Clustering spatio-temporal series of confirmed COVID-19 deaths in Europe

The impact of the COVID-19 pandemic varied significantly across different countries, with important consequences in the definition of control and response strategies. In this work, to investigate the heterogeneity of this crisis, we analyse the spatial patterns of deaths attributed to COVID-19 in several European countries. To this end, we propose a Bayesian nonparametric approach, based on mixture of Gaussian processes coupled with Dirichlet process, to group the COVID-19 mortality curves. The model provides a flexible framework for the analysis of time series data, allowing the inclusion in the clustering procedure of different features of the series, such as spatial correlations, time varying parameters and measurement errors. We evaluate the proposed methodology on the death counts recorded at NUTS-2 regional level for several European countries in the period from March 2020 to February 2021.

Modelling trajectories of parentally reported and physician-confirmed atopic dermatitis in a birth cohort study

BACKGROUND

In a population-based birth cohort, we aimed to identify longitudinal trajectories of atopic dermatitis (AD) during childhood using data from different sources (validated questionnaires and healthcare records). We investigated the impact of different AD definitions on such trajectories and their relationships with various risk factors.

METHODS

Of the 1184 children born into the study, 1083 had information on current AD for at least three follow-ups from birth to age 11 years and were included in the analysis for parentally reported AD (PRAD). Data were transcribed from healthcare records for 916 of 1184 children for the analysis of doctor-diagnosed AD (DDAD). We also derived a composite definition of AD (CDAD) (at least two of the following: PRAD, DDAD, current use of AD treatment). Using latent class analysis (LCA), we determined longitudinal profiles of AD using the three definitions. Filaggrin (FLG) genotype data were available for 803 white participants.

RESULTS

For PRAD, LCA identified four AD classes ('no AD', 'persistent', 'early-onset remitting' and 'late-onset'). For DDAD and CDAD, the optimal number of phenotypes was three ('no AD', 'persistent' and 'early-onset remitting'). Although AD classes at population level appeared similar in different models, a considerable proportion of children (n = 485, 45%) moved between classes. The association with FLG genotype, atopic diseases and early-life risk factors was inconsistent across different definitions, but the association with oral food challenge-confirmed peanut allergy was similar, with a nine- to 11-fold increase among children in the persistent AD class. In a CDAD model, compared with the early-onset remitting class, those with persistent AD were significantly more likely to have (at age 3 years) moderate/severe AD, polysensitization and current wheeze, and were less likely to have been breastfed.

CONCLUSIONS

Standardized composite definitions of AD may help to define AD cases with more precision and identify more consistent long-term trajectories.

Clinica: An Open-Source Software Platform for Reproducible Clinical Neuroscience Studies

We present Clinica (www.clinica.run), an open-source software platform designed to make clinical neuroscience studies easier and more reproducible. Clinica aims for researchers to (i) spend less time on data management and processing, (ii) perform reproducible evaluations of their methods, and (iii) easily share data and results within their institution and with external collaborators. The core of Clinica is a set of automatic pipelines for processing and analysis of multimodal neuroimaging data (currently, T1-weighted MRI, diffusion MRI, and PET data), as well as tools for statistics, machine learning, and deep learning. It relies on the brain imaging data structure (BIDS) for the organization of raw neuroimaging datasets and on established tools written by the community to build its pipelines. It also provides converters of public neuroimaging datasets to BIDS (currently ADNI, AIBL, OASIS, and NIFD). Processed data include image-valued scalar fields (e.g., tissue probability maps), meshes, surface-based scalar fields (e.g., cortical thickness maps), or scalar outputs (e.g., regional averages). These data follow the ClinicA Processed Structure (CAPS) format which shares the same philosophy as BIDS. Consistent organization of raw and processed neuroimaging files facilitates the execution of single pipelines and of sequences of pipelines, as well as the integration of processed data into statistics or machine learning frameworks. The target audience of Clinica is neuroscientists or clinicians conducting clinical neuroscience studies involving multimodal imaging, and researchers developing advanced machine learning algorithms applied to neuroimaging data.

Early Cognitive, Structural, and Microstructural Changes in Presymptomatic C9orf72 Carriers Younger Than 40 Years

Importance

Presymptomatic carriers of chromosome 9 open reading frame 72 (C9orf72) mutation, the most frequent genetic cause of frontotemporal lobar degeneration and amyotrophic lateral sclerosis, represent the optimal target population for the development of disease-modifying drugs. Preclinical biomarkers are needed to monitor the effect of therapeutic interventions in this population.

Objectives

To assess the occurrence of cognitive, structural, and microstructural changes in presymptomatic C9orf72 carriers.

Design, Setting, and Participants

The PREV-DEMALS study is a prospective, multicenter, observational study of first-degree relatives of individuals carrying the C9orf72 mutation. Eighty-four participants entered the study between October 2015 and April 2017; 80 (95%) were included in cross-sectional analyses of baseline data. All participants underwent neuropsychological testing and magnetic resonance imaging; 63 (79%) underwent diffusion tensor magnetic resonance imaging. Gray matter volumes and diffusion tensor imaging metrics were calculated within regions of interest. Anatomical and microstructural differences between individuals who carried the C9orf72 mutation (C9+) and those who did not carry the C9orf72 mutation (C9-) were assessed using linear mixed-effects models. Data were analyzed from October 2015 to April 2017.

Main Outcomes and Measures

Differences in neuropsychological scores, gray matter volume, and white matter integrity between C9+ and C9- individuals.

Results

Of the 80 included participants, there were 41 C9+ individuals (24 [59%] female; mean [SD] age, 39.8 [11.1] years) and 39 C9- individuals (24 [62%] female; mean [SD] age, 45.2 [13.9] years). Compared with C9- individuals, C9+ individuals had lower mean (SD) praxis scores (163.4 [6.1] vs 165.3 [5.9]; P = .01) and intransitive gesture scores (34.9 [1.6] vs 35.7 [1.5]; P = .004), atrophy in 8 cortical regions of interest and in the right thalamus, and white matter alterations in 8 tracts. When restricting the analyses to participants younger than 40 years, compared with C9- individuals, C9+ individuals had lower praxis scores and intransitive gesture scores, atrophy in 4 cortical regions of interest and in the right thalamus, and white matter alterations in 2 tracts.

Conclusions and Relevance

Cognitive, structural, and microstructural alterations are detectable in young C9+ individuals. Early and subtle praxis alterations, underpinned by focal atrophy of the left supramarginal gyrus, may represent an early and nonevolving phenotype related to neurodevelopmental effects of C9orf72 mutation. White matter alterations reflect the future phenotype of frontotemporal lobar degeneration/amyotrophic lateral sclerosis, while atrophy appears more diffuse. Our results contribute to a better understanding of the preclinical phase of C9orf72 disease and of the respective contribution of magnetic resonance biomarkers.

Trial Registration

clinicaltrials.gov Identifier: NCT02590276.

Reproducible evaluation of classification methods in Alzheimer's disease: Framework and application to MRI and PET data

A large number of papers have introduced novel machine learning and feature extraction methods for automatic classification of Alzheimer's disease (AD). However, while the vast majority of these works use the public dataset ADNI for evaluation, they are difficult to reproduce because different key components of the validation are often not readily available. These components include selected participants and input data, image preprocessing and cross-validation procedures. The performance of the different approaches is also difficult to compare objectively. In particular, it is often difficult to assess which part of the method (e.g. preprocessing, feature extraction or classification algorithms) provides a real improvement, if any. In the present paper, we propose a framework for reproducible and objective classification experiments in AD using three publicly available datasets (ADNI, AIBL and OASIS). The framework comprises: i) automatic conversion of the three datasets into a standard format (BIDS); ii) a modular set of preprocessing pipelines, feature extraction and classification methods, together with an evaluation framework, that provide a baseline for benchmarking the different components. We demonstrate the use of the framework for a large-scale evaluation on 1960 participants using T1 MRI and FDG PET data. In this evaluation, we assess the influence of different modalities, preprocessing, feature types (regional or voxel-based features), classifiers, training set sizes and datasets. Performances were in line with the state-of-the-art. FDG PET outperformed T1 MRI for all classification tasks. No difference in performance was found for the use of different atlases, image smoothing, partial volume correction of FDG PET images, or feature type. Linear SVM and L2-logistic regression resulted in similar performance and both outperformed random forests. The classification performance increased along with the number of subjects used for training. Classifiers trained on ADNI generalized well to AIBL and OASIS. All the code of the framework and the experiments is publicly available: general-purpose tools have been integrated into the Clinica software (www.clinica.run) and the paper-specific code is available at: https://gitlab.icm-institute.org/aramislab/AD-ML.

Temperature-controlled laminar airflow (TLA) device in the treatment of children with severe atopic eczema: Open-label, proof-of-concept study

BACKGROUND

Children with severe, persistent atopic eczema (AE) have limited treatment options, often requiring systemic immunosuppression.

OBJECTIVE

To evaluate the effect of the temperature-controlled laminar airflow (TLA) treatment in children/adolescents with severe AE.

METHODS

We recruited 15 children aged 2-16 years with long-standing, severe AE and sensitization to ≥1 perennial inhalant allergen. Run-in period of 6-10 weeks (3 visits) was followed by 12-month treatment with overnight TLA (Airsonett^® , Sweden). The primary outcome was eczema severity (SCORAD-Index and Investigator Global Assessment-IGA). Secondary outcomes included child/family dermatology quality of life and family impact questionnaires (CDQLI, FDQLI, DFI), patient-oriented eczema measure (POEM), medication requirements and healthcare contacts. The study is registered as ISRCTN65865773.

RESULTS

There was a significant reduction in AE severity ascertained by SCORAD and IGA during the 12-month intervention period (P < .001). SCORAD was reduced from a median of 34.9 [interquartile range 28.75-45.15] at Baseline to 17.2 [12.95-32.3] at the final visit, and IGA improved significantly from 4 [3-4] to 2 [1-3]. We observed a significant improvement in FDQLI (16.0 [12.25-19.0] to 12 [8-18], P = .023) and DFI (P = .011), but not CDQLI or POEM. Compared to 6-month period prior to enrolment, there was a significant reduction at six months after the start of the intervention in potent topical corticosteroids (P = .033). The exploratory cluster analysis revealed two strongly divergent patterns of response, with 9 patients classified as responders, and 6 as non-responders.

CONCLUSION AND CLINICAL RELEVANCE

Addition of TLA device to standard pharmacological treatment may be an effective add-on to the management of difficult-to-control AE.

Characterization of oxidized oligomers from polyethylene films by mass spectrometry and NMR spectroscopy before and after biodegradation by a Rhodococcus rhodochrous strain

The objective of this work was to develop a new approach to assess the specificity and the efficiency of biodegradation of oxidized oligomers extracted from aged HDPE polyethylene films and to bring insight on the mechanisms occurring during biodegradation. ¹H NMR spectroscopy and LC Orbitrap™ mass spectrometry were combined together with data processing using Kendrick mass defect calculation and Van Krevelen Diagram. We showed that the molecular weight of extracted oligomers was lower than 850 Da with maximum chain length of 55 carbon atoms. The oligomers were divided into 11 classes of molecules with different oxidation state ranging from 0 to 10. All classes included series of chemically related compounds including up to 19 molecules. 95% of the soluble oligomers were assimilated by a strain of Rhodococcus rhodocchrous after 240 days of incubation. Large highly oxidized molecules completely disappeared while the other classes of molecules were still represented. Molecules containing 0-1 oxygen atom were less degraded. A strong shift to smaller molecules (<450 Da, 25 carbon atoms) was observed suggesting that longer molecules disappeared more rapidly than the smaller ones. It opens new perspectives on biodegradation processes as not only intracellular β-oxidation must be considered but also extracellular mechanisms leading to chain cleavages.

Osteoma of the internal auditory canal. Case report and literature review

Osteomas of the internal auditory canal are rare lesions, with only 12 reported cases in the world literature. Symptoms are those of eighth nerve compression, and include unilateral hearing loss and vestibular weakness, thus mimicking symptoms of acoustic neuroma. We report a patient with an osteoma of the internal auditory canal, along with a review of the literature. We note age and sex characteristics from the literature, give evidence of localized trauma as a possible etiologic factor for this lesion, and discuss the pitfalls of relying exclusively on magnetic resonance imaging in the workup of suspected retrocochlear lesions.