Surface enhanced Raman spectroscopy based analysis of SARS-CoV-2 spike protein binding to ACE2 receptor

The SARS-CoV-2 virus is still a challenge because of its diversity and mutations. The binding interactions of the angiotensin converting enzyme 2 (ACE2) receptor and the spike protein are relevant for the SARS-CoV-2 virus to enter the cell. Consequently, it is important and helpful to analyze binding activities and the changes in the structure of the ACE2 receptor and the spike protein. Surface enhanced Raman spectroscopy is able to analyze small concentrations of the proteins without contact, non-invasively and label-free. In this work, we present a SERS based approach in the visible wavelength range to analyze and study the binding interactions of the ACE2 receptor and the spike protein. SERS measurements of the ACE2 receptor, the spike protein and the ACE2-spike complex were performed. Additionally, an inhibitor was used to prevent the spike protein from binding to ACE2 and to compare the results. The analysis of the measured SERS spectra reveals structural differences and changes due to binding activities. Thus, we show that the performed SERS based approach can help for rapid and non-invasive analysis of binding interactions of the ACE2-spike complex and also of protein binding in general. © 2023 SPIE.

A new strategy to integrate silver nanowires with waterborne coating to improve their antimicrobial and antiviral properties

PurposeThis study aims to focus on the preparation and characterization of the silver nanowire (AgNWs), as well as their application as antimicrobial and antivirus activities either with incorporation on the waterborne coating formulation or on their own.Design/methodology/approachPrepared AgNWs are characterized by different analytical instruments, such as ultraviolet-visible spectroscope, scanning electron microscope and X-ray diffraction spectrometer. All the paint formulation's physical and mechanical qualities were tested using American Society for Testing and Materials, a worldwide standard test procedure. The biological activities of the prepared AgNWs and the waterborne coating based on AgNWs were investigated. And, their effects on pathogenic bacteria, antioxidants, antiviral activity and cytotoxicity were also investigated.FindingsThe obtained results of the physical and mechanical characteristics of the paint formulation demonstrated the formulations' greatest performance, as well as giving good scrub resistance and film durability. In the antimicrobial activity, the paint did not have any activity against bacterial pathogen, whereas the AgNWs and AgNWs with paint have similar activity against bacterial pathogen with inhibition zone range from 10 to 14 mm. The development of antioxidant and cytotoxicity activity of the paint incorporated with AgNWs were also observed. The cytopathic effects of herpes simplex virus type 1 (HSV-1) were reduced in all three investigated modes of action when compared to the positive control group (HSV-1-infected cells), suggesting that these compounds have promising antiviral activity against a wide range of viruses, including DNA and RNA viruses.Originality/valueThe new waterborne coating based on nanoparticles has the potential to be promising in the manufacturing and development of paints, allowing them to function to prevent the spread of microbial infection, which is exactly what the world requires at this time.

Quantification of oil and gas methane emissions in the Delaware and Marcellus basins using a network of continuous tower-based measurements

According to the United States Environmental Protection Agency (US EPA), emissions from oil and gas infrastructure contribute 30 % of all anthropogenic methane (CH4) emissions in the US. Studies in the last decade have shown emissions from this sector to be substantially larger than bottom-up assessments, including the EPA inventory, highlighting both the increased importance of methane emissions from the oil and gas sector in terms of their overall climatological impact and the need for independent monitoring of these emissions. In this study we present continuous monitoring of regional methane emissions from two oil and gas basins using tower-based observing networks. Continuous methane measurements were taken at four tower sites in the northeastern Marcellus basin from May 2015 through December 2016 and five tower sites in the Delaware basin in the western Permian from March 2020 through April 2022. These measurements, an atmospheric transport model, and prior emission fields are combined using an atmospheric inversion to estimate monthly methane emissions in the two regions. This study finds the mean overall emission rate from the Delaware basin during the measurement period to be 146–210 Mg CH4 h-1 (energy-normalized loss rate of 1.1 %–1.5 %, gas-normalized rate of 2.5 %–3.5 %). Strong temporal variability in the emissions was present, with the lowest emission rates occurring during the onset of the COVID-19 pandemic. Additionally, a synthetic model–data experiment performed using the Delaware tower network shows that the presence of intermittent sources is not a significant source of uncertainty in monthly quantification of the mean emission rate. In the Marcellus, this study finds the overall mean emission rate to be 19–28 Mg CH4 h-1 (gas-normalized loss rate of 0.30 %–0.45 %), with relative consistency in the emission rate over time. These totals align with aircraft top-down estimates from the same time periods. In both basins, the tower network was able to constrain monthly flux estimates within ±20 % uncertainty in the Delaware and ±24 % uncertainty in the Marcellus. The results from this study demonstrate the ability to monitor emissions continuously and detect changes in the emissions field, even in a basin with relatively low emissions and complex background conditions.

Preface

The title of the ConferenceXXII Conference of PhD Students and Young Scientists "Interdisciplinary topics in mining and geology”The location and the date of the conferencevirtual event – online conference, June 29th to July 1st, 2022 in Wrocław, PolandXXIInd Conference of PhD Students and Young Scientists "Interdisciplinary topics in mining and geology” continues a series of events that started in 2000 at Wrocław University of Science and Technology. Scientific programme of the Conference focuses on four thematic panels:1. Mining Engineering: sustainable development, digitalisation in mining, problems of securing, protecting and using remnants of old mining works, underground mining, opencast mining, mineral processing, waste management, mining machinery, mine transport, economics in mining, mining aeronautics, ventilation and air conditioning in mines,2. Earth and Space Sciences: geology, hydrogeology, environmental protection, extraterrestrial resources, groundwater and medicinal waters, engineering and environmental protection, geotourism,3. Geoengineering: environmental protection, applied geotechnics, rock and soil mechanics, geohazards,4. Geoinformation: mining geodesy, GIS, photogrammetry and remote sensing, geodata modeling and analysis.The XXII Conference of PhD Students and Young Scientists was held as a virtual event, that is as a virtual, online conference in real-time. The reason why the Organizing Committee decided to change the traditional formula of the event to online formula was related to the concern for the health of the participants due to the COVID-19 epidemic.The XXII Conference of PhD Students and Young Scientists took place from June 29th to July 1st, 2022 in Wroclaw, Poland. That is the organizers worked and managed the event from the Wrocław University of Science and Technology Geocentre building. Because the conference focused on four thematic panels, four different special opening lectures were delivered by wellknown scientists- Professor Jan Zalasiewicz (University of Leicester, England)- Associate Professor Artur Krawczyk (AGH University of Science and Technology, Poland)- Professor Biljana Kovacević-Zelić (University of Zagreb, Croatia)- Assistant Professor Eduard Kan (Tashkent Institute of Irrigation and Agricultural Mechanizations Engineers, Uzbekistan).The Conference was divided into 8 oral sessions (with 33 presentations) and 1 poster session (with 33 posters). The amount of time provided to one presentation was 15 minutes, after presentation there was 5 minutes available for discussion. The poster session was available throughout the event, and the posters were available for online viewing on the Conference's website with the possibility of make discussion and ask questions in real time via zoom meeting application as well. Every day of the Conference one "virtual coffee break” was devoted for discussion between participants and question and answer session for the Organizers.There were 96 registered participants from 13 countries. The online XXII Conference of PhD Students and Young Scientists was conducted using the Zoom meeting platform with commemorative screen shots taken. By tradition two competitions, for the best oral presentation and for the best poster were held. The award for the best oral presentation was given ex aequo to Julia Tiganj (TH Georg Agricola University of Applied Sciences, Germany) for the presentation entitled Post-mining goes international: hurdles to climate neutrality using the example of China and Oksana Khomiak, Jörg Benndorf (TU Bergakademie Freiberg, Germany) for the presentation entitled Spectral analysis of ore hyperspectral images at different stages of the mining value chain, whereas the best poster was awarded to Adam Wróblewski, Jacek Wodecki, Paweł Trybała, Radosław Zimroz (Wrocław University of Science and technology, Poland) for the poster entitled Large underground structures geometry evaluation based on point cloud data analysis.List of Scientific Committee, Organizing Committee, Editorial Team are available i this pdf.

Exploring the authentication of COVID-19 vaccines using Surface-enhanced handheld Raman spectroscopy (SERS) equipped with orbital Raster scattering and machine learning

COVID-19 is a novel coronavirus first emerging in Wuhan, China in December 2019 and has since spread rapidly across the globe escalating into a worldwide pandemic causing millions of fatalities. Emergency response to the pandemic included social distancing and isolation measures as well as the escalation of vaccination programmes. The most popular COVID-19 vaccines are nucleic acid-based. The vast spread and struggles in containment of the virus has allowed a gap in the market to emerge for counterfeit vaccines. This study investigates the use of handheld Raman spectroscopy as a method for nucleic acid-based vaccine authentication and utilises machine learning analytics to assess the efficacy of the method. Conventional Raman spectroscopy requires a large workspace, is cumbersome and energy consuming, and handheld Raman systems show limitations with regards to sensitivity and sample detection. Surface Enhanced Raman spectroscopy (SERS) however, shows potential as an authentication technique for vaccines, allowing identification of characteristic nucleic acid bands in spectra. SERS showed strong identification potential through Correlation in Wavelength Space (CWS) with all vaccine samples obtaining an r value of approximately 1 when plotted against themselves. Variance was observed between some excipients and a selected number of DNA-based vaccines, possibly attributed to the stability of the SERS colloid where the colloid-vaccine complex had been measured over different time intervals. Further development of the technique would include optimisation of the SERS method, stability studies and more comprehensive analysis and interpretation of a greater sample size. © 2023 IEEE.

Pathogen spectrum analysis of acute respiratory infection among inpatients before and after the outbreak of COVID-19 pandemic in Huairou district of Beijing

Objective To analyze the composition and epidemiological characteristics of respiratory pathogens in hospitalized patients with respiratory tract infections in Huairou district before and after the outbreak of corona virus disease 2019（COVID-19）. Methods Respiratory specimens were collected from hospitalized patients who met the case definition in Huairou district during the period of January 2018 and December 2021. The samples were tested for influenza virus, respiratory syncytial virus, adenovirus, parainfluenza virus, metapneumovirus, coronavirus, rhinovirus, bocavirus, enterovirus, mycoplasma pneumoniae, chlamydophila pneumoniae and other respiratory pathogens by using ABI 7500 real-time fluorescent quantitative PCR assay. Results From January 2018 to December 2021, a total of 1 148 samples were tested and the overall positive rate was 24. 65%（283cases）. The positive detection rate after the outbreak of COVID-19 in 2020-2021（79/522） was significantly lower than that before the outbreak of COVID-19 in 2018-2019（204/626）（15. 13% vs 32. 59%, χ~2=46. 683, P＜0. 01）. The positive rates in children aged 0-＜2 years and 2-＜5 years after the outbreak of COVID-19were 46. 15% and 45. 45% respectively, were significantly higher than those in other age groups （χ~2=73. 053,P＜0. 01）. Mycoplasma pneumoniae（12. 75%）, enterovirus（10. 29%） and adenovirus（10. 29%） were the top three pathogens before the outbreak, while, after the outbreak, the top three pathogens were syncytial virus（21. 52%）, parainfluenza（17. 72%） and rhinovirus（17. 72%）. In Huairou district, the detection rate of respiratory pathogens peaked in winter, there was also a small peak in summer. Conclusion After the outbreak of COVID-19, children under 5 years old are still the main population for respiratory infection control. The change of pathogen spectrum before and after the outbreak of covid-19 is helpful for clinician to recognize and diagnose the disease.

Classification of Respiratory Syncytial Virus and Sendai Virus Using Portable Near-Infrared Spectroscopy and Chemometrics

There is evidence that it may be possible to detect viruses and viral infection optically using techniques such as Raman and infrared (IR) spectroscopy and hence open the possibility of rapid identification of infected patients. However, high-resolution Raman and IR spectroscopy instruments are laboratory-based and require skilled operators. The use of low-cost portable or field-deployable instruments employing similar optical approaches would be highly advantageous. In this work, we use chemometrics applied to low-resolution near-IR (NIR) reflectance/absorbance spectra to investigate the potential for simple low-cost virus detection suitable for widespread societal deployment. We present the combination of near-IR spectroscopy (NIRS) and chemometrics to distinguish two respiratory viruses, respiratory syncytial virus (RSV), the principal cause of severe lower respiratory tract infections in infants worldwide, and Sendai virus (SeV), a prototypic paramyxovirus. Using a low-cost and portable spectrometer, three sets of RSV and SeV spectra, dispersed in phosphate-buffered saline (PBS) medium or Dulbecco's modified eagle medium (DMEM), were collected in long- and short-term experiments. The spectra were preprocessed and analyzed by partial least-squares discriminant analysis (PLS-DA) for virus type and concentration classification. Moreover, the virus type/concentration separability was visualized in a low-dimensional space through data projection. The highest virus-type classification accuracy obtained in PBS and DMEM is 85.8% and 99.7%, respectively. The results demonstrate the feasibility of using portable NIR spectroscopy as a valuable tool for rapid, on- site, and low-cost virus prescreening for RSV and SeV with the further possibility of extending this to other respiratory viruses such as SARS-CoV-2.

Vascular Spectrum of Imaging Findings in COVID-19: Ischemic, Hemorrhagic, and Thromboembolic Complications

Abstract Ischemic strokes secondary to occlusion of large vessels have been described in patients with COVID-19. Also, venous thrombosis and pulmonary thromboembolism have been related to the disease. Vascular occlusion may be associated with a prothrombotic state due to COVID-19-related coagulopathy and endotheliopathy. Intracranial hemorrhagic lesions can additionally be seen in these patients. The causative mechanism of hemorrhage could be associated with anticoagulant therapy or factors such as coagulopathy and endotheliopathy. We report on cases of ischemic, thrombotic, and hemorrhagic complications in six patients diagnosed with SARS-CoV-2 infection. Chest computed tomography (CT) showed typical SARS-CoV-2 pneumonia findings in all the cases, which were all confirmed by either serology or reverse transcription polymerase chain reaction (RT-PCR) tests.

Automatic Analysis of Isothermal Amplification via Impedance Time-Constant-Domain Spectroscopy: A SARS-CoV-2 Case Study

The development of sensitive and affordable testing devices for infectious diseases is essential to preserve public health, especially in pandemic scenarios. In this work, we have developed an attractive analytical method to monitor products of genetic amplification, particularly the loop-mediated isothermal amplification reaction (RT-LAMP). The method is based on electrochemical impedance measurements and the distribution of relaxation times model, to provide the so-called time-constant-domain spectroscopy (TCDS). The proposed method is tested for the SARS-CoV-2 genome, since it has been of worldwide interest due to the COVID-19 pandemic. Particularly, once the method is calibrated, its performance is demonstrated using real wastewater samples. Moreover, we propose a simple classification algorithm based on TCDS data to discriminate among positive and negative samples. Results show how a TCDS-based method provides an alternative mechanism for label-free and automated assays, exhibiting robustness and specificity for genetic detection.

Customizable Fabrication Process for Flexible Carbon-Based Electrochemical Biosensors

In recent research, 3D printing has become a powerful technique and has been applied in the last few years to carbon-based materials. A new generation of 3D-printed electrodes, more affordable and easier to obtain due to rapid prototyping techniques, has emerged. We propose a customizable fabrication process for flexible (and rigid) carbon-based biosensors, from biosensor design to printable conductive inks. The electrochemical biosensors were obtained on a 50 µm Kapton® (polyimide) substrate and transferred to a 500 µm PDMS substrate, using a 3D-extrusion-based printing method. The main features of our fabrication process consist of short-time customization implementation, fast small-to-medium batch production, ease of electrochemical spectroscopy measurements, and very good resolution for an extrusion-based printing method (100 µm). The sensors were designed for future integration into a smart wound dressing for wound monitoring and other biomedical applications. We increased their sensibility with electro-deposited gold nanoparticles. To assess the biosensors' functionality, we performed surface functionalization with specific anti-N-protein antibodies for SARS-CoV 2 virus, with promising preliminary results.

Synthesis, DFT and In Silico Anti-COVID Evaluation of Novel Tetrazole Analogues

A new series of 3-aryl/heteroaryl-2-(1H-tetrazol-5-yl) acrylamides have been synthesized through catalyst-free, one-pot cascade reactions, utilizing click chemistry approach and evaluated for their anti-COVID activities against two proteins in silico. The structural properties of the synthesized molecules were evaluated based on DFT calculations. Total energy of the synthesized tetrazole compounds were obtained through computational analysis which indicate the high stability of the synthesized compounds. The Frontier Molecular Orbitals (FMO) and associated energies and molecular electrostatic potential (MEP) surfaces were generated for the compounds. Spectral analysis by DFT gave additional evidence to the structural properties of the synthesized molecules. All tetrazole analogues come under good ADMET data as they followed the standard value for ADMET parameters. Docking studies offered evidence of the molecules displaying excellent biological properties as an anti-Covid drug. Compound 4 g exhibited excellent anti-COVID-19 properties with four hydrogen binding interactions with amino acids GLN 2.486 Å, GLN 2.436 Å, THR 2.186 Å and HSD 2.468 Å with good full-fitness score (–1189.12) and DeltaG (–7.19). Similarly, compound 4d shown potent activity against anti-COVID-19 mutant protein (PDB: 3K7H) with three hydrogen binding interactions, i.e., SER 2.274 Å, GLU 1.758 Å and GLU 1.853 Å with full-fitness score of –786.60) and DeltaG (–6.85). The result of these studies revealed that the compounds have the potential to become lead molecules in the drug discovery process.

Carbon trading and COVID-19: a hybrid machine learning approach for international carbon price forecasting.

Accurate carbon price forecasting can better allocate carbon emissions and thus ensure a balance between economic development and potential climate impacts. In this paper, we propose a new two-stage framework based on processes of decomposition and re-estimation to forecast prices across international carbon markets. We focus on the Emissions Trading System (ETS) in the EU, as well as the five main pilot schemes in China, spanning the period from May 2014 to January 2022. In this way, the raw carbon prices are first separated into multiple sub-factors and then reconstructed into factors of 'trend' and 'period' with the use of Singular Spectrum Analysis (SSA). Once the subsequences have been thus decomposed, we further apply six machine learning and deep learning methods, allowing the data to be assembled and thus facilitating the prediction of the final carbon price values. We find that from amongst these machine learning models, the Support vector regression (SSA-SVR) and Least squares support vector regression (SSA-LSSVR) stand out in terms of performance for the prediction of carbon prices in both the European ETS and equivalent models in China. Another interesting finding to come out of our experiments is that the sophisticated algorithms are far from being the best performing models in the prediction of carbon prices. Even after accounting for the impacts of the COVID-19 pandemic and other macro-economic variables, as well as the prices of other energy sources, our framework still works effectively.

Early Detection of Covid Using Spectral Analysis of Cough and Deep Convolutional Neural Network

Now-a-days, there are numerous techniques and ICT tools for the detection of Covid-19. But, these techniques are working with the help;of culminated or peak of symptoms. However, there is a demanding need for the early detection of Covid with self-reported symptoms or even without any symptoms, which makes it easier for further diagnosis or treatment. This research paper proposes a novel approach for the early detection of Covid with the spectral analysis of Cough sound using discrete wavelet transform (DWT), followed by deep convolution neural network (DCNN) based classification. The proposed method with the cough spectral analysis and Deep Learning based algorithm returns the covid infection probability. The empirical results show that the proposed method of covid detection using cough spectral analysis using DWT and deep learning achieves better accuracy, while compared to the conventional methods. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Low-Dimensional Compounds Containing Bioactive Ligands. Part XX: Crystal Structures, Cytotoxic, Antimicrobial Activities and DNA/BSA Binding of Oligonuclear Zinc Complexes with Halogen Derivatives of 8-Hydroxyquinoline

Two tetranuclear [Zn4Cl2(ClQ)6]·2DMF (1) and [Zn4Cl2(ClQ)6(H2O)2]·4DMF (2), as well as three dinuclear [Zn2(ClQ)3(HClQ)3]I3 (3), [Zn2(dClQ)2(H2O)6(SO4)] (4) and [Zn2(dBrQ)2(H2O)6(SO4)] (5), complexes (HClQ = 5-chloro-8-hydroxyquinoline, HdClQ = 5,7-dichloro-8-hydroxyquinoline and HdBrQ = 5,7-dibromo-8-hydroxyquinoline) were prepared as possible anticancer or antimicrobial agents and characterized by IR spectroscopy, elemental analysis and single crystal X-ray structure analysis. The stability of the complexes in solution was verified by NMR spectroscopy. Antiproliferative activity and selectivity of the prepared complexes were studied using in vitro MTT assay against the HeLa, A549, MCF-7, MDA-MB-231, HCT116 and Caco-2 cancer cell lines and on the Cos-7 non-cancerous cell line. The most sensitive to the tested complexes was Caco-2 cell line. Among the tested complexes, complex 3 showed the highest cytotoxicity against all cell lines. Unfortunately, all complexes showed only poor selectivity to normal cells, except for complex 5, which showed a certain level of selectivity. Antibacterial potential was observed for complex 5 only. Moreover, the DNA/BSA binding potential of complexes 1–3 was investigated by UV-vis and fluorescence spectroscopic methods.

EVALUATION OF AIR TRAFFIC IN THE CONTEXT OF THE COVID-19 PANDEMIC

The Covid-19 pandemic unexpectedly shook the entire global economy, causing it to destabilize over a long period of time. One of the sectors that was particularly hit hard was air traffic, and the changes that have taken place in it have been unmatched by any other crisis in history. The purpose of this article was to identify the time series describing the number of airline flights in Poland in the context of the Covid-19 pandemic. The article first presents selected statistics and indicators showing the situation of the global and domestic aviation market during the pandemic. Then, based on the data on the number of flights in Poland, the identification of the time series describing the number of flights by airlines was made. The discrete wavelet transformation (DWT) was used to determine the trend, while for periodicity verification, first statistical tests (Kruskal-Wallis test and Friedman test) and then spectral analysis were used. The confirmation of the existence of weekly seasonality allowed for the identification of the studied series as the sum of the previously determined trend and the seasonal component, as the mean value from the observations on a given day of the week. The proposed model was compared with the 7-order moving average model, as one of the most popular in the literature. As the obtained results showed, the model developed by the authors was better at identifying the studied series than the moving average. The errors were significantly lower, which made the presented solution more effective. This confirmed the validity of using wavelet analysis in the case of irregular behaviour of time series, and also showed that both spectral analysis and statistical tests (Kruskal-Walis and Fridman) proved successful in identifying the seasonal factor in the time series. The method used allowed for a satisfactory identification of the model for empirical data, however, it should be emphasized that the aviation services market is influenced by many variables and the forecasts and scenarios created should be updated and modified on an ongoing basis. © 2022 Warsaw University of Technology. All rights reserved.

Discovering a Dihydrofluorescein Analogue as a Promising Fluorescence Substrate to HRP

Horseradish peroxidase (HRP) combined with its fluorescence substrates is attracting increasing attention for biochemical analysis. Amplex red is the most widely used fluorescence substrate to HRP;however, it suffers from some drawbacks, such as nonspecific responsiveness toward carboxylesterases. Discovering a new small molecular fluorescence substrate with improved sensitivity and selectivity for HRP is thus desired. Herein, three dihydrofluorescein derivatives (DCFHs) are presented to serve as HRP substrates through fluorescence turn-on methods. The most promising one, 2,7-dichloro-9-(2-(hydroxymethyl)phenyl)-9H-xanthene-3,6-diol (DCFH-1), exhibited excellent sensitivity in the detection of HRP. Moreover, DCFH-1 does not respond to carboxylesterase, thus holding advantages over Amplex red. In the further study, the detection reagent in the commercial ELISA kits was replaced with DCFH-1 to establish a new fluorescence ELISA, which works very well in the quantification of inflammatory cytokine biomarkers from in vitro models.

Structure, Morphology, and Surface Chemistry of Surgical Masks and Their Evolution up to 10 Washing Cycles

The Covid-19 crisis has led to a massive surge in the use of surgical masks worldwide, causing risks of shortages and high pollution. Various decontamination techniques are currently being studied to reduce these risks by allowing the reuse of masks. In this study, surgical masks were washed up to 10 times, each cycle under the same conditions. The consequences of the washing cycles on the structure, fiber morphology, and surface chemistry have been studied through several characterization techniques: scanning electron microscopy, wetting angle measurements, infrared spectroscopy, X-ray diffraction, and X-ray photoelectrons spectroscopy. The washing process did not induce large changes in the hydrophobicity of the surface, the contact angle remaining constant throughout the cycles. The composition observed in the IR spectrum also remained unchanged for washed masks up to 10 cycles. Some slight variations were observed during X-ray analysis: the crystallinity of the fibers as well as the size of the crystals increases with the number of wash cycles. The XPS analysis shows that after 10 cycles, the surface of the masks underwent a slight oxidation. In the SEM images, changes were observed in the arrangement of the fibers, which are more visible the more times the mask has been washed: they align themselves in bundles, form areas with holes in the mask layer, and are crushed in some areas. © 2023 American Chemical Society

Disposable face masks release micro particles to the aqueous environment after simulating sunlight aging: Microplastics or non-microplastics?

This study focuses on characterizing microplastics and non-microplastics released from surgical masks (SMs), N95 masks (N95), KN95 masks (KN95), and children's masks (CMs) after simulating sunlight aging. Based on micro-Raman spectrum analysis, it was found that the dominant particles released from masks were non-microplastics (66.76–98.85%). Unfortunately, CMs released the most microplastics, which is 8.92 times more than SMs. The predominant size range of microplastics was 30–500 µm, and the main polymer types were PP and PET. Compared with the whole SMs, the microplastic particles released from the cutting-SMs increased conspicuously, which is 12.15 times that of the whole SMs. The main components of non-microplastics include β-carotene, microcrystalline cellulose 102, and eight types of minerals. Furthermore, non-microplastics were mainly fibrous and fragmented in appearance, similar to the morphology of microplastics. After 15 days of UVA-aging, the fibers of the face layers had cracks to varying degrees. It was estimated that these four types of masks can release at least 31.5 trillion microplastics annually in China. Overall, this study demonstrated that the masks could release a large quantity of microplastics and non-microplastics to the environment after sunlight aging, deserving urgent attention in the future study. © 2022 Elsevier B.V.

Comparative Analysis of COVID 19 Detection from Cough Speech Using Machine Learning Classifiers

Corona Virus Disease-2019, or COVID-19, has been on the rise since its emergence, so its early detection is necessary to stop it from spreading rapidly. Speech detection is one of the best ways to detect it at an early stage as it exhibits variations in the nasopharyngeal cavity and can be performed ubiquitously. In this research, three standard databases are used for detection of COVID-19 from speech signal. The feature set includes the baseline perceptual features such as spectral centroid, spectral crest, spectral decrease, spectral entropy, spectral flatness, spectral flux, spectral kurtosis, spectral roll off point, spectral skewness, spectral slope, spectral spread, harmonic to noise ratio, and pitch. 05 ML based classification techniques have been employed using these features. It has been observed that Generalized Additive Model (GAM) classifier offers an average of 95% and a maximum of 97.55% accuracy for COVID-19 detection from cough signals. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Structural characterization and anti-inflammatory activity of fucoidan isolated from Ecklonia maxima stipe

Enzyme-assisted hydrolysis is frequendy used as a cost-effective and efficient method to obtain functional ingredients from bioresources. This study involved die enzyme-assisted hydrolyzation and purification of fucoidan from Ecklonia maxima stipe and die investigation of its anti-inflammatory activity in lipopolysaccharide (LPS)-induced RAW 264.7 cells. Fucoidans of Viscozyme-assisted hydrolysate from E. maxima (EMSFs) harvested in Jeju, Korea. Structural and chemical characterizations were performed using fourier transform infrared spectroscopy, scanning electron microscope, and monosaccharide analysis. Among fucoidans, EMSF6 was rich in fucose and sulfate and had a similar structural character to commercial fucoidan. EMSF6 showed a strong inhibitory effect on nitric oxide generation in LPS-induced RAW 264.7 cells and significantly decreased die production of LPS-induced pro-inflammatory cytokines, including interleukin-6, interleukin-1 p, and tumor necrosis factor a. The anti-inflammatory potential of EMSF6 was mediated through the down-regulation of inducible nitric oxide synthase and cyclooxygenase-2 expression. Thus, fucoidans from&temppound;. maxima stipe are promising candidates for functional food products.