Preparation and Properties of an Efficient Purification Material for Personal Protection

At present, the filtration of virus and other small particles in the air by meltblown cloth produced by electret treatment mainly depends on its electrostatic adsorption mechanism. However, because the surface charge of melt blown fabric can not be maintained for a long time, it can not maintain high efficiency filtration for a long time. Therefore, there is no guarantee for the medical staffs to not be infected by COVID-19. Therefore, it is necessary to improve the mechanical filtration efficiency of melt blown fabric in the situation of an electric charge loss. In this paper, nylon 6 (PA6) nanofibers were electrospun on melt blown cloth by electrospinning technology, and a sandwich material with melt blown cloth as surface layer and PA6 nanofibers as middle layer was made by hot- pressing technology;the surface morphology, thermal and mechanical properties of the sandwich material were characterized, and its filtration performance was tested. The experimental results show that the surface integrity of the sandwich material is high, and the diameter of nanofibers can reach about 67 nm;without the electret treatment, the filtration efficiency of the sandwich material for particles in an size of 0.2 μm is more than 95%,while the filtration efficiency of non-woven fabric is zero;the filtration resistance of the material is about 284 Pa, which is suitable for personal protection. © 2023 Chengdu University of Science and Technology. All rights reserved.

Optimization and Characterization of Quercetin Vitamin C Nano-Phytosome Formulation

Objective: To design an optimal formulation for quercetin and vitamin C nano-phytosome. Method(s): Nano-phytosomes are prepared by the thin layer hydration technique using a 2-level-5-factor design experimental. A total of 32 experimental formulas were used for data analysis. The ratio of quercetin: soy lecithin (X1), the ratio of quercetin: cholesterol (X2), stirring speed (X3), stirring temperature (X4), and stirring time (X5) were used as independent factors, while globule size as a dependent factor. Data analysis was carried out by Design Expert12 application. Characterization of the optimal formula included physicochemical evaluation, globule size analysis, zeta potential, polydispersity index, entrapment efficiency, Transition Electron Microscopy (TEM) analysis, and FTIR analysis. Result(s): The optimal formula consisted of quercetin: vitamin C: lecithin: cholesterol ratio of 1: 1: 1.046: 0.105 mol;stirring speed 763.986 rpm;stirring time of 59 min, at temperature 51.73 degreeC which produced 59.26 nm average globule size, PDI value 0.66;zeta potential value-35.93+/-0.95 mV and average SPAN value 0.61. This formulation showed entrapment efficiency of quercetin 91.69+/-0.18 % and vitamin C 90.82+/-0.13 %. The TEM and FITR analysis showed the morphological of the globules and interactions between the drugs, soy lecithin, and cholesterol to form nano-phytosomes. Conclusion(s): The conditions to obtain the optimal formula for quercetin vitamin C nano-phytosome consisted of quercetin: vitamin C: lecithin: cholesterol ratio of 1: 1: 1.046: 0.105 mol;stirring speed 763.986 rpm;stirring time of 59 min, and at temperature 51.73 degreeC.Copyright © 2023 The Authors.

TwIdw—A Novel Method for Feature Extraction from Unstructured Texts

Featured ApplicationThe research has a potential application in the field of fake news detection. By using the feature extraction technique, TwIdw, proposed in this paper, more relevant and informative features can be extracted from the text data, which can lead to an enhancement in the accuracy of the classification models employed in these tasks.This research proposes a novel technique for fake news classification using natural language processing (NLP) methods. The proposed technique, TwIdw (Term weight–inverse document weight), is used for feature extraction and is based on TfIdf, with the term frequencies replaced by the depth of the words in documents. The effectiveness of the TwIdw technique is compared to another feature extraction method—basic TfIdf. Classification models were created using the random forest and feedforward neural networks, and within those, three different datasets were used. The feedforward neural network method with the KaiDMML dataset showed an increase in accuracy of up to 3.9%. The random forest method with TwIdw was not as successful as the neural network method and only showed an increase in accuracy with the KaiDMML dataset (1%). The feedforward neural network, on the other hand, showed an increase in accuracy with the TwIdw technique for all datasets. Precision and recall measures also confirmed good results, particularly for the neural network method. The TwIdw technique has the potential to be used in various NLP applications, including fake news classification and other NLP classification problems.

Characterization of varieties biofortified in iron and zinc and local varieties of common bean

Iron and zinc are important minerals in humans in sub Saharan Africa, whose deficiency is known as "hidden hunger" due to the lack of recognised symptoms in the early stages. Although iron deficiency is the most prevalent, zinc is also involved in inhibition of replication of viruses, including the corona virus (COVID-19). In North Kivu and South Kivu provinces where more than 50% of common bean is produced and consumed in Democratic Republic of Congo, 36% and 47% of preschool children are anemic due to iron deficiency. This paradox is mainly due to insufficiency of iron-rich foods. The aim of this study is to characterise 59 iron and zinc biofortified varieties together with six local varieties of common bean for a potential selection programme in Butembo town in the Democratic Republic of Congo. We focused on 15 qualitative and five quantitative parameters. The qualitative parameters were helpful to distinguish the different morphotypes and for cluster analysis. In addition to the descriptive statistics, the quantitative data were used for Pearson correlation and for principal component analysis, PCA. Qualitative parameters enabled grouping of the study genotypes into 14 morphotypes according to the aspect and colour of the seed coat, the colour around the hilum and the size of seeds. Clustering grouped the 65 genotypes into 12 clusters with the most similar genotypes grouped in the same cluster. Quantitative parameters showed that the study genotypes were dissimilar (P=0.00). A positive correlation was obtained between the days to flowering and the days to maturity (P<0.05) and between the number of pods per plant and the days to flowering. A strong correlation was found between the number of pods per plant and seeds per pod (P<0.01). In contrast, a negative correlation was observed between the 100 seed weight and the number of seeds per pod. The PCA represented on two perpendicular axes showed 64.1% of the total variance of which the 42.3% is explained by the first axis and 21.8% by the second axis. Overall, the study genotypes are morphologically and quantitatively different and thus can be used in a selection programme.

Impact of La3+doping on temperature coefficient of resistivity and peak temperature of La x Ca0.89-xSr0.11MnO3 films prepared by sol-gel spin coating method

Herein, refined LaxCa0.89-xSr0.11MnO3 (LCSMO, x = 0.65, 0.68, 0.71 and 0.74) films were prepared through the sol-gel spin-coating. The influence of La3+ content on the structural properties of LCSMO films was investigated by X-ray diffraction and Atomic force microscope, demonstrating that LCSMO films can grow well on SrTiO3 (00l) substrate. Besides, X-ray photoemission spectroscopy verified the double exchange (DE) effect was weakened with La3+ dopant. The La3+ doping and interconnected grains boundaries (GBs) led to the weakening DE effect and GBs scattering, respectively. Due to superior GBs connectivity, the resistivity of LCSMO films was less than 7.1 x 10(-4) Omega.cm at low temperature of 100 K. Importantly, it is an effective control method to keep the temperature (T-k) corresponding to temperature coefficient of resistivity (TCR) at room temperature with Sr2+ content as constant in LCSMO films. At x = 0.71, the peak TCR value was found to be 8.84%/K and corresponding T-k was 283.15 K. These results are beneficial for advanced application of uncooling infrared bolometer.

Effects of Different Types of Recombinant SARS-CoV-2 Spike Protein on Circulating Monocytes' Structure.

This study investigated the biological effects on circulating monocytes after challenge with SARS-CoV-2 recombinant spike protein. Whole blood collected from seven ostensibly healthy healthcare workers was incubated for 15 min with 2 and 20 ng/mL final concentration of recombinant spike protein of Ancestral, Alpha, Delta, and Omicron variants. Samples were analyzed with Sysmex XN and DI-60 analyzers. Cellular complexity (i.e., the presence of granules, vacuoles and other cytoplasmic inclusions) increased in all samples challenged with the recombinant spike protein of the Ancestral, Alpha, and Delta variants, but not in those containing Omicron. The cellular content of nucleic acids was constantly decreased in most samples, achieving statistical significance in those containing 20 ng/mL of Alpha and Delta recombinant spike proteins. The heterogeneity of monocyte volumes significantly increased in all samples, achieving statistical significance in those containing 20 ng/mL of recombinant spike protein of the Ancestral, Alpha and Delta variants. The monocyte morphological abnormalities after spike protein challenge included dysmorphia, granulation, intense vacuolization, platelet phagocytosis, development of aberrant nuclei, and cytoplasmic extrusions. The SARS-CoV-2 spike protein triggers important monocyte morphological abnormalities, more evident in cells challenged with recombinant spike protein of the more clinically severe Alpha and Delta variants.

The effects of city morphology on airborne transmission of COVID-19. Case study: Port Said City, Egypt.

Looking beyond COVID-19 outbreak, Scholars continue to develop innovative approaches to bring the city on to health and safety. Recent studies have indicated that urban spaces could produce or propagate pathogens, which is an urgent topic at the city level. However, there is a dearth of studies investigating the interrelationship between urban morphology and pandemics outbreak at the neighborhood level. Accordingly, this research will trace the effect of cities morphologies on the rate of spread of COVID-19 through a simulation study held on five areas that form the urban morphology of Port Said City, using Envi-met software. Results are explored based on the degree of concentration and rate of diffusion of coronavirus particles. It was observed on a regular basis that wind speed has a directly proportional relationship with the diffusion of the particles and an inversely proportional relationship with the concentration of the particles. However, certain urban characteristics led to inconsistent and opposing results like wind tunnels, shaded arcades, height variance, and spacious in-between spaces. Moreover, it is obvious that the city morphology is being transformed over time toward safer conditions; urban areas constructed recently have low vulnerability to respiratory pandemics outbreak compared to older areas.

The Suitability of Using the Ferret as a Model Organism in Biomedical Research

This paper reviews the literature data considering the use of the ferret as a model organism in biomedical research. The albino ferret is the most used variant from the ferret species as an animal model in biomedi-cal research. Its use as a pet is also known. Similarities have been identified between ferrets and humans in terms of the physiology and morphology of the respi-ratory system, but also on the pathogenesis or evolu-tion of some diseases on other organs and systems. Ferrets are intensively used in the study of respiratory diseases of viral nature, such as influenza and SARS-CoV, in hypersecretory respiratory disorders, such as cystic fibrosis, in the neuroendocrinology and neuro-anatomy, and even in gastric ulcer research. Through genetic engineering, transgenic ferrets have been ob-tained, such individuals reproducing the studied pa-thology much more faithfully.

Morphofunctional changes in the conducting and respiratory parts of the bronchopulmonary system in COVID-19 (analytical review)

The article reflects the analysis of the state of the problem concerning the pathogenesis of COVID-19 (CoronaVirus Disease 2019), the epidemic of which began from the end of 2019 to the beginning of 2020, from the position of the bronchopulmonary system, which is associated with the main route of penetration of the virus of this infectious disease (SARS-CoV-1) with the development of severe pneumonia, often fatal. And if the study and results of the study of this problem are to a certain extent highlighted in the literature, then they concerned mainly the respiratory regions of the respiratory tract (RT), given that mainly complications leading to death are associated with them. At the same time, studies on conducting RT are reflected in a few foreign works, although there are data suggesting their not small contribution to the pathogenesis of COVID-19. In the domestic literature, studies of this kind have not been reflected. The aim of the work is to analyze, reflect and assess the role of possible morphological and functional regional changes in the mucous membrane of the RT in the pathogenesis of COVID-19. Methodologically, the analysis was carried out using a systematic approach based on the material of various databases of biomedical and biomedical scientific information, including such as Index Medicus, PubMed, EMBASE, Cochrane, and others for the period, mainly 2020. Results. An analysis of the mechanisms of influence on the morphofunctional state of the mucous membrane of the RT of coronavirus infection is given, depending on their departments, including conductive and respiratory. Methods and approaches to the study of such influence are reflected. The possible role of disturbances in the morphofunctional state and defense mechanisms of the lungs is shown, hypotheses and paradigms regarding the pathogenesis of coronavirus infection are presented. The results of the analysis indicate that there is a whole complex of morphological and functional disorders that determine the development of this pathology, its virulence. The fundamental role of the epithelium of the respiratory tract has been established and multicellular receptor tropism in the pathogenesis of COVID-19 is reflected. In conclusion, it is pointed out that the essential role of epithelial cells not only of the respiratory, but also of the conductive parts of the LTP, mainly ciliates, which are involved in the pathogenesis of COVID-19 not only from the position of functional receptors, but also in terms of violations of the earliest leading protective mechanism - mucociliary clearance aggravating the "vicious circle of pathogenesis" of this pathology.Copyright © 2021 Infectious Diseases: News, Opinions, Training. All rights reserved.

Covid-19 Approved Mask Detection using Mathematical Morphology

Face detection has multiple applications including recognition, people identification and detection of facial expressions. With the current pandemic crisis and due to the measures imposed to prevent Covid-19 spreading, the wearing of a protection mask became mandatory. The object of interest of this paper is to detect the wearing of an approved mask face using Viola Jones algorithm, aggregate channel features (ACF) and mathematical morphology. COVID-19 virus spread through the air, so it is necessary that all the materials used for manufacturing of the face masks to filter properly the air, and only the approved face masks to be used in order to control the spread infection. The algorithm used for face detection is Viola Jones with notable success in real time face detection and real time impression speed in face detection. Identification of the approved face mask against Covid-19 virus is made with a trained ACF detector. Eye detection, necessary to check if the face mask is properly placed, is based on mathematical morphology operators. These operations used together are robust with high results on the image processing. © 2023 SPIE.

Morphological Changes in Blood Cells as Indicators for Disease Progression in COVID-19

BACKGROUND: COVID-19 as a pandemic has caused an alarming increase in mortality and morbidity. Viral-induced morphologic changes in the peripheral blood cells are well characterized in certain infections and can direct diagnostic workup to ensure timely therapeutic intervention. This study describes the morphological changes of blood cells in various stages of COVID disease. MATERIALS AND METHODS: A total of thousand COVID-positive patients admitted in the tertiary care center were taken for the study. They were classified as mild, moderate, and severe based on the clinical criteria suggested by World Health Organization. Peripheral smears of the patients were analyzed, and the morphological changes in various blood cells were correlated with the disease stage and coagulation parameters. RESULT(S): The study demonstrated significant morphological changes in the blood cells of COVID patients during the course of disease progression and during the onset of COVID-associated coagulopathy. Leukocytosis, neutrophilia, and toxic changes in neutrophils were seen in the severe stage of the disease and in COVID coagulopathy suggesting these are important indicators of disease severity. Activated lymphocyte was found to be the most common morphological presentation seen in all patients irrespective of the disease stage, whereas plasmacytoid lymphocytes were an important finding in severe-stage disease. Schistocytes an important finding in any other coagulopathy was present only in 1% of cases of COVID coagulopathy. CONCLUSION(S): The study demonstrated significant morphological changes in the blood cells of COVID-positive patients during the course of disease progression. Comprehensive daily complete blood count and peripheral smear examination should be undertaken in patients hospitalized with COVID-19 to predict potential clinical deterioration and signs of disease progression. These morphological changes in peripheral smear can be used as one of the factors indicating disease progression which can formulate for further evaluation. Since follow-up and post-COVID morphological examination were not done, additional research in this aspect can shed light on the clinical categorization of COVID patients based on the morphological findings.Copyright © 2023 Journal of Applied Hematology Published by Wolters Kluwer - Medknow.

Computer-aided comprehensive explorations of RNA structural polymorphism through complementary simulation methods

While RNA folding was originally seen as a simple problem to solve, it has been shown that the promiscuous interactions of the nucleobases result in structural polymorphism, with several competing structures generally observed for non-coding RNA. This inherent complexity limits our understanding of these molecules from experiments alone, and computational methods are commonly used to study RNA. Here, we discuss three advanced sampling schemes, namely Hamiltonian-replica exchange molecular dynamics (MD), ratchet-and-pawl MD and discrete path sampling, as well as the HiRE-RNA coarse-graining scheme, and highlight how these approaches are complementary with reference to recent case studies. While all computational methods have their shortcomings, the plurality of simulation methods leads to a better understanding of experimental findings and can inform and guide experimental work on RNA polymorphism.Copyright ©

The effects of COVID-19 on placental morphology.

The impact of the COVID-19 infection, caused by Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), during the pandemic has been considerably more severe in pregnant women than non-pregnant women. Therefore, a review detailing the morphological alterations and physiological changes associated with COVID-19 during pregnancy and the effect that these changes have on the feto-placental unit is of high priority. This knowledge is crucial for these mothers, their babies and clinicians to ensure a healthy life post-pandemic. Hence, we review the placental morphological changes due to COVID-19 to enhance the general understanding of how pregnant mothers, their placentas and unborn children may have been affected by this pandemic. Based on current literature, we deduced that COVID-19 pregnancies were oxygen deficient, which could further result in other pregnancy-related complications like preeclampsia and IUGR. Therefore, we present an up-to-date review of the COVID-19 pathophysiological implications on the placenta, covering the function of the placenta in COVID-19, the effects of this virus on the placenta, its functions and its link to other gestational complications. Furthermore, we highlight the possible effects of COVID-19 therapeutic interventions on pregnant mothers and their unborn children. Based on the literature, we strongly suggest that consistent surveillance for the mothers and infants from COVID-19 pregnancies be prioritised in the future. Though the pandemic is now in the past, its effects are long-term, necessitating the monitoring of clinical manifestations in the near future.

Deep learning and Gaussian Mixture Modelling clustering mix. A new approach for fetal morphology view plane differentiation.

The last three years have been a game changer in the way medicine is practiced. The COVID-19 pandemic changed the obstetrics and gynecology scenery. Pregnancy complications, and even death, are preventable due to maternal-fetal monitoring. A fast and accurate diagnosis can be established by a doctor + Artificial Intelligence combo. The aim of this paper is to propose a framework designed as a merger between Deep learning algorithms and Gaussian Mixture Modelling clustering applied in differentiating between the view planes of a second trimester fetal morphology scan. The deep learning methods chosen for this approach were ResNet50, DenseNet121, InceptionV3, EfficientNetV2S, MobileNetV3Large, and Xception. The framework establishes a hierarchy of the component networks using a statistical fitness function and the Gaussian Mixture Modelling clustering method, followed by a synergetic weighted vote of the algorithms that gives the final decision. We have tested the framework on two second trimester morphology scan datasets. A thorough statistical benchmarking process has been provided to validate our results. The experimental results showed that the synergetic vote of the framework outperforms the vote of each stand-alone deep learning network, hard voting, soft voting, and bagging strategy.

Preparation of PLA-based SMS nonwoven composites and its applications in protective apparel

The global COVID-19 pandemic has triggered a huge demand for the protective nonwovens. However, the main raw material of nonwovens comes from petroleum, and the massive consumption of petroleum-based polymers brings great pressure to ecosystem. Therefore, it is significant to develop biodegradable protective barrier products. In this work, a polylactic-based composite (a tri-layer nonwovens composed of spunbond, meltblown and spunbond, SMS) was prepared and applied for protective apparel. The surface morphology and chemical changes of the fibers were characterized and analyzed by scanning electron microscope (SEM), Fourier transform infrared spectroscopy (FTIR) and energy dispersive spectroscopy (EDS). The liquid contact angle and permeability, breathability and moisture permeability, frictional charge and mechanical strength of the samples were evaluated and compared. The samples degradability was also recorded. The results demonstrate that the optimum formula for anti-fouling treatment on SMS is F-30. The treated fabric possesses superior liquid repellency and anti-permeability, with contact angles of water and alcohol at 128° and 115° respectively, while the alcohol repellent grade reaches level 7. The treated sample has less strength loss but exhibits favorable breathability, moisture permeability and anti-static properties, which can meet the requirements of protective apparels. After fluorine resin coating, the composite still provide excellent degradation performance, and the weight loss rate reaches more than 80% after 10 days water degradation. These results provide new insights for the application of PLA-based SMS in biodegradable protective apparel. © 2023 The Textile Institute.

Classifying Microscopic Images of Reactive Lymphocytosis Using Two-Step Tandem AI Models

The practical applications of automatic recognition and categorization technology for next-generation systems are desired in the clinical laboratory. We approached the identification of reactive lymphocytosis using artificial intelligence (AI) technology and studied its clinical usefulness for blood smear screening. This study created one- and two-step AI models for the identification of reactive lymphocytosis. The ResNet-101 model was applied for deep learning. The original image set for supervised AI training consisted of 5765 typical nucleated blood cell images. The subjects for clinical assessment were 25 healthy cases, 25 erythroblast cases, and 25 reactive lymphocytosis cases. The total accuracy (mean ± standard deviation) of the one- and two-step models were 0.971 ± 0.047 and 0.977 ± 0.024 in healthy, 0.938 ± 0.040 and 0.978 ± 0.018 in erythroblast, and 0.856 ± 0.056 and 0.863 ± 0.069 in reactive lymphocytosis cases, respectively. The two-step AI model showed a sensitivity of 0.960 and a specificity of 1.000 between healthy and reactive lymphocytosis cases. As our two-step tandem AI model showed high performance for identifying reactive lymphocytosis in blood smear screening, we plan to apply this method to the development of AI models to differentiate reactive and neoplastic lymphocytosis.

Fusion-Based Segmentation and Classification of Novel Coronavirus or Pneumonia from Chest X-ray Images Using Machine Learning Techniques

The deadfall widespread of coronavirus (SARS-Co V-2) disease has trembled every part of the earth and has significant disruption to health support systems in different countries. In spite of such existing difficulties and disagreements for testing the coronavirus disease, an advanced and low-cost technique is required to classify the disease. For the sense of reason, supervised machine learning (ML) along with image processing has turned out as a strong technique to detect coronavirus from human chest X-rays. In this work, the different methodologies to identify coronavirus (SARS-CoV-2) are discussed. It is essential to expand a fully automatic detection system to restrict the carrying of the virus load through contact. Various deep learning structures are present to detect the SARS-CoV-2 virus such as ResNet50, Inception-ResNet-v2, AlexNet, Vgg19, etc. A dataset of 10,040 samples has been used in which the count of SARS-CoV-2, pneumonia and normal images are 2143, 3674, and 4223 respectively. The model designed by fusion of neural network and HOG transform had an accuracy of 98.81% and a sensitivity of 98.65%. © 2022 IEEE.

Special Isue: Virtual anatomy, histology and embryology in research and education. (Special Isue: Virtual anatomy, histology and embryology in research and education.)

This issue contains 13 articles on the use of virtual anatomy, histology and embryology in research and education;digital histological morphometry of the human pineal gland in a postmortem study, with endocrine and neurological clinical implications;an international collaborative approach to learning histology using a virtual microscope;delivery anatomy kits to help keep practical veterinary classes during the COVID-19 pandemic;how virtual animal anatomy facilitated a successful transition to online instruction and supported student learning during the coronavirus pandemic;using videos in active learning in veterinary anatomy;dissection videos as a virtual veterinary anatomy peer learning tool at the University of Tehran during the COVID-19 pandemic;a new virtual platform for teaching comparative animal neuroanatomy based on metameric slices of the central nervous system;application of student remote and distance research in neuroanatomy by mapping Dscaml1 expression with a LacZ gene trap in mouse brain;implementing a multi-colour genetic marker analysis technique for embryology education;impact of COVID-19 on student attainment and pedagogical needs when undertaking independent scientific research;extended reality veterinary medicine case studies for diagnostic veterinary imaging instruction and assessing student perceptions and examination performance and students' performance in teaching neuroanatomy using traditional and technology-based methods. 16 proceedings from the Trans-European Pedagogic Anatomy Research Group (TEPARG) Hybrid Meeting entitled "Hybrid Anatomy Education: Barriers and Enablers for Students and Educators" held in Barcelona, Spain, during 5 March 2022, are also included.

Eco-Friendly Hierarchical Nanoporous Microfiber Respirator Filters Fabricated Using Rotary Jet Spinning Technology (RJS)

The current global health crisis caused by the SARS-CoV-2 virus (COVID-19) has increased the use of personal protective equipment, especially face masks, leading to the disposal of a large amount of plastic waste causing an environmental crisis due to the use of non-biodegradable and non-recyclable polymers, such as polypropylene and polyester. In this work, an eco-friendly biopolymer, polylactic acid (PLA), was used to manufacture hierarchical nanoporous microfiber biofilters via a single-step rotary jet spinning (RJS) technique. The process parameters that aid the formation of nanoporosity within the microfibers were discussed. The microstructure of the fibers was analyzed by scanning electron microscopy (SEM) and a noninvasive X-ray microtomography (XRM) technique was employed to study the three-dimensional (3D) morphology and the porous architecture. Particulate matter (PM) and aerosol filtration efficiency were tested by OSHA standards with a broad range (10-1000 nm) of aerosolized saline droplets. The viral penetration efficiency was tested using the phi X174 bacteriophage (similar to 25 nm) with an envelope, mimicking the spike protein structure of SARS-CoV-2. Although these fibers have a similar size used in N95 filters, the developed biofilters present superior filtration efficiency (similar to 99%) while retaining better breathability (<4% pressure drop) than N95 respirator filters.