Automatic deep learning-driven label-free image-guided patch clamp system

Patch clamp recording of neurons is a labor-intensive and time-consuming procedure. Here, we demonstrate a tool that fully automatically performs electrophysiological recordings in label-free tissue slices. The automation covers the detection of cells in label-free images, calibration of the micropipette movement, approach to the cell with the pipette, formation of the whole-cell configuration, and recording. The cell detection is based on deep learning. The model is trained on a new image database of neurons in unlabeled brain tissue slices. The pipette tip detection and approaching phase use image analysis techniques for precise movements. High-quality measurements are performed on hundreds of human and rodent neurons. We also demonstrate that further molecular and anatomical analysis can be performed on the recorded cells. The software has a diary module that automatically logs patch clamp events. Our tool can multiply the number of daily measurements to help brain research. Patch clamp recording of neurons is slow and labor-intensive. Here the authors present a method for automated deep learning driven label-free image guided patch clamp physiology to perform measurements on hundreds of human and rodent neurons.Peer reviewe

Management of Adult Patients With Drug Reaction With Eosinophilia and Systemic Symptoms: A Delphi-Based International Consensus.

IMPORTANCE Drug reaction with eosinophilia and systemic symptoms (DRESS) is a rare but potentially fatal drug hypersensitivity reaction. To our knowledge, there is no international consensus on its severity assessment and treatment. OBJECTIVE To reach an international, Delphi-based multinational expert consensus on the diagnostic workup, severity assessment, and treatment of patients with DRESS. DESIGN, SETTING, AND PARTICIPANTS The Delphi method was used to assess 100 statements related to baseline workup, evaluation of severity, acute phase, and postacute management of DRESS. Fifty-seven international experts in DRESS were invited, and 54 participated in the survey, which took place from July to September 2022. MAIN OUTCOMES/MEASURES The degree of agreement was calculated with the RAND-UCLA Appropriateness Method. Consensus was defined as a statement with a median appropriateness value of 7 or higher (appropriate) and a disagreement index of lower than 1. RESULTS In the first Delphi round, consensus was reached on 82 statements. Thirteen statements were revised and assessed in a second round. A consensus was reached for 93 statements overall. The experts agreed on a set of basic diagnostic workup procedures as well as severity- and organ-specific further investigations. They reached a consensus on severity assessment (mild, moderate, and severe) based on the extent of liver, kidney, and blood involvement and the damage of other organs. The panel agreed on the main lines of DRESS management according to these severity grades. General recommendations were generated on the postacute phase follow-up of patients with DRESS and the allergological workup. CONCLUSIONS AND RELEVANCE This Delphi exercise represents, to our knowledge, the first international expert consensus on diagnostic workup, severity assessment, and management of DRESS. This should support clinicians in the diagnosis and management of DRESS and constitute the basis for development of future guidelines