Background. Multiple sclerosis (MS) is a chronic autoimmune inflammatory disease. Low vitamin D levels have been reported to be a risk factor for MS, and genetic variances could be implicated. The ...aim of this study was to evaluate the association of MS with rs10766197 polymorphism of CYP2R1 gene and rs10877012 polymorphism of CYP27B1 gene. The second aim was to analyse whether these polymorphisms are associated with the severity of the progression of MS. Material and Methods. In a case-control study, we included 116 MS patients and 226 controls, all of whom were Mexican Mestizo. MS was diagnosed by McDonald criteria (2017). A complete neurological evaluation was performed to evaluate the severity of disease progression. Serum 25-hydroxyvitamin D 25(OH) vitamin D levels were measured by ELISA. Single nucleotide polymorphisms rs10766197 of CYP2R1 gene and rs10877012 SNP of CYP27B1 gene were genotyped by real-time PCR. Results. Serum 25(OH) vitamin D levels were lower in MS patients than in controls (p=0.009). No differences were observed between serum 25(OH) vitamin D levels of MS patients with severe progression compared to low progression (p=0.88). A higher frequency of the A allele of CYP2R1 rs10766197 was observed between MS patients and controls (p=0.05). No differences were observed in the frequency of T allele of CYP27B1 rs10877012 (p=0.65). In subanalysis, patients with GA+AA genotypes of CYP2R1 rs10766197 had an increased risk of MS compared to controls (p=0.03). No increased risk was observed in GT+TT genotypes of CYP27B1 rs10877012 (p=0.63). No differences were observed in allele frequencies of either polymorphism between patients with severe vs. low disease progression. Conclusion. Lower serum 25(OH) vitamin D levels were observed in MS patients than in controls, although these levels were not associated with disease progression. Carriers of GA+AA genotypes of CYP2R1 rs10766197 had an increased risk of MS. None of these polymorphisms was associated with severe progression of MS.
Background: An inadequate response to initial empirical treatment of community acquired pneumonia (CAP) represents a challenge for clinicians and requires early identification and intervention. A ...study was undertaken to quantify the incidence of failure of empirical treatment in CAP, to identify risk factors for treatment failure, and to determine the implications of treatment failure on the outcome. Methods: A prospective multicentre cohort study was performed in 1424 hospitalised patients from 15 hospitals. Early treatment failure (<72 hours), late treatment failure, and in-hospital mortality were recorded. Results: Treatment failure occurred in 215 patients (15.1%): 134 early failure (62.3%) and 81 late failure (37.7%). The causes were infectious in 86 patients (40%), non-infectious in 34 (15.8%), and undetermined in 95. The independent risk factors associated with treatment failure in a stepwise logistic regression analysis were liver disease, pneumonia risk class, leucopenia, multilobar CAP, pleural effusion, and radiological signs of cavitation. Independent factors associated with a lower risk of treatment failure were influenza vaccination, initial treatment with fluoroquinolones, and chronic obstructive pulmonary disease (COPD). Mortality was significantly higher in patients with treatment failure (25% v 2%). Failure of empirical treatment increased the mortality of CAP 11-fold after adjustment for risk class. Conclusions: Although these findings need to be confirmed by randomised studies, they suggest possible interventions to decrease mortality due to CAP.
The advent of next-generation survey instruments, such as the Vera C. Rubin Observatory and its Legacy Survey of Space and Time (LSST), is opening a window for new research in time-domain astronomy. ...The Extended LSST Astronomical Time-Series Classification Challenge (ELAsTiCC) was created to test the capacity of brokers to deal with a simulated LSST stream. Our aim is to develop a next-generation model for the classification of variable astronomical objects. We describe ATAT, the Astronomical Transformer for time series And Tabular data, a classification model conceived by the ALeRCE alert broker to classify light curves from next-generation alert streams. ATAT was tested in production during the first round of the ELAsTiCC campaigns. ATAT consists of two transformer models that encode light curves and features using novel time modulation and quantile feature tokenizer mechanisms, respectively. ATAT was trained on different combinations of light curves, metadata, and features calculated over the light curves. We compare ATAT against the current ALeRCE classifier, a balanced hierarchical random forest (BHRF) trained on human-engineered features derived from light curves and metadata. When trained on light curves and metadata, ATAT achieves a macro F1 score of $82.9 0.4$ in 20 classes, outperforming the BHRF model trained on 429 features, which achieves a macro F1 score of $79.4 The use of transformer multimodal architectures, combining light curves and tabular data, opens new possibilities for classifying alerts from a new generation of large etendue telescopes, such as the Vera C. Rubin Observatory, in real-world brokering scenarios.
Objective
To assess the intra‐ and interobserver reproducibility of musculoskeletal ultrasonography (US) in detecting inflammatory shoulder changes in patients with rheumatoid arthritis, and to ...determine the agreement between US and the Shoulder Pain and Disability Index (SPADI) and the Disabilities of the Arm, Shoulder, and Hand (DASH) questionnaire, using magnetic resonance imaging (MRI) as a gold standard.
Methods
Eleven rheumatologists investigated 10 patients in 2 rounds independently and blindly of each other by US. US results were compared with shoulder function tests and MRI.
Results
The positive and negative predictive values (NPVs) for axillary recess synovitis (ARS) were 0.88 and 0.43, respectively, for posterior recess synovitis (PRS) were 0.36 and 0.97, respectively, for subacromial/subdeltoid bursitis (SASB) were 0.85 and 0.28, respectively, and the NPV for biceps tenosynovitis (BT) was 1.00. The intraobserver kappa was 0.62 for ARS, 0.59 for PRS, 0.51 for BT, and 0.70 for SASB. The intraobserver kappa for power Doppler US (PDUS) signal was 0.91 for PRS, 0.77 for ARS, 0.94 for SASB, and 0.53 for BT. The interobserver maximum kappa was 0.46 for BT, 0.95 for ARS, 0.52 for PRS, and 0.61 for SASB. The interobserver reliability of PDUS was 1.0 for PRS, 0.1 for ARS, 0.5 for BT, and 1.0 for SASB. P values for the SPADI and DASH versus cuff tear on US were 0.02 and 0.01, respectively; all other relationships were not significant.
Conclusion
Overall agreements between gray‐scale US and MRI regarding synovitis of the shoulder varied considerably, but excellent results were seen for PDUS. Measures of shoulder function have a poor relationship with US and MRI. Improved standardization of US scanning technique could further reliability of shoulder US.
The advent of next-generation survey instruments, such as the Vera C. Rubin Observatory and its Legacy Survey of Space and Time (LSST), is opening a window for new research in time-domain astronomy. ...The Extended LSST Astronomical Time-Series Classification Challenge (ELAsTiCC) was created to test the capacity of brokers to deal with a simulated LSST stream. We describe ATAT, the Astronomical Transformer for time series And Tabular data, a classification model conceived by the ALeRCE alert broker to classify light-curves from next-generation alert streams. ATAT was tested in production during the first round of the ELAsTiCC campaigns. ATAT consists of two Transformer models that encode light curves and features using novel time modulation and quantile feature tokenizer mechanisms, respectively. ATAT was trained on different combinations of light curves, metadata, and features calculated over the light curves. We compare ATAT against the current ALeRCE classifier, a Balanced Hierarchical Random Forest (BHRF) trained on human-engineered features derived from light curves and metadata. When trained on light curves and metadata, ATAT achieves a macro F1-score of 82.9 +- 0.4 in 20 classes, outperforming the BHRF model trained on 429 features, which achieves a macro F1-score of 79.4 +- 0.1. The use of Transformer multimodal architectures, combining light curves and tabular data, opens new possibilities for classifying alerts from a new generation of large etendue telescopes, such as the Vera C. Rubin Observatory, in real-world brokering scenarios.
En este artículo se muestran, a través de mapas bidimensionales, los resultados procedentes de un proyecto de investigación mediante el cual se han identificado las variables externas a tener en ...cuenta en el diseño de una herramienta de eLearning para conseguir evaluar, posteriormente, su uso real. La técnica empleada para ello es la «Elaboración de mapas conceptuales». Asimismo, se observa la necesidad de tener en cuenta cómo llevar a cabo la gestión de la herramienta por parte del usuario. Finalmente, se ha analizado la fiabilidad de nuestros mapas.
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