CT-derived coronary artery calcium density is affected by regional lesion distribution and image reconstruction parameters.

PURPOSE: The prognostic relevance of coronary artery calcium (CAC) density, assessed from cardiac CT scans, is established. However, the influence of CAC distribution, volume, image reconstruction, and clinical factors on CAC density warrants further examination. METHODS: In this study, 120 patients underwent non-contrast ECG-gated cardiac CT scans using a prospectively defined CAC scoring protocol with 1-, 3-, and 5-mm thick image reconstructions, both with and without a 20% image overlap. We segmented CAC in all reconstructions and assessed the relationship between CAC density, volume, and number of detected calcifications/patient. RESULTS: Overall, 75/120 (63%) patients (66% men, mean age 63 ± 11 years) presented CAC across 342 segments. CAC density, CAC volume, and the number of detected calcifications decreased with increasing slice thickness (p < 0.001 for all); these effects were slightly reduced by image overlap (p < 0.001 for all). Higher CAC density correlated with greater CAC volume (ρ = 0.62; p < 0.001) and more calcified segments per person (ρ = 0.32; p = 0.006). Higher CAC density was also associated with lower patient weight (beta: -0.6, 95%CI: -1.1--0.1, p = 0.022) and increased high-density lipoprotein (HDL) levels (beta: 0.7, 95%CI: 0.0-1.4, p = 0.046). In a multivariable analysis adjusted for clinical covariates, lower CAC density was associated with broader CAC distribution (i.e., a higher number of calcified segments at a given CAC volume; beta-coefficient: -58.9; 95%CI: -84.7 to -33.1; p < 0.001). CONCLUSION: CAC density is significantly impacted by regional CAC distribution and image reconstruction, potentially confounding its prognostic value. Accounting for these factors may improve patient risk assessment, management, and cardiovascular health outcomes.

Pulmonary perfusion defect volume on dual-energy CT: prognostic marker of adverse events in patients with suspected pulmonary embolism.

To assess whether quantification of pulmonary perfusion defects on dual-energy computed tomography (DECT) relates to adverse events beyond clinical parameters and traditional embolus detection in patients with suspected pulmonary embolism (PE). We included consecutive patients who underwent DECT to rule out acute PE in 2018-2020 and recorded incident adverse events, defined as a composite of short-term (< 30 days) in-hospital all-cause mortality or admission to intensive care unit. Relative perfusion defect volume (PDV) was measured on DECT and indexed by total lung volume. PDV was then related to adverse events using logistic regressions adjusting for clinical parameters, clinical PE pre-test probability (Wells score), and visual PE burden on pulmonary angiography (Qanadli score). Among 136 included patients (63 [46%] females; age: 70 ± 14 years), 19/136 (14%) experienced adverse events during a median hospitalization of 7.5 (4-14) days. Overall, 7/19 (37%) events occurred in those without visible emboli but with measurable perfusion defects. An increase of PDV by one standard deviation was associated with over two times higher odds of adverse events (OR = 2.24; 95%CI:1.37-3.65; p = 0.001). This association remained significant after adjusting for the Wells and Qanadli scores (OR = 2.34; 95%CI:1.20-4.60; p = 0.013). PDV significantly increased the combined discriminatory capacity of Wells and Qanadli scores (AUC 0.76 vs. 0.80; p = 0.011 for difference). DECT-derived PDV may represent a prognostic imaging marker with incremental value beyond clinical and traditional imaging findings, improving risk stratification and aiding clinical management in patients with suspected PE.

Development of a multiplex Q-PCR to detect Trichoderma harzianum Rifai strain T22 in plant roots.

The fungal species Trichoderma harzianum is widely used as a biological agent in crop protection. To verify the continued presence of this fungus on plant roots manually inoculated with T. harzianum strain T22, a Q-PCR was designed using specific probes for this particular strain. To develop these molecular diagnostic tools, genome mining was first carried out to retrieve putative new regions by which different strains of T. harzianum could be distinguished. Subsequently, Sanger sequencing of the L-aminoacid oxidase gene (aox1) in T. harzianum was applied to determine the mutations differing between various strains isolated from the Trichoderma collection of Koppert Biological Systems. Based on the sequence information obtained, a set of hydrolysis probes was subsequently developed which discriminated T. harzianum T22 strains varying in only a single nucleotide. Probes designed for two strains uniquely recognized the respective strains in Q-PCR with a detection limit of 12,5ng DNA. Titration assays in which T. harzianum DNA from distinct strains was varied further underscored the specificity of the probes. Lastly, fungal DNA extracted from roots of greenhouse cultured tomato plants was analyzed using the probe-based assay. DNA from T. harzianum strain T22 could readily be identified on roots of greenhouse reared tomato plants inoculated with varying concentrations up to one week after treatment with a detection limit of 3e6 colony forming units of T. harzianum T22. We conclude that the Q-PCR method is a reliable and robust method for assessing the presence and quantity of T. harzianum strain T22 in manually inoculated plant material. Our method provides scope for the development of DNA based strain specific identification of additional strains of Trichoderma and other fungal biological control agents.