Histological Typing in Patients With Cardiac Amyloidosis: JACC Review Topic of the Week.

Cardiac amyloidosis is increasingly recognized as a treatable form of heart failure. Highly effective specific therapies have recently become available for the 2 most frequent forms of cardiac amyloidosis: immunoglobulin light chain amyloidosis and transthyretin (ATTR) amyloidosis. Nevertheless, initiation of specific therapies requires recognition of cardiac amyloidosis and appropriate characterization of the amyloid type. Although noninvasive diagnosis is possible for ATTR cardiac amyloidosis, histological demonstration and typing of amyloid deposits is still required for a substantial number of patients with ATTR and in all patients with light chain amyloidosis and other rarer forms of cardiac amyloidosis. Amyloid histological typing can be performed using different techniques: mass spectrometry, immunohistochemistry, and immunoelectron microscopy. This review describes which patients require histological confirmation of cardiac amyloidosis along with when and how to type amyloid deposits in histologic specimens. Furthermore, it covers the characteristics and limitations of the different typing methods that are available in clinical practice.

Unravelling individual rhythmic abilities using machine learning.

Humans can easily extract the rhythm of a complex sound, like music, and move to its regular beat, like in dance. These abilities are modulated by musical training and vary significantly in untrained individuals. The causes of this variability are multidimensional and typically hard to grasp in single tasks. To date we lack a comprehensive model capturing the rhythmic fingerprints of both musicians and non-musicians. Here we harnessed machine learning to extract a parsimonious model of rhythmic abilities, based on behavioral testing (with perceptual and motor tasks) of individuals with and without formal musical training (n = 79). We demonstrate that variability in rhythmic abilities and their link with formal and informal music experience can be successfully captured by profiles including a minimal set of behavioral measures. These findings highlight that machine learning techniques can be employed successfully to distill profiles of rhythmic abilities, and ultimately shed light on individual variability and its relationship with both formal musical training and informal musical experiences.