An optimized D-dimer cut-off value to predict pulmonary thromboembolism in COVID-19 patients.

Pulmonary thromboembolism (PTE) is a common complication in coronavirus disease 2019 (COVID-19) patients. Elevated D-dimer levels are observed even in the absence of PTE, reducing its discriminative ability as a screening test. It is unknown whether conventional D-dimer cut-off values, as used in the YEARS algorithm, apply to COVID-19 patients. This study aimed to determine the optimal D-dimer cut-off value to predict PTE in COVID-19 patients. All confirmed COVID-19 patients with a computed tomography pulmonary angiography (CTPA) performed ≤5 days after admission due to suspicion of PTE between March 2020 and February 2021, at Medisch Spectrum Twente, The Netherlands, were retrospectively analyzed. The association between PTE and D-dimer levels prior to CTPA, and other potential predictors, was analyzed using logistic regression analyses. The optimal cut-off value was identified using receiver operating characteristic (ROC) curve analyses. In 142 patients, PTE prevalence was 20.4%. The optimal cut-off value was 750 ng/mL (sensitivity 100%; specificity 19.5%; negative predictive value 100%; positive predictive value 24.2%). In total, 15 of 113 (13%) patients without PTE had a D-dimer level ≥500 and <750 ng/mL. In our population of patients hospitalized with COVID-19, a D-dimer level <750 ng/mL safely excluded PTE. Compared to the YEARS 500 ng/mL cut-off value, 13% fewer patients are in need of a CTPA, with similar sensitivity. Future research is required for external validation.

Semi-automated colony-forming unit counting for biosafety level 3 laboratories.

Biosafety level 3 decontamination precautions motivate measuring microbial colonies using consumable photography instead of expensive automated plate counters or smartphones, and assaying drug treatments-with multiple concentrations per treatment, replicates, and controls-produces hundreds of images. Here, we present a protocol for semi-automated image analysis by hand-tuning three parameters. The parameters control for non-uniform colony growth and artifacts such as lid condensation, reflections, and plating streaks. We describe steps to prepare images, tune parameters, and plot dose-response relationships. For complete details on the use and execution of this protocol, please refer to Larkins-Ford et al.1.

Effects of sublethal single, simultaneous and sequential abiotic stresses on phenotypic traits of Arabidopsis thaliana.

Plant responses to abiotic stresses are complex and dynamic, and involve changes in different traits, either as the direct consequence of the stress, or as an active acclimatory response. Abiotic stresses frequently occur simultaneously or in succession, rather than in isolation. Despite this, most studies have focused on a single stress and single or few plant traits. To address this gap, our study comprehensively and categorically quantified the individual and combined effects of three major abiotic stresses associated with climate change (flooding, progressive drought and high temperature) on 12 phenotypic traits related to morphology, development, growth and fitness, at different developmental stages in four Arabidopsis thaliana accessions. Combined sublethal stresses were applied either simultaneously (high temperature and drought) or sequentially (flooding followed by drought). In total, we analysed the phenotypic responses of 1782 individuals across these stresses and different developmental stages. Overall, abiotic stresses and their combinations resulted in distinct patterns of effects across the traits analysed, with both quantitative and qualitative differences across accessions. Stress combinations had additive effects on some traits, whereas clear positive and negative interactions were observed for other traits: 9 out of 12 traits for high temperature and drought, 6 out of 12 traits for post-submergence and drought showed significant interactions. In many cases where the stresses interacted, the strength of interactions varied across accessions. Hence, our results indicated a general pattern of response in most phenotypic traits to the different stresses and stress combinations, but it also indicated a natural genetic variation in the strength of these responses. This includes novel results regarding the lack of a response to drought after submergence and a decoupling between leaf number and flowering time after submergence. Overall, our study provides a rich characterization of trait responses of Arabidopsis plants to sublethal abiotic stresses at the phenotypic level and can serve as starting point for further in-depth physiological research and plant modelling efforts.