How to Inject Ictal SPECT? From Manual to Automated Injection

BackgroundSuccessful surgery depends on the accurate localization of epileptogenic zone before surgery. Ictal SPECT is the only imaging modality that allows identification of the ictal onset zone by measuring the regional cerebral blood flow at the time of injection. The main limitations of ictal SPECT in epilepsy are the complex methodology of the tracer injection during a seizure. To overcome these limitations, we present the main features of the first automated injector for ictal SPECT (epijet, LemerPax; La Chapelle -sur-Erdre; France). In this study we compared traditional manual injection with automated injection for ictal SPECT in122 patients with drug-resistant epilepsy. MethodsThe study included 55 consecutive prospective patients with drug-resistant epilepsy undergoing injection with the automated injector. The control group was our retrospective database of a historic pool of 67 patients, injected manually from 2014-2016. Calculated annual exposure/radioactive dose for operators was measured. Injection time, seizure focus localization with ictal SPECT, as well as repeated hospitalizations related to fails injections were compared in these two groups of patients. ResultsThere were no differences in the average injection time with epijet (13 s) compared with the traditional manual injection (14s). The seizure focus was successfully localized with ictal SPECT with epijet in 44/55 (80%) patients and with manual injection in 46/67 (68%) patients (p=0.694). Repeated studies were required in 16/67 (23%) patients in the manual injection group compared to 4 patients (7%) in the epijet group (p=0.022). Calculated annual exposure/dose for operators of 0.39 mSv/year and administered dose error inferior to 5% are other advantages of epijet. ConclusionThe first results using epijet are promising in adjustment of the injection dose, reducing the rate of radiation exposure for patients and nurses, maintaining the same injection time and allowing high SPECT accuracy. These preliminary results support the use of an automated injection system to inject radioactive ictal SPECT doses in epilepsy units.

Deep learning image recognition enables efficient genome editing in zebrafish by automated injections

One of the most popular techniques in zebrafish research is microinjection, as it is a rapid and efficient way to genetically manipulate early developing embryos, and to introduce microbes or tracers at larval stages.Here we demonstrate the development of a machine learning software that allows for microinjection at a trained target site in zebrafish eggs at unprecedented speed. The software is based on the open-source deep-learning library Inception v3.In a first step, the software distinguishes wells containing embryos at one-cell stage from wells to be skipped with an accuracy of 93%. A second step was developed to pinpoint the injection site. Deep learning allows to predict this location on average within 42 µm to manually annotated sites. Using a Graphics Processing Unit (GPU), both steps together take less than 100 milliseconds. We first tested our system by injecting a morpholino into the middle of the yolk and found that the automated injection efficiency is as efficient as manual injection (~ 80%). Next, we tested both CRISPR/Cas9 and DNA construct injections into the zygote and obtained a comparable efficiency to that of an experienced experimentalist. Combined with a higher throughput, this results in a higher yield. Hence, the automated injection of CRISPR/Cas9 will allow high-throughput applications to knock out and knock in relevant genes to study their mechanisms or pathways of interest in diverse areas of biomedical research.