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Background: Native knee joint infections, while uncommon, present a serious condition predominantly instigated by bacteria such as Staphylococcus aureus. Without timely intervention, they can result in joint destruction or sepsis, with risk factors encompassing preexisting medical conditions and iatrogenic procedures. The diagnostic process includes a comprehensive patient history, clinical evaluation, laboratory testing, imaging studies, and microbiological investigations. Treatment typically involves joint aspiration and arthroscopy. This study aims to examine and establish correlations between diagnostic criteria and treatment modalities, enhancing the speed and specificity of future therapeutic strategies. Materials and methods: The present study is a retrospective cohort study conducted at a 1200-bed university clinic between 2007 and 2017, with an in-depth examination of patient details, symptoms, treatments, and outcomes. A scoring system was developed to classify the severity of knee joint impairment, categorizing patients on the basis of hospital stay duration, surgeries, and postoperative factors such as recurring symptoms, pain, and range of motion. Results: This study of 116 patients with knee joint infections revealed that clinical symptoms such as pain, swelling, and effusion are common but not definitive for diagnosis. Laboratory analysis revealed no significant differences in CRP or leukocyte counts between cultures positive or negative for pathogens. Hospital stay and disease severity are influenced by factors such as age, sex, presence of polyarthritis, neutrophil count, and type of pathogen, with higher weight and cortisone treatment associated with poorer outcomes. Conclusions: This study highlights the diagnostic challenges in native knee joint infections, revealing the need for comprehensive approaches given the nonspecificity of clinical symptoms and laboratory findings. This underscores the importance of advancing research through standardized methodologies and prospective studies to increase the accuracy of diagnosis and the effectiveness of treatment in this field.

Background and objective: With advancement in technology, it has been possible to use minimally invasive surgical approaches for performing pyeloplasty for the treatment of ureteral strictures. This study aims to investigate the current trends of pyeloplasty and reconstructive ureteral procedures. Methods: We analyzed the nationwide German hospital billing database (Destatis) from 2006 to 2022. Linear regression models were utilized for the identification of trends over time. Key findings and limitations: A total of 34 975 pyeloplasties and 37 470 cases with ureteral reconstruction procedures were analyzed. The total number of pyeloplasties increased from 1990 procedures in 2006 to 2251 in 2019, before declining again down to 1916 procedures in 2022 (p = 0.783). Open pyeloplasty cases decreased from 79.5% in 2006 to 17.6% in 2022, while those using the robotic approach increased from 0.3% in 2006 to 35.9% in 2022 (p

Abstract The International Staging System for multiple myeloma recently underwent a second revision (R2-ISS) to include gain/amplification of 1q21 and account for the additive prognostic significance of multiple high-risk features. The phase 3 ICARIA-MM (isatuximab–pomalidomide–dexamethasone vs. pomalidomide–dexamethasone) and IKEMA (isatuximab–carfilzomib–dexamethasone vs. carfilzomib–dexamethasone) studies provide large datasets for retrospectively validating the prognostic value of the R2-ISS in relapsed/refractory multiple myeloma. Of 609 pooled patients, 68 (11.2%) were reclassified as R2-ISS stage I, 136 (22.3%) as R2-ISS stage II, 204 (33.5%) as R2-ISS stage III, 55 (9.0%) as stage IV, and 146 (24.0%) “Not classified”. Median progression-free survival was shorter among those reclassified as R2-ISS stage II (HR 1.52, 95% CI 0.979–2.358), stage III (HR 2.59, 95% CI 1.709–3.923), and stage IV (HR 3.51, 95% CI 2.124–5.784) versus stage I. Adding isatuximab led to longer progression-free survival versus doublet therapy (adjusted HR 0.544 [95% CI 0.436–0.680]), with a consistent treatment effect observed across all R2-ISS stages. This is the first study to validate the R2-ISS with novel agents, including anti-CD38 monoclonal antibodies, and to show that R2-ISS, as a prognostic scoring system, can be applied to patients with relapsed/refractory multiple myeloma.

\textbf{Background:}Rhizomania counts as the most important disease in sugar beet (\textit{Beta vulgaris} L.) for which no plant protection is available, leaving plant breeding as the only defence strategy at the moment. Five resistance genes have been detected on the same chromosome and further studies suggested that these might be different alleles at two resistance clusters. Nevertheless, it was postulated that rhizomania resistance might be a quantitative trait with multiple unknown minor resistance genes. Here, we present a first attempt at genomic prediction of rhizomania resistance in a population that was genotyped using single nucleotide polymorphism (SNP) markers. \newline \textbf{Methods:} First, genomic prediction was performed using all SNPs. Next, we calculated the variable importance for each SNP using machine learning and performed genomic prediction by including the SNPs incrementally in the prediction model based on their variable importance. Using this method, we selected the optimal number of SNPs that maximised the prediction accuracy. Furthermore, we performed genomic prediction with SNP pairs. We also performed feature selection with SNP pairs using the information about the variable importance of the single SNPs. \newline \textbf{Results:} From the four methods under investigation, the latter led to the highest prediction accuracy. These results lead to the following conclusions: (I) The genotypes that were resistant at all known resistance genes, provided the highest possible variation of virus concentrations that the machine can measure. Thus, it can be assumed that more genes must be involved in the resistance towards rhizomania. (II) We show that prediction models that include SNP interactions increased the prediction accuracy. \newline \textbf{Conclusions:} Altogether, our findings suggest that rhizomania resistance is a complex quantitative trait that is affected by multiple genes as well as their interaction.

Abstract Background The ever-increasing availability of high-density genomic markers in the form of single nucleotide polymorphisms (SNPs) enables genomic prediction, i.e. the inference of phenotypes based solely on genomic data, in the field of animal and plant breeding, where it has become an important tool. However, given the limited number of individuals, the abundance of variables (SNPs) can reduce the accuracy of prediction models due to overfitting or irrelevant SNPs. Feature selection can help to reduce the number of irrelevant SNPs and increase the model performance. In this study, we investigated an incremental feature selection approach based on ranking the SNPs according to the results of a genome-wide association study that we combined with random forest as a prediction model, and we applied it on several animal and plant datasets. Results Applying our approach to different datasets yielded a wide range of outcomes, i.e. from a substantial increase in prediction accuracy in a few cases to minor improvements when only a fraction of the available SNPs were used. Compared with models using all available SNPs, our approach was able to achieve comparable performances with a considerably reduced number of SNPs in several cases. Our approach showcased state-of-the-art efficiency and performance while having a faster computation time. Conclusions The results of our study suggest that our incremental feature selection approach has the potential to improve prediction accuracy substantially. However, this gain seems to depend on the genomic data used. Even for datasets where the number of markers is smaller than the number of individuals, feature selection may still increase the performance of the genomic prediction. Our approach is implemented in R and is available at https://github.com/FelixHeinrich/GP_with_IFS/ .

Research indicates that student achievement enhances if coupled with information and communication technology (ICT). However, regardless of its extreme importance, there are examples of ICT underuse across all levels of classroom teaching and learning in Pakistan. Evidence from previous research studies suggests that teachers' perceptions, attitudes and motivation towards ICT determine the practical use of ICT in classroom teaching. The aim of this study was to find the effect of university teachers' perceptions, attitude, and motivation towards their readiness for the integration of ICT in their classroom teaching. A sample of 377 university teachers from 51 higher education institutions responded to the survey instrument. The data were analyzed by using Exploratory Factor Analysis (EFA) and Multiple Regression Analysis. The analysis of data revealed a statistically significant effect of university teachers' perceptions, attitude and motivation towards teachers' readiness for the integration of ICT in their classroom teaching. Based on the findings, the study recommends that university teachers with basic ICT literacy should be encouraged to have a positive attitude toward technology use for discharging professional responsibilities.