Competencias genéricas en la universidad: recursos en línea para entenderlas y aplicarlas

Postprint (published version

Prediction of thrombo-embolic risk in patients with hypertrophic cardiomyopathy (HCM Risk-CVA)

Aims Atrial fibrillation (AF) and thrombo-embolism (TE) are associated with reduced survival in hypertrophic cardiomyopathy (HCM), but the absolute risk of TE in patients with and without AF is unclear. The primary aim of this study was to derive and validate a model for estimating the risk of TE in HCM. Exploratory analyses were performed to determine predictors of TE, the performance of the CHA2DS2-VASc score, and outcome with vitamin K antagonists (VKAs). Methods and results A retrospective, longitudinal cohort of seven institutions was used to develop multivariable Cox regression models fitted with pre-selected predictors. Bootstrapping was used for validation. Of 4821 HCM patients recruited between 1986 and 2008, 172 (3.6%) reached the primary endpoint of cerebrovascular accident (CVA), transient ischaemic attack (TIA), or systemic peripheral embolus within 10 years. A total of 27.5% of patients had a CHA2DS2-VASc score of 0, of whom 9.8% developed TE during follow-up. Cox regression revealed an association between TE and age, AF, the interaction between age and AF, TE prior to first evaluation, NYHA class, left atrial (LA) diameter, vascular disease, and maximal LV wall thickness. There was a curvilinear relationship between LA size and TE risk. The model predicted TE with a C-index of 0.75 [95% confidence interval (CI) 0.70-0.80] and the D-statistic was 1.30 (95% CI 1.05-1.56). VKA treatment was associated with a 54.8% (95% CI 31-97%, P = 0.037) relative risk reduction in HCM patients with AF. Conclusions The study shows that the risk of TE in HCM patients can be identified using a small number of simple clinical features. LA size, in particular, should be monitored closely, and the assessment and treatment of conventional vascular risk factors should be routine practice in older patients. Exploratory analyses show for the first time evidence for a reduction of TE with VKA treatment. The CHA2DS2-VASc score does not appear to correlate well with the clinical outcome in patients with HCM and should not be used to assess TE risk in this population

Creación de un SPOC (Small Private Open Course) colaborativo entre docentes y estudiantes para la mejora del aprendizaje de la Farmacología

Depto. de Farmacología, Farmacognosia y BotánicaFac. de FarmaciaFALSEsubmitte

A comparison of Covid-19 early detection between convolutional neural networks and radiologists

[EN] Background The role of chest radiography in COVID-19 disease has changed since the beginning of the pandemic from a diagnostic tool when microbiological resources were scarce to a different one focused on detecting and monitoring COVID-19 lung involvement. Using chest radiographs, early detection of the disease is still helpful in resource-poor environments. However, the sensitivity of a chest radiograph for diagnosing COVID-19 is modest, even for expert radiologists. In this paper, the performance of a deep learning algorithm on the first clinical encounter is evaluated and compared with a group of radiologists with different years of experience. Methods The algorithm uses an ensemble of four deep convolutional networks, Ensemble4Covid, trained to detect COVID-19 on frontal chest radiographs. The algorithm was tested using images from the first clinical encounter of positive and negative cases. Its performance was compared with five radiologists on a smaller test subset of patients. The algorithm's performance was also validated using the public dataset COVIDx. Results Compared to the consensus of five radiologists, the Ensemble4Covid model achieved an AUC of 0.85, whereas the radiologists achieved an AUC of 0.71. Compared with other state-of-the-art models, the performance of a single model of our ensemble achieved nonsignificant differences in the public dataset COVIDx. Conclusion The results show that the use of images from the first clinical encounter significantly drops the detection performance of COVID-19. The performance of our Ensemble4Covid under these challenging conditions is considerably higher compared to a consensus of five radiologists. Artificial intelligence can be used for the fast diagnosis of COVID-19.Project Chest screening for patients with COVID 19 (COV2000750 Special COVID19 resolution) funded by Instituto de Salud Carlos III. Project DIRAC (INNVA1/2020/42) funded by the Agencia Valenciana de la Innovacion, Generalitat Valenciana.Albiol Colomer, A.; Albiol, F.; Paredes Palacios, R.; Plasencia-Martínez, JM.; Blanco Barrio, A.; García Santos, JM.; Tortajada, S.... (2022). A comparison of Covid-19 early detection between convolutional neural networks and radiologists. Insights into Imaging. 13(1):1-12. https://doi.org/10.1186/s13244-022-01250-311213

Therapeutic Effect of a Poly(ADP-Ribose) Polymerase-1 Inhibitor on Experimental Arthritis by Downregulating Inflammation and Th1 Response

Poly(ADP-ribose) polymerase-1 (PARP-1) synthesizes and transfers ADP ribose polymers to target proteins, and regulates DNA repair and genomic integrity maintenance. PARP-1 also plays a crucial role in the progression of the inflammatory response, and its inhibition confers protection in several models of inflammatory disorders. Here, we investigate the impact of a selective PARP-1 inhibitor in experimental arthritis. PARP-1 inhibition with 5-aminoisoquinolinone (AIQ) significantly reduces incidence and severity of established collagen-induced arthritis, completely abrogating joint swelling and destruction of cartilage and bone. The therapeutic effect of AIQ is associated with a striking reduction of the two deleterious components of the disease, i.e. the Th1-driven autoimmune and inflammatory responses. AIQ downregulates the production of various inflammatory cytokines and chemokines, decreases the antigen-specific Th1-cell expansion, and induces the production of the anti-inflammatory cytokine IL-10. Our results provide evidence of the contribution of PARP-1 to the progression of arthritis and identify this protein as a potential therapeutic target for the treatment of rheumatoid arthritis

Casos clínicos como recursos educativos abiertos para fomentar el aprendizaje significativo de la Farmacología General

Depto. de Farmacología, Farmacognosia y BotánicaFac. de FarmaciaFALSEsubmitte

Thymidine Kinase 2 Deficiency-Induced Mitochondrial DNA Depletion Causes Abnormal Development of Adipose Tissues and Adipokine Levels in Mice

Mammal adipose tissues require mitochondrial activity for proper development and differentiation. The components of the mitochondrial respiratory chain/oxidative phosphorylation system (OXPHOS) are encoded by both mitochondrial and nuclear genomes. The maintenance of mitochondrial DNA (mtDNA) is a key element for a functional mitochondrial oxidative activity in mammalian cells. To ascertain the role of mtDNA levels in adipose tissue, we have analyzed the alterations in white (WAT) and brown (BAT) adipose tissues in thymidine kinase 2 (Tk2) H126N knockin mice, a model of TK2 deficiency-induced mtDNA depletion. We observed respectively severe and moderate mtDNA depletion in TK2-deficient BAT and WAT, showing both tissues moderate hypotrophy and reduced fat accumulation. Electron microscopy revealed altered mitochondrial morphology in brown but not in white adipocytes from TK2-deficient mice. Although significant reduction in mtDNA-encoded transcripts was observed both in WAT and BAT, protein levels from distinct OXPHOS complexes were significantly reduced only in TK2-deficient BAT. Accordingly, the activity of cytochrome c oxidase was significantly lowered only in BAT from TK2-deficient mice. The analysis of transcripts encoding up to fourteen components of specific adipose tissue functions revealed that, in both TK2-deficient WAT and BAT, there was a consistent reduction of thermogenesis related gene expression and a severe reduction in leptin mRNA. Reduced levels of resistin mRNA were found in BAT from TK2-deficient mice. Analysis of serum indicated a dramatic reduction in circulating levels of leptin and resistin. In summary, our present study establishes that mtDNA depletion leads to a moderate impairment in mitochondrial respiratory function, especially in BAT, causes substantial alterations in WAT and BAT development, and has a profound impact in the endocrine properties of adipose tissues

Hyperprolactinaemia in first episode psychosis - a longitudinal assessment

Little is known about hyperprolactinaemia (HPL) in first episode psychosis (FEP) patients. We investigated longitudinal changes in serum prolactin in FEP, and the relationship between HPL, and antipsychotic medication and stress. Serum prolactin was recorded in FEP patients at recruitment and again, 3 and 12 months later. HPL was defined as a serum prolactin level greater than 410 mIU/L (~19.3ng/ml) for males, and a serum prolactin level greater than 510 mIU/L (~24.1ng/ml) for females. From a total of 174 people with serum prolactin measurements at study recruitment, 43% (n=74) had HPL, whilst 27% (n=21/78) and 27% (n=26/95) had HPL at 3 and 12 months respectively. We observed higher serum prolactin levels in females versus males (p<0.001), and in antipsychotic treated (n=68) versus antipsychotic naïve patients (p<0.0001). Prolactin levels were consistently raised in FEP patients taking risperidone, amisulpride and FGAs compared to other antipsychotics. No significant relationship was observed between perceived 3 stress scores (β=7.13, t =0.21, df=11, p=0.0.84 95% CI -72.91-87.16), or objective life stressors (β=-21.74, t=-0.31, df=8, p=0.77 95% CI -218.57-175.09) and serum prolactin. Our study found elevated rates of HPL over the course of the first 12 months of illness. We found no evidence to support the notion that stress is related to elevated serum prolactin at the onset of psychosis

Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London

A comparison of Covid-19 early detection between convolutional neural networks and radiologists

Background The role of chest radiography in COVID-19 disease has changed since the beginning of the pandemic from a diagnostic tool when microbiological resources were scarce to a different one focused on detecting and monitoring COVID-19 lung involvement. Using chest radiographs, early detection of the disease is still helpful in resource-poor environments. However, the sensitivity of a chest radiograph for diagnosing COVID-19 is modest, even for expert radiologists. In this paper, the performance of a deep learning algorithm on the first clinical encounter is evaluated and compared with a group of radiologists with different years of experience. Methods The algorithm uses an ensemble of four deep convolutional networks, Ensemble4Covid, trained to detect COVID-19 on frontal chest radiographs. The algorithm was tested using images from the first clinical encounter of positive and negative cases. Its performance was compared with five radiologists on a smaller test subset of patients. The algorithm's performance was also validated using the public dataset COVIDx. Results Compared to the consensus of five radiologists, the Ensemble4Covid model achieved an AUC of 0.85, whereas the radiologists achieved an AUC of 0.71. Compared with other state-of-the-art models, the performance of a single model of our ensemble achieved nonsignificant differences in the public dataset COVIDx. Conclusion The results show that the use of images from the first clinical encounter significantly drops the detection performance of COVID-19. The performance of our Ensemble4Covid under these challenging conditions is considerably higher compared to a consensus of five radiologists. Artificial intelligence can be used for the fast diagnosis of COVID-19.Project Chest screening for patients with COVID 19 (COV2000750 Special COVID19 resolution) funded by Instituto de Salud Carlos III. Project DIRAC (INNVA1/2020/42) funded by the Agencia Valenciana de la Innovación, Generalitat Valenciana.Peer reviewe