Comparison of disinfection quality in nurseries of Nanjing during 2019 - 2021

Objective To analyze the disinfection quality and influencing factors of nurseries in Nanjing during 2019-2021 so as to provide a scientific basis for optimizing preventive disinfection strategies and measures in nurseries. Methods Environmental samples from 389 nurseries in Nanjing from January 2019 to December 2021 were tested and the change of disinfection quality qualification rate was compared. Results The overall disinfection qualification rate of nurseries of year 2019-2021 were 96.32% 95.85% and 94.60% respectively showing a downward trend χ2trend = 8.67 P<0.05 . Specifically disinfection qualification rate of object surfaces staff hands and tableware showed a downward trend while the disinfection qualification rate of dynamic air showed an upward trend and the differences were statistically significant χ2trend = 23.17 12.32 5.37 21.48 P<0.05 . The total qualification rate of disinfection in Jiangning and Liuhe districts increased during 2019-2021 χ2trend = 21.46 24.05 P<0.05 . Conclusion Disinfection quality of nurseries in Nanjing has declined by year during 2019-2021 especially the object surfaces and staff hands. It is urgent to optimize and refine the strategies and measures for preventive disinfection in nurseries strengthen the training of personnel on disinfection knowledge and ensure the quality of disinfection in nurseries. © 2023 Chinese Journal of General Surgery. All rights reserved.

Exploration of potential mechanism of SARS-CoV-2 induced immune inflammatory response leading to progression of atherosclerosis and prediction of traditional Chinese medicine for preventing and treating based on bioinformatics.

Objective To explore the core targets and important pathways of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) induced atherosclerosis (AS) progression from the perspective of immune inflammation, so as to predict the potential prevention and treatment of traditional Chinese medicine (TCM). Methods Microarray data were obtained from the Gene Expression Omnibus (GEO) database for coronavirus disease 2019 (COVID-19) patients and AS patients, and the "limmar" and "Venn" packages were used to screen out the common differentially expressed genes (DEGs) genes in both diseases. The gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) analyses were performed on the common DEGs to annotate their functions and important pathways. The two gene sets were scored for immune cells and immune function to assess the level of immune cell infiltration. The protein-protein interaction (PPI) network was constructed by STRING database, and the CytoHubba plug-in of Cytoscape was used to identify the hub genes. Two external validation datasets were introduced to validate the hub genes and obtain the core genes. Immuno-infiltration analysis and gene set enrichment analysis (GSEA) were performed on the core genes respectively. Finally the potential TCM regulating the core genes were predicted by Coremine Medical database. Results A total of 7898 genes related to COVID-19, 471 genes related to AS progression;And 51 common DEGs, including 32 highly expressed genes and 19 low expressed genes were obtained. GO and KEGG analysis showed that common DEGs, which were mainly localized in cypermethrin-encapsulated vesicles, platelet alpha particles, phagocytic vesicle membranes and vesicles, were involved in many biological processes such as myeloid differentiation factor 88 (MyD88)-dependent Toll-like receptor signaling pathway transduction, interleukin-8 (IL-8) production and positive regulation, IL-6 production and positive regulation to play a role in regulating nicotinamide adenine dinucleotide phosphate oxidase activity, Toll-like receptor binding and lipopeptide and glycosaminoglycan binding through many biological pathways, including Toll-like receptor signaling pathways, neutrophil extracellular trap formation, complement and coagulation cascade reactions. The results of immune infiltration analysis demonstrated the state of immune microenvironment of COVID-19 and AS. A total of 5 hub genes were obtained after screening, among which Toll-like receptor 2 (TLR2), cluster of differentiation 163 (CD163) and complement C1q subcomponent subunit B (C1QB) genes passed external validation as core genes. The core genes showed strong correlation with immune process and inflammatory response in both immune infiltration analysis and GSEA enrichment analysis. A total of 35 TCMs, including Chuanxiong (Chuanxiong Rhizoma), Taoren (Persicae Semen), Danggui (Angelicae Sinensis Radix), Huangqin (Scutellariae Radix), Pugongying (Taraxaci Herba), Taizishen (Pseudostellariae Radix), Huangjing (Polygonati Rhizoma), could be used as potential therapeutic agents. Conclusion TLR2, CD163 and C1QB were the core molecules of SARS-CoV-2-mediated immune inflammatory response promoting AS progression, and targeting predicted herbs were potential drugs to slow down AS progression in COVID-19 patients.Copyright © 2023 Editorial Office of Chinese Traditional and Herbal Drugs. All rights reserved.

RISK OF APS AND aPLs POSITIVE PATIENTS FOR THROMBOSIS WITH COVID-19

BackgroundCoagulopathy, thromboembolic events and DIC during COVID-19 infection has been reported. Antiphospholipid antibodies (aPLs), present in 1–5 % of healthy individuals. aPLs are associated with the risk of antiphospholipid syndrome (APS) which is associated with higher risk of thrombosis.ObjectivesWe wanted to see if patients with known APS or aPLs only are at higher risk of a thrombotic event compared to control when developed COVID-19. We retrospectively review EMR for over a year for thrombotic events in patients with COVID and prior history of APS or aPLs only and matched them to control.MethodsPatient characteristics and laboratory testing were summarized according to the following groups: APS, aPLs detected or control. The control were matched according to age and gender for each group. Continuous variable were summarized as median (range) and mean (standard deviation), while categorical variables were reported as frequency (percentage). The binary patient outcome of thrombotic event, hospitalization for COVID, death, and composite event (the combined occurrence of thrombotic events, hospitalization, death) were calculated and interpreted as the multiplicative increase in odds of the given outcome for aPL group compared to control group. Multivariable logistic regression models were adjusted for potential risk factors (immobilization, hypertension, coronary artery disease, diabetes mellitus, and smoking) one at a time due to the rare occurrences of events studied.ResultsIn single variable analysis (unadjusted) the odds of the patient having a thrombotic event was approximately 27 times higher in patients with aPL only compared to Controls (P<0.001). We see similar results in multivariable analyses (adjusted) adjusting for the following variables one at a time: immobilization, hypertension, coronary artery disease, diabetes mellitus, and smoking. In each of the multivariable analyses, the adjusted odds of a thrombotic event was between approximately 24 and 29 times higher in patients with aPL Antibody Only compared to Controls (all P<0.001) indicating that association of aPL Antibody Only with thrombotic event was independent of immobilization, hypertension, coronary artery disease, diabetes, and smoking. There was no statistically significant risk of thrombosis in APS group vs control. Majority of patients with APS were on chronic anticoagulation.ConclusionWe found a statistical significantly difference in patient with aPLs only versus control regarding risk of thrombosis when developed COVID-19. No statistically significant risk was noted in patients with APS. While chronic anticoagulation in APS patients is protective it seemed that patient with aPLs only do carry a high risk of thrombosis if any inciting factors like COVID-19.References[1]C. Huang, Y. Wang, X. Li et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China, Lancet 395 (2020)[2]A. Jayarangaiah, P.T. Kariyanna, X. Chen, A. Jayarangaiah, A. Kumar, COVID-19- Associated coagulopathy: an exacerbated immunothrombosis response, Clin. Appl. Thromb. 26 (2020)[3]Y. Zhang, M. Xiao, S. Zhang et al. Coagulopathy and antiphospholipid antibodies in patients with Covid-19, N. Engl. J. Med. 382 (2020)[4]K.J. Lackner, N. Müller-Calleja, Pathogenesis of antiphospholipid syndrome: recent insights and emerging concepts, Expert Rev. Clin. Immunol. 15 (2019)Acknowledgements:NIL.Disclosure of InterestsNone Declared.

A COVID-19 Detection Model Based on Convolutional Neural Network and Residual Learning

A model that can obtain rapid and accurate detection of coronavirus disease 2019 (COVID-19) plays a significant role in treating and preventing the spread of disease transmission. However, designing such a model that can balance the detection accuracy and weight parameters of memory well to deploy a mobile device is challenging. Taking this point into account, this paper fuses the convolutional neural network and residual learning operations to build a multi-class classification model, which improves COVID-19 pneumonia detection performance and keeps a trade-off between the weight parameters and accuracy. The convolutional neural network can extract the COVID-19 feature information by repeated convolutional operations. The residual learning operations alleviate the gradient problems caused by stacking convolutional layers and enhance the ability of feature extraction. The ability further enables the proposed model to acquire effective feature information at a low cost, which can make our model keep small weight parameters. Extensive validation and comparison with other models of COVID-19 pneumonia detection on the well-known COVIDx dataset show that (1) the sensitivity of COVID-19 pneumonia detection is improved from 88.2% (non-COVID-19) and 77.5% (COVID-19) to 95.3% (non-COVID-19) and 96.5% (COVID-19), respectively. The positive predictive value is also respectively increased from 72.8% (non-COVID-19) and 89.0% (COVID-19) to 88.8% (non-COVID-19) and 95.1% (COVID-19). (2) Compared with the weight parameters of the COVIDNet-small network, the value of the proposed model is 13 M, which is slightly higher than that (11.37 M) of the COVIDNet-small network. But, the corresponding accuracy is improved from 85.2% to 93.0%. The above results illustrate the proposed model can gain an efficient balance between accuracy and weight parameters. © 2023 Tech Science Press. All rights reserved.

Adversarial Attacks on Deep Temporal Point Process

Temporal point processes have many applications, from crime forecasting to modeling earthquake aftershocks sequences. Due to the flexibility and expressiveness of deep learning, neural network-based approaches have recently shown promise for modeling point process intensities. However, there is a lack of research on the robustness of such models in regards to adversarial attacks and natural shocks to systems. Precisely, while neural point processes may outperform simpler parametric models on in-sample tests, how these models perform when encountering adversarial examples or sharp non-stationary trends remains unknown. Current work proposes several white-box and blackbox adversarial attacks against temporal point processes modeled by deep neural networks. Extensive experiments confirm that predictive performance and parametric modeling of neural point processes are vulnerable to adversarial attacks. Additionally, we evaluate the vulnerability and performance of these models in the presence of non-stationary abrupt changes, using the crimes dataset, during the Covid-19 pandemic, as an example. © 2022 IEEE.

[Progress in research of epidemiology of 2019-nCoV reinfection].

Continuous evolution of Omicron variant of 2019-nCoV has resulted in a rapid and simultaneous emergences of novel sub-variants with increased immune escape ability, higher reinfection risk and shorter time interval between infections. Compared with the first infection, the reinfection would still pose exceed risk to people's health although the clinical manifestations of the reinfection might be milder and the risk for severe illness or death is lower. The reinfection is highly associated with people's vaccination status, immunity level, age, working and residential factors. Those who have not received 2019-nCoV vaccination, the elderly and those with comorbidities, especially the previous 2019-nCoV patients with severe/critical illness, are at high risk for the reinfection. Booster doses of vaccine might play an additional role in the prevention of the reinfection and severe illness on the basis of natural immunity.

Nearly mobile: pandemic im/mobilities and lockdown strategies amongst Asian migrant professionals in England

This article proposes ‘nearly mobile' as a productive concept to illustrate the im/mobility experiences amongst Asian skilled migrants in England during a global health crisis. The concept of ‘nearly mobile' describes how mobility practices and aspirations have been heavily restricted during national lockdowns, while still remaining fluid and relational, with both debilitating and empowering potentials. The ambiguity of ‘nearly' offers both frustration and hope, when the desired ‘mobility as normal' seems just out of reach. The concept of 'nearly mobile' offers two main dimensions. First, it highlights how migrants navigate through ‘shock precarities' by safe-zoning–utilising and spatialising voluntary immobility to create unique work and living spaces based on daily assessments of risk and responsibility. Second, ‘nearly mobile' reflects a continuous yearning for a return to normality, where forecasting and imagining become practical ways of seizing opportunities that emerge amidst crisis and uncertainty. We propose 'nearly mobile' as a productive framework for revaluing im/mobility in times of rising localism, protectionism, and the normalisation of transnational biopolitical regimes, which underpin a possible ‘new normal' beyond the pandemic. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

Supply chain agility as the antecedent to firm sustainability in the post COVID-19

Purpose: COVID-19 has caused critical supply chain problems, especially in sustainable supply chain management, but very few empirical studies have been explored how to improve the firm sustainability through supply chain endeavours such as supply chain agility to manage the impacts of COVID-19. This paper aims to develop a model to incorporate supply chain agility and supply chain relationships that link firm sustainability to mitigate the impacts of the pandemic. Design/methodology/approach: The authors conducted an online survey and collected 203 valid responses from businesses in the United Arab Emirates, and employed an exploratory factor analysis, mediated regression analysis and structural equation modelling methodology to test the models and hypotheses. Findings: The authors find that the adoption of supply chain agility can improve supply chain relationships and positively impact sustainability. Meanwhile, supply chain relationships partially mediate the relationship between supply chain agility and sustainability. In addition, sustainability mitigates the impacts of COVID-19 pandemic on supply chains. Originality/value: The results provide fruitful insights and implications for the challenges and uncertainties caused by the pandemic post COVID-19 and provide several directions for further research. © 2023, Emerald Publishing Limited.

A Deep Learning Network for Detecting COVID-19 From Chest X-Ray Images

Rapid and accurate detection of COVID-19 plays a significant role in treating and preventing the spread of disease transmission. To this end, we fuse the convolutional neural network and residual learning operation to build a multi-class classification model, which has a few parameters and is more conducive to be deployed on a mobile device. Extensive experiments show that our proposed model gains competitive performance. Compared with the COVIDNet-small network, the sensitivity of COVID-19 pneumonia detection is improved from 88.2% (non-COVID-19) and 77.5% (COVID-19) to 95.3% (non-COVID-19) and 96.5% (COVID-19). Alternatively, the Positive predictive value is increased from 72.8% (non-COVID-19) and 89.0% (COVID-19) to 88.8% (non-COVID-19) and 95.1 % (COVID-19). The accuracy is also improved from 85.2 % to 93.0 %, which is very close to the value (93.3 %) of the COVIDNet-large network. But, the weight parameters (13M) of the proposed model are slightly higher than that (11.37M) of the COVIDNet-small network, but only about one-third of that (37.85M) of the COVIDNet-large network. © 2022 IEEE.

Prediction Model for Inclusive Finance Development Considering the Impact of COVID-19: The Case of China

Models in previous studies about inclusive finance often include economic data while excludes public online statements. In this paper Random Forest Regression (RFR) model is trained on the annual influencing factors and annual financial inclusion index to predict quarterly financial inclusion index by the quarterly influencing factors to expand the size of data. Then, BOW model tf-idf algorithm is used to convert COVID-19 – loan related online statements into word vectors. Lastly, these influencing factors of different lag periods are passed into the RFR model to compare their performance. Result of models shows that there is impact the epidemic has on the development of inclusive finance, and the lag period of the impact opinion texts on financial inclusion is 2 quarters. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A review on quality control, toxicity and clinical application of Amomum tsao-ko Crevost & Lemarie

The dried fruit of Amomum tsao-ko Crevost & Lemarie, a perennial herb of Cardamom in Zingiberaceae, has been widely used in food and as a folk medicine. It is used not only as an important food additive and spice for removing peculiar smell and improving taste, but also as a traditional Chinese medicine with significant efficacy in treating many kinds of disorders. Based on the high edible and medicinal value, large amounts of investigation have been reported for A. tsaoko in the past several years. This review specifically summarises its quality control, toxicology and clinical application, about which no literature had systematically reviewed, based on our best acknowledge. The current quality control of A. tsaoko is based on the content of volatile oil, which should be no less than 1.4% according to the Pharmacopoeia of the People's Republic of China (2020 edition), while a more possible Q-Markers should be developed to focus on either a specific bioactive ingredient or a component correlated with a certain clinical efficacy. Toxicity research suggests that A. tsaoko actually belongs to the non-toxic substance, although citral and 1,8-cineole, two main components of A. tsaoko, is hepatotoxic for the former and displays low acute toxicity and sub-chronic oral toxicity for the latter, but no obviously accumulative toxicity has so far been discovered for A. tsaoko. In clinical practice, A. tsaoko is often used in treatment of dampness/cold resistance, malaria, vomiting, fullness and epigastric distension across additional disorders, such as SARS, COVID-19 and hepatitis.Copyright © 2022 The Author(s)

Analysis of a Typical COVID-19 Outbreak Spread by Tourist Groups

Background: Novel coronavirus disease (SARS-COV-2 infection or COVID-19) is a respiratory tract infection that has been linked to severe acute respiratory syndrome transmitted particularly through touching and respiration. The purpose of this study is to understand the epidemiological characteristics of COVID-19 cases in a typical tourist-related outbreak and explore the possible route for its transmission. Method(s): All data and epidemiological survey reports of COVID-19 cases in the outbreak were reported by provincial and urban (county) Centers for Disease Control and Prevention and Health Commissions nationwide from October 16th to November 5th, 2021. The epidemiological survey reports included information on gender, age, source of infection (imported from other provinces or locally acquired), daily life track and itinerary, date of symptom onset, and date of diagnosis. The data were analyzed using descriptive statistical methods, one-way analysis of variance, independent t-test, and Chi-square tests. Histograms and percentage stacked area plots were used to describe the epidemiological characteristics of the outbreaks. Result(s): The COVID-19 outbreak associated with the tourist groups has involved 551 COVID-19 cases, with a median age of 44 years (interquartile range: 30-59 years), gradually spreading from the northwestern region to the national level across 15 provinces of China. One-fifth of the cases (16.0%) had traveled to Ejin Banner, resulting in 68 second-generation cases. We estimated an outbreak on 11 flights and 19 trains, accounting for a total of 27 confirmed cases. In addition, 42 clusters of outbreak cases were also reported to occur, 21 (50.0%) in households and 10 (23.81%) in restaurants. About 106 confirmed cases were related to the gatherings in restaurants. The median incubation period for this COVID-19 outbreak was 7 days (inter-quartile range: 5-10 days). Conclusion(s): The survey results indicated that this COVID-19 outbreak originated in Ejin Banner and was spread by tourist groups, which was a typical infection outbreak promoted by travel. Our results further confirmed that travel needs to be more strictly weighed in pandemics like COVID-19, and people need to pay more attention to the prevention against infectious diseases, particularly when traveling in a tourist group.Copyright © 2022 Zheng et al.

Related Return Patterns Adjustment Under the Impact of COVID-19 on Hedge Funds

The latest threat to global health is an ongoing outbreak of a respiratory disease known as COVID-19 and has become a global concern. The exponential spread of the COVID-19 pandemic shook up global markets and caused major adjustments to the world economy. In this paper, we investigate whether these changes affected hedge fund return patterns. We decompose hedge fund index returns into Fama-French factors using data from 2017 - 2019 and compare it to decompositions using data from 2020 and 2021 to date. Our empirical results suggest that the Fama-French factor exposures changed on the conventional hedge funds. This has reflected that COVID-19 has an impact on the return patterns of the hedge funds we selected. The findings have implications for investors and major players in the investment markets. Our research is useful for predicting how the performance of hedge funds changes in market disruption. © 2022 ACM.

The Effect of Proximity to Universities on House Prices after the COVID Outbreak

COVID-19 has made virtual interactions an integral part of learning modes. This made it possible for college students to live further away from school than before, which might change the house price neighboring universities. This article studies the effect of proximity to school on house prices after the COVID-19 outbreak using a non-parametric difference-in-differences approach with property-level transaction data surrounding 128 universities in the U.S. The results show that house prices within 0.5 miles of universities experienced a maximum decrease of approximately 7% after three months of the outbreak. The effects vary for universities that implemented different teaching modes of in-person, hybrid, and online. Since house prices are important indicators for local economic conditions, the results help local homeowners, investors, and governments in their decision-making processes. © 2023 by the author.

COVID-19 and systemic anti-cancer therapy.

The corona virus disease 2019 (COVID-19) pandemic continues to severely impact healthcare systems around the world, and patients with cancer are even worse affected owing to compromised immune status and greater exposure risk. In the present review, we retrieved the relevant literature including guidelines and consensuses directly related to the purpose of this study from the PubMed database, and then summarized the research data on cancer and COVID-19, aiming to discuss the personal protection, systemic anti-cancer therapy, outcome of co-infection, and the clinical management strategy in this population. We found that patients with malignant tumors had a higher chance of suffering COVID-19, co-infection of whom had an even worse clinical prognosis, especially for those with lung cancer or hematologic cancers. Systemic chemotherapy may delay the clearance of severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) virus of human body, and thus have a negative impact on the clinical outcomes of COVID-19, while certain endocrine therapy and targeted drugs having limited or no impact. There has been no sufficient evidence for the impact of immune checkpoint therapy on the outcomes of COVID-19 till now. It is of great value to strengthen the personal protection of patients, adjust the anti-tumor treatments rationally and optimize the clinical management processes.Copyright © 2022, Editorial Office of China Oncology. All rights reserved.

Prussian Blue@Zeolitic imidazolate framework composite toward solar-triggered biodecontamination

Metal-organic frameworks (MOFs) featuring composition and bandstructure diversity, are an emerging class of photoresponsive disinfectants. In this study, we demonstrated the superiority of core–shell arranged photoactive MOFs (prussian blue (PB) and zeolitic imidazolate framework (ZIF-8)) for pathogen inactivation in terms of biocidal efficiency and broad-spectrum sensitivity. Reactive oxygen species (ROS) production was significantly promoted after the integration of PB due to the photosensitization effect and initiation of in situ Fenton reaction. Favorably, another inactivation channel was also opened owing to the unique photothermal effect of PB. Attributed to the facilitated ROS intracellular penetration by heat, the composite outperforms not only individual component but anatase TiO2 in pathogen elimination. Specifically, the Staphylococcus aureus (S. aureus) inactivation efficiency of the composite (6.6 log) is 2, 1.8 and 5.1 times higher than that of PB (3.3 log), ZIF-8 (3.7 log) and TiO2 (1.3 log) over 45 min of simulated sunlight illumination. Significantly, the infectivity of Bacillus anthracis and murine coronavirus in droplets on composite-coated filter surface could be greatly reduced (approximately 3 log reduction in colony number/coronavirus titer) within few minutes of solar exposure, indicative of the great potential of MOF composites toward life-threatening microbial infection prevention. © 2022 Elsevier B.V.

Forearm multimodal recognition based on IAHP-entropy weight combination

Biometrics are the among most popular authentication methods due to their advantages over traditional methods, such as higher security, better accuracy and more convenience. The recent COVID-19 pandemic has led to the wide use of face masks, which greatly affects the traditional face recognition technology. The pandemic has also increased the focus on hygienic and contactless identity verification methods. The forearm is a new biometric that contains discriminative information. In this paper, we proposed a multimodal recognition method that combines the veins and geometry of a forearm. Five features are extracted from a forearm Near-Infrared (Near-Infrared) image: SURF, local line structures, global graph representations, forearm width feature and forearm boundary feature. These features are matched individually and then fused at the score level based on the Improved Analytic Hierarchy Process-entropy weight combination. Comprehensive experiments were carried out to evaluate the proposed recognition method and the fusion rule. The matching results showed that the proposed method can achieve a satisfactory performance. © 2022 The Authors. IET Biometrics published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

Privacy-preserving COVID-19 contact tracing solution based on blockchain

The COVID-19 pandemic has severely affected daily life and caused a great loss to the global economy. Due to the very urgent need for identifying close contacts of confirmed patients in the current situation, the development of automated contact tracing app for smart devices has attracted more attention all over the world. Compared with expensive manual tracing approach, automated contact tracing apps can offer fast and precise tracing service, however, over-pursing high efficiency would lead to the privacy-leaking issue for app users. By combing with the benign properties (e.g., anonymity, decentralization, and traceability) of blockchain, we propose an efficient privacy-preserving solution in automated tracing scenario. Our main technique is a combination of non-interactive zero-knowledge proof and multi-signature with public key aggregation. By means of aggregating multiple signatures from different contacts at the mutual commitment phase, we only need fewer zero-knowledge proofs to complete the task of identifying contacts. It inherently leads to the benefits of saving storage and consuming less time for running verification algorithm on blockchain. Furthermore, we perform an experimental comparison by timing the execution of signature verification with and without aggregate signature, respectively. It shows that our solution can actually preserve the full-fledged privacy protection property with a lower computational cost. © 2022

Lifetime prediction of non-woven face masks in ocean and contributions to microplastics and dissolved organic carbon

This study explored the degradation behavior of three types of disposable face masks in simulated seawater via the accelerated aging experiments. Microplastics (MPs) and dissolved organic carbon (DOC) were monitored in UV- and thermal-treated mask suspensions and their concentrations increased slowly in the early stage at 50 ℃ and 58 ℃. Owing to the high energy supply, the release rates of MPs and DOC at 76 ℃ were much faster than the above two temperatures. The time-temperature superposition principle (TTSP) was used to superpose the MPs/DOC release kinetics from other tested temperatures to the reference temperature and its applicability was verified by the similar activation energy. Then, a release kinetics model was established and fitted well with the superposed MP data (R2 ≥ 0.96). Since less than 0.1 % of carbon was leached, the superposed DOC data was roughly modelled by the exponential function (R2 ≥ 0.90). According to the TTSP and the established kinetics models, about 15 years were estimated to decompose half of a certain marine mask waste, together with leaching 0.21 ± 0.02 mg∙g-mask−1 of DOC. If mask consumption remains the same before 2025, they would contribute 40000–230000 tonnes of MPs and 13–97 tonnes of DOC to the ocean by 2040. © 2022