Ceramide promotes lytic reactivation of Epstein-Barr virus in gastric carcinoma.

Epstein-Barr virus (EBV) has a lifelong latency period after initial infection. Rarely, however, when the EBV immediate early gene BZLF1 is expressed by a specific stimulus, the virus switches to the lytic cycle to produce progeny viruses. We found that EBV infection reduced levels of various ceramide species in gastric cancer cells. As ceramide is a bioactive lipid implicated in the infection of various viruses, we assessed the effect of ceramide on the EBV lytic cycle. Treatment with C6-ceramide (C6-Cer) induced an increase in the endogenous ceramide pool and increased production of the viral product as well as BZLF1 expression. Treatment with the ceramidase inhibitor ceranib-2 induced EBV lytic replication with an increase in the endogenous ceramide pool. The glucosylceramide synthase inhibitor Genz-123346 inhibited C6-Cer-induced lytic replication. C6-Cer induced extracellular signal-regulated kinase 1/2 (ERK1/2) and CREB phosphorylation, c-JUN expression, and accumulation of the autophagosome marker LC3B. Treatment with MEK1/2 inhibitor U0126, siERK1&2, or siCREB suppressed C6-Cer-induced EBV lytic replication and autophagy initiation. In contrast, siJUN transfection had no impact on BZLF1 expression. The use of 3-methyladenine (3-MA), an inhibitor targeting class III phosphoinositide 3-kinases (PI3Ks) to inhibit autophagy initiation, resulted in reduced beclin-1 expression, along with suppressed C6-Cer-induced BZLF1 expression and LC3B accumulation. Chloroquine, an inhibitor of autophagosome-lysosome fusion, increased BZLF1 protein intensity and LC3B accumulation. However, siLC3B transfection had minimal effect on BZLF1 expression. The results suggest the significance of ceramide-related sphingolipid metabolism in controlling EBV latency, highlighting the potential use of drugs targeting sphingolipid metabolism for treating EBV-positive gastric cancer.IMPORTANCEEpstein-Barr virus remains dormant in the host cell but occasionally switches to the lytic cycle when stimulated. However, the exact molecular mechanism of this lytic induction is not well understood. In this study, we demonstrate that Epstein-Barr virus infection leads to a reduction in ceramide levels. Additionally, the restoration of ceramide levels triggers lytic replication of Epstein-Barr virus with increase in phosphorylation of extracellular signal-regulated kinase 1/2 (ERK1/2) and CREB. Our study suggests that the Epstein-Barr virus can inhibit lytic replication and remain latent through reduction of host cell ceramide levels. This study reports the regulation of lytic replication by ceramide in Epstein-Barr virus-positive gastric cancer.

Combination Analysis of PCDHGA12 and CDO1 DNA Methylation in Bronchial Washing Fluid for Lung Cancer Diagnosis.

BACKGROUND: When suspicious lesions are observed on computer-tomography (CT), invasive tests are needed to confirm lung cancer. Compared with other procedures, bronchoscopy has fewer complications. However, the sensitivity of peripheral lesion through bronchoscopy including washing cytology is low. A new test with higher sensitivity through bronchoscopy is needed. In our previous study, DNA methylation of PCDHGA12 in bronchial washing cytology has a diagnostic value for lung cancer. In this study, combination of PCDHGA12 and CDO1 methylation obtained through bronchial washing cytology was evaluated as a diagnostic tool for lung cancer. METHODS: A total of 187 patients who had suspicious lesions in CT were enrolled. PCDHGA12 methylation test, CDO1 methylation test, and cytological examination were performed using 3-plex LTE-qMSP test. RESULTS: Sixty-two patients were diagnosed with benign diseases and 125 patients were diagnosed with lung cancer. The sensitivity of PCDHGA12 was 74.4% and the specificity of PCDHGA12 was 91.9% respectively. CDO1 methylation test had a sensitivity of 57.6% and a specificity of 96.8%. The combination of both PCDHGA12 methylation test and CDO1 methylation test showed a sensitivity of 77.6% and a specificity of 90.3%. The sensitivity of lung cancer diagnosis was increased by combining both PCDHGA12 and CDO1 methylation tests. CONCLUSION: Checking DNA methylation of both PCDHGA12 and CDO1 genes using bronchial washing fluid can reduce the invasive procedure to diagnose lung cancer.

Deep Learning Approaches for lncRNA-Mediated Mechanisms: A Comprehensive Review of Recent Developments.

This review paper provides an extensive analysis of the rapidly evolving convergence of deep learning and long non-coding RNAs (lncRNAs). Considering the recent advancements in deep learning and the increasing recognition of lncRNAs as crucial components in various biological processes, this review aims to offer a comprehensive examination of these intertwined research areas. The remarkable progress in deep learning necessitates thoroughly exploring its latest applications in the study of lncRNAs. Therefore, this review provides insights into the growing significance of incorporating deep learning methodologies to unravel the intricate roles of lncRNAs. By scrutinizing the most recent research spanning from 2021 to 2023, this paper provides a comprehensive understanding of how deep learning techniques are employed in investigating lncRNAs, thereby contributing valuable insights to this rapidly evolving field. The review is aimed at researchers and practitioners looking to integrate deep learning advancements into their lncRNA studies.

Recent Advances in Deep Learning for Protein-Protein Interaction Analysis: A Comprehensive Review.

Deep learning, a potent branch of artificial intelligence, is steadily leaving its transformative imprint across multiple disciplines. Within computational biology, it is expediting progress in the understanding of Protein-Protein Interactions (PPIs), key components governing a wide array of biological functionalities. Hence, an in-depth exploration of PPIs is crucial for decoding the intricate biological system dynamics and unveiling potential avenues for therapeutic interventions. As the deployment of deep learning techniques in PPI analysis proliferates at an accelerated pace, there exists an immediate demand for an exhaustive review that encapsulates and critically assesses these novel developments. Addressing this requirement, this review offers a detailed analysis of the literature from 2021 to 2023, highlighting the cutting-edge deep learning methodologies harnessed for PPI analysis. Thus, this review stands as a crucial reference for researchers in the discipline, presenting an overview of the recent studies in the field. This consolidation helps elucidate the dynamic paradigm of PPI analysis, the evolution of deep learning techniques, and their interdependent dynamics. This scrutiny is expected to serve as a vital aid for researchers, both well-established and newcomers, assisting them in maneuvering the rapidly shifting terrain of deep learning applications in PPI analysis.

HRGAN: A Generative Adversarial Network Producing Higher-Resolution Images than Training Sets.

The generative adversarial network (GAN) has demonstrated superb performance in generating synthetic images in recent studies. However, in the conventional framework of GAN, the maximum resolution of generated images is limited to the resolution of real images that are used as the training set. In this paper, in order to address this limitation, we propose a novel GAN framework using a pre-trained network called evaluator. The proposed model, higher resolution GAN (HRGAN), employs additional up-sampling convolutional layers to generate higher resolution. Then, using the evaluator, an additional target for the training of the generator is introduced to calibrate the generated images to have realistic features. In experiments with the CIFAR-10 and CIFAR-100 datasets, HRGAN successfully generates images of 64 × 64 and 128 × 128 resolutions, while the training sets consist of images of 32 × 32 resolution. In addition, HRGAN outperforms other existing models in terms of the Inception score, one of the conventional methods to evaluate GANs. For instance, in the experiment with CIFAR-10, a HRGAN generating 128 × 128 resolution demonstrates an Inception score of 12.32, outperforming an existing model by 28.6%. Thus, the proposed HRGAN demonstrates the possibility of generating higher resolution than training images.

Effects and Mechanism of Particulate Matter on Tendon Healing Based on Integrated Analysis of DNA Methylation and RNA Sequencing Data in a Rat Model.

Exposure to particulate matter (PM) has been linked with the severity of various diseases. To date, there is no study on the relationship between PM exposure and tendon healing. Open Achilles tenotomy of 20 rats was performed. The animals were divided into two groups according to exposure to PM: a PM group and a non-PM group. After 6 weeks of PM exposure, the harvest and investigations of lungs, blood samples, and Achilles tendons were performed. Compared to the non-PM group, the white blood cell count and tumor necrosis factor-alpha expression in the PM group were significantly higher. The Achilles tendons in PM group showed significantly increased inflammatory outcomes. A TEM analysis showed reduced collagen fibrils in the PM group. A biomechanical analysis demonstrated that the load to failure value was lower in the PM group. An upregulation of the gene encoding cyclic AMP response element-binding protein (CREB) was detected in the PM group by an integrated analysis of DNA methylation and RNA sequencing data, as confirmed via a Western blot analysis showing significantly elevated levels of phosphorylated CREB. In summary, PM exposure caused a deleterious effect on tendon healing. The molecular data indicate that the action mechanism of PM may be associated with upregulated CREB signaling.

Estimation with Uncertainty via Conditional Generative Adversarial Networks.

Conventional predictive Artificial Neural Networks (ANNs) commonly employ deterministic weight matrices; therefore, their prediction is a point estimate. Such a deterministic nature in ANNs causes the limitations of using ANNs for medical diagnosis, law problems, and portfolio management in which not only discovering the prediction but also the uncertainty of the prediction is essentially required. In order to address such a problem, we propose a predictive probabilistic neural network model, which corresponds to a different manner of using the generator in the conditional Generative Adversarial Network (cGAN) that has been routinely used for conditional sample generation. By reversing the input and output of ordinary cGAN, the model can be successfully used as a predictive model; moreover, the model is robust against noises since adversarial training is employed. In addition, to measure the uncertainty of predictions, we introduce the entropy and relative entropy for regression problems and classification problems, respectively. The proposed framework is applied to stock market data and an image classification task. As a result, the proposed framework shows superior estimation performance, especially on noisy data; moreover, it is demonstrated that the proposed framework can properly estimate the uncertainty of predictions.

Prediction of survival risks with adjusted gene expression through risk-gene networks.

MOTIVATION: Network-based analysis of biomedical data has been extensively studied over the last decades. As a successful application, gene networks have been used to illustrate interactions among genes and explain the associated phenotypes. However, the gene network approaches have not been actively applied for survival analysis, which is one of the main interests of biomedical research. In addition, a few previous studies using gene networks for survival analysis construct networks mainly from prior knowledge, such as pathways, regulations and gene sets, while the performance considerably depends on the selection of prior knowledge. RESULTS: In this paper, we propose a data-driven construction method for survival risk-gene networks as well as a survival risk prediction method using the network structure. The proposed method constructs risk-gene networks with survival-associated genes using penalized regression. Then, gene expression indices are hierarchically adjusted through the networks to reduce the variance intrinsic in datasets. By illustrating risk-gene structure, the proposed method is expected to provide an intuition for the relationship between genes and survival risks. The risk-gene network is applied to a low grade glioma dataset, and produces a hypothesis of the relationship between genetic biomarkers of low and high grade glioma. Moreover, with multiple datasets, we demonstrate that the proposed method shows superior prediction performance compared to other conventional methods. AVAILABILITY AND IMPLEMENTATION: The R package of risk-gene networks is freely available in the web at http://cdal.korea.ac.kr/NetDA/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Treatment of Adenoviral Acute Respiratory Distress Syndrome Using Cidofovir With Extracorporeal Membrane Oxygenation.

Adenovirus infections are associated with respiratory (especially upper respiratory) infection and gastrointestinal disease and occur primarily in infants and children. Although rare in adults, severe lower respiratory adenovirus infections including pneumonia are reported in specific populations, such as military recruits and immunocompromised patients. Antiviral treatment is challenging due to limited clinical experience and lack of well-controlled randomized trials. Several previously reported cases of adenoviral pneumonia showed promising efficacy of cidofovir. However, few reports discussed the efficacy of cidofovir in acute respiratory distress syndrome (ARDS). We experienced 3 cases of adenoviral pneumonia associated with ARDS and treated with cidofovir and respiratory support, including extracorporeal membrane oxygenation (ECMO). All 3 patients showed a positive clinical response to cidofovir and survival at 28 days. Cidofovir with early ECMO therapy may be a therapeutic option in adenoviral ARDS. A literature review identified 15 cases of adenovirus pneumonia associated with ARDS.

MetaSwin: a unified meta vision transformer model for medical image segmentation.

Transformers have demonstrated significant promise for computer vision tasks. Particularly noteworthy is SwinUNETR, a model that employs vision transformers, which has made remarkable advancements in improving the process of segmenting medical images. Nevertheless, the efficacy of training process of SwinUNETR has been constrained by an extended training duration, a limitation primarily attributable to the integration of the attention mechanism within the architecture. In this article, to address this limitation, we introduce a novel framework, called the MetaSwin model. Drawing inspiration from the MetaFormer concept that uses other token mix operations, we propose a transformative modification by substituting attention-based components within SwinUNETR with a straightforward yet impactful spatial pooling operation. Additionally, we incorporate of Squeeze-and-Excitation (SE) blocks after each MetaSwin block of the encoder and into the decoder, which aims at segmentation performance. We evaluate our proposed MetaSwin model on two distinct medical datasets, namely BraTS 2023 and MICCAI 2015 BTCV, and conduct a comprehensive comparison with the two baselines, i.e., SwinUNETR and SwinUNETR+SE models. Our results emphasize the effectiveness of MetaSwin, showcasing its competitive edge against the baselines, utilizing a simple pooling operation and efficient SE blocks. MetaSwin's consistent and superior performance on the BTCV dataset, in comparison to SwinUNETR, is particularly significant. For instance, with a model size of 24, MetaSwin outperforms SwinUNETR's 76.58% Dice score using fewer parameters (15,407,384 vs 15,703,304) and a substantially reduced training time (300 vs 467 mins), achieving an improved Dice score of 79.12%. This research highlights the essential contribution of a simplified transformer framework, incorporating basic elements such as pooling and SE blocks, thus emphasizing their potential to guide the progression of medical segmentation models, without relying on complex attention-based mechanisms.

Deep Transfer Learning Using Real-World Image Features for Medical Image Classification, with a Case Study on Pneumonia X-ray Images.

Deep learning has profoundly influenced various domains, particularly medical image analysis. Traditional transfer learning approaches in this field rely on models pretrained on domain-specific medical datasets, which limits their generalizability and accessibility. In this study, we propose a novel framework called real-world feature transfer learning, which utilizes backbone models initially trained on large-scale general-purpose datasets such as ImageNet. We evaluate the effectiveness and robustness of this approach compared to models trained from scratch, focusing on the task of classifying pneumonia in X-ray images. Our experiments, which included converting grayscale images to RGB format, demonstrate that real-world-feature transfer learning consistently outperforms conventional training approaches across various performance metrics. This advancement has the potential to accelerate deep learning applications in medical imaging by leveraging the rich feature representations learned from general-purpose pretrained models. The proposed methodology overcomes the limitations of domain-specific pretrained models, thereby enabling accelerated innovation in medical diagnostics and healthcare. From a mathematical perspective, we formalize the concept of real-world feature transfer learning and provide a rigorous mathematical formulation of the problem. Our experimental results provide empirical evidence supporting the effectiveness of this approach, laying the foundation for further theoretical analysis and exploration. This work contributes to the broader understanding of feature transferability across domains and has significant implications for the development of accurate and efficient models for medical image analysis, even in resource-constrained settings.

Deep learning in CRISPR-Cas systems: a review of recent studies.

In genetic engineering, the revolutionary CRISPR-Cas system has proven to be a vital tool for precise genome editing. Simultaneously, the emergence and rapid evolution of deep learning methodologies has provided an impetus to the scientific exploration of genomic data. These concurrent advancements mandate regular investigation of the state-of-the-art, particularly given the pace of recent developments. This review focuses on the significant progress achieved during 2019-2023 in the utilization of deep learning for predicting guide RNA (gRNA) activity in the CRISPR-Cas system, a key element determining the effectiveness and specificity of genome editing procedures. In this paper, an analytical overview of contemporary research is provided, with emphasis placed on the amalgamation of artificial intelligence and genetic engineering. The importance of our review is underscored by the necessity to comprehend the rapidly evolving deep learning methodologies and their potential impact on the effectiveness of the CRISPR-Cas system. By analyzing recent literature, this review highlights the achievements and emerging trends in the integration of deep learning with the CRISPR-Cas systems, thus contributing to the future direction of this essential interdisciplinary research area.

Deep Learning Techniques with Genomic Data in Cancer Prognosis: A Comprehensive Review of the 2021-2023 Literature.

Deep learning has brought about a significant transformation in machine learning, leading to an array of novel methodologies and consequently broadening its influence. The application of deep learning in various sectors, especially biomedical data analysis, has initiated a period filled with noteworthy scientific developments. This trend has majorly influenced cancer prognosis, where the interpretation of genomic data for survival analysis has become a central research focus. The capacity of deep learning to decode intricate patterns embedded within high-dimensional genomic data has provoked a paradigm shift in our understanding of cancer survival. Given the swift progression in this field, there is an urgent need for a comprehensive review that focuses on the most influential studies from 2021 to 2023. This review, through its careful selection and thorough exploration of dominant trends and methodologies, strives to fulfill this need. The paper aims to enhance our existing understanding of applications of deep learning in cancer survival analysis, while also highlighting promising directions for future research. This paper undertakes aims to enrich our existing grasp of the application of deep learning in cancer survival analysis, while concurrently shedding light on promising directions for future research in this vibrant and rapidly proliferating field.

Machine learning for small interfering RNAs: a concise review of recent developments.

The advent of machine learning and its subsequent integration into small interfering RNA (siRNA) research heralds a new epoch in the field of RNA interference (RNAi). This review emphasizes the urgency and relevance of assimilating the plethora of contributions and advancements in this domain, particularly focusing on the period of 2019-2023. Given the rapid progression of deep learning technologies, our synthesis of recent research is paramount to staying apprised of the state-of-the-art methods being utilized. It not only offers a comprehensive insight into the confluence of machine learning and siRNA but also serves as a beacon, guiding future explorations in this intersectional research field. Our rigorous examination of studies promises a discerning perspective on the contemporary landscape of machine learning applications in siRNA design and function. This review is an effort to foster further discourse and propel academic inquiry in this multifaceted domain.

Recent Advancements in Deep Learning Using Whole Slide Imaging for Cancer Prognosis.

This review furnishes an exhaustive analysis of the latest advancements in deep learning techniques applied to whole slide images (WSIs) in the context of cancer prognosis, focusing specifically on publications from 2019 through 2023. The swiftly maturing field of deep learning, in combination with the burgeoning availability of WSIs, manifests significant potential in revolutionizing the predictive modeling of cancer prognosis. In light of the swift evolution and profound complexity of the field, it is essential to systematically review contemporary methodologies and critically appraise their ramifications. This review elucidates the prevailing landscape of this intersection, cataloging major developments, evaluating their strengths and weaknesses, and providing discerning insights into prospective directions. In this paper, a comprehensive overview of the field aims to be presented, which can serve as a critical resource for researchers and clinicians, ultimately enhancing the quality of cancer care outcomes. This review's findings accentuate the need for ongoing scrutiny of recent studies in this rapidly progressing field to discern patterns, understand breakthroughs, and navigate future research trajectories.

Transformer Architecture and Attention Mechanisms in Genome Data Analysis: A Comprehensive Review.

The emergence and rapid development of deep learning, specifically transformer-based architectures and attention mechanisms, have had transformative implications across several domains, including bioinformatics and genome data analysis. The analogous nature of genome sequences to language texts has enabled the application of techniques that have exhibited success in fields ranging from natural language processing to genomic data. This review provides a comprehensive analysis of the most recent advancements in the application of transformer architectures and attention mechanisms to genome and transcriptome data. The focus of this review is on the critical evaluation of these techniques, discussing their advantages and limitations in the context of genome data analysis. With the swift pace of development in deep learning methodologies, it becomes vital to continually assess and reflect on the current standing and future direction of the research. Therefore, this review aims to serve as a timely resource for both seasoned researchers and newcomers, offering a panoramic view of the recent advancements and elucidating the state-of-the-art applications in the field. Furthermore, this review paper serves to highlight potential areas of future investigation by critically evaluating studies from 2019 to 2023, thereby acting as a stepping-stone for further research endeavors.

Discovering potential pathways between type 2 diabetes mellitus and diabetic retinopathy: A big data analysis of the South Korean National Sample Cohort.

Diabetes mellitus, a prevalent metabolic disorder, is associated with a multitude of complications that necessitate vigilant management post-diagnosis. A notable complication, diabetic retinopathy, could lead to intense ocular injury, including vision impairment and blindness, due to the impact of the disease. Studying the transition from diabetes to diabetic retinopathy is paramount for grasping and halting the progression of complications. In this study, we examine the statistical correlation between type 2 diabetes mellitus and retinal disorders classified elsewhere, ultimately proposing a comprehensive disease network. The National Sample Cohort of South Korea, containing approximately 1 million samples and primary diagnoses based on the International Statistical Classification of Diseases and Related Health Problems 10th Revision classification, was utilized for this retrospective analysis. The diagnoses of both conditions displayed a statistically significant correlation with a chi-square test value of P < .001, and the t test for the initial diagnosis date also yielded a P < .001 value. The devised network, comprising 27 diseases and 142 connections, was established through statistical evaluations. This network offers insight into potential pathways leading to diabetic retinopathy and intermediary diseases, encouraging medical researchers to further examine various risk factors associated with these connections.

SuperstarGAN: Generative adversarial networks for image-to-image translation in large-scale domains.

Image-to-image translation with generative adversarial networks (GANs) has been extensively studied in recent years. Among the models, StarGAN has achieved image-to-image translation for multiple domains with a single generator, whereas conventional models require multiple generators. However, StarGAN has several limitations, including the lack of capacity to learn mappings among large-scale domains; furthermore, StarGAN can barely express small feature changes. To address the limitations, we propose an improved StarGAN, namely SuperstarGAN. We adopted the idea, first proposed in controllable GAN (ControlGAN), of training an independent classifier with the data augmentation techniques to handle the overfitting problem in the classification of StarGAN structures. Since the generator with a well-trained classifier can express small features belonging to the target domain, SuperstarGAN achieves image-to-image translation in large-scale domains. Evaluated with a face image dataset, SuperstarGAN demonstrated improved performance in terms of Fréchet Inception distance (FID) and learned perceptual image patch similarity (LPIPS). Specifically, compared to StarGAN, SuperstarGAN exhibited decreased FID and LPIPS by 18.1% and 42.5%, respectively. Furthermore, we conducted an additional experiment with interpolated and extrapolated label values, indicating the ability of SuperstarGAN to control the degree of expression of the target domain features in generated images. Additionally, SuperstarGAN was successfully adapted to an animal face dataset and a painting dataset, where it can translate styles of animal faces (i.e., a cat to a tiger) and styles of painters (i.e., Hassam to Picasso), respectively, which explains the generality of SuperstarGAN regardless of datasets.

Machine Learning Big Data Analysis of the Impact of Air Pollutants on Rhinitis-Related Hospital Visits.

This study seeks to elucidate the intricate relationship between various air pollutants and the incidence of rhinitis in Seoul, South Korea, wherein it leveraged a vast repository of data and machine learning techniques. The dataset comprised more than 93 million hospital visits (n = 93,530,064) by rhinitis patients between 2013 and 2017. Daily atmospheric measurements were captured for six major pollutants: PM10, PM2.5, O3, NO2, CO, and SO2. We employed traditional correlation analyses alongside machine learning models, including the least absolute shrinkage and selection operator (LASSO), random forest (RF), and gradient boosting machine (GBM), to dissect the effects of these pollutants and the potential time lag in their symptom manifestation. Our analyses revealed that CO showed the strongest positive correlation with hospital visits across all three categories, with a notable significance in the 4-day lag analysis. NO2 also exhibited a substantial positive association, particularly with outpatient visits and hospital admissions and especially in the 4-day lag analysis. Interestingly, O3 demonstrated mixed results. Both PM10 and PM2.5 showed significant correlations with the different types of hospital visits, thus underlining their potential to exacerbate rhinitis symptoms. This study thus underscores the deleterious impacts of air pollution on respiratory health, thereby highlighting the importance of reducing pollutant levels and developing strategies to minimize rhinitis-related hospital visits. Further research considering other environmental factors and individual patient characteristics will enhance our understanding of these intricate dynamics.

Horizontal Arrays of One-Dimensional van der Waals Heterostructures as Transistor Channels.

The nanotube/dielectric interface plays an essential role in achieving superb switching characteristics of carbon nanotube-based transistors for energy-efficient computation. Formation of van der Waals heterostructures with hexagonal boron nitride nanotubes could be an effective means to reduce interface state density, but the need for isolating nanotubes during the formation of coaxial outer layers has hindered the fabrication of their horizontal arrays. Here, we develop a strategy to create isolated heterostructure arrays using aligned carbon nanotubes grown on a quartz substrate as starting materials. Air-suspended arrays of carbon nanotubes are prepared by a dry transfer technique and then used as templates for the coaxial wrapping of boron nitride nanotubes. We then fabricate the transistors, where boron nitride serves as interfacial layers between carbon nanotube channels and conventional gate dielectrics, showing hysteresis-free characteristics owing to the improved interfaces. We have also gained a deeper understanding of the strain applied on inner carbon nanotubes, as well as the inhomogeneity of the outer coating, by characterizing individual heterostructures over trenches and on a substrate surface. The device fabrication and characterization presented here essentially do not require elaborate electron microscopy, thus paving the way for the practical use of one-dimensional van der Waals heterostructures for nanoelectronics.