Supply chain accountability, COVID-19, and violations of workers' rights in the global clothing supply chain

Purpose: This study aims to contribute to the debate on the efficacy of softer regulations to prevent violations of workers' rights in the global clothing supply chain. Design/methodology/approach: This study draws on value trap and adverse incorporations as a theoretical lens to understand the reasons behind the continued violations of workers' rights. The empirical findings are based on an analysis of 24 semi-structured interviews with workers and owners. Extensive documentary evidence to track the plight of workers in Bangladeshi clothing factories during the pandemic. Findings: The study demonstrates how imbalances in supply chain relationships allow retailers to take advantage of the pandemic. The authors find that some retailers worsened the working conditions by cancelling orders, demanding discounts on old orders and forcing suppliers to agree to a lower price for new orders. Large brands and retailers' responses to the COVID-19 pandemic remind us that softer regulations, such as third-party audits, are likely to be ineffective given the power imbalance at the heart of the supply chain. Practical implications: The study presents a case for regulatory frameworks and intense stakeholder activism to encourage large retailers and brands to behave responsibly. This is especially important when a supply chain is value-trapped and workers are adversely incorporated and unprotected. Originality/value: Drawing on studies on adverse incorporations, value-trapped supply chains and the plight of workers during the COVID-19 pandemic, the study offers a broader understanding of the continued violation of workers' rights and the efficacy of softer regulations. © 2023, Emerald Publishing Limited.

The Impact of the COVID-19 Pandemic on Makkah City's Health Programs Performance

Background: Coronavirus (Covid-19) is a viral illness caused by a recently discovered coronavirus that began in the Chinese city of Wuhan in December 2019.(1). The impact of this global pandemic affects all social, psychological, and economic aspects of society, and health(1,2). The aim of the Saudi preventive health programs for Community health services was to increase awareness and decrease preventable diseases. This study aimed to assess the impact of the COVID-19 pandemic on key performance indicators of health programs at Makkah Al-Mukarramah City. Material and Methods: This comparative descriptive study was conducted to assess health programs' key performance indicators and statistics before COVID19 in 2019, in comparison with 2020 and 2021. KPI and statistics of health programs collected the data including that on chronic diseases Preventive programs, age categories and healthy life programs.

Comprehensive evaluation of six interventions for hospitalized patients with COVID-19: A propensity score matching study.

Purpose: The purpose of this study was to evaluate the effectiveness of either hydroxychloroquine, triple combination therapy (TCT), favipiravir, dexamethasone, remdesivir, or COVID-19 convalescent plasma (CCP) in comparison with standard-of-care for hospitalized patients with COVID-19 using real-world data from Saudi Arabia. Patients and methods: A secondary database analysis was conducted using the Saudi Ministry of Health database for patients with COVID-19. Adult (≥ 18 years) hospitalized patients with COVID-19 between March 2020 and January 2021 were included in the analysis. A propensity score matching technique was used to establish comparable groups for each therapeutic approach. Lastly, an independent t-test and chi-square test were used to compare the matching groups in the aspects of the duration of hospitalization, length of stay (LOS) in intensive care units (ICU), in-hospital mortality, and composite poor outcome. Multilevel logistic regression model was used to assess the association between the severity stage of COVID-19 and the outcomes while using the medication or intervention used as a grouping variable in the model. Results: The mean duration of hospitalization was significantly longer for patients who received TCT, favipiravir, dexamethasone, or CCP compared to patients who did not receive these therapies, with a mean difference ranging between 2.2 and 4.9 days for dexamethasone and CCP, respectively. Furthermore, the use of favipiravir or CCP was associated with a longer stay in ICU. Remdesivir was the only agent associated with in-hospital mortality benefit. A higher risk of mortality and poorer composite outcome were associated with the use of favipiravir or dexamethasone. However, the logistic regression model reveled that the difference between the two matched cohorts was due to the severity stage not the medication. Additionally, the use of hydroxychloroquine, TCT, or CCP had no impact on the incidence of in-hospital mortality or composite poor outcomes. Conclusion: Remdesivir was the only agent associated with in-hospital mortality benefit. The observed worsened treatment outcomes associated with the use of dexamethasone or FPV shall be attributed to the severity stage rather than the medication use. In light of these varied results, additional studies are needed to continue evaluating the actual benefits of these therapies.

AI-based adaptive personalized content presentation and exercises navigation for an effective and engaging E-learning platform.

Effective and engaging E-learning becomes necessary in unusual conditions such as COVID-19 pandemic, especially for the early stages of K-12 education. This paper proposes an adaptive personalized E-learning platform with a novel combination of Visual/Aural/Read, Write/Kinesthetic (VARK) presentation or gamification and exercises difficulty scaffolding through skipping/hiding/ reattempting. Cognitive, behavior and affective adaptation means are included in developing a dynamic learner model, which detects and corrects each student's learning style and cognitive level. As adaptation targets, the platform provides adaptive content presentation in two groups (VARK and gamification), adaptive exercises navigation and adaptive feedback. To achieve its goal, the platform utilizes a Deep Q-Network Reinforcement Learning (DQN-RL) and an online rule-based decision making implementation. The platform interfaces front-end dedicated website and back-end adaptation algorithms. An improvement in learning effectiveness is achieved comparing the post-test to the pre-test in a pilot experiment for grade 3 mathematics curriculum. Both groups witnessed academic performance and satisfaction level improvements, most importantly, for the students who started the experiment with a relatively low performance. VARK group witnessed a slightly more improvement and higher satisfaction level, since interactive activities and games in the kinesthetic presentation can provide engagement, while keeping other presentation styles available, when needed.

SARS-CoV-2 Detection using Colorimetric Plasmonic Sensors: A Proof-of-Concept Computational Study.

Traditional molecular techniques for SARS-CoV-2 viral detection are time-consuming and can exhibit a high probability of false negatives. In this work, we present a computational study of SARS-CoV-2 detection using plasmonic gold nanoparticles. The resonance wavelength of a SARS-CoV-2 virus was recently estimated to be in the near-infrared region. By engineering gold nanospheres to specifically bind with the outer surface of the SARS-CoV-2 virus, the resonance frequency can be shifted to the visible range (380 nm - 700 nm). Moreover, we show that broadband absorption will emerge in the visible spectrum when the virus is partially covered with gold nanoparticles at a specific coverage percentage. This broadband absorption can be used to guide the development of an efficient and accurate colorimetric plasmon sensor for COVID-19 detection. Our observation also suggests that this technique is unaffected by the number of protein spikes present on the virus outer surface, hence can pave a potential path for a label-free COVID-19 diagnostic tool independent of the number of protein spikes.

Aloin A inhibits SARS CoV-2 replication by targeting its binding with ACE2 - Evidence from modeling-supported molecular dynamics simulation.

The current study aimed to expand on the recently published results and assess the inhibitory efficacy of aloin A against SARS CoV-2. In vitro testing of aloin A against SARS CoV-2 proteases (i.e., MPro and PLPro) showed weak to moderate activity (IC50 = 68.56 ± 1.13 µM and 24.77 ± 1.57 µM, respectively). However, aloin A was able to inhibit the replication of SARS CoV-2 in Vero E6 cells efficiently with an IC50 of 0.095 ± 0.022 µM. Depending on the reported poor permeability of aloin A alongside its insignificant protease inhibitory activities presented in this study, we ran a number of extensive virtual screenings and physics-based simulations to determine the compound's potential mode of action. As a result, RBD-ACE2 was identified as a key target for aloin A. Results from 600 ns-long molecular dynamics (MD) simulation experiments pointed to aloin A's role as an RBD-ACE2 destabilizer. Therefore, the results of this work may pave the way for further development of this scaffold and the eventual production of innovative anti-SARS CoV-2 medicines with several mechanisms of action.Communicated by Ramaswamy H. Sarma.

A Deep Batch Normalized Convolution Approach for Improving COVID-19 Detection from Chest X-ray Images.

Pre-trained machine learning models have recently been widely used to detect COVID-19 automatically from X-ray images. Although these models can selectively retrain their layers for the desired task, the output remains biased due to the massive number of pre-trained weights and parameters. This paper proposes a novel batch normalized convolutional neural network (BNCNN) model to identify COVID-19 cases from chest X-ray images in binary and multi-class frameworks with a dual aim to extract salient features that improve model performance over pre-trained image analysis networks while reducing computational complexity. The BNCNN model has three phases: Data pre-processing to normalize and resize X-ray images, Feature extraction to generate feature maps, and Classification to predict labels based on the feature maps. Feature extraction uses four repetitions of a block comprising a convolution layer to learn suitable kernel weights for the features map, a batch normalization layer to solve the internal covariance shift of feature maps, and a max-pooling layer to find the highest-level patterns by increasing the convolution span. The classifier section uses two repetitions of a block comprising a dense layer to learn complex feature maps, a batch normalization layer to standardize internal feature maps, and a dropout layer to avoid overfitting while aiding the model generalization. Comparative analysis shows that when applied to an open-access dataset, the proposed BNCNN model performs better than four other comparative pre-trained models for three-way and two-way class datasets. Moreover, the BNCNN requires fewer parameters than the pre-trained models, suggesting better deployment suitability on low-resource devices.

Adsorption of Molnupiravir anti-COVID-19 drug over B12N12 and Al12N12 nanocarriers: a DFT study.

The potentiality of B12N12 and Al12N12 nanocarriers to adsorb Molnupiravir anti-COVID-19 drug, for the first time, was herein elucidated using a series of quantum mechanical calculations. Density function theory (DFT) was systematically utilized. Interaction (Eint) and adsorption (Eads) energies showed higher negative values for Molnupiravir···Al12N12 complexes compared with Molnupiravir···B12N12 analogs. Symmetry-adapted perturbation theory (SAPT) results proclaimed that the adsorption process was predominated by electrostatic forces. Notably, the alterations in the distributions of the molecular orbitals ensured that the B12N12 and Al12N12 nanocarriers were efficient candidates for delivering the Molnupiravir drug. From the thermodynamic perspective, the adsorption process of Molnupiravir drug over B12N12 and Al12N12 nanocarriers had spontaneous and exothermic nature. The ESP, QTAIM, NCI, and DOS observations exposed the tendency of BN and Al12N12 to adsorb the Molnupiravir drug. Overall, these findings proposed that the B12N12 and Al12N12 nanocarriers are efficient aspirants for the development of the Molnupiravir anti-COVID-19 drug delivery process.Communicated by Ramaswamy H. Sarma.

General two-parameter distribution: Statistical properties, estimation, and application on COVID-19.

In this paper, we introduced a novel general two-parameter statistical distribution which can be presented as a mix of both exponential and gamma distributions. Some statistical properties of the general model were derived mathematically. Many estimation methods studied the estimation of the proposed model parameters. A new statistical model was presented as a particular case of the general two-parameter model, which is used to study the performance of the different estimation methods with the randomly generated data sets. Finally, the COVID-19 data set was used to show the superiority of the particular case for fitting real-world data sets over other compared well-known models.

A potential association between obesity and reduced effectiveness of COVID-19 vaccine-induced neutralizing humoral immunity.

Due to the adverse effects of obesity on host immunity, this study investigated the effectiveness of COVID-19 vaccines (BNT162b2, ChAdOx-nCov-2019, and mRNA-1273) in inducing anti-SARS-CoV-2 Spike (S) neutralizing antibodies among individuals with various obesity classes (class I, II, III, and super obesity). Sera from vaccinated obese individuals (n = 73) and normal BMI controls (n = 46) were subjected to S-based enzyme-linked immunosorbent assay (ELISA) and serum-neutralization test (SNT) to determine the prevalence and titer of anti-SARS-CoV-2 neutralizing antibodies. Nucleocapsid-ELISA was also utilized to distinguish between immunity acquired via vaccination only versus vaccination plus recovery from infection. Data were linked to participant demographics including age, gender, past COVID-19 diagnosis, and COVID-19 vaccination profile. S-based ELISA demonstrated high seroprevalence rates (>97%) in the study and control groups whether samples with evidence of past infection were included or excluded. Interestingly, however, SNT demonstrated a slightly significant reduction in both the rate and titer of anti-SARS-CoV-2 neutralizing antibodies among vaccinated obese individuals (60/73; 82.19%) compared to controls (45/46; 97.83%). The observed reduction in COVID-19 vaccine-induced neutralizing humoral immunity among obese individuals occurs independently of gender, recovery from past infection, and period from last vaccination. Our data suggest that COVID-19 vaccines are highly effective in inducing protective humoral immunity. This effectiveness, however, is potentially reduced among obese individuals which highlight the importance of booster doses to improve their neutralizing immunity. Further investigations on larger sample size remain necessary to comprehensively conclude about the effect of obesity on COVID-19 vaccine effectiveness on humoral immunity induction.

Mathematical model for the novel coronavirus (2019-nCOV) with clinical data using fractional operator.

Coronavirus infection (COVID-19) is a considerably dangerous disease with a high demise rate around the world. There is no known vaccination or medicine until our time because the unknown aspects of the virus are more significant than our theoretical and experimental knowledge. One of the most effective strategies for comprehending and controlling the spread of this epidemic is to model it using a powerful mathematical model. However, mathematical modeling with a fractional operator can provide explanations for the disease's possibility and severity. Accordingly, basic information will be provided to identify the kind of measure and intrusion that will be required to control the disease's progress. In this study, we propose using a fractional-order SEIARPQ model with the Caputo sense to model the coronavirus (COVID-19) pandemic, which has never been done before in the literature. The stability analysis, existence, uniqueness theorems, and numerical solutions of such a model are displayed. All results were numerically simulated using MATLAB programming. The current study supports the applicability and influence of fractional operators on real-world problems.

Discovery of Novel Thioquinazoline-N-aryl-acetamide/N-arylacetohydrazide Hybrids as Anti-SARS-CoV-2 Agents: Synthesis, in vitro Biological Evaluation, and Molecular Docking Studies.

In the current investigation, two novel series of (tetrahydro)thioquinazoline-N-arylacetamides and (tetrahydro)thioquinazoline-N-arylacetohydrazides were designed, synthesized and investigated for their antiviral activity against SARS-CoV-2. The thioquinazoline-N-arylacetamide 17g as well as the tetrahydrothioquinazoline-N-arylacetohydrazides 18c and 18f showed potent antiviral activity with IC50 of 21.4, 38.45 and 26.4 µM, respectively. In addition, 18c and 18f demonstrated potential selectivity toward the SARS-CoV-2 over the host cells with SI of 10.67 and 16.04, respectively. Further evaluation of the mechanism of action of the three derivatives 17g, 18c, and 18f displayed that they can inhibit the virus at the adsorption as well as at the replication stages, in addition to their virucidal properties. In addition, 17g, 18c, and 18f demonstrated satisfactory physicochemical properties as well as drug-likeness properties to be further optimized for the discovery of novel antiviral agents. The docking simulation predicted the binding pattern of the target compounds rationalizing their differential activity based on their hydrophobic interaction and fitting in the hydrophobic S2 subsite of the binding site.

Consequences of SARS-CoV-2 Infection in Pregnant Women and Their Infants: A Systematic Review.

Coronavirus disease 2019 (COVID-19) is a worldwide health problem, particularly for pregnant women. This review assesses the effects of COVID-19 on pregnant women and their infants. A systematic search was performed of studies published on PubMed, Web of Science, Google Scholar, and Embase from January 2020 to January 2021, without restriction by language. This review included 27 studies (22 from China, one from the United States, one from Honduras, one from Italy, one from Iran, and one from Spain), which cumulatively evaluated 386 pregnant women with clinically confirmed COVID-19 and their 334 newborns. Of the 386 pregnant women, 356 had already delivered their infants, four had medical abortions at the time of research, 28 were still pregnant, and two died from COVID-19 before they were able to give birth. Cesarean sections were performed on 71% of pregnant women with COVID-19 to give birth. Fever and cough were common symptoms among women. Premature rupture of membranes, distress, and preterm birth were pregnancy complications. Low birth weight and a short gestational age were common outcomes for newborns. The common laboratory findings among pregnant women were lymphopenia, leukocytosis, and elevated levels of C-reactive protein. Chest computed tomography revealed abnormal viral lung changes in 73.3% of women. Eleven infants tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. There was no evidence of vertical transmission. Most infants were observed to have lymphopenia and thrombocytopenia. The clinical features of pregnant women were found to be similar to those of generally infected patients. There is evidence of adverse pregnancy and neonatal outcomes caused by COVID-19.

Antibiotic resistance profile of common uropathogens during COVID-19 pandemic: hospital based epidemiologic study.

BACKGROUND: Antimicrobial resistance has a direct impact on the ability to treat common infections, and this was worsened during the COVID-19 pandemic. Worldwide surveillance studies are lacking and resistance rates vary spatially, so frequent local surveillance reports are required to guide antimicrobial stewardship efforts. This study aims to report our common local uropathogens and their antibiogram profiles in our community during the COVID era. METHODS: A retrospective study included patients referred to our urology units with urine culture and sensitivity. All bacterial strains were identified, and their antibiotic susceptibilities were tested. RESULTS: Out of 2581 urine culture results recruited, 30% showed microbiological proof of infection. The majority, 486 (63.4%), were isolated from females. The most frequent isolates were Escherichia coli (44.4%) and Staphylococcus aureus (17.8%). The resistance rates ranged from 26.9 to 79.7%. Piperacillin-tazobactam antibiotic had the lowest resistance rate. The multi-drug resistance pattern was recorded in 181 (23.9%) of the isolates; 159/597 (26.6%) Gram-negative and 22/160 (13.8%) Gram-positive isolates. CONCLUSIONS: Alarming rates of antimicrobial resistance were detected, which stresses the significance of following infection control policies and establishing national antimicrobial stewardship standards.

SARS-CoV-2-free residual proteins mediated phenotypic and metabolic changes in peripheral blood monocytic-derived macrophages in support of viral pathogenesis.

The large-scale dissemination of coronavirus disease-2019 (COVID-19) and its serious complications have pledged the scientific research communities to uncover the pathogenesis mechanisms of its etiologic agent, severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Methods of unveiling such mechanisms are rooted in understanding the viral agent's interactions with the immune system, including its ability to activate macrophages, due to their suggested role in prolonged inflammatory phases and adverse immune responses. The objective of this study is to test the effect of SARS-CoV-2-free proteins on the metabolic and immune responses of macrophages. We hypothesized that SARS-CoV-2 proteins shed during the infection cycle may dynamically induce metabolic and immunologic alterations with an inflammatory impact on the infected host cells. It is imperative to delineate such alterations in the context of macrophages to gain insight into the pathogenesis of these highly infectious viruses and their associated complications and thus, expedite the vaccine and drug therapy advent in combat of viral infections. Human monocyte-derived macrophages were treated with SARS-CoV-2-free proteins at different concentrations. The phenotypic and metabolic alterations in macrophages were investigated and the subsequent metabolic pathways were analyzed. The obtained results indicated that SARS-CoV-2-free proteins induced concentration-dependent alterations in the metabolic and phenotypic profiles of macrophages. Several metabolic pathways were enriched following treatment, including vitamin K, propanoate, and the Warburg effect. These results indicate significant adverse effects driven by residual viral proteins that may hence be considered determinants of viral pathogenesis. These findings provide important insight as to the impact of SARS-CoV-2-free residual proteins on the host cells and suggest a potential new method of management during the infection and prior to vaccination.

Weighted power Maxwell distribution: Statistical inference and COVID-19 applications.

During the course of this research, we came up with a brand new distribution that is superior; we then presented and analysed the mathematical properties of this distribution; finally, we assessed its fuzzy reliability function. Because the novel distribution provides a number of advantages, like the reality that its cumulative distribution function and probability density function both have a closed form, it is very useful in a wide range of disciplines that are related to data science. One of these fields is machine learning, which is a sub field of data science. We used both traditional methods and Bayesian methodologies in order to generate a large number of different estimates. A test setup might have been carried out to assess the effectiveness of both the classical and the Bayesian estimators. At last, three different sets of Covid-19 death analysis were done so that the effectiveness of the new model could be demonstrated.

Mouth rinses efficacy on salivary SARS-CoV-2 viral load: A randomized clinical trial.

Considering the global trend to confine the COVID-19 pandemic by applying various preventive health measures, preprocedural mouth rinsing has been proposed to mitigate the transmission risk of SARS-CoV-2 in dental clinics. The study aimed to investigate the effect of different mouth rinses on salivary viral load in COVID-19 patients. This study was a single-center, randomized, double-blind, six-parallel-group, placebo-controlled clinical trial that investigated the effect of four mouth rinses (1% povidone-iodine, 1.5% hydrogen peroxide, 0.075% cetylpyridinium chloride, and 80 ppm hypochlorous acid) on salivary SARS-CoV-2 viral load relative to the distilled water and no-rinse control groups. The viral load was measured by quantitative reverse transcription PCR (RT-qPCR) at baseline and 5, 30, and 60 min post rinsing. The viral load pattern within each mouth rinse group showed a reduction overtime; however, this reduction was only statistically significant in the hydrogen peroxide group. Further, a significant reduction in the viral load was observed between povidone-iodine, hydrogen peroxide, and cetylpyridinium chloride compared to the no-rinse group at 60 min, indicating their late antiviral potential. Interestingly, a similar statistically significant reduction was also observed in the distilled water control group compared to the no-rinse group at 60 min, proposing mechanical washing of the viral particles through the rinsing procedure. Therefore, results suggest using preprocedural mouth rinses, particularly hydrogen peroxide, as a risk-mitigation step before dental procedures, along with strict adherence to other infection control measures.

Antibiotic consumption in hospitals during COVID-19 pandemic: a comparative study.

INTRODUCTION: Coronavirus disease 2019 (COVID-19) results in similar clinical characteristics as bacterial respiratory tract infections and can potentially lead to antibiotic overuse. This study aimed to determine the changes in hospital antimicrobial usage before and during the COVID-19 pandemic. METHODOLOGY: We compared antimicrobial consumption data for 2019 and 2020. Inpatient antibiotic consumption was determined and expressed as a defined daily dose (DDD) per 100 occupied bed days, following the World Health Organization (WHO) methods. The WHO Access, Watch, and Reserve (AWaRe) classification was used. RESULTS: The total antimicrobial consumption in 2020 increased by 16.3% compared to consumption in 2019. In 2020, there was a reduction in fourth-generation cephalosporins (-30%), third-generation cephalosporins (-29%), and combinations of penicillins (-23%). In contrast, antibiotics that were consumed more during 2020 compared with 2019 included linezolid (374%), vancomycin (66.6%), and carbapenem (7%). Linezolid is the only antibiotic from the Reserve group on the hospital's formulary. Antibiotic usage from the Access group was reduced by 17%, while antibiotic usage from the Watch group and the Reserve group was increased by 3% and 374%, respectively. CONCLUSIONS: The findings show a significant shift in antibiotic usage from the Access group to the Watch and Reserve groups. The Watch and Reserve groups are known to be associated with increased resistance to antibiotics. Therefore, antimicrobial stewardship should be increased and maintained during the pandemic to ensure appropriate antibiotic use.