Neonatal IL-4 Over-Exposure is Accompanied by Macrophage Accumulation in Dura Mater After Instant Anti-inflammatory Cytokine Response in CSF.

Multiple studies have shown that clinical events resulting into neonatal IL-4 over-exposure, such as asthma in early life and food allergy, were associated with brain damage and that the neuroinflammation induced by them might lead to cognitive impairments, anxiety-/depressive-like behaviors. IL-4 is the most major elevated cytokine in periphery when these clinical events occur and peripheral IL-4 level positively correlates with the severity of those events. Our previous studies have verified that neonatal IL-4 over-exposure induced a delayed neuroinflammatory damage in rodents, which might have adverse implications for brain development and cognition. Neuroinflammation in brain parenchyma is often accompanied by changes in CSF cytokines levels. However, whether the cytokines levels in CSF change after neonatal IL-4 over-exposure is unknown. Here, we found a delayed pro-inflammatory cytokines response (higher IL-6, IL-1ß and, TNF levels) in both hippocampus and CSF after an instant anti-inflammatory cytokine response in IL-4 over-exposed rats. Moreover, the pro-inflammatory cytokines response appeared earlier in CSF than in hippocampus. The level of each of the pro-inflammatory cytokines in CSF positively correlated with that in hippocampus at the age of postnatal day 42. More microglia numbers/activation and higher M-CSF level in the hippocampus in IL-4 over-exposed rats were also observed. Furthermore, there were more macrophages with inflammatory activation in dural mater of IL-4 over-exposed rats. In sum, neonatal IL-4 over-exposure in rats induces delayed inflammation in CSF, suggesting CSF examination may serve as a potential method in predicting delayed neuroinflammation in brain following neonatal IL-4 over-exposure.

ClusterX: a novel representation learning-based deep clustering framework for accurate visual inspection in virtual screening.

Molecular clustering analysis has been developed to facilitate visual inspection in the process of structure-based virtual screening. However, traditional methods based on molecular fingerprints or molecular descriptors limit the accuracy of selecting active hit compounds, which may be attributed to the lack of representations of receptor structural and protein-ligand interaction during the clustering. Here, a novel deep clustering framework named ClusterX is proposed to learn molecular representations of protein-ligand complexes and cluster the ligands. In ClusterX, the graph was used to represent the protein-ligand complex, and the joint optimisation can be used efficiently for learning the cluster-friendly features. Experiments on the KLIFs database show that the model can distinguish well between the binding modes of different kinase inhibitors. To validate the effectiveness of the model, the clustering results on the virtual screening dataset further demonstrated that ClusterX achieved better or more competitive performance against traditional methods, such as SIFt and extended connectivity fingerprints. This framework may provide a unique tool for clustering analysis and prove to assist computational medicinal chemists in visual decision-making.