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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Enhancement of chemosensitivity in 5-fluorouracil-resistant colon cancer cells with carcinoembryonic antigen-specific RNA aptamer.

Colorectal cancer (CRC) is one of the most common cancers, and rates of incidence and diagnosis of CRC have gradually increased. Carcinoembryonic antigen (CEA) is overexpressed in patients with CRC and is associated with cell adhesion, anoikis resistance, and promotion of metastasis to the liver. 5-Fluorouracil (5-FU) is a chemotherapeutic drug used to treat cancer, including CRC. However, a major issue of 5-FU therapy is the occurrence of chemoresistance, and the fact that 5-FU induces CEA overexpression, which may induce the 5-FU resistance. We previously isolated a CEA-specific RNA aptamer that was able to inhibit hepatic metastasis of colon cancer cells in a mouse model. In the present study, we tested whether protecting CEA using the CEA aptamer could enhance 5-FU sensitivity in chemoresistant LS174T colon cancer cells. We observed that the CEA aptamer sensitized the 5-FU-resistant colon cancer cell line to 5-FU more than five-fold (IC50 ~ 5.995 µM), compared with cells treated with 5-FU alone (IC50 ~ 31.46 µM). Moreover, treatment with CEA aptamer combined with 5-FU synergistically regressed growth of chemoresistant tumors in mouse xenografted models. Combinatorial treatment of 5-FU and CEA aptamer augmented caspase-8 activity in the 5-FU-resistant colon cancer cell line via aptamer-mediated disruption of CEA interaction with death receptor 5 and in mouse xenograft tumors. In conclusion, CEA-specific aptamer improved 5-FU sensitivity in chemoresistant colon cancer cells in vitro and in vivo, and thus represents a novel 5-FU adjuvant to overcome the chemoresistance in CRC patients.

Logarithmic-Domain Array Interpolation for Improved Direction of Arrival Estimation in Automotive Radars.

In automotive radar systems, a limited number of antenna elements are used to estimate the angle of the target. Therefore, array interpolation techniques can be used for direction of arrival (DOA) estimation to achieve high angular resolution. In general, to generate interpolated array elements from original array elements, the method of linear least squares (LLS) is used. When the LLS method is used, the amplitudes of the interpolated array elements may not be equivalent to those of the original array elements. In addition, through the transformation matrix obtained from the LLS method, the phases of the interpolated array elements are not precisely generated. Therefore, we propose an array transformation matrix that generates accurate phases for interpolated array elements to improve DOA estimation performance, while maintaining constant amplitudes of the array elements. Moreover, to enhance the effect of our interpolation method, a power calibration method for interpolated received signals is also proposed. Through the simulation, we confirm that the array interpolation accuracy and DOA estimation performance of the proposed method are improved compared to those of the conventional method. Moreover, the performance and effectiveness of our proposed method are also verified using data obtained from the commercial radar system. Because the proposed method exhibits better performance when applied to actual measurement data, it can be utilized in commercial automotive radar systems.

Effects of Human Mesenchymal Stem Cells Coculture on Calcium-Induced Differentiation of Normal Human Keratinocytes.

The influence of mesenchymal stem cells (MSCs) on keratinocytes in altered microenvironments is poorly understood. Here, we cocultured umbilical cord blood-derived MSCs with normal human epidermal keratinocytes to evaluate their paracrine effect in the presence of high extracellular calcium (Ca2+ ) concentration. High Ca2+ environment to keratinocytes can disrupt normal skin barrier function due to abnormal/premature differentiation of keratinocytes. Surprisingly, we found that MSCs suppress both proliferation and differentiation of keratinocytes under a high Ca2+ environment in transforming growth factors ß1 (TGFß1)-dependent manner. Furthermore, we determined that MSCs can regulate the mitogen-activated protein kinases, phosphatidylinositol 3-kinase/protein kinase B, and protein kinase C pathways in Ca2+ -induced differentiated keratinocytes. Knockdown of TGFß1 from MSCs results in decreased suppression of differentiation with significantly increased proliferation of keratinocytes compared with control MSCs. MSCs-derived TGFß1 further induced growth inhibition of keratinocyte in high extracellular Ca2+ environment as analyzed by a decrease in DNA synthesis, accumulation of phosphorylated retinoblastoma protein, cdc2, and increased mRNA level of p21, and independent of TGFß1/SMAD pathway. Taken together, we found that MSCs-derived TGFß1 is a critical regulator of keratinocyte function, and involves multiple proximal signaling cascades. Stem Cells 2017;35:1592-1602.

Specific and Efficient Regression of Cancers Harboring KRAS Mutation by Targeted RNA Replacement.

Mutations in the KRAS gene, which persistently activate RAS function, are most frequently found in many types of human cancers. Here, we proposed and verified a new approach against cancers harboring the KRAS mutation with high cancer selectivity and efficient anti-cancer effects based on targeted RNA replacement. To this end, trans-splicing ribozymes from Tetrahymena group I intron were developed, which can specifically target and reprogram the mutant KRAS G12V transcript to induce therapeutic gene activity in cells. Adenoviral vectors containing the specific ribozymes with downstream suicide gene were constructed and then infection with the adenoviruses specifically downregulated KRAS G12V expression and killed KRAS G12V-harboring cancer cells additively upon pro-drug treatment, but it did not affect the growth of wild-type KRAS-expressing cells. Minimal liver toxicity was noted when the adenoviruses were administered systemically in vivo. Importantly, intratumoral injection of the adenoviruses with pro-drug treatment specifically and significantly impeded the growth of xenografted tumors harboring KRAS G12V through a trans-splicing reaction with the target RNA. In contrast, xenografted tumors harboring wild-type KRAS were not affected by the adenoviruses. Therefore, RNA replacement with a mutant KRAS-targeting trans-splicing ribozyme is a potentially useful therapeutic strategy to combat tumors harboring KRAS mutation.

Development of RNA aptamer that inhibits methyltransferase activity of dengue virus.

OBJECTIVES: To develop an RNA aptamer specific for the methyltransferase (MTase) of dengue virus (DENV) which is essential for viral genome replication and translation acting directly on N-7 and 2'-O-methylation of the type-I cap structure of the viral RNA. RESULTS: We identified 2'-fluoro-modified RNA aptamers that can specifically bind DENV serotype 2 (DENV2) MTase using systematic evolution of ligands by exponential enrichment technology. We truncated the chosen aptamer into a 45-mer RNA sequence that can bind DENV2 MTase with K d  ~ 28 nM and inhibit N-7 methylation activity of the protein. Moreover, the 45-mer truncated aptamer could not only bind with an K d  ~ 15.6 nM but also inhibit methylation activity of DENV serotype 3 (DENV3) MTase. The 45-mer aptamer competitively impeded binding of both DENV2 and DENV3 genomic RNA to MTase of each serotype. CONCLUSION: The selected 45-mer truncated RNA aptamer specifically and avidly bound DENV MTase and competitively inhibited its methylation activity, and thus could be useful for the development of anti-DENV agents.

Inhibition of Japanese encephalitis virus (JEV) replication by specific RNA aptamer against JEV methyltransferase.

Japanese encephalitis virus (JEV) is the most common etiological agent of epidemic viral encephalitis. JEV encodes a single methyltransferase (MTase) domain located at the N-terminal region of the viral nonstructural protein NS5. JEV MTase is essential for viral replication and specifically catalyzes methylation of the viral RNA cap, which occurs exclusively in the cytoplasm. Therefore, JEV MTase is a potential target for antiviral therapy. Here, we identified specific and avid RNA aptamer (Kd âˆ¼ 12 nM) with modified 2'-O-methyl pyrimidines against JEV MTase. The RNA aptamer efficiently inhibited viral cap methylation activity of MTase and interfered with JEV production in cells. Moreover, we generated a 24-mer truncated aptamer that could specifically bind to JEV MTase with high affinity (Kd ∼16 nM). The 24-mer aptamer efficiently inhibited JEV production and replication in cells. Therefore, MTase-specific RNA aptamer might be useful as an anti-JEV agent.