Multifunctional composite coatings with hydrophobic, UV-resistant, anti-oxidative, and photothermal performance for healthcare.

Personal protective textiles have attracted extensive interest since Corona Virus Disease 2019 has broken out. Moreover, developing eco-friendly, multifunctional waterproof, and breathable surface is of great importance but still faces enormous challenges. Notably, good hydrophobicity and breathability are necessary for protective textiles, especially protective clothing and face masks for healthcare. Herein, the multifunctional composite coatings with good UV-resistant, anti-oxidative, hydrophobic, breathable, and photothermal performance has been rapidly created to meet protective requirements. First, the gallic acid and chitosan polymer was coated onto the cotton fabric surface. Subsequently, the modified silica sol was anchored on the coated cotton fabric surface. The successful fabrication of composite coatings was verified by RGB values obtained from the smartphone and K/S value. The present work is an advance for realizing textile hydrophobicity by utilizing fluorine-free materials, compared with the surface hydrophobicity fabricated with conventional fluorinated materials. The surface free energy has been reduced from 84.2 to27.6 mJ/m2 so that the modified cotton fabric could repel the ethylene glycol, hydrochloric acid, and sodium hydroxide solutions, respectively. Besides, the composite coatings possesses lower adhesion to deionized water. After 70 cycles of the sandpaper abrasion, the fluorine-free hydrophobic coatings still exhibits good hydrophobicity with WCA of 124.6 ± 0.9°, with overcoming the intrinsic drawback of the poor abrasion resistance of hydrophobic surfaces. Briefly, the present work may provide a universal strategy for rapidly creating advanced protective coatings to meet personal healthcare, and a novel method for detecting RGB values of composite coatings by smartphone.

Organic-free growth of gold nanosheets inside 3D bacterial cellulose as highly efficient and robust antibacterial biopolymers.

Without any chemical agent, gold nanosheets (AuNSs) were controllable synthesized through a facile photo-induced reduction within bacterial cellulose (BC) biopolymers. Compared with traditional polymers, AuNSs modified BC biopolymers (AuNSs@BC) biopolymers exhibited similar levels of softness, ductility, and better tensile strength. The in situ constructing of AuNSs@BC biopolymers was demonstrated to provide great reusability and antibacterial activities and towards both of Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli). The optimized AuNSs@BC biopolymers remain at least 95% antibacterial activities after three cycles. The facile and shape-controlled synthesis of AuNSs@BC biopolymers is believed to be useful for the design and application of biomass-based medical dressing. Supplementary Information: The online version contains supplementary material available at 10.1007/s10853-022-07273-x.

Mathematical Model and Optimization Methods of Wide-Scale Pooled Sample Testing for COVID-19

Currently, coronavirus disease 2019 (COVID-19) has become the most severe infectious disease affecting the world, which has spread around the world to more than 200 countries in 2020. Until the number of COVID-19 vaccines is insufficient, nucleic acid testing is considered as an effective way to screen virus carriers and control the spread of the virus. Considering that the medical resources and infection rates are different across various countries and regions, if all infected areas adopt the traditional individual nucleic acid testing method, the workload will be heavy and time-consuming. Therefore, this will not lead to the control of the pandemic. After Wuhan completed a citywide nucleic acid testing in May 2020, China basically controlled the spread of COVID-19 and entered the post-epidemic period. Since then, although some cities in China, such as Qingdao, Xinjiang, Beijing, and Dalian, have experienced a local epidemic resurgence, the pandemic was quickly suppressed through wide-scale pooled nucleic acid testing methods. Combined with the successful experience of mass nucleic acid testing in China, this study introduces two main pooled testing methods used in two cities with a population of more than ten million people, Wuhan's 'five-in-one';and Qingdao's 'ten-in-one';rapid pooled testing methods. This study proposes an improved method for optimising the second round of 'ten-in-one';pooled testing, known as 'the pentagram mini-pooled testing method';, which speeds up the testing process (as a result of reducing the numbers of testing by 40%) and significantly reduces the cost. Qingdao's optimised 'ten-in-one';pooled testing method quickly screens out the infections by running fewer testing samples. This study also mathematically examines the probabilistic principles and applicability conditions for pooled testing of COVID-19. Herein, the study theoretically determines the optimal number of samples that could successfully be combined into a pool under different infection rates. Then, it quantitatively discusses the applicability and principles for choosing the pooled testing instead of individual testing. Overall, this research offers a reference for other countries with different infection rates to help them in implementing the mass testing for COVID-19 to reduce the spread of coronavirus.

An Evaluation of the Performance of Five Burnout Screening Tools: A Multicentre Study in Anaesthesiology, Intensive Care, and Ancillary Staff.

Burnout is an important occupational hazard and early detection is paramount in preventing negative sequelae in physicians, patients, and healthcare systems. Several screening tools have been developed to replace lengthy diagnostic tools for large-scale screening, however, comprehensive head-to-head evaluation for performance and accuracy are lacking. The primary objective of this study was to compare the diagnostic performance of five burnout screening tools, including a novel rapid burnout screening tool (RBST). This was a cross-sectional study involving 493 hospital staff (anaesthesiology and intensive care doctors, nurses, and ancillary staff) at the COVID-19 frontline across four hospitals in Singapore between December 2020 and April 2021. The Maslach Burnout Inventory-Human Services Survey (MBI-HSS) was used as the reference standard. Five burnout screening tools, the single-item MBI measure of burnout (SI-MBI), dual-item MBI (DI-MBI), abbreviated MBI (aMBI), Single Item Burnout Question (SIBOQ), and the RBST, were administered via a 36-item online survey. Tools were administered simultaneously and responses were anonymised. Burnout prevalence was 19.9%. The RBST and the SI-MBI had the two highest accuracies (87.8% and 81.9% respectively) and AUROC scores (0.86, 95% CI: 0.83-0.89 and 0.86, 95% CI: 0.82-0.89 respectively). However, the accuracy of the RBST was significantly higher than the SI-MBI (p < 0.0001), and it had the highest positive likelihood ratio (+LR = 7.59, 95% CI 5.65-10.21). Brief screening tools detect burnout albeit with a wide range of accuracy. This can strain support services and resources. The RBST is a free screening tool that can detect burnout with a high degree of accuracy.

Predicting hosts based on early SARS-CoV-2 samples and analyzing the 2020 pandemic.

The SARS-CoV-2 pandemic has raised concerns in the identification of the hosts of the virus since the early stages of the outbreak. To address this problem, we proposed a deep learning method, DeepHoF, based on extracting viral genomic features automatically, to predict the host likelihood scores on five host types, including plant, germ, invertebrate, non-human vertebrate and human, for novel viruses. DeepHoF made up for the lack of an accurate tool, reaching a satisfactory AUC of 0.975 in the five-classification, and could make a reliable prediction for the novel viruses without close neighbors in phylogeny. Additionally, to fill the gap in the efficient inference of host species for SARS-CoV-2 using existing tools, we conducted a deep analysis on the host likelihood profile calculated by DeepHoF. Using the isolates sequenced in the earliest stage of the COVID-19 pandemic, we inferred that minks, bats, dogs and cats were potential hosts of SARS-CoV-2, while minks might be one of the most noteworthy hosts. Several genes of SARS-CoV-2 demonstrated their significance in determining the host range. Furthermore, a large-scale genome analysis, based on DeepHoF's computation for the later pandemic in 2020, disclosed the uniformity of host range among SARS-CoV-2 samples and the strong association of SARS-CoV-2 between humans and minks.

Digital twins-based remote semi-physical commissioning of flow-type smart manufacturing systems.

The COVID-19 has become a global pandemic that dramatically impacted human lives and economic activities. Due to the high risk of getting affected in high-density population areas and the implementation of national emergency measures under the COVID-19 pandemic, both travel and transportation among cities become difficult for engineers and equipment. Consequently, the costly physical commissioning of a new manufacturing system is greatly hindered. As an emerging technology, digital twins can achieve semi-physical simulation to avoid the vast cost of physical commissioning of the manufacturing system. Therefore, this paper proposes a digital twins-based remote semi-physical commissioning (DT-RSPC) approach for open architecture flow-type smart manufacturing systems. A digital twin system is developed to enable the remote semi-physical commissioning. The proposed approach is validated through a case study of digital twins-based remote semi-physical commissioning of a smartphone assembly line. The results showed that combining the open architecture design paradigm with the proposed digital twins-based approach makes the commissioning of a new flow-type smart manufacturing system more sustainable.

Rapid construction and advanced technology for a Covid-19 field hospital in Wuhan, China

A specialised emergency field hospital was constructed in record time in Wuhan, China shortly after the initial outbreak of the Covid-19 pandemic. Covering an area of 34 000 m2 and providing 1000 beds, Huoshenshan Hospital was more advanced and had more rigorous isolation systems than most current infectious-disease hospitals, but it was delivered in just 10 days. The rapid construction benefited from a unique modular design, over 4000 people who worked around the clock and a series of advanced technologies such as building information modelling, modular construction and 5G communications. The hospital played a crucial role in controlling the pandemic in China.