224 research outputs found
A non-rigid registration approach for quantifying myocardial contraction in tagged MRI using generalized information measures.
International audienceWe address the problem of quantitatively assessing myocardial function from tagged MRI sequences. We develop a two-step method comprising (i) a motion estimation step using a novel variational non-rigid registration technique based on generalized information measures, and (ii) a measurement step, yielding local and segmental deformation parameters over the whole myocardium. Experiments on healthy and pathological data demonstrate that this method delivers, within a reasonable computation time and in a fully unsupervised way, reliable measurements for normal subjects and quantitative pathology-specific information. Beyond cardiac MRI, this work redefines the foundations of variational non-rigid registration for information-theoretic similarity criteria with potential interest in multimodal medical imaging
Recalage variationnel non rigide d'images par f-information exclusive
Nous nous intéressons à la mise en correspondance non rigide dense d'images dans des contextes monomodaux avec fortes variations photométriques/texturales ou multimodaux, et étudions à cette fin des critÚres de similarité statistiques fondés sur des mesures d'information généralisées au sein de la classe d'Ali-Silvey. Nous introduisons une nouvelle classe de fonctionnelles, dénommées f-informations exclusives, et développons un cadre variationnel générique bien-posé pour leur optimisation sur des espaces de déformations non paramétriques et paramétriques, généralisant les méthodes par information mutuelle. Cette approche est appliquée à l'alignement aveugle robuste de visages sous éclairement arbitraire et pour des déformations faciales complexes
DĂ©tection de motifs graphiques dans des images de documents anciens
International audienceLa dĂ©tection de motifs graphiques consiste Ă rechercher dans une collection d'images de documents, les occurences les plus similaires Ă une requĂȘte image. Dans cet article, nous proposons un systĂšme non supervisĂ© pour la dĂ©tection de motifs, sans besoin de segmentation prĂ©alable, en nous inspirant de techniques rĂ©centes en vision par ordinateur. Notre approche s'appuie sur une dĂ©composition des documents en fenĂȘtres de tailles variĂ©es et une description de ces fenĂȘtres par sac de mots visuels, le tout hors-ligne afin de diminuer le temps de calcul. Une technique de compression des donnĂ©es, proposĂ©e tout rĂ©cemment en recherche d'images, permet de maintenir une quantitĂ© de mĂ©moire raisonnable, mais nĂ©cessite d'approximer le calcul de distance Ă la requĂȘte. De premiers rĂ©sultats encourageants sont obtenus sur la base de documents DocExplore, une base de documents mĂ©diĂ©vaux. Abstract-Pattern spotting consists of retrieving the most similar graphical patterns from a collection of document images. Inspired by the recent advances in computer vision and word spotting techniques, we propose in this paper an unsupervised, segmentation-free pattern spotting system. Overall, the system includes a powerful patch-based framework, the bag of visual word model with an offline sliding window mechanism to avoid heavy computational burden during the retrieval process. Our system takes advantage of the most recent powerful compression and distance approximation techniques (product quantization and asymmetric distance computation) to efficiently index the great number of sub-windows produced by sliding windows and allows to retrieve small sized queries in a large indexed corpus
Metabolism
Background: Cardiovascular disease is the leading cause of deaths in nonalcoholic steatohepatitis (NASH) patients. Mouse models, while widely used for drug development, do not fully replicate human NASH nor integrate the associated cardiac dysfunction, i.e. heart failure with preserved ejection fraction (HFpEF). To overcome these limitations, we established a nutritional hamster model developing both NASH and HFpEF. We then evaluated the effects of the dual peroxisome proliferator activated receptor alpha/delta agonist elafibranor developed for the treatment of NASH patients. Methods: Male Golden Syrian hamsters were fed for 10 to 20 weeks with a free choice diet, which presents hamsters with a choice between control chow diet with normal drinking water or a high fat/high cholesterol diet with 10% fructose enriched drinking water. Biochemistry, histology and echocardiography analysis were performed to characterize NASH and HFpEF. Once the model was validated, elafibranor was evaluated at 15 mg/kg/day orally QD for 5 weeks. Results: Hamsters fed a free choice diet for up to 20 weeks developed NASH, including hepatocyte ballooning (as confirmed with cytokeratin-18 immunostaining), bridging fibrosis, and a severe diastolic dysfunction with restrictive profile, but preserved ejection fraction. Elafibranor resolved NASH, with significant reduction in ballooning and fibrosis scores, and improved diastolic dysfunction with significant reduction in E/A and E/E' ratios. Conclusion: Our data demonstrate that the free choice diet induced NASH hamster model replicates the human phenotype and will be useful for validating novel drug candidates for the treatment of NASH and associated HfpEF
Association of polygenic score for major depression with response to lithium in patients with bipolar disorder
Lithium is a first-line medication for bipolar disorder (BD), but only one in three patients respond optimally to the drug. Since evidence shows a strong clinical and genetic overlap between depression and bipolar disorder, we investigated whether a polygenic susceptibility to major depression is associated with response to lithium treatment in patients with BD. Weighted polygenic scores (PGSs) were computed for major depression (MD) at different GWAS p value thresholds using genetic data obtained from 2586 bipolar patients who received lithium treatment and took part in the Consortium on Lithium Genetics (ConLi+Gen) study. Summary statistics from genome-wide association studies in MD (135,458 cases and 344,901 controls) from the Psychiatric Genomics Consortium (PGC) were used for PGS weighting. Response to lithium treatment was defined by continuous scores and categorical outcome (responders versus non-responders) using measurements on the Alda scale. Associations between PGSs of MD and lithium treatment response were assessed using a linear and binary logistic regression modeling for the continuous and categorical outcomes, respectively. The analysis was performed for the entire cohort, and for European and Asian sub-samples. The PGSs for MD were significantly associated with lithium treatment response in multi-ethnic, European or Asian populations, at various p value thresholds. Bipolar patients with a low polygenic load for MD were more likely to respond well to lithium, compared to those patients with high polygenic load [lowest vs highest PGS quartiles, multi-ethnic sample: ORâ=â1.54 (95% CI: 1.18â2.01) and European sample: ORâ=â1.75 (95% CI: 1.30â2.36)]. While our analysis in the Asian sample found equivalent effect size in the same direction: ORâ=â1.71 (95% CI: 0.61â4.90), this was not statistically significant. Using PGS decile comparison, we found a similar trend of association between a high genetic loading for MD and lower response to lithium. Our findings underscore the genetic contribution to lithium response in BD and support the emerging concept of a lithium-responsive biotype in BD
COVID-19 trajectories among 57 million adults in England: a cohort study using electronic health records
BACKGROUND:
Updatable estimates of COVID-19 onset, progression, and trajectories underpin pandemic mitigation efforts. To identify and characterise disease trajectories, we aimed to define and validate ten COVID-19 phenotypes from nationwide linked electronic health records (EHR) using an extensible framework.
METHODS:
In this cohort study, we used eight linked National Health Service (NHS) datasets for people in England alive on Jan 23, 2020. Data on COVID-19 testing, vaccination, primary and secondary care records, and death registrations were collected until Nov 30, 2021. We defined ten COVID-19 phenotypes reflecting clinically relevant stages of disease severity and encompassing five categories: positive SARS-CoV-2 test, primary care diagnosis, hospital admission, ventilation modality (four phenotypes), and death (three phenotypes). We constructed patient trajectories illustrating transition frequency and duration between phenotypes. Analyses were stratified by pandemic waves and vaccination status.
FINDINGS:
Among 57â032â174 individuals included in the cohort, 13â990â423 COVID-19 events were identified in 7â244â925 individuals, equating to an infection rate of 12·7% during the study period. Of 7â244â925 individuals, 460â737 (6·4%) were admitted to hospital and 158â020 (2·2%) died. Of 460â737 individuals who were admitted to hospital, 48â847 (10·6%) were admitted to the intensive care unit (ICU), 69â090 (15·0%) received non-invasive ventilation, and 25â928 (5·6%) received invasive ventilation. Among 384â135 patients who were admitted to hospital but did not require ventilation, mortality was higher in wave 1 (23â485 [30·4%] of 77â202 patients) than wave 2 (44â220 [23·1%] of 191â528 patients), but remained unchanged for patients admitted to the ICU. Mortality was highest among patients who received ventilatory support outside of the ICU in wave 1 (2569 [50·7%] of 5063 patients). 15â486 (9·8%) of 158â020 COVID-19-related deaths occurred within 28 days of the first COVID-19 event without a COVID-19 diagnoses on the death certificate. 10â884 (6·9%) of 158â020 deaths were identified exclusively from mortality data with no previous COVID-19 phenotype recorded. We observed longer patient trajectories in wave 2 than wave 1.
INTERPRETATION:
Our analyses illustrate the wide spectrum of disease trajectories as shown by differences in incidence, survival, and clinical pathways. We have provided a modular analytical framework that can be used to monitor the impact of the pandemic and generate evidence of clinical and policy relevance using multiple EHR sources.
FUNDING:
British Heart Foundation Data Science Centre, led by Health Data Research UK
SARS-CoV-2 susceptibility and COVID-19 disease severity are associated with genetic variants affecting gene expression in a variety of tissues
Variability in SARS-CoV-2 susceptibility and COVID-19 disease severity between individuals is partly due to
genetic factors. Here, we identify 4 genomic loci with suggestive associations for SARS-CoV-2 susceptibility
and 19 for COVID-19 disease severity. Four of these 23 loci likely have an ethnicity-specific component.
Genome-wide association study (GWAS) signals in 11 loci colocalize with expression quantitative trait loci
(eQTLs) associated with the expression of 20 genes in 62 tissues/cell types (range: 1:43 tissues/gene),
including lung, brain, heart, muscle, and skin as well as the digestive system and immune system. We perform
genetic fine mapping to compute 99% credible SNP sets, which identify 10 GWAS loci that have eight or fewer
SNPs in the credible set, including three loci with one single likely causal SNP. Our study suggests that the
diverse symptoms and disease severity of COVID-19 observed between individuals is associated with variants across the genome, affecting gene expression levels in a wide variety of tissue types
The Eleventh and Twelfth Data Releases of the Sloan Digital Sky Survey: Final Data from SDSS-III
The third generation of the Sloan Digital Sky Survey (SDSS-III) took data from 2008 to 2014 using the original SDSS wide-field imager, the original and an upgraded multi-object fiber-fed optical spectrograph, a new near-infrared high-resolution spectrograph, and a novel optical interferometer. All of the data from SDSS-III are now made public. In particular, this paper describes Data Release 11 (DR11) including all data acquired through 2013 July, and Data Release 12 (DR12) adding data acquired through 2014 July (including all data included in previous data releases), marking the end of SDSS-III observing. Relative to our previous public release (DR10), DR12 adds one million new spectra of galaxies and quasars from the Baryon Oscillation Spectroscopic Survey (BOSS) over an additional 3000 deg2 of sky, more than triples the number of H-band spectra of stars as part of the Apache Point Observatory (APO) Galactic Evolution Experiment (APOGEE), and includes repeated accurate radial velocity measurements of 5500 stars from the Multi-object APO Radial Velocity Exoplanet Large-area Survey (MARVELS). The APOGEE outputs now include the measured abundances of 15 different elements for each star. In total, SDSS-III added 5200 deg2 of ugriz imaging; 155,520 spectra of 138,099 stars as part of the Sloan Exploration of Galactic Understanding and Evolution 2 (SEGUE-2) survey; 2,497,484 BOSS spectra of 1,372,737 galaxies, 294,512 quasars, and 247,216 stars over 9376 deg2; 618,080 APOGEE spectra of 156,593 stars; and 197,040 MARVELS spectra of 5513 stars. Since its first light in 1998, SDSS has imaged over 1/3 of the Celestial sphere in five bands and obtained over five million astronomical spectra. \ua9 2015. The American Astronomical Society
SCORE2-Diabetes: 10-year cardiovascular risk estimation in type 2 diabetes in Europe
Aims: To develop and validate a recalibrated prediction model (SCORE2-Diabetes) to estimate the 10-year risk of cardiovascular disease (CVD) in individuals with type 2 diabetes in Europe. Methods and results: SCORE2-Diabetes was developed by extending SCORE2 algorithms using individual-participant data from four large-scale datasets comprising 229 460 participants (43 706 CVD events) with type 2 diabetes and without previous CVD. Sex-specific competing risk-adjusted models were used including conventional risk factors (i.e. age, smoking, systolic blood pressure, total, and HDL-cholesterol), as well as diabetes-related variables (i.e. age at diabetes diagnosis, glycated haemoglobin [HbA1c] and creatinine-based estimated glomerular filtration rate [eGFR]). Models were recalibrated to CVD incidence in four European risk regions. External validation included 217 036 further individuals (38 602 CVD events), and showed good discrimination, and improvement over SCORE2 (C-index change from 0.009 to 0.031). Regional calibration was satisfactory. SCORE2-Diabetes risk predictions varied several-fold, depending on individuals' levels of diabetes-related factors. For example, in the moderate-risk region, the estimated 10-year CVD risk was 11% for a 60-year-old man, non-smoker, with type 2 diabetes, average conventional risk factors, HbA1c of 50 mmol/mol, eGFR of 90 mL/min/1.73 m2, and age at diabetes diagnosis of 60 years. By contrast, the estimated risk was 17% in a similar man, with HbA1c of 70 mmol/mol, eGFR of 60 mL/min/1.73 m2, and age at diabetes diagnosis of 50 years. For a woman with the same characteristics, the risk was 8% and 13%, respectively. Conclusion: SCORE2-Diabetes, a new algorithm developed, calibrated, and validated to predict 10-year risk of CVD in individuals with type 2 diabetes, enhances identification of individuals at higher risk of developing CVD across Europe
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