Single-Cell Profiling Uncovers Evolutionary Divergence of Hypocretin/Orexin Neuronal Subpopulations.

Brain nuclei are traditionally defined by their anatomy, activity, and expression of specific markers. The hypothalamus contains discrete neuronal populations that coordinate fundamental behavioral functions, including sleep and wakefulness, in all vertebrates. Particularly, the diverse roles of hypocretin/orexin (Hcrt)-releasing neurons suggest functional heterogeneity among Hcrt neurons.

A telencephalon cell type atlas for goldfish reveals diversity in the evolution of spatial structure and cell types.

Teleost fish form the largest group of vertebrates and show a tremendous variety of adaptive behaviors, making them critically important for the study of brain evolution and cognition. The neural basis mediating these behaviors remains elusive. We performed a systematic comparative survey of the goldfish telencephalon.

Sexual dimorphism in synaptic inputs to the mouse amygdala and orbital cortex.

The medial amygdala (MeA) is a sexually dimorphic brain region that regulates fear responses, emotional memories, and social behaviors. It is known to be larger and contains more cells in males. The MeA integrates information through input connections from olfactory regions, bed nucleus of the stria terminalis, ventral hippocampus, and thalamic and hypothalamic structures.

Neuronal types in the mouse amygdala and their transcriptional response to fear conditioning.

The amygdala is a brain region primarily associated with emotional response. The use of genetic markers and single-cell transcriptomics can provide insights into behavior-associated cell state changes. Here we present a detailed cell-type taxonomy of the adult mouse amygdala during fear learning and memory consolidation.

Machine learning algorithm for early detection of end-stage renal disease.

Background: End stage renal disease (ESRD) describes the most severe stage of chronic kidney disease (CKD), when patients need dialysis or renal transplant. There is often a delay in recognizing, diagnosing, and treating the various etiologies of CKD. The objective of the present study was to employ machine learning algorithms to develop a prediction model for progression to ESRD based on a large-scale multidimensional database.