Association between nitrogen dioxide concentration and hypertension hospitalization: A multicenter time-series analysis in Xinxiang, China

Hypertension is a common chronic disease, and air pollution is strongly associated with hypertension hospitalization. However, the association between nitrogen dioxide (NO2)1 concentration and hypertension hospitalization has rarely been studied. We collected daily data on hypertension hospitalizations, air pollutants, and meteorology from January 1, 2016 to October 31, 2021. After controlling for the effects of seasonal and long-term trends, weather conditions, weekdays, holidays, and during the novel coronavirus crown epidemic, a generalized additive model with over discrete Poisson regression was used to simulate the association between NO2 concentration and hypertension hospitalizations while quantifying hypertension hospitalizations, hospital stays, and hospital costs attributable to NO2. The results showed that each 10 μg/m3 increase in NO2 concentration was significantly associated with the relative risk (RR) of hypertension admission in Xinxiang, with the greatest effect at lag04 (RR = 1.107;95% confidence interval, 1.046–1.172). Hypertension hospitalizations attributed to NO2 during the study period accounted for 9.70% (484) of the total hypertension hospitalizations, equivalent to 4202 hospital days and 338.55 thousand United States dollars (USD). Increased NO2 concentration increases the risk of hypertension hospitalization in Xinxiang, which poses a significant health and economic burden to society and patients. The findings of this study provide a basis for developing stricter environmental pollutant standards.

Impact of Anthropogenic Emission Reduction during COVID-19 on Air Quality in Nanjing, China

To avoid the spread of COVID-19, China has implemented strict lockdown policies and control measures, resulting in a dramatic decrease in air pollution and improved air quality. In this study, the air quality model WRF-Chem and the latest MEIC2019 and MEIC2020 anthropogenic emission inventories were used to simulate the air quality during the COVID-19 lockdown in 2020 and the same period in 2019. By designing different emission scenarios, this study explored the impact of the COVID-19 lockdown on the concentration of air pollutants emitted by different sectors (industrial sector and transportation sector) in Nanjing for the first time. The results indicate that influenced by the COVID-19 lockdown policies, compared with the same period in 2019, the concentrations of PM2.5, PM10, and NO2 in Nanjing decreased by 15%, 17.1%, and 20.3%, respectively, while the concentration of O3 increased by 45.1% in comparison;the concentrations of PM2.5, PM10 and NO2 emitted by industrial sector decreased by 30.7%, 30.8% and 14.0% respectively;the concentrations of PM2.5, PM10 and NO2 emitted by transportation sector decreased by 15.6%, 15.7% and 26.2% respectively. The COVID-19 lockdown has a greater impact on the concentrations of PM2.5 and PM10 emitted by the industrial sector, while the impact on air pollutants emitted by the transportation sector is more reflected in the concentration of NO2. This study provides some theoretical basis for the treatment of air pollutants in different departments in Nanjing.

Spatio-temporal heterogeneity in the international trade resilience during COVID-19

The COVID-19 pandemic and subsequent lockdowns have created immeasurable health and economic crises, leading to unprecedented disruptions to world trade. The COVID-19 pandemic shows diverse impacts on different economies that suffer and recover at different rates and degrees. This research aims to evaluate the spatio-temporal heterogeneity of international trade network vulnerabilities in the current crisis to understand the global production resilience and prepare for the future crisis. We applied a series of complex network analysis approaches to the monthly international trade networks at the world, regional, and country scales for the pre- and post- COVID-19 outbreak period. The spatio-temporal patterns indicate that countries and regions with an effective COVID-19 containment such as East Asia show the strongest resilience, especially Mainland China, followed by high-income countries with fast vaccine roll-out (e.g., U.S.), whereas low-income countries (e.g., Africa) show high vulnerability. Our results encourage a comprehensive strategy to enhance international trade resilience when facing future pandemic threats including effective non-pharmaceutical measures, timely development and rollout of vaccines, strong governance capacity, robust healthcare systems, and equality via international cooperation. The overall findings elicit the hidden global trading disruption, recovery, and growth due to the adverse impact of the COVID-19 pandemic.

Response and duration of serum anti-SARS-CoV-2 antibodies induced by the third dose of an inactivated vaccine: A prospective longitudinal cohort study at 21 serial time points over 641 days.

Given the prevalence of low-pathogenic but highly infectious Omicron variants, a cohort study was conducted to assess the response and duration of novel coronavirus-inactivated vaccine-induced antibodies 1 year after the third dose (Day 641). Blood samples were collected and anti-spike neutralizing antibodies (neutralizing antibody), total antibodies against the receptor-binding domain of the spike protein (total antibody), and immunoglobulin G antibodies against the spike protein (IgG antibody) were determined. Antibody kinetics and attenuation were evaluated. The results showed that the levels of neutralizing, total, and IgG antibodies on Day 641 were 98.05 IU/mL, 152.8 AU/mL, and 7.68 S/CO, respectively. Levels of anti-SARS-CoV-2 antibodies were higher in the younger subgroup than in the older subgroup at several time points after the second and third doses. The seropositive rate of neutralizing antibodies providing protection from infection or severe infection was 46.87% and 87.5%, and the seropositive rates of total antibody and IgG antibody were maintained at 100% and 90.63%, respectively. The half-lives of neutralizing, total, and IgG antibodies were 186.89, 363.04, and 417.50 days, respectively. Collectively, anti-SARS-CoV-2 antibodies may provide a certain degree of protection from infection 1 year after the third dose and high protection from severe infection.

Non-pharmaceutical interventions for COVID-19 reduced the incidence of infectious diseases: a controlled interrupted time-series study.

BACKGROUND: Non-pharmaceutical interventions (NPIs) have been implemented worldwide to suppress the spread of coronavirus disease 2019 (COVID-19). However, few studies have evaluated the effect of NPIs on other infectious diseases and none has assessed the avoided disease burden associated with NPIs. We aimed to assess the effect of NPIs on the incidence of infectious diseases during the COVID-19 pandemic in 2020 and evaluate the health economic benefits related to the reduction in the incidence of infectious diseases. METHODS: Data on 10 notifiable infectious diseases across China during 2010-2020 were extracted from the China Information System for Disease Control and Prevention. A two-stage controlled interrupted time-series design with a quasi-Poisson regression model was used to examine the impact of NPIs on the incidence of infectious diseases. The analysis was first performed at the provincial-level administrative divisions (PLADs) level in China, then the PLAD-specific estimates were pooled using a random-effect meta-analysis. RESULTS: A total of 61,393,737 cases of 10 infectious diseases were identified. The implementation of NPIs was associated with 5.13 million (95% confidence interval [CI] 3.45‒7.42) avoided cases and USD 1.77 billion (95% CI 1.18‒2.57) avoided hospital expenditures in 2020. There were 4.52 million (95% CI 3.00‒6.63) avoided cases for children and adolescents, corresponding to 88.2% of total avoided cases. The top leading cause of avoided burden attributable to NPIs was influenza [avoided percentage (AP): 89.3%; 95% CI 84.5‒92.6]. Socioeconomic status and population density were effect modifiers. CONCLUSIONS: NPIs for COVID-19 could effectively control the prevalence of infectious diseases, with patterns of risk varying by socioeconomic status. These findings have important implications for informing targeted strategies to prevent infectious diseases.

Atomic layer deposition of ZnO on polypropylene nonwovens for photocatalytic antibacterial facemasks.

Addressing respiratory infectious diseases remains one of the main priorities due to the increased risk of exposure caused by population growth, increasing international travel and commerce, and most recently, the COVID-19 outbreak. In the war against respiratory diseases, facemasks are powerful tools to obstruct the penetration of microorganisms, thereby protecting the wearer from infections. Nonetheless, the intercepted microorganisms on the surface of facemasks may proliferate and lead to secondary infection. To solve this problem, atomic layer deposition is introduced to deposit uniform and mechanically robust ZnO layers on polypropylene (PP) nonwoven fabrics, a widely used raw material in fabricating facemasks. The loading of ZnO demonstrates no adverse effects on the separation performance of facemasks, and the filtration efficiency of the facemasks towards different types of nanoparticles remains higher than 98.9%. Moreover, the modified PP nonwoven fabrics are granted with excellent antibacterial activity and photocatalytic sterilization ability, which can inactivate both germ-negative and germ-positive bacteria (E. coliandS. aureus) effectively with and without light illumination. Therefore, the modified PP nonwoven fabrics are potential candidates to be used as the outer layer on facemasks and endow them with photocatalytic antibacterial activity.

Evaluation on the inclusion behavior of ß-cyclodextrins with lycorine and its hydrochloride.

Lycorine (Lyc) and its hydrochloride (Lyc∙HCl) as effective drugs can fight against many diseases including novel coronavirus (COVID-19) based on their antiviral and antitumor mechanism. Beta-cyclodextrin (ß-CD) is considered a promising carrier in improving its efficacy while minimizing cytotoxicity due to the good spatial compatibility with Lyc. However, the detailed mechanism of inclusion interaction still remains to be further evaluated. In this paper, six inclusion complexes based on ß-CDs, Lyc and Lyc∙HCl were processed through ultrasound in the mixed solvent of ethanol and water, and their inclusion behavior was characterized after lyophilization. It was found that the inclusion complexes based on sulfobutyl-beta-cyclodextrin (SBE-ß-CD) and Lyc∙HCl had the best encapsulation effect among prepared inclusion complexes, which may be attributed to the electrostatic interaction between sulfonic group of SBE-ß-CD and quaternary amino group of Lyc∙HCl. Moreover, the complexes based on SBE-ß-CD displayed pH-sensitive drug release property, good solubilization, stability and blood compatibility, indicating their potential as suitable drug carriers for Lyc and Lyc∙HCl.

Spatio-temporal heterogeneity in the international trade resilience during COVID-19.

The COVID-19 pandemic and subsequent lockdowns have created immeasurable health and economic crises, leading to unprecedented disruptions to world trade. The COVID-19 pandemic shows diverse impacts on different economies that suffer and recover at different rates and degrees. This research aims to evaluate the spatio-temporal heterogeneity of international trade network vulnerabilities in the current crisis to understand the global production resilience and prepare for the future crisis. We applied a series of complex network analysis approaches to the monthly international trade networks at the world, regional, and country scales for the pre- and post- COVID-19 outbreak period. The spatio-temporal patterns indicate that countries and regions with an effective COVID-19 containment such as East Asia show the strongest resilience, especially Mainland China, followed by high-income countries with fast vaccine roll-out (e.g., U.S.), whereas low-income countries (e.g., Africa) show high vulnerability. Our results encourage a comprehensive strategy to enhance international trade resilience when facing future pandemic threats including effective non-pharmaceutical measures, timely development and rollout of vaccines, strong governance capacity, robust healthcare systems, and equality via international cooperation. The overall findings elicit the hidden global trading disruption, recovery, and growth due to the adverse impact of the COVID-19 pandemic.

3D bi-directional transformer U-Net for medical image segmentation.

As one of the popular deep learning methods, deep convolutional neural networks (DCNNs) have been widely adopted in segmentation tasks and have received positive feedback. However, in segmentation tasks, DCNN-based frameworks are known for their incompetence in dealing with global relations within imaging features. Although several techniques have been proposed to enhance the global reasoning of DCNN, these models are either not able to gain satisfying performances compared with traditional fully-convolutional structures or not capable of utilizing the basic advantages of CNN-based networks (namely the ability of local reasoning). In this study, compared with current attempts to combine FCNs and global reasoning methods, we fully extracted the ability of self-attention by designing a novel attention mechanism for 3D computation and proposed a new segmentation framework (named 3DTU) for three-dimensional medical image segmentation tasks. This new framework processes images in an end-to-end manner and executes 3D computation on both the encoder side (which contains a 3D transformer) and the decoder side (which is based on a 3D DCNN). We tested our framework on two independent datasets that consist of 3D MRI and CT images. Experimental results clearly demonstrate that our method outperforms several state-of-the-art segmentation methods in various metrics.

Discovery of Novel Chinese Medicine Compounds Targeting 3CL Protease by Virtual Screening and Molecular Dynamics Simulation.

The transmission and infectivity of COVID-19 have caused a pandemic that has lasted for several years. This is due to the constantly changing variants and subvariants that have evolved rapidly from SARS-CoV-2. To discover drugs with therapeutic potential for COVID-19, we focused on the 3CL protease (3CLpro) of SARS-CoV-2, which has been proven to be an important target for COVID-19 infection. Computational prediction techniques are quick and accurate enough to facilitate the discovery of drugs against the 3CLpro of SARS-CoV-2. In this paper, we used both ligand-based virtual screening and structure-based virtual screening to screen the traditional Chinese medicine small molecules that have the potential to target the 3CLpro of SARS-CoV-2. MD simulations were used to confirm these results for future in vitro testing. MCCS was then used to calculate the normalized free energy of each ligand and the residue energy contribution. As a result, we found ZINC15676170, ZINC09033700, and ZINC12530139 to be the most promising antiviral therapies against the 3CLpro of SARS-CoV-2.

Clinical presentation of acute primary angle closure during the COVID-19 epidemic lockdown.

Purpose: This study aimed to investigate the clinical presentation of acute primary angle closure (APAC) during the COVID-19 epidemic lockdown in Wuhan. Methods: Consecutive patients seeking APAC treatment at the Wuhan Aier Eye Hospital during the 76 days (January 23-April 8, 2020) when the lockdown policy was implemented due to the COVID-19 pandemic were compared to those during the same period the following year (January 23-April 8, 2021), when the lockdown policy was not implemented. The cohorts were compared to assess demographic variables and clinical presentations. Results: A total of 54 patients (64 eyes) were included in the 2020, compared with 46 patients (51 eyes) in the 2021. Demographic factors were similar between the groups. Significantly more patients developed blindness in the 2020 cohort (21.87%) than in the 2021 cohort (7.84%). Patients in the 2020 showed a longer time from symptom to treatment (241.84 ± 211.95 h in 2020 vs. 121.53 ± 96.12 h in 2021; P = 0.001), higher intraocular pressure at presentation (52.63 ± 12.45 mmHg in 2020 vs. 45.16 ± 9.79 mmHg in 2021; P = 0.001), larger pupil diameter (5.47 ± 1.62 mm in 2020 vs. 4.33 ± 1.27 mm in 2021; P = 0.001), and more glaucomatous optic neuropathy diagnoses [20/64 eyes (31.25%) in 2020 vs. 7/51 eyes (13.73%) in 2021; P = 0.03]. Conclusion: The time between the onset of APAC symptoms and its treatment during the COVID-19 epidemic lockdown was significantly prolonged, which increased the blindness rate of APAC patients.

Fuzzy Front-End Vertical External Involvement, Corporate Social Responsibility and Firms' New Product Development Performance in the VUCA Age: From an Organizational Learning Perspective

Fuzzy front-end (FFE) innovation is the important initial phase of manufacturing companies' new product development (NPD) process, which can be a factor that determines manufacturers' NPD performance and sustainable growth. This study seeks to investigate the role of FFE vertical external involvement (FFE customer involvement and FFE supplier involvement) in advancing firms' corporate social responsibility (CSR) and NPD performance in the COVID-19-influenced age of volatility, uncertainty, complexity and ambiguity (VUCA). This study adopts an organizational learning (OL) perspective as its theoretical foundation in constructing a comprehensive framework by developing a series of hypotheses. In addition, we test the mediating effect of CSR on the relationship between FFE vertical external involvement and firm NPD performance. We use the structural equation modeling method to examine our hypotheses empirically based on data collected from 548 Chinese manufacturing companies. The findings illustrate that FFE customer involvement and FFE supplier involvement are vital drivers of a firm's CSR engagement. Furthermore, we find that FFE supplier involvement plays a more significant role than FFE customer involvement in contributing to NPD performance. Finally, we find that a firm's CSR engagement positively mediates the relationship between FFE vertical external involvement and NPD performance.

Human Genetic Variants Associated with COVID-19 Severity are Enriched in Immune and Epithelium Regulatory Networks.

Human genetic variants can influence the severity of symptoms infected with SARS-COV-2. Several genome-wide association studies have identified human genomic risk single nucleotide polymorphisms (SNPs) associated with coronavirus disease 2019 (COVID-19) severity. However, the causal tissues or cell types underlying COVID-19 severity are uncertain. In addition, candidate genes associated with these risk SNPs were investigated based on genomic proximity instead of their functional cellular contexts. Here, we compiled regulatory networks of 77 human contexts and revealed those risk SNPs' enriched cellular contexts and associated risk SNPs with transcription factors, regulatory elements, and target genes. Twenty-one human contexts were identified and grouped into two categories: immune cells and epithelium cells. We further aggregated the regulatory networks of immune cells and epithelium cells. These two aggregated regulatory networks were investigated to reveal their association with risk SNPs' regulation. Two genomic clusters, the chemokine receptors cluster and the oligoadenylate synthetase (OAS) cluster, showed the strongest association with COVID-19 severity, and they had different regulatory programs in immune and epithelium contexts. Our findings were supported by analysis of both SNP array and whole genome sequencing-based genome wide association study (GWAS) summary statistics. Supplementary Information: The online version contains supplementary material available at 10.1007/s43657-022-00066-x.

Emergency logistics network optimization with time window assignment.

During natural disasters or accidents, an emergency logistics network aims to ensure the distribution of relief supplies to victims in time and efficiently. When the coronavirus disease 2019 (COVID-19) emerged, the government closed the outbreak areas to control the risk of transmission. The closed areas were divided into high-risk and middle-/low-risk areas, and travel restrictions were enforced in the different risk areas. The distribution of daily essential supplies to residents in the closed areas became a major challenge for the government. This study introduces a new variant of the vehicle routing problem with travel restrictions in closed areas called the two-echelon emergency vehicle routing problem with time window assignment (2E-EVRPTWA). 2E-EVRPTWA involves transporting goods from distribution centers (DCs) to satellites in high-risk areas in the first echelon and delivering goods from DCs or satellites to customers in the second echelon. Vehicle sharing and time window assignment (TWA) strategies are applied to optimize the transportation resource configuration and improve the operational efficiency of the emergency logistics network. A tri-objective mathematical model for 2E-EVRPTWA is also constructed to minimize the total operating cost, total delivery time, and number of vehicles. A multi-objective adaptive large neighborhood search with split algorithm (MOALNS-SA) is proposed to obtain the Pareto optimal solution for 2E-EVRPTWA. The split algorithm (SA) calculates the objective values associated with each solution and assigns multiple trips to shared vehicles. A non-dominated sorting strategy is used to retain the optimal labels obtained with the SA algorithm and evaluate the quality of the multi-objective solution. The TWA strategy embedded in MOALNS-SA assigns appropriate candidate time windows to customers. The proposed MOALNS-SA produces results that are comparable with the CPLEX solver and those of the self-learning non-dominated sorting genetic algorithm-II, multi-objective ant colony algorithm, and multi-objective particle swarm optimization algorithm for 2E-EVRPTWA. A real-world COVID-19 case study from Chongqing City, China, is performed to test the performance of the proposed model and algorithm. This study helps the government and logistics enterprises design an efficient, collaborative, emergency logistics network, and promote the healthy and sustainable development of cities.

Construction and application of a cold chain logistics supervision platform for agricultural products in port areas during the COVID-19 pandemic

Diversification of agricultural imports is an important strategy for ensuring food security in China. During the COVID-19 pandemic, agricultural cold chain logistics are facing new challenges but also a new round of rapid development opportunities. It's particularly critical to determine how to build the first line of defense in port areas for the supervision of agricultural products to ensure the safety and reliability of incoming cargo. In recent years, China has continued to strengthen the construction of its regulatory traceability system for imported agricultural products, but there are still weak links and a lack of connectivity in the overall construction of the system. In this paper, we propose a supervision platform that addresses the problems of weak correlation and traceability of the cold chain logistics information on agricultural products in port areas during the COVID-19 pandemic. Relying on low-power-based radio frequency identification technology, the platform is combined with wireless sensor networks to collect key real-time data on the microenvironment, including temperature and humidity, influencing the cold chain logistics links of imported agricultural products. The platform unites multiple entities, including production, distribution, operation, and supervision entities, to build a pool of basic information data, such as inspection and quarantine certificates, nucleic acid test reports, and disinfection certificates for imported agricultural products. It integrates the core technologies of the new generation, the Internet of Things, the Internet, big data, cloud computing, and block chain, and builds a standard data flow and database to identify the key links and elements affecting the quality and safety of agricultural products from a large amount of data with the help of advanced algorithms. We establish risk controls and early warnings for imported agricultural products, build a traceability model, and then explore the creation of a comprehensive supervision platform in the port area, undertaking key data cleaning, integration, analysis, and sharing. The platform provides powerful support for government supervision, analysis, and decision-making, including emergency responses and public inquiries, combines "physical defense" and "human defense" in the cold chain logistics network for imported agricultural products, and promotes the improvement of the supervisory system for cold chain logistics of agricultural products in port areas.

Multisystem inflammatory syndrome (MIS-C) with SARS-CoV-2 omicron variant BA.2.38 in a four-year-old Chinese girl: A case report.

We report a severe COVID-19 complicated with MIS-C in a girl treated by the author in China, and discuss the current research status and progress in the diagnosis and therapy of MIS-C in children. The patient was a 4-year-old child previously healthy who was referred to the hospital with a complaint of fever, finally, Multisystem inflammatory syndrome was diagnosed with COVID-19.

Medical services for sports injuries and illnesses in the Beijing 2022 Olympic Winter Games.

BACKGROUND: Beijing 2022 Olympic Winter Games was the second Games held amid the COVID-19 pandemic. To a certain extent, it has altered the way sporting activities operate. There is a lack of knowledge on injury risk and illness occurrence in elite winter sport athletes amid the COVID-19 pandemic. This study aimed to describe the incidence of injuries and illnesses sustained during the XXIV Olympic Winter Games in Beijing from February 4 to 20, 2022. METHODS: We recorded the daily number of injuries and illnesses among athletes reported by Beijing 2022 medical staff in the polyclinic, medical venues, and ambulance. We calculated injury and illness incidence as the number of injuries or illnesses occurring during competition or training, respectively, with incidence presented as injuries/illnesses per 100 athlete-days. RESULTS: In total, 2,897 athletes from 91 nations experienced injury or illness. Beijing 2022 medical staff reported 326 injuries and 80 illnesses, equaling 11.3 injuries and 2.8 illnesses per 100 athletes over the 17-day period. Altogether, 11% of the athletes incurred at least one injury and nearly 3% incurred at least one illness. The number of injured athletes was highest in the skating sports (n=104), followed by alpine skiing (n=53), ice track (n=37), freestyle skiing (n=36), and ice hockey (n=35), and was the lowest in the Nordic skiing disciplines (n=20). Of the 326 injuries, 14 (4.3%) led to an estimated absence from training or competition of more than 1 week. A total of 52 injured athletes were transferred to hospitals for further care. The number of athletes with illness (n=80) was the highest for skating (n=33) and Nordic skiing (n=22). A total of 50 illnesses (62.5%) were admitted to the department of dentistry/ophthalmology/otolaryngology, and the most common cause of illness was other causes, including preexisting illness and medicine (n=52, 65%). CONCLUSION: Overall, 11% of athletes incurred at least one injury during the Games, which is similar to the findings during the Olympic Winter Games in 2014 and 2018. Regarding illness, 2% of athletes were affected, which is approximately one-third of the number affected in the 2018 Olympic Winter Games.

Evaluation of the cycle threshold values of RT-PCR for SARS-CoV-2 in COVID-19 patients in predicting epidemic dynamics and monitoring surface contamination.

To evaluate the application of cycle threshold (Ct) values of coronavirus disease 2019 (COVID-19) patients in predicting epidemic dynamics and monitoring surface contamination. The Ct value of reverse transcriptase-polymerase chain reaction for SARS­CoV-2 from COVID-19 patients inbound overseas in Xiamen, China was collected from October 2020 to December 2021, and the correlation of patients' Ct values with epidemic dynamics and surface contamination was evaluated. The results showed that there was an extreme inverse correlation of positivity rate in the current calendar month (ORF1ab, r = -0.692, P = 0.004; N,r = -0.629, P = 0.012) and the following calendar month (ORF1ab,r = -0.801, P = 0.001; N,r = -0.620, P = 0.018) with the median Ct values. Ct value showed better performance for monitoring surface contamination, with the area under the curve value 0.808(95 %CI: 0.748-0.869) for ORF1ab and 0.807(95 %CI:0.746-0.868) for the N gene. The patients' ORF1ab Ct value< 29.09 or N Ct value< 28.03 were 11.25 times and 10.48 times more likely to result in surface contamination than those with ORF1ab Ct value ≥ 29.09 or N Ct value≥ 28.03 (OR:11.25,95 % CI: 5.52-22.35; OR:10.48,95 % CI:5.29-20.70). Ct values were associated with the positivity rate in the current or following calendar month and predicted the epidemic dynamics. The Ct values can be used as a predictor for monitoring surface contamination to develop public health responses to COVID-19.