Continuous evolution and emerging lineage of seasonal human coronaviruses: A multicenter surveillance study.

The seasonal human coronaviruses (HCoVs) have zoonotic origins, repeated infections, and global transmission. The objectives of this study are to elaborate the epidemiological and evolutionary characteristics of HCoVs from patients with acute respiratory illness. We conducted a multicenter surveillance at 36 sentinel hospitals of Beijing Metropolis, China, during 2016-2019. Patients with influenza-like illness (ILI) and severe acute respiratory infection (SARI) were included, and submitted respiratory samples for screening HCoVs by multiplex real-time reverse transcription-polymerase chain reaction assays. All the positive samples were used for metatranscriptomic sequencing to get whole genomes of HCoVs for genetical and evolutionary analyses. Totally, 321 of 15 677 patients with ILI or SARI were found to be positive for HCoVs, with an infection rate of 2.0% (95% confidence interval, 1.8%-2.3%). HCoV-229E, HCoV-NL63, HCoV-OC43, and HCoV-HKU1 infections accounted for 18.7%, 38.3%, 40.5%, and 2.5%, respectively. In comparison to ILI cases, SARI cases were significantly older, more likely caused by HCoV-229E and HCoV-OC43, and more often co-infected with other respiratory pathogens. A total of 179 full genome sequences of HCoVs were obtained from 321 positive patients. The phylogenetical analyses revealed that HCoV-229E, HCoV-NL63 and HCoV-OC43 continuously yielded novel lineages, respectively. The nonsynonymous to synonymous ratio of all key genes in each HCoV was less than one, indicating that all four HCoVs were under negative selection pressure. Multiple substitution modes were observed in spike glycoprotein among the four HCoVs. Our findings highlight the importance of enhancing surveillance on HCoVs, and imply that more variants might occur in the future.

Use of polypropylene fibers extracted from recycled surgical face masks in cement mortar.

The use of personal protective equipment (PPE), particularly single-use surgical face masks (FMs), has increased drastically owing to the ongoing Covid-19 pandemic. This study aimed to demonstrate the feasibility of utilizing recycled FM fibers in cement mortar. For this, FMs were used by removing the inner nose wires and ear loops and cutting them into two different sizes: 10 mm × 5 mm and 20 mm × 5 mm. The FMs were then introduced into five mixtures at 0 (control), 0.10, 0.15, 0.20, and 0.25 % by volume. The following mechanical properties of the mixtures were then tested: workability, density, porosity, water absorption, and the related strengths (compressive, direct tensile, and flexural). In addition, the microstructures of the mixtures were analyzed using a scanning electron microscope. The results revealed that introducing FM fibers, particularly an FM with a 5 mm diameter and 10 mm length, in the mortar increased both the tensile and flexural strengths. Among the various combinations of FMs studied, a mixture containing 0.15 % FMs exhibited the best performance. The findings of this research reveal that FMs can be reused as fibers to enhance the tensile and flexural strengths of cement mortar.

Production of singlet oxygen from photosensitizer erythrosine for facile inactivation of coronavirus on mask.

The global health crisis caused by the COVID-19 pandemic has led to a surge in demand and use of personal protective equipment (PPE) such as masks, putting great pressure on social production and the environment.It is urgent to find an efficient and non-destructive disinfection method for the safe reuse of PPE. This study proposes a PPE disinfection method that uses erythrosine, a U.S. Food and Drug Administration-approved food dye, as photosensitizer to produce singlet oxygen for virus inactivation, and indicates the completion of disinfection by its photobleaching color change.After spraying 100 µL of 10 µM erythrosine on the surface of the mask for 3 times and light exposure for 25 min, the titer of coronavirus decreased by more than 99.999%, and the color of erythrosine on the mask surface disappeared. In addition, the structure of the mask was intact and the filtration efficiency was maintained at > 95% after 10 cycles of erythrosine treatment.Therefore, this disinfection method can provide at least 10 cycles of reuse with the advantages of high safety and convenient, and the completion of disinfection can be indicated by its photobleaching, which is suitable for hospitals and daily life to reduce the consumption of PPE.

Recalling Self-efficacious Memories Reduces COVID-19-Related Fear.

Purpose: Manipulating perceived self-efficacy can mitigate the negative impact of trauma and increase ability to adapt to stress. It is possible that a similar domain-based manipulation aimed at anxiety around the pandemic might mitigate the negative mental health impact of COVID-19. The current experimental study assessed whether a self-efficacy induction would be effective in reducing COVID-19 distress. Methods: Participants were randomized to a self-efficacy autobiographical memory induction or control condition. We hypothesized that individuals in the self-efficacy group would exhibit lower levels of fear on an implicit measure of emotional states following exposure to COVID-19-related stimuli. Results: A significant increase in general self-efficacy and self-confidence was found in the self-efficacy group from pre- to post-induction. Individuals in the self-efficacy group had significantly lower levels of fear counts on the implicit measure of emotional states than the control group following exposure to COVID-19-related stimuli. Conclusions: Results suggest that (1) self-efficacy can be increased among individuals with high levels of COVID-19-related distress using an autobiographical memory induction and (2) doing so reduces fear processing among these individuals when exposed to COVID-19 stimuli. This is relevant for future intervention as it reveals a possible mechanism for reducing and recovering from COVID-19-related distress. Supplementary Information: The online version contains supplementary material available at 10.1007/s10608-023-10377-6.

Recycling spent masks to fabricate flexible hard carbon anode toward advanced sodium energy storage.

The massive discard of spent masks during the COVID-19 pandemic imposes great environmental anxiety to the human society, which calls for a reliable and sustainable outlet to mitigate this issue. In this work, we demonstrate a green design strategy of recycling the spent masks to fabricate hard carbon fabrics toward high-efficient sodium energy storage. After a simple carbonization treatment, flexible hard carbon fabrics composed of interwoven microtubular fibers are obtained. When serving as binder-free anodes of sodium-ion batteries, a large Na-ion storage capacity of 280 mAh g-1 is achieved for the optimized sample. More impressively, the flexible anode exhibits an initial coulombic efficiency of as high as 86% and excellent rate/cycling performance. The real-life practice of the flexible hard carbon is realized in the full-cells. The present study affords an enlightening approach for the recycling fabrication of high value-added hard carbon materials from the spent masks for advanced sodium energy storage.

[Incidence of pressure ulcers due to prone position in patients admitted to the ICU for Covid-19]. / Incidencia de úlceras por presión secundarias al decúbito prono en pacientes ingresados en unidades de cuidados intensivos por SARS-CoV-2.

The appearance of pressure ulcers (PU) is one of the frequent complications of prone position (PP), due to prolonged pressure and shear forces. Objectives: To compare the incidence of pressure ulcers secondary to prone position and describe their location among four Intensive Care Units (ICU) of public hospitals. Methods: Multicenter descriptive and retrospective observational study. The population consisted of patients admitted to the ICU between February 2020 and May 2021, diagnosed with Covid-19 who required prone decubitus. The variables studied were sociodemographic, days of admission to the ICU, total hours on PP, PU prevention, location, stage, frequency of postural changes, nutrition and protein intake. Data collection was carried out through the clinical history of the different computerized databases of each hospital. Descriptive analysis and association between variables were performed using SPSS vs.20.0. Results: A total of 574 patients were admitted for Covid-19, 43.03% were pronated. 69.6% were men, median age was 66 (IQR 55-74) and BMI 30.7 (RIC 27-34.2). Median ICU stay was 28 days (IQR 17-44.2), median hours on PD per patient 48 h (IQR 24-96). The incidence of PU occurrence was 56.3%, 76.2% of patients presented a PU, the most frequent location was the forehead (74.9%). There were significant differences between hospitals in terms of PU incidence (p = 0.002), location (p < 0.001) and median duration of hours per PD episode (p = 0.001). Conclusions: The incidence of pressure ulcers due to the prone position was very high. There is great variability in the incidence of pressure ulcers between hospitals, location and average duration of hours per episode of prone position.

Microplastics/nanoplastics released from facemasks as contaminants of emerging concern.

Facemasks have become a global medical necessity and are a key preventive measure against COVID-19. Typically, facemasks (FMs) are fabricated from non-renewable polymers, particularly polypropylene (PP) and polyethylene (PE), which release secondary microplastic (MPs) due to the chemical, physical, and biological processes. In light of the widespread usage and improper disposal of single-use facemasks, there is concern about their environmental impact since they contribute to plastic pollution during and after pandemics. The repercussions of this have led to millions of tons of plastic waste being dumped into the environment. Due to lack of awareness and improper disposal, the occurrence of micro/nanoplastics released from facemasks in wastewater treatment plants and landfills poses a concern. Infiltration of wastewater treatment processes by micro/nanoplastics at various levels can be problematic because of their chemical nature and broad but small size. Thus, operational and process stability issues can arise during wastewater treatment processes. In addition, landfilling and illegal waste disposal are being used to dispose of potentially infectious COVID-19 waste, leading to an environmental threat to animal and human health and exacerbating plastic pollution. This paper reviews the fate of facemasks in the environment and the repercussions of improper waste management of facemasks in wastewater treatment plants, landfills, and ultimately the environment.

Efficient, Breathable, and Compostable Multilayer Air Filter Material Prepared from Plant-Derived Biopolymers.

State-of-art face masks and respirators are fabricated as single-use devices using microfibrous polypropylene fabrics, which are challenging to be collected and recycled at a community scale. Compostable face masks and respirators can offer a viable alternative to reducing their environmental impact. In this work, we have developed a compostable air filter produced by electrospinning a plant-derived protein, zein, on a craft paper-based substrate. The electrospun material is tailored to be humidity tolerant and mechanically durable by crosslinking zein with citric acid. The electrospun material demonstrated a high particle filtration efficiency (PFE) of 91.15% and a high pressure drop (PD) of 191.2 Pa using an aerosol particle diameter of 75 ± 2 nm at a face velocity of 10 cm/s. We deployed a pleated structure to reduce the PD or improve the breathability of the electrospun material without compromising the PFE over short- and long-duration tests. Over a 1 h salt loading test, the PD of a single-layer pleated filter increased from 28.9 to 39.1 Pa, while that of the flat sample increased from 169.3 to 327 Pa. The stacking of pleated layers enhanced the PFE while retaining a low PD; a two-layer stack with a pleat width of 5 mm offers a PFE of 95.4 ± 0.34% and a low PD of 75.2 ± 6.1 Pa.

High-Sensitivity CMOS-Integrated Floating Gate-Based UVC Sensors.

We report on novel UVC sensors based on the floating gate (FG) discharge principle. The device operation is similar to that of EPROM non-volatile memories UV erasure, but the sensitivity to ultraviolet light is strongly increased by using single polysilicon devices of special design with low FG capacitance and long gate periphery (grilled cells). The devices were integrated without additional masks into a standard CMOS process flow featuring a UV-transparent back end. Low-cost integrated UVC solar blind sensors were optimized for implementation in UVC sterilization systems, where they provided feedback on the radiation dose sufficient for disinfection. Doses of ~10 µJ/cm2 at 220 nm could be measured in less than a second. The device can be reprogrammed up to 10,000 times and used to control ~10-50 mJ/cm2 UVC radiation doses typically employed for surface or air disinfection. Demonstrators of integrated solutions comprising UV sources, sensors, logics, and communication means were fabricated. Compared with the existing silicon-based UVC sensing devices, no degradation effects that limit the targeted applications were observed. Other applications of the developed sensors, such as UVC imaging, are also discussed.

The Ultra fit community mask-Toward maximal respiratory protection via personalized face fit.

Effective masking policies to prevent the spread of airborne infections depend on public access to masks with high filtration efficacy. However, poor face-fit is almost universally present in pleated multilayer disposable face masks, severely limiting both individual and community respiratory protection. We developed a set of simple mask modifications to mass-manufactured disposable masks, the most common type of mask used by the public, that dramatically improves both their personalized fit and performance in a low-cost and scalable manner. These modifications comprise a user-moldable full mask periphery wire, integrated earloop tension adjusters, and an inner flange to trap respiratory droplets. We demonstrate that these simple design changes improve quantitative fit factor by 320%, triples the level of protection against aerosolized droplets, and approaches the model efficacy of N95 respirators in preventing the community spread of COVID-19, for an estimated additional cost of less than 5 cents per mask with automated production.

Three birds, one stone: Disinfecting and turning waste medical masks into valuable carbon dots for sodium hydrosulfite and Fe3+ detection enabled by a simple hydrothermal treatment.

Disposable medical masks are widely used to prevent respiratory infections due to their ability to block virus particles from entering the human body. The coronavirus disease 2019 (COVID-19) pandemic highlighted the importance of medical masks, leading to their widespread use around the world. However, a large number of disposable medical masks have been discarded, some carrying viruses, which have posed a grave threat to the environment and people's health, as well as wasting resources. In this study, a simple hydrothermal method was used for the disinfection of waste medical masks under high-temperature conditions as well as for their transformation into high-value-added carbon dots (CDs, a new type of carbon nanomaterial) with blue-emissive fluorescence, without high energy consumption or environmental pollution. Moreover, the mask-derived CDs (m-CDs) could not only be used as fluorescent probes for sensing sodium hydrosulfite (Na2S2O4), which is widely used in the food and textile industries but is seriously harmful to human health, but also be used for detecting Fe3+ which is harmful to the environment and human health due to its wide use in industries.

Face mask recognition system using CNN model.

COVID-19 epidemic has swiftly disrupted our day-to-day lives affecting the international trade and movements. Wearing a face mask to protect one's face has become the new normal. In the near future, many public service providers will expect the clients to wear masks appropriately to partake of their services. Therefore, face mask detection has become a critical duty to aid worldwide civilization. This paper provides a simple way to achieve this objective utilising some fundamental Machine Learning tools as TensorFlow, Keras, OpenCV and Scikit-Learn. The suggested technique successfully recognises the face in the image or video and then determines whether or not it has a mask on it. As a surveillance job performer, it can also recognise a face together with a mask in motion as well as in a video. The technique attains excellent accuracy. We investigate optimal parameter values for the Convolutional Neural Network model (CNN) in order to identify the existence of masks accurately without generating over-fitting.

Microscale combinatorial stimulation of human myeloid cells reveals inflammatory priming by viral ligands.

Cells sense a wide variety of signals and respond by adopting complex transcriptional states. Most single-cell profiling is carried out today at cellular baseline, blind to cells' potential spectrum of functional responses. Exploring the space of cellular responses experimentally requires access to a large combinatorial perturbation space. Single-cell genomics coupled with multiplexing techniques provide a useful tool for characterizing cell states across several experimental conditions. However, current multiplexing strategies require programmatic handling of many samples in macroscale arrayed formats, precluding their application in large-scale combinatorial analysis. Here, we introduce StimDrop, a method that combines antibody-based cell barcoding with parallel droplet processing to automatically formulate cell population × stimulus combinations in a microfluidic device. We applied StimDrop to profile the effects of 512 sequential stimulation conditions on human dendritic cells. Our results demonstrate that priming with viral ligands potentiates hyperinflammatory responses to a second stimulus, and show transcriptional signatures consistent with this phenomenon in myeloid cells of patients with severe COVID-19.

Leveraging MOOCs for learners in economically disadvantaged regions.

While Massive Online Open Courses (MOOCs) have seen a surge in enrollments in higher education around the world especially during the Covid-19 pandemic, it is unclear if learners from the economically disadvantaged regions (EDR) are also able to capitalize on them. Specifically, challenges related to using MOOCs in these regions have been reported in the literature. Thus, the objective of this paper is to address the pedagogical challenge by investigating approaches to leverage MOOCs for learners in EDR. Drawing from the ARCS (i.e. Attention, Relevance, Confidence and Satisfaction) model, we proposed an embedded MOOCs approach where bite-sized MOOCs segments are integrated into in-class lectures under the guidance of the instructors. The effectiveness of the embedded MOOCs approach was evaluated and compared with other instructional methods. Results from randomized experiments showed that the embedded MOOCs approach had higher evaluations in terms of attention, relevance and satisfaction than face-to-face learning approach. In addition, the embedded MOOCs approach outperformed asynchronously blended MOOCs in enhancing students' relevance perception. Regression analysis further revealed that attention, confidence, and satisfaction perceptions were positively associated with students' intention to adopt the embedded MOOCs approach in their future studies. The findings shed light on how to utilize MOOCs and re-use content in MOOCs for global benefits and enable new pedagogical developments. The findings also underscore the importance of local social support and offline interactions to support the online learning materials.

Development of a reusable low-cost facemask with a recycled hydrophobic layer for preventive health care.

The current work focuses on designing a low-cost, reusable, and highly efficient facemask for protection from respiratory droplets that cause COVID-19, other infection-causing organisms, and dust allergies. Several masks available in the market are single-use that would choke the environment through plastic pollution or are expensive for the commoner to afford. In the present study, the facemask incorporates a waste-derived polyethylene terephthalate (PET) layer and a non-woven polypropylene (PP) layer sandwiched between two tightly woven cotton layers. Combining these layers provides comfort and breathability, besides high bacterial and particulate filtration efficiency. Moreover, the unique PET layer provides mechanical strength and a 3D shape that enables hindrance-free speaking and prevents spectacle fogging. Compared to commercial N95 masks, the developed mask can be reused up to 30 washes and recycled with zero waste discharge ensuing green technology. Moreover, the mask was produced at an affordable cost of Rs. 17 (0.22 USD), including labor charges, and sold at a 100% profit margin @ Rs.35 (0.45 USD) per unit. Further, the mask was certified by neutral testing agencies and provided to a population of more than 6 lakhs, thus significantly contributing to the mitigation of COVID-19.

Enhanced Filtration Efficiency of Natural Materials with the Addition of Electrospun Poly(vinylidene fluoride-co-hexafluoropropylene) Fibres.

Pollutants and infectious diseases can spread through air with airborne droplets and aerosols. A respiratory mask can decrease the amount of pollutants we inhale and it can protect us from airborne diseases. With the onset of the COVID-19 pandemic, masks became an everyday item used by a lot of people around the world. As most of them are for a single use, the amount of non-recyclable waste increased dramatically. The plastic from which the masks are made pollutes the environment with various chemicals and microplastic. Here, we investigated the time- and size-dependent filtration efficiency (FE) of aerosols in the range of 25.9 to 685.4 nm of five different natural materials whose FE was enhanced using electrospun poly(vinylidene fluoride-co-hexafluoropropylene) (PVDF) fibres. A scanning electron microscope (SEM) was used to determine the morphology and structure of the natural materials as well as the thickness of the PVDF fibres, while the phase of the electrospun fibres was determined by Raman spectroscopy. A thin layer of the electrospun PVDF fibres with the same grammage was sandwiched between two sheets of natural materials, and their FE increased up to 80%. By varying the grammature of the electrospun polymer, we tuned the FE of cotton from 82.6 to 99.9%. Thus, through the optimization of the grammage of the electrospun polymer, the amount of plastic used in the process can be minimized, while achieving sufficiently high FE.

ESA-FedGNN: Efficient secure aggregation for federated graph neural networks.

Graph Neural Network (GNN) architecture is a state-of-the-art model, which can obtain complete node embedding features and rich data information by aggregating the information of nodes and neighbors. Therefore, GNNs are widely used in electronic shopping, drug discovery (especially for the treatment of COVID-19) and other fields, promoting the explosive development of machine learning. However, user interaction, data sharing and circulation are highly sensitive to privacy, and centralized storage can lead to data isolation. Therefore, Federated Learning with high efficiency and strong security and privacy enhancement technology based on secure aggregation can improve the security dilemma faced by GNN. In this paper, we propose an Efficient Secure Aggregation for Federated Graph Neural Network(ESA-FedGNN), which can efficiently reduce the cost of communication and avoid computational redundancy while ensuring data privacy. Firstly, a novel secret sharing scheme based on numerical analysis is proposed, which employs Fast Fourier Transform to improve the computational power of the neural network in sharing phase, and leverages Newton Interpolation method to deal with the disconnection and loss of the client in reconstruction phase. Secondly, a regular graph embedding based on geometric distribution is proposed, which optimizes the aggregation speed by using data parallelism. Finally, a double mask is adopted to ensure privacy and prevent malicious adversaries from stealing model parameters. We achieve O ( log N log ( log N ) ) improvements compared to O N 2 in state-of-the-art works. This research helps to provide security solutions related to the practical development and application of privacy-preserving graph neural network technology.