The aim of this study was to assess inter- and intraobserver agreement of the traditional systems (Ruedi-Allgower, AO [Arbeitsgemeinschaft für Osteosynthesefragen], and Topliss) and the newly proposed Leonetti classification system of pilon fractures. We studied all patients at our center who underwent pilon fracture surgery over a 2-year period: 68 patients (70 legs) were included. Four observers independently classified each pilon fracture according to the Ruedi-Allgower, AO, Topliss, and Leonetti systems by evaluating radiographs and computed tomography images on 2 occasions. The inter- and intraobserver agreements were calculated using the Fleiss kappa test. Interobserver reliability was good for AO types (A, B, and C) and Ruedi-Allgower (κ = 0.71 and 0.61, respectively), whereas the interobserver reliability was moderate for AO groups (A1, A2, A3, B1, B2, B3, C1, C2, and C3), Topliss families, Topliss subfamilies, Leonetti types, and Leonetti subtypes. Intraobserver reproducibility was excellent for the Ruedi-Allgower classification, AO types, and Topliss families and good for AO groups, Topliss subfamilies, and Leonetti types and subtypes. Ruedi-Allgower and AO classification systems are the most reliable among those currently used for pilon fractures, but with lower agreement at the AO group level. The use of Topliss and Leonetti classification systems is not recommended because of less favorable results.
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http://dx.doi.org/10.1053/j.jfas.2019.07.002 | DOI Listing |
Microb Genom
January 2025
GMT Science 75 route de Lyons-La-Foret, Rouen F-76000, France.
Microbiome profiling tools rely on reference catalogues, which significantly affect their performance. Comparing them is, however, challenging, mainly due to differences in their native catalogues. In this study, we present a novel standardized benchmarking framework that makes such comparisons more accurate.
View Article and Find Full Text PDFInt J Syst Evol Microbiol
January 2025
School of Life Science, Nanyang Normal University, Nanyang 473061, PR China.
Two novel yeast strains, NYNU 236247 and NYNU 23523, were isolated from the leaves of Hance, collected in the Tianchi Mountain National Forest Park, Henan Province, central China. Phylogenetic analysis of the D1/D2 domain of the large subunit rRNA gene and the internal transcribed spacer (ITS) region revealed the closest relatives of the strains are three described species: , and . The novel species differed from the type strains of these three species by 12 to 22 nucleotide substitutions and 1 gap (~2.
View Article and Find Full Text PDFInt J Syst Evol Microbiol
January 2025
Division of Neurogenetics, Center for Neurological Diseases and Cancer, Nagoya University Graduate School of Medicine, Nagoya, Japan.
Small, obligately anaerobic strains 13CB8C, 13CB11C, 13CB18C and 13GAM1G were isolated from a faecal sample in a patient with Parkinson's disease with a history of duodenal resection. After conducting a comprehensive polyphasic taxonomic analysis including genomic analysis, we propose the establishment of one new genus and four new species. The novel bacteria are sp.
View Article and Find Full Text PDFJ Drug Target
January 2025
Department of Pharmaceutics, Sinhgad College of Pharmacy, Vadgaon (Bk.), Pune-411041, Maharashtra, India.
Ferulic acid (FA) is a phenolic compound obtained naturally and is a versatile antioxidant identified for its potential in managing hypertension. However, its application is constrained due to its classification as a BCS Class IV moiety. To address this, we concentrated on improving its solubility and permeability by developing nanostructured lipid carriers (NLCs) of FA using emulsification probe sonication technique.
View Article and Find Full Text PDFChem Biodivers
January 2025
Department of Horticultural Science, Faculty of Agriculture, Jahrom University, Jahrom, Iran.
The approaches used to determine the medicinal properties of the plants are often destructive, labor-intensive, time-consuming, and expensive, making it impossible to analyze their quality analysis online. Performance of hyperspectral imaging (HSI) integrated with intelligent techniques to overcome these problems was investigated in this research. For this purpose, three classification methods-support vector machine, random forest (RF), and extreme gradient boosting-were studied for the classification of plants in three classes of medicinal, edible, and ornamental for the organs of leaf, stem, flower, and root.
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