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Predictors of lung function test severity and outcome in systemic sclerosis-associated interstitial lung disease
Systemic sclerosis-related interstitial lung disease (SSc-ILD) is the leading cause of death in SSc. In this study, we aimed to describe the baseline severity and evolution of forced vital capacity (FVC) and diffusing capacity for carbon monoxide (DLCO) in patients with SSc-ILD and to assess the baseline clinical, biological and high-resolution CT scan (HRCT) predictors of this evolution. Baseline and serial FVC and DLCO were collected in 75 SSc-ILD patients followed during 6.4±4.2 years (n = 557 individual data). FVC and DLCO evolution was modelled using a linear mixed model with random effect. During follow-up, FVC was stable while DLCO significantly decreased (-1.5±0.3%/year (p<0.0001). Baseline NYHA functional class III/IV, extensive SSc-ILD on HRCT and DLCO<80% were associated with a lower baseline FVC. Absence of digital ulcers extensive SSc-ILD, and FVC<80% and were associated with a lower baseline DLCO. Presence or history of digital ulcers and presence of pulmonary hypertension at baseline or during follow-up were associated with a faster decline of DLCO overtime. Neither age, gender, subtype of SSc nor specificity of autoantibodies were associated with baseline severity or outcome of lung function tests. In this SSc-ILD population, FVC was therefore stable while DLCO significantly declined over time. ILD extension was associated with baseline FVC and DLCO but not with their evolution. Presence or history of digital ulcers and pulmonary hypertension were predictors of a faster decline of DLCO over time.
Journal Article
Next generation systematics
\"We live in an age of ubiquitous genomics. Next generation sequencing (NGS) technology, both widely adopted today and advancing at pace, has transformed today's data landscape, opening up an enormous source of heritable characters to the comparative biologist. Its impact on systematics, like many other fields of biology, has been felt throughout its breadth: from defining species boundaries to estimating their evolutionary histories. This volume examines the broad range of ways in which NGS data are being used in systematics and in the fields that it underpins, from biodiversity prospecting to evo-devo. The authors draw on contemporary case studies to demonstrate state-of-the-art applications of NGS data. These, along with novel analyses, comprehensive reviews and lively perspectives are combined to produce an authoritative account of contemporary issues in systematics that have been advanced and impacted by the recent adoption of NGS\"-- Provided by publisher.
Book
Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning
Raman optical spectroscopy promises label-free bacterial detection, identification, and antibiotic susceptibility testing in a single step. However, achieving clinically relevant speeds and accuracies remains challenging due to weak Raman signal from bacterial cells and numerous bacterial species and phenotypes. Here we generate an extensive dataset of bacterial Raman spectra and apply deep learning approaches to accurately identify 30 common bacterial pathogens. Even on low signal-to-noise spectra, we achieve average isolate-level accuracies exceeding 82% and antibiotic treatment identification accuracies of 97.0±0.3%. We also show that this approach distinguishes between methicillin-resistant and -susceptible isolates of
Staphylococcus aureus
(MRSA and MSSA) with 89±0.1% accuracy. We validate our results on clinical isolates from 50 patients. Using just 10 bacterial spectra from each patient isolate, we achieve treatment identification accuracies of 99.7%. Our approach has potential for culture-free pathogen identification and antibiotic susceptibility testing, and could be readily extended for diagnostics on blood, urine, and sputum.
The use of Raman spectroscopy for pathogen identification is hampered by the weak Raman signal and phenotypic diversity of bacterial cells. Here the authors generate an extensive dataset of bacterial Raman spectra and apply deep learning to identify common bacterial pathogens and predict antibiotic treatment from noisy Raman spectra.
Journal Article
Changes to virus taxonomy and to the International Code of Virus Classification and Nomenclature ratified by the International Committee on Taxonomy of Viruses (2021)
This article reports the changes to virus taxonomy approved and ratified by the International Committee on Taxonomy of Viruses (ICTV) in March 2021. The entire ICTV was invited to vote on 290 taxonomic proposals approved by the ICTV Executive Committee at its meeting in October 2020, as well as on the proposed revision of the International Code of Virus Classification and Nomenclature (ICVCN). All proposals and the revision were ratified by an absolute majority of the ICTV members. Of note, ICTV mandated a uniform rule for virus species naming, which will follow the binomial 'genus-species' format with or without Latinized species epithets. The Study Groups are requested to convert all previously established species names to the new format. ICTV has also abolished the notion of a type species, i.e., a species chosen to serve as a name-bearing type of a virus genus. The remit of ICTV has been clarified through an official definition of ‘virus’ and several other types of mobile genetic elements. The ICVCN and ICTV Statutes have been amended to reflect these changes.
Journal Article
Th17 and CD24hiCD27+ regulatory B lymphocytes are biomarkers of response to biologics in rheumatoid arthritis
Background
The aim was to describe the regulatory B and T cells (Breg and Treg) and T helper 17 (Th17) lymphocytes before and under treatment with biologic drugs, and to assess their potential predictive value as biomarkers of response in rheumatoid arthritis (RA).
Methods
This was a non-randomised, single-centre, prospective study. Patients with active RA (American College of Rheumatology (ACR)/European League Against Rheumatism (EULAR) 2010) who required the initiation or switch to any biologic drug except rituximab were included. The main judgement criterion was the frequency and absolute number of CD24
hi
CD27
+
Breg and CD24
hi
CD38
hi
T2/Breg cells, CD25
hi
CD127
low
Treg and CD45RA
−
CD161
+
CCR6
+
Th17 cells measured at inclusion in both patients and controls, and after 1, 3 and 6 months of treatment (M1, M3 and M6) in patients with RA, and compared with the M6 response to treatment (EULAR response and Disease Activity Score in 28 joints (DAS28) remission).
Results
Thirty-one patients with RA and 17 controls were included. There was a reduction in T2/Breg frequency at M0 in patients (
p
< 0.001) and absolute numbers (
p
= 0.014) and in immunopositive vs
.
immunonegative RA (
p
= 0.016). DAS28 remission at M6 was associated with increased frequency of Treg (
p
= 0.01). A higher level of CD24
hi
CD27
+
Breg at baseline was associated with DAS28 remission at M6 (
p
= 0.04) and a good EULAR response at M6 for abatacept-treated patients (
p
= 0.01). A lower M0 level of Th17 was associated with a good EULAR response at M6 (
p
= 0.007), notably under anti-cytokine drugs (
p
= 0.048).
Conclusions
Altogether, these data, although preliminary, suggest that phenotyping of T and B cells has potential value for the stratification of biologic drugs, notably with respect to choosing between abatacept and anti-cytokine blockade.
Journal Article
Group Classification and Numerical Study of Gross-Pitaevskii Systems
We carry out a preliminary group classification for the 3+1 Gross-Pitaevskii system and the complete group classification for the 1 + 1 integrable component of the Gross-Pitaevskii system. For the exceptional potentials that the group classification sheds light a numerical study was performed.
Journal Article