Photoluminescence enhancement of CdSe quantum dots: A case of organogel-nanoparticle symbiosis

Highly fluorescent organogels (QD-organogel), prepared by combining a pseudopeptidic macrocycle and different types of CdSe quantum dots (QDs), have been characterized using a battery of optical and microscopic techniques. The results indicate that the presence of the QDs not only does not disrupt the supramolecular organization of the internal fibrillar network of the organogel to a significant extent, but it also decreases the critical concentration of gelator needed to form stable and thermoreversible organogels. Regarding the photophysical properties of the QDs, different trends were observed depending on the presence of a ZnS inorganic shell around the CdSe core. Thus, while the core-shell QDs preserve their photophysical properties in the organogel medium, a high to moderate increase of the fluorescence intensity (up to 528%) and the average lifetime (up to 1.7), respectively, was observed for the core QDs embedded in the organogel. The results are relevant for the development of luminescent organogels based on quantum dots, which have potential applications as advanced hybrid materials in different fields. © 2012 American Chemical Society.Fil: Wadhavane, Prashant D.. Universitat Jaume I; EspañaFil: Galian, Raquel Eugenia. Universidad de Valencia; EspañaFil: Izquierdo, M. Angeles. Universitat Jaume I; EspañaFil: Aguilera Sigalat, Jordi. Universidad de Valencia; EspañaFil: Galindo, Francisco. Universitat Jaume I; EspañaFil: Schmidt, Luciana Carina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Valencia; EspañaFil: Burguete, M. Isabel. Universitat Jaume I; EspañaFil: Pérez Prieto, Julia. Universidad de Valencia; EspañaFil: Luis, Santiago V.. Universitat Jaume I; Españ

Delineating COVID-19 subgroups using routine clinical data identifies distinct in-hospital outcomes

The COVID-19 pandemic has been a great challenge to healthcare systems worldwide. It highlighted the need for robust predictive models which can be readily deployed to uncover heterogeneities in disease course, aid decision-making and prioritise treatment. We adapted an unsupervised data-driven model—SuStaIn, to be utilised for short-term infectious disease like COVID-19, based on 11 commonly recorded clinical measures. We used 1344 patients from the National COVID-19 Chest Imaging Database (NCCID), hospitalised for RT-PCR confirmed COVID-19 disease, splitting them equally into a training and an independent validation cohort. We discovered three COVID-19 subtypes (General Haemodynamic, Renal and Immunological) and introduced disease severity stages, both of which were predictive of distinct risks of in-hospital mortality or escalation of treatment, when analysed using Cox Proportional Hazards models. A low-risk Normal-appearing subtype was also discovered. The model and our full pipeline are available online and can be adapted for future outbreaks of COVID-19 or other infectious disease

Author Correction: Delineating COVID-19 subgroups using routine clinical data identifies distinct in-hospital outcomes

Correction to: Scientific Reports, published online 20 June 2023 The original version of this Article contained an error in the name of author, Andrew Scarsbrook which was incorrectly given as Prof Andrew Scarsbrook. He is a member of the NCCID Collaborative team. The original Article has been corrected