Search

Asthma is a heterogeneous disease with several phenotypes, endotypes and severity degrees, in which different T-cell subpopulations are involved. These cells express specific miRNAs (i.e. inflamma-miRs) that can be released to serum in exosomes after activation and be used as biomarkers of underlying inflammation. Thus, we aim to evaluate specific T-cell miRNA signatures in serum exosomes from different subgroups of asthmatic patients.Samples from healthy donors (N = 30) and patients (N = 119) with different asthma endotypes (T2We detected five miRNAs with high confidence in serum exosomes: miR-16-5p, miR-21-5p, miR-126-3p, miR146a-5p and miR-215-5p. All of them, except miR-16-5p were upregulated in MSA patients compared to MA. A logistic regression model including each of these miRNAs was created to discriminate both conditions, rendering a ROC curve AUC of 0.896 (0.830-0.961). miR-21-5p and miR-126-3p, both involved in Th1/Th2 differentiation, were specifically augmented in T2Immune-related miRNAs, including miR-21-5p, miR-126-3p, miR-146a-5p and miR-215-5p, can be used as clinically relevant non-invasive biomarkers of the phenotype/endotype and severity of asthma.

Severe eosinophilic asthma is a high-burden disease. Mepolizumab has been effective in several randomized clinical trials. However, such success might not be applicable to patients treated in usual clinical practice. The objectives of this article are to evaluate the efficacy of mepolizumab in severe uncontrolled eosinophilic asthma under usual clinical practice, and to determine characteristics associated with the response to this treatment.We have conducted a retrospective, multicentre study, including all adult patients with severe uncontrolled eosinophilic asthma in Galicia, Spain, on whom mepolizumab treatment was started before June 2020, at least 6 months before the time of inclusion, and had received at least one dose of the drug. Patient characteristics, clinical data, respiratory function and comorbidities were collected at baseline and at the 6-month-follow-up. Responders and super-responders were defined according to clinical response and requirement of systemic corticosteroids.122 patients (mean age 58 years old) were included. In the follow-up treatment 6 months later, 75.4% of the patients were well-controlled, displaying a significant reduction in blood eosinophil counts (p 0.001), hospital admissions and disease exacerbations (p 0.001), and had their systemic glucocorticosteroid dose significantly reduced (p 0.001). The inhaled corticosteroid dose was also lowered (p 0.01) after 6 months of treatment. Around two-thirds had a clinically significant increase in FEV1, 95% of the patients were considered responders and 43% super-responders.In routine clinical practice, mepolizumab is effective in patients with severe eosinophilic asthma and it has a good safety profile.

Introduction This study was aimed to identify risk factors associated with unfavorable outcomes (composite outcome variable: mortality and need for mechanical ventilation) in patients hospitalized in Galicia with COVID-19 pneumonia. Methods Retrospective, multicenter, observational study carried out in the 8 Galician tertiary hospitals. All Patients admitted with confirmed COVID-19 pneumonia from 1st of March to April 24th, 2020 were included. A multivariable logistic regression analysis was performed in order to identify the relationship between risk factors, therapeutic interventions and the composite outcome variable. Results A total of 1292 patients (56.1% male) were included. Two hundred and twenty-five (17.4%) died and 327 (25.3%) reached the main outcome variable. Age [odds ratio (OR) = 1.03 (95% confidence interval (CI): 1.01–1.04)], CRP quartiles 3 and 4 [OR = 2.24 (95% CI: 1.39–3.63)] and [OR = 3.04 (95% CI: 1.88–4.92)], respectively, Charlson index [OR = 1.16 (95%CI: 1.06–1.26)], SaO2 upon admission [OR = 0.93 (95% CI: 0.91–0.95)], hydroxychloroquine prescription [OR = 0.22 (95%CI: 0.12–0.37)], systemic corticosteroids prescription [OR = 1.99 (95%CI: 1.45–2.75)], and tocilizumab prescription [OR = 3.39 (95%CI: 2.15–5.36)], significantly impacted the outcome. Sensitivity analysis using different alternative logistic regression models identified consistently the ratio admissions/hospital beds as a predictor of the outcome [OR = 1.06 (95% CI: 1.02–1.11)]. Conclusion These findings may help to identify patients at hospital admission with a higher risk of death and may urge healthcare authorities to implement policies aimed at reducing deaths by increasing the availability of hospital beds.

The coronavirus disease caused by SARS-Cov-2 is a pandemic with millions of confirmed cases around the world and a high death toll. Currently, the real-time polymerase chain reaction (RT-PCR) is the standard diagnostic method for determining COVID-19 infection. Various failures in the detection of the disease by means of laboratory samples have raised certain doubts about the characterisation of the infection and the spread of contacts.In clinical practice, chest radiography (RT) and chest computed tomography (CT) are extremely helpful and have been widely used in the detection and diagnosis of COVID-19. RT is the most common and widely available diagnostic imaging technique, however, its reading by less qualified personnel, in many cases with work overload, causes a high number of errors to be committed. Chest CT can be used for triage, diagnosis, assessment of severity, progression, and response to treatment. Currently, artificial intelligence (AI) algorithms have shown promise in image classification, showing that they can reduce diagnostic errors by at least matching the diagnostic performance of radiologists.This review shows how AI applied to thoracic radiology speeds up and improves diagnosis, allowing to optimise the workflow of radiologists. It can provide an objective evaluation and achieve a reduction in subjectivity and variability. AI can also help to optimise the resources and increase the efficiency in the management of COVID-19 infection. Resumen: La enfermedad causada por el coronavirus SARS-CoV-2 es una pandemia con millones de casos confirmados en todo el mundo, y un alto número de fallecimientos. Actualmente, la reacción en cadena de la polimerasa en tiempo real (RT-PCR) es el método de diagnóstico estándar para determinar la infección por COVID-19. Diversos fracasos en la detección de la enfermedad por medio de muestras de laboratorio han planteado ciertas dudas sobre la caracterización de la infección y la propagación a los contactos.En la práctica clínica, la radiografía de tórax (RT) y la tomografía computarizada (TC) de tórax son extremadamente útiles y se han utilizado extensamente en la detección y el diagnóstico de la COVID-19. La RT es la técnica de diagnóstico por imagen más común, y la que está más ampliamente disponible, sin embargo, su lectura por personal menos cualificado, en muchos casos con sobrecarga de trabajo, hace que se cometa un gran número de errores. La TC de tórax se puede utilizar para el triaje, el diagnóstico, la evaluación de la gravedad, la progresión y la respuesta al tratamiento. Actualmente, los algoritmos de inteligencia artificial (IA) han resultado prometedores en la clasificación de imágenes, mostrando que pueden reducir los errores de diagnóstico, como mínimo igualando el rendimiento diagnóstico de los radiólogos.Esta revisión muestra cómo la IA aplicada a la RT acelera y mejora el diagnóstico, lo que permite optimizar el flujo de trabajo de los radiólogos. Puede proporcionar una evaluación objetiva y lograr una reducción de la subjetividad y la variabilidad. La IA también puede ayudar a optimizar los recursos y aumentar la eficiencia en la gestión de la infección por COVID-19.

This study was aimed at evaluating whether once-daily regimens (od-r) show benefits in adherence when compared to twice-daily (td-r).Prospective, multicenter, 6-month follow-up study with two visits. The main objective was to compare adherence assessed by the electronic prescription refill rate (EPRR) and by the 10-item Test of Adherence to Inhalers (TAI) in patients with od-r and td-r. Suboptimal adherence was defined as TAI50 or EPRR ≤ 80%. The effect of suboptimal adherence on meaningful clinical outcomes and the concordance between EPRR and TAI were also examined.One hundred and ninety-seven patients (47.3 ± 15.9 years, 65% women) were included and 180 completed the study. TAI score was50 in 29.8% od-r patients and 46.9% in td-r (p = 0.01) and EPRR was ≤80% in 22.6% and 37.5% respectively (p = 0.02). The correlation between the two methods was moderate (rho = 0.548; p0.001). There were no significant differences in FEVMean adherence rates were greater with od-r than with td-r, but we did not observe an effect on clinical outcomes.