Search

We tested whether CRP combined with the neutrophil-to-lymphocyte ratio (NLR) optimizes the prediction of infectious inflammation in hemodialysis patients. We conducted a retrospective study of 774 (mean age 71.1 ± 12.8 years, 35% women) hemodialysis patients from our institution, hospitalized between 2007 and 2021 for various reasons, with CRP levels available at admission. Infection was defined according to the International Sepsis Definition Conference criteria. An algorithm for the optimal CRP and NLR cutoff points for predicting infection was developed based on a decision tree analysis in the training cohort (n = 620) and then tested in the validation cohort (n = 154). A CRP level above 40 mg/L (obtained as the cutoff point in predicting infections in the training group, using ROC curve analysis) predicted an infection diagnosis with a sensitivity of 75% and a specificity of 76% with an odds ratio (OR) of 9.37 (95% CI: 5.36–16.39), according to a multivariate logistic regression analysis. Whereas, CRP levels above 23 mg/L together with an NLR above 9.7 predicted an infection diagnosis with a sensitivity of 69% and a specificity of 84% with an OR of 25.59 (95% CI: 9.73–67.31). All these results were reproduced in the validation set. Combined use of CRP with NLR may lower the CRP cutoff point in distinguishing between infectious and noninfectious inflammation in hemodialysis patients. [ABSTRACT FROM AUTHOR]

Educational Data Mining is widely used for predicting student's performance. It's a challenging task because a plethora of features related to demographics, personality traits, socio-economic, and environmental may affect students' performance. Such varying features may depend on the level of study, program offered, nature of subject, and geographical location. This study attempted to predict the final semester's results of students studying Doctor of Veterinary Medicine (DVM) based on their pre-admission academic achievements, demographics, and first semester performance. The imbalanced data led to non-generic prediction models, so it was addressed through synthetic minority oversampling technique. Among five prediction models, the Support Vector Machine led the best with 92% accuracy. The decision tree model identified key features affecting students' performance. The analysis led to the conclusion that marks obtained in Biology, Islamiat, and Urdu at Matric and English at Intermediate level affected the students' performance in their final semester. The findings provide useful information to predict students' performance and guidelines for academic institutes' management regarding improving students' achievement. It is speculated that adoption of digital transformation may help reduce difficulty faced in data collection and analysis. [ABSTRACT FROM AUTHOR]

The molecular weight and isoelectric point of the proteins are very important parameters that control their subcellular localization and subsequent function. Although the genome sequence data of the plant kingdom improved enormously, the proteomic details have been poorly elaborated. Therefore, we have calculated the molecular weight and isoelectric point of the plant proteins and reported them in this database. A database, PlantMWpIDB, containing protein data from 342 plant proteomes was created to provide information on plant proteomes for hypothesis formulation in basic research and for biotechnological applications. The Molecular weight and isoelectric point (pI) are important molecular parameters of proteins that are useful when conducting protein studies involving 2D gel electrophoresis, liquid chromatography-mass spectrometry, and X-ray protein crystallography. PlantMWpIDB provides an easy-to-use and efficient interface for search options and generates a summary of basic protein parameters. The database represents a virtual 2D proteome map of plants, and the molecular weight and pI of a protein can be obtained by searching on the name of a protein, a keyword, or by a list of accession numbers. The PlantMWpIDB database also allows one to query protein sequences. The database can be found in the following link https://plantmwpidb.com/. The individual 2D virtual proteome map of the plant kingdom will enable us to understand the proteome diversity between different species. Further, the molecular weight and isoelectric point of individual proteins can enable us to understand their functional significance in different species. [ABSTRACT FROM AUTHOR]