Erratum to "Optical Detection of Distal Lung Enzyme Activity in Human Inflammatory Lung Disease".

[This corrects the article DOI: 10.34133/2021/9834163.].

Toward cardiac tissue characterization using machine learning and light-scattering spectroscopy.

Significance: The non-destructive characterization of cardiac tissue composition provides essential information for both planning and evaluating the effectiveness of surgical interventions such as ablative procedures. Although several methods of tissue characterization, such as optical coherence tomography and fiber-optic confocal microscopy, show promise, many barriers exist that reduce effectiveness or prevent adoption, such as time delays in analysis, prohibitive costs, and limited scope of application. Developing a rapid, low-cost non-destructive means of characterizing cardiac tissue could improve planning, implementation, and evaluation of cardiac surgical procedures.

Towards Intraoperative Quantification of Atrial Fibrosis Using Light-Scattering Spectroscopy and Convolutional Neural Networks.

Light-scattering spectroscopy (LSS) is an established optical approach for characterization of biological tissues. Here, we investigated the capabilities of LSS and convolutional neural networks (CNNs) to quantitatively characterize the composition and arrangement of cardiac tissues. We assembled tissue constructs from fixed myocardium and the aortic wall with a thickness similar to that of the atrial free wall.

Optical Detection of Distal Lung Enzyme Activity in Human Inflammatory Lung Disease.

There is a need to develop platforms delineating inflammatory biology of the distal human lung. We describe a platform technology approach to detect enzyme activity and observe drug inhibition in the distal human lung using a combination of matrix metalloproteinase (MMP) optical reporters, fibered confocal fluorescence microscopy (FCFM), and a bespoke delivery device. .

Towards Automated Quantification of Atrial Fibrosis in Images from Catheterized Fiber-Optics Confocal Microscopy Using Convolutional Neural Networks.

Clinical approaches for quantification of atrial fibrosis are currently based on digital image processing of magnetic resonance images. Here, we introduce and evaluate a comprehensive framework based on convolutional neural networks for quantifying atrial fibrosis from images acquired with catheterized fiber-optics confocal microscopy (FCM). FCM images in three regions of the atria were acquired in the beating heart in situ in an established transgenic animal model of atrial fibrosis.

Development of an alveolar transbronchial catheter for concurrent fiber optics based imaging and fluid delivery.

Optical molecular imaging is an emerging field and high resolution optical imaging of the distal lung parenchyma has been made possible with the advent of clinically approved fiber based imaging modalities. However, currently, there is no single method of allowing the simultaneous imaging and delivery of targeted molecular imaging agents. The objective of this research is to create a catheterized device capable of fulfilling this need.