Background/aims

Recently, ultrasound signals termed 'lung water comets' associated with pulmonary edema have been correlated with adverse clinical events in dialysis patients. These comets fluctuate substantially during the ultrasound exam highlighting the need for objective quantitative measurement methods.

Methods

We developed an image-processing algorithm for the detection and quantification of lung comets. Quantification measures included comet number (comet count) and the fraction of the ultrasound beams with comet findings (comet fraction). We used this algorithm in a pilot study in 20 stable dialysis outpatients to identify associations between ultrasound comets and clinical parameters including blood pressure (BP), percent blood volume reduction on dialysis (%BV), ejection fraction (EF), and ultrafiltration on dialysis (UF).

Results

Positive findings included associations with lung comet measurements with pre-dialysis Diastolic BP (r = 0.534, p = 0.015), subject age (r = -0.446, p = 0.049), and a combination of EF and end dialysis %BV reduction (r = -0.585, p = 0.028). Comet fraction and comet count were closely correlated due to the inherent relationship between these two metrics (r = 0.973, p < 0.001). Negative findings included ultrasound comets that did not change from beginning to end of dialysis (p = 0.756), and were not significantly correlated with single dialysis treatment UF (p = 0.522), subject body weight (p = 0.208), or BMI (p = 0.358).

Conclusions

Ultrasound signal processing methods may help quantify lung ultrasound comets. Additional findings include algorithmic lung comet measurement that did not change significantly during single dialysis sessions in these stable outpatients, but were associated with cardiovascular and fluid status parameters.

Within the islets of Langerhans, beta cells orchestrate synchronized insulin secretion, a pivotal aspect of metabolic homeostasis. Despite the inherent heterogeneity and multimodal activity of individual cells, intercellular coupling acts as a homogenizing force, enabling coordinated responses through the propagation of intercellular waves. Disruptions in this coordination are implicated in irregular insulin secretion, a hallmark of diabetes. Recently, innovative approaches, such as integrating multicellular calcium imaging with network analysis, have emerged for a quantitative assessment of the cellular activity in islets. However, different groups use distinct experimental preparations, microscopic techniques, apply different methods to process the measured signals and use various methods to derive functional connectivity patterns. This makes comparisons between findings and their integration into a bigger picture difficult and has led to disputes in functional connectivity interpretations. To address these issues, we present here a systematic analysis of how different approaches influence the network representation of islet activity. Our findings show that the choice of methods used to construct networks is not crucial, although care is needed when combining data from different islets. Conversely, the conclusions drawn from network analysis can be heavily affected by the pre-processing of the time series, the type of the oscillatory component in the signals, and by the experimental preparation. Our tutorial-like investigation aims to resolve interpretational issues, reconcile conflicting views, advance functional implications, and encourage researchers to adopt connectivity analysis. As we conclude, we outline challenges for future research, emphasizing the broader applicability of our conclusions to other tissues exhibiting complex multicellular dynamics.

DNA-binding transcription factors (TFs) remain challenging to target with molecular probes. Many TFs function in part through interaction with Mediator, a 26-subunit complex that controls RNA polymerase II activity genome-wide. We sought to block p53 function by disrupting the p53-Mediator interaction. Through rational design and activity-based screening, we characterize a stapled peptide, with functional mimics of both p53 activation domains, that blocks p53-Mediator binding and selectively inhibits p53-dependent transcription in human cells; importantly, this "bivalent" peptide has negligible impact, genome-wide, on non-p53 target genes. Our proof-of-concept strategy circumvents the TF entirely and targets the TF-Mediator interface instead, with desired functional outcomes (i.e., selective inhibition of p53 activation). Furthermore, these results demonstrate that TF activation domains represent viable starting points for Mediator-targeting molecular probes, as an alternative to large compound libraries. Different TFs bind Mediator through different subunits, suggesting this strategy could be broadly applied to selectively alter gene expression programs.