Transthyretin amyloid cardiomyopathy, an often unrecognized cause of heart failure, is now treatable with a transthyretin stabilizer. It is therefore important to identify at-risk patients who can undergo targeted testing for earlier diagnosis and treatment, prior to the development of irreversible heart failure. Here we show that a random forest machine learning model can identify potential wild-type transthyretin amyloid cardiomyopathy using medical claims data. We derive a machine learning model in 1071 cases and 1071 non-amyloid heart failure controls and validate the model in three nationally representative cohorts (9412 cases, 9412 matched controls), and a large, single-center electronic health record-based cohort (261 cases, 39393 controls). We show that the machine learning model performs well in identifying patients with cardiac amyloidosis in the derivation cohort and all four validation cohorts, thereby providing a systematic framework to increase the suspicion of transthyretin cardiac amyloidosis in patients with heart failure.

The protein kinase C (PKC) enzymes have long been established as critical for synaptic plasticity. However, it is unknown whether Ca-dependent PKC isozymes are activated in dendritic spines during plasticity and, if so, how this synaptic activity is encoded by PKC. Here, using newly developed, isozyme-specific sensors, we demonstrate that classical isozymes are activated to varying degrees and with distinct kinetics. PKCα is activated robustly and rapidly in stimulated spines and is the only isozyme required for structural plasticity. This specificity depends on a PDZ-binding motif present only in PKCα. The activation of PKCα during plasticity requires both NMDA receptor Ca flux and autocrine brain-derived neurotrophic factor (BDNF)-TrkB signaling, two pathways that differ vastly in their spatiotemporal scales of signaling. Our results suggest that, by integrating these signals, PKCα combines a measure of recent, nearby synaptic plasticity with local synaptic input, enabling complex cellular computations such as heterosynaptic facilitation of plasticity necessary for efficient hippocampus-dependent learning.

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This paper presents the results of an experimental study comparing cortical activation in the brain when generating solutions using brainstorming, morphological analysis, and TRIZ. Twelve engineering students were given the same three design tasks, respectively, using the three solution generation techniques. Students generated solutions while change in oxygenated blood along the prefrontal cortex (PFC) was measured using functional near-infrared spectroscopy. The results show that generating solutions using brainstorming, morphological analysis, and TRIZ leads to differences in cortical activation, specifically along the region of the brain associated with spatial working memory, cognitive flexibility, and abstract reasoning, called the left dorsolateral prefrontal cortex (left DLPFC). Brainstorming evokes a high average blood oxygenation level dependent (BOLD) response in the left DLPFC early during the solution generation process but this high response is not sustained. In comparison, morphological analysis and TRIZ evoke multiple high average BOLD responses across the solution generation process. Not only was the high average BOLD response sustained but the density of network coordination among brain regions across the PFC was greater for morphological analysis and TRIZ. Higher density is a proxy for higher cognitive effort. The brain regions most central to coordination also varied. During brainstorming the right hemisphere, in a region associated with memory encoding (right PFC), was most activated. During morphological analysis, the left hemisphere, the left DLPFC was most activated. During TRIZ, both the middle and left hemisphere included regions of high activation. These results indicate neuro-cognitive differences of activation patterns, cognitive effort over time, and brain regions central for coordination when using these three concept generation techniques. Future research can begin to explore neuro-cognitive differences as a result of these techniques over multiple uses and the effects of design education.