The impact of single-stranded RNAs on the dimerization of double-stranded RNA-dependent protein kinase PKR.

The RNA-binding protein PKR serves as a crucial antiviral innate immune factor that globally suppresses translation by sensing viral double-stranded RNA (dsRNA) and by phosphorylating the translation initiation factor eIF2α. Recent findings have unveiled that single-stranded RNAs (ssRNAs), including in vitro transcribed (IVT) mRNA, can also bind to and activate PKR. However, the precise mechanism underlying PKR activation by ssRNAs, remains incompletely understood. Here, we developed a NanoLuc Binary Technology (NanoBiT)-based in vitro PKR dimerization assay to assess the impact of ssRNAs on PKR dimerization. Our findings demonstrate that, akin to double-stranded polyinosinic:polycytidylic acid (polyIC), an encephalomyocarditis virus (EMCV) RNA, as well as NanoLuc luciferase (Nluc) mRNA, can induce PKR dimerization. Conversely, homopolymeric RNA lacking secondary structure fails to promote PKR dimerization, underscoring the significance of secondary structure in this process. Furthermore, adenovirus VA RNA 1, another ssRNA, impedes PKR dimerization by competing with Nluc mRNA. Additionally, we observed structured ssRNAs capable of forming G-quadruplexes induce PKR dimerization. Collectively, our results indicate that ssRNAs have the ability to either induce or inhibit PKR dimerization, thus representing potential targets for the development of antiviral and anti-inflammatory agents.

Detection of aspiration from images of a videofluoroscopic swallowing study adopting deep learning.

OBJECTIVES: A videofluoroscopic swallowing study (VFSS) is conducted to detect aspiration. However, aspiration occurs within a short time and is difficult to detect. If deep learning can detect aspirations with high accuracy, clinicians can focus on the diagnosis of the detected aspirations. Whether VFSS aspirations can be classified using rapid-prototyping deep-learning tools was studied. METHODS: VFSS videos were separated into individual image frames. A region of interest was defined on the pharynx. Three convolutional neural networks (CNNs), namely a Simple-Layer CNN, Multiple-Layer CNN, and Modified LeNet, were designed for the classification. The performance results of the CNNs were compared in terms of the areas under their receiver-operating characteristic curves (AUCs). RESULTS: A total of 18,333 images obtained through data augmentation were selected for the evaluation. The different CNNs yielded sensitivities of 78.8%-87.6%, specificities of 91.9%-98.1%, and overall accuracies of 85.8%-91.7%. The AUC of 0.974 obtained for the Simple-Layer CNN and Modified LeNet was significantly higher than that obtained for the Multiple-Layer CNN (AUC of 0.936) (p < 0.001). CONCLUSIONS: The results of this study show that deep learning has potential for detecting aspiration with high accuracy.

Deep learning for preliminary profiling of panoramic images.

OBJECTIVE: This study explored the feasibility of using deep learning for profiling of panoramic radiographs. STUDY DESIGN: Panoramic radiographs of 1000 patients were used. Patients were categorized using seven dental or physical characteristics: age, gender, mixed or permanent dentition, number of presenting teeth, impacted wisdom tooth status, implant status, and prosthetic treatment status. A Neural Network Console (Sony Network Communications Inc., Tokyo, Japan) deep learning system and the VGG-Net deep convolutional neural network were used for classification. RESULTS: Dentition and prosthetic treatment status exhibited classification accuracies of 93.5% and 90.5%, respectively. Tooth number and implant status both exhibited 89.5% classification accuracy; impacted wisdom tooth status exhibited 69.0% classification accuracy. Age and gender exhibited classification accuracies of 56.0% and 75.5%, respectively. CONCLUSION: Our proposed preliminary profiling method may be useful for preliminary interpretation of panoramic images and preprocessing before the application of additional artificial intelligence techniques.

Performance of deep learning technology for evaluation of positioning quality in periapical radiography of the maxillary canine.

OBJECTIVES: The aim of the present study was to create and test an automatic system for assessing the technical quality of positioning in periapical radiography of the maxillary canines using deep learning classification and segmentation techniques. METHODS: We created and tested two deep learning systems using 500 periapical radiographs (250 each of good- and bad-quality images). We assigned 350, 70, and 80 images as the training, validation, and test datasets, respectively. The learning model of system 1 was created with only the classification process, whereas system 2 consisted of both the segmentation and classification models. In each model, 500 epochs of training were performed using AlexNet and U-net for classification and segmentation, respectively. The segmentation results were evaluated by the intersection over union method, with values of 0.6 or more considered as success. The classification results were compared between the two systems. RESULTS: The segmentation performance of system 2 was recall, precision, and F measure of 0.937, 0.961, and 0.949, respectively. System 2 showed better classification performance values than those obtained by system 1. The area under the receiver operating characteristic curve values differed significantly between system 1 (0.649) and system 2 (0.927). CONCLUSIONS: The deep learning systems we created appeared to have potential benefits in evaluation of the technical positioning quality of periapical radiographs through the use of segmentation and classification functions.

Long-Term Durability Test for the Left Ventricular Assist System EVAHEART under the Physiologic Pulsatile Load.

The EVAHEART Left Ventricular Assist System (LVAS) was designed for the long-term support of a patient with severe heart failure. It has an original water lubrication system for seal and bearing and wear on these parts was considered one of its critical failure modes. A durability test focusing on wear was designed herein. We developed a mock loop, which generates a physiologic pulsatile flow and is sufficiently durable for a long-term test. The pulsatile load and the low fluid viscosity enable the creation of a severe condition for the mechanical seal. A total of 18 EVAHEART blood pumps completed 2 years of operation under the pulsatile condition without any failure. It indicated the EVAHEART blood pump had a greater than 90% reliability with a 88% confidence level. The test was continued with six blood pumps and achieved an average of 8.6 years, which was longer than the longest clinical use in Japan. The test result showed that no catastrophic, critical, marginal, or minor failures of the blood pump or their symptoms were observed. The seal performance was maintained after the test. Moreover, the surface roughness did not change, which showed any burn or abnormal wear occurred. The original water lubrication system equipped in EVAHEART LVAS prevent severe wear on the seal and the bearing, and it can be used in the bridge to transplant and destination therapy.

In vitro hemodynamic evaluation of ventricular suction conditions of the EVAHEART ventricular assist pump.

PURPOSE: Mismatches between pump output and venous return in a continuous-flow ventricular assist device may elicit episodes of ventricular suction. This research describes a series of in vitro experiments to characterize the operating conditions under which the EVAHEART centrifugal blood pump (Sun Medical Technology Research Corp., Nagano, Japan) can be operated with minimal concern regarding left ventricular (LV) suction. METHODS: The pump was interposed into a pneumatically driven pulsatile mock circulatory system (MCS) in the ventricular apex to aorta configuration. Under varying conditions of preload, afterload, and systolic pressure, the speed of the pump was increased step-wise until suction was observed. Identification of suction was based on pump inlet pressure. RESULTS: In the case of reduced LV systolic pressure, reduced preload (=10 mmHg), and afterload (=60 mmHg), suction was observed for speeds=2,200 rpm. However, suction did not occur at any speed (up to a maximum speed of 2,400 rpm) when preload was kept within 10-14 mmHg and afterload=80 mmHg. Although in vitro experiments cannot replace in vivo models, the results indicated that ventricular suction can be avoided if sufficient preload and afterload are maintained. CONCLUSION: Conditions of hypovolemia and/or hypotension may increase the risk of suction at the highest speeds, irrespective of the native ventricular systolic pressure. However, in vitro guidelines are not directly transferrable to the clinical situation; therefore, patient-specific evaluation is recommended, which can be aided by ultrasonography at various points in the course of support.