Effect of sutureless securement on hemodialysis catheter-related bloodstream infection

The use of sutureless securement devices during catheterization might reduce the risk of catheter-related bloodstream infection (CRBSI) by suppressing catheter-exit infection and catheter dislodgement. However, the effectiveness of these devices in reducing CRBSI risk when securing hemodialysis catheters has not been explored. This single-center retrospective observational study examined 211 non-tunneled hemodialysis catheters (NTHCs) from 110 hemodialysis inpatients, of which 121 were secured using conventional skin sutures (Suture group) and 90 with GRIP-LOK (GRIP-LOK group). The stabilized inverse probability of treatment (SIPT)-weighting method was used to generate a new population (SIPT-weighted model) without group differences for each of the 12 predictors of CRBSI development (i.e., age, sex, dialysis history, concomitant acute kidney injury or diabetes, concurrent use of immunosuppressant drugs or aspirin, NTHC insertion site, methicillin-resistant Staphylococcus aureus, carriage, bacteremia event within 3 months before catheterization, hemoglobin level, and serum albumin titer). The effect of GRIP-LOK compared with sutures on CRBSI in the SIPT-weighted model was evaluated using univariate SIPT-weighted Cox proportional regression analysis, which showed a significant CRBSI suppression effect of GRIP-LOK compared with sutures (hazard ratio: 0.17 [95% CI 0.04–0.78], p = 0.023). GRIP-LOK affords a lower risk of CRBSI due to indwelling NTHCs than conventional securement using sutures.

The Model Counting Competition 2020

Many computational problems in modern society account to probabilistic reasoning, statistics, and combinatorics. A variety of these real-world questions can be solved by representing the question in (Boolean) formulas and associating the number of models of the formula directly with the answer to the question. Since there has been an increasing interest in practical problem solving for model counting over the past years, the Model Counting Competition was conceived in fall 2019. The competition aims to foster applications, identify new challenging benchmarks, and promote new solvers and improve established solvers for the model counting problem and versions thereof. We hope that the results can be a good indicator of the current feasibility of model counting and spark many new applications. In this article, we report on details of the Model Counting Competition 2020, about carrying out the competition, and the results. The competition encompassed three versions of the model counting problem, which we evaluated in separate tracks. The first track featured the model counting problem, which asks for the number of models of a given Boolean formula. On the second track, we challenged developers to submit programs that solve the weighted model counting problem. The last track was dedicated to projected model counting. In total, we received a surprising number of nine solvers in 34 versions from eight groups.

Single-Drop Analysis of Epinephrine and Uric Acid on a Screen-Printed Carbon Electrode

This work demonstrates the analysis of epinephrine (EP) and uric acid (UA) in a single drop (the volume of the test solution was only 50 µL) using a screen-printed carbon electrode (SPCE) sensor and square-wave voltammetry (SWV). The limit of detection, limit of quantification, linearity, accuracy, precision, and robustness were validated. The normality of the experimental data was tested and confirmed for both methods. Heteroscedasticity was checked by residual analysis followed by a statistical F-test. The latter was confirmed for both analytes. The low relative standard deviations (RSD) at all calibration points and repetitive slopes justified the use of a calibration curve; therefore, the standard addition methodology was avoided (the latter is common in electroanalysis, but time-consuming). Since the conditions for using an ordinary least squares (OLS) regression were not met, weighted linear regression (WLR) was used to improve the accuracy of the analytical results at low concentrations of the analytes. In this manner, the best weighted model was determined and used for the quantification. A comparison was made between the OLS and WLR methods to show the necessity of using the WLR method for EP and UA analysis. The newly developed and validated methods were also shown to be effective in the analysis of real samples. The content of EP in an EP auto-injector and UA in human urine was tested by employing the best weighted model. For EP and UA, the accuracy in terms of the average recovery value was 101.01% and 94.35%, and precision in terms of RSD was 5.65% and 2.75%, respectively. A new analytical methodology is presented that uses a low volume (a single drop), and it offers the advantage of electroanalysis for on-site analysis, where conventional chromatographic techniques cannot be easily employed. Furthermore, the developed technique has additional advantages in terms of speed, cost, and miniaturization.

Creating a Composite Index to Target Recruitment of UK Students from Areas of Low Participation in Higher Education

This research presents a framework through which a new Higher Education Access and Deprivation (HEAD) composite index was created to assist UK universities in efficiently recruiting and supporting students from areas with traditionally low engagement rates in higher education. The index was designed to be easily adaptable by university staff to suit their own work priorities and/or an institution’s strategic requirements by utilising open socio-demographic data and construction techniques that require minimal technical statistical skills. Although Cornwall was used as the study area in this research, this index has been designed such that it can be readily applied elsewhere. Two differently weighted models were created using the framework – one with equal weights and the other based on the frequency the constituent indicators appeared in the reviewed academic literature. Both models were mapped across Cornwall, identifying areas of deprivation at a finer resolution than under the current widely used Participation of Local Areas (POLAR) methodology. The weighted model performed marginally better than both the equal weighted model and the current POLAR methodology when validated against external data, and additionally it worked well in both rural and urban environments leading to it being selected as the new HEAD index. The HEAD index maps smaller scale areas of deprivation than previously available, and by enabling users to investigate the underlying socio-demographic characteristics of an area, it also allows universities to create interventions, support, and policies that best targets the students they aim to recruit and teach.

Hybrid Probabilistic Inference with Logical and Algebraic Constraints: a Survey

Real world decision making problems often involve both discrete and continuous variables and require a combination of probabilistic and deterministic knowledge. Stimulated by recent advances in automated reasoning technology, hybrid (discrete+continuous) probabilistic reasoning with constraints has emerged as a lively and fast growing research field. In this paper we provide a survey of existing techniques for hybrid probabilistic inference with logic and algebraic constraints. We leverage weighted model integration as a unifying formalism and discuss the different paradigms that have been used as well as the expressivity-efficiency trade-offs that have been investigated. We conclude the survey with a comparative overview of existing implementations and a critical discussion of open challenges and promising research directions.