In Vitro Self-Circularization Methods Based on Self-Splicing Ribozyme.

In vitro circular RNA (circRNA) preparation methods have been gaining a lot of attention recently as several reports suggest that circRNAs are more stable, with better performances in cells and in vivo, than linear RNAs in various biomedical applications. Self-splicing ribozymes are considered a major in vitro circRNA generation method for biomedical applications due to their simplicity and efficiency in the circularization of the gene of interest. This review summarizes, updates, and discusses the recently developed self-circularization methods based on the self-splicing ribozyme, such as group I and II intron ribozymes, and the pros and cons of each method in preparing circRNA in vitro.

Anuria after kidney transplantation diagnosed as early recurrence of focal segmental glomerulosclerosis combined with acute calcineurin inhibitor nephrotoxicity: a case report and literature review.

BACKGROUND: Primary focal segmental glomerulosclerosis (FSGS) is a glomerular disease that sometimes recurs in patients after kidney transplantation (KT) and increases the risk of graft loss. Proteinuria is a common early sign of recurrent FSGS, but an abrupt decrease in urine volume is rare. Herein, we report a patient with early recurrence of FSGS with anuria following KT. CASE PRESENTATION: A 55-year-old man with end-stage kidney disease caused by primary FSGS experienced anuria on postoperative day 2 following deceased donor KT. Laboratory results revealed that serum tacrolimus trough levels were consistently elevated at the time of anuria. At first, we considered acute calcineurin inhibitor (CNI) nephrotoxicity based on graft biopsy on light microscopy, laboratory findings, and clinical courses. However, the allograft function did not recover even after discontinuation of CNI, and recurrent FSGS was diagnosed 2 weeks later on electron microscopy. A total of 13 sessions of plasmapheresis and two administrations of rituximab (375 mg/m2) were required to treat recurrent FSGS. The patient achieved a partial response, and the spot urine protein-to-creatinine ratio decreased from 15.5 g/g creatinine to 5.2 g/g creatinine. At 5 months following KT, the serum creatinine level was stable at 1.15 mg/dL. CONCLUSIONS: These findings highlight that anuria can occur in cases of early recurrence of FSGS combined with acute CNI nephrotoxicity.

Severe Acute Kidney Injury Associated with Transformation of Chronic Myelomonocytic Leukemia into Acute Myeloid Leukemia: A Case Report.

Chronic myelomonocytic leukemia (CMML) is a rare hematologic disorder that infrequently causes acute kidney injury (AKI). CMML can transform into acute myeloid leukemia (AML), which can be accompanied by a deterioration in kidney function. However, severe AKI due to extramedullary manifestations of AML is rare. Herein, we present the case of a 67-year-old male patient with CMML that transformed into AML with severe AKI necessitating hemodialysis. The cause of the AKI was the AML transformation. The patient, with stable kidney function after chemotherapy for CMML, presented with a sudden decline in kidney function. Hemodialysis was initiated because of severe AKI, and histopathologic evaluation of the kidney biopsy specimen revealed severe, diffuse mixed inflammatory cell infiltrates in the interstitium and c-kit-immunopositive myeloblast-like cells. A bone marrow biopsy was performed because of the kidney biopsy findings suggesting that leukemic infiltration led to the diagnosis of AML. The patient received chemotherapy for AML, and his kidney function recovered. As illustrated in this case, severe AKI can develop as an early extramedullary manifestation during transformation from CMML to AML. Therefore, in patients with CMML and rapidly declining renal function, transformation into AML should be considered and histopathologically confirmed by kidney biopsy.

Generation of hepatitis C virus-resistant liver cells by genome editing-mediated stable expression of RNA aptamer.

Hepatitis C virus (HCV) infections frequently recur after liver transplantation in patients with HCV-related liver diseases. Approximately 30% of these patients progress to cirrhosis within 5 years after surgery. In this study, we proposed an effective therapeutic strategy to overcome the recurrence of HCV. CRISPR-Cas9 was used to insert an expression cassette encoding an RNA aptamer targeting HCV NS5B replicase as an anti-HCV agent into adeno-associated virus integration site 1 (AAVS1), known as a "safe harbor," in a hepatocellular carcinoma cell line to confer resistance to HCV. The RNA aptamer expression system based on a dihydrofolate reductase minigene was precisely knocked in into AAVS1, leading to the stable expression of aptamer RNA in the developed cell line. HCV replication was effectively inhibited at both the RNA and protein levels in cells transfected with HCV RNA or infected with HCV. RNA immunoprecipitation and competition experiments strongly suggested that this HCV inhibition was due to the RNA aptamer-mediated sequestration of HCV NS5B. No off-target insertion of the RNA aptamer expression construct was observed. The findings suggest that HCV-resistant liver cells produced by genome editing technology could be used as a new alternative in the development of a treatment for HCV-induced liver diseases.

Real-time monitoring strategies for optimization of in vitro transcription and quality control of RNA.

RNA-based therapeutics and vaccines are opening up new avenues for modern medicine. To produce these useful RNA-based reagents, in vitro transcription (IVT) is an important reaction that primarily determines the yield and quality of the product. Therefore, IVT condition should be well optimized to achieve high yield and purity of transcribed RNAs. To this end, real-time monitoring of RNA production during IVT, which allows for fine tuning of the condition, would be required. Currently, light-up RNA aptamer and fluorescent dye pairs are considered as useful strategies to monitor IVT in real time. Fluorophore-labeled antisense probe-based methods can also be used for real-time IVT monitoring. In addition, a high-performance liquid chromatography (HPLC)-based method that can monitor IVT reagent consumption has been developed as a powerful tool to monitor IVT reaction in near real-time. This mini-review briefly introduces some strategies and examples for real-time IVT monitoring and discusses pros and cons of IVT monitoring methods.

Efficient circular RNA engineering by end-to-end self-targeting and splicing reaction using Tetrahymena group I intron ribozyme.

Circular RNA (circRNA) has various advantages over linear mRNA that is gaining success as a new vaccine and therapeutic agent. Thus, circRNA and its engineering methods have attracted attention recently. In this study, we developed a new in vitro circRNA engineering method by end-to-end self-targeting and splicing (STS) reaction using Tetrahymena group I intron ribozyme. We found that only the P1 helix structure of the group I intron was enough to generate circRNA by STS reaction. The efficacy of circRNA generation by STS reaction was comparable to the method using a permuted intron-exon (PIE) reaction. However, an end-to-end STS reaction does not introduce any extraneous fragments, such as an intronic scar that can be generated by PIE reaction and might trigger unwanted innate immune responses in cells, into circRNA sequences. Moreover, generated circRNA was efficiently purified by ion pair-reversed phase high-pressure liquid chromatography and used for cell-based analysis. Of note, efficient protein expression and stability with least innate immune induction by the circRNA with coxsackievirus B3 IRES were observed in cells. In conclusion, our new in vitro circRNA strategy can effectively generate highly useful circRNAs in vitro as an alternative circRNA engineering method.

Adenovirus VA RNAs impair maturation of primary microRNA.

BACKGROUND: Adenovirus expresses two non-coding virus-associated (VA) RNAs: VA I RNA and VA II RNA. Adenovirus-expressed VA RNAs interfere with the microRNA (miRNA) pathway by competing with precursor miRNAs. The processing pattern of primary miRNA (pri-miRNA) and factors to affect its processing are not exactly known when using adenovirus for the delivery of pri-miRNA. METHODS: To observe pri-miRNA processing, plasmid construct encoding pri-miRNA was co-transfected with VA I/II RNA expression plasmid, or recombinant adenovirus encoding pri-miRNA was generated and infected. Levels of miRNAs, VA I RNA and VA II RNA were analyzed by a quantitative real-time PCR (RT-PCR). VA I-II full-length RNA was analyzed by a RT-PCR. RNA immunoprecipitation analysis to pull-down the VA I-II full-length RNA binding with Drosha was conducted with Drosha antibody. RESULTS: pri-miRNA was normally processed into mature miRNA when it was expressed in cells using plasmid. However, miRNA maturation was impaired when pri-miRNA was delivered and expressed using adenovirus. Of note, pri-miRNA processing was observed to be blocked by VA RNA expression. Such blocked processing could be recovered by introducing antisense RNA of VA RNA, anti-3'VA RNA. In addition, VA RNAs were transcribed into VA I-II full-length RNA, which was found to bind and sequester Drosha. CONCLUSIONS: Adenovirus infection downregulated the processing of pri-miRNAs in cells, and such downregulation could be derived from VA I-II full-length RNAs in pri-miRNA-like form through competitively binding to Drosha protein. These results indicated that the expression of adenovirus VA RNAs should be inhibited for successful delivery and expression of pri-miRNA or shRNA in cells using adenovirus.

DNN-Based Estimation for Misalignment State of Automotive Radar Sensor.

The reliability and safety of advanced driver assistance systems and autonomous vehicles are highly dependent on the accuracy of automotive sensors such as radar, lidar, and camera. However, these sensors can be misaligned compared to the initial installation state due to external shocks, and it can cause deterioration of their performance. In the case of the radar sensor, when the mounting angle is distorted and the sensor tilt toward the ground or sky, the sensing performance deteriorates significantly. Therefore, to guarantee stable detection performance of the sensors and driver safety, a method for determining the misalignment of these sensors is required. In this paper, we propose a method for estimating the vertical tilt angle of the radar sensor using a deep neural network (DNN) classifier. Using the proposed method, the mounting state of the radar can be easily estimated without physically removing the bumper. First, to identify the characteristics of the received signal according to the radar misalignment states, radar data are obtained at various tilt angles and distances. Then, we extract range profiles from the received signals and design a DNN-based estimator using the profiles as input. The proposed angle estimator determines the tilt angle of the radar sensor regardless of the measured distance. The average estimation accuracy of the proposed DNN-based classifier is over 99.08%. Therefore, through the proposed method of indirectly determining the radar misalignment, maintenance of the vehicle radar sensor can be easily performed.

FPGA Implementation of the Chirp-Scaling Algorithm for Real-Time Synthetic Aperture Radar Imaging.

Synthetic aperture radar (SAR), which can generate images of regions or objects, is an important research area of radar. The chirp scaling algorithm (CSA) is a representative SAR imaging algorithm. The CSA has a simple structure comprising phase compensation and fast Fourier transform (FFT) operations by replacing interpolation for range cell migration correction (RCMC) with phase compensation. However, real-time processing still requires many computations and a long execution time. Therefore, it is necessary to develop a hardware accelerator to improve the speed of algorithm processing. In addition, the demand for a small SAR system that can be mounted on a small aircraft or drone and that satisfies the constraints of area and power consumption is increasing. In this study, we proposed a CSA-based SAR processor that supports FFT and phase compensation operations and presents field-programmable gate array (FPGA)-based implementation results. We also proposed a modified CSA flow that simplifies the traditional CSA flow by changing the order in which the transpose operation occurs. Therefore, the proposed CSA-based SAR processor was designed to be suitable for modified CSA flow. We designed the multiplier for FFT to be shared for phase compensation, thereby achieving area efficiency and simplifying the data flow. The proposed CSA-based SAR processor was implemented on a Xilinx UltraScale+ MPSoC FPGA device and designed using Verilog-HDL. After comparing the execution times of the proposed SAR processor and the ARM cortex-A53 microprocessor, we observed a 136.2-fold increase in speed for the 4096 × 4096-pixel image.

Pros and Cons of In Vitro Methods for Circular RNA Preparation.

mRNA is gaining success as a new therapeutic agent and vaccine. However, mRNA has limitations in stability. To overcome the shortcomings of mRNA, circular RNA is emerging as a new modality. In this review, several current methods of manufacturing circular RNA in vitro are introduced and their advantages and disadvantages are reviewed. Furthermore, this study discusses which fields and directions of research and development are needed for the increase in the efficacy and productivity of circular RNA as a therapeutic agent and vaccine formulation.

Autoencoder-Based Target Detection in Automotive MIMO FMCW Radar System.

In general, a constant false alarm rate algorithm (CFAR) is widely used to automatically detect targets in an automotive frequency-modulated continuous wave (FMCW) radar system. However, if the number of guard cells, the number of training cells, and the probability of false alarm are set improperly in the conventional CFAR algorithm, the target detection performance is severely degraded. Therefore, we propose a method using a convolutional neural network-based autoencoder (AE) to replace the CFAR algorithm in the multiple-input and multiple-output FMCW radar system. In the AE, the entire detection result is compressed at the encoder side, and only significant signal components are recovered on the decoder side. In this work, by changing the number of hidden layers and the number of filters in each layer, the structure of the AE showing a high signal-to-noise ratio in the target detection result is determined. To evaluate the performance of the proposed method, the AE-based target detection result is compared with the target detection results of conventional CFAR algorithms. As a result of calculating the correlation coefficient with the data marked with the actual target position, the proposed AE-based target detection shows the highest similarity with a correlation of 0.73 or higher.

Therapeutic Application of Genome Editing Technologies in Viral Diseases.

Viral infections can be fatal and consequently, they are a serious threat to human health. Therefore, the development of vaccines and appropriate antiviral therapeutic agents is essential. Depending on the virus, it can cause an acute or a chronic infection. The characteristics of viruses can act as inhibiting factors for the development of appropriate treatment methods. Genome editing technology, including the use of clustered regularly interspaced short palindromic repeats (CRISPR)-CRISPR-associated (Cas) proteins, zinc-finger nucleases (ZFNs), and transcription activator-like effector nucleases (TALENs), is a technology that can directly target and modify genomic sequences in almost all eukaryotic cells. The development of this technology has greatly expanded its applicability in life science research and gene therapy development. Research on the use of this technology to develop therapeutics for viral diseases is being conducted for various purposes, such as eliminating latent infections or providing resistance to new infections. In this review, we will look at the current status of the development of viral therapeutic agents using genome editing technology and discuss how this technology can be used as a new treatment approach for viral diseases.

A Scalable Readout IC Based on Wideband Noise Cancelling for Full-Rate Scanning of High-Density Microelectrode Arrays.

This paper presents a highly scalable readout IC for high-density microelectrode arrays (MEAs). Although the recent development of large-scale high-density MEAs provides opportunities to achieve sub-cellular neural recording over a wide network area, it is challenging to implement the readout IC that can operate with such MEAs. The requirement of high-speed recording in large-scale arrays induces wideband-noise folding, which makes it challenging to achieve a good noise performance for high-fidelity neural recording. Moreover, for the wideband readout, the major noise contributor changes from the readout circuit to the cell-electrode interface. In this paper, we first show why the interface noise becomes the dominant noise source and elucidate its component that contributes the most: sealing resistance. Then, we propose a new readout circuit structure, which can effectively cancel the wideband interface noise. As a result, the signal-to-noise ratio of input neural spike signals is improved dramatically in all cell-attachment or sealing conditions. Particularly, it is shown that under weakly sealed conditions, the spikes can be detected only when the proposed wideband noise cancellation technique is applied.

Compressive Sensing-Based SAR Image Reconstruction from Sparse Radar Sensor Data Acquisition in Automotive FMCW Radar System.

In this paper, we propose a method for reconstructing synthetic aperture radar (SAR) images by applying a compressive sensing (CS) technique to sparsely acquired radar sensor data. In general, SAR image reconstruction algorithms require radar sensor data acquired at regular spatial intervals. However, when the speed of the radar-equipped platform is not constant, it is difficult to consistently perform regular data acquisitions. Therefore, we used the CS-based signal recovery method to efficiently reconstruct SAR images even when regular data acquisition was not performed. In the proposed method, we used the l1-norm minimization to overcome the non-uniform data acquisition problem, which replaced the Fourier transform and inverse Fourier transform in the conventional SAR image reconstruction method. In addition, to reduce the phase distortion of the recovered signal, the proposed method was applied to each of the in-phase and quadrature components of the acquired radar sensor data. To evaluate the performance of the proposed method, we conducted experiments using an automotive frequency-modulated continuous wave radar sensor. Then, the quality of the SAR image reconstructed with data acquired at regular intervals was compared with the quality of images reconstructed with data acquired at non-uniform intervals. Using the proposed method, even if only 70% of the regularly acquired radar sensor data was used, a SAR image having a correlation of 0.83 could be reconstructed.

Simultaneous Target Classification and Moving Direction Estimation in Millimeter-Wave Radar System.

In this study, we propose a method to identify the type of target and simultaneously determine its moving direction in a millimeter-wave radar system. First, using a frequency-modulated continuous wave (FMCW) radar sensor with the center frequency of 62 GHz, radar sensor data for a pedestrian, a cyclist, and a car are obtained in the test field. Then, a You Only Look Once (YOLO)-based network is trained with the sensor data to perform simultaneous target classification and moving direction estimation. To generate input data suitable for the deep learning-based classifier, a method of converting the radar detection result into an image form is also proposed. With the proposed method, we can identify the type of each target and its direction of movement with an accuracy of over 95%. Moreover, the pre-trained classifier shows an identification accuracy of 85% even for newly acquired data that have not been used for training.

Identification of Human Motion Using Radar Sensor in an Indoor Environment.

In this paper, we propose a method of identifying human motions, such as standing, walking, running, and crawling, using a millimeter wave radar sensor. In our method, two signal processing is performed in parallel to identify the human motions. First, the moment at which a person's motion changes is determined based on the statistical characteristics of the radar signal. Second, a deep learning-based classification algorithm is applied to determine what actions a person is taking. In each of the two signal processing, radar spectrograms containing the characteristics of the distance change over time are used as input. Finally, we evaluate the performance of the proposed method with radar sensor data acquired in an indoor environment. The proposed method can find the moment when the motion changes with an error rate of 3%, and also can classify the action that a person is taking with more than 95% accuracy.

Dual-Mode Radar Sensor for Indoor Environment Mapping.

In this paper, we introduce mapping results in an indoor environment based on our own developed dual-mode radar sensor. Our radar system uses a frequency-modulated continuous wave (FMCW) with a center frequency of 62 GHz and a multiple-input multiple-output antenna system. In addition, the FMCW radar sensor we designed is capable of dual-mode detection, which alternately transmits two waveforms using different bandwidths within one frame. The first waveform is for long-range detection, and the second waveform is for short-range detection. This radar system is mounted on a small robot that moves in indoor environments such as rooms or hallways, and the radar and the robot send and receive necessary information to each other. The radar estimates the distance, velocity, and angle information of targets around the radar-equipped robot. Then, the radar receives information about the robot's motion from the robot, such as its speed and rotation angle. Finally, by combining the motion information and the detection results, the radar-equipped robot maps the indoor environment while finding its own position. Compared to the actual map data, the radar-based mapping is effectively achieved through the radar system we developed.

Aptamers for Anti-Viral Therapeutics and Diagnostics.

Viral infections cause a host of fatal diseases and seriously affect every form of life from bacteria to humans. Although most viral infections can receive appropriate treatment thereby limiting damage to life and livelihood with modern medicine and early diagnosis, new types of viral infections are continuously emerging that need to be properly and timely treated. As time is the most important factor in the progress of many deadly viral diseases, early detection becomes of paramount importance for effective treatment. Aptamers are small oligonucleotide molecules made by the systematic evolution of ligands by exponential enrichment (SELEX). Aptamers are characterized by being able to specifically bind to a target, much like antibodies. However, unlike antibodies, aptamers are easily synthesized, modified, and are able to target a wider range of substances, including proteins and carbohydrates. With these advantages in mind, many studies on aptamer-based viral diagnosis and treatments are currently in progress. The use of aptamers for viral diagnosis requires a system that recognizes the binding of viral molecules to aptamers in samples of blood, serum, plasma, or in virus-infected cells. From a therapeutic perspective, aptamers target viral particles or host cell receptors to prevent the interaction between the virus and host cells or target intracellular viral proteins to interrupt the life cycle of the virus within infected cells. In this paper, we review recent attempts to use aptamers for the diagnosis and treatment of various viral infections.

Targeted suicide gene therapy for liver cancer based on ribozyme-mediated RNA replacement through post-transcriptional regulation.

Hepatocellular carcinoma (HCC) has high fatality rate and limited therapeutic options. Here, we propose a new anti-HCC approach with high cancer-selectivity and efficient anticancer effects, based on adenovirus-mediated Tetrahymena group I trans-splicing ribozymes specifically inducing targeted suicide gene activity through HCC-specific replacement of telomerase reverse transcriptase (TERT) RNA. To confer potent anti-HCC effects and minimize hepatotoxicity, we constructed post-transcriptionally enhanced ribozyme constructs coupled with splicing donor and acceptor site and woodchuck hepatitis virus post-transcriptional regulatory element under the control of microRNA-122a (miR-122a). Adenovirus encoding post-transcriptionally enhanced ribozyme improved trans-splicing reaction and decreased human TERT (hTERT) RNA level, efficiently and selectively retarding hTERT-positive liver cancers. Adenovirus encoding miR-122a-regulated ribozyme caused selective liver cancer cytotoxicity, the efficiency of which depended on ribozyme expression level relative to miR-122a level. Systemic administration of adenovirus encoding the post-transcriptionally enhanced and miR-regulated ribozyme caused efficient anti-cancer effects at a single dose of low titers and least hepatotoxicity in intrahepatic multifocal HCC mouse xenografts. Minimal liver toxicity, tissue distribution, and clearance pattern of the recombinant adenovirus were observed in normal animals administered either systemically or via the hepatic artery. Post-transcriptionally regulated RNA replacement strategy mediated by a cancer-specific ribozyme provides a clinically relevant, safe, and efficient strategy for HCC treatment.

YOLO-Based Simultaneous Target Detection and Classification in Automotive FMCW Radar Systems.

This paper proposes a method to simultaneously detect and classify objects by using a deep learning model, specifically you only look once (YOLO), with pre-processed automotive radar signals. In conventional methods, the detection and classification in automotive radar systems are conducted in two successive stages; however, in the proposed method, the two stages are combined into one. To verify the effectiveness of the proposed method, we applied it to the actual radar data measured using our automotive radar sensor. According to the results, our proposed method can simultaneously detect targets and classify them with over 90% accuracy. In addition, it shows better performance in terms of detection and classification, compared with conventional methods such as density-based spatial clustering of applications with noise or the support vector machine. Moreover, the proposed method especially exhibits better performance when detecting and classifying a vehicle with a long body.