Genotyping of clinically relevant human adenoviruses by array-in-well hybridization assay

AbstractA robust oligonucleotide array-in-well hybridization assay using novel up-converting phosphor reporter technology was applied for genotyping clinically relevant human adenovirus types. A total of 231 adenovirus-positive respiratory, ocular swab, stool and other specimens from 219 patients collected between April 2010 and April 2011 were included in the study. After a real-time PCR amplification targeting the adenovirus hexon gene, the array-in-well assay identified the presence of B03 (n = 122; 57.5% of patients), E04 (29; 13.7%), C02 (21; 9.9%), D37 (14; 6.6%), C01 (12; 5.7%), C05 (5; 2.4%), D19 (4; 1.9%), C06 (2; 0.9%), D08 (1; 0.5%), A31 (1; 0.5%) and F41 (1; 0.5%) genotypes among the clinical sample panel. The typing result was obtained for all specimens that could be amplified (n = 223; 97%), and specificity of the typing was confirmed by sequencing specimens representing each of the different genotypes. No hybridization signal was obtained in adenovirus-negative specimens or specimens with other viruses (n = 30). The array-in-well hybridization assay has great potential as a rapid and multiplex platform for the typing of clinically relevant human adenovirus genotypes in different specimen types

Smoking is a predictor of complications in all types of surgery : a machine learning-based big data study

Background: Machine learning algorithms are promising tools for smoking status classification in big patient data sets. Smoking is a risk factor for postoperative complications in major surgery. Whether this applies to all surgery is unknown. The aims of this retrospective cohort study were to develop a machine learning algorithm for clinical record-based smoking status classification and to determine whether smoking and former smoking predict complications in all surgery types. Methods: All surgeries performed in a Finnish hospital district from 1 January 2015 to 31 December 2019 were analysed. Exclusion criteria were age below 16 years, unknown smoking status, and unknown ASA class. A machine learning algorithm was developed for smoking status classification. The primary outcome was 90-day overall postoperative complications in all surgeries. Secondary outcomes were 90-day overall complications in specialties with over 10 000 surgeries and critical complications in all surgeries. Results: The machine learning algorithm had precisions of 0.958 for current smokers, 0.974 for ex-smokers, and 0.95 for never-smokers. The sample included 158 638 surgeries. In adjusted logistic regression analyses, smokers had increased odds of overall complications (odds ratio 1.17; 95 per cent c.i. 1.14 to 1.20) and critical complications (odds ratio 1.21; 95 per cent c.i. 1.14 to 1.29). Corresponding odds ratios of ex-smokers were 1.09 (95 per cent c.i. 1.06 to 1.13) and 1.09 (95 per cent c.i. 1.02 to 1.17). Smokers had increased odds of overall complications in all specialties with over 10 000 surgeries. ASA class was the most important complication predictor. Conclusion: Machine learning algorithms are feasible for smoking status classification in big surgical data sets. Current and former smoking predict complications in all surgery types.Peer reviewe

On Some Structural and Spectroscopic Aspects of Rare Earth Oxycompounds

The aim of this account is to present the use and the advantages of different experimental and theoretical methods in the study of the structural and spectroscopic properties of rare earth (RE) oxyfluorides. The structural characterization was carried out with the X-ray and neutron powder diffraction techniques combined with the analysis of the acquired data with the Rietveld profile refinement method. The detailed spectroscopic studies were also used by employing the optical absorption and luminescence as well as inelastic neutron scattering data. Simple spectroscopic measurements gave, however, only qualitative description of the spectroscopic properties studied. More sophisticated and quantitative means was obtained by the application of the phenomenological crystal field theory to the spectroscopic data. On the other hand, the structural data was also used as initial input to electrostatic point charge calculation in order to extract the spectroscopic information. The structural and spectroscopic studies comprised the verification of the exact crystal and energy level structures and the characterization of the different interactions modifying the spectroscopic properties of the RE$\text{}^{3+}$ ions. Finally, the results from the theoretical model were used to explain the evolution of the crystal field effect on the 4f$\text{}^{N}$ energy level structure of the RE$\text{}^{3+}$ ion in the RE oxyfluorides series