Nitrogen-rich and core-sheath polyamide/polyethyleneimine@Zr-MOF for iodine adsorption and nerve agent simulant degradation.

Radioactive nuclides and highly toxic organophosphates are typical deadly threats. Materials with the function of radioactive substances adsorption and organophosphates degradation provide double protection. Herein, dual-functional polyamide (PA)/polyethyleneimine (PEI)@Zr-MOF fiber composite membranes, fabricated by in-situ solvothermal growth of Zr-MOF on PA/PEI electrospun fiber membranes, are designed for protection against two typical model compounds of iodine and dimethyl 4-nitrophenyl phosphate (DMNP). Benefiting from the unique core-sheath structure composed of inner nitrogen-rich fibers and outer porous Zr-MOF, the composite membranes rapidly enrich iodine through abundant active sites of the outer sheath and form complexes with the amine of inner PEI, exhibiting a highly competitive adsorption capacity of 609 mg g-1. Moreover, it can adsorb and degrade DMNP with the synergy of PEI component and Zr-MOF, achieving an 80 % removal of DMNP within 7 min without any additional co-catalyst. This work provides a feasible strategy to fabricate dual-functional materials that protect against radioactive and organophosphorus contaminants.

Correction: Motivating factors for physical activity participation among individuals with chronic obstructive pulmonary disease: A qualitative study applying the motivation, opportunity, and ability model.

[This corrects the article DOI: 10.1371/journal.pone.0303858.].

Motivating factors for physical activity participation among individuals with chronic obstructive pulmonary disease: A qualitative study applying the motivation, opportunity, and ability model.

OBJECTIVE: The study aims to explore the driving forces behind physical activity engagement among patients with chronic obstructive pulmonary disease, focusing on motivation, opportunity, and capability. DESIGN: A phenomenological qualitative study applied the motivation, opportunity, and capability model, conducted in two respiratory units of a Chinese university hospital. METHODS: Participants, selected by age, gender, and illness duration, included inpatients during the interview sessions and those recently discharged within six months. One-on-one semi-structured interviews were recorded, transcribed, and analyzed by the Colaizzi seven-step method. RESULTS: Seventeen participants diagnosed with chronic obstructive pulmonary disease for over one year aged between 66 (range: 42-96) participated. Three major themes were identified: Inspiring participation motivation-transitioning from recognizing significance to habit formation; Offering participation opportunities-reiterating demand for personalized strategies and ideal environmental settings; Enhancing participation capability-addressing strategies for overcoming fears, setting goals, ensuring safety, and adjusting activity levels. CONCLUSIONS: This research underscores the vital role of inspiring participation motivation, offering opportunities, and enhancing the capability for participation in effective engagement. Advocating increased attention from healthcare departments, fostering interdisciplinary collaboration, improving activity guidance and counseling effectiveness, and considering individual preferences can significantly benefit those patients with chronic obstructive pulmonary disease who hesitate or are unable to participate in physical activities, thereby increasing the dose of non-leisure time physical activity.

Validation of Baveno VII criteria for clinically significant portal hypertension by two-dimensional shear wave elastography.

BACKGROUND: The Baveno VII consensus proposed criteria for the non-invasively diagnosis of clinically significant portal hypertension (CSPH) in patients with compensated advanced chronic liver disease (cACLD). The performance of Baveno VII criteria for assessing CSPH by two-dimensional shear wave elastography (2D-SWE) had not been well validated. We aimed to validate the performance of Baveno VII criteria for rule-in and rule-out CSPH by 2D-SWE. METHOD: This is an international multicenter study including cACLD patients from China and Croatia with paired liver stiffness measurement (LSM), spleen stiffness measurement (SSM) by 2D-SWE, and hepatic venous pressure gradient (HVPG) were included. CSPH was defined as HVPG ≥ 10 mmHg. RESULT: A total of 146 patients with cACLD were enrolled, and finally 118 patients were included in the analysis. Among them, CSPH was documented in 79 (66.9%) patients. Applying the Baveno VII criteria for rule-out CSPH by 2D-SWE, [LSM ≤ 15 kPa and platelet count ≥ 150 × 109/L] OR SSM < 21 kPa, could exclude CSPH with sensitivity > 90% (93.5 or 98.7%) but negative predictive value < 90% (74.1 or 85.7%). Using the Baveno VII criteria for rule-in CSPH by 2D-SWE, LSM ≥ 25 kPa OR SSM ≥ 50 kPa, could diagnose CSPH with 100% specificity and 100% positive predictive values. CONCLUSION: Baveno VII criteria by 2D-SWE showed a good diagnostic performance for ruling in but not for ruling out CSPH, which might become an emerging non-invasive elastography tool to select the patients who needed non-selective beta blocker therapy.

Copper Catalyzed Three-Component Ullmann C-S Coupling in PEG for the Synthesis of 6-Aryl/alkylthio-purines.

To tackle the environmental unfriendly issue in existing synthesis strategies for 6-substitued thiopurine derivatives, such as poor step economy, frequent use of malodorous organic sulfur starting materials, toxic organic solvents, and equivalent dosage of base, we have developed a CuI-catalyzed base-free three-component Ullmann C-S coupling synthetic strategy, featured using inorganic salt Na2S as the sulfur source and nontoxic PEG-600 as the solvent. The newly developed strategy is particularly effective for the synthesis of 6-arylthiopurines. The high catalytic efficiency in PEG-600 can be rationalized by the high soluble ability of CuI catalyst, likely due to the presence of multiple oxygen coordination sites in PEG.

Recent progress in TiO2-biochar-based photocatalysts for water contaminants treatment: strategies to improve photocatalytic performance.

Toxic organic pollutants in wastewater have seriously damaged human health and ecosystems. Photocatalytic degradation is a potential and efficient tactic for wastewater treatment. Among the entire carbon family, biochar has been developed for the adsorption of pollutants due to its large specific surface area, porous skeleton structure, and abundant surface functional groups. Hence, combining adsorption and photocatalytic decomposition, TiO2-biochar photocatalysts have received considerable attention and have been extensively studied. Owing to biochar's adsorption, more active sites and strong interactions between contaminants and photocatalysts can be achieved. The synergistic effect of biochar and TiO2 nanomaterials substantially improves the photocatalytic capacity for pollutant degradation. TiO2-biochar composites have numerous attractive properties and advantages, culminating in infinite applications. This review discusses the characteristics and preparation techniques of biochar, presents in situ and ex situ synthesis approaches of TiO2-biochar nanocomposites, explains the benefits of TiO2-biochar-based compounds for photocatalytic degradation, and emphasizes the strategies for enhancing the photocatalytic efficiency of TiO2-biochar-based photocatalysts. Finally, the main difficulties and future advancements of TiO2-biochar-based photocatalysis are highlighted. The review gives an exhaustive overview of recent progress in TiO2-biochar-based photocatalysts for organic contaminants removal and is expected to encourage the development of robust TiO2-biochar-based photocatalysts for sewage remediation and other environmentally friendly uses.

Research progress in artificial intelligence assisted diabetic retinopathy diagnosis.

Diabetic retinopathy (DR) is one of the most common retinal vascular diseases and one of the main causes of blindness worldwide. Early detection and treatment can effectively delay vision decline and even blindness in patients with DR. In recent years, artificial intelligence (AI) models constructed by machine learning and deep learning (DL) algorithms have been widely used in ophthalmology research, especially in diagnosing and treating ophthalmic diseases, particularly DR. Regarding DR, AI has mainly been used in its diagnosis, grading, and lesion recognition and segmentation, and good research and application results have been achieved. This study summarizes the research progress in AI models based on machine learning and DL algorithms for DR diagnosis and discusses some limitations and challenges in AI research.

A General Synthesis of Cross-Conjugated Enynones through Pd Catalyzed Sonogashira Coupling with Triazine Esters.

The palladium-catalyzed Sonogashira coupling of α, ß-unsaturated acid derivatives offers a diversity-oriented synthetic strategy for cross-conjugated enynones. However, the susceptibility of the unsaturated C-C bonds adjacent to the carbonyl group toward Pd catalysts makes the direct conversion of α, ß-unsaturated derivatives as acyl electrophiles to cross-conjugated ketones rare. This work presents a highly selective C-O activation approach to prepare cross-conjugated enynones using α, ß-unsaturated triazine esters as acyl electrophiles. Under base and phosphine ligand-free conditions, NHC-Pd(II)-Allyl precatalyst alone catalyzed the cross-coupling of α, ß-unsaturated triazine esters with terminal alkynes efficiently, yielding 31 cross-conjugated enynones with diverse functional groups. This method demonstrates the potential of triazine-mediated C-O activation for preparing highly functionalized ketones.

Palladium-catalysed Suzuki-Miyaura coupling of α,ß-unsaturated superactive triazine esters.

Palladium-catalysed Suzuki-Miyaura couplings of α,ß-unsaturated acid derivatives are challenging due to the susceptibility of their CC bonds adjacent to carbonyl groups. In this work, we describe a highly selective C-O activation approach to this transformation using superactive triazine esters and organoborons as coupling partners. 42 α,ß-unsaturated ketones with diverse functional groups have been prepared with this method. The mechanistic investigation unveiled that the dual function of triazine for activating the C-O bond and stabilizing non-covalent interactions between the catalyst and substrate is critical for the reaction's success. The method's efficiency, functional group compatibility and unique mechanism make it a valuable alternative to classic methods.

Research progress on diagnosing retinal vascular diseases based on artificial intelligence and fundus images.

As the only blood vessels that can directly be seen in the whole body, pathological changes in retinal vessels are related to the metabolic state of the whole body and many systems, which seriously affect the vision and quality of life of patients. Timely diagnosis and treatment are key to improving vision prognosis. In recent years, with the rapid development of artificial intelligence, the application of artificial intelligence in ophthalmology has become increasingly extensive and in-depth, especially in the field of retinal vascular diseases. Research study results based on artificial intelligence and fundus images are remarkable and provides a great possibility for early diagnosis and treatment. This paper reviews the recent research progress on artificial intelligence in retinal vascular diseases (including diabetic retinopathy, hypertensive retinopathy, retinal vein occlusion, retinopathy of prematurity, and age-related macular degeneration). The limitations and challenges of the research process are also discussed.

Hypermixed Convolutional Neural Network for Retinal Vein Occlusion Classification.

Retinal vein occlusion (RVO) is one of the most common retinal vascular diseases leading to vision loss if not diagnosed and treated in time. RVO can be classified into two types: CRVO (blockage of the main retinal veins) and BRVO (blockage of one of the smaller branch veins). Automated diagnosis of RVO can improve clinical workflow and optimize treatment strategies. However, to the best of our knowledge, there are few reported methods for automated identification of different RVO types. In this study, we propose a new hypermixed convolutional neural network (CNN) model, namely, the VGG-CAM network, that can classify the two types of RVOs based on retinal fundus images and detect lesion areas using an unsupervised learning method. The image data used in this study is collected and labeled by three senior ophthalmologists in Shanxi Eye Hospital, China. The proposed network is validated to accurately classify RVO diseases and detect lesions. It can potentially assist in further investigating the association between RVO and brain vascular diseases and evaluating the optimal treatments for RVO.

Prediction of corneal astigmatism based on corneal tomography after femtosecond laser arcuate keratotomy using a pix2pix conditional generative adversarial network.

Purpose: This study aimed to develop a deep learning model to generate a postoperative corneal axial curvature map of femtosecond laser arcuate keratotomy (FLAK) based on corneal tomography using a pix2pix conditional generative adversarial network (pix2pix cGAN) for surgical planning. Methods: A total of 451 eyes of 318 nonconsecutive patients were subjected to FLAK for corneal astigmatism correction during cataract surgery. Paired or single anterior penetrating FLAKs were performed at an 8.0-mm optical zone with a depth of 90% using a femtosecond laser (LenSx laser, Alcon Laboratories, Inc.). Corneal tomography images were acquired from Oculus Pentacam HR (Optikgeräte GmbH, Wetzlar, Germany) before and 3 months after the surgery. The raw data required for analysis consisted of the anterior corneal curvature for a range of ± 3.5 mm around the corneal apex in 0.1-mm steps, which the pseudo-color corneal curvature map synthesized was based on. The deep learning model used was a pix2pix conditional generative adversarial network. The prediction accuracy of synthetic postoperative corneal astigmatism in zones of different diameters centered on the corneal apex was assessed using vector analysis. The synthetic postoperative corneal axial curvature maps were compared with the real postoperative corneal axial curvature maps using the structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR). Results: A total of 386 pairs of preoperative and postoperative corneal tomography data were included in the training set, whereas 65 preoperative data were retrospectively included in the test set. The correlation coefficient between synthetic and real postoperative astigmatism (difference vector) in the 3-mm zone was 0.89, and that between surgically induced astigmatism (SIA) was 0.93. The mean absolute errors of SIA for real and synthetic postoperative corneal axial curvature maps in the 1-, 3-, and 5-mm zone were 0.20 ± 0.25, 0.12 ± 0.17, and 0.09 ± 0.13 diopters, respectively. The average SSIM and PSNR of the 3-mm zone were 0.86 ± 0.04 and 18.24 ± 5.78, respectively. Conclusion: Our results showed that the application of pix2pix cGAN can synthesize plausible postoperative corneal tomography for FLAK, showing the possibility of using GAN to predict corneal tomography, with the potential of applying artificial intelligence to construct surgical planning models.

Multi-Model Domain Adaptation for Diabetic Retinopathy Classification.

Diabetic retinopathy (DR) is one of the most threatening complications in diabetic patients, leading to permanent blindness without timely treatment. However, DR screening is not only a time-consuming task that requires experienced ophthalmologists but also easy to produce misdiagnosis. In recent years, deep learning techniques based on convolutional neural networks have attracted increasing research attention in medical image analysis, especially for DR diagnosis. However, dataset labeling is expensive work and it is necessary for existing deep-learning-based DR detection models. For this study, a novel domain adaptation method (multi-model domain adaptation) is developed for unsupervised DR classification in unlabeled retinal images. At the same time, it only exploits discriminative information from multiple source models without access to any data. In detail, we integrate a weight mechanism into the multi-model-based domain adaptation by measuring the importance of each source domain in a novel way, and a weighted pseudo-labeling strategy is attached to the source feature extractors for training the target DR classification model. Extensive experiments are performed on four source datasets (DDR, IDRiD, Messidor, and Messidor-2) to a target domain APTOS 2019, showing that MMDA produces competitive performance for present state-of-the-art methods for DR classification. As a novel DR detection approach, this article presents a new domain adaptation solution for medical image analysis when the source data is unavailable.

Genetic dissection of glutathione S-transferase omega-1: identification of novel downstream targets and Alzheimer's disease pathways.

Alzheimer's disease (AD) is affected by genetic factors. Polymorphisms in the glutathione S-transferase omega-1 (Gsto1) gene have been shown by genetic correlation analyses performed in different ethnic populations to be genetic risk factors for AD. Gene expression profile data from BXD recombinant inbred mice were used in combination with genetic and bioinformatic analyses to characterize the mechanisms underlying regulation of Gsto1 variation regulation and to identify network members that may contribute to AD risk or progression. Allele-specific assays confirmed that variation in Gsto1 expression is controlled by cis-expression quantitative trait loci. We found that Gsto1 mRNA levels were related to several central nervous system traits, such as glial acidic fibrillary protein levels in the caudate putamen, cortical gray matter volume, and hippocampus mossy fiber pathway volume. We identified 2168 genes whose expression was highly correlated with that of Gsto1. Some genes were enriched for the most common neurodegenerative diseases. Some Gsto1-related genes identified in this study had previously been identified as susceptibility genes for AD, such as APP, Grin2b, Ide, and Psenen. To evaluate the relationships between Gsto1 and candidate network members, we transfected astrocytes with Gsto1 siRNA and assessed the effect on putative downstream effectors. We confirmed that knockdown of Gsto1 had a significant influence on Pa2g4 expression, suggesting that Pa2g4 may be a downstream effector of Gsto1, and that both genes interact with other genes in a network during AD pathogenesis.

The prevalence and influencing factors for compassion fatigue among nurses in Fangcang shelter hospitals: A cross-sectional study.

AIM: We aim to investigate the prevalence and associated factors for compassion fatigue among nurses in Fangcang Shelter Hospitals in Wuhan. Studies have shown that compassion fatigue was more common among nurses than other health-care providers, and its predictors were also different. In recent years, most studies have investigated compassion fatigue in emergency and oncology nurses, whereas there is little information on compassion fatigue among nurses from the frontline of Fangcang Shelter Hospitals during the COVID-19 pandemic. METHODS: A descriptive, cross-sectional design was used in this study. An online survey was conducted among nurses (n = 972) of five Fangcang Shelter Hospitals in Wuhan, Hubei province, China, from 6 March to 10 March 2020. A self-administered questionnaire including demographic information, work-related information, General Health Questionnaire, Perceived Stress Scale and Compassion Fatigue Scale was used. RESULTS: The prevalence of compassion fatigue among nurses in Fangcang Shelter Hospitals was moderate, and most cases were mild. There was a significant relationship between compassion fatigue and work-related factors, mental health and perceived stress among nurses working in Fangcang Shelter Hospitals. CONCLUSIONS: Various factors contribute to compassion fatigue, including lower job satisfaction and job adaptability, less praise from patients, more fear of infection and more perceived stress. A good working atmosphere, organizational support and psychological consultation are essential to alleviate nurses' compassion fatigue during the anti-epidemic period.

Advances in artificial intelligence applications for ocular surface diseases diagnosis.

In recent years, with the rapid development of computer technology, continual optimization of various learning algorithms and architectures, and establishment of numerous large databases, artificial intelligence (AI) has been unprecedentedly developed and applied in the field of ophthalmology. In the past, ophthalmological AI research mainly focused on posterior segment diseases, such as diabetic retinopathy, retinopathy of prematurity, age-related macular degeneration, retinal vein occlusion, and glaucoma optic neuropathy. Meanwhile, an increasing number of studies have employed AI to diagnose ocular surface diseases. In this review, we summarize the research progress of AI in the diagnosis of several ocular surface diseases, namely keratitis, keratoconus, dry eye, and pterygium. We discuss the limitations and challenges of AI in the diagnosis of ocular surface diseases, as well as prospects for the future.

The Formation of 14H-LPSO in Mg-9Gd-2Y-2Zn-0.5Zr Alloy during Heat Treatment.

There is a new long-period stacking ordered structure in Mg-RE-Zn magnesium alloys, namely the LPSO phase, which can effectively improve the yield strength, elongation, and corrosion resistance of Mg alloys. According to different types of Mg-RE-Zn alloy systems, two transformation modes are involved in the heat treatment transformation process. The first is the alloy without LPSO phase in the as-cast alloy, and the MgxRE phase changes to 14H-LPSO phase. The second is the alloy containing LPSO phase in the as-cast state, and the 14H-LPSO phase is obtained by the transformations of 6H, 18R, and 24R. The effects of different solution parameters on the second phase of Mg-9Gd-2Y-2Zn-0.5Zr alloy were studied by scanning electron microscopy (SEM), transmission electron microscopy (TEM), and X-ray diffraction (XRD). The precipitation mechanism of 14H-LPSO phase during solution treatment was further clarified. At a solution time of 13 h, the grain size increased rapidly initially and then decreased slightly with increasing solution temperature. The analysis of the volume fraction of the second phase and lattice constant showed that Gd and Y elements in the alloy precipitated from the matrix and formed 14H-LPSO phase after solution treatment at 490 °C for 13 h. At this time, the hardness of the alloy reached the maximum of 74.6 HV. After solution treatment at 500 °C for 13 h, the solid solution degree of the alloy increases, and the grain size and hardness of the alloy remain basically unchanged.

Research on an Intelligent Lightweight-Assisted Pterygium Diagnosis Model Based on Anterior Segment Images.

AIMS: The lack of primary ophthalmologists in China results in the inability of basic-level hospitals to diagnose pterygium patients. To solve this problem, an intelligent-assisted lightweight pterygium diagnosis model based on anterior segment images is proposed in this study. METHODS: Pterygium is a common and frequently occurring disease in ophthalmology, and fibrous tissue hyperplasia is both a diagnostic biomarker and a surgical biomarker. The model diagnosed pterygium based on biomarkers of pterygium. First, a total of 436 anterior segment images were collected; then, two intelligent-assisted lightweight pterygium diagnosis models (MobileNet 1 and MobileNet 2) based on raw data and augmented data were trained via transfer learning. The results of the lightweight models were compared with the clinical results. The classic models (AlexNet, VGG16 and ResNet18) were also used for training and testing, and their results were compared with the lightweight models. A total of 188 anterior segment images were used for testing. Sensitivity, specificity, F1-score, accuracy, kappa, area under the concentration-time curve (AUC), 95% CI, size, and parameters are the evaluation indicators in this study. RESULTS: There are 188 anterior segment images that were used for testing the five intelligent-assisted pterygium diagnosis models. The overall evaluation index for the MobileNet2 model was the best. The sensitivity, specificity, F1-score, and AUC of the MobileNet2 model for the normal anterior segment image diagnosis were 96.72%, 98.43%, 96.72%, and 0976, respectively; for the pterygium observation period anterior segment image diagnosis, the sensitivity, specificity, F1-score, and AUC were 83.7%, 90.48%, 82.54%, and 0.872, respectively; for the surgery period anterior segment image diagnosis, the sensitivity, specificity, F1-score, and AUC were 84.62%, 93.50%, 85.94%, and 0.891, respectively. The kappa value of the MobileNet2 model was 77.64%, the accuracy was 85.11%, the model size was 13.5 M, and the parameter size was 4.2 M. CONCLUSION: This study used deep learning methods to propose a three-category intelligent lightweight-assisted pterygium diagnosis model. The developed model can be used to screen patients for pterygium problems initially, provide reasonable suggestions, and provide timely referrals. It can help primary doctors improve pterygium diagnoses, confer social benefits, and lay the foundation for future models to be embedded in mobile devices.

Prevalence and Influencing Factors on Fatigue of First-line Nurses Combating with COVID-19 in China: A Descriptive Cross-Sectional Study.

Nurses' work-related fatigue has been recognized as a threat to nurse health and patient safety. The aim of this study was to assess the prevalence of fatigue among first-line nurses combating with COVID-19 in Wuhan, China, and to analyze its influencing factors on fatigue. A multi-center, descriptive, cross-sectional design with a convenience sample was used. The statistical population consisted of the first-line nurses in 7 tertiary general hospitals from March 3, 2020 to March 10, 2020 in Wuhan of China. A total of 2667 samples from 2768 contacted participants completed the investgation, with a response rate of 96.35%. Social-demographic questionnaire, work-related questionnaire, Fatigue Scale-14, Generalized Anxiety Disorder-7, Patient Health Questionnaire-9, and Chinese Perceived Stress Scale were used to conduct online survey. The descriptive statistic of nurses' social-demographic characteristics was conducted, and the related variables of work, anxiety, depression, perceived stress and fatigue were analyzed by t-tests, nonparametric test and Pearson's correlation analysis. The significant factors which resulted in nurses' fatigue were further analyzed by multiple linear regression analysis. The median score for the first-line nurses' fatigue in Wuhan was 4 (2, 8). The median score of physical and mental fatigue of them was 3 (1, 6) and 1 (0, 3) respectively. According to the scoring criteria, 35.06% nurses (n=935) of all participants were in the fatigue status, their median score of fatigue was 10 (8, 11), and the median score of physical and mental fatigue of them was 7 (5, 8) and 3 (2, 4) respectively. Multiple linear regression analysis revealed the participants in the risk groups of anxiety, depression and perceived stress had higher scores on physical and mental fatigue and the statistically significant positive correlation was observed between the variables and nurses' fatigue, the frequency of exercise and nurses' fatigue had a statistically significant negative correlation, and average daily working hours had a significantly positive correlation with nurses' fatigue, and the frequency of weekly night shift had a low positive correlation with nurses' fatigue (P<0.01). There was a moderate level of fatigue among the first-line nurses fighting against COVID-19 pandemic in Wuhan, China. Government and health authorities need to formulate and take effective intervention strategies according to the relevant risk factors, and undertake preventive measures aimed at reducing health hazards due to increased work-related fatigue among first-line nurses, and to enhance their health status and provide a safe occupational environment worldwide. Promoting both medical and nursing safety while combating with the pandemic currently is warranted.

Factors associated with insomnia among Chinese front-line nurses fighting against COVID-19 in Wuhan: A cross-sectional survey.

AIM: To investigate the prevalence of insomnia among front-line nurses fighting against COVID-19 in Wuhan, China, and analyse its influencing factors. BACKGROUND: Insomnia is an important factor that can affect the health and work quality of nurses. However, there is a lack of big-sample studies exploring factors that affect the insomnia of nurses fighting against COVID-19. METHOD: This cross-sectional study using the Ascension Insomnia Scale, Fatigue Scale-14 and Perceived Stress Scale took place in March 2020. Participants were 1,794 front-line nurses from four tertiary-level general hospitals. RESULTS: The prevalence of insomnia among participants was 52.8%. Insomnia was predicted by gender, working experience, chronic diseases, midday nap duration, direct participation in the rescue of patients with COVID-19, frequency of night shifts, professional psychological assistance during the pandemic, negative experiences (such as family, friends or colleagues being seriously ill or dying due to COVID-19), the degree of fear of COVID-19, fatigue and perceived stress. CONCLUSION: The level of insomnia among participants was higher than the normal level. Interventions based on influencing factors should be implemented to ensure nurses' sleep quality. IMPLICATIONS FOR NURSING MANAGEMENT: An in-depth understanding of the influencing factors of insomnia among front-line nurses can help nurse managers develop solutions to improve front-line nurses' sleep quality, which will enhance the physical and mental conditions of nurses and promote the quality of care.