Application of Localized Surface Plasmon Resonance of Conjugated Gold Nanoparticles in Spectral Diagnosis of SARS-CoV-2: A Numerical Study.

Severe respiratory syndrome COVID-19 (SARS-CoV-2) outbreak has became the most important global health issue, and simultaneous efforts to fast and low-cost diagnosis of this virus were performed by researchers. One of the most usual tests was colorimetric methods based on the change of color of gold nanoparticles in the presence of viral antibodies, antigens, and other biological agents. This spectral change can be due to the aggregation of the particles or the shift of localized surface plasmon resonance due to the electrical interactions of surface agents. It is known that surface agents could easily shift the absorption peak of metallic nanocolloids which is attributed to the localized surface plasmon resonance. Experimental diagnosis assays for colorimetric detection of SARS-CoV-2 using Au NPs were reviewed, and the shift of absorption peak was studied from the viewpoint of numerical analysis. Using the numerical method, the refractive index and real and imaginary parts of the effective relative permittivity of the viral biological shell around Au NPs were obtained. This model gives a quantitative description of colorimetric assays of the detection of SARS-CoV-2 using Au NPs.

The role of Industry 4.0 technologies on performance measurement systems of supply chains during global pandemics: an interval-valued intuitionistic hesitant fuzzy approach

PurposeThis paper aims to investigate Supply Chain (SC) Performance Measurement Systems (PMSs) (SCPMSs) that are suitable and applicable to evaluate SC performance during unexpected events such as global pandemics. Furthermore, the contribution of Industry 4.0 Disruptive Technologies (IDTs) to implement SCPMSs during such Black Swan events is investigated in this study.Design/methodology/approachThe research methodology is based upon a novel qualitative and quantitative mixed-method. A Systematic Literature Review (SLR) was initially employed to identify two complete lists of SCPMSs and IDTs. Then, a novel Interval-Valued Intuitionistic Hesitant-Fuzzy (IVIHF)-Delphi method was firstly developed in this paper to screen the extracted SCPMSs. Afterward, the Propriety, Economic, Acceptable, Resource, Legal (PEARL) indicator of the Hanlon method was innovatively applied to prioritize the identified IDTs for each finalized SCPMS.FindingsTwo high-score SCPMSs including the SC operations reference (SCOR) model and sustainable SCPMS were recommended to improve measuring the performance of the pharmaceutical SC of emerging economies such as Iran in which the societal, biological and economic issues were undeniable, particularly during unexpected events. Employing nine IDTs such as simulation, big data analytics, cloud technologies, etc., would facilitate implementing sustainable SCPMS from distinct perspectives.Originality/valueThis is one of the first papers to provide in-depth insights into determining the priority of contribution of IDTs in applying different SCPMSs during global pandemics. Proposing a novel multi-layer mixed-methodology involving SLR, IVIHF-Delphi, and the PEARL indicator of the Hanlon method is another originality offered by this paper.

Learning from an Experience, Challenges and Approaches in the Workplace during COVID-19 Pandemic: A Content Analysis of International Documents (preprint)

Background The present study aims to investigate international measures of pandemic control at the workplace based on the guidelines of international organizations and learn from them and their experiences.Methods We conducted the qualitative study through the content analysis method. The search method included the guidelines published regarding the prevention and response in dealing with the COVID-19 pandemic in workplaces.Results We extracted eleven categories, consisting of legal requirements and duties of employees and employers, structure and program changes, risk assessment, risk communication, information and training, internal and external consultation and cooperation, provision of facilities and tools and workplace hygiene, special conditions, special groups, closing and reopening workplaces, reducing contact and exposure and mental health.Conclusions Protecting employees during a pandemic requires a multifaceted approach and strong advocacy. The operational plan of pandemic control should be developed according to the level of risk, and the support should be appropriate to the conditions of the employees and adapted to their needs.

Exploration roles and responsibilities of trade unions in preventing and responding to emergencies against epidemics: The case of COVID-19.

BACKGROUND AND AIM: The coronavirus disease 209 (COVID-19) pandemic has been affecting various strata of society including different guilds. Each of these segments has its role to play in controlling epidemics. Accordingly, this study aimed to explore trade unions' roles and responsibilities in the prevention and emergency response to epidemic, including the COVID-19 pandemic. MATERIALS AND METHODS: The present qualitative research was conducted using directed content analysis. Participants were selected by a purposeful sampling method. Data were collected through semistructured interviews and field notes and validated through Lincoln and Guba's (1985) evaluative criteria. Data were analyzed by MAXQDA software. RESULTS: Data analysis, constant comparison, and class integration provided a total of seven main themes, which were extracted into four domains of Plan, Implementation, Review, and Action. The main themes were categorized into the dimensions of each domain, so that the Plan domain included three dimensions of union/guild contexts, leadership and staff participation, and planning. The Implementation domain included two dimensions of support and operations. The Assessment domain had a performance evaluation dimension, and the Action domain was made up of an improvement dimension. CONCLUSION: Relying on their organizational and social capacities, trade unions can facilitate the leadership and participation of employees and communities for appropriate policies and making resilient decisions to control epidemics and other roles and responsibilities related to health.

The danger of the toxicity and inefficacy of alcohol-based hand rubs in Iran during COVID-19: a cross-sectional study.

BACKGROUND: The use of disinfectants and alcohol-based hand rubs (ABHR) to prevent COVID-19 transmission increased in the first wave of the infection. To meet the increased demand, the Iranian Ministry of Health issued an emergency use authorization allowing new manufacturers to enter the market, despite the limited capacity for surveillance of these products during COVID-19. Methanol poisoning outbreaks spread rapidly, and more people died from methanol poisoning than COVID-19 in some cities. The aim of this study was to analyze some ABHRs in the Iranian market to see if (a) ABHRs are standard and suitable for hand antisepsis and (b) contained potentially dangerous toxic alcohols. METHOD: Between February and March 2020, 64 brands of ABHR were conveniently collected from pharmacies, supermarkets, and shops selling hygienic products and analyzed using Gas Chromatography. World Health Organization and Food and Drug Administration guidelines were used to define minimum requirements for ABHR. For estimating the risk for acute methanol poisoning, we assumed a serum methanol concentration of 200 mg/L following ABHR ingestion was sufficient to cause intoxication. This threshold concentration would be achieved in an average 75-kg adult after consuming 8000 mg (or eight grams) methanol in 1-2 h. RESULTS: The median [IQR] (range) concentration of ethanol, isopropanol, and methanol were 59% v/v [32.2, 68] (0, 99), 0 mg/L [0, 0] (0, 197,961), and 0 mg/L [0, 0] (0, 680,100), respectively. There was a strong negative correlation between methanol and ethanol contents of hand rubbers (r= -0.617, p < 0.001). Almost 47% of ABHRs complied with minimum standards. In 12.5% of ABHRs, high concentrations of methanol were observed, which have no antiseptic properties but could cause acute methanol poisoning if ingested. CONCLUSION: COVID-19 initiated a policy for distribution and use of ABHR with little control. As ABHR and masks are still accepted preventive measures of the disease, non-standard ABHR compositions may increase the population's risk to both COVID-19 infection and methanol poisoning.

Spatiotemporal association between weather and Covid-19 explored by machine learning

The Covid-19 epidemic led to loss of the lives of many people in the world and had a very negative impact on the mental and physical health of humans. One of the effective ways to preventive strategies regarding is to study the impact of climatic parameters. This research introduces a new spatiotemporal methodology to explore the association between Covid-19 and hourly data of weather. This methodology developed based on machine learning using unsupervised clustering method. Six counties considered for finding association and the cities that have similar climatic temporal changes clustered and compared with cities that have similar number of Covid-19 cases. For this goal, a new model is developed for finding similarities between clusters, which indicates the association between weather and Covid-19. The result shows similarities are about 57% for wind speed, 63% for temperature, 63% for surface pressure, and 42% for elevation. Then result evaluated sing Kendall's tau_b and Spearman's rho which shows the proposed methodology has an acceptable result.

Guillain-Barre Syndrome and COVID-19 Vaccine: A Report of Nine Patients

Introduction: Guillain-Barre Syndrome (GBS) is an autoimmune acute inflammatory demyelinating polyneuropathy usually elicited by an upper respiratory tract infection. Several studies reported GBS associated with Coronavirus Disease 2019 (COVID-19) infection. In this study, we described nine GBS patients following the COVID-19 vaccine.Methods: In this study, nine patients were introduced from six referral centers for neuromuscular disorders in Iran between April 8 and June 20, 2021. Four patients received the Sputnik V, three patients received the Sinopharm, and two cases received the AstraZeneca vaccine. All patients were diagnosed with GBS evidenced by nerve conduction studies and/or cerebrospinal fluid analysis.Results: The median age of the patients was 54.22 years (ranged 26-87 years), and seven patients were male. The patients were treated with Intravenous Immunoglobulin (IVIg) or Plasma Exchange (PLEX). All patients were discharged with some improvements.Conclusion: The link between the COVID-19 vaccine and GBS is not well understood. Given the prevalence of GBS over the population, this association may be coincidental;therefore, more studies are needed to investigate a causal relationship.

An enhanced ant colony optimizer with Cauchy-Gaussian fusion and novel movement strategy for multi-threshold COVID-19 X-ray image segmentation.

The novel coronavirus pneumonia (COVID-19) is a respiratory disease of great concern in terms of its dissemination and severity, for which X-ray imaging-based diagnosis is one of the effective complementary diagnostic methods. It is essential to be able to separate and identify lesions from their pathology images regardless of the computer-aided diagnosis techniques. Therefore, image segmentation in the pre-processing stage of COVID-19 pathology images would be more helpful for effective analysis. In this paper, to achieve highly effective pre-processing of COVID-19 pathological images by using multi-threshold image segmentation (MIS), an enhanced version of ant colony optimization for continuous domains (MGACO) is first proposed. In MGACO, not only a new move strategy is introduced, but also the Cauchy-Gaussian fusion strategy is incorporated. It has been accelerated in terms of convergence speed and has significantly enhanced its ability to jump out of the local optimum. Furthermore, an MIS method (MGACO-MIS) based on MGACO is developed, where it applies the non-local means, 2D histogram as the basis, and employs 2D Kapur's entropy as the fitness function. To demonstrate the performance of MGACO, we qualitatively analyze it in detail and compare it with other peers on 30 benchmark functions from IEEE CEC2014, which proves that it has a stronger capability of solving problems over the original ant colony optimization for continuous domains. To verify the segmentation effect of MGACO-MIS, we conducted a comparison experiment with eight other similar segmentation methods based on real pathology images of COVID-19 at different threshold levels. The final evaluation and analysis results fully demonstrate that the developed MGACO-MIS is sufficient to obtain high-quality segmentation results in the COVID-19 image segmentation and has stronger adaptability to different threshold levels than other methods. Therefore, it has been well-proven that MGACO is an excellent swarm intelligence optimization algorithm, and MGACO-MIS is also an excellent segmentation method.

Whale optimization with random contraction and Rosenbrock method for COVID-19 disease prediction.

Coronavirus Disease 2019 (COVID-19), instigated by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has hugely impacted global public health. To identify and intervene in critically ill patients early, this paper proposes an efficient, intelligent prediction model based on the machine learning approach, which combines the improved whale optimization algorithm (RRWOA) with the k-nearest neighbor (KNN) classifier. In order to improve the problem that WOA is prone to fall into local optimum, an improved version named RRWOA is proposed based on the random contraction strategy (RCS) and the Rosenbrock method. To verify the capability of the proposed algorithm, RRWOA is tested against nine classical metaheuristics, nine advanced metaheuristics, and seven well-known WOA variants based on 30 IEEE CEC2014 competition functions, respectively. The experimental results in mean, standard deviation, the Friedman test, and the Wilcoxon signed-rank test are considered, proving that RRWOA won first place on 18, 24, and 25 test functions, respectively. In addition, a binary version of the algorithm, called BRRWOA, is developed for feature selection problems. An efficient prediction model based on BRRWOA and KNN classifier is proposed and compared with seven existing binary metaheuristics based on 15 datasets of UCI repositories. The experimental results show that the proposed algorithm obtains the smallest fitness value in eleven datasets and can solve combinatorial optimization problems, indicating that it still performs well in discrete cases. More importantly, the model was compared with five other algorithms on the COVID-19 dataset. The experiment outcomes demonstrate that the model offers a scientific framework to support clinical diagnostic decision-making. Therefore, RRWOA is an effectively improved optimizer with efficient value.

Fireworks explosion boosted Harris Hawks optimization for numerical optimization: Case of classifying the severity of COVID-19.

Harris Hawks optimization (HHO) is a swarm optimization approach capable of handling a broad range of optimization problems. HHO, on the other hand, is commonly plagued by inadequate exploitation and a sluggish rate of convergence for certain numerical optimization. This study combines the fireworks algorithm's explosion search mechanism into HHO and proposes a framework for fireworks explosion-based HHo to address this issue (FWHHO). More specifically, the proposed FWHHO structure is comprised of two search phases: harris hawk search and fireworks explosion search. A search for fireworks explosion is done to identify locations where superior hawk solutions may be developed. On the CEC2014 benchmark functions, the FWHHO approach outperforms the most advanced algorithms currently available. Moreover, the new FWHHO framework is compared to four existing HHO and fireworks algorithms, and the experimental results suggest that FWHHO significantly outperforms existing HHO and fireworks algorithms. Finally, the proposed FWHHO is employed to evolve a kernel extreme learning machine for diagnosing COVID-19 utilizing biochemical indices. The statistical results suggest that the proposed FWHHO can discriminate and classify the severity of COVID-19, implying that it may be a computer-aided approach capable of providing adequate early warning for COVID-19 therapy and diagnosis.

Multi-verse Optimizer with Rosenbrock and Diffusion Mechanisms for Multilevel Threshold Image Segmentation from COVID-19 Chest X-Ray Images.

Coronavirus Disease 2019 (COVID-19) is the most severe epidemic that is prevalent all over the world. How quickly and accurately identifying COVID-19 is of great significance to controlling the spread speed of the epidemic. Moreover, it is essential to accurately and rapidly identify COVID-19 lesions by analyzing Chest X-ray images. As we all know, image segmentation is a critical stage in image processing and analysis. To achieve better image segmentation results, this paper proposes to improve the multi-verse optimizer algorithm using the Rosenbrock method and diffusion mechanism named RDMVO. Then utilizes RDMVO to calculate the maximum Kapur's entropy for multilevel threshold image segmentation. This image segmentation scheme is called RDMVO-MIS. We ran two sets of experiments to test the performance of RDMVO and RDMVO-MIS. First, RDMVO was compared with other excellent peers on IEEE CEC2017 to test the performance of RDMVO on benchmark functions. Second, the image segmentation experiment was carried out using RDMVO-MIS, and some meta-heuristic algorithms were selected as comparisons. The test image dataset includes Berkeley images and COVID-19 Chest X-ray images. The experimental results verify that RDMVO is highly competitive in benchmark functions and image segmentation experiments compared with other meta-heuristic algorithms.

Explaining the role and responsibilities of the National Anti-Coronavirus Headquarters in prevention and emergency response to pandemics in the workplace: a qualitative study on COVID-19 experience in Iran.

BACKGROUND: In recent years, the Coronavirus disease 2019 (COVID-19) have greatly affected the safety of life and the economy. Taking rapid measures to reduce these problems requires effective and efficient decisions by various departments and headquarters in a country. The purpose of this study was to investigate the role and responsibilities of the National Anti-Corona Headquarters (NACH) in the workplace during the pandemic. METHODS: This study was a qualitative study conducted using a triangulation approach. Data were obtained through semi-structured interviews with 18 participants with a purposive sampling technique as well as the review of related documents and records in response to the COVID-19 pandemic. The inductive and deductive approach was used for the content analysis of data in the Plan-Do-Check-Act (PDCA) model of the ISO45001 management system. RESULTS: Based on the results, four themes (plan, do, check, and act) were considered as the main domains. Subthemes include understanding the needs and expectations of interested parties; specific policy-making for organizations/workplaces; leadership and organizational commitment; addressing risks and opportunities; providing resources; competence of individuals and organizations; awareness; communication; information documentation; emergency response; monitoring, analyze, and evaluate performance; management review; non-compliance and corrective action; and improvement in pandemic control. CONCLUSION: To ensure the effectiveness and efficiency of organizations to deal with pandemics, the NACH must implement these responsibilities and play a pivotal role in responding to pandemics and using the participation of other government agencies and society. The findings of this study can be useful from national to local levels.

Multi-verse Optimizer with Rosenbrock and Diffusion Mechanisms for Multilevel Threshold Image Segmentation from COVID-19 Chest X-Ray Images

Coronavirus Disease 2019 (COVID-19) is the most severe epidemic that is prevalent all over the world. How quickly and accurately identifying COVID-19 is of great significance to controlling the spread speed of the epidemic. Moreover, it is essential to accurately and rapidly identify COVID-19 lesions by analyzing Chest X-ray images. As we all know, image segmentation is a critical stage in image processing and analysis. To achieve better image segmentation results, this paper proposes to improve the multi-verse optimizer algorithm using the Rosenbrock method and diffusion mechanism named RDMVO. Then utilizes RDMVO to calculate the maximum Kapur's entropy for multilevel threshold image segmentation. This image segmentation scheme is called RDMVO-MIS. We ran two sets of experiments to test the performance of RDMVO and RDMVO-MIS. First, RDMVO was compared with other excellent peers on IEEE CEC2017 to test the performance of RDMVO on benchmark functions. Second, the image segmentation experiment was carried out using RDMVO-MIS, and some meta-heuristic algorithms were selected as comparisons. The test image dataset includes Berkeley images and COVID-19 Chest X-ray images. The experimental results verify that RDMVO is highly competitive in benchmark functions and image segmentation experiments compared with other meta-heuristic algorithms.

A spatiotemporal analysis of the impact of the COVID-19 outbreak on noise pollution in Tehran, Iran.

Noise pollution is one of the non-natural hazards in cities. Long-term exposure to this kind of pollution has severe destructive effects on human health, including mental illness, stress, anxiety, hormonal disorders, hypertension and therefore also cardiovascular disease. One of the primary sources of noise pollution in cities is transportation. The COVID-19 outbreak caused a significant change in the pattern of transportation in cities of Iran. In this article, we studied the spatial and temporal patterns of noise pollution levels in Tehran before and after the outbreak of this disease. An overall analysis from one year before until one year after the outbreak, which showed that noise pollution in residential areas of Tehran had increased by 7% over this period. In contrast, it had diminished by about 2% in the same period in the city centre and around Tehran's Grand Bazaar. Apart from these changes, we observed no specific pattern in other city areas. However, a monthly data analysis based on the t-test, the results show that the early months of the virus outbreak were associated with a significant pollution reduction. However, this reduction in noise pollution was not sustained; instead a gradual increase in pollution occurred over the following months. In the months towards the end of the period analysed, noise pollution increased to a level even higher than before the outbreak. This increase can be attributed to the gradual reopening of businesses or people ignoring the prevailing conditions.

ACSN: Attention capsule sampling network for diagnosing COVID-19 based on chest CT scans.

Automated diagnostic techniques based on computed tomography (CT) scans of the chest for the coronavirus disease (COVID-19) help physicians detect suspected cases rapidly and precisely, which is critical in providing timely medical treatment and preventing the spread of epidemic outbreaks. Existing capsule networks have played a significant role in automatic COVID-19 detection systems based on small datasets. However, extracting key slices is difficult because CT scans typically show many scattered lesion sections. In addition, existing max pooling sampling methods cannot effectively fuse the features from multiple regions. Therefore, in this study, we propose an attention capsule sampling network (ACSN) to detect COVID-19 based on chest CT scans. A key slices enhancement method is used to obtain critical information from a large number of slices by applying attention enhancement to key slices. Then, the lost active and background features are retained by integrating two types of sampling. The results of experiments on an open dataset of 35,000 slices show that the proposed ACSN achieve high performance compared with state-of-the-art models and exhibits 96.3% accuracy, 98.8% sensitivity, 93.8% specificity, and 98.3% area under the receiver operating characteristic curve.

Boosted crow search algorithm for handling multi-threshold image problems with application to X-ray images of COVID-19.

COVID-19 is pervasive and threatens the safety of people around the world. Therefore, now, a method is needed to diagnose COVID-19 accurately. The identification of COVID-19 by X-ray images is a common method. The target area is extracted from the X-ray images by image segmentation to improve classification efficiency and help doctors make a diagnosis. In this paper, we propose an improved crow search algorithm (CSA) based on variable neighborhood descent (VND) and information exchange mutation (IEM) strategies, called VMCSA. The original CSA quickly falls into the local optimum, and the possibility of finding the best solution is significantly reduced. Therefore, to help the algorithm avoid falling into local optimality and improve the global search capability of the algorithm, we introduce VND and IEM into CSA. Comparative experiments are conducted at CEC2014 and CEC'21 to demonstrate the better performance of the proposed algorithm in optimization. We also apply the proposed algorithm to multi-level thresholding image segmentation using Renyi's entropy as the objective function to find the optimal threshold, where we construct 2-D histograms with grayscale images and non-local mean images and maximize the Renyi's entropy on top of the 2-D histogram. The proposed segmentation method is evaluated on X-ray images of COVID-19 and compared with some algorithms. VMCSA has a significant advantage in segmentation results and obtains better robustness than other algorithms. The available extra info can be found at https://github.com/1234zsw/VMCSA.

Active deep learning from a noisy teacher for semi-supervised 3D image segmentation: Application to COVID-19 pneumonia infection in CT.

Supervised deep learning has become a standard approach to solving medical image segmentation tasks. However, serious difficulties in attaining pixel-level annotations for sufficiently large volumetric datasets in real-life applications have highlighted the critical need for alternative approaches, such as semi-supervised learning, where model training can leverage small expert-annotated datasets to enable learning from much larger datasets without laborious annotation. Most of the semi-supervised approaches combine expert annotations and machine-generated annotations with equal weights within deep model training, despite the latter annotations being relatively unreliable and likely to affect model optimization negatively. To overcome this, we propose an active learning approach that uses an example re-weighting strategy, where machine-annotated samples are weighted (i) based on the similarity of their gradient directions of descent to those of expert-annotated data, and (ii) based on the gradient magnitude of the last layer of the deep model. Specifically, we present an active learning strategy with a query function that enables the selection of reliable and more informative samples from machine-annotated batch data generated by a noisy teacher. When validated on clinical COVID-19 CT benchmark data, our method improved the performance of pneumonia infection segmentation compared to the state of the art.

Spatiotemporal Surveillance of COVID-19 Based on Epidemiological Features: Evidence from Northeast Iran

Spatiotemporal analysis of COVID-19 cases based on epidemiological characteristics leads to more refined findings about health inequalities and better allocation of medical resources in a spatially and timely fashion. While existing literature has explored the spatiotemporal clusters of COVID-19 worldwide, little attention has been paid to investigate the space-time clusters based on epidemiological features. This study aims to identify COVID-19 clusters by epidemiological factors in Golestan province, one of the highly affected areas in Iran. This cross-sectional study used GIS techniques, including local spatial autocorrelations, directional distribution statistics, and retrospective space-time Poisson scan statistics. The results demonstrated that Golestan has been facing an upward trend of epidemic waves, so the case fatality rate (CFR) of the province was roughly 2.5 times the CFR in Iran. Areas with a more proportion of young adults were more likely to generate space-time clusters. Most high-risk clusters have emerged since early June 2020. The infection first appeared in the west and southwest of the province and gradually spread to the center, east, and northeast regions. The results also indicated that the detected clusters based on epidemiological features varied across the province. This study provides an opportunity for health decision-makers to prioritize disease-prone areas and more vulnerable populations when allocating medical resources.