Efecto del número de indicadores por factor sobre la identificación y estimación en modelos aditivos de análisis factorial confirmatorio

La matriz multirrasgo-multimétodo (MRMM) es un diseño de investigación de larga tradición en Psicología. Las técnicas de análisis de datos adecuadas para una correcta extracción de conclusiones han estado sujetas a controversia. Parece, no obstante, que diversos modelos de análisis factorial confirmatorio resultan muy adecuados. De entre los diversos modelos, dos de ellos han recibido gran atención, el modelo completo, que apareció primero en la literatura, y el de unicidades correlacionadas, que parece una alternativa razonable a los problemas que aparecen en el primero. Los resultados de ambos modelos en la literatura se refieren a situaciones con un solo indicador por combinación rasgo-método. La presente investigación simula datos de matrices MRMM para múltiples indicadores por combinación rasgo-método y somete a prueba la adecuación de las estimaciones de ambos modelos. Los resultados apuntan a un mejor comportamiento del modelo completo, si bien los sesgos, aunque triviales en cuantía, aumentan conforme aumenta la correlación entre los métodos

Efecto del número de indicadores por factor sobre la identificación y estimación en modelos aditivos de análisis factorial confirmatorio

La matriz multirrasgo-multimétodo (MRMM) es un diseño de investigación de larga tradición en Psicología. Las técnicas de análisis de datos adecuadas para una correcta extracción de conclusiones han estado sujetas a controversia. Parece, no obstante, que diversos modelos de análisis factorial confirmatorio resultan muy adecuados. De entre los diversos modelos, dos de ellos han recibido gran atención, el modelo completo, que apareció primero en la literatura, y el de unicidades correlacionadas, que parece una alternativa razonable a los problemas que aparecen en el primero. Los resultados de ambos modelos en la literatura se refieren a situaciones con un solo indicador por combinación rasgo-método. La presente investigación simula datos de matrices MRMM para múltiples indicadores por combinación rasgo-método y somete a prueba la adecuación de las estimaciones de ambos modelos. Los resultados apuntan a un mejor comportamiento del modelo completo, si bien los sesgos, aunque triviales en cuantía, aumentan conforme aumenta la correlación entre los métodos

Challenges in Clinical Metaproteomics Highlighted by the Analysis of Acute Leukemia Patients with Gut Colonization by Multidrug-Resistant Enterobacteriaceae.

The microbiome has a strong impact on human health and disease and is, therefore, increasingly studied in a clinical context. Metaproteomics is also attracting considerable attention, and such data can be efficiently generated today owing to improvements in mass spectrometry-based proteomics. As we will discuss in this study, there are still major challenges notably in data analysis that need to be overcome. Here, we analyzed 212 fecal samples from 56 hospitalized acute leukemia patients with multidrug-resistant Enterobactericeae (MRE) gut colonization using metagenomics and metaproteomics. This is one of the largest clinical metaproteomic studies to date, and the first metaproteomic study addressing the gut microbiome in MRE colonized acute leukemia patients. Based on this substantial data set, we discuss major current limitations in clinical metaproteomic data analysis to provide guidance to researchers in the field. Notably, the results show that public metagenome databases are incomplete and that sample-specific metagenomes improve results. Furthermore, biological variation is tremendous which challenges clinical study designs and argues that longitudinal measurements of individual patients are a valuable future addition to the analysis of patient cohorts

Prediction of poor outcome in clostridioides difficile infection: A multicentre external validation of the toxin B amplification cycle

Producción CientíficaClassification of patients according to their risk of poor outcomes in Clostridioides difficile infection (CDI) would enable implementation of costly new treatment options in a subset of patients at higher risk of poor outcome. In a previous study, we found that low toxin B amplification cycle thresholds (Ct) were independently associated with poor outcome CDI. Our objective was to perform a multicentre external validation of a PCR-toxin B Ct as a marker of poor outcome CDI. We carried out a multicentre study (14 hospitals) in which the characteristics and outcome of patients with CDI were evaluated. A subanalysis of the results of the amplification curve of real-time PCR gene toxin B (XpertTM C. difficile) was performed. A total of 223 patients were included. The median age was 73.0 years, 50.2% were female, and the median Charlson index was 3.0. The comparison of poor outcome and non–poor outcome CDI episodes revealed, respectively, the following results: median age (years), 77.0 vs 72.0 (p = 0.009); patients from nursing homes, 24.4% vs 10.8% (p = 0.039); median leukocytes (cells/μl), 10,740.0 vs 8795.0 (p = 0.026); and median PCR-toxin B Ct, 23.3 vs 25.4 (p = 0.004). Multivariate analysis showed that a PCR-toxin B Ct cut-off <23.5 was significantly and independently associated with poor outcome CDI (p = 0.002; OR, 3.371; 95%CI, 1.565–7.264). This variable correctly classified 68.5% of patients. The use of this microbiological marker could facilitate early selection of patients who are at higher risk of poor outcome and are more likely to benefit from newer and more costly therapeutic options

Spread of a SARS-CoV-2 variant through Europe in the summer of 2020.

Following its emergence in late 2019, the spread of SARS-CoV-21,2 has been tracked by phylogenetic analysis of viral genome sequences in unprecedented detail3–5. Although the virus spread globally in early 2020 before borders closed, intercontinental travel has since been greatly reduced. However, travel within Europe resumed in the summer of 2020. Here we report on a SARS-CoV-2 variant, 20E (EU1), that was identified in Spain in early summer 2020 and subsequently spread across Europe. We find no evidence that this variant has increased transmissibility, but instead demonstrate how rising incidence in Spain, resumption of travel, and lack of effective screening and containment may explain the variant’s success. Despite travel restrictions, we estimate that 20E (EU1) was introduced hundreds of times to European countries by summertime travellers, which is likely to have undermined local efforts to minimize infection with SARS-CoV-2. Our results illustrate how a variant can rapidly become dominant even in the absence of a substantial transmission advantage in favourable epidemiological settings. Genomic surveillance is critical for understanding how travel can affect transmission of SARS-CoV-2, and thus for informing future containment strategies as travel resumes. © 2021, The Author(s), under exclusive licence to Springer Nature Limited

The first wave of the COVID-19 epidemic in Spain was associated with early introductions and fast spread of a dominating genetic variant

The coronavirus disease 2019 (COVID-19) pandemic has affected the world radically since 2020. Spain was one of the European countries with the highest incidence during the first wave. As a part of a consortium to monitor and study the evolution of the epidemic, we sequenced 2,170 samples, diagnosed mostly before lockdown measures. Here, we identified at least 500 introductions from multiple international sources and documented the early rise of two dominant Spanish epidemic clades (SECs), probably amplified by superspreading events. Both SECs were related closely to the initial Asian variants of SARS-CoV-2 and spread widely across Spain. We inferred a substantial reduction in the effective reproductive number of both SECs due to public-health interventions (Re < 1), also reflected in the replacement of SECs by a new variant over the summer of 2020. In summary, we reveal a notable difference in the initial genetic makeup of SARS-CoV-2 in Spain compared with other European countries and show evidence to support the effectiveness of lockdown measures in controlling virus spread, even for the most successful genetic variants