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Respiratory syncytial virus (RSV) primarily infects the respiratory epithelium, but growing evidence suggests that it may also be responsible for neurologic sequelae. In 3-dimensional microphysiologic peripheral nerve cultures, RSV infected neurons, macrophages, and dendritic cells along 2 distinct trajectories depending on the initial viral load. Low-level infection was transient, primarily involved macrophages, and induced moderate chemokine release with transient neural hypersensitivity. Infection with higher viral loads was persistent, infected neuronal cells in addition to monocytes, and induced robust chemokine release followed by progressive neurotoxicity. In spinal cord cultures, RSV infected microglia and dendritic cells but not neurons, producing a moderate chemokine expression pattern. The persistence of infection was variable but could be identified in dendritic cells as long as 30 days postinoculation. This study suggests that RSV can disrupt neuronal function directly through infection of peripheral neurons and indirectly through infection of resident monocytes and that inflammatory chemokines likely mediate both mechanisms. [ABSTRACT FROM AUTHOR]

Phosphorylation of the serine residues in estrogen receptor (ER) α is important in transcriptional activation. Hence, methods to detect such posttranslational modification events are valuable. We describe, in detail, the analysis of the phosphorylated ERα by electrophoretic separation of proteins and subsequent immunoblotting techniques. In particular, phosphorylation of the ERα is one possible outcome of activation of the putative membrane estrogen receptor (mER), GPR30 or GPER1. Hence, phosphorylation represents a crosstalk event between GPR30 and ERα and may be important in estrogen-regulated physiology., (© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

In this paper, we present a novel strategy for automatic segmentation of biomedical images acquired from bio-engineered nerve tissues exhibiting variable morphological characteristics. Automatic image segmentation is one step towards the end goal of automatic analysis of the impact of various neurotoxic drug treatments on these artificial nerve tissues. We propose a deep learning architecture to perform this task. Our proposed architecture can be seen as a variation of U-Net that helps deal with a small manually annotated training data set. We present promising preliminary results and our human expert analysis shows that in some cases the model is even more precise in detecting the relevant morphological characteristics of the tissue compared to the manually annotated data. In the future, our model can be adapted for end-to-end automatic analysis of treated tissues. Moreover, based on a very small set of annotated data, it provides a reasonable segmentation to be used by human annotators. This will reduce the time of manual annotation significantly and streamline the process of generating a larger manually annotated data set for training our final ideal segmentation model.

Phosphorylation of the serine residues in estrogen receptor (ER) α is important in transcriptional activation. Hence, methods to detect such posttranslational modification events are valuable. We describe, in detail, the analysis of the phosphorylated ERα by electrophoretic separation of proteins and subsequent immuno-blotting techniques. In particular, phosphorylation of the ERα is one possible outcome of activation of the putative membrane estrogen receptor (mER), GPR30. Hence, phosphorylation represents a cross talk event between GPR30 and ERα and may be important in estrogen-regulated physiology.