Color-variable dual-dyed photodynamic antimicrobial polyethylene terephthalate (PET)/cotton blended fabrics.

The urgent demand for scalable, potent, color variable, and comfortable antimicrobial textiles as personal protection equipment (PPE) to help reduce infection transmission in hospitals and healthcare facilities has significantly increased since the start of the COVID-19 pandemic. Here, we explored photodynamic antimicrobial polyethylene terephthalate/cotton (TC) blended fabrics comprised of photosensitizer-conjugated cotton fibers and polyethylene terephthalate (PET) fibers dyed with disperse dyes. A small library of TC blended fabrics was constructed wherein the PET fibers were embedded with traditional disperse dyes dominating the fabric color, thereby enabling variable color expression, while the cotton fibers were covalently coupled with the photosensitizer thionine acetate as the microbicidal agent. Physical (SEM, CLSM, TGA, XPS and mechanical strength) and colorimetric (K/S and CIELab values) characterization methods were employed to investigate the resultant fabrics, and photooxidation studies with DPBF demonstrated the ability of these materials to generate reactive oxygen species (i.e., singlet oxygen) upon visible light illumination. The best results demonstrated a photodynamic inactivation of 99.985% (~ 3.82 log unit reduction, P = 0.0021) against Gram-positive S. aureus, and detection limit inactivation (99.99%, 4 log unit reduction, P ≤ 0.0001) against Gram-negative E. coli upon illumination with visible light (60 min; ~ 300 mW/cm2; λ ≥ 420 nm). Enveloped human coronavirus 229E showed a photodynamic susceptibility of ~ 99.99% inactivation after 60 min illumination (400-700 nm, 65 ± 5 mW/cm2). The presence of the disperse dyes on the fabrics showed no significant effects on the aPDI results, and furthermore, appeared to provide the photosensitizer with some measure of protection from photobleaching, thus improving the photostability of the dual-dyed fabrics. Taken together, these results suggest the feasibility of low cost, scalable and color variable thionine-conjugated TC blended fabrics as potent self-disinfecting textiles.

Changes in primary and secondary aerosols during a controlled Chinese New Year.

Large reductions in anthropogenic emissions during the Chinese New Year (CNY) holiday in Beijing have been well reported. However, the changes during the CNY of 2021 are different because most people stayed in Beijing to control the spread of coronavirus disease (COVID-19). Here a high-resolution aerosol mass spectrometer (HR-AMS) was deployed for characterization of the changes in size-resolved aerosol composition and sources during the CNY. We found that the reductions in traffic-related NOx and fossil fuel-related organic aerosol (OA), and cooking OA (1.3-12.7%) during the CNY of 2021 were much smaller than those in previous CNY holidays of 2013, 2015, and 2020. In contrast, the mass concentrations of secondary aerosol species except nitrate showed ubiquitous increases (17.6-30.4%) during the CNY of 2021 mainly due to a 4-day severe haze episode. OA composition also changed substantially during the CNY of 2021. In particular, we observed a large increase by nearly a factor of 2 in oxidized primary OA likely from biomass burning, and a decrease of 50.1% in aqueous-phase secondary OA. A further analysis of the severe haze episode during the CNY illustrated a rapid transition of secondary formation from photochemical to aqueous-phase processing followed by a scavenging process, leading to significant changes in aerosol composition, size distributions, and oxidation degree of OA. A parameterization relationship between oxygen-to-carbon (O/C) and f44 (fraction of m/z 44 in OA) from a collocated capture vaporizer aerosol chemical speciation monitor (CV-ACSM) was developed, which has a significant implication for characterization of OA evolution and the impacts on hygroscopicity due to the rapidly increased deployments of CV-ACSM worldwide.

C1R, CCL2, and TNFRSF1A Genes in Coronavirus Disease-COVID-19 Pathway Serve as Novel Molecular Biomarkers of GBM Prognosis and Immune Infiltration.

Glioblastoma multiforme (GBM) is a prevalent intracranial brain tumor associated with a high rate of recurrence and treatment difficulty. The prediction of novel molecular biomarkers through bioinformatics analysis may provide new clues into early detection and eventual treatment of GBM. Here, we used data from the GTEx and TCGA databases to identify 1923 differentially expressed genes (DEGs). GO and KEGG analyses indicated that DEGs were significantly enriched in immune response and coronavirus disease-COVID-19 pathways. Survival analyses revealed a significant correlation between high expression of C1R, CCL2, and TNFRSF1A in the coronavirus disease-COVID-19 pathway and the poor survival in GBM patients. Cell experiments indicated that the mRNA expression levels of C1R, CCL2, and TNFRSF1A in GBM cells were very high. Immune infiltration analysis revealed a significant difference in the proportion of immune cells in tumor and normal tissue, and the expression levels of C1R, CCL2, and TNFRSF1A were associated with immune cell infiltration of GBM. Additionally, the protein-protein interaction networks of C1R, CCL2, and TNFRSF1A involved a total of 65 nodes and 615 edges. These results suggest that C1R, CCL2, and TNFRSF1A may be used as molecular biomarkers of prognosis and immune infiltration in GBM patients in the future.

Machine learning elucidates the impact of short-term emission changes on air pollution in Beijing

The Chinese Spring Festival (CSF) is the most solemn traditional festival in China, and the substantial changes in anthropogenic activities in megacities provide a unique natural experiment to assess the influence of short-term emission changes on air quality. Here we applied a machine learning based random forest algorithm to six-year aerosol composition measurements in urban Beijing during the CSFs of 2012–2020 to quantify the relative contributions of meteorology and emission changes to air quality. Our results demonstrate large variabilities of air pollutants during the CSF due to the meteorological changes and holiday effect. By removing the meteorological effect, we found that the reduced emissions during CSF caused an average decrease of 5.1% for non-refractory PM2.5 with chloride and primary organic aerosol being the largest (8.8–18.7%) while the changes in secondary species were small. The COVID-19 lockdown during 2020 led to additional reductions of primary species by 16.3–36.8%, yet increases in nitrate and secondary organic aerosol due to enhanced secondary production. Our study has a significant implication that reducing local traffic and cooking emissions is far from enough for mitigating air pollution in winter in megacities due to the nonlinear effect of secondary production and regional transport. A synergetic control of multiple precursors, e.g., NOx and ammonia, is of great importance to reduce secondary aerosol and improve air quality.

Effectiveness Evaluation of a Primary School-Based Intervention against Heatwaves in China.

BACKGROUND: Evidence of the effectiveness of intervention against extreme heat remains unclear, especially among children, one of the vulnerable populations. This study aimed to evaluate the effectiveness of a primary school-based intervention program against heatwave and climate change in China to provide evidence for development of policies for adaptation to climate change. METHODS: Two primary schools in Dongtai City, Jiangsu Province, China, were randomly selected as intervention and control schools (CTR registration number: ChiCTR2200056005). Health education was conducted at the intervention school to raise students' awareness and capability to respond to extreme heat during May to September in 2017. Knowledge, attitude, and practice (KAP) of students and their parents at both schools were investigated by questionnaire surveys before and after intervention. The changes in KAP scores after intervention were evaluated using multivariable difference-in-difference (DID) analysis, controlling for age, sex, etc. Results: The scores of knowledge, attitude, and practice of students and their parents increased by 19.9% (95%CI: 16.3%, 23.6%) and 22.5% (95%CI: 17.8%, 27.1%); 9.60% (95%CI: 5.35%, 13.9%) and 7.22% (95%CI: 0.96%, 13.5%); and 9.94% (95%CI: 8.26%, 18.3%) and 5.22% (95%CI: 0.73%, 9.71%), respectively, after intervention. The KAP score changes of boys were slightly higher than those of girls. Older students had higher score changes than younger students. For parents, the higher the education level, the greater the score change, and change in scores was greater in females than in males. All the health education activities in the program were significantly correlated with the changes in KAP scores of primary school students after intervention, especially those curricula with interesting activities and experiential learning approaches. CONCLUSIONS: Heat and health education program in primary school was an effective approach to improve cognition and behavior for both students and their parents to better adapt to heatwaves and climate change. The successful experience can be generalized to respond to the increasing extreme weather/climate events in the context of climate change, such as heatwaves, and other emergent occasions or public health education, such as the control and prevention of COVID-19.

Cumulative effects of ambient particulate matter pollution on deaths: A multicity analysis of mortality displacement.

BACKGROUND: Systematic evaluations of the cumulative effects and mortality displacement of ambient particulate matter (PM) pollution on deaths are lacking. We aimed to discern the cumulative effect profile of PM exposure, and investigate the presence of mortality displacement in a large-scale population. METHODS: We conducted a time-series analysis with different exposure-lag models on 13 cities in Jiangsu, China, to estimate the effects of PM pollution on non-accidental, cardiovascular, and respiratory mortality (2015-2019). Over-dispersed Poisson generalized additive models were integrated with distributed lag models to estimate cumulative exposure effects, and assess mortality displacement. RESULTS: Pooled cumulative effect estimates with lags of 0-7 and 0-14 days were substantially larger than those with single-day and 2-day moving average lags. For each 10 µg/m3 increment in PM2.5 concentration with a cumulative lag of 0-7 days, we estimated an increase of 0.50 % (95 % CI: 0.29, 0.72), 0.63 % (95 % CI: 0.38, 0.88), and 0.50 % (95 % CI: 0.01, 1.01) in pooled estimates of non-accidental, cardiovascular, and respiratory mortality, respectively. Both PM10 and PM2.5 were associated with significant increases in non-accidental and cardiovascular mortality with a cumulative lag of 0-14 days. We observed mortality displacement within 30 days for non-accidental, cardiovascular, and respiratory deaths. CONCLUSIONS: Our findings suggest that risk assessment based on single-day or 2-day moving average lag structures may underestimate the adverse effects of PM pollution. The cumulative effects of PM exposure on non-accidental and cardiovascular mortality can last up to 14 days. Evidence of mortality displacement for non-accidental, cardiovascular, and respiratory deaths was found.

Nitrate and secondary organic aerosol dominated particle light extinction in Beijing due to clean air action

Air quality in China has been continuously improved since clean air action in 2013, yet the visibility was not improved simultaneously. Here we employed a new method by integrating highly-time resolved aerosol compositions with particle light extinction (bext) into positive matrix factorization to quantify the different contributors to visibility degradation during four seasons in Beijing. Our results show that ammonium nitrate-related factor contributed dominantly to bext during all seasons (31–48%) and played more significant roles during low-visibility periods. Secondary organic aerosol (SOA) was an important contributor of bext (27–35%) in autumn and spring while primary OA related sources were more important in winter (37%). An increase in aerosol mass extinction efficiency and similarly important roles of ammonium nitrate and SOA in visibility degradation were also observed during COVID-19 lockdown. Our results point towards a future challenge in improving visibility in China due to the increased contributions of nitrate and SOA in PM2.5. Future emission controls with a priority to decrease nitrate would benefit both air quality and visibility.

Low CD4 T cell count predicts radiological progression in severe and critically ill COVID-19 patients: a case control study.

BACKGROUND: Novel coronavirus disease (COVID-19) has spread globally and caused over 3 million deaths, posing great challenge on public health and medical systems. Limited data are available predictive factors for disease progression. We aim to assess clinical and radiological predictors for pulmonary aggravation in severe and critically ill COVID-19 patients. METHODS: Patients with confirmed COVID-19 in Renmin Hospital of Wuhan University, China, between Feb. 6th, 2020 and Feb. 21st, 2020 were retrospectively collected. Enrolled patients were divided into non-progression group and progression group based on initial and follow-up chest CTs. Clinical, laboratory, and radiological variables were analyzed. RESULTS: During the study period, 162 patients were identified and a total of 126 patients, including 97 (77.0%) severe cases and 29 (23.0%) critically ill cases were included in the final analysis. Median age was 66.0 (IQR, 56.0-71.3) years. Median time from onset to initial chest CT was 15.0 (IQR, 12.0-20.0) days and median interval to follow-up was 7.0 (IQR, 5.0-7.0) days. Compared with those who did not progress (n=111, 88.1%), patients in the progression group (n=15, 11.9%) had significantly higher percentage of peak body temperature >38 °C (P=0.002), lower platelet count (P=0.011), lower CD4 T cell count (P=0.002), lower CD8 count (P=0.011), higher creatine kinase level (P=0.002), and lower glomerular filtration rate (P=0.018). On both univariate and multivariable analysis, only CD4 T cell count <200/µL was significant (OR, 6.804; 95% CI, 1.450-31.934; P=0.015) for predicting pulmonary progression. CONCLUSIONS: Low CD4 T cell count predicts progression of pulmonary change in severe and critically ill patients with COVID-19.

Use of antibacterial drugs in 616 COVID-19 patients in Shanghai

OBJECTIVE: To analyze the use of antibiotics in patients with coronavirus disease 2019(COVID-19) in Shanghai and to provide evidence for the treatment of COVID-19 and the management of antibacterial drugs. METHODS: The clinical data of 616 patients with COVID-19 in Shanghai Public Health Clinical Center from 20 th, Jan. 2020 to 30 th Apr., 2020 were collected retrospectively, including demographic data, time of admission, time of discharge, and use of antibacterial drugs. All patients were followed up until they were discharged. The frequency of antibacterial drug usage, AUD and the situation of antibacterial using were analyzed. RESULTS: Among 616 patients, 137 were mild, 382 were common, 79 were severe and 18 were critical severe. There were 343 males with an average age of 41.1 years and a median length of stay of 16 days, 273 female cases with an average age of 42.8 years and a median length of stay of 14 days. A total of 165 patients(26.8%) received antibiotics therapy. The usage rates of antibiotics in the mild, common, severe and critical severe subgroups were 4.3%, 21.7%, 73.4% and 100.0%, respectively, which was closely related to clinical classification. The overall usage rates of antibacterial drugs in hospitalized patients gradually decreased with the increase of months. In common patients, the usage rates of antibacterial drugs in March and April were significantly lower than that in January and February. The AUD in all patients was 25.3. As the clinical classification worsened, the AUD in each subgroup gradually increased(0.9, 11.9, 46.2, and 143.8). In total, mild and common patients, the AUD showed a downward trend in January, February, March and April. The total frequency of antibacterial drugs was 286 times, and the top 5 most frequently used drugs were fluoroquinolones, beta-lactamase/beta-lactamase inhibitors, carbapenems, cephalosporins, and linezolid. In severe patients, the antibacterial drugs were mainly restricted use grade antibiotics, and in critical severe patients were mainly special use grade antibiotics. CONCLUSION: In the treatment of COVID-19 patients, the usage rates of antibacterial drugs and AUD were related to clinical classification. As our knowledge and understanding of COVID-19 deepen, our usage rates and strategies of antibacterial drugs are being adjusted, in order to avoid inappropriate use of antibacterial drugs as much as possible.

Evaluation of superinfection, antimicrobial usage, and airway microbiome with metagenomic sequencing in COVID-19 patients: A cohort study in Shanghai.

BACKGROUND: In COVID-19 patients, information regarding superinfection, antimicrobial assessment, and the value of metagenomic sequencing (MS) could help develop antimicrobial stewardship. METHOD: This retrospective study analyzed 323 laboratory-confirmed COVID-19 patients for co-infection rate and antimicrobial usage in the Shanghai Public Health Clinical Center (SPHCC) from January 23rd to March 14th 2020. The microbiota composition was also investigated in patients with critically severe COVID-19. RESULTS: The total population co-infection rate was 17/323 (5.3%) and 0/229 (0), 4/78 (5.1%), and 13/16 (81.3%) for the mild, severe, and critically severe subgroups, respectively. Proven fungal infection was significantly associated with a higher mortality rate (p = 0.029). In critically severe patients, the rate of antimicrobials and carbapenem usage were 16/16 (100%) and 13/16 (81.3%), respectively, in which the preemptive and empiric antimicrobial days accounted for 51.6% and 30.1%, respectively. Targeted therapy only accounted for 18.3%. MS was implemented to detect non-COVID-19 virus co-existence and the semi-quantitative surveillance of bacteremia, with clear clinical benefit seen in cases with MS-based precision antimicrobial management. Airway microbiome analysis suggested that the microbiota compositions in critically severe COVID-19 patients were likely due to intubation and mechanical ventilation. CONCLUSIONS: In the SPHCC cohort, we observed a non-negligible rate of super-infection, especially for the critically ill COVID-19 patients. Fungal co-infection requires intensive attention due to the high risk of mortality, and the clinical benefit of MS in guiding antimicrobial management warrants further investigation.

Coronavirus pandemic reduced China's CO2 emissions in short-term, while stimulus packages may lead to emissions growth in medium- and long-term.

Coronavirus has confined human activities, which caused significant reductions in coal, oil, and natural gas consumptions in China since January of 2020. We compile industrial, transport, and construction data to estimate the reductions in energy-related CO2 emissions during the first quarter of 2020 in China. Our results show that the fossil fuel related CO2 emissions decreased by 18.7% (182 MtCO2) in the first quarter of 2020 compared with the same period last year, including reductions of 12.2% (92 MtCO2) in industry sectors, 61.9% (62 MtCO2) in transport, and 23.9% (28 MtCO2) in construction. The figure in annual CO2 emission reductions is expected to limit with an estimate of 1.6%. However, to achieve the economic target for the 13th Five-Year-Plan, stimulus packages including investments in "shovel-ready" infrastructure projects issued by China's central and local governments to response the COVID-19 may increase CO2 emissions with a higher speed in the coming years. Thus, sustainable stimulus packages are needed for accelerating China's climate goals.

Anxiety, depression and cognitive emotion regulation strategies in Chinese nurses during the COVID-19 outbreak.

AIMS: To explore the association between cognitive emotion regulation strategies and anxiety and depression among nurses during the COVID-19 outbreak. BACKGROUND: Nurses play a vital role in responding to the COVID-19 outbreak, but many of them suffer from psychological problems due to the excessive workload and stress. Understanding the correlation between cognitive emotion regulation strategies and anxiety and depression will promote targeted psychosocial interventions for these affected nurses. METHODS: This cross-sectional study of 586 nurses was conducted in Eastern China. Participants completed online questionnaires that investigated anxiety, depression and cognitive emotion regulation strategies. RESULTS: The prevalence of nurses' anxiety and depression was 27.6% and 32.8%, respectively. Lower self-blame, rumination and catastrophizing, as well as greater acceptance and positive refocusing, were related to fewer symptoms of anxiety or depression. CONCLUSION: The cognitive emotion regulation strategies of acceptance and positive refocusing contribute to reducing anxiety or depression. These strategies should be considered when implementing psychotherapeutic interventions to improve nurses' adverse emotional symptoms. IMPLICATIONS FOR NURSING MANAGEMENT: This study highlights the need to assess cognitive emotion regulation strategies use in screening for anxiety and depression. Nurse managers should develop psychosocial interventions including appropriate strategies to help nurses with adverse emotions during a pandemic.

Endoscopic mask innovation and protective measures changes during the coronavirus disease-2019 pandemic: Experience from a Chinese hepato-biliary-pancreatic unit.

Endoscopy is widely used as a clinical diagnosis and treatment method for certain hepatobiliary and pancreatic diseases. However, due to the distinctive epidemiological characteristics of severe acute respiratory syndrome coronavirus 2, the virus causing coronavirus disease-2019 (COVID-19), healthcare providers are exposed to the patient's respiratory and gastrointestinal fluids, rendering endoscopy a high risk for transmitting a nosocomial infection. This article introduces preventive measures for endoscopic treatment enacted in our medical center during COVID-19, including the adjustment of indications, the application of endoscope protective equipment, the design and application of endoscopic masks and splash-proof films, and novel recommendations for bedside endoscope pre-sterilization.

D-dimer as a biomarker for disease severity and mortality in COVID-19 patients: a case control study.

BACKGROUND: Over 5,488,000 cases of coronavirus disease-19 (COVID-19) have been reported since December 2019. We aim to explore risk factors associated with mortality in COVID-19 patients and assess the use of D-dimer as a biomarker for disease severity and clinical outcome. METHODS: We retrospectively analyzed the clinical, laboratory, and radiological characteristics of 248 consecutive cases of COVID-19 in Renmin Hospital of Wuhan University, Wuhan, China from January 28 to March 08, 2020. Univariable and multivariable logistic regression methods were used to explore risk factors associated with in-hospital mortality. Correlations of D-dimer upon admission with disease severity and in-hospital mortality were analyzed. Receiver operating characteristic curve was used to determine the optimal cutoff level for D-dimer that discriminated those survivors versus non-survivors during hospitalization. RESULTS: Multivariable regression that showed D-dimer > 2.0 mg/L at admission was the only variable associated with increased odds of mortality [OR 10.17 (95% CI 1.10-94.38), P = 0.041]. D-dimer elevation (≥ 0.50 mg/L) was seen in 74.6% (185/248) of the patients. Pulmonary embolism and deep vein thrombosis were ruled out in patients with high probability of thrombosis. D-dimer levels significantly increased with increasing severity of COVID-19 as determined by clinical staging (Kendall's tau-b = 0.374, P = 0.000) and chest CT staging (Kendall's tau-b = 0.378, P = 0.000). In-hospital mortality rate was 6.9%. Median D-dimer level in non-survivors (n = 17) was significantly higher than in survivors (n = 231) [6.21 (3.79-16.01) mg/L versus 1.02 (0.47-2.66) mg/L, P = 0.000]. D-dimer level of > 2.14 mg/L predicted in-hospital mortality with a sensitivity of 88.2% and specificity of 71.3% (AUC 0.85; 95% CI = 0.77-0.92). CONCLUSIONS: D-dimer is commonly elevated in patients with COVID-19. D-dimer levels correlate with disease severity and are a reliable prognostic marker for in-hospital mortality in patients admitted for COVID-19.

A chemical cocktail during the COVID-19 outbreak in Beijing, China: Insights from six-year aerosol particle composition measurements during the Chinese New Year holiday.

The rapidly spread coronavirus disease (COVID-19) has limited people's outdoor activities and hence caused substantial reductions in anthropogenic emissions around the world. However, the air quality in some megacities has not been improved as expected due to the complex responses of aerosol chemistry to the changes in precursors and meteorology. Here we demonstrate the responses of primary and secondary aerosol species to the changes in anthropogenic emissions during the COVID-19 outbreak in Beijing, China along with the Chinese New Year (CNY) holiday effects on air pollution by using six-year aerosol particle composition measurements. Our results showed large reductions in primary aerosol species associated with traffic, cooking and coal combustion emissions by 30-50% on average during the CNY, while the decreases in secondary aerosol species were much small (5-12%). These results point towards a future challenge in mitigating secondary air pollution because the reduced gaseous precursors may not suppress secondary aerosol formation efficiently under stagnant meteorological conditions. By analyzing the long-term measurements from 2012 to 2020, we found considerable increases in the ratios of nitrate to sulfate, secondary to primary OA, and sulfur and nitrogen oxidation capacity despite the overall decreasing trends in mass concentrations of most aerosol species, suggesting that the decreases in anthropogenic emissions have facilitated secondary formation processes during the last decade. Therefore, a better understanding of the mechanisms driving the chemical responses of secondary aerosol to the changes in anthropogenic emissions under complex meteorological environment is essential for future mitigation of air pollution in China.