan diseases are critical for developing effective therapies. One such condition, fibrodysplasia ossificans progressiva (FOP; MIM#135100), is characterized by progressive heterotopic ossification (HO) that leads to severe disability. Individuals with FOP are extremely sensitive to even minor traumatic events. There has been substantial recent interest in clinical trials for novel and urgently-needed treatments for FOP. The International Clinical Council on FOP (ICC) was established in 2016 to provide consolidated and coordinated advice on the best practices for clinical care and clinical research for individuals who suffer from FOP. The Clinical Trials Committee of the ICC developed a focused list of key considerations that encompass the specific and unique needs of the FOP community - considerations that are endorsed by the entire ICC. These considerations complement established protocols for developing and executing robust clinical trials by providing a foundation for helping to ensure the safety of subjects with FOP in clinical research trials.

PurposeWith recent substantial improvements in modern computing, interest in quantitative imaging with CT has seen a dramatic increase. As a result, the need to both create and analyze large, high-quality datasets of clinical studies has increased as well. At present, no efficient, widely available method exists to accomplish this. The purpose of this technical note is to describe an open-source high-throughput computational pipeline framework for the reconstruction and analysis of diagnostic CT imaging data to conduct large-scale quantitative imaging studies and to accelerate and improve quantitative imaging research.MethodsThe pipeline consists of two, primary "blocks": reconstruction and analysis. Reconstruction is carried out via a graphics processing unit (GPU) queuing framework developed specifically for the pipeline that allows a dataset to be reconstructed using a variety of different parameter configurations such as slice thickness, reconstruction kernel, and simulated acquisition dose. The analysis portion then automatically analyzes the output of the reconstruction using "modules" that can be combined in various ways to conduct different experiments. Acceleration of analysis is achieved using cluster processing. Efficiency and performance of the pipeline are demonstrated using an example 142 subject lung screening cohort reconstructed 36 different ways and analyzed using quantitative emphysema scoring techniques.ResultsThe pipeline reconstructed and analyzed the 5112 reconstructed datasets in approximately 10 days, a roughly 72× speedup over previous efforts using the scanner for reconstructions. Tightly coupled pipeline quality assurance software ensured proper performance of analysis modules with regard to segmentation and emphysema scoring.ConclusionsThe pipeline greatly reduced the time from experiment conception to quantitative results. The modular design of the pipeline allows the high-throughput framework to be utilized for other future experiments into different quantitative imaging techniques. Future applications of the pipeline being explored are robustness testing of quantitative imaging metrics, data generation for deep learning, and use as a test platform for image-processing techniques to improve clinical quantitative imaging.

PurposeWith recent substantial improvements in modern computing, interest in quantitative imaging with CT has seen a dramatic increase. As a result, the need to both create and analyze large, high-quality datasets of clinical studies has increased as well. At present, no efficient, widely available method exists to accomplish this. The purpose of this technical note is to describe an open-source high-throughput computational pipeline framework for the reconstruction and analysis of diagnostic CT imaging data to conduct large-scale quantitative imaging studies and to accelerate and improve quantitative imaging research.MethodsThe pipeline consists of two, primary "blocks": reconstruction and analysis. Reconstruction is carried out via a graphics processing unit (GPU) queuing framework developed specifically for the pipeline that allows a dataset to be reconstructed using a variety of different parameter configurations such as slice thickness, reconstruction kernel, and simulated acquisition dose. The analysis portion then automatically analyzes the output of the reconstruction using "modules" that can be combined in various ways to conduct different experiments. Acceleration of analysis is achieved using cluster processing. Efficiency and performance of the pipeline are demonstrated using an example 142 subject lung screening cohort reconstructed 36 different ways and analyzed using quantitative emphysema scoring techniques.ResultsThe pipeline reconstructed and analyzed the 5112 reconstructed datasets in approximately 10 days, a roughly 72× speedup over previous efforts using the scanner for reconstructions. Tightly coupled pipeline quality assurance software ensured proper performance of analysis modules with regard to segmentation and emphysema scoring.ConclusionsThe pipeline greatly reduced the time from experiment conception to quantitative results. The modular design of the pipeline allows the high-throughput framework to be utilized for other future experiments into different quantitative imaging techniques. Future applications of the pipeline being explored are robustness testing of quantitative imaging metrics, data generation for deep learning, and use as a test platform for image-processing techniques to improve clinical quantitative imaging.

Genome-wide association studies (GWASs) are highly effective at identifying common risk variants for schizophrenia. Rare risk variants are also important contributors to schizophrenia etiology but, with the exception of large copy number variants, are difficult to detect with GWAS. Exome and genome sequencing, which have accelerated the study of rare variants, are expensive so alternative methods are needed to aid detection of rare variants. Here we re-analyze an Irish schizophrenia GWAS dataset (n = 3,473) by performing identity-by-descent (IBD) mapping followed by exome sequencing of individuals identified as sharing risk haplotypes to search for rare risk variants in coding regions. We identified 45 rare haplotypes (>1 cM) that were significantly more common in cases than controls. By exome sequencing 105 haplotype carriers, we investigated these haplotypes for functional coding variants that could be tested for association in independent GWAS samples. We identified one rare missense variant in PCNT but did not find statistical support for an association with schizophrenia in a replication analysis. However, IBD mapping can prioritize both individual samples and genomic regions for follow-up analysis but genome rather than exome sequencing may be more effective at detecting risk variants on rare haplotypes.

ObjectiveTo examine associations of HLA class I and class II alleles with ankylosing spondylitis (AS) in three cohorts of patients of European, Asian and African ancestry.MethodsHLA-A, HLA-B, HLA-C, HLA-DRB1, HLA-DQB1 and HLA-DPB1 alleles were genotyped in 1948 unrelated white and 67 African-American patients with AS from the Prospective Study of Outcomes in Ankylosing Spondylitis cohort, the North American Spondylitis Consortium and Australo-Anglo-American Spondyloarthritis Consortium, 990 white and 245 African-American Controls and HLA-B alleles in 442 Han Chinese patients with AS and 346 controls from Shanghai and Gansu, China. In addition to the case:control analyses, HLA-B*27-negative patients with AS were analysed separately, and logistic regression and 'relative predispositional effects' (RPE) analyses were carried out to control for the major effect of HLA-B*27 on disease susceptibility.ResultsAlthough numerous associations were seen between HLA alleles and AS in whites, among HLA-B*27-negative patients with AS , positive associations were seen with HLA-A*29, B*38, B*49, B*52, DRB1*11 and DPB1*03:01 and negative associations with HLA-B*07, HLA-B*57, HLA-DRB1*15:01, HLA-DQB1*02:01 and HLA-DQB1*06:02. Additional associations with HLA-B*14 and B*40 (B60) were observed via RPE analysis, which excludes the HLA-B*27 alleles. The increased frequency of HLA-B*40:01 and decreased frequency of HLA-B*07 was also seen in Han Chinese and African-Americans with AS. HLA-B*08 was decreased in whites with acute anterior uveitis.ConclusionsThese data, analysing the largest number of patients with AS examined to date in three ethnic groups, confirm that other HLA class I and II alleles other than HLA-B*27 to be operative in AS predisposition.

BACKGROUND:Global health diplomacy (GHD) is a burgeoning field bridging the priorities of global health and foreign affairs. Given the increasing need to mobilize disparate global health stakeholders coupled with the need to design complex public health partnerships to tackle issues of international concern, effective and timely cooperation among state actors is critical. Health Attachés represent this coordination focal point and are key diplomatic professionals at the forefront of GHD. Despite their unique mandate, little is published about this profession and the perspectives of those who work in the field. METHODS:Through purposive sampling, we performed in-depth qualitative interviews with seven Health Attachés: three foreign Health Attachés accredited to the United States and four U.S. Health Attachés accredited to foreign governments. Our interviews explored four key topics: the role and mission of Health Attachés, skills needed to perform GHD, examples of successes and challenges in accomplishing their respective missions, and suggestions for the future development of the diplomatic profession. RESULTS:We identified several lessons to apply to the growing field of GHD. First, GHD actors need to receive appropriate training to successfully negotiate the intersection of global health and foreign affairs. Participants suggested several areas of training that would benefit GHD actors: diplomacy and negotiation, applied science, and cross-cultural competency. Second, participants articulated the need for a career path for GHD practitioners, increased opportunities for on-the-job training and mentored experiences, and GHD competencies with defined levels of mastery that can be used in occupational evaluation and career development. CONCLUSIONS:Our findings indicate that skills in diplomacy and negotiation, applied science, and cross cultural competency are essential for the statecraft of Health Attachés. Additionally, establishing a clear career pathway for Health Attachés is critical for future maturation of the profession and for fostering effective global health action that aligns public health and foreign diplomacy outcomes. Achieving these goals would ensure that this special cadre of diplomats could effectively practice GHD and would also better position Health Attachés to take the lead in advancing shared global health goals among nation states in a new era of twenty-first century diplomacy.

PurposeWe present a method for generating a T2 MR-based probabilistic model of tumor occurrence in the prostate to guide the selection of anatomical sites for targeted biopsies and serve as a diagnostic tool to aid radiological evaluation of prostate cancer.Materials and methodsIn our study, the prostate and any radiological findings within were segmented retrospectively on 3D T2-weighted MR images of 266 subjects who underwent radical prostatectomy. Subsequent histopathological analysis determined both the ground truth and the Gleason grade of the tumors. A randomly chosen subset of 19 subjects was used to generate a multi-subject-derived prostate template. Subsequently, a cascading registration algorithm involving both affine and non-rigid B-spline transforms was used to register the prostate of every subject to the template. Corresponding transformation of radiological findings yielded a population-based probabilistic model of tumor occurrence. The quality of our probabilistic model building approach was statistically evaluated by measuring the proportion of correct placements of tumors in the prostate template, i.e., the number of tumors that maintained their anatomical location within the prostate after their transformation into the prostate template space.ResultsProbabilistic model built with tumors deemed clinically significant demonstrated a heterogeneous distribution of tumors, with higher likelihood of tumor occurrence at the mid-gland anterior transition zone and the base-to-mid-gland posterior peripheral zones. Of 250 MR lesions analyzed, 248 maintained their original anatomical location with respect to the prostate zones after transformation to the prostate.ConclusionWe present a robust method for generating a probabilistic model of tumor occurrence in the prostate that could aid clinical decision making, such as selection of anatomical sites for MR-guided prostate biopsies.

Schizophrenia and bipolar disorder are two distinct diagnoses that share symptomology. Understanding the genetic factors contributing to the shared and disorder-specific symptoms will be crucial for improving diagnosis and treatment. In genetic data consisting of 53,555 cases (20,129 bipolar disorder [BD], 33,426 schizophrenia [SCZ]) and 54,065 controls, we identified 114 genome-wide significant loci implicating synaptic and neuronal pathways shared between disorders. Comparing SCZ to BD (23,585 SCZ, 15,270 BD) identified four genomic regions including one with disorder-independent causal variants and potassium ion response genes as contributing to differences in biology between the disorders. Polygenic risk score (PRS) analyses identified several significant correlations within case-only phenotypes including SCZ PRS with psychotic features and age of onset in BD. For the first time, we discover specific loci that distinguish between BD and SCZ and identify polygenic components underlying multiple symptom dimensions. These results point to the utility of genetics to inform symptomology and potential treatment.

In type 2 diabetes, amyloid oligomers, chronic hyperglycemia, lipotoxicity, and pro-inflammatory cytokines are detrimental to beta-cells, causing apoptosis and impaired insulin secretion. The histone acetyl transferase p300, involved in remodeling of chromatin structure by epigenetic mechanisms, is a key ubiquitous activator of the transcriptional machinery. In this study, we report that loss of p300 acetyl transferase activity and expression leads to beta-cell apoptosis, and most importantly, that stress situations known to be associated with diabetes alter p300 levels and functional integrity. We found that proteasomal degradation is the mechanism subserving p300 loss in beta-cells exposed to hyperglycemia or pro-inflammatory cytokines. We also report that melatonin, a hormone produced in the pineal gland and known to play key roles in beta-cell health, preserves p300 levels altered by these toxic conditions. Collectively, these data imply an important role for p300 in the pathophysiology of diabetes.

OBJECTIVE:Opioid analgesics may be prescribed to ankylosing spondylitis (AS) patients with pain that is unresponsive to antirheumatic treatment. Our study assessed factors associated with opioid usage in AS. METHODS:A prospective cohort of 706 patients with AS meeting modified New York criteria followed at least 2 years underwent comprehensive clinical evaluation of disease activity and functional impairment. These were assessed by the Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) and Bath Ankylosing Spondylitis Functional Index (BASFI). Radiographic severity was assessed by the Bath Ankylosing Spondylitis Radiology Index and modified Stokes Ankylosing Spondylitis Scoring System. Medications taken concurrently with opioids, as well as C-reactive protein (CRP) levels and erythrocyte sedimentation rate (ESR), were determined at each study visit, performed every 6 months. Analyses were carried out at baseline, and longitudinal multivariable models were developed to identify factors independently associated with chronic and intermittent opioid usage over time. RESULTS:Factors significantly associated with opioid usage, especially chronic opioid use, included longer disease duration, smoking, lack of exercise, higher disease activity (BASDAI) and functional impairment (BASFI), depression, radiographic severity, and cardiovascular disease. Patients taking opioids were more likely to be using anxiolytic, hypnotic, antidepressant, and muscle relaxant medications. Multivariable analysis underscored the association with smoking, older age, antitumor necrosis factor agent use, and psychoactive drugs, as well as with subjective but not objective determinants of disease activity. CONCLUSION:Opioid usage was more likely to be associated with subjective measures (depression, BASDAI, BASFI) than objective measures (CRP, ESR), suggesting that pain in AS may derive from sources other than spinal inflammation alone.

BACKGROUND:Global health diplomacy (GHD) is a burgeoning field bridging the priorities of global health and foreign affairs. Given the increasing need to mobilize disparate global health stakeholders coupled with the need to design complex public health partnerships to tackle issues of international concern, effective and timely cooperation among state actors is critical. Health Attachés represent this coordination focal point and are key diplomatic professionals at the forefront of GHD. Despite their unique mandate, little is published about this profession and the perspectives of those who work in the field. METHODS:Through purposive sampling, we performed in-depth qualitative interviews with seven Health Attachés: three foreign Health Attachés accredited to the United States and four U.S. Health Attachés accredited to foreign governments. Our interviews explored four key topics: the role and mission of Health Attachés, skills needed to perform GHD, examples of successes and challenges in accomplishing their respective missions, and suggestions for the future development of the diplomatic profession. RESULTS:We identified several lessons to apply to the growing field of GHD. First, GHD actors need to receive appropriate training to successfully negotiate the intersection of global health and foreign affairs. Participants suggested several areas of training that would benefit GHD actors: diplomacy and negotiation, applied science, and cross-cultural competency. Second, participants articulated the need for a career path for GHD practitioners, increased opportunities for on-the-job training and mentored experiences, and GHD competencies with defined levels of mastery that can be used in occupational evaluation and career development. CONCLUSIONS:Our findings indicate that skills in diplomacy and negotiation, applied science, and cross cultural competency are essential for the statecraft of Health Attachés. Additionally, establishing a clear career pathway for Health Attachés is critical for future maturation of the profession and for fostering effective global health action that aligns public health and foreign diplomacy outcomes. Achieving these goals would ensure that this special cadre of diplomats could effectively practice GHD and would also better position Health Attachés to take the lead in advancing shared global health goals among nation states in a new era of twenty-first century diplomacy.

A clinical validation of the bone scan lesion area (BSLA) as a quantitative imaging biomarker was performed in metastatic castration-resistant prostate cancer (mCRPC). BSLA was computed from whole-body bone scintigraphy at baseline and week 12 posttreatment in a cohort of 198 mCRPC subjects (127 treated and 71 placebo) from a clinical trial involving a different drug from the initial biomarker development. BSLA computation involved automated image normalization, lesion segmentation, and summation of the total area of segmented lesions on bone scan AP and PA views as a measure of tumor burden. As a predictive biomarker, treated subjects with baseline BSLA [Formula: see text] had longer survival than those with higher BSLA ([Formula: see text] and [Formula: see text]). As a surrogate outcome biomarker, subjects were categorized as progressive disease (PD) if the BSLA increased by a prespecified 30% or more from baseline to week 12 and non-PD otherwise. Overall survival rates between PD and non-PD groups were statistically different ([Formula: see text] and [Formula: see text]). Subjects without PD at week 12 had longer survival than subjects with PD: median 398 days versus 280 days. BSLA has now been demonstrated to be an early surrogate outcome for overall survival in different prostate cancer drug treatments.

In type 2 diabetes, amyloid oligomers, chronic hyperglycemia, lipotoxicity, and pro-inflammatory cytokines are detrimental to beta-cells, causing apoptosis and impaired insulin secretion. The histone acetyl transferase p300, involved in remodeling of chromatin structure by epigenetic mechanisms, is a key ubiquitous activator of the transcriptional machinery. In this study, we report that loss of p300 acetyl transferase activity and expression leads to beta-cell apoptosis, and most importantly, that stress situations known to be associated with diabetes alter p300 levels and functional integrity. We found that proteasomal degradation is the mechanism subserving p300 loss in beta-cells exposed to hyperglycemia or pro-inflammatory cytokines. We also report that melatonin, a hormone produced in the pineal gland and known to play key roles in beta-cell health, preserves p300 levels altered by these toxic conditions. Collectively, these data imply an important role for p300 in the pathophysiology of diabetes.

Eukaryotic microbes have three primary mechanisms for obtaining nutrients and energy: phagotrophy, photosynthesis and osmotrophy. Traits associated with the latter two functions arose independently multiple times in the eukaryotes. The Fungi successfully coupled osmotrophy with filamentous growth, and similar traits are also manifested in the Pseudofungi (oomycetes and hyphochytriomycetes). Both the Fungi and the Pseudofungi encompass a diversity of plant and animal parasites. Genome-sequencing efforts have focused on host-associated microbes (mutualistic symbionts or parasites), providing limited comparisons with free-living relatives. Here we report the first draft genome sequence of a hyphochytriomycete 'pseudofungus'; Hyphochytrium catenoides Using phylogenomic approaches, we identify genes of recent viral ancestry, with related viral derived genes also present on the genomes of oomycetes, suggesting a complex history of viral coevolution and integration across the Pseudofungi. H. catenoides has a complex life cycle involving diverse filamentous structures and a flagellated zoospore with a single anterior tinselate flagellum. We use genome comparisons, drug sensitivity analysis and high-throughput culture arrays to investigate the ancestry of oomycete/pseudofungal characteristics, demonstrating that many of the genetic features associated with parasitic traits evolved specifically within the oomycete radiation. Comparative genomics also identified differences in the repertoire of genes associated with filamentous growth between the Fungi and the Pseudofungi, including differences in vesicle trafficking systems, cell-wall synthesis pathways and motor protein repertoire, demonstrating that unique cellular systems underpinned the convergent evolution of filamentous osmotrophic growth in these two eukaryotic groups.

Eukaryotic microbes have three primary mechanisms for obtaining nutrients and energy: phagotrophy, photosynthesis and osmotrophy. Traits associated with the latter two functions arose independently multiple times in the eukaryotes. The Fungi successfully coupled osmotrophy with filamentous growth, and similar traits are also manifested in the Pseudofungi (oomycetes and hyphochytriomycetes). Both the Fungi and the Pseudofungi encompass a diversity of plant and animal parasites. Genome-sequencing efforts have focused on host-associated microbes (mutualistic symbionts or parasites), providing limited comparisons with free-living relatives. Here we report the first draft genome sequence of a hyphochytriomycete 'pseudofungus'; Hyphochytrium catenoides Using phylogenomic approaches, we identify genes of recent viral ancestry, with related viral derived genes also present on the genomes of oomycetes, suggesting a complex history of viral coevolution and integration across the Pseudofungi. H. catenoides has a complex life cycle involving diverse filamentous structures and a flagellated zoospore with a single anterior tinselate flagellum. We use genome comparisons, drug sensitivity analysis and high-throughput culture arrays to investigate the ancestry of oomycete/pseudofungal characteristics, demonstrating that many of the genetic features associated with parasitic traits evolved specifically within the oomycete radiation. Comparative genomics also identified differences in the repertoire of genes associated with filamentous growth between the Fungi and the Pseudofungi, including differences in vesicle trafficking systems, cell-wall synthesis pathways and motor protein repertoire, demonstrating that unique cellular systems underpinned the convergent evolution of filamentous osmotrophic growth in these two eukaryotic groups.

The purpose of this study is to compare disease severity in ankylosing spondylitis (AS) in three ethnic groups. We assessed 925 AS patients (57 Blacks, 805 Whites, 63 Latinos) enrolled in the longitudinal Prospective Study of Outcomes in AS (PSOAS) for functional impairment, disease activity, and radiographic severity. Comparisons of clinical characteristics and HLA-B27 frequency for each group were performed, in two multivariable regression models, we compared the baseline Bath Ankylosing Spondylitis Radiographic Index (BASRI) and modified Stokes Ankylosing Spondylitis Spine Score (mSASSS) by ethnicity, adjusting for covariates. Blacks had greater functional impairment (Bath Ankylosing Spondylitis Functional Index) (median 62.5 vs. 27.8 in Whites and 38.1 in Latinos; p

The purpose of this study is to compare disease severity in ankylosing spondylitis (AS) in three ethnic groups. We assessed 925 AS patients (57 Blacks, 805 Whites, 63 Latinos) enrolled in the longitudinal Prospective Study of Outcomes in AS (PSOAS) for functional impairment, disease activity, and radiographic severity. Comparisons of clinical characteristics and HLA-B27 frequency for each group were performed, in two multivariable regression models, we compared the baseline Bath Ankylosing Spondylitis Radiographic Index (BASRI) and modified Stokes Ankylosing Spondylitis Spine Score (mSASSS) by ethnicity, adjusting for covariates. Blacks had greater functional impairment (Bath Ankylosing Spondylitis Functional Index) (median 62.5 vs. 27.8 in Whites and 38.1 in Latinos; p

PurposeLung cancer screening with low-dose CT has recently been approved for reimbursement, heralding the arrival of such screening services worldwide. Computer-aided detection (CAD) tools offer the potential to assist radiologists in detecting nodules in these screening exams. In lung screening, as in all CT exams, there is interest in further reducing radiation dose. However, the effects of continued dose reduction on CAD performance are not fully understood. In this work, we investigated the effect of reducing radiation dose on CAD lung nodule detection performance in a screening population.MethodsThe raw projection data files were collected from 481 patients who underwent low-dose screening CT exams at our institution as part of the National Lung Screening Trial (NLST). All scans were performed on a multidetector scanner (Sensation 64, Siemens Healthcare, Forchheim Germany) according to the NLST protocol, which called for a fixed tube current scan of 25 effective mAs for standard-sized patients and 40 effective mAs for larger patients. The raw projection data were input to a reduced-dose simulation software to create simulated reduced-dose scans corresponding to 50% and 25% of the original protocols. All raw data files were reconstructed at the scanner with 1 mm slice thickness and B50 kernel. The lungs were segmented semi-automatically, and all images and segmentations were input to an in-house CAD algorithm trained on higher dose scans (75-300 mAs). CAD findings were compared to a reference standard generated by an experienced reader. Nodule- and patient-level sensitivities were calculated along with false positives per scan, all of which were evaluated in terms of the relative change with respect to dose. Nodules were subdivided based on size and solidity into categories analogous to the LungRADS assessment categories, and sub-analyses were performed.ResultsFrom the 481 patients in this study, 82 had at least one nodule (prevalence of 17%) and 399 did not (83%). A total of 118 nodules were identified. Twenty-seven nodules (23%) corresponded to LungRADS category 4 based on size and composition, while 18 (15%) corresponded to LungRADS category 3 and 73 (61%) corresponded to LungRADS category 2. For solid nodules ≥8 mm, patient-level median sensitivities were 100% at all three dose levels, and mean sensitivities were 72%, 63%, and 63% at original, 50%, and 25% dose, respectively. Overall mean patient-level sensitivities for nodules ranging from 3 to 45 mm were 38%, 37%, and 38% at original, 50%, and 25% dose due to the prevalence of smaller nodules and nonsolid nodules in our reference standard. The mean false-positive rates were 3, 5, and 13 per case.ConclusionsCAD sensitivity decreased very slightly for larger nodules as dose was reduced, indicating that reducing the dose to 50% of original levels may be investigated further for use in CT screening. However, the effect of dose was small relative to the effect of the nodule size and solidity characteristics. The number of false positives per scan increased substantially at 25% dose, illustrating the importance of tuning CAD algorithms to very challenging, high-noise screening exams.

PurposeLung cancer screening with low-dose CT has recently been approved for reimbursement, heralding the arrival of such screening services worldwide. Computer-aided detection (CAD) tools offer the potential to assist radiologists in detecting nodules in these screening exams. In lung screening, as in all CT exams, there is interest in further reducing radiation dose. However, the effects of continued dose reduction on CAD performance are not fully understood. In this work, we investigated the effect of reducing radiation dose on CAD lung nodule detection performance in a screening population.MethodsThe raw projection data files were collected from 481 patients who underwent low-dose screening CT exams at our institution as part of the National Lung Screening Trial (NLST). All scans were performed on a multidetector scanner (Sensation 64, Siemens Healthcare, Forchheim Germany) according to the NLST protocol, which called for a fixed tube current scan of 25 effective mAs for standard-sized patients and 40 effective mAs for larger patients. The raw projection data were input to a reduced-dose simulation software to create simulated reduced-dose scans corresponding to 50% and 25% of the original protocols. All raw data files were reconstructed at the scanner with 1 mm slice thickness and B50 kernel. The lungs were segmented semi-automatically, and all images and segmentations were input to an in-house CAD algorithm trained on higher dose scans (75-300 mAs). CAD findings were compared to a reference standard generated by an experienced reader. Nodule- and patient-level sensitivities were calculated along with false positives per scan, all of which were evaluated in terms of the relative change with respect to dose. Nodules were subdivided based on size and solidity into categories analogous to the LungRADS assessment categories, and sub-analyses were performed.ResultsFrom the 481 patients in this study, 82 had at least one nodule (prevalence of 17%) and 399 did not (83%). A total of 118 nodules were identified. Twenty-seven nodules (23%) corresponded to LungRADS category 4 based on size and composition, while 18 (15%) corresponded to LungRADS category 3 and 73 (61%) corresponded to LungRADS category 2. For solid nodules ≥8 mm, patient-level median sensitivities were 100% at all three dose levels, and mean sensitivities were 72%, 63%, and 63% at original, 50%, and 25% dose, respectively. Overall mean patient-level sensitivities for nodules ranging from 3 to 45 mm were 38%, 37%, and 38% at original, 50%, and 25% dose due to the prevalence of smaller nodules and nonsolid nodules in our reference standard. The mean false-positive rates were 3, 5, and 13 per case.ConclusionsCAD sensitivity decreased very slightly for larger nodules as dose was reduced, indicating that reducing the dose to 50% of original levels may be investigated further for use in CT screening. However, the effect of dose was small relative to the effect of the nodule size and solidity characteristics. The number of false positives per scan increased substantially at 25% dose, illustrating the importance of tuning CAD algorithms to very challenging, high-noise screening exams.

Mutations in more than 250 genes are implicated in inherited retinal dystrophy; the encoded proteins are involved in a broad spectrum of pathways. The presence of unsolved families after highly parallel sequencing strategies suggests that further genes remain to be identified. Whole-exome and -genome sequencing studies employed here in large cohorts of affected individuals revealed biallelic mutations in ARHGEF18 in three such individuals. ARHGEF18 encodes ARHGEF18, a guanine nucleotide exchange factor that activates RHOA, a small GTPase protein that is a key component of tight junctions and adherens junctions. This biological pathway is known to be important for retinal development and function, as mutation of CRB1, encoding another component, causes retinal dystrophy. The retinal structure in individuals with ARHGEF18 mutations resembled that seen in subjects with CRB1 mutations. Five mutations were found on six alleles in the three individuals: c.808A>G (p.Thr270Ala), c.1617+5G>A (p.Asp540Glyfs∗63), c.1996C>T (p.Arg666∗), c.2632G>T (p.Glu878∗), and c.2738_2761del (p.Arg913_Glu920del). Functional tests suggest that each disease genotype might retain some ARHGEF18 activity, such that the phenotype described here is not the consequence of nullizygosity. In particular, the p.Thr270Ala missense variant affects a highly conserved residue in the DBL homology domain, which is required for the interaction and activation of RHOA. Previously, knock-out of Arhgef18 in the medaka fish has been shown to cause larval lethality which is preceded by retinal defects that resemble those seen in zebrafish Crumbs complex knock-outs. The findings described here emphasize the peculiar sensitivity of the retina to perturbations of this pathway, which is highlighted as a target for potential therapeutic strategies.

Inherited retinal disease is a common cause of visual impairment and represents a highly heterogeneous group of conditions. Here, we present findings from a cohort of 722 individuals with inherited retinal disease, who have had whole-genome sequencing (n = 605), whole-exome sequencing (n = 72), or both (n = 45) performed, as part of the NIHR-BioResource Rare Diseases research study. We identified pathogenic variants (single-nucleotide variants, indels, or structural variants) for 404/722 (56%) individuals. Whole-genome sequencing gives unprecedented power to detect three categories of pathogenic variants in particular: structural variants, variants in GC-rich regions, which have significantly improved coverage compared to whole-exome sequencing, and variants in non-coding regulatory regions. In addition to previously reported pathogenic regulatory variants, we have identified a previously unreported pathogenic intronic variant in CHM in two males with choroideremia. We have also identified 19 genes not previously known to be associated with inherited retinal disease, which harbor biallelic predicted protein-truncating variants in unsolved cases. Whole-genome sequencing is an increasingly important comprehensive method with which to investigate the genetic causes of inherited retinal disease.

We simultaneously investigated the genetic landscape of ankylosing spondylitis, Crohn's disease, psoriasis, primary sclerosing cholangitis and ulcerative colitis to investigate pleiotropy and the relationship between these clinically related diseases. Using high-density genotype data from more than 86,000 individuals of European ancestry, we identified 244 independent multidisease signals, including 27 new genome-wide significant susceptibility loci and 3 unreported shared risk loci. Complex pleiotropy was supported when contrasting multidisease signals with expression data sets from human, rat and mouse together with epigenetic and expressed enhancer profiles. The comorbidities among the five immune diseases were best explained by biological pleiotropy rather than heterogeneity (a subgroup of cases genetically identical to those with another disease, possibly owing to diagnostic misclassification, molecular subtypes or excessive comorbidity). In particular, the strong comorbidity between primary sclerosing cholangitis and inflammatory bowel disease is likely the result of a unique disease, which is genetically distinct from classical inflammatory bowel disease phenotypes.

We simultaneously investigated the genetic landscape of ankylosing spondylitis, Crohn's disease, psoriasis, primary sclerosing cholangitis and ulcerative colitis to investigate pleiotropy and the relationship between these clinically related diseases. Using high-density genotype data from more than 86,000 individuals of European ancestry, we identified 244 independent multidisease signals, including 27 new genome-wide significant susceptibility loci and 3 unreported shared risk loci. Complex pleiotropy was supported when contrasting multidisease signals with expression data sets from human, rat and mouse together with epigenetic and expressed enhancer profiles. The comorbidities among the five immune diseases were best explained by biological pleiotropy rather than heterogeneity (a subgroup of cases genetically identical to those with another disease, possibly owing to diagnostic misclassification, molecular subtypes or excessive comorbidity). In particular, the strong comorbidity between primary sclerosing cholangitis and inflammatory bowel disease is likely the result of a unique disease, which is genetically distinct from classical inflammatory bowel disease phenotypes.