Characteristics of unintentional childhood injury during COVID-19: a single-center comparative study.

Background: There are many articles related to child injuries during the coronavirus disease of 2019 (COVID-19) in other countries, but only few studies in this field in China. This study analyzes the clinical characteristics of unintentional childhood injury during the pandemic, to provide reference for the prevention of unintentional childhood injury in the context of pandemic. Methods: A comparative study was performed on the medical data of 2,497 children with unintentional injury who were hospitalized at Chengdu Women's and Children's Central Hospital between 1 January, 2018 and 31 May, 2021. The study period was divided into 2 periods, before the pandemic (1 January, 2018 to 31 May, 2019), during the pandemic (1 January, 2020 to 31 May, 2021). The number of unintentional childhood injuries and age distribution before and during the pandemic were compared. Group differences were examined using Mann-Whitney U for continuous variables and Chi-squared or Kruskal-Wallis tests for categorical variables. Results: There were significant differences in age, accident location, hospitalization days, and medical expenses before and during the pandemic (P<0.05). During the pandemic, the number of children's unintentional injuries increased by 34.24% (1,066 vs. 1,431, P=0.000), and the significantly increased types of unintentional injuries included foreign bodies, falls, crush injuries, and sharp injuries. During the pandemic, the highest proportion of unintentional injury to children was foreign bodies, whereas the proportion of falls was the highest before the pandemic. During the pandemic, the number of foreign body injuries in toddler was significantly higher than before the pandemic (P=0.001), but the number of falls, crush injuries, and sharp injuries in preschooler was significantly higher (P<0.05). Conclusions: In the circumstance of the COVID-19, the number of foreign bodies, falls, crush injuries, and sharp injuries, in children increased significantly. It is necessary to strengthen the prevention of foreign bodies in toddler, and falls, crush injuries, and sharp injuries in preschooler.

Applications and Services

The Institute of Electrical and Electronic Engineers (IEEE) Future Networks International Network Generations Roadmap (INGR) Applications and Services Working Group developed a Transdisciplinary Framework that is sustainable, structured, flexible, adaptable, and scalable framework that extends across end-to-end ecosystems, and caters to different stages of priorities, resources, and technologies. The framework may be used by academic stakeholders for new research topics of interest, industry stakeholders to develop solutions for roadmap identified opportunities while minimizing negative risks, and government stakeholders for governance and policy development. The 2022 edition provides additional details on the Applications and Services Transdisciplinary Framework from Smart Cities, developed in the 1st edition, and was extended towards Smart Communities that include both urban and non-urban areas in the 2021 edition. This edition of the IEEE INGR Application and Services roadmap chapter includes: •Applications and Services Framework: a dynamic sustainable framework for applications and services that extends across end-to-end ecosystems, and caters to the priorities, resources, and technologies for local urban and non-urban areas. ○ Ecosystem of Ecosystems: intra-ecosystem and inter-ecosystem alignments for agriculture, education, electrical power, health care, media and entertainment, public safety, transportation, and water distribution and wastewater treatment ecosystems. ○ Network of Networks: Future networks components (access, service delivery, operations and service management, and network extensions), use case categories and network operations enhancements. ○ Governance Function of Functions: strategic and governance related functions to support local area objectives that include economic development, quality of life, stakeholder attraction and retention, and policy development. •Transdisciplinary Framework Scenarios and Use Cases: smart cities, smart regions, and pandemic planning scenarios The Applications and Services working group will extend the reach and depth of this framework to add new ecosystems and enhance the existing ecosystems already addressed for future INGR editions. © 2022 IEEE.

Air Quality Index (AQI) Did Not Improve during the COVID-19 Lockdown in Shanghai, China, in 2022, Based on Ground and TROPOMI Observations

The lockdowns from the coronavirus disease of 2019 (COVID-19) have led to a reduction in anthropogenic activities and have hence reduced primary air pollutant emissions, which were reported to have helped air quality improvements. However, air quality expressed by the air quality index (AQI) did not improve in Shanghai, China, during the COVID-19 outbreak in the spring of 2022. To better understand the reason, we investigated the variations of nitrogen dioxide (NO2), ozone (O3), PM2.5 (particular matter with an aerodynamic diameter of less than 2.5 μm), and PM10 (particular matter with an aerodynamic diameter of less than 10 μm) by using in situ and satellite measurements from 1 March to 31 June 2022 (pre-, full-, partial-, and post-lockdown periods). The results show that the benefit of the significantly decreased ground-level PM2.5, PM10, and NO2 was offset by amplified O3 pollution, therefore leading to the increased AQI. According to the backward trajectory analyses and multiple linear regression (MLR) model, the anthropogenic emissions dominated the observed changes in air pollutants during the full-lockdown period relative to previous years (2019–2021), whereas the long-range transport and local meteorological parameters (temperature, air pressure, wind speed, relative humidity, and precipitation) influenced little. We further identified the chemical mechanism that caused the increase in O3 concentration. The amplified O3 pollution during the full-lockdown period was caused by the reduction in anthropogenic nitrogen oxides (NOx) under a VOC-limited regime and high background O3 concentrations owing to seasonal variations. In addition, we found that in the downtown area, ground-level PM2.5, PM10, and NO2 more sensitively responded to the changes in lockdown measures than they did in the suburbs. These findings provide new insights into the impact of emission control restrictions on air quality and have implications for air pollution control in the future. © 2023 by the authors.

Infectious disorders: HIV, hepatitis C, and COVID-19 pandemics

This chapter instead focuses on the neuropsychological manifestations and neuropathological underpinnings of three prominent pandemic infectious diseases: human immunodeficiency virus (HIV), hepatitis C virus (HCV), and the novel coronavirus disease of 2019 (COVID-19), given their substantial global prevalence. It shows how these pandemics highlight the complexities of characterizing neurocognition across varying dimensions of clinical disease. Through a predominantly neuropsychological lens, the chapter discusses how variations in disease duration, severity, degree of recovery, and treatment can affect brain health and related outcomes. Toward this end, it discusses how these mechanisms intersect and diverge in HIV and HCV, two historically severe infectious diseases that now have undergone significant advances in treatment. In turn, the chapter draws upon insights gained from researching the neuropsychological complications of these diseases in order to inform the assessment and diagnosis of neurocognitive impairment in the context of COVID-19. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

X-Ray Image Classification for COVID-19 Using Transfer Learning

SARS-CoV-2, the cause of one of the significant pandemics in history, first appeared in Wuhan, China. It spreads rapidly, with symptoms like fever, cough, tiredness, and loss of taste or smell. We came up with many measures where the most effective was vaccines. Yet it's not enough against the rapidly appearing waves of SARS-CoV-2. A deep learning algorithm has proven efficient in detecting Covid-19 based on pneumonia and respiratory problems. These problems have been identified with the help of CT scans and X-ray images. It'll make it a lot easier to determine who's Infected and would save a lot of time and expenses overall would provide for extensive relief in the Covid-19 pandemic. This paper uses publically available COVID-19 X-Ray and CT Scan images to create a dataset. The Deep Learning based model is used to train and test the dataset. In the experiment, the overall accuracy is 98%, and in the testing process, the overall accuracy is 99%. © 2023 The authors and IOS Press.

Pharmacists and pharmacy services in COVID-19 literature: A bibliometirc analysis.

Background: The COVID-19 pandemic had an enormous impact on the global economy and healthcare. Pharmacists were vital members of the healthcare system, and they participated in various strategies to reduce the effect of the pandemic. Numerous papers were published discussing their roles during the pandemic. Bibliometric analysis was used to measure the impact of publications on this topic and assessed them qualitatively and quantitatively over a specific time. Objective: Evaluate published literature pertaining to the roles of pharmacists and pharmacy services during the pandemic and identify gaps. Methods: An electronic search was conducted on PubMed database using a specific query. Eligible publications were published in English between January 2020 and January 2022 and discussed the role of pharmacists, pharmacies, and pharmacy departments during the pandemic. Clinical trials, studies on pharmacy education/training, and conference abstracts were excluded. Results: Of 954 records retrieved, 338 (35.4%) from 67 countries were included. Most papers (n = 113; 33.4%) were from the community pharmacy sector, followed by the clinical pharmacy sector (n = 89; 26.3%). Sixty-one (18%) papers were multinational, mostly involving two countries. The average number of citations of the included papers was 6 times (range 0-89). The most common MeSH terms were 'humans', 'hospitals', and 'telemedicine', where the former frequently co-appeared with the terms 'COVID-19' and 'pharmacists.' Conclusions: Results from this study illustrate the innovative and proactive strategies developed by pharmacists during the pandemic. Pharmacists from around the world are encouraged to share their experiences for stronger healthcare systems to counter future pandemics and environmental disasters.

Road crashes in Adelaide metropolitan region, the consequences of COVID-19.

Background: Many countries instituted lockdown rules as the COVID-19 pandemic progressed, however, the effects of COVID-19 on transportation safety vary widely across countries and regions. In several situations, it has been shown that although the COVID-19 closure has decreased average traffic flow, it has also led to an increase in speeding, which will indeed increase the severity of crashes and the number of fatalities and serious injuries. Methods: At the local level, Generalized linear Mixed (GLM) modelling is used to look at how often road crashes changed in the Adelaide metropolitan area before and after the COVID-19 pandemic. The Geographically Weighted Generalized Linear Model (GWGLM) is also used to explore how the association between the number of crashes and the factors that explain them varies across census blocks. Using both no-spatial and spatial models, the effects of urban structure elements like land use mix, road network design, distance to CBD, and proximity to public transit on the frequency of crashes at the local level were studied. Results: This research showed that lockdown orders led to a mild reduction (approximately 7%) in crash frequency. However, this decrease, which has occurred mostly during the first three months of the lockdown, has not systematically alleviated traffic safety risks in the Greater Adelaide Metropolitan Area. Crash hotspots shifted from areas adjacent to workplaces and education centres to green spaces and city fringes, while crash incidence periods switched from weekdays to weekends and winter to summer. Implications: The outcomes of this research provided insights into the impact of shifting driving behaviour on safety during disorderly catastrophes such as COVID-19.

Personalized predictions of adverse side effects of the COVID-19 vaccines.

Background: Misconceptions about adverse side effects are thought to influence public acceptance of the Coronavirus disease 2019 (COVID-19) vaccines negatively. To address such perceived disadvantages of vaccines, a novel machine learning (ML) approach was designed to generate personalized predictions of the most common adverse side effects following injection of six different COVID-19 vaccines based on personal and health-related characteristics. Methods: Prospective data of adverse side effects following COVID-19 vaccination in 19943 participants from Iran and Switzerland was utilized. Six vaccines were studied: The AZD1222, Sputnik V, BBIBP-CorV, COVAXIN, BNT162b2, and the mRNA-1273 vaccine. The eight side effects were considered as the model output: fever, fatigue, headache, nausea, chills, joint pain, muscle pain, and injection site reactions. The total input parameters for the first and second dose predictions were 46 and 54 features, respectively, including age, gender, lifestyle variables, and medical history. The performances of multiple ML models were compared using Area Under the Receiver Operating Characteristic Curve (ROC-AUC). Results: The total number of people receiving the first dose of the AZD1222, Sputnik V, BBIBP-CorV, COVAXIN, BNT162b2, and mRNA-1273 were 6022, 7290, 5279, 802, 277, and 273, respectively. For the second dose, the numbers were 2851, 5587, 3841, 599, 242 and 228. The Logistic Regression model for predicting different side effects of the first dose achieved ROC-AUCs of 0.620-0.686, 0.685-0.716, 0.632-0.727, 0.527-0.598, 0.548-0.655, 0.545-0.712 for the AZD1222, Sputnik V, BBIBP-CorV, COVAXIN, BNT162b2 and mRNA-1273 vaccines, respectively. The second dose models yielded ROC-AUCs of 0.777-0.867, 0.795-0.848, 0.857-0.906, 0.788-0.875, 0.683-0.850, and 0.486-0.680, respectively. Conclusions: Using a large cohort of recipients vaccinated with COVID-19 vaccines, a novel and personalized strategy was established to predict the occurrence of the most common adverse side effects with high accuracy. This technique can serve as a tool to inform COVID-19 vaccine selection and generate personalized factsheets to curb concerns about adverse side effects.

Sensitivity to treatment and score bands of the Infants and Toddlers Dermatology Quality of Life questionnaire.

Background: The Infants and Toddlers Dermatology Quality of Life (InToDermQoL) questionnaire is the first dermatology-specific proxy health related QoL instrument for children from birth to 4 years. Score meaning bands and the sensitivity to successful therapeutic intervention are important to interpret the clinical meaning of an instrument. Objective: The aim of the present study was to check the sensitivity to successful therapeutic intervention and establish score bands of the InToDermQoL questionnaire. Methods: Parents or grandparents of 424 children with skin diseases from Spain, Malta, Croatia, Romania, Greece, and Ukraine filled in national language versions of the InToDermQoL questionnaire. Disease severity of children with atopic dermatitis was assessed by SCORAD (Scoring atopic dermatitis). Cohen's d was used to assess the responsiveness of the instrument. Results: The mean total InToDermQoL scores significantly decreased after treatment. Severity grading of the SCORAD scores gave stratification of the InToDermQoL severity grades based on 95% confidence intervals. Scores below a calculated minimal important difference of 2 corresponded to no effect on patient's health related QoL. Limitations: Score banding may be slightly different across patient population and study context. Conclusion: All 3 age-specific versions of the InToDermQoL questionnaire showed sensitivity to treatment. Score bands for the InToDermQoL questionnaire have been established.

Transcriptomics secondary analysis of severe human infection with SARS-CoV-2 identifies gene expression changes and predicts three transcriptional biomarkers in leukocytes.

SARS-CoV-2 is the causative agent of COVID-19, which has greatly affected human health since it first emerged. Defining the human factors and biomarkers that differentiate severe SARS-CoV-2 infection from mild infection has become of increasing interest to clinicians. To help address this need, we retrieved 269 public RNA-seq human transcriptome samples from GEO that had qualitative disease severity metadata. We then subjected these samples to a robust RNA-seq data processing workflow to calculate gene expression in PBMCs, whole blood, and leukocytes, as well as to predict transcriptional biomarkers in PBMCs and leukocytes. This process involved using Salmon for read mapping, edgeR to calculate significant differential expression levels, and gene ontology enrichment using Camera. We then performed a random forest machine learning analysis on the read counts data to identify genes that best classified samples based on the COVID-19 severity phenotype. This approach produced a ranked list of leukocyte genes based on their Gini values that includes TGFBI, TTYH2, and CD4, which are associated with both the immune response and inflammation. Our results show that these three genes can potentially classify samples with severe COVID-19 with accuracy of ∼88% and an area under the receiver operating characteristic curve of 92.6--indicating acceptable specificity and sensitivity. We expect that our findings can help contribute to the development of improved diagnostics that may aid in identifying severe COVID-19 cases, guide clinical treatment, and improve mortality rates.

Multiclass Classification of COVID-19, Pneumonia, or Nornal Lungs Based on Chest X-Ray Images with Ensemble Deep Learning

Coronavirus Disease of 2019 (COVID-19) has a high transmission and death rate. It is important to diagnose COVID-19 accurately and distinguish it clearly from other common lung diseases, e.g., pneumonia. Both diseases are detectable from chest X-Ray images. Therefore, an ensemble deep learning model is applied for multiclass classification of COVID-19, pneumonia, or normal lungs based on chest X-Ray images. ResNet50, VGG16, and InceptionV3 pretrained CNN models are employed to form an ensemble model. The chest X-Ray images are preprocessed in three steps, i.e., cropping, resizing, and normalization. Then, the pretrained models are trained with a new classifier at the top layer of the model. After the classifier is trained, then the pretrained ResNet50, VGG16, and InceptionV3 are fine-Tuned. Lastly, the decisions from each model are assembled using Soft Voting. The ensemble deep learning model which produces the best result, which is formed by combining pretrained and fine-Tuned ResNet50, VGG16, and InceptionV3 models, results weighted accuracy of 0.9752, weighted sensitivity of 0.9612, and weighted specificity of 0.9804. © 2022 IEEE.

COVID-19: X-ray image classification using transfer learning DCNN approach

COVID-19 is a coronavirus that causes sickness in the human respiratory system. It is the most recent virus that is wreaking havoc on the entire world. It spreads mainly through contact with an infected person. There are some vaccinations available to prevent this condition now. The flu causes symptoms such as fever, coughing, and breathing difficulties in humans. COVID-19: Classification of X-Ray Images This paper suggests using a Deep Convolution Neural Network-based Transfer Learning methodology. Deep CNN learns picture patterns and classifies X-RAY pictures using transfer learning technology. A dataset is created using publicly available photos of COVID-19 X-Ray. All images have been resized and rotated by 2 to 20 degrees. The file contains 6677 COVID-19 pictures and 5753 stock pictures. DCNN predictability is 99.64 percent on a training set, while on a test set, it is 99.79 percent. After the transfer of learning, predictive accuracy on the training set is 99.19 percent, while predictive accuracy on the test set is 99.31 percent. © 2022 Author(s).

ß-D-mannuronic Acid (M2000) and Inflammatory Cytokines in COVID-19; An In vitro Study.

coronavirus disease of 2019 (COVID-19) can be complicated by acute respiratory distress syndrome (ARDS) and may be associated with cytokine storm and multiorgan failure. Anti-inflammatory agents, such as systemic corticosteroids, monoclonal antibodies, and nonsteroidal anti-inflammatory drugs (NSAIDs) can be used for this purpose. In this study, we evaluated the immunomodulatory effect of mannuronic acid (M2000), which is a novel NSAID, on COVID-19-related cytokine storms. This study was conducted in vitro on blood samples of 30 COVID-19 patients who presented with ARDS to a referral center. Peripheral blood mononuclear cells (PBMCs) were isolated from blood samples and incubated with phorbol myristate acetate for 24 hours. M2000 was administered with the dosages of 25 µg/well and 50 µg/well after 4 hours of incubation at 37°C. The quantitative real-time polymerase chain reaction (qRT-PCR) was conducted to assess mRNA gene expression. Enzyme-linked immunosorbent assay (ELISA) was performed to evaluate the supernatant PBMC levels of interleukin (IL)-6, IL-17, tumor necrosis factor (TNF)-α, and interferon (IFN)-γ. Both mRNA expression and the supernatant PBMC levels of IL-17, TNF-α, IL­6, and IFN­Î³ were decreased in PBMCs of COVID-19 patients treated with M2000 compared with the control  group. For the first time, it was observed that M2000 could be effective in alleviating the inflammatory cascade of COVID-19 patients based on an in vitro model. After further studies in vitro and in animal models, M2000 could be considered a novel NSAID drug in COVID-19 patients.

Utility of National Early Warning Score 2 to risk-stratify coronavirus disease of 2019 patients in the emergency department: A retrospective cohort study.

Background: The application of a risk stratification pathway is necessary for the emergency department (ED) to assess the severity of the disease and the need for escalation of therapy. We aimed to implement the National Early Warning Score 2 (NEWS2) pathway at triage to differentiate patients who are stable or critically ill with no invasive investigations at the time of admission during the coronavirus disease of 2019 (COVID-19) era in comparison to other clinical risk scores. Methods: One hundred and four patients were collected from April 1, 2021, to June 1, 2021, during the second wave of the COVID-19 pandemic at an academic medical center in India. The NEWS2 scoring system and the quick sepsis-related organ failure assessment (qSOFA) score were introduced as part of the initial assessment in the triage area of the ED. Data were assessed using the area under the receiving operating characteristic (AUROC) curve for NEWS2 and qSOFA scores, respectively. Results: In the study, NEWS2 classification indicated that 25% of patients required continuous monitoring, of which 12.7% subsequently deteriorated within 24 h of admission and 7% died. Both, NEWS2 (threshold 0; 1, AUROC 0.883; 95%; confidence interval [CI] 0.8-0.966) and qSOFA (threshold 0; 1, AUROC 0.851; 95% CI 0.766-29 0.936) effectively identified COVID-19 patients in the ED at risk for clinical deterioration. There was no significant difference in the diagnostic performance of qSOFA and NEWS2 (DeLong's test P = 0.312). Conclusion: Both NEWS2 and qSOFA effectively-identified COVID-19 patients in the ED at risk for clinical deterioration with no significant statistical difference. However, a triage level risk stratification score can be developed with the inclusion of blood parameters on admission to further validate the practice.

Comparison of anxiety and depressive symptoms of head and neck cancer patients in a closed-loop management system before and during the 2019 coronavirus pandemic: a comparative study.

BACKGROUND: The coronavirus disease of 2019 (COVID-19) poses an unprecedented challenge to health and the financial system, especially the healthcare of patients with cancer. However, the research on the negative effect of the pandemic on the anxiety and depressive symptoms of cancer patients in closed-loop is rarely reported at present. In view of the limitations of previous studies. In this study, we compared the anxiety and depressive symptoms of head and neck cancer patients in the closed-loop management system before and during the 2019 coronavirus pandemic. METHODS: In this comparative study, a total of 526 head and neck cancer patients (269 and 257 patients before and during the COVID-19 pandemic) were enrolled in the present study. The two groups of patients' median age (53 years, 52 years), female distribution (70.26%, 66.15%) and male distribution (29.74%, 33.85%) were analyzed before and after the COVID-19 epidemic. They received questionnaires using the standardized data forms of Self-Rating Anxiety Scale (SAS) and the Self-Rating Depression Scale (SDS) to collect the relevant data of patients for retrospective investigation. For data analysis, either the chi-squared test or Fisher's exact test was employed for categorical variables, and we described the time trend of psychological states before and after the outbreak with Cochran-Armitage trend (CAT) test. RESULTS: A total of 526 head and neck cancer patients were included in the final analysis; 26.85% and 50.19% of cases experienced anxiety and depression during the COVID-19 epidemic. In contrast, 18.22% and 33.46% of cases had experienced anxiety and depression before the pandemic. According to the statistical results, the prevalence of anxiety and depression in patients during the COVID-19 epidemic was higher compared to that of patients prior to the pandemic (P=0.018). More importantly, both anxiety and depression incidence trends increased significantly before and after the outbreak of COVID-19 (P=0.000). CONCLUSIONS: The present study demonstrates the significant impact of COVID-19 on the psychological states of cancer patients in the case of closed-loop management system, effectively indicating the need for appropriate changes in treatment decisions, enhanced psychotherapy, and interventions to reduce the incidence of anxiety, depression, and even suicide during this pandemic.

Alveolar-arterial Oxygen Gradient in COVID-19 Pneumonia Initiated on Noninvasive Ventilation: Looking into the Mortality-prediction Ability.

How to cite this article: Karim HMR, Esquinas AM. Alveolar-arterial Oxygen Gradient in COVID-19 Pneumonia Initiated on Noninvasive Ventilation: Looking into the Mortality-prediction Ability. Indian J Crit Care Med 2022;26(10):1152.

Misdiagnosis of intramural ectopic pregnancy and invasive gestational trophoblastic disease on ultrasound: A challenging case at Tu Du Hospital in Vietnam in COVID-19 pandemic peak and mini-review of literature.

Differentiation between intramural ectopic pregnancy and molar ectopic pregnancy is very difficult because of their exceptional rarity. Herein, we present a misdiagnosed case of intramural pregnancy and invasive trophoblastic disease on ultrasound. A 45-year-old female patient was admitted to our tertiary referral hospital due to abdominal pain and unusual ultrasonography findings. Initially, a diagnosis of intramural ectopic pregnancy was identified based on transvaginal color Doppler ultrasonography, 3-dimensional ultrasound, and serial serum beta-human chorionic gonadotropin, thus the patient underwent laparotomy with hysterectomy. However, the histopathological endpoint showed an invasive trophoblastic disease. Clinically, this pathology should be included in the differential diagnosis of intramural ectopic pregnancy since an imaging scan remains quite unclear.

Sludge-based activated carbon from two municipal sewage sludge precursors for improved secondary wastewater-treatment discharge-effluent

This study investigated the use of sludge-based activated carbon (SBAC) sorbent as an integrated waste-to-resources approach for the removal of contaminants from wastewater. We measured the ability of SBAC sorbents from two types of municipal sewage sludge (SS) precursors (thickened waste SS “TWSS-SBAC” and biosolids “Bio-SBAC”) from a Canadian wastewater treatment plant (WWTP) to stabilise emerging contaminants (ECs) from precursor SS and to remove ECs from the discharged effluent. The ECs were from pharmaceutical and personal care products (PPCPs), including antibiotics, disinfectants, and antibacterial hand-sanitisers and soaps, which were commonly used during the COVID-19 (coronavirus disease of 2019) pandemic. We measured the removal efficacy of Bio-SBAC at two dosages (1g/L and 10g/L) and TWSS-SBAC at one dosage (1g/L) via 30-min batch adsorption tests for eleven PPCPs at mean concentrations of 2–2337ng/L in the discharged effluent, and compared the results with those of other techniques and sorbents reported in literature. At both dosages, Bio-SBAC removed PPCPs, including four blood regulator compounds that have been extensively used since the pandemic outbreak (furosemide, gemfibrozil, glyburide, and warfarin), with their levels decreasing below the detection limit. The percentage removal for ibuprofen, 2-hydroxy-ibuprofen, and naproxen were 91.6–99.8% using 1g/L. The antimicrobial compounds triclosan and triclocarban were completely removed at both dosages. Ninety-nine percentage of bisphenol A was removed at 1g/L dosage and was completely removed at 10g/L. TWSS-SBAC showed similar performance as Bio-SBAC in removing PPCPs from the final effluent to improve the quality of wastewater discharged from a WWTP.

A narrative review of COVID-19-related acute respiratory distress syndrome (CARDS): "typical" or "atypical" ARDS?

Background and Objective: The coronavirus disease of 2019 (COVID-19) is highly infectious and mainly involves the respiratory system, with some patients rapidly progress to acute respiratory distress syndrome (ARDS), which is the leading cause of death in COVID-19 patients. Hence, fully understanding the features of COVID-19-related ARDS (CARDS) and early management of this disease would improve the prognosis and reduce the mortality of severe COVID-19. With the development of recent studies which have focused on CARDS, whether CARDS is "typical" or "atypical" ARDS has become a hotly debated topic. Methods: We searched for relevant literature from 1999 to 2021 published in PubMed by using the following keywords and their combinations: "COVID-19", "CARDS", "ARDS", "pathophysiological mechanism", "clinical manifestations", "prognosis", and "clinical trials". Then, we analyzed, compared and highlighted the differences between classic ARDS and CARDS from all of the aspects above. Key Content and Findings: Classical ARDS commonly occurs within 1 week after a predisposing cause, yet the median time from symptoms onset to CARDS is longer than that of classical ARDS, manifesting within a period of 9.0-12.0 days. Although the lung mechanics exhibited in CARDS grossly match those of classical ARDS, there are some atypical manifestations of CARDS: the severity of hypoxemia seemed not to be proportional to injury of lung mechanics and an increase of thrombogenic processes. Meanwhile, some patients' symptoms do not correspond with the extent of the organic injury: a chest computed tomography (CT) will reveal the severe and diffuse lung injuries, yet the clinical presentations of patients can be mild. Conclusions: Despite the differences between the CARDS and ARDS, in addition to the treatment of antivirals, clinicians should continue to follow the accepted evidence-based framework for managing all ARDS cases, including CARDS.

Development and integration of VGG and dense transfer-learning systems supported with diverse lung images for discovery of the Coronavirus identity.

The contagious SARS-CoV-2 has had a tremendous impact on the life and health of many communities. It was first rampant in early 2019 and so far, 539 million cases of COVID-19 have been reported worldwide. This is reminiscent of the 1918 influenza pandemic. However, we can detect the infected cases of COVID-19 by analysing either X-rays or CT, which are presumably considered the least expensive methods. In the existence of state-of-the-art convolutional neural networks (CNNs), which integrate image pre-processing techniques with fully connected layers, we can develop a sophisticated AI system contingent on various pre-trained models. Each pre-trained model we involved in our study assumed its role in extracting some specific features from different chest image datasets in many verified sources, such as (Mendeley, Kaggle, and GitHub). First, for CXR datasets associated with the CNN trained model from the beginning, whereby is comprised of four layers beginning with the Conv2D layer, which comprises 32 filters, followed by the MaxPooling and afterwards, we reiterated similarly. We used two techniques to avoid overgeneralization, the early stopping and the Dropout techniques. After all, the output was one neuron to classify both cases of 0 or 1, followed by a sigmoid function; in addition, we used the Adam optimizer owing to the more improved outcomes than what other optimizers conducted; ultimately, we referred to our findings by using a confusion matrix, classification report (Recall & Precision), sensitivity and specificity; in this approach, we achieved a classification accuracy of 96%. Our three integrated pre-trained models (VGG16, DenseNet201, and DenseNet121) yielded a remarkable test accuracy of 98.81%. Besides, our merged models (VGG16, DenseNet201) trained on CT images with the utmost effort; this model held an accurate test of 99.73% for binary classification with the (Normal/Covid-19) scenario. Comparing our results with related studies shows that our proposed models were superior to the previous CNN machine learning models in terms of various performance metrics. Our pre-trained model associated with the CT dataset achieved 100% of the F1score and the loss value was approximately 0.00268.