The advantages of lexicon-based sentiment analysis in an age of machine learning.

Assessing whether texts are positive or negative-sentiment analysis-has wide-ranging applications across many disciplines. Automated approaches make it possible to code near unlimited quantities of texts rapidly, replicably, and with high accuracy. Compared to machine learning and large language model (LLM) approaches, lexicon-based methods may sacrifice some in performance, but in exchange they provide generalizability and domain independence, while crucially offering the possibility of identifying gradations in sentiment.

Macrophage-Induced Pro-Fibrotic Gene Expression in Tubular Cells after Ischemia/Reperfusion Is Paralleled but Not Directly Mediated by C5a/C5aR1 Signaling.

Ischemia/reperfusion (I/R) is inevitable during kidney transplantation and causes acute kidney injury (AKI), which affects immediate outcome and leads to chronic changes such as fibrotic remodeling of the graft. We investigated pro-fibrotic signaling after I/R, focusing on the complement component and receptor C5a/C5aR1 and macrophage/tubule crosstalk. Male Dark Agouti rats were subjected to I/R and their kidneys were harvested 10 min, 6 h, 24 h, 3 days, 5 days and 8 weeks after reperfusion.

Chronic frames of social inequality: How mainstream media frame race, gender, and wealth inequality.

How social inequality is described—as advantage or disadvantage—critically shapes individuals’ responses to it [e.g., B.

Can Gene Expression Analysis in Zero-Time Biopsies Predict Kidney Transplant Rejection?

Zero-time biopsies are taken to determine the quality of the donor organ at the time of transplantation. Histological analyses alone have so far not been able to identify parameters that allow the prediction of subsequent rejection episodes or graft survival. This study investigated whether gene expression analyses of zero-time biopsies might support this prediction.