| 1. |
- Stratoulias, Vassilis, et al.
(author)
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ARG1-expressing microglia show a distinct molecular signature and modulate postnatal development and function of the mouse brain
- 2023
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In: Nature Neuroscience. - : Nature Publishing Group. - 1097-6256 .- 1546-1726. ; 26:6, s. 1008-1020
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Journal article (peer-reviewed)abstract
- Molecular diversity of microglia, the resident immune cells in the CNS, is reported. Whether microglial subsets characterized by the expression of specific proteins constitute subtypes with distinct functions has not been fully elucidated. Here we describe a microglial subtype expressing the enzyme arginase-1 (ARG1; that is, ARG1+ microglia) that is found predominantly in the basal forebrain and ventral striatum during early postnatal mouse development. ARG1+ microglia are enriched in phagocytic inclusions and exhibit a distinct molecular signature, including upregulation of genes such as Apoe, Clec7a, Igf1, Lgals3 and Mgl2, compared to ARG1- microglia. Microglial-specific knockdown of Arg1 results in deficient cholinergic innervation and impaired dendritic spine maturation in the hippocampus where cholinergic neurons project, which in turn results in impaired long-term potentiation and cognitive behavioral deficiencies in female mice. Our results expand on microglia diversity and provide insights into microglia subtype-specific functions.
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| 2. |
- Duarte-Salles, Talita, et al.
(author)
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Thirty-Day Outcomes of Children and Adolescents With COVID-19: An International Experience.
- 2021
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In: Pediatrics. - : American Academy of Pediatrics (AAP). - 1098-4275 .- 0031-4005. ; 148:3
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Journal article (peer-reviewed)abstract
- To characterize the demographics, comorbidities, symptoms, in-hospital treatments, and health outcomes among children and adolescents diagnosed or hospitalized with coronavirus disease 2019 (COVID-19) and to compare them in secondary analyses with patients diagnosed with previous seasonal influenza in 2017-2018.International network cohort using real-world data from European primary care records (France, Germany, and Spain), South Korean claims and US claims, and hospital databases. We included children and adolescents diagnosed and/or hospitalized with COVID-19 at age <18 between January and June 2020. We described baseline demographics, comorbidities, symptoms, 30-day in-hospital treatments, and outcomes including hospitalization, pneumonia, acute respiratory distress syndrome, multisystem inflammatory syndrome in children, and death.A total of 242158 children and adolescents diagnosed and 9769 hospitalized with COVID-19 and 2084180 diagnosed with influenza were studied. Comorbidities including neurodevelopmental disorders, heart disease, and cancer were more common among those hospitalized with versus diagnosed with COVID-19. Dyspnea, bronchiolitis, anosmia, and gastrointestinal symptoms were more common in COVID-19 than influenza. In-hospital prevalent treatments for COVID-19 included repurposed medications (<10%) and adjunctive therapies: systemic corticosteroids (6.8%-7.6%), famotidine (9.0%-28.1%), and antithrombotics such as aspirin (2.0%-21.4%), heparin (2.2%-18.1%), and enoxaparin (2.8%-14.8%). Hospitalization was observed in 0.3% to 1.3% of the cohort diagnosed with COVID-19, with undetectable (n < 5 per database) 30-day fatality. Thirty-day outcomes including pneumonia and hypoxemia were more frequent in COVID-19 than influenza.Despite negligible fatality, complications including hospitalization, hypoxemia, and pneumonia were more frequent in children and adolescents with COVID-19 than with influenza. Dyspnea, anosmia, and gastrointestinal symptoms could help differentiate diagnoses. A wide range of medications was used for the inpatient management of pediatric COVID-19.
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| 3. |
- Fico, Giuseppe, et al.
(author)
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What do healthcare professionals need to turn risk models for type 2 diabetes into usable computerized clinical decision support systems? Lessons learned from the MOSAIC project
- 2019
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In: BMC Medical Informatics and Decision Making. - : Springer Science and Business Media LLC. - 1472-6947. ; 19
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Journal article (peer-reviewed)abstract
- Background: To understand user needs, system requirements and organizational conditions towards successful design and adoption of Clinical Decision Support Systems for Type 2 Diabetes (T2D) care built on top of computerized risk models. Methods: The holistic and evidence-based CEHRES Roadmap, used to create eHealth solutions through participatory development approach, persuasive design techniques and business modelling, was adopted in the MOSAIC project to define the sequence of multidisciplinary methods organized in three phases, user needs, implementation and evaluation. The research was qualitative, the total number of participants was ninety, about five-seventeen involved in each round of experiment. Results: Prediction models for the onset of T2D are built on clinical studies, while for T2D care are derived from healthcare registries. Accordingly, two set of DSSs were defined: the first, T2D Screening, introduces a novel routine; in the second case, T2D Care, DSSs can support managers at population level, and daily practitioners at individual level. In the user needs phase, T2D Screening and solution T2D Care at population level share similar priorities, as both deal with risk-stratification. End-users of T2D Screening and solution T2D Care at individual level prioritize easiness of use and satisfaction, while managers prefer the tools to be available every time and everywhere. In the implementation phase, three Use Cases were defined for T2D Screening, adapting the tool to different settings and granularity of information. Two Use Cases were defined around solutions T2D Care at population and T2D Care at individual, to be used in primary or secondary care. Suitable filtering options were equipped with "attractive" visual analytics to focus the attention of end-users on specific parameters and events. In the evaluation phase, good levels of user experience versus bad level of usability suggest that end-users of T2D Screening perceived the potential, but they are worried about complexity. Usability and user experience were above acceptable thresholds for T2D Care at population and T2D Care at individual. Conclusions: By using a holistic approach, we have been able to understand user needs, behaviours and interactions and give new insights in the definition of effective Decision Support Systems to deal with the complexity of T2D care.
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| 4. |
- Kostka, Kristin, et al.
(author)
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Unraveling COVID-19: A Large-Scale Characterization of 4.5 Million COVID-19 Cases Using CHARYBDIS.
- 2022
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In: Clinical epidemiology. - 1179-1349. ; 14, s. 369-384
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Journal article (peer-reviewed)abstract
- Routinely collected real world data (RWD) have great utility in aiding the novel coronavirus disease (COVID-19) pandemic response. Here we present the international Observational Health Data Sciences and Informatics (OHDSI) Characterizing Health Associated Risks and Your Baseline Disease In SARS-COV-2 (CHARYBDIS) framework for standardisation and analysis of COVID-19 RWD.We conducted a descriptive retrospective database study using a federated network of data partners in the United States, Europe (the Netherlands, Spain, the UK, Germany, France and Italy) and Asia (South Korea and China). The study protocol and analytical package were released on 11th June 2020 and are iteratively updated via GitHub. We identified three non-mutually exclusive cohorts of 4,537,153 individuals with a clinical COVID-19 diagnosis or positive test, 886,193 hospitalized with COVID-19, and 113,627 hospitalized with COVID-19 requiring intensive services.We aggregated over 22,000 unique characteristics describing patients with COVID-19. All comorbidities, symptoms, medications, and outcomes are described by cohort in aggregate counts and are readily available online. Globally, we observed similarities in the USA and Europe: more women diagnosed than men but more men hospitalized than women, most diagnosed cases between 25 and 60 years of age versus most hospitalized cases between 60 and 80 years of age. South Korea differed with more women than men hospitalized. Common comorbidities included type 2 diabetes, hypertension, chronic kidney disease and heart disease. Common presenting symptoms were dyspnea, cough and fever. Symptom data availability was more common in hospitalized cohorts than diagnosed.We constructed a global, multi-centre view to describe trends in COVID-19 progression, management and evolution over time. By characterising baseline variability in patients and geography, our work provides critical context that may otherwise be misconstrued as data quality issues. This is important as we perform studies on adverse events of special interest in COVID-19 vaccine surveillance.
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| 5. |
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| 6. |
- Laurie, Steven, et al.
(author)
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The RD-Connect Genome-Phenome Analysis Platform : Accelerating diagnosis, research, and gene discovery for rare diseases
- 2022
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In: Human Mutation. - : John Wiley & Sons. - 1059-7794 .- 1098-1004. ; 43:6, s. 717-733
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Journal article (peer-reviewed)abstract
- Rare disease patients are more likely to receive a rapid molecular diagnosis nowadays thanks to the wide adoption of next-generation sequencing. However, many cases remain undiagnosed even after exome or genome analysis, because the methods used missed the molecular cause in a known gene, or a novel causative gene could not be identified and/or confirmed. To address these challenges, the RD-Connect Genome-Phenome Analysis Platform (GPAP) facilitates the collation, discovery, sharing, and analysis of standardized genome-phenome data within a collaborative environment. Authorized clinicians and researchers submit pseudonymised phenotypic profiles encoded using the Human Phenotype Ontology, and raw genomic data which is processed through a standardized pipeline. After an optional embargo period, the data are shared with other platform users, with the objective that similar cases in the system and queries from peers may help diagnose the case. Additionally, the platform enables bidirectional discovery of similar cases in other databases from the Matchmaker Exchange network. To facilitate genome-phenome analysis and interpretation by clinical researchers, the RD-Connect GPAP provides a powerful user-friendly interface and leverages tens of information sources. As a result, the resource has already helped diagnose hundreds of rare disease patients and discover new disease causing genes.
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| 7. |
- Moreno-Martos, David, et al.
(author)
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Characteristics and outcomes of COVID-19 patients with COPD from the United States, South Korea, and Europe
- 2023
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In: Wellcome Open Research. - : F1000 Research Ltd. - 2398-502X. ; 7
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Journal article (peer-reviewed)abstract
- Background: Characterization studies of COVID-19 patients with chronic obstructive pulmonary disease (COPD) are limited in size and scope. The aim of the study is to provide a large-scale characterization of COVID-19 patients with COPD. Methods: We included thirteen databases contributing data from January-June 2020 from North America (US), Europe and Asia. We defined two cohorts of patients with COVID-19 namely a ‘diagnosed’ and ‘hospitalized’ cohort. We followed patients from COVID-19 index date to 30 days or death. We performed descriptive analysis and reported the frequency of characteristics and outcomes among COPD patients with COVID-19. Results: The study included 934,778 patients in the diagnosed COVID-19 cohort and 177,201 in the hospitalized COVID-19 cohort. Observed COPD prevalence in the diagnosed cohort ranged from 3.8% (95%CI 3.5-4.1%) in French data to 22.7% (95%CI 22.4-23.0) in US data, and from 1.9% (95%CI 1.6-2.2) in South Korean to 44.0% (95%CI 43.1-45.0) in US data, in the hospitalized cohorts. COPD patients in the hospitalized cohort had greater comorbidity than those in the diagnosed cohort, including hypertension, heart disease, diabetes and obesity. Mortality was higher in COPD patients in the hospitalized cohort and ranged from 7.6% (95%CI 6.9-8.4) to 32.2% (95%CI 28.0-36.7) across databases. ARDS, acute renal failure, cardiac arrhythmia and sepsis were the most common outcomes among hospitalized COPD patients. Conclusion: COPD patients with COVID-19 have high levels of COVID-19-associated comorbidities and poor COVID-19 outcomes. Further research is required to identify patients with COPD at high risk of worse outcomes.
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| 8. |
- Palacio, Montse, et al.
(author)
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Prediction of neonatal respiratory morbidity by quantitative ultrasound lung texture analysis: a multicenter study.
- 2017
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In: American journal of obstetrics and gynecology. - : Elsevier BV. - 1097-6868 .- 0002-9378. ; 217:2
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Journal article (peer-reviewed)abstract
- Prediction of neonatal respiratory morbidity may be useful to plan delivery in complicated pregnancies. The limited predictive performance of the current diagnostic tests together with the risks of an invasive procedure limits the use of fetal lung maturity assessment.The objective of the study was to evaluate the performance of quantitative ultrasound texture analysis (quantusFLM) to predict neonatal respiratory morbidity in preterm and early-term (<39.0 weeks) deliveries.This was a prospective multicenter study in 20 centers worldwide. Fetal lung ultrasound images were obtained at 25.0-38.6 weeks' gestation within 48 hours of delivery, stored in Digital Imaging and Communication in Medicine format, and analyzed with quantusFLM. Physicians were blinded to the analysis. At delivery, perinatal outcomes and the occurrence of neonatal respiratory morbidity, defined as either respiratory distress syndrome or transient tachypnea of the newborn, were registered. The performance of the ultrasound texture analysis test to predict neonatal respiratory morbidity was evaluated.A total of 883 images were collected, but 17.2% were discarded because of poor image quality or exclusion criteria, leaving 730 observations for the final analysis. The prevalence of neonatal respiratory morbidity was 13.8% (101 of 730). The quantusFLM predicted neonatal respiratory morbidity with a sensitivity, specificity, and positive and negative predictive value of 74.3% (75 of 101), 88.6% (557 of 629), 51.0% (75 of 147), and 95.5% (557 of 583), respectively. Accuracy was 86.5% (632 of 730) and positive and negative likelihood ratios were 6.5 and 0.3, respectively.The quantusFLM predicted neonatal respiratory morbidity with an accuracy similar to that previously reported for other tests with the advantage of being a noninvasive technique.
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| 9. |
- Posada-Marin, Jose A., et al.
(author)
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Global Impacts of El Nino on Terrestrial Moisture Recycling
- 2023
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In: Geophysical Research Letters. - 0094-8276 .- 1944-8007. ; 50:10
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Journal article (peer-reviewed)abstract
- The global impacts of El Nino on precipitation have been long-recognized, but more understanding of the mechanisms behind this influence is needed. For instance, previous studies have largely overlooked the potential impacts of El Nino on terrestrial moisture recycling (TMR). We perform a 40-year forward tracking simulation to derive a global climatology of recycled precipitation and use a composite analysis to investigate how El Nino affects TMR. We identify seven regions where the El Nino impact on TMR is most significant and find that, in these regions, changes in precipitation and TMR are directly related: they increase or decrease together. In addition, we find a marked latitudinal contrast between the Southern Hemisphere, where TMR increases during El Nino, and the Northern Hemisphere and the tropics, where it decreases. Our results indicate that the weakening and strengthening of TMR can be behind precipitation changes caused by El Nino globally.
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| 10. |
- Roel, Elena, et al.
(author)
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Characteristics and Outcomes of Over 300,000 Patients with COVID-19 and History of Cancer in the United States and Spain.
- 2021
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In: Cancer epidemiology, biomarkers & prevention. - 1538-7755. ; 30:10, s. 1884-1894
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Journal article (peer-reviewed)abstract
- We described the demographics, cancer subtypes, comorbidities, and outcomes of patients with a history of cancer and coronavirus disease 2019 (COVID-19). Second, we compared patients hospitalized with COVID-19 to patients diagnosed with COVID-19 and patients hospitalized with influenza.We conducted a cohort study using eight routinely collected health care databases from Spain and the United States, standardized to the Observational Medical Outcome Partnership common data model. Three cohorts of patients with a history of cancer were included: (i) diagnosed with COVID-19, (ii) hospitalized with COVID-19, and (iii) hospitalized with influenza in 2017 to 2018. Patients were followed from index date to 30 days or death. We reported demographics, cancer subtypes, comorbidities, and 30-day outcomes.We included 366,050 and 119,597 patients diagnosed and hospitalized with COVID-19, respectively. Prostate and breast cancers were the most frequent cancers (range: 5%-18% and 1%-14% in the diagnosed cohort, respectively). Hematologic malignancies were also frequent, with non-Hodgkin's lymphoma being among the five most common cancer subtypes in the diagnosed cohort. Overall, patients were aged above 65 years and had multiple comorbidities. Occurrence of death ranged from 2% to 14% and from 6% to 26% in the diagnosed and hospitalized COVID-19 cohorts, respectively. Patients hospitalized with influenza (n = 67,743) had a similar distribution of cancer subtypes, sex, age, and comorbidities but lower occurrence of adverse events.Patients with a history of cancer and COVID-19 had multiple comorbidities and a high occurrence of COVID-19-related events. Hematologic malignancies were frequent.This study provides epidemiologic characteristics that can inform clinical care and etiologic studies.
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