Active learning for efficient analysis of high-throughput nanopore data.

MOTIVATION: As the third-generation sequencing technology, nanopore sequencing has been used for high-throughput sequencing of DNA, RNA, and even proteins. Recently, many studies have begun to use machine learning technology to analyze the enormous data generated by nanopores. Unfortunately, the success of this technology is due to the extensive labeled data, which often suffer from enormous labor costs. Therefore, there is an urgent need for a novel technology that can not only rapidly analyze nanopore data with high-throughput, but also significantly reduce the cost of labeling. To achieve the above goals, we introduce active learning to alleviate the enormous labor costs by selecting the samples that need to be labeled. This work applies several advanced active learning technologies to the nanopore data, including the RNA classification dataset (RNA-CD) and the Oxford Nanopore Technologies barcode dataset (ONT-BD). Due to the complexity of the nanopore data (with noise sequence), the bias constraint is introduced to improve the sample selection strategy in active learning. Results: The experimental results show that for the same performance metric, 50% labeling amount can achieve the best baseline performance for ONT-BD, while only 15% labeling amount can achieve the best baseline performance for RNA-CD. Crucially, the experiments show that active learning technology can assist experts in labeling samples, and significantly reduce the labeling cost. Active learning can greatly reduce the dilemma of difficult labeling of high-capacity nanopore data. We hope active learning can be applied to other problems in nanopore sequence analysis. AVAILABILITY AND IMPLEMENTATION: The main program is available at https://github.com/guanxiaoyu11/AL-for-nanopore. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Prediction of urinary dickkopf-3 for AKI, sepsis-associated AKI, and PICU mortality in children.

BACKGROUND: Preoperative urinary dickkopf-3 (DKK3) is proposed as an early biomarker for the prediction of acute kidney injury (AKI) in patients undergoing cardiac surgery. We explored the clinical utility of urinary DKK3 for the early predictive value for AKI, sepsis-associated AKI (SA-AKI), and pediatric intensive care unit (PICU) mortality in critically ill children. METHODS: Urine samples were collected during the first 24 h after admission for measurement of DKK3. AKI diagnosis was based on serum creatinine and urine output using the KDIGO criteria. SA-AKI was defined as AKI that occurred in children who met the sepsis criteria in accordance with the surviving sepsis campaign international guidelines for children. RESULTS: Of the 420 children, 73 developed AKI, including 24 with SA-AKI, and 30 died during the PICU stay. The urinary DKK3 level was significantly associated with AKI, SA-AKI, and PICU mortality, even after adjustment for confounders. The area under the receiver operating characteristic curve of urinary DKK3 for the discrimination of AKI, SA-AKI, and PICU mortality was 0.70, 0.80, and 0.78, respectively. CONCLUSION: Urinary DKK3 was independently associated with an increased risk for AKI, SA-AKI, and PICU mortality and may be predictive of the aforementioned issues in critically ill children. IMPACT: Urinary dickkopf-3 (DKK3) has been identified as a preoperative biomarker for the prediction of acute kidney injury (AKI) following cardiac surgery or coronary angiography in adult patients. However, little is known about the clinical utility of urinary DKK3 in pediatric cohorts. This study demonstrated that urinary DKK3 is capable of early predicting AKI and pediatric intensive care unit (PICU) mortality and discriminating sepsis-associated AKI (SA-AKI) from other types of AKI. Urinary DKK3 may be an early biomarker for predicting AKI, SA-AKI, and PICU mortality in critically ill children.

Spectroscopic Visualization of Flat Bands in Magic-Angle Twisted Monolayer-Bilayer Graphene: Coexistence of Localization and Delocalization.

Recent transport studies have demonstrated the great potential of twisted monolayer-bilayer graphene (TMBG) as a new platform to host moiré flat bands with a higher tunability than twisted bilayer graphene (TBG). However, a direct visualization of the flat bands in TMBG and its comparison with the ones in TBG remain unexplored. Here, via fabricating on a single sample with exactly the same twist angle of ∼1.13°, we present a direct comparative study between TMBG and TBG using scanning tunneling microscopy and spectroscopy. We observe a sharp density of states peak near the Fermi energy in tunneling spectroscopy, confirming unambiguously the existence of flat electronic bands in TMBG. The bandwidth of this flat-band peak is found to be slightly narrower than that of the TBG, validating previous theoretical predictions. Remarkably, by measuring spatially resolved spectroscopy, combined with continuum model calculation, we show that the flat-band states in TMBG exhibit a unique layer-resolved localization-delocalization coexisting feature, which offers an unprecedented possibility to utilize their cooperation on exploring novel correlation phenomena. Our work provides important microscopic insight of flat-band states for better understanding the emergent physics in graphene moiré systems.

Aging and Mesenchymal Stem Cells: Therapeutic Opportunities and Challenges in the Older Group.

With aging, a portion of cells, including mesenchymal stem cells (MSCs), become senescent, and these senescent cells accumulate and promote various age-related diseases. Therefore, the older age group has become a major population for MSC therapy, which is aimed at improving tissue regeneration and function of the aged body. However, the application of MSC therapy is often unsatisfying in the aged group. One reasonable conjecture for this correlation is that aging microenvironment reduces the number and function of MSCs. Cellular senescence also plays an important role in MSC function impairment. Thus, it is necessary to explore the relationship between senescence and MSCs for improving the application of MSCs in the elderly. Here, we present the influence of aging on MSCs and the characteristics and functional changes of senescent MSCs. Furthermore, current therapeutic strategies for improving MSC therapy in the elderly group are also discussed.

[Cross subject personality assessment based on electroencephalogram functional connectivity and domain adaptation].

The research shows that personality assessment can be achieved by regression model based on electroencephalogram (EEG). Most of existing researches use event-related potential or power spectral density for personality assessment, which can only represent the brain information of a single region. But some research shows that human cognition is more dependent on the interaction of brain regions. In addition, due to the distribution difference of EEG features among subjects, the trained regression model can not get accurate results of cross subject personality assessment. In order to solve the problem, this research proposes a personality assessment method based on EEG functional connectivity and domain adaption. This research collected EEG data from 45 normal people under different emotional pictures (positive, negative and neutral). Firstly, the coherence of 59 channels in 5 frequency bands was taken as the original feature set. Then the feature-based domain adaptation was used to map the feature to a new feature space. It can reduce the distribution difference between training and test set in the new feature space, so as to reduce the distribution difference between subjects. Finally, the support vector regression model was trained and tested based on the transformed feature set by leave-one-out cross-validation. What's more, this paper compared the methods used in previous researches. The results showed that the method proposed in this paper improved the performance of regression model and obtained better personality assessment results. This research provides a new method for personality assessment.

Portable detection of colorectal cancer SW620 cells by using a personal glucose meter.

It is of great value to develop general, low-cost and even household methods for colorectal cancer detection. Here, a portable detection strategy based on a personal glucose meter (PGM) was designed for meeting this purpose. In this strategy, the anti-EpCAM coated magnet beads (MBs) were used as capture probes for enriching cancer cells and the aptamer modified and invertase loaded graphene oxides (GO) were used as report probes for producing glucose signal. This method is sensitive with detection limit as low as 560 cells, and demonstrates excellent detection specificity. Meanwhile, we succeeded in the specific detection of target cells in 20% human serum samples, indicating this method has great prospect in clinical diagnosis. Moreover, this method presents favourable universality for detecting different colorectal cancer cells by just using different recognition aptamers. Importantly, this method can be implemented for the target cell detection at room temperature without any expensive and large-scale instruments but a portable PGM. Therefore, this portable detection method possesses great potential in point-of-care detection of colorectal cancer cells.

A Dual-Adversarial Model for Cross-Time and Cross-Subject Cognitive Workload Decoding.

Electroencephalogram (EEG) signals are widely utilized in the field of cognitive workload decoding (CWD). However, when the recognition scenario is shifted from subject-dependent to subject-independent or spans a long period, the accuracy of CWD deteriorates significantly. Current solutions are either dependent on extensive training datasets or fail to maintain clear distinctions between categories, additionally lacking a robust feature extraction mechanism. In this paper, we tackle these issues by proposing a Bi-Classifier Joint Domain Adaptation (BCJDA) model for EEG-based cross-time and cross-subject CWD. Specifically, the model consists of a feature extractor, a domain discriminator, and a Bi-Classifier, containing two sets of adversarial processes for domain-wise alignment and class-wise alignment. In the adversarial domain adaptation, the feature extractor is forced to learn the common domain features deliberately. The Bi-Classifier also fosters the feature extractor to retain the category discrepancies of the unlabeled domain, so that its classification boundary is consistent with the labeled domain. Furthermore, different adversarial distance functions of the Bi-Classifier are adopted and evaluated in this model. We conduct classification experiments on a publicly available BCI competition dataset for recognizing low, medium, and high cognitive workload levels. The experimental results demonstrate that our proposed BCJDA model based on cross-gradient difference maximization achieves the best performance.

Intermittent clearance of p21-highly-expressing cells extends lifespan and confers sustained benefits to health and physical function.

A key challenge in aging research is extending lifespan in tandem with slowing down functional decline so that life with good health (healthspan) can be extended. Here, we show that monthly clearance, starting from 20 months, of a small number of cells that highly express p21Cip1 (p21high) improves cardiac and metabolic function and extends both median and maximum lifespans in mice. Importantly, by assessing the health and physical function of these mice monthly until death, we show that clearance of p21high cells improves physical function at all remaining stages of life, suggesting healthspan extension. Mechanistically, p21high cells encompass several cell types with a relatively conserved proinflammatory signature. Clearance of p21high cells reduces inflammation and alleviates age-related transcriptomic signatures of various tissues. These findings demonstrate the feasibility of healthspan extension in mice and indicate p21high cells as a therapeutic target for healthy aging.

A Spiking Neural Network with Adaptive Graph Convolution and LSTM for EEG-Based Brain-Computer Interfaces.

Electroencephalography (EEG) signals classification is essential for the brain-computer interface (BCI). Recently, energy-efficient spiking neural networks (SNNs) have shown great potential in EEG analysis due to their ability to capture the complex dynamic properties of biological neurons while also processing stimulus information through precisely timed spike trains. However, most existing methods do not effectively mine the specific spatial topology of EEG channels and temporal dependencies of the encoded EEG spikes. Moreover, most are designed for specific BCI tasks and lack some generality. Hence, this study presents a novel SNN model with the customized spike-based adaptive graph convolution and long short-term memory (LSTM), termed SGLNet, for EEG-based BCIs. Specifically, we first adopt a learnable spike encoder to convert the raw EEG signals into spike trains. Then, we tailor the concepts of the multi-head adaptive graph convolution to SNN so that it can make good use of the intrinsic spatial topology information among distinct EEG channels. Finally, we design the spike-based LSTM units to further capture the temporal dependencies of the spikes. We evaluate our proposed model on two publicly available datasets from two representative fields of BCI, notably emotion recognition, and motor imagery decoding. The empirical evaluations demonstrate that SGLNet consistently outperforms existing state-of-the-art EEG classification algorithms. This work provides a new perspective for exploring high-performance SNNs for future BCIs with rich spatiotemporal dynamics.

A Temporal-Spectral Fused and Attention-based Deep Model for Automatic Sleep Staging.

Sleep staging is a vital process for evaluating sleep quality and diagnosing sleep-related diseases. Most of the existing automatic sleep staging methods focus on time-domain information and often ignore the transformation relationship between sleep stages. To deal with the above problems, we propose a Temporal-Spectral fused and Attention-based deep neural Network model (TSA-Net) for automatic sleep staging, using a single-channel electroencephalogram (EEG) signal. The TSA-Net is composed of a two-stream feature extractor, feature context learning, and conditional random field (CRF). Specifically, the two-stream feature extractor module can automatically extract and fuse EEG features from time and frequency domains, considering that both temporal and spectral features can provide abundant distinguishing information for sleep staging. Subsequently, the feature context learning module learns the dependencies between features using the multi-head self-attention mechanism and outputs a preliminary sleep stage. Finally, the CRF module further applies transition rules to improve classification performance. We evaluate our model on two public datasets, Sleep-EDF-20 and Sleep-EDF-78. In terms of accuracy, the TSA-Net achieves 86.64% and 82.21% on the Fpz-Cz channel, respectively. The experimental results illustrate that our TSA-Net can optimize the performance of sleep staging and achieve better staging performance than state-of-the-art methods.

MTRT: Motion Trajectory Reconstruction Transformer for EEG-Based BCI Decoding.

Brain computer interface (BCI) is a system that directly uses brain neural activities to communicate with the outside world. Recently, the decoding of the human upper limb based on electroencephalogram (EEG) signals has become an important research branch of BCI. Even though existing research models are capable of decoding upper limb trajectories, the performance needs to be improved to make them more practical for real-world applications. This study is attempt to reconstruct the continuous and nonlinear multi-directional upper limb trajectory based on Chinese sign language. Here, to reconstruct the upper limb motion trajectory effectively, we propose a novel Motion Trajectory Reconstruction Transformer (MTRT) neural network that utilizes the geometric information of human joint points and EEG neural activity signals to decode the upper limb trajectory. Specifically, we use human upper limb bone geometry properties as reconstruction constraints to obtain more accurate trajectory information of the human upper limbs. Furthermore, we propose a MTRT neural network based on this constraint, which uses the shoulder, elbow, and wrist joint point information and EEG signals of brain neural activity during upper limb movement to train its parameters. To validate the model, we collected the synchronization information of EEG signals and upper limb motion joint points of 20 subjects. The experimental results show that the reconstruction model can accurately reconstruct the motion trajectory of the shoulder, elbow, and wrist of the upper limb, achieving superior performance than the compared methods. This research is very meaningful to decode the limb motion parameters for BCI, and it is inspiring for the motion decoding of other limbs and other joints.

Drug-loaded mucoadhesive microneedle patch for the treatment of oral submucous fibrosis.

Oral submucous fibrosis is a chronic, inflammatory and potentially malignant oral disease. Local delivery of triamcinolone to lesion site is a commonly used therapy. The existing methods for local drug delivery include topical administration and submucosal injection. However, in the wet and dynamic oral microenvironment, these methods have drawbacks such as limited drug delivery efficiency and injection pain. Therefore, it is urgently needed to develop an alternative local drug delivery system with high efficiency and painlessness. Inspired by the structure of band-aid, this study proposed a novel double-layered mucoadhesive microneedle patch for transmucosal drug delivery. The patch consisted of a mucoadhesive silk fibroin/tannic acid top-layer and a silk fibroin microneedle under-layer. When applying the annealing condition for the medium content of ß-sheets of silk fibroin, the microneedles in under-layer displayed both superior morphology and mechanical property. The mechanical strength of per needle (0.071N) was sufficient to penetrate the oral mucosa. Sequentially, the gelation efficiency of silk fibroin and tannic acid in top-layer was maximized as the weight ratio of tannic acid to silk fibroin reached 5:1. Moreover, in vitro results demonstrated the double-layered patch possessed undetectable cytotoxicity. The sustained release of triamcinolone was observed from the double-layered patch for at least 7 days. Furthermore, compared with other commercial buccal patches, the double-layered patch exhibited an enhanced wet adhesion strength of 37.74 kPa. In addition, ex vivo mucosal tissue penetration experiment confirmed that the double-layered patch could reach the lamina propria, ensuring effective drug delivery to the lesion site of oral submucous fibrosis. These results illustrate the promising potential of the drug-loaded mucoadhesive microneedle patch for the treatment of oral submucous fibrosis.

A comprehensive review on motion trajectory reconstruction for EEG-based brain-computer interface.

The advance in neuroscience and computer technology over the past decades have made brain-computer interface (BCI) a most promising area of neurorehabilitation and neurophysiology research. Limb motion decoding has gradually become a hot topic in the field of BCI. Decoding neural activity related to limb movement trajectory is considered to be of great help to the development of assistive and rehabilitation strategies for motor-impaired users. Although a variety of decoding methods have been proposed for limb trajectory reconstruction, there does not yet exist a review that covers the performance evaluation of these decoding methods. To alleviate this vacancy, in this paper, we evaluate EEG-based limb trajectory decoding methods regarding their advantages and disadvantages from a variety of perspectives. Specifically, we first introduce the differences in motor execution and motor imagery in limb trajectory reconstruction with different spaces (2D and 3D). Then, we discuss the limb motion trajectory reconstruction methods including experiment paradigm, EEG pre-processing, feature extraction and selection, decoding methods, and result evaluation. Finally, we expound on the open problem and future outlooks.

Tunable non-volatile memories based on 2D InSe/h-BN/GaSe heterostructures towards potential multifunctionality.

Floating-gate memories based on two-dimensional van der Waal (2D vdW) heterostructures play an important role in the development of next-generation information technology. The diversity of 2D vdW materials and their heterostructures provides flexibility in the design of novel storage architectures. However, 2D InSe/h-BN/GaSe heterostructures are rarely reported in the field of tunable non-volatile memories, probably due to the quality limitation of materials and complex interfaces from stackings. Herein, a floating-gate 2D InSe/h-BN/GaSe memory with high performance and atmosphere stability is demonstrated. It exhibits both a large ON/OFF current ratio of ∼105 and a good extinction ratio of ∼103, with an estimated maximum storage capacity of 5.1 × 1012 cm-2. Moreover, the storage performance can be regulated by optimizing the thickness of the insulating h-BN layer. Different device configurations have been explored to validate the working mechanism. Furthermore, a simulation of biological synaptic behavior is achieved on the same prototype device. The enhanced non-volatile characteristics enable the exploration of the integrated 2D memory and potential multifunctionality.

Cross-Task Cognitive Workload Recognition Based on EEG and Domain Adaptation.

Cognitive workload recognition is pivotal to maintain the operator's health and prevent accidents in the human-robot interaction condition. So far, the focus of workload research is mostly restricted to a single task, yet cross-task cognitive workload recognition has remained a challenge. Furthermore, when extending to a new workload condition, the discrepancy of electroencephalogram (EEG) signals across various cognitive tasks limits the generalization of the existed model. To tackle this problem, we propose to construct the EEG-based cross-task cognitive workload recognition models using domain adaptation methods in a leave-one-task-out cross-validation setting, where we view any task of each subject as a domain. Specifically, we first design a fine-grained workload paradigm including working memory and mathematic addition tasks. Then, we explore four domain adaptation methods to bridge the discrepancy between the two different tasks. Finally, based on the supporting vector machine classifier, we conduct experiments to classify the low and high workload levels on a private EEG dataset. Experimental results demonstrate that our proposed task transfer framework outperforms the non-transfer classifier with improvements of 3% to 8% in terms of mean accuracy, and the transfer joint matching (TJM) consistently achieves the best performance.

Estimating High-Order Brain Functional Networks in Bayesian View for Autism Spectrum Disorder Identification.

Brain functional network (BFN) has become an increasingly important tool to understand the inherent organization of the brain and explore informative biomarkers of neurological disorders. Pearson's correlation (PC) is the most widely accepted method for constructing BFNs and provides a basis for designing new BFN estimation schemes. Particularly, a recent study proposes to use two sequential PC operations, namely, correlation's correlation (CC), for constructing the high-order BFN. Despite its empirical effectiveness in identifying neurological disorders and detecting subtle changes of connections in different subject groups, CC is defined intuitively without a solid and sustainable theoretical foundation. For understanding CC more rigorously and providing a systematic BFN learning framework, in this paper, we reformulate it in the Bayesian view with a prior of matrix-variate normal distribution. As a result, we obtain a probabilistic explanation of CC. In addition, we develop a Bayesian high-order method (BHM) to automatically and simultaneously estimate the high- and low-order BFN based on the probabilistic framework. An efficient optimization algorithm is also proposed. Finally, we evaluate BHM in identifying subjects with autism spectrum disorder (ASD) from typical controls based on the estimated BFNs. Experimental results suggest that the automatically learned high- and low-order BFNs yield a superior performance over the artificially defined BFNs via conventional CC and PC.

The Diagnostic Value of the Thermostatic Amplification of Ribonucleic Acid in Bronchoalveolar Lavage Fluid in Smear-Negative Pulmonary Tuberculosis.

Objective: This study aimed to determine the value of the simultaneous amplification and testing for Mycobacterium tuberculosis in bronchoalveolar lavage fluid (BALF) in the diagnosis of smear-negative pulmonary tuberculosis (PTB). Methods: A total of 316 patients were selected, of which 197 had smear-negative PTB (observation group), and 119 did not have TB (control group). Bronchoscopy was performed in both groups, and BALF samples were collected for acid-fast bacilli smears, simultaneous amplification/testing for TB (SAT-TB), and BACTEC MGIT 960 cultures. The sensitivity, specificity, positive predictive, and negative predictive values of SAT-TB in BALF for the diagnosis of negative TB were calculated. Results: The sensitivity of SAT-TB detection was 45.18%, which was significantly higher than smears and slightly lower than cultures. The specificity of SAT-TB was 99.16%, which differed slightly from the other two methods. The positive predictive value was 98.89%, which was not significantly different from the other two methods. The negative predictive value of SAT-TB was 58.91%, which was higher than smears and slightly lower than cultures. Conclusion: The very high specificity and negative prediction of SAT-TB in BALF means that the method has great application value for the rapid diagnosis of smear-negative PTB.

Cellular Senescence and Periodontitis: Mechanisms and Therapeutics.

Periodontitis is a chronic inflammatory disease which increases in prevalence and severity in the older population. Aging is a leading risk factor for periodontitis, which exacerbates alveolar bone loss and results in tooth loss in the elderly. However, the mechanism by which aging affects periodontitis is not well understood. There is considerable evidence to suggest that targeting cellular senescence could slow down the fundamental aging process, and thus alleviate a series of age-related pathological conditions, likely including alveolar bone loss. Recently, it has been discovered that the senescent cells accumulate in the alveolar bone and promote a senescence-associated secretory phenotype (SASP). Senescent cells interacting with bacteria, together with secreted SASP components altering the local microenvironment and inducing paracrine effects in neighboring cells, exacerbate the chronic inflammation in periodontal tissue and lead to more alveolar bone loss. This review will probe into mechanisms underlying excessive alveolar bone loss in periodontitis with aging and discuss potential therapeutics for the treatment of alveolar bone loss targeting cellular senescence and the SASP. Inspecting the relationship between cellular senescence and periodontitis will lead to new avenues of research in this field and contribute to developing potential translatable clinical interventions to mitigate or even reverse the harmful effects of aging on oral health.

A mussel-pearl side chain interaction in mercury(II) and phenol removal by sulfur-functionalized covalent organic frameworks: A DFT study.

The covalent organic framework materials (COFs) with excellent chemical and physical characteristics have been rapidly developed as adsorbents in the application of environmental remediation. In the design of COFs, the selection of functional groups and side chains is of great significance. Herein, density function theory (DFT) method is used to illustrate the adsorption behavior and mechanism of three sulfur-functionalized COFs (S-COFs) for the adsorption of mercury(II) and phenol. According to the analysis of geometric configurations and electronic properties, it demonstrated that the side chains of S-COFs with high flexibility and concentrated sulfur-functional groups, acting like a closed mussel which tightly confined the contaminants, the highest adsorption was -24.32 kcal/mol. The adsorption mechanism of phenol and mercury(II) on S-COFs was elucidated. For phenol, hydrogen bonds and π-π stacking interaction played an important role in the adsorption process, while the coordination interaction was dominated for the adsorption of mercury(II). This research explains the importance of selecting appropriate functional groups and side chains for COFs in the removal of contaminants in the molecular scale, and reveals the great potential of COFs in environmental remediation applications.

Increased levels of lactate dehydrogenase and hypertension are associated with severe illness of COVID-19.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is amid an ongoing pandemic. It has been shown that patients with cardiovascular comorbidities are at higher risk of severe illness of COVID-19. AIM: To find out the relationship between cardiovascular comorbidities and severe illness of COVID-19. METHODS: The clinical data of 140 COVID-19 patients treated from January 22, 2020 to March 3, 2020 at our hospital were retrospectively collected. The clinical characteristics were compared between patients with mild illness and those with severe illness. RESULTS: There were 75 male patients and 65 female patients (53.6% vs 46.4%). The mean age was 45.4 ± 14.6 years (range, 2-85 years). Most of the patients had mild illness (n = 114, 81.4%) and 26 patients had severe illness (18.6%). The most common symptom was fever (n = 110, 78.6%), followed by cough (n = 82, 58.6%) and expectoration (n = 51, 36.4%). Eight patients were asymptomatic but were positive for severe acute respiratory syndrome coronavirus 2 RNA. Patients with severe illness were significantly more likely to be hypertensive than those with mild illness [(10/26, 38.4%) vs (22/114, 19.3%), P = 0.036]. The levels of lactate dehydrogenase were significantly higher in the severe illness group than in the mild illness group (299.35 ± 68.82 vs 202.94 ± 63.87, P < 0.001). No patient died in either the severe illness or the mild illness group. CONCLUSION: Hypertension and elevated levels of lactate dehydrogenase may be associated with severe illness of COVID-19.