Atypical monoarthritis presentation in children with oligoarticular juvenile idiopathic arthritis: A case series

Background: Oligoarticular juvenile idiopathic arthritis (oligoJIA), the most common chronic inflammatory arthritis of childhood, usually involves the knees and ankles. Severe oligoJIA monoarthritis presenting in a joint other than knees and ankles, is rare. Findings: We report four children who presented with severe isolated arthritis of the hip, wrist or elbow and were diagnosed with oligoJIA. All four were girls with a median age of 11.5 years. Those with hip arthritis also met the classification criteria for juvenile-onset spondylarthopathy. Median duration of symptoms prior to diagnosis was 9.5 months. Three children had already cartilage loss or erosive disease at diagnosis. Conclusions: Children diagnosed with oligoJIA that present with monoarthritis of the hip, wrist and elbow can have aggressive disease. Girls with positive HLA-B27 presenting with isolated hip arthritis could meet the classification criteria for both oligoJIA and juvenile-onset SpA. Early referral to specialized care may improve their diagnosis, treatment and outcome

NNMobile-Net: Rethinking CNN Design for Deep Learning-Based Retinopathy Research

Retinal diseases (RD) are the leading cause of severe vision loss or blindness. Deep learning-based automated tools play an indispensable role in assisting clinicians in diagnosing and monitoring RD in modern medicine. Recently, an increasing number of works in this field have taken advantage of Vision Transformer to achieve state-of-the-art performance with more parameters and higher model complexity compared to Convolutional Neural Networks (CNNs). Such sophisticated and task-specific model designs, however, are prone to be overfitting and hinder their generalizability. In this work, we argue that a channel-aware and well-calibrated CNN model may overcome these problems. To this end, we empirically studied CNN's macro and micro designs and its training strategies. Based on the investigation, we proposed a no-new-MobleNet (nn-MobileNet) developed for retinal diseases. In our experiments, our generic, simple and efficient model superseded most current state-of-the-art methods on four public datasets for multiple tasks, including diabetic retinopathy grading, fundus multi-disease detection, and diabetic macular edema classification. Our work may provide novel insights into deep learning architecture design and advance retinopathy research.Comment: Code will publish soon: https://github.com/Retinal-Research/NNMOBILE-NE

Oral Morphine Versus Ibuprofen Administered at Home for Postoperative Orthopedic Pain in Children: a Randomized Controlled Trial

BACKGROUND: Oral morphine for postoperative pain after minor pediatric surgery, while increasingly popular, is not supported by evidence. We evaluated whether oral morphine was superior to ibuprofen for at-home management of children\u27s postoperative pain. METHODS: We conducted a randomized superiority trial comparing oral morphine (0.5 mg/kg) with ibuprofen (10 mg/kg) in children 5 to 17 years of age who had undergone minor outpatient orthopedic surgery (June 2013 to September 2016). Participants took up to 8 doses of the intervention drug every 6 hours as needed for pain at home. The primary outcome was pain, according to the Faces Pain Scale - Revised, for the first dose. Secondary outcomes included additional analgesic requirements, adverse effects, unplanned health care visits and pain scores for doses 2 to 8. RESULTS: We analyzed data for 77 participants in each of the morphine and ibuprofen groups. Both interventions decreased pain scores with no difference in efficacy. The median difference in pain score before and after the first dose of medication was 1 (interquartile range 0-1) for both morphine and ibuprofen ( INTERPRETATION: Morphine was not superior to ibuprofen, and both drugs decreased pain with no apparent difference in efficacy. Morphine was associated with significantly more adverse effects, which suggests that ibuprofen is a better first-line option after minor surgery. TRIAL REGISTRATION: ClinicalTrials.gov, no. NCT01686802

Influence of the segmentation on the characterization of cerebral networks of structural damage for patients with disorders of consciousness

Disorders of consciousness (DOC) are a consequence of a variety of severe brain injuries. DOC commonly results in anatomical brain modifications, which can affect cortical and sub-cortical brain structures. Postmortem studies suggest that severity of brain damage correlates with level of impairment in DOC. In-vivo studies in neuroimaging mainly focus in alterations on single structures. Recent evidence suggests that rather than one, multiple brain regions can be simultaneously affected by this condition. In other words, DOC may be linked to an underlying cerebral network of structural damage. Recently, geometrical spatial relationships among key sub-cortical brain regions, such as left and right thalamus and brain stem, have been used for the characterization of this network. This approach is strongly supported on automatic segmentation processes, which aim to extract regions of interests without human intervention. Nevertheless, patients with DOC usually present massive structural brain changes. Therefore, segmentation methods may highly influence the characterization of the underlying cerebral network structure. In this work, we evaluate the level of characterization obtained by using the spatial relationships as descriptor of a sub-cortical cerebral network (left and right thalamus) in patients with DOC, when different segmentation approaches are used (FSL, Free-surfer and manual segmentation). Our results suggest that segmentation process may play a critical role for the construction of robust and reliable structural characterization of DOC conditions

Connecting Transitions in Galaxy Properties to Refueling

We relate transitions in galaxy structure and gas content to refueling, here defined to include both the external gas accretion and the internal gas processing needed to renew reservoirs for star formation. We analyze two z = 0 data sets: a high-quality ~200 galaxy sample (the Nearby Field Galaxy Survey, data release herein) and a volume-limited ~3000 galaxy sample with reprocessed archival data. Both reach down to baryonic masses ~10^9 M_☉ and span void-to-cluster environments. Two mass-dependent transitions are evident: (1) below the "gas-richness threshold" scale (V ~ 125 km s^(–1)), gas-dominated quasi-bulgeless Sd-Im galaxies become numerically dominant; while (2) above the "bimodality" scale (V ~ 200 km s^(–1)), gas-starved E/S0s become the norm. Notwithstanding these transitions, galaxy mass (or V as its proxy) is a poor predictor of gas-to-stellar mass ratio M_(gas)/M_*. Instead, M_(gas)/M_* correlates well with the ratio of a galaxy's stellar mass formed in the last Gyr to its preexisting stellar mass, such that the two ratios have numerically similar values. This striking correspondence between past-averaged star formation and current gas richness implies routine refueling of star-forming galaxies on Gyr timescales. We argue that this refueling underlies the tight M_(gas)/M_* versus color correlations often used to measure "photometric gas fractions." Furthermore, the threshold and bimodality scale transitions reflect mass-dependent demographic shifts between three refueling regimes—accretion-dominated, processing-dominated, and quenched. In this picture, gas-dominated dwarfs are explained not by inefficient star formation but by overwhelming gas accretion, which fuels stellar mass doubling in ≾1 Gyr. Moreover, moderately gas-rich bulged disks such as the Milky Way are transitional, becoming abundant only in the narrow range between the threshold and bimodality scales

Atypical monoarthritis presentation in children with oligoarticular juvenile idiopathic arthritis: a case series

Abstract Background Oligoarticular juvenile idiopathic arthritis (oligoJIA), the most common chronic inflammatory arthritis of childhood, usually involves the knees and ankles. Severe oligoJIA monoarthritis presenting in a joint other than knees and ankles, is rare. Findings We report four children who presented with severe isolated arthritis of the hip, wrist or elbow and were diagnosed with oligoJIA. All four were girls with a median age of 11.5\ua0years. Those with hip arthritis also met the classification criteria for juvenile-onset spondylarthopathy. Median duration of symptoms prior to diagnosis was 9.5\ua0months. Three children had already cartilage loss or erosive disease at diagnosis. Conclusions Children diagnosed with oligoJIA that present with monoarthritis of the hip, wrist and elbow can have aggressive disease. Girls with positive HLA-B27 presenting with isolated hip arthritis could meet the classification criteria for both oligoJIA and juvenile-onset SpA. Early referral to specialized care may improve their diagnosis, treatment and outcome

Functional connectivity analysis for thalassemia disease based on a graphical lasso model

International audienceThalassemia is a congenital disorder of hemoglobin synthesis which can lead to thromboembolic events and stroke in the brain. In this work we propose to use a functional connectivity model to discriminate between control and diseased subjects. Our connectivity measure is based on functional magnetic resonance imaging, and hence common variations of the blood oxygenation level in spatially distant areas. Analyzing this connectivity could highlight abnormal neuronal activation and provide us with a descriptor (bio-marker) of the disease. To estimate the connectivity, we propose a robust learning scheme based on the graphical lasso model, whose hyperparameter is validated within a cross-validation scheme. To analyze model fit, we transfer the mean connectivity from the control group to the thalassemic patient group. Our null hypothesis is that the model learned on control subjects is perfectly adequate (in the maximum likelihood sense) to describe the patients. The results of the permutation test suggest that the some patients with thalassemia do not have the same connectivity structure as the control

Low-frequency fluctuation amplitude analysis of resting-state fMRI for sickle cell disease patients

International audienceSickle cell disease may result in neurological damage and strokes, leading to morbidity and mortality. The inability of conventional magnetic resonance imaging to predict impending stroke underlies the need for other neuroimaging markers risk. In this study, we analyzed neuronal processes at resting state and more particularly how this disease affects the default mode network. The amplitude of low frequency fluctuations was used to reflect areas of spontaneous BOLD signal across brain regions. We compared the activations of sickle cell disease patients to a control group with variance analysis and t-test. Significant differences in different parts among the two groups were observed, especially in the default mode network areas and cortical regions near large cerebral arteries. These findings suggest that sickle cell disease can cause some activation modifications near vessels, and these changes could be used a biomarker of the malady