Search

Abstract Segatella is a prevalent genus within individuals’ gut microbiomes worldwide, especially in non-Western populations. Although metagenomic assembly and genome isolation have shed light on its genetic diversity, the lack of available isolates from this genus has resulted in a limited understanding of how members’ genetic diversity translates into phenotypic diversity. Within the confines of a single gut microbiome, we have isolated 63 strains from diverse lineages of Segatella. We performed comparative analyses that exposed differences in cellular morphologies, preferences in polysaccharide utilization, yield of short-chain fatty acids, and antibiotic resistance across isolates. We further show that exposure to Segatella isolates either evokes strong or muted transcriptional responses in human intestinal epithelial cells. Our study exposes large phenotypic differences within related Segatella isolates, extending this to host-microbe interactions.

Abstract Background Opicapone is a third-generation catechol-O-methyl-transferase inhibitor currently used for the treatment of motor fluctuations in Parkinson’s disease. Its benefit and safety have been established by clinical trials; however, data about its use in a real-life context, and particularly in an Italian population of patients with Parkinson’s disease, are missing. Objectives We aimed to gather data about the real-life tolerability/safety of opicapone when used for the treatment of Parkinson’s disease-related motor fluctuations. Methods We enrolled 152 consecutive patients with Parkinson’s disease and followed them for 2 years after opicapone introduction. We obtained baseline clinical and demographical information, including disease duration, stage, phenotype, as well as axial and non-motor symptoms. We collected the reasons for any treatment interruption and adverse events emerging after opicapone introduction. Results Eighty-nine (58%) patients reported adverse events and 46 (30%) patients discontinued the treatment. Adverse events occurred less frequently in “earlier” patients accordingly to the disease course and l-Dopa treatment pathway; a motor fluctuation duration ≥12 months and Hoehn and Yahr scale score ≥2.5 were the main predictors of therapy withdrawal. Conclusions This study confirms the good tolerability/safety profile of opicapone in a real-life setting and provides country-specific data for Italian patients with Parkinson’s disease.

Abstract A mechanistic understanding of algal growth is essential for maintaining a sustainable environment in an era of climate change and population expansion. It is known that algal growth is tightly controlled by complex interactive physical and chemical conditions. Many mathematical models have been proposed to describe the relation of algal growth and environmental parameters, but experimental verification has been difficult due to the lack of tools to measure cell growth under precise physical and chemical conditions. As such, current models depend on the specific testing systems, and the fitted growth kinetic constants vary widely for the same organisms in the existing literature. Here, we present a microfluidic platform where both light intensity and nutrient gradients can be well controlled for algal cell growth studies. In particular, light shading is avoided, a common problem in macroscale assays. Our results revealed that light and nitrogen colimit the growth of algal cells, with each contributing a Monod growth kinetic term in a multiplicative model. We argue that the microfluidic platform can lead towards a general culture system independent algal growth model with systematic screening of many environmental parameters. Our work advances technology for algal cell growth studies and provides essential information for future bioreactor designs and ecological predictions.

We present BoundaryLayerDynamics.jl, a new code for turbulence-resolving simulations of atmospheric boundary-layer flows as well as canonical turbulent flows in channel geometries. The code performs direct numerical simulation as well as large-eddy simulation using a hybrid (pseudo)spectral and finite-difference approach with explicit time advancement. Written in Julia, the code strives to be flexible and adaptable without sacrificing performance, and extensive automated tests aim to ensure that the implementation is and remains correct. We show that the simulation results are in agreement with published results and that the performance is on par with an existing Fortran implementation of the same methods.

ObjectiveThis study was undertaken to assess the impact of immunosuppressive and immunomodulatory therapies on the severity of coronavirus disease 2019 (COVID-19) in people with multiple sclerosis (PwMS).MethodsWe retrospectively collected data of PwMS with suspected or confirmed COVID-19. All the patients had complete follow-up to death or recovery. Severe COVID-19 was defined by a 3-level variable: mild disease not requiring hospitalization versus pneumonia or hospitalization versus intensive care unit (ICU) admission or death. We evaluated baseline characteristics and MS therapies associated with severe COVID-19 by multivariate and propensity score (PS)-weighted ordinal logistic models. Sensitivity analyses were run to confirm the results.ResultsOf 844 PwMS with suspected (n = 565) or confirmed (n = 279) COVID-19, 13 (1.54%) died; 11 of them were in a progressive MS phase, and 8 were without any therapy. Thirty-eight (4.5%) were admitted to an ICU; 99 (11.7%) had radiologically documented pneumonia; 96 (11.4%) were hospitalized. After adjusting for region, age, sex, progressive MS course, Expanded Disability Status Scale, disease duration, body mass index, comorbidities, and recent methylprednisolone use, therapy with an anti-CD20 agent (ocrelizumab or rituximab) was significantly associated (odds ratio [OR] = 2.37, 95% confidence interval [CI] = 1.18-4.74, p = 0.015) with increased risk of severe COVID-19. Recent use (

Abstract Objectives We describe the Residras registry, dedicated to Dravet syndrome (DS) and to other phenotypes related to SCN1A mutations, as a paradigm of registry for rare and complex epilepsies. Our primary objectives are to present the tools and framework of the integrative platform, the main characteristics emerging from the patient cohort included in the registry, with emphasis on demographic, clinical outcome, and mortality. Methods Standardized data of enrolled pediatric and adult patients were collected in 24 Italian expert centers and regularly updated at least on a yearly basis. Patients were prospectively enrolled, at registry starting, but historical retrospective data were also included. Results At present, 281 individuals with DS and a confirmed SCN1A mutation are included. Most patients have data available on epilepsy (n = 263) and their overall neurological condition (n = 255), based on at least one follow‐up update. Median age at first clinical assessment was 2 years (IQR 0–9) while at last follow‐up was 11 years (IQR 5–18.5). During the 7‐year activity of the registry, five patients died resulting in a mortality rate of 1.84 per 1000‐person‐years. When analyzing clinical changes over the first 5‐year follow‐up, we observed a significant difference in cognitive function (P

Abstract Introduction The purpose of the study is to investigate, by T2 relaxation, non‐lesional white matter (WM) in relapsing–remitting (RR) multiple sclerosis (MS). Methods Twenty stable RR MS patients underwent 1.5T Magnetic Resonance Imaging (MRI) with 3D Fluid‐Attenuated Inversion‐Recovery (FLAIR), 3D‐T1‐weighted, and T2‐relaxation multi‐echo sequences. The Lesion Segmentation Tool processed FLAIR images to identify focal lesions (FLs), whereas T1 images were segmented to identify WM and FL sub‐volumes with T1 hypo‐intensity. Non‐lesional WM was obtained as the segmented WM, excluding FL volumes. The multi‐echo sequence allowed decomposition into myelin water, intra‐extracellular water, and free water (Fw), which were evaluated on the segmented non‐lesional WM. Correlation analysis was performed between the non‐lesional WM relaxation parameters and Expanded Disability Status Scale (EDSS), disease duration, patient age, and T1 hypo‐intense FL volumes. Results The T1 hypo‐intense FL volumes correlated with EDSS. On the non‐lesional WM, the median Fw correlated with EDSS, disease duration, age, and T1 hypo‐intense FL volumes. Bivariate EDSS correlation of FL volumes and WM T2‐relaxation parameters did not improve significance. Conclusion T2 relaxation allowed identifying subtle WM alterations, which significantly correlated with EDSS, disease duration, and age but do not seem to be EDSS‐predictors independent from FL sub‐volumes in stable RR patients. Particularly, the increase in the Fw component is suggestive of an uninvestigated prodromal phenomenon in brain degeneration.

Abstract Background US Food and Drug Administration has issued Emergency Use Authorizations for hundreds of serological assays to support Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) diagnosis. The aim of this study is to evaluate, for the first time in children, the performance of three widely utilized SARS-CoV-2 serology commercial assays, Diesse Diagnostics (IgG, IgA, IgM) and Roche Diagnostics, both Roche Nucleocapsid (N) IgG and Roche Spike (S) IgG assays. Methods Sensitivity and 95% confidence intervals (CIs) were estimated for each of the three different serological tests and mixed and direct comparison were performed. Univariate and multivariate Poisson regression models were fitted to calculate incidence rate ratios and 95% CIs as estimate of the effects of age, gender, time on the serology title. A p-value

Introduction: The disease activity associated with the drug-utilization patterns of biologic Disease Modifying Anti-Rheumatic Drugs (DMARDs) is poorly investigated in real-world studies on rheumatoid arthritis (RA) patients. To investigate the relationship between biologic DMARD initiation/discontinuations in RA patients identified in the healthcare administrative databases of Tuscany and the Disease Activity Score 28 (DAS28) reported in the medical charts.Methods: This retrospective population-based study included RA’s first-ever biologic DMARD users of the Pisa University Hospital from 2014 to 2016. Patients were followed up until 31 December 2019. We evaluated the DAS28 recorded before (T0) and after (T1) the biologic DMARD initiation and before (TD0) and after (TD1) discontinuations. Patients were classified as “off-target” (DAS28 > 3.2) or “in-target” (DAS28 ≤ 3.2). We described the disease activity trends at initiation and discontinuation.Results: Ninety-five users were included (73 women, mean age 59.6). Among 70 patients (74%) with at least three DAS28 measures, 28 (40.0%) were off-target at T0 and 38 (54.3%) in-target at T1. Thirty-three (47%) patients had at least one discontinuation, among those with at least three DAS28 assessments. In the disease activity trend, disease stability or improvement was observed in 28 out of 37 (75.7%) patients at initiation and in 24 out of 37 (64.9%) at discontinuation.Discussion: Biologic DMARD discontinuations identified in the healthcare administrative databasese of Tuscany are frequently observed in situations of controlled RA disease. Further studies are warranted to confirm that these events can be used in studies using healthcare administrative databases as proxies of treatment effectiveness.

The purpose of this study is to demonstrate that T2‐weighted imaging with very long echo time (TE > 300 ms) can provide relevant information in neurodegenerative/inflammatory disorder. Twenty patients affected by relapsing–remitting multiple sclerosis with stable disease course underwent 1.5 T 3D FLAIR, 3D T1‐weighted, and a multi‐echo sequence with 32 echoes (TE = 10–320 ms). Focal lesions (FL) were identified on FLAIR. T1‐images were processed to segment deep gray matter (dGM), white matter (WM), FL sub‐volumes with T1 hypo‐intensity (T1FL), and dGM volumes (atrophy). Clinical‐radiological parameters included Expanded Disability Status Scale (EDSS), disease duration, patient age, T1FL, and dGM atrophy. Correlation analysis was performed between the mean signal intensity (SI) computed on the non‐lesional dGM and WM at different TE versus the clinical‐radiological parameters. Multivariable linear regressions were fitted to the data to assess the association between the dependent variable EDSS and the independent variables obtained by T1FL lesion load and the mean SI of dGM and WM at the different TE. A clear trend is observed, with a systematic strengthening of the significance of the correlation at longer TE for all the relationships with the clinical‐radiological parameters, becoming significant (p < 0.05) for EDSS, T1FL volumes, and dGM atrophy. Multivariable linear regressions show that at shorter TE, the SI of the T2‐weighted sequences is not relevant for describing the EDSS variability while the T1FL volumes are relevant, and vice versa, at very‐long TEs (around 300 ms); the SI of the T2‐weighted sequences significantly (p < 0.05) describes the EDSS variability. By very long TE, the SI primarily originates from water with a T2 longer than 250 ms and/or free water, which may be arising from the perivascular space (PVS). Very‐long T2‐weighting might detect dilated PVS and represent an unexplored MR approach in neurofluid imaging of neurodegenerative/inflammatory diseases. [ABSTRACT FROM AUTHOR]

Obtaining accurate and dense three-dimensional estimates of turbulent wall-bounded flows is notoriously challenging, and this limitation negatively impacts geophysical and engineering applications, such as weather forecasting, climate predictions, air quality monitoring, and flow control. This study introduces a physics-informed variational autoencoder model that reconstructs realizable three-dimensional turbulent velocity fields from two-dimensional planar measurements thereof. Physics knowledge is introduced as soft and hard constraints in the loss term and network architecture, respectively, to enhance model robustness and leverage inductive biases alongside observational ones. The performance of the proposed framework is examined in a turbulent open-channel flow application at friction Reynolds number R e τ = 250. The model excels in precisely reconstructing the dynamic flow patterns at any given time and location, including turbulent coherent structures, while also providing accurate time- and spatially-averaged flow statistics. The model outperforms state-of-the-art classical approaches for flow reconstruction such as the linear stochastic estimation method. Physical constraints provide a modest but discernible improvement in the prediction of small-scale flow structures and maintain better consistency with the fundamental equations governing the system when compared to a purely data-driven approach. [ABSTRACT FROM AUTHOR]

Abstract Validation of algorithms for selecting patients from healthcare administrative databases (HAD) is recommended. This PATHFINDER study section is aimed at testing algorithms to select rheumatoid arthritis (RA) patients from Tuscan HAD (THAD) and assessing RA diagnosis time interval between the medical chart date and that of THAD. A population was extracted from THAD. The information of the medical charts at the Rheumatology Unit of Pisa University Hospital represented the reference. We included first ever users of biologic disease modifying anti-rheumatic drugs (bDMARDs) between 2014 and 2016 (index date) with at least a specialist visit at the Rheumatology Unit of the Pisa University Hospital recorded from 2013 to the index date. Out of these, we tested four index tests (algorithms): (1) RA according to hospital discharge records or emergency department admissions (ICD-9 code, 714*); (2) RA according to exemption code from co-payment (006); (3) RA according to hospital discharge records or emergency department admissions AND RA according to exemption code from co-payment; (4) RA according to hospital discharge records or emergency department admissions OR RA according to exemption code from co-payment. We estimated sensitivity, specificity, positive and negative predicted values (PPV and NPV) with 95% confidence interval (95% CI) and the RA diagnosis median time interval (interquartile range, IQR). Two sensitivity analyses were performed. Among 277 reference patients, 103 had RA. The fourth algorithm identified 96 true RA patients, PPV 0.78 (95% CI 0.70–0.85), sensitivity 0.93 (95% CI 0.86–0.97), specificity 0.84 (95% CI 0.78–0.90), and NPV 0.95 (95% CI 0.91–0.98). The sensitivity analyses confirmed performance. The time measured between the actual RA diagnosis date recorded in medical charts and that assumed in THAD was 2.2 years (IQR 0.5–8.4). In conclusion, this validation showed the fourth algorithm as the best. The time interval elapsed between the actual RA diagnosis date in medical charts and that extrapolated from THAD has to be considered in the design of future studies.