Praterbullock2849

Furthermore, those that were adherent to COVID guidelines were also less likely to donate (OR=0·583; P-value=0·000).

We suggest that blood collection services consider specialist campaigns that focus on the altruistic motivation of donors during the crisis and that they continue to communicate the additional safety measures in place with the aim of reducing the fear of infection whilst donating blood.

We suggest that blood collection services consider specialist campaigns that focus on the altruistic motivation of donors during the crisis and that they continue to communicate the additional safety measures in place with the aim of reducing the fear of infection whilst donating blood.

Blood donors, compared to non-donors, are more likely to show a preference to help others either by sharing resources to directly compensate those in need or indirectly by punishing those who act unfairly. Knowing the dominant cooperative preference for blood donors will inform the development of targeted interventions. We test which preference dominates and an initial intervention based on these findings.

We report two studies. The first compares compensation and punishment preferences in blood donors and non-donors (N=372) using a third-party-compensation-and-punishment game. Based on the results of Study 1, Study 2 (N=151) is a feasibility experiment of an intervention based on advantageous inequality aversion ('As a healthy person, you can give blood and help those less healthy than you'.).

Blood donors, compared to non-donors, have a preference for compensation. Organ donors have a preference for punishment. Those exposed to the advantageous inequality aversion intervention, compared to control conditions, show a greater behavioural propensity to donate blood (this was especially the case for non-donors).

Blood donors have a clear preference for direct helping through compensation that can be translated into a simple effective intervention to enhance blood donor recruitment and retention.

Blood donors have a clear preference for direct helping through compensation that can be translated into a simple effective intervention to enhance blood donor recruitment and retention.

To examine patterns of recurrence of benign phonotraumatic vocal fold lesions over time for insights into pathophysiology.

Case series with mathematical modeling.

Medical records and stroboscopic exams of adults who underwent microlaryngoscopic resection of phonotraumatic vocal fold lesions over a 13-year period were reviewed for time to recurrence after surgery. Uniform and log-normal probability distributions were fitted to the time to recurrence curves for vocal fold polyps, midfold masses, and pseudocysts. Model fits were compared using the Akaike information criterion corrected, a standard measure of the goodness of fit. Stochastic simulations were used to verify that the mechanistic hypotheses were concordant with the selected probability distributions and empiric data.

Of 567 patients who underwent microlaryngoscopic resection, 65 had a recurrence (16 polyps, 14 midfold masses, and 35 pseudocysts). Midfold mass and pseudocyst recurrences were predominantly seen in younger women. Polyps were best fit by a uniform distribution rather than log-normal, whereas midfold masses and pseudocysts were better fit by log-normal rather than uniform. Stochastic simulations suggest that polyps recur sporadically according to a paroxysmal-developmental model, whereas midfold mass and pseudocyst recurrences follow a force-multiplication, damage-accumulation process.

Vocal fold polyps are acute lesions evenly distributed by age and gender that recur uniformly over time, suggesting they arise from sudden tissue reactions to phonotraumatic stress. Pseudocysts and midfold fibrous masses are chronic lesions predominantly found in young women that recur with log-normal distribution over time, suggesting gradual damage accumulation in larynges predisposed to enhanced phonotrauma.

4 Laryngoscope, 2021.

4 Laryngoscope, 2021.Among breast cancer subtypes, triple-negative breast cancer (TNBC) is the most aggressive with the worst prognosis and the highest rates of metastatic disease. To identify TNBC gene signatures, we applied the network-based methodology implemented by the SWIM software to gene expression data of TNBC patients in The Cancer Genome Atlas (TCGA) database. SWIM enables to predict key (switch) genes within the co-expression network, whose perturbations in expression pattern and abundance may contribute to the (patho)biological phenotype. Here, SWIM analysis revealed an interesting interplay between the genes encoding the transcription factors HMGA1, FOXM1, and MYBL2, suggesting a potential cooperation among these three switch genes in TNBC development. The correlative nature of this interplay in TNBC was assessed by in vitro experiments, demonstrating how they may actually modulate the expression of each other.The rapid development of advanced microscopy techniques over recent decades has significantly increased the quality of imaging and our understanding of subcellular structures, such as the organization of the filaments of the cytoskeleton using fluorescence and electron microscopy. However, these recent improvements in imaging techniques have not been matched by similar development of techniques for computational analysis of the images of filament networks that can now be obtained. Hence, for a wide range of applications, reliable computational analysis of such two-dimensional methods remains challenging. Here, we present a new algorithm for tracing of filament networks. This software can extract many important parameters from grayscale images of filament networks, including the mesh hole size, and filament length and connectivity (also known as Coordination Number). compound library inhibitor In addition, the method allows sub-networks to be distinguished in two-dimensional images using intensity thresholding. We show that the algorithm can be used to analyze images of cytoskeleton networks obtained using different advanced microscopy methods. We have thus developed a new improved method for computational analysis of two-dimensional images of filamentous networks that has wide applications for existing imaging techniques. The algorithm is available as open-source software.