Impact on sulfadiazine bio-accessibility in soils through organic diffusive gradients in thin films (o-DGT): Differentiation based on microplastic polymers, aging, and soil properties.

Due to the similar sources of swage irrigation, organic fertilizer, and sludge application, microplastics (MPs) and antibiotics coexist inevitably in the agriculture soils. However, the impacts of MPs with different polymer types and aging status on the bio-accessibility of co-existing antibiotics in soils remained unclear. Therefore, we using the diffusive gradients films for organic compounds devices (o-DGT) to evaluated the distribution of sulfadiazine (SDZ) in both paddy soil and saline soil amended with 0.5 % (w/w) MPs. Four polymer types (polyethylene: PE, polypropylene: PP, polyamide: PA, and polyethylene terephthalate: PET) and two aging statuses (aged PE and aged PP) of MPs were used in this study. Results showed that soil properties significantly influence the partition of SDZ in soil and soil solution, and SDZ gained a lower degradation rate but higher mobility in saline soil. MPs pose different impacts on partition of SDZ between paddy soil and saline soil. Notably, PP reduced the labile solid phase-solution phase partition coefficient (Kdl) by 17.7 % in paddy soil, while PE, PP, and aPE increased the Kdl value by 2.00, 1.62, and 2.81 times in saline soil. Besides, in saline soil, all the MPs reduced the SDZ concentration in the soil solution, while significantly increased the SDZ in o-DGT phase. Conversely, MPs did not impact the SDZ's o-DGT concentration in paddy soil. Additionally, MPs increased the R value of SDZ in two soils, especially in saline soil. It suggested that MPs could potentially enhance the resupply of SDZ from soil to plants, particularly under saline conditions. Furthermore, aged MPs had a more pronounced effect on these indicators compared to virgin MPs in saline soil. Therefore, MPs in soil poses a potential risk for biota's uptake of SDZ, particularly in fragile environment. Moreover, the risk intensifies with aged MPs.

Prediction of drug-disease associations based on reinforcement symmetric metric learning and graph convolution network.

Accurately identifying novel indications for drugs is crucial in drug research and discovery. Traditional drug discovery is costly and time-consuming. Computational drug repositioning can provide an effective strategy for discovering potential drug-disease associations. However, the known experimentally verified drug-disease associations is relatively sparse, which may affect the prediction performance of the computational drug repositioning methods. Moreover, while the existing drug-disease prediction method based on metric learning algorithm has achieved better performance, it simply learns features of drugs and diseases only from the drug-centered perspective, and cannot comprehensively model the latent features of drugs and diseases. In this study, we propose a novel drug repositioning method named RSML-GCN, which applies graph convolutional network and reinforcement symmetric metric learning to predict potential drug-disease associations. RSML-GCN first constructs a drug-disease heterogeneous network by integrating the association and feature information of drugs and diseases. Then, the graph convolutional network (GCN) is applied to complement the drug-disease association information. Finally, reinforcement symmetric metric learning with adaptive margin is designed to learn the latent vector representation of drugs and diseases. Based on the learned latent vector representation, the novel drug-disease associations can be identified by the metric function. Comprehensive experiments on benchmark datasets demonstrated the superior prediction performance of RSML-GCN for drug repositioning.

[A Family with Congenital Dysfibrinogenemia and Blood Transfusion].

OBJECTIVE: To investigate a family with congenital dysfibrinogenemia, and analyze the risk of hemorrhage and thrombosis and blood transfusion strategies. METHODS: Prothrombin time (PT), activated partial thromboplastin time (APTT) and thrombin time (TT) of the proband and her family members were detected by automatic coagulometer, fibrinogen (Fg) activity and antigen were detected by Clauss method and PT algorithm respectively. Meanwhile, thromboelastometry was analyzed for proband and her family members. Then, peripheral blood samples of the proband and her family members were collected, and all exons of FGA, FGB and FGG and their flanks were amplified by PCR and sequenced to search for gene mutations. RESULTS: The proband had normal APTT and PT, slightly prolonged TT, reduced level of Fg activity (Clauss method). The Fg of the proband's aunt, son and daughter all decreased to varying degrees. The results of thromboelastogram indicated that Fg function of the proband and her family members (except her son) was basically normal. Gene analysis showed that there were 6233 G/A (p.AαArg35His) heterozygous mutations in exon 2 of FGA gene in the proband, her children and aunt. In addition, 2 polymorphic loci were found in the family, they were FGA gene g.9308A/G (p.AαThr331Ala) and FGB gene g.12628G/A (p.BßArg478Iys) polymorphism, respectively. The proband was injected with 10 units of cryoprecipitate 2 hours before delivery to prevent bleeding, and no obvious bleeding occurred during and after delivery. CONCLUSION: Heterozygous mutation of 6233G/A (p.AαArg35His) of FGA gene is the biogenetic basis of the disease in this family with congenital dysfibrinogenemia.

High-resolution single-photon imaging with physics-informed deep learning.

High-resolution single-photon imaging remains a big challenge due to the complex hardware manufacturing craft and noise disturbances. Here, we introduce deep learning into SPAD, enabling super-resolution single-photon imaging with enhancement of bit depth and imaging quality. We first studied the complex photon flow model of SPAD electronics to accurately characterize multiple physical noise sources, and collected a real SPAD image dataset (64 × 32 pixels, 90 scenes, 10 different bit depths, 3 different illumination flux, 2790 images in total) to calibrate noise model parameters. With this physical noise model, we synthesized a large-scale realistic single-photon image dataset (image pairs of 5 different resolutions with maximum megapixels, 17250 scenes, 10 different bit depths, 3 different illumination flux, 2.6 million images in total) for subsequent network training. To tackle the severe super-resolution challenge of SPAD inputs with low bit depth, low resolution, and heavy noise, we further built a deep transformer network with a content-adaptive self-attention mechanism and gated fusion modules, which can dig global contextual features to remove multi-source noise and extract full-frequency details. We applied the technique in a series of experiments including microfluidic inspection, Fourier ptychography, and high-speed imaging. The experiments validate the technique's state-of-the-art super-resolution SPAD imaging performance.

Resonant-Type Piezoelectric Pump Driven by Piezoelectric Stacks and a Rhombic Micro Displacement Amplifier.

To obtain a high flow rate, a resonant-type piezoelectric pump is designed, fabricated, and studied in this paper. The pump consists of four parts: a piezoelectric vibrator, a pump chamber, a check valve and a compressible space. The designed piezoelectric vibrator is composed of a rhombic micro displacement amplifier, counterweight blocks and two piezoelectric stacks with low-voltage drive and a large output displacement. ANSYS software (Workbench 19.0) simulation results show that at the natural frequency of 946 Hz, the designed piezoelectric vibrator will produce the maximum output displacement. The bilateral deformation is symmetrical, and the phase difference is zero. Frequency, voltage, and backpressure characteristics of the piezoelectric pump are investigated. The experimental results show that at a certain operating frequency, the flow rate and the backpressure of the piezoelectric pump both increase with the increase in voltage. When the applied voltage is 150 Vpp, the flow rate reaches a peak of 367.48 mL/min at 720 Hz for one diaphragm pump, and reaches a peak of 700.15 mL/min at 716 Hz for two diaphragm pumps.

The promoting effect of modified Dioscorea pills on vascular remodeling in chronic cerebral hypoperfusion via the Ang/Tie signaling pathway.

Objective: The objective of this study was to investigate the effect of modified Dioscorea pills (MDP) on microcirculatory remodeling in the hippocampus of rats with chronic cerebral hypoperfusion (CCH) through the angiopoietin (Ang)/tyrosine kinase receptor tyrosine kinase with immunoglobulin-like and EGF-like domains (Ang receptor) 2 (Tie-2) signaling pathways, which may underlie the cognitive improvement observed in CCH rats. Methods: Forty male Sprague-Dawley rats raised under specific pathogen-free conditions were randomly divided into three groups: control group (10 rats), model group (15 rats), and MDP group (15 rats). The rats in the model group and MDP group underwent bilateral common carotid artery occlusion using the 2-vessel occlusion (2-VO) method to induce CCH. Rats in the control group underwent the same surgical procedures as those in the model group, except for ligation and occlusion of the carotid arteries. After 1 week of 2-VO, rats in the MDP group were administered MDP condensed decoction intragastrically at a dose of 1 ml/100 g body weight (prepared by the Preparation Room of Hubei Provincial Hospital of Traditional Chinese Medicine) for 45 days, while rats in the other two groups received normal saline intragastrically with the same dose and duration as the MDP group. After the intervention, all rats were euthanized, and brain perfusion was performed to obtain the hippocampal tissue for analysis. Immunohistochemical staining for CD43 was performed to assess microvessel density (MVD); western blot and the reverse transcription-polymerase chain reaction (RT-PCR) were used to analyze the expression of proteins and genes in angiopoietin-1 (Ang-1), angiopoietin-2 (Ang-2), Tie-2, and vascular endothelial growth factor (VEGF) proteins and genes in the hippocampal tissue and compute the Ang-1/Ang-2 ratio. Results: MDP treatment reduced neuronal loss and promoted restoration of the damaged hippocampal structure in CCH rats. The model group showed significantly higher MVD (14.93 ± 1.92) compared to the control group (5.78 ± 1.65) (P < 0.01), whereas MDP treatment further increased MVD (21.19 ± 2.62). Western blot and RT-PCR analysis revealed that CCH significantly increased the expression of Ang-1, Ang-2, Tie-2, and VEGF proteins and genes, while MDP treatment further significantly upregulated the expression of these proteins and genes. In addition, MDP significantly elevated the gene and protein expression of the Ang-1/Ang-2 ratio compared to the control group (P = 0.041, P = 0.029). Conclusion: CCH induces microvascular neogenesis in the hippocampus, and MDP promotes angiogenesis and microcirculation remodeling in CCH rats via the Ang/Tie signaling pathway, which may be an important mechanism for its restorative effects on hippocampal perfusion and improvement of cognitive function in CCH rats.

U-Shape Transformer for Underwater Image Enhancement.

The light absorption and scattering of underwater impurities lead to poor underwater imaging quality. The existing data-driven based underwater image enhancement (UIE) techniques suffer from the lack of a large-scale dataset containing various underwater scenes and high-fidelity reference images. Besides, the inconsistent attenuation in different color channels and space areas is not fully considered for boosted enhancement. In this work, we built a large scale underwater image (LSUI) dataset, which covers more abundant underwater scenes and better visual quality reference images than existing underwater datasets. The dataset contains 4279 real-world underwater image groups, in which each raw image's clear reference images, semantic segmentation map and medium transmission map are paired correspondingly. We also reported an U-shape Transformer network where the transformer model is for the first time introduced to the UIE task. The U-shape Transformer is integrated with a channel-wise multi-scale feature fusion transformer (CMSFFT) module and a spatial-wise global feature modeling transformer (SGFMT) module specially designed for UIE task, which reinforce the network's attention to the color channels and space areas with more serious attenuation. Meanwhile, in order to further improve the contrast and saturation, a novel loss function combining RGB, LAB and LCH color spaces is designed following the human vision principle. The extensive experiments on available datasets validate the state-of-the-art performance of the reported technique with more than 2dB superiority. The dataset and demo code are available at https://bianlab.github.io/.

Fusion-based underwater image enhancement with category-specific color correction and dehazing.

Underwater imaging is usually affected by water scattering and absorption, resulting in image blur and color distortion. In order to achieve color correction and dehazing for different underwater scenes, in this paper we report a fusion-based underwater image enhancement technique. First, statistics of the hue channel of underwater images are used to divide the underwater images into two categories: color-distorted images and non-distorted images. Then, category-specific combinations of color compensation and color constancy algorithms are used to remove the color shift. Second, a ground-dehazing algorithm using haze-line prior is employed to remove the haze in the underwater image. Finally, a channel-wise fusion method based on the CIE L* a* b* color space is used to fuse the color-corrected image and dehazed image. For experimental validation, we built a setup to acquire underwater images. The experimental results validate that the category-specific color correction strategy is robust to different categories of underwater images and the fusion strategy simultaneously removes haze and corrects color casts. The quantitative metrics on the UIEBD and EUVP datasets validate its state-of-the-art performance.

Weighted sampling-adaptive single-pixel sensing.

The novel single-pixel sensing technique that uses an end-to-end neural network for joint optimization achieves high-level semantic sensing, which is effective but computation-consuming for varied sampling rates. In this Letter, we report a weighted optimization technique for sampling-adaptive single-pixel sensing, which only needs to train the network once for any dynamic sampling rate. Specifically, we innovatively introduce a weighting scheme in the encoding process to characterize different patterns' modulation efficiencies, in which the modulation patterns and their corresponding weights are updated iteratively. The optimal pattern series with the highest weights is employed for light modulation in the experimental implementation, thus achieving highly efficient sensing. Experiments validated that once the network is trained with a sampling rate of 1, the single-target classification accuracy reaches up to 95.00% at a sampling rate of 0.03 on the MNIST dataset and 90.20% at a sampling rate of 0.07 on the CCPD dataset for multi-target sensing.

Image-free multi-character recognition.

The recently developed image-free sensing technique decouples semantic information directly from compressed measurements without image reconstruction, which maintains the advantages of both the light hardware and software. However, the existing attempts have failed to classify multi-semantic information with multiple targets in the practical fieldof-view. In this Letter, we report a novel image-free sensing technique to tackle the multi-target recognition challenge for the first time, to the best of our knowledge. Different from the convolutional layer stack of image-free single-pixel networks, the reported convolutional recurrent neural network (CRNN) uses the bidirectional LSTM architecture to predict the distribution of multiple characters simultaneously. The framework enables capture of the long-range dependencies, providing a high recognition accuracy of multiple characters. We demonstrate the technique's effectiveness in license plate detection, which achieves a recognition accuracy of 87.60% at a sampling rate of 5% with a refresh rate higher than 100 FPS.

Ultrahigh-security single-pixel semantic encryption.

Single-pixel encryption is a recently developed encryption technique enabling the ciphertext amount to be decreased. It adopts modulation patterns as secret keys and uses reconstruction algorithms for image recovery in the decryption process, which are time-consuming and can easily be illegally deciphered if the patterns are exposed. We report an image-free single-pixel semantic encryption technique that significantly enhances security. The technique extracts semantic information directly from the ciphertext without image reconstruction, which significantly reduces computing resources for end-to-end real-time decoding. Moreover, we introduce a stochastic mismatch between keys and ciphertext, with random measurement shift and dropout, which effectively enhances the difficulty of illegal deciphering. Experiments on the MNIST dataset validate that 78 coupling measurements (0.1 sampling rate) with stochastic shift and random dropout achieved 97.43% semantic decryption accuracy. In the worst situation, when all the keys are illegally obtained by unauthorized attackers, only 10.80% accuracy can be achieved (39.47% in an ergodic manner).

Recovery preparedness of global air transport influenced by COVID-19 pandemic: Policy intervention analysis.

The outbreak of COVID-19 constitutes an unprecedented disruption globally, in which risk management framework is on top priority in many countries. Travel restriction and home/office quarantine are some frequently utilized non-pharmaceutical interventions, which bring the worst crisis of airline industry compared with other transport modes. Therefore, the post-recovery of global air transport is extremely important, which is full of uncertainty but rare to be studied. The explicit/implicit interacted factors generate difficulties in drawing insights into the complicated relationship and policy intervention assessment. In this paper, a Causal Bayesian Network (CBN) is utilized for the modelling of the post-recovery behaviour, in which parameters are synthesized from expert knowledge, open-source information and interviews from travellers. The tendency of public policy in reaction to COVID-19 is analyzed, whilst sensitivity analysis and forward/backward belief propagation analysis are conducted. Results show the feasibility and scalability of this model. On condition that no effective health intervention method (vaccine, medicine) will be available soon, it is predicted that nearly 120 days from May 22, 2020, would be spent for the number of commercial flights to recover back to 58.52%-60.39% on different interventions. This intervention analysis framework is of high potential in the decision making of recovery preparedness and risk management for building the new normal of global air transport.

Acute psychological impact on COVID-19 patients in Hubei: a multicenter observational study.

We conducted a multicentre cross-sectional survey of COVID-19 patients to evaluate the acute psychological impact on the patients with coronavirus disease 2019 (COVID-19) during isolation treatment based on online questionnaires from 2 February to 5 March 2020. A total of 460 COVID-19 patients from 13 medical centers in Hubei province were investigated for their mental health status using online questionnaires (including Patient Health Questionnaire-9, Generalized Anxiety Disorder-7, Patient Health Questionnaire-15, and Insomnia Severity Index scales). Among all 460 COVID-19 patients, 187 (40.65%) of them were healthcare workers (HCWs). 297 (64.57%) of them were females. The most common psychological problems were somatization symptoms (66.09%, n = 304), followed by depression (53.48%, n = 246), anxiety (46.30%, n = 213), problems of insomnia (42.01%, n = 171), and then self-mutilating or suicidal thoughts (23.26%, n = 107). Of all the patients, 15.65% (n = 72) had severe somatization symptoms, and 2.83% (n = 13) had severe (almost every day) self-mutilating or suicidal thoughts. The most common psychological problems for HCWs were somatization symptoms (67.84%, n = 125), followed by depression (51.87%, n = 97), anxiety (44.92%, n = 84), problems of insomnia (36.18%, n = 55), and then self-mutilating or suicidal thoughts (20.86%, n = 39). Patients with lower education levels were found to be associated with higher incidence of self-mutilating or suicidal thoughts (odds ratio [OR], 2.68, 95% confidence interval [95% CI], 1.66-4.33 [P < 0.001]). Patients with abnormal body temperature were found to be associated with higher incidence of self-mutilating or suicidal thoughts (OR, 3.97, 95% CI, 2.07-7.63 [P < 0.001]), somatic symptoms (OR, 2.06, 95% CI, 1.20-3.55 [P = 0.009]) and insomnia (OR, 1.66, 95% CI, 1.04-2.65 [P = 0.033]). Those with suspected infected family members displayed a higher prevalence of anxiety than those without infected family members (OR, 1.61, 95% CI, 1.1-2.37 [P = 0.015]). Patients at the age of 18-44 years old had fewer somatic symptoms than those aged over 45 years old (OR, 1.91, 95% CI, 1.3-2.81 [P = 0.001]). In conclusion, COVID-19 patients tended to have a high prevalence of adverse psychological events. Early identification and intervention should be conducted to avoid extreme events such as self-mutilating or suicidal impulsivity for COVID-19 patients, especially for those with low education levels and females who have undergone divorce or bereavement.

Prevalence of psychological disorders in the COVID-19 epidemic in China: A real world cross-sectional study.

OBJECTIVE: This study aimed to explore the prevalence of psychological disorders and associated factors at different stages of the COVID-19 epidemic in China. METHODS: The mental health status of respondents was assessed via the Patient Health Questionnaire-9 (PHQ-9), Insomnia Severity Index (ISI) and the Generalized Anxiety Disorder 7 (GAD-7) scale. RESULTS: 5657 individuals participated in this study. History of chronic disease was a common risk factor for severe present depression (OR 2.2, 95% confidence interval [CI], 1.82-2.66, p < 0.001), anxiety (OR 2.41, 95% CI, 1.97-2.95, p < 0.001), and insomnia (OR 2.33, 95% CI, 1.83-2.95, p < 0.001) in the survey population. Female respondents had a higher risk of depression (OR 1.61, 95% CI, 1.39-1.87, p < 0.001) and anxiety (OR 1.35, 95% CI, 1.15-1.57, p < 0.001) than males. Among the medical workers, confirmed or suspected positive COVID-19 infection as associated with higher scores for depression (confirmed, OR 1.87; suspected, OR 4.13), anxiety (confirmed, OR 3.05; suspected, OR 3.07), and insomnia (confirmed, OR 3.46; suspected, OR 4.71). LIMITATION: The cross-sectional design of present study presents inference about causality. The present psychological assessment was based on an online survey and on self-report tools, albeit using established instruments. We cannot estimate the participation rate, since we cannot know how many potential subjects received and opened the link for the survey. CONCLUSIONS: Females, non-medical workers and those with a history of chronic diseases have had higher risks for depression, insomnia, and anxiety. Positive COVID-19 infection status was associated with higher risk of depression, insomnia, and anxiety in medical workers.

Protective effect of Aster tataricus extract on NLRP3-mediated pyroptosis of bladder urothelial cells.

Aster tataricus L.f. is a traditional Eastern Asian herbal medicine used for the relief of uroschesis-related illnesses and has been demonstrated clinically to exert satisfied effects. However, the mechanism of its therapeutic action remains unclear. The present study aimed to evaluate the protective mechanism of Aster tataricus extract (ATE) on CYP or LPS + ATP-induced interstitial cystitis (IC), we successfully constructed the induced IC Sprague-Dawley (SD) rat model and IC human urothelium cell (SV-HUC-1) model. The main compounds of ATE were determined by LC-MS. After intervention, the changes on the bladder wall morphology and inflammation were observed in each group. SV-HUC1 cell viability was measured by MTT and double stained with Hoechst 33342 and propidium iodide (PI). The expression levels of NLRP3, Pro-caspase-1, Caspsae-1 p20, GSDMD, GSDMD-N and Cleave-IL-1ß in vivo and in vitro in different groups were detected by Western blotting. ATE significantly alleviated oedema and haemorrhage and reduced the inflammation index and histopathological score in SD rat bladder. The results of cell revealed that ATE could improve cell viability and decrease pyroptosis ratio. The expression of NLRP3 and other pyroptosis-related protein was remarkably decreased by ATE both in vivo and in vitro. ATE may be used as an inhibitor of NLRP3 in treating IC. The discovery of NLRP3/Caspase-1/GSDMD-N as a new protective pathway provides a new direction for protecting cell against IC.

Resilience of Alzheimer's Disease to COVID-19.

BACKGROUND: Facing the novel coronavirus disease 2019 (COVID-19), most vulnerable individuals are seniors, especially those with comorbidities. More attention needs to been paid to the COVID-19 patients with Alzheimer's disease (AD), which is the top age-related neurodegenerative disease. OBJECTIVE: Since it is unclear whether AD patients are prone to COVID-19 infection and progression to severe stages, we report for the first time a retrospective analysis of the clinical characteristics of AD patients with COVID-19 pneumonia. METHODS: We conducted a retrospective cohort study of the clinical data of 19 AD patients with COVID-19 pneumonia, compared with 23 non-AD COVID-19 patients admitted at the same time to our hospital. Demographic, clinical, laboratory, radiological, and treatment data were collected and analyzed. RESULTS: Between AD patients and non-AD patients with COVID-19 pneumonia, the pneumonia severity was not significantly different. AD patients had a higher clustering onset than non-AD patients. The median duration from symptom onset to hospitalization were shorter in AD patients than non-AD patients, indicating the former were sent to the hospital by their family or from nursing home earlier than the later. The median duration from hospitalization to discharge seemed shorter in AD patients than non-AD patients. Dementia patients seemed less likely to report fatigue. It is noticed that more AD patients might have pericardial effusion than the non-AD patients. CONCLUSION: AD patients with COVID-19 were in milder conditions with a better prognosis than non-AD patients. AD patients who had adequate access to healthcare showed resilience to COVID-19 with shorter hospital stays.

Management of a Parkinson's disease patient with severe COVID-19 pneumonia.

Elderly populations with underlying chronic diseases are more vulnerable to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and have higher mortality. Parkinson's disease (PD) is a neurodegenerative disease that occurs more often in elderly people. Currently, little is known about whether patients with PD are more susceptible to novel coronavirus disease 2019 (COVID-19) and whether the treatment of PD would affect the management of COVID-19 or vice versa. Here, we report a case of a PD patient with severe COVID-19 pneumonia in Wuhan, China. After diagnosis of COVID-19, this PD patient had worsening of motor symptoms, complicated with acute hypoxemic respiratory failure, urinary tract infection, and acute encephalopathy. In addition to treatment for COVID-19 and urinary tract infection, we adjusted anti-PD medicine by stepwise increasing of dose, resulting in better control of her mobility symptoms and non-motor symptoms.

The evaluation of sleep disturbances for Chinese frontline medical workers under the outbreak of COVID-19.

OBJECTIVE: To evaluate sleep disturbances of Chinese frontline medical workers (FMW) under the outbreak of coronavirus disease 2019 (COVID-19), and make a comparison with non-FMW. METHODS: The medical workers from multiple hospitals in Hubei Province, China, volunteered to participate in this cross-sectional study. An online questionnaire, including Pittsburgh Sleep Quality Index (PSQI), Athens Insomnia Scale (AIS) and Visual Analogue Scale (VAS), was used to evaluate sleep disturbances and mental status. Sleep disturbances were defined as PSQI>6 points or/and AIS>6 points. We compared the scores of PSQI, AIS, anxiety and depression VAS, as well as prevalence of sleep disturbances between FMW and non-FMW. RESULTS: A total of 1306 subjects (801 FMW and 505 non-FMW) were enrolled. Compared to non-FMW, FMW had significantly higher scores of PSQI (9.3 ± 3.8 vs 7.5 ± 3.7; P < 0.001; Cohen's d = 0.47), AIS (6.9 ± 4.3 vs 5.3 ± 3.8; P < 0.001; Cohen's d = 0.38), anxiety (4.9 ± 2.7 vs 4.3 ± 2.6; P < 0.001; Cohen's d = 0.22) and depression (4.1 ± 2.5 vs 3.6 ± 2.4; P = 0.001; Cohen's d = 0.21), as well as higher prevalence of sleep disturbances according to PSQI > 6 points (78.4% vs 61.0%; relative risk [RR] = 1.29; P < 0.001) and AIS > 6 points (51.7% vs 35.6%; RR = 1.45; P < 0.001). CONCLUSION: FMW have higher prevalence of sleep disturbances and worse sleep quality than non-FMW. Further interventions should be administrated for FMW, aiming to maintain their healthy condition and guarantee their professional performance in the battle against COVID-19.