Single-Cell Patch-Clamp/Proteomics of Human Alzheimer's Disease iPSC-Derived Excitatory Neurons Versus Isogenic Wild-Type Controls Suggests Novel Causation and Therapeutic Targets.

Standard single-cell (sc) proteomics of disease states inferred from multicellular organs or organoids cannot currently be related to single-cell physiology. Here, a scPatch-Clamp/Proteomics platform is developed on single neurons generated from hiPSCs bearing an Alzheimer's disease (AD) genetic mutation and compares them to isogenic wild-type controls. This approach provides both current and voltage electrophysiological data plus detailed proteomics information on single-cells.

Multiclass classification of environmental chemical stimuli from unbalanced plant electrophysiological data.

Plant electrophysiological response contains useful signature of its environment and health which can be utilized using suitable statistical analysis for developing an inverse model to classify the stimulus applied to the plant. In this paper, we have presented a statistical analysis pipeline to tackle a multiclass environmental stimuli classification problem with unbalanced plant electrophysiological data. The objective here is to classify three different environmental chemical stimuli, using fifteen statistical features, extracted from the plant electrical signals and compare the performance of eight different classification algorithms.

A time-series analysis of blood-based biomarkers within a 25-year longitudinal dolphin cohort.

Causal interactions and correlations between clinically-relevant biomarkers are important to understand, both for informing potential medical interventions as well as predicting the likely health trajectory of any individual as they age. These interactions and correlations can be hard to establish in humans, due to the difficulties of routine sampling and controlling for individual differences (e.g.