Real-Time Temperature-Controlled Retinal Laser Irradiation in Rabbits.

Purpose: Subdamaging thermal retinal laser therapy has the potential to induce regenerative stimuli in retinal diseases, but validated dosimetry is missing. Real-time optoacoustic temperature determination and control could close this gap. This study investigates a first in vivo application. Methods: Two iterations of a control module that were optically coupled in between a continuous-wave commercial laser source and a commercial slit lamp were evaluated on chinchilla rabbits. The module allows extraction of the temperature rise in real time and can control the power of the therapy laser such that a predefined temperature rise at the retina is quickly achieved and held constant. Irradiations with aim temperatures from 45°C to 69°C were performed on a diameter of 200 µm and a heating time of 100 ms. Results: We analyzed 424 temperature-guided irradiations in nine eyes of five rabbits. The mean difference between the measured and aim temperature was -0.04°C ± 0.98°C. The following ED50 values for visibility thresholds could be determined: 58.6°C for funduscopic visibility, 57.7°C for fluorescein angiography, and 57.0°C for OCT. In all measurements, the correlation of tissue effect was higher to the temperature than to the average heating laser power used. Conclusions: The system was able to reliably perform temperature-guided irradiations, which allowed for better tissue effect control than simple power control. This approach could enhance the accuracy, safety, and reproducibility of thermal stimulating laser therapy. Translational Relevance: This study is a bridge between preclinical ex vivo experiments and a pilot clinical study.

Deep Learning to Decipher the Progression and Morphology of Axonal Degeneration.

Axonal degeneration (AxD) is a pathological hallmark of many neurodegenerative diseases. Deciphering the morphological patterns of AxD will help to understand the underlying mechanisms and develop effective therapies. Here, we evaluated the progression of AxD in cortical neurons using a novel microfluidic device together with a deep learning tool that we developed for the enhanced-throughput analysis of AxD on microscopic images. The trained convolutional neural network (CNN) sensitively and specifically segmented the features of AxD including axons, axonal swellings, and axonal fragments. Its performance exceeded that of the human evaluators. In an in vitro model of AxD in hemorrhagic stroke induced by the hemolysis product hemin, we detected a time-dependent degeneration of axons leading to a decrease in axon area, while axonal swelling and fragment areas increased. Axonal swellings preceded axon fragmentation, suggesting that swellings may be reliable predictors of AxD. Using a recurrent neural network (RNN), we identified four morphological patterns of AxD (granular, retraction, swelling, and transport degeneration). These findings indicate a morphological heterogeneity of AxD in hemorrhagic stroke. Our EntireAxon platform enables the systematic analysis of axons and AxD in time-lapse microscopy and unravels a so-far unknown intricacy in which AxD can occur in a disease context.