Coronavirus Disease 2019 (COVID-19): A Perspective from China

Knowledge and willingness to implement cardiopulmonary resuscitation among the general public: A study in Hubei Province, China

BACKGROUND

The incidence of out-of-hospital cardiac arrest (OHCA) in China is high, yet the treatment rate remains low. Research on the current state of the public's awareness of and willingness to provide cardiopulmonary resuscitation (CPR) in Hubei Province is scarcer.

OBJECTIVES

The objective of this research was to explore the general public's knowledge, confidence, and willingness to perform CPR in Hubei Province, China.

METHODS

In this descriptive study we used convenience sampling to select 1, 849 permanent residents of Hubei Province as survey respondents. A self-designed questionnaire included four sections: demographic information, knowledge about CPR, confidence in skill implementation, willingness to implement it, factors that may influence CPR use.

RESULTS

Four hundred ten individuals (22 %) had a knowledge score of moderate or higher. The following factors were statistically significant in CPR knowledge scores: gender, age, education, place of residence (last three years), presence of a cohabitant over age sixty years, occupation related to medicine, and participation in CPR training (p < 0.05). Only 81 (9.8 %) were confident in performing CPR. There were 708 (77.2 %) members of the public who were willing to perform CPR on strangers. Gender, age, education, place of residence (last three years), and having a medically-related occupation were associated with willingness to perform CPR (p < 0.05).

CONCLUSIONS

Public knowledge of CPR in Hubei needs improvement, with a strong willingness but inadequate background knowledge for rescue. Government should broaden CPR training paths to enhance survival rates of OHCA patients.

Potential role of lung ultrasonography in outpatient follow-up of patients with COVID-19. A systematic review

INTRODUCTION AND AIM

Currently, the usefulness of lung ultrasound in the follow-up of patients after hospital discharge for SARS-CoV-2 pneumonia is not well known. The main objective of this systematic review is to investigate the persistence of alterations in lung ultrasound of patients who have had COVID-19 pneumonia.

METHODS

A systematic review has been carried out following the PRISMA regulations in the PubMed, EMBASE, Web of Science and Google Scholar database from January 2020 to May 2023 using the combination of MeSH terms: "lung ultrasound", "ultrasonography", "lung alterations", "persistence", "follow-up", "consequences", "hospital discharge", "COVID", "COVID-19", "SARS-CoV-2". Studies were selected that described alterations in the lung ultrasound of patients after having suffered from COVID-19 pneumonia. The JBI Critical Appraisal Tools were used to assess the risk of bias of the studies. No meta-analysis techniques were performed, the results being compared narratively.

RESULTS

From two to six months after COVID-19 pneumonia, pulmonary ultrasound abnormalities appear frequently and are proportional to the intensity of the initial episode. The most frequent anomalies are irregularities in the pleural line, the presence of B lines and/or subpleural consolidations, predominantly in the basal regions of the thorax. These findings seem to correlate with those of the chest CT.

CONCLUSIONS

Lung ultrasound offers technical and economic advantages that should be considered for the study of patients after hospital discharge for COVID-19.

Virucidal activity of olanexidine gluconate against SARS-CoV-2

Antiseptics have been used for infection control against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Ethanol (EtOH) was found to be effective against SARS-CoV-2, while chlorhexidine gluconate (CHG) was less effective. Therefore, virucidal activity may differ between different classes of antiseptic agents. In this study, the efficacy of antiseptics against SARS-CoV-2 was evaluated, and effective agents for infection control were identified. The following antiseptics were used in this study: 1.5% olanexidine gluconate (OLG); 80% EtOH; 1% sodium hypochlorite (NaClO); 0.2% benzalkonium chloride (BKC); 1% povidone-iodine (PVP-I); 0.5%, 1% and 1.5% CHG; and 0.5% alkyldiaminoethylglycine hydrochloride (AEG). The virucidal activity was evaluated at 0, 0.5, 1, 2 and 3 min according to EN14476. After 0.5 min of exposure, 1.5% OLG, 80% EtOH, 1% NaClO, 0.2% BKC and 1% PVP-I inactivated SARS-CoV-2 below the detection limit. The virus survived in the presence of 0.5% CHG, 1% CHG or 0.5% AEG for 3 min. The virucidal activity of 1.5% CHG was insufficient after 0.5 min of exposure. The results showed that virucidal activity against SARS-CoV-2 differs depending on the class of antiseptic agents used under clean conditions. Despite belonging to the same class of biguanide antiseptics, OLG was more effective against SARS-CoV-2 than CHG.

Investigating the effect of echinacea extraction syrup on the outcomes of lower respiratory infections in patients with COVID-19: a randomized clinical trial study

INTRODUCTION

Many COVID-19 patients experience mild to severe symptoms, including respiratory system involvement. Different treatment instructions have been suggested for patients with COVID-19. Echinacea has known antiviral effects. However, there is still not enough evidence that it is effective in treating COVID-19. This study was conducted with the aim of determining the effect of Echinacea extract syrup on the outcomes of the lower respiratory tract in patients with COVID-19.

METHODS

In this single-blind randomized controlled trial, 40 patients with COVID-19 who were inpatients in the hospitals of Mashhad University of Medical Sciences, Iran, were randomly selected and assigned to two equal control and experimental groups (n = 20). In addition to receiving routine care and treatment (oxygen supply, remdesivir, enoxaparin and heparin), the experimental group received 5 cubic centimeter (CC) of Imogen syrup three times a day for 5 days each. The control group only received routine care and treatment. The data were collected on the first, third and fifth days after hospitalization and were analyzed using descriptive and analytical tests in Statistical Package for the Social Sciences (SPSS). The significance level was set at p < 0.05.

RESULTS

The mean white blood cell count in the experimental group after the intervention decreased significantly compared to that before the intervention (t = 0.434, p = 0.045, df = 19). Arterial oxygen pressure increased significantly in both the experimental group (t = 4.382, p = 0.000, df = 19) and control group (t = 3.239, p = 0.004, df = 19), however no statistical differences were observed between experimental and control groups after intervention. The level of lung involvement (p = 0.320) and cough symptoms (P = 0.347) were not significantly different between the experimental and control groups after the intervention. In addition, there were no significant differences between the experimental and control groups in terms of the mean oxygen saturation, temperature, and number of breaths per minute on the first, third, and fifth day (p > 0.05).

DISCUSSION

The consumption of Echinacea extract syrup may not be able to improve the symptoms of acute lower respiratory tract infection in patients with COVID-19 with 3 daily doses for 5 days. More studies should be conducted to investigate the clinical effects of Echinacea extract in the treatment of patients with pulmonary complications.

TRIAL REGISTRATION

IRCT20130522013423N2.

Risk factors for anxiety, depression, stress, and their comorbidities among nurses: a prospective cohort from 2020 to 2022

BACKGROUND

Nurses are at the forefront of healthcare delivery during the COVID-19, placing them at an increased risk for mental health issues. This study aimed to identify the risk factors for anxiety, depression, stress, and their comorbidities among nurses during the 2020-2022 period.

METHODS

A prospective cohort of nurses in Zunyi City, China, was followed from 2020 to 2022. The Self-Rating Anxiety Scale (SAS), Self-Rating Depression Scale (SDS), and Perceived Stress Scale (PSS) were used to assess anxiety, depression, and stress, respectively. A self-reported questionnaire was utilized to collect data on demographic characteristics, lifestyle habits, socioeconomic status, work-related factors, and medical history. The Healthy Eating Index (HEI) was calculated to evaluate diet quality. Univariate and multivariate logistic regression analyses were conducted to examine the associations between risk factors and mental health outcomes.

RESULTS

Among the 516 participating nurses, the incidence rates of new-onset anxiety, depression, and stress were 27.1%, 33.9%, and 39.9%, respectively. Frontline experience with infected patients, night shift work, longer working hours, and higher body mass index were consistently associated with increased risks of anxiety, depression, and stress, whereas a higher healthy eating index score was linked to reduced odds of these mental health outcomes. Notably, higher BMI (≥ 28: OR = 1.67, 95% CI: 1.12-2.16, p = 0.011), night shifts (> 1/week: OR = 5.12, 95% CI: 3.64-5.99, p < 0.001), longer working hours (> 40/week: OR = 1.99, 95% CI: 1.66-2.89, p < 0.001), and frontline experience (OR = 6.11, 95% CI: 4.52-8.88, p < 0.001) significantly increased comorbidity risk, while higher HEI (> 3: OR = 0.51, 95% CI: 0.36-0.70, p < 0.001) reduced the risk.

CONCLUSION

Our study highlights the considerable mental health burden among nurses during the 2020-2022 period and identifies key risk factors associated with anxiety, depression, stress, and their comorbidities. These findings underscore the importance of providing targeted interventions and support for nurses, including workload management, work-life balance promotion, and healthy lifestyle encouragement, to mitigate the negative consequences of identified risk factors and improve mental health outcomes.

Comparison of X-ray and CT Images of COVID-19 Caused by the Wild-Type and Alpha-Variant SARS-CoV-2

Introduction This study aimed to determine the characteristics of coronavirus disease 2019 (COVID-19) pneumonia caused by the wild type and the alpha variant in patients. This study included patients with COVID-19 admitted to Fukui General Hospital between October 31, 2020, and April 30, 2021. Methods Pneumonia occurrence rate, chest X-ray, and computed tomography (CT) findings were evaluated by two radiologists. The time since the onset and presence of pneumonia were also investigated. Results Out of 128 patients, 43 had pneumonia. The pneumonia detection rates using chest radiography were 15.6% (20/128) and 33.6% (43/128) using CT (p = 0.0008). Of the pneumonia cases detected by CT, 32.0% (8/25) of the wild type and 66.7% (12/18) of the alpha variant were detected by X-rays (p = 0.0246). The main finding of pneumonia was a higher percentage of ground-glass opacities than consolidation in both the wild type and alpha variant. In the alpha variant, multiple signs of air bubbles were observed in four patients on chest CT; however, these were not observed in the wild type (p = 0.014). Conclusion The imaging features of pneumonia may be different in variants of COVID-19 compared to those in the wild type. CT helps to detect pneumonia and identify features in patients with COVID-19 because it is difficult to detect COVID-19 pneumonia using plain chest radiographs.

Association of plasma microRNAs with COVID-19 severity and outcome

OBJECTIVE

As one of the remarkable host responses to SARS-CoV-2 infection, circulating microRNAs (miRNAs) represent important diagnostic and prognostic diseases biomarkers. The study is a step towards highlighting the role of miRNAs in COVID-19 pathogenesis and severity.

METHODS

In this case-control study, miRCURY LNA miRNA PCR plasma panel (168 miRNAs) was applied and the expression of the altered miRNAs was then analysed by quantitative real time PCR for 120 COVID-19 patients (30 mild, 30 moderate, 30 severe, and 30 critical) and 30 healthy subjects.

RESULTS

The initial screening showed that 30 miRNAs displayed altered expression, out of them, only eleven miRNAs (miR-885-5p, miR-141-3p, miR-21-5p, miR-127-3p, miR-99b-5p, let-7d-3p, miR-375, miR-1260a, miR-139-5p, miR-28-5p and miR-34a-5p) were dysregulated in the plasma of COVID-19 patients; all of them were significantly overexpressed. By applying ROC curve analysis, AUC for the eleven miRNAs were ranged from 0.65 to 0.83, and the AUC for the combined miRNAs was 0.93. Ten miRNAs (miR-141-3p, miR-181a-5p, miR-221-3p, miR-223-5p, miR99b-5p, Let-7d-3p, miR-375, miR-199a-5p, miR-139-5p and miR-28-5p) exhibited a significant change in their expression between different severity groups. Patients with positive outcome were found to have increased miR-375 and decreased miR-99b-5p expression levels. Bioinformatic prediction showed that, out of the eleven dysregulated miRNAs, five miRNAs (miR-139-5p, -34a-5p, -28-5p, -21-5p and -885-5p) have the ability to regulate at least two genes related to COVID-19 according to KEGG database.

CONCLUSION

miRNAs are dysregulated in COVID-19 patients and associated with severity degree and patients' outcome.

Therapeutic applications of carbohydrate-based compounds: a sweet solution for medical advancement

Carbohydrates, one of the most abundant biomolecules found in nature, have been seen traditionally as a dietary component of foods. Recent findings, however, have unveiled their medicinal potential in the form of carbohydrates-derived drugs. Their remarkable structural diversity, high optical purity, bioavailability, low toxicity and the presence of multiple functional groups have positioned them as a valuable scaffold and an exciting frontier in contemporary therapeutics. At present, more than 170 carbohydrates-based therapeutics have been granted approval by varying regulatory agencies such as United States Food and Drug Administration (FDA), Japan Pharmaceuticals and Medical Devices Agency (PMDA), Chinese National Medical Products Administration (NMPA), and the European Medicines Agency (EMA). This article explores an overview of the fascinating potential and impact of carbohydrate-derived compounds as pharmacological agents and drug delivery vehicles.

Detection and prioritization of COVID-19 infected patients from CXR images: Analysis of AI-assisted diagnosis in clinical settings

In this paper, we present the significant results from the Covid Radiographic imaging System based on AI (Co.R.S.A.) project, which took place in Italy. This project aims to develop a state-of-the-art AI-based system for diagnosing Covid-19 pneumonia from Chest X-ray (CXR) images. The contributions of this work are manifold: the release of the public CORDA dataset, a deep learning pipeline for Covid-19 detection and prioritization, the clinical validation of the developed solution by expert radiologists, and an in-depth analysis of possible biases embedded in the data and in the models, in order to build more trust in our AI-based pipeline. The proposed detection model is based on a two-step approach that provides reliable results based on objective radiological findings. Our prioritization scheme ensures the ordering of the patients so that severe cases are presented first. We showcase the impact of our pipeline on radiologists' workflow with a clinical study, allowing us to assess the real benefits in terms of accuracy and time efficiency. Project homepage: https://corsa.di.unito.it/.

Impact of COVID-19 on nephropathy in diabetes mellitus type-II patients: a systematic literature review and meta-analysis

BACKGROUND

Recent reports have revealed that nephropathy leading to kidney injury (KI) is a prevalent complication of COVID-19 and is linked to high mortality and morbidity in diabetes mellitus type II (DM-T-II) patients. This systematic literature review and meta-analysis aimed to critically analyze existing studies and evidence on the impact of COVID-19 on nephropathy and kidney injury in diabetes mellitus type II (DM-T-II) patients.

METHOD

A systematic search was conducted in the Web of Science (WoS), PubMed and Cochrane databases for relevant studies published between March 2020 and July 2023. To ensure the integrity of the systematic literature review and meta-analysis, observational studies that specifically reported post-COVID-19 kidney injury in DM-T2 patients were included, whereas we did not include articles in the press, meta-analyses, case reports, case series, Diabetes Type-I articles or non-English papers. The primary outcome was kidney injury in patients with type II diabetes after contracting COVID-19. The protocol for this study was published on PROSPERO (registration number CRD42023413887).

RESULTS

Initially, 6,339 articles were included in the search, from which only 6 observational studies were selected by following the 2020 PRISMA statement. The quality of the evidence was assessed by a tool provided by the National Institutes of Health (observational studies). The total number of participants included in the studies was 14,723. Our systematic literature review and meta-analysis provide compelling evidence that kidney injury is a prevalent complication of COVID-19 infection in the type II diabetes population, with a pooled odds ratio of 2.27 (95% CI: 2.05-2.51; p < 0.00001), often necessitating hospitalization and hemodialysis in severe cases.

CONCLUSION

Covid-19 is associated with a two-fold increase in nephropathy and acute kidney injury in diabetes mellitus type 2 patients compared to non-diabetic patients. This implies that kidney injury is more likely to occur in diabetes mellitus type 2 patients post Covid infection.

The severity assessment and nucleic acid turning-negative-time prediction in COVID-19 patients with COPD using a fused deep learning model

BACKGROUND

Previous studies have shown that patients with pre-existing chronic obstructive pulmonary diseases (COPD) were more likely to be infected with coronavirus disease (COVID-19) and lead to more severe lung lesions. However, few studies have explored the severity and prognosis of COVID-19 patients with different phenotypes of COPD.

PURPOSE

The aim of this study is to investigate the value of the deep learning and radiomics features for the severity evaluation and the nucleic acid turning-negative time prediction in COVID-19 patients with COPD including two phenotypes of chronic bronchitis predominant patients and emphysema predominant patients.

METHODS

A total of 281 patients were retrospectively collected from Hohhot First Hospital between October 2022 and January 2023. They were divided to three groups: COVID-19 group of 95 patients, COVID-19 with emphysema group of 94 patients, COVID-19 with chronic bronchitis group of 92 patients. All patients underwent chest computed tomography (CT) scans and recorded clinical data. The U-net model was pretrained to segment the pulmonary involvement area on CT images and the severity of pneumonia were evaluated by the percentage of pulmonary involvement volume to lung volume. The 107 radiomics features were extracted by pyradiomics package. The Spearman method was employed to analyze the correlation of the data and visualize it through a heatmap. Then we establish a deep learning model (model 1) and a fusion model (model 2) combined deep learning with radiomics features to predict nucleic acid turning-negative time.

RESULTS

COVID-19 patients with emphysema was lowest in the lymphocyte count compared to COVID-19 patients and COVID-19 companied with chronic bronchitis, and they have the most extensive range of pulmonary inflammation. The lymphocyte count was significantly correlated with pulmonary involvement and the time for nucleic acid turning negative (r=-0.145, P < 0.05). Importantly, our results demonstrated that model 2 achieved an accuracy of 80.9% in predicting nucleic acid turning-negative time.

CONCLUSION

The pre-existing emphysema phenotype of COPD severely aggravated the pulmonary involvement of COVID-19 patients. Deep learning and radiomics features may provide more information to accurately predict the nucleic acid turning-negative time, which is expected to play an important role in clinical practice.

Exploring the relationship between cycle threshold values and oral manifestations in COVID-19: a comprehensive overview

OBJECTIVE

This cross-sectional study aimed to compare oral manifestations between severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2)-positive and SARS-CoV-2-negative patients and to examine associations between oral symptoms, Ct values of E and N SARS-CoV-2 viral genes, and the implications of low Ct values indicating a high viral load, which is a predictive factor for the outcome of COVID-19.

METHODS

A total of 353 participating patients were aged ≥18 years with clinical manifestations of COVID-19 infection and tested for SARS-CoV-2 carriage at the medical center, by reverse transcriptase polymerase chain reaction (RT-PCR). All patients filled out an anonymous digital questionnaire regarding oral and general symptoms and overall medical health.  Results: A significant association was found between SARS-CoV-2 carriage and dry mouth, unpleasant taste and changes in taste (p < 0.001); for example, 37.4% of the 147 SARS-CoV-2- positive participants had a dry mouth, compared to 18.9% of the 206 SARS-CoV-2- negative participants. Oral blisters were experienced by patients with an E gene Ct value of 10-20 (50%) or 21-30 (50%) (p = 0.041). Bad breath, dry mouth, unpleasant taste and changes in taste were mostly present in participants whose Ct values of both E and N genes were between 21 and 30.  Conclusions: This study found significant associations between low Ct values of E and N SARS-CoV-2 viral genes and high viral load, indicating that Ct values can serve as predictive factors for COVID-19 outcomes. The findings suggest that while oral symptoms are present, the Ct values and associated high viral loads are more critical indicators of disease severity and prognosis.

A radiomics model utilizing CT for the early detection and diagnosis of severe community-acquired pneumonia

BACKGROUND

Community-Acquired Pneumonia (CAP) remains a significant global health concern, with a subset of cases progressing to Severe Community-Acquired Pneumonia (SCAP). This study aims to develop and validate a CT-based radiomics model for the early detection of SCAP to enable timely intervention and improve patient outcomes.

METHODS

A retrospective study was conducted on 115 CAP and SCAP patients at Southern Medical University Shunde Hospital from January to December 2021. Using the Pyradiomics package, 107 radiomic features were extracted from CT scans, refined via intra-class and inter-class correlation coefficients, and narrowed down using the Least Absolute Shrinkage and Selection Operator (LASSO) regression model. The predictive performance of the radiomics-based model was assessed through receiver operating characteristic (ROC) analysis, employing machine learning classifiers such as k-Nearest Neighbors (KNN), Support Vector Machine (SVM), Logistic Regression (LR), and Random Forest (RF), trained and validated on datasets split 7:3, with a training set (n = 80) and a validation set (n = 35).

RESULTS

The radiomics model exhibited robust predictive performance, with the RF classifier achieving superior precision and accuracy compared to LR, SVM, and KNN classifiers. Specifically, the RF classifier demonstrated a precision of 0.977 (training set) and 0.833 (validation set), as well as an accuracy of 0.925 (training set) and 0.857 (validation set), suggesting its superior performance in both metrics. Decision Curve Analysis (DCA) was utilized to evaluate the clinical efficacy of the RF classifier, demonstrating a favorable net benefit within the threshold ranges of 0.1 to 0.8 for the training set and 0.2 to 0.7 for the validation set.

CONCLUSIONS

The radiomics model developed in this study shows promise for early SCAP detection and can improve clinical decision-making.

Impact of Long COVID-19 on Health Outcomes Among Adults With Preexisting Cardiovascular Disease and Hypertension: A Systematic Review

Background

This review summarizes the impact of long COVID (LC) on the health of adults with preexisting cardiovascular disease (CVD) and hypertension.

Methods

We searched Medline, Web of Science (Core Collection), and the Cumulative Index to Nursing and Allied Health Literature (CINAHL), without language restrictions, for articles published from December 1, 2019 through October 10, 2023, to ensure all relevant studies were captured. We included studies that enrolled adults (aged ≥18 years) diagnosed with CVD prior to COVID-19 infection whose infection was subsequently determined to be LC per the World Health Organization definition. We excluded studies with adults diagnosed with CVD concurrent with or subsequent to COVID-19 or with those who solely self-reported LC. We used a custom-built data extraction form to collect a range of study characteristics. Study quality was assessed using modified versions of the National Heart, Lung, and Blood Institute quality-assessment tools.

Results

A total of 13,779 studies were identified; 53 were included in the final analysis. Of these, 27 were of good quality and 26 were of fair quality. Health outcomes consisted of the presence of prolonged symptoms of LC (n = 29), physiological health outcomes (n = 20), lifestyle behaviours (n = 19), psycho-social outcomes (n = 13), CVD complications (n = 5), and death and hospital readmission (n = 5). Thirty-four studies incorporated 2 or more outcomes, and 19 integrated only 1.

Conclusions

Given the significant impact of LC among individuals with preexisting CVD, specially tailored clinical management is needed for members of this population. Additional studies on the impact of LC among those with CVD and other underlying conditions also would be beneficial.

The Epidemiology of Pathogens in Community-Acquired Pneumonia Among Children in Southwest China Before, During and After COVID-19 Non-pharmaceutical Interventions: A Cross-Sectional Study

OBJECTIVE

This study aimed to investigate the pathogen epidemiology of community-acquired pneumonia (CAP) among children in Southwest China before, during and after the COVID-19 non-pharmaceutical interventions (NPIs).

METHODS

Pathogen data of hospitalised children with CAP, including multiple direct immunofluorescence test for seven viruses, bacterial culture and polymerase chain reaction (PCR) for Mycoplasma pneumoniae, were analysed across three phases: Phase I (pre-NPIs: 1 January 2019 to 31 December 2019), Phase II (NPI period: 1 January 2020 to 31 December 2020) and Phase III (post-NPIs: 1 January 2023 to 31 December 2023).

RESULTS

A total of 7533 cases were enrolled, including 2444, 1642 and 3447 individuals in Phases I, II and III, respectively. M. pneumoniae predominated in Phases I and III (23.4% and 35.5%, respectively). In Phase II, respiratory syncytial virus (RSV) emerged as the primary pathogen (20.3%), whereas detection rates of influenza A virus (Flu A) and M. pneumoniae were at a low level (1.8% and 9.6%, respectively). In Phase III, both Flu A (15.8%) and M. pneumoniae epidemic rebounded, whereas RSV detection rate returned to Phase I level, and detection rates of Streptococcus pneumoniae and Haemophilus influenzae decreased significantly compared to those in Phase I. Detection rates of adenovirus and parainfluenza virus type 3 decreased phase by phase. Age-stratified analysis and monthly variations supported the above findings. Seasonal patterns of multiple pathogens were disrupted during Phases II and III.

CONCLUSIONS

COVID-19 NPIs exhibited a distinct impact on CAP pathogen epidemic among children, with post-NPIs increases observed in M. pneumoniae and Flu A prevalence. Continuous pathogen monitoring is crucial for effective prevention and control of paediatric CAP.

Investigation of the psychiatric factors that determine the fear of COVID-19 in healthcare workers and hospital staff in a university hospital in Turkey

To examine the association of health and hospital workers' fears of coronavirus disease 2019 (COVID-19) with anxiety, anxiety sensitivity, depression, and sociodemographic variables during the COVID-19 pandemic. A total of 527 participants (237 men/289 women) were included, 222 of the participants were doctors, 99 nurses, 22 assistant health personnel, and 182 hospital personnel without health education. Participants filled in the sociodemographic data form, Beck Depression Inventory, Beck Anxiety Inventory, Anxiety Sensitivity Index-3, and Fear of COVID-19 Inventory. In linear regression analysis, independent predictors of the fear of COVID-19 were determined as Beck Anxiety Inventory (p < 0.001), Beck Depression Inventory (p = 0.001), and Anxiety Sensitivity Index-3 Physical subscale (p = 0.001). The fear of COVID-19 is associated with the physical subscale of anxiety, depression, and anxiety sensitivity.

Nanomaterials-based electrochemical biosensors for diagnosis of COVID-19

Since the outbreak of corona virus disease 2019 (COVID-19), this pandemic has caused severe death and infection worldwide. Owing to its strong infectivity, long incubation period, and nonspecific symptoms, the early diagnosis is essential to reduce risk of the severe illness. The electrochemical biosensor, as a fast and sensitive technique for quantitative analysis of body fluids, has been widely studied to diagnose different biomarkers caused at different infective stages of COVID-19 virus (SARS-CoV-2). Recently, many reports have proved that nanomaterials with special architectures and size effects can effectively promote the biosensing performance on the COVID-19 diagnosis, there are few comprehensive summary reports yet. Therefore, in this review, we will pay efforts on recent progress of advanced nanomaterials-facilitated electrochemical biosensors for the COVID-19 detections. The process of SARS-CoV-2 infection in humans will be briefly described, as well as summarizing the types of sensors that should be designed for different infection processes. Emphasis will be supplied to various functional nanomaterials which dominate the biosensing performance for comparison, expecting to provide a rational guidance on the material selection of biosensor construction for people. Finally, we will conclude the perspective on the design of superior nanomaterials-based biosensors facing the unknown virus in future.

Return to play of young and adult professional athletes after COVID-19: A scoping review

Background/objective

Given the persistence of COVID-19 under various facets and mutations, there is an urgent need to understand the debate on a safe return to play for professional athletes (young and adults) recovering from the infection. This work offers a scoping and comprehensive review on the topic during the first two years of the pandemic event by providing an identification of main clusters of research, relevant gaps and significant insights for future investigation.

Methods

The literature is selected using the search engines of: PubMed®, SCIENCEDIRECT, and SCOPUS. Further criteria for selection are: Time range of 2020-2022; Scope: Return to play of professional athletes recovering from COVID-19 infection; 3) Types of publications: Research papers, reviews, practice guidelines, case reports; 4) Language: English. Two independent researchers performed a quality check on a random sample (n = 30%) of publications.

Results

Main results reveal four research clusters deepening the analysis on: myocarditis, cardiac diseases and return to play, training and rehabilitation, mass screening and risk assessment, and sport and bio-psycho-social sphere for a safe return to play. Major collaborations occur between UK-South Africa, UK-USA, USA-Canada, and USA-Australia.

Conclusions

Important gaps refer to a lack of investigation on a safe return to play for female athletes in mostly all sports disciplines; on the other hand, sport and the bio-psycho-social sphere of the athlete is a fast-growing topic. Both deserve further attention in the immediate future to improve ad-hoc sport and exercise practices.

Insights from a multi-country study: Lessons for future nursing education from community clinical practice amid the COVID-19 pandemic

Background

The COVID-19 pandemic prompted adjustments in education, raising concerns about students' competency achievement. Despite these changes, aspects like student engagement (SE), basic needs fulfillment (BNF), and stress levels (SL) in nursing students during community-based clinical practice remain understudied.

Objectives

This study aims to examine the relationships between students' competencies achievement (SCA), student engagement, basic needs fulfillment, and stress levels among nursing students engaging in community-based clinical practice during the COVID-19 pandemic.

Design

and Methods: A cross-sectional online survey was conducted with 451 nursing students from Indonesia, Malaysia, and India. Online questionnaires assessing SCA, SE, BNF, and SL were administered between November and December 2021.

Results

The study involved 131 participants from Indonesia, 138 from Malaysia, and 182 from India, with an average age of 22.52. Multivariate analysis, employing linear regression revealed that across the three countries, online student engagement demonstrated the strongest association with SCA (B: 0.701; p-value: 0.0001). However, specific factors-stress levels, learning methods, and study year-showed greater relevance in Indonesia, India, and Malaysia, respectively.

Conclusions

The COVID-19 pandemic significantly impacted nursing students' teaching and learning experiences during clinical practice. Enhancing online engagement between academic lecturers and students is imperative for attaining clinical competencies.

Asymptotic analysis on a new stochastic epidemic model involving isolation mechanism

In this paper, a new stochastic epidemic model is established and the dynamical behavior of its solutions is studied for this model. A deterministic epidemic model (ordinary differential equation) is first proposed by considering the isolation mechanism, and the transmission probability function is determined by a Wells-Riley model method to analyze the transmission in the quarantine. For this deterministic model, the basic reproduction number R0 is computed and it is used to determine the existence of disease-free and positive equilibria. The linearized stability of the equilibria is also discussed by analyzing the distribution of eigenvalues of the linear system. Following that, a corresponding stochastic epidemic model is further established by introducing stochastic disturbance. Then, the extinction result of the model is derived also with the help of the basic reproduction number R0s. Furthermore, by applying the theory of Markov semigroups, it is proved that the densities of the distributions of the solutions can converge to an invariant density or sweeping under certain conditions. At last, some numerical simulations are provided and discussed to illustrate the practicability of the model and the obtained theoretical results.

Effect of the COVID-19 Pandemic on Gambling Behavior in Mainland Chinese Gamblers in Macau: Cross-Sectional Survey Study

BACKGROUND

This study examined the effects of the COVID-19 pandemic on the gambling behavior of individuals who were already actively engaged in such pursuits. We aimed to uncover the intricate consequences of the pandemic on this specific demographic, emphasizing the importance of understanding the complex connection between public health concerns such as the COVID-19 pandemic and gambling behavior from a public health perspective. In addition to identifying immediate impacts, this study holds significance in assessing potential long-term public health implications for the broader gambling industry.

OBJECTIVE

This study investigated how the COVID-19 pandemic has affected the gambling behavior of Mainland Chinese tourists in Macau from a public health perspective. We aimed to understand the changing patterns of gambling habits within this specific demographic by comparing their behavior before and during the pandemic, with a particular emphasis on the evolving dynamics of gambling and their public health consequences. This study provides a detailed exploration of the impact and implications of global health emergencies on this particular demographic's gambling behaviors and preferences.

METHODS

This study used a robust cross-sectional analysis involving a sample of 334 Mainland Chinese gamblers with prior experiences in casinos in Macau. The sample deliberately encompassed individuals involved in gambling before and during the COVID-19 pandemic. Data were collected through carefully designed questionnaires to gather information on gambling habits, preferences, and observed behavioral changes in the sample.

RESULTS

This study unveiled a notable shift in Mainland Chinese gamblers' behavior during the COVID-19 pandemic. A considerable number of participants opted for web-based platforms over traditional land-based casinos, resulting in reduced budgets, less time spent on gambling, and decreased participation in social gambling. Remarkably, there was a notable surge in online gambling, indicating a noteworthy adaptability of gamblers to changing circumstances. These findings emphasize the dynamic nature of gambling habits during global public health emergencies, revealing the resilient and evolving preferences of Mainland Chinese gamblers in response to the challenges posed by the pandemic.

CONCLUSIONS

This study highlights the negative impact of the COVID-19 pandemic on casino gambling, notably evident in a significant decline in Mainland Chinese tourists visiting Macau for gambling. There is a noticeable shift from traditional gambling to web-based alternatives, with individuals seeking options within the pandemic constraints. Furthermore, the findings point out an increase in gambling among the younger generation and behavioral changes in individuals with mood disorders. The findings of this study emphasize the critical need for proactive measures to address evolving gambling preferences and associated risks during public health crises; furthermore, these findings underscore the importance of adaptive strategies within the gambling industry, as well as the necessity for effective public health interventions and regulatory frameworks to respond to unprecedented challenges with efficacy and precision.

A Hybrid Deep Learning CNN model for COVID-19 detection from chest X-rays

Coronavirus disease (COVID-2019) is emerging in Wuhan, China in 2019. It has spread throughout the world since the year 2020. Millions of people were affected and caused death to them till now. To avoid the spreading of COVID-2019, various precautions and restrictions have been taken by all nations. At the same time, infected persons are needed to identify and isolate, and medical treatment should be provided to them. Due to a deficient number of Reverse Transcription Polymerase Chain Reaction (RT-PCR) tests, a Chest X-ray image is becoming an effective technique for diagnosing COVID-19. In this work, the Hybrid Deep Learning CNN model is proposed for the diagnosis COVID-19 using chest X-rays. The proposed model consists of a heading model and a base model. The base model utilizes two pre-trained deep learning structures such as VGG16 and VGG19. The feature dimensions from these pre-trained models are reduced by incorporating different pooling layers, such as max and average. In the heading part, dense layers of size three with different activation functions are also added. A dropout layer is supplemented to avoid overfitting. The experimental analyses are conducted to identify the efficacy of the proposed hybrid deep learning with existing transfer learning architectures such as VGG16, VGG19, EfficientNetB0 and ResNet50 using a COVID-19 radiology database. Various classification techniques, such as K-Nearest Neighbor (KNN), Naive Bayes, Random Forest, Support Vector Machine (SVM), and Neural Network, were also used for the performance comparison of the proposed model. The hybrid deep learning model with average pooling layers, along with SVM-linear and neural networks, both achieved an accuracy of 92%.These proposed models can be employed to assist radiologists and physicians in avoiding misdiagnosis rates and to validate the positive COVID-19 infected cases.

A comprehensive investigation on the receptor BSG expression reveals the potential risk of healthy individuals and cancer patients to 2019-nCoV infection

BACKGROUND

Coronavirus disease-2019 (COVID-19) pandemic is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a newly emerging coronavirus. BSG (basigin) is involved in the tumorigenesis of multiple tumors and recently emerged as a novel viral entry receptor for SARS-CoV-2. However, its expression profile in normal individuals and cancer patients are still unclear.

METHODS

We performed a comprehensive analysis of the expression and distribution of BSG in normal tissues, tumor tissues, and cell lines via bioinformatics analysis and experimental verification. In addition, we investigated the expression of BSG and its isoforms in multiple malignancies and adjacent normal tissues, and explored the prognostic values across pan-cancers. Finally, we conducted function analysis for co-expressed genes with BSG.

RESULTS

We found BSG was highly conserved in different species, and was ubiquitously expressed in almost all normal tissues and significantly increased in some types of cancer tissues. Moreover, BSG at mRNA expression level was higher than ACE2 in normal lung tissues, and lung cancer tissues. High expression of BSG indicated shorter overall survival (OS) in multiple tumors. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses indicated that BSG is mostly enriched in genes for mitochondria electron transport, oxidoreduction-driven active transmembrane transporter activity, mitochondrial inner membrane, oxidative phosphorylation, and genes involving COVID-19.

CONCLUSIONS

Our present work emphasized the value of targeting BSG in the treatment of COVID-19 and cancer, and also provided several novel insights for understanding the SARS-CoV-2 pandemic.

Post-COVID-19 physical and cognitive impairments and associations with quality of life: a cross-sectional study

Background and objective

This study aimed to compare physical and cognitive functions between post-coronavirus disease 2019 (COVID-19) participants and healthy matched controls and investigate associations between physical and cognitive impairments with quality of life.

Methods

Twenty-three post-COVID-19 participants and 23 age and sex-matched healthy people without a history of COVID-19 were included. Physical function was assessed using the Medical Research Council Sum Score (MRC-SS), 2 min Step Test, Modified Borg Scale, and Short Physical Performance Battery (SPPB) Test. Cognitive function was assessed using the Montreal Cognitive Assessment (MoCA), Trail Making Test (TMT), and Stroop test, and the quality of life was evaluated using the Euro Quality of Life-5 Dimensions-3 Levels (EQ-5D-3l) questionnaire. Assessments were performed by a physical therapist in one session.

Results

Mann-Whitney U test showed that in the post-COVID-19 group, compared to the control group, the number of steps in the 2 min Step Test (p < 0.001, ES = 0.57) and the scores of the SPPB (p = 0.03, ES = 0.32), MoCA (p = 0.003, ES = 0.44), Stroop test (p < 0.001, ES = 0.75), and the EQ-5D-3l visual analog scale (p = 0.027, ES = 0.32) were significantly lower. In addition, the Modified Borg Scale score (p < 0.001, ES = 0.6), TMT-A (p = 0.013, ES = 0.36) and TMT-B (p = 0.016, ES = 0.35) times, and the Stroop time (p < 0.001, ES = 0.61) were significantly higher in the post-COVID-19 group. There were no significant between-group differences in the MRC-SS score (p = 0.055, ES = 0.28). Furthermore, there were significant moderate to high associations between physical and cognitive functions and the quality of life in post-COVID-19 participants.

Conclusions

On average 4 months after symptomatic COVID-19, post-COVID-19 participants had significant impairments in physical and cognitive functions compared to healthy matched controls that were significantly correlated with the quality of life. These findings highlight the need for a comprehensive assessment to plan appropriate management strategies.

Multiparametric Cardiovascular Magnetic Resonance in Nonhospitalized COVID-19 Infection Subjects: An Intraindividual Comparison Study

PURPOSE

To investigate intraindividual cardiac structural and functional changes before and after COVID-19 infection in a previously healthy population with a 3T cardiac magnetic resonance (CMR).

MATERIALS AND METHODS

A total of 39 unhospitalized patients with COVID-19 were recruited. They participated in our previous study as non-COVID-19 healthy volunteers undergoing baseline CMR examination and were recruited to perform a repeated CMR examination after confirmed COVID-19 infection in December 2022. The CMR parameters were measured and compared between before and after COVID-19 infection with paired t tests. The laboratory measures including myocardial enzymes and inflammatory indicators were also collected when performing repeated CMR.

RESULTS

The median duration was 393 days from the first to second CMR and 26 days from clinical symptoms onset to the second CMR. Four patients (10.3%, 4/39) had the same late gadolinium enhancement pattern at baseline and repeated CMR and 5 female patients (12.8%, 5/39) had myocardial T2 ratio >2 (2.07 to 2.27) but with normal T2 value in post-COVID-19 CMR. All other CMR parameters were in normal ranges before and after COVID-19 infection. Between before and after the COVID-19 infection, there were no significant differences in cardiac structure, function, and tissue characterization, no matter with or without symptoms (fatigue, chest discomfort, palpitations, shortness of breath, and insomnia/sleep disorders) (all P >0.05). The laboratory measures at repeated CMR were in normal ranges in all participants.

CONCLUSIONS

These intraindividual CMR studies showed unhospitalized patients with COVID-19 with normal myocardial enzymes had no measurable CMR abnormalities, which can help alleviate wide social concerns about COVID-19-related myocarditis.

PulmoNet: a novel deep learning based pulmonary diseases detection model

Pulmonary diseases are various pathological conditions that affect respiratory tissues and organs, making the exchange of gas challenging for animals inhaling and exhaling. It varies from gentle and self-limiting such as the common cold and catarrh, to life-threatening ones, such as viral pneumonia (VP), bacterial pneumonia (BP), and tuberculosis, as well as a severe acute respiratory syndrome, such as the coronavirus 2019 (COVID-19). The cost of diagnosis and treatment of pulmonary infections is on the high side, most especially in developing countries, and since radiography images (X-ray and computed tomography (CT) scan images) have proven beneficial in detecting various pulmonary infections, many machine learning (ML) models and image processing procedures have been utilized to identify these infections. The need for timely and accurate detection can be lifesaving, especially during a pandemic. This paper, therefore, suggested a deep convolutional neural network (DCNN) founded image detection model, optimized with image augmentation technique, to detect three (3) different pulmonary diseases (COVID-19, bacterial pneumonia, and viral pneumonia). The dataset containing four (4) different classes (healthy (10,325), COVID-19 (3,749), BP (883), and VP (1,478)) was utilized as training/testing data for the suggested model. The model's performance indicates high potential in detecting the three (3) classes of pulmonary diseases. The model recorded average detection accuracy of 94%, 95.4%, 99.4%, and 98.30%, and training/detection time of about 60/50 s. This result indicates the proficiency of the suggested approach when likened to the traditional texture descriptors technique of pulmonary disease recognition utilizing X-ray and CT scan images. This study introduces an innovative deep convolutional neural network model to enhance the detection of pulmonary diseases like COVID-19 and pneumonia using radiography. This model, notable for its accuracy and efficiency, promises significant advancements in medical diagnostics, particularly beneficial in developing countries due to its potential to surpass traditional diagnostic methods.

Physical activity and depressive symptoms during the fifth wave of COVID-19 pandemic: Implication for public policy and administrators

Depression is a public mental health problem that can progress to suicidal ideation, literature suggests regular physical activity may ameliorate it. The study assessed the link between physical activity and depression symptoms during the fifth wave of the COVID-19 pandemic and the Academic Staff Union (ASU) strike among undergraduates. Four hundred and eighteen undergraduates were recruited and participated in the study. Participants completed the International Physical Activity Questionnaire-Short Form (IPAQ-SF) and Patient Health Questionnaire-9 (PHQ-9) to measure depression severity. The result on PA showed that about one-third of the participants were inactive, above half were moderately active, while a few achieved high PA levels. Above one-fifth of the participants experienced minimal or no depression while a good percent had mild, moderate, moderately severe, and severe depression. Non-parametric tests between PA total score and depression total score with demographic variables were not significant. Spearman's correlation showed a strong negative relationship between PHQ-9 scores and IPAQ-SF scores. This suggests that a high PA level is associated with lower depression symptoms. The COVID-19 pandemic and the ASU strike experiences resulted in increased depression among undergraduates. The university administration needs to formulate an urgent policy to promote PA among undergraduates and provide treatment for the affected students.

Performance of chest X-ray scoring in predicting disease severity and outcomes of patients hospitalised with COVID-19 in Bangladesh

Introduction

Evaluation of potential outcomes of COVID-19-affected pneumonia patients using computed tomography scans may not be conceivable in low-resource settings. Thus, we aimed to evaluate the performance of chest X-ray scoring in predicting the disease severity and outcomes of adults hospitalised with COVID-19.

Methods

This was a retrospective chart analysis consuming data from COVID-19-positive adults who had chest X-ray availability and were admitted to a temporary COVID unit, in Bangladesh from 23rd April 2020 to 15th November 2021. At least one clinical intensivist and one radiologist combinedly reviewed each admission chest X-ray for the different lung findings. Chest X-ray scoring varied from 0 to 8, depending on the area of lung involvement with 0 indicating no involvement and 8 indicating ⩾75% involvement of both lungs. The receiver operating characteristic curve was used to determine the optimum chest X-ray cut-off score for predicting the fatal outcomes.

Result

A total of 218 (82.9%) out of 263 COVID-19-affected adults were included in the study. The receiver operating characteristic curve demonstrated the optimum cut-off as ⩾3 and ⩾5 for disease severity and death, respectively. In multivariate logistic regression analysis, a chest X-ray score of ⩾3 was found to be independently associated with disease severity (aOR: 8.70; 95% CI: 3.82, 19.58, p < 0.001) and a score of ⩾5 with death (aOR: 16.53; 95% CI: 4.74, 57.60, p < 0.001) after adjusting age, sex, antibiotic usage before admission, history of fever, cough, diabetes mellitus, hypertension, total leukocytes count and C-reactive protein.

Conclusion

Using chest X-ray scoring derived cut-off at admission might help to identify the COVID-19-affected adults who are at risk of severe disease and mortality. This may help to initiate early and aggressive management of such patients, thereby reducing their fatal outcomes.

Auto-detection of the coronavirus disease by using deep convolutional neural networks and X-ray photographs

The most widely used method for detecting Coronavirus Disease 2019 (COVID-19) is real-time polymerase chain reaction. However, this method has several drawbacks, including high cost, lengthy turnaround time for results, and the potential for false-negative results due to limited sensitivity. To address these issues, additional technologies such as computed tomography (CT) or X-rays have been employed for diagnosing the disease. Chest X-rays are more commonly used than CT scans due to the widespread availability of X-ray machines, lower ionizing radiation, and lower cost of equipment. COVID-19 presents certain radiological biomarkers that can be observed through chest X-rays, making it necessary for radiologists to manually search for these biomarkers. However, this process is time-consuming and prone to errors. Therefore, there is a critical need to develop an automated system for evaluating chest X-rays. Deep learning techniques can be employed to expedite this process. In this study, a deep learning-based method called Custom Convolutional Neural Network (Custom-CNN) is proposed for identifying COVID-19 infection in chest X-rays. The Custom-CNN model consists of eight weighted layers and utilizes strategies like dropout and batch normalization to enhance performance and reduce overfitting. The proposed approach achieved a classification accuracy of 98.19% and aims to accurately classify COVID-19, normal, and pneumonia samples.

A Qualitative Study of the Experience of COVID-19 Patients in Burkina Faso

In Burkina Faso, the health system is characterized by systemic insufficient and antiquated health-care infrastructures. Consequently, few health-care establishments have the required resources to diagnose and manage patients with COVID-19, and fewer still have intensive care facilities for severely ill patients with COVID. Furthermore, there is a widespread scarcity of qualified health-care staff. The aim of this study was to explore the experiences of patients with COVID-19 who recovered after being cared for in Bobo Dioulasso and Ouagadougou. Using individual semistructured interviews, we performed a cross-sectional qualitative, descriptive study from June 12 to 30, 2020 with the aid of 13 well-educated patients who had survived COVID-19. The results reveal that prior to hospital admission, the main reason that prompted patients to seek care was onset of symptoms of COVID-19, regardless of whether they had been in contact with suspected or confirmed cases. Transmission was mainly believed to have occurred in the community, in the hospital, and during travel. Patient management was punctuated by frequent self-medication with medicinal plants or pharmaceutical drugs. The participants reported a negative perception of hospitalization or home-based management, with several forms of stigmatization, but a positive perception influenced by the satisfactory quality of management in health-care centers. This report of patient experiences could be helpful in improving the management of COVID-19 in Burkina Faso, both in the health-care setting and in home-based care.

Role of Imaging in Diagnosis and Management of COVID-19: Evidence-Based Approaches

Imaging has been demonstrated to play a crucial role in both the diagnosis and management of COVID-19. Depending on resources, pre-test probability, and risk factors for severe disease progression, real-time polymerase chain reaction (RT-PCR) testing may be followed by chest radiography (CXR) or chest computed tomography (CT) to further aid in diagnosis or excluding COVID-19 disease. SARS-CoV-2 has been shown not only to pathologically impact the pulmonary system, but also the cardiovascular, gastrointestinal, and neurological systems to name a few. Imaging has again proven useful in further investigating and managing extrapulmonary disease, with the use of echocardiogram, CT angiography of the cardiovascular and cerebrovascular structures, MRI of the brain, as well as ultrasound of the abdomen and CT of the abdomen and pelvis proving particularly useful. Research in artificial intelligence and its application in the diagnosis of COVID-19 and disease severity prediction is underway, and point-of-care ultrasound is an emerging bedside technique that may allow for more efficient and timely diagnosis of COVID-19.

The Role of Traditional Chinese Medicine and Chinese Pharmacopoeia in the Evaluation and Treatment of COVID-19

The epidemic prompted by COVID-19 continues to spread, causing a great risk to the general population's safety and health. There are still no drugs capable of curing it. Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS) are the two other diseases caused by coronaviruses. Traditional Chinese Medicine (TCM) showed benefits in treating SARS and MERS by preventing the disease early, substantially mitigating symptoms, shortening the treatment period, and minimizing risks and adverse reactions caused by hormone therapy. Although several vaccines have been developed and are being used for the treatment of COVID-19, existing vaccines cannot provide complete protection against the virus due to the rapid evolution and mutation of the virus, as mutated viral epitopes evade the vaccine's target and decrease the efficacy of vaccines. Thus, there is a need to develop alternative options. TCM has demonstrated positive effects in the treatment of COVID-19. Previous research studies on TCM showed broad-spectrum antiviral activity, offering a range of possibilities for their potential use against COVID-19. This study shed some light on common TCM used for SARS and MERS outbreaks and their effective use for COVID-19 management. This study provides new insights into COVID-19 drug discovery.

Significant Differences in the Effects of Nitrogen Doping on Pristine Biochar and Graphene-like Biochar for the Adsorption of Tetracycline

To improve the adsorption efficiency of pollutants by biochar, preparing graphene-like biochar (GBC) or nitrogen-doped biochar are two commonly used methods. However, the difference in the nitrogen doping (N-doping) effects upon the adsorption of pollutants by pristine biochar (PBC) and GBC, as well as the underlying mechanisms, are still unclear. Take the tetracycline (TC) as an example, the present study analyzed the characteristics of the adsorption of TCs on biochars (PBC, GBC, N-PBC, N-GBC), and significant differences in the effects of N-doping on the adsorption of TCs by PBC and GBC were consistently observed at different solution properties. Specifically, N-doping had varied effects on the adsorption performance of PBC, whereas it uniformly improved the adsorption performance of GBC. To interpret the phenomenon, the N-doping upon the adsorption was revealed by the QSAR model, which indicated that the pore filling (VM) and the interactions between TCs with biochars (Ead-v) were found to be the most important two factors. Furthermore, the density functional theory (DFT) results demonstrated that N-doping slightly affects biochar's chemical reactivity. The van der Waals (vdWs) and electrostatic interactions are the main forces for TCs-biochars interactions. Moreover, N-doping mostly strengthened the electrostatic interactions of TCs-biochars, but the vdWs interactions of most samples remained largely unaffected. Overall, the revealed mechanism of N-doping on TCs adsorption by biochars will enhance our knowledge of antibiotic pollution remediation.

Prevalence and associated risk factors for mental health problems among young adults in Fiji Island during COVID-19: a cross-sectional study

Introduction

The COVID-19 pandemic has had a significant impact on mental health globally. To understand the impact of the pandemic on mental health in Fiji, this study aimed to investigate the prevalence of anxiety disorder and depression among the young adults.

Method

An online survey was conducted to assess the prevalence of anxiety disorder and depression among the general population in Suva, Fiji during the COVID-19 pandemic. A total of 1,119 Fiji adults participated in the study. The study was conducted between May 20 to June 30, 2022, using a snowball sampling via social media platforms. The Generalized Anxiety Disorder (GAD-7) and Patient Health Questionnaire (PHQ-9) scales were used to measure anxiety and depression, respectively. The COVID-19 related stressors was evaluated using the adapted SARS stressors assessment. Univariate and multivariate logistic regression analysis was performed to determine the factors influencing mental health among respondents.

Results

The result shows that a significant portion of individuals experienced each of the stressors, with the highest prevalence seen for hearing information about the severity of COVID-19. The prevalence of anxiety and depression was found to be 45% and 49%, respectively. Being female, having pre-existing illness and COVID-19 stressors were a risk factor to develop anxiety and depression. On the other hand, employed individuals and having high BMI was a protective factor against developing depression during COVID-19 lockdown.

Conclusion

These findings highlight the importance of addressing the mental health needs of the Fijian population during the COVID-19 pandemic and beyond.

A medical multimodal large language model for future pandemics

Deep neural networks have been integrated into the whole clinical decision procedure which can improve the efficiency of diagnosis and alleviate the heavy workload of physicians. Since most neural networks are supervised, their performance heavily depends on the volume and quality of available labels. However, few such labels exist for rare diseases (e.g., new pandemics). Here we report a medical multimodal large language model (Med-MLLM) for radiograph representation learning, which can learn broad medical knowledge (e.g., image understanding, text semantics, and clinical phenotypes) from unlabelled data. As a result, when encountering a rare disease, our Med-MLLM can be rapidly deployed and easily adapted to them with limited labels. Furthermore, our model supports medical data across visual modality (e.g., chest X-ray and CT) and textual modality (e.g., medical report and free-text clinical note); therefore, it can be used for clinical tasks that involve both visual and textual data. We demonstrate the effectiveness of our Med-MLLM by showing how it would perform using the COVID-19 pandemic "in replay". In the retrospective setting, we test the model on the early COVID-19 datasets; and in the prospective setting, we test the model on the new variant COVID-19-Omicron. The experiments are conducted on 1) three kinds of input data; 2) three kinds of downstream tasks, including disease reporting, diagnosis, and prognosis; 3) five COVID-19 datasets; and 4) three different languages, including English, Chinese, and Spanish. All experiments show that our model can make accurate and robust COVID-19 decision-support with little labelled data.

Development and external validation of a prediction model for the transition from mild to moderate or severe form of COVID-19

OBJECTIVES

COVID-19 pandemic seems to be under control. However, despite the vaccines, 5 to 10% of the patients with mild disease develop moderate to critical forms with potential lethal evolution. In addition to assess lung infection spread, chest CT helps to detect complications. Developing a prediction model to identify at-risk patients of worsening from mild COVID-19 combining simple clinical and biological parameters with qualitative or quantitative data using CT would be relevant to organizing optimal patient management.

METHODS

Four French hospitals were used for model training and internal validation. External validation was conducted in two independent hospitals. We used easy-to-obtain clinical (age, gender, smoking, symptoms' onset, cardiovascular comorbidities, diabetes, chronic respiratory diseases, immunosuppression) and biological parameters (lymphocytes, CRP) with qualitative or quantitative data (including radiomics) from the initial CT in mild COVID-19 patients.

RESULTS

Qualitative CT scan with clinical and biological parameters can predict which patients with an initial mild presentation would develop a moderate to critical form of COVID-19, with a c-index of 0.70 (95% CI 0.63; 0.77). CT scan quantification improved the performance of the prediction up to 0.73 (95% CI 0.67; 0.79) and radiomics up to 0.77 (95% CI 0.71; 0.83). Results were similar in both validation cohorts, considering CT scans with or without injection.

CONCLUSION

Adding CT scan quantification or radiomics to simple clinical and biological parameters can better predict which patients with an initial mild COVID-19 would worsen than qualitative analyses alone. This tool could help to the fair use of healthcare resources and to screen patients for potential new drugs to prevent a pejorative evolution of COVID-19.

CLINICAL TRIAL REGISTRATION

NCT04481620.

CLINICAL RELEVANCE STATEMENT

CT scan quantification or radiomics analysis is superior to qualitative analysis, when used with simple clinical and biological parameters, to determine which patients with an initial mild presentation of COVID-19 would worsen to a moderate to critical form.

KEY POINTS

• Qualitative CT scan analyses with simple clinical and biological parameters can predict which patients with an initial mild COVID-19 and respiratory symptoms would worsen with a c-index of 0.70. • Adding CT scan quantification improves the performance of the clinical prediction model to an AUC of 0.73. • Radiomics analyses slightly improve the performance of the model to a c-index of 0.77.

Concurrent trastuzumab deruxtecan-induced interstitial lung disease and COVID-19 in the treatment of advanced breast cancer

Patients with cancer are at an increased risk of developing coronavirus disease 2019 (COVID-19) infection. Trastuzumab deruxtecan (T-DXd) is an antibody-drug conjugate (ADC) against epidermal growth factor receptor 2 (HER2)-positive cancer, known to cause drug-induced interstitial lung disease (DILD), including drug-induced pneumonitis. A 60-year-old woman with breast cancer developed a fever during treatment with T-DXd and was diagnosed with COVID-19. The fever persisted for approximately 3 weeks, and chest computed tomography showed multiple consolidations with bilateral peripheral predominance. Since the clinical course was atypical for COVID-19 due to the long duration of the fever and the CT pattern was frequently seen in T-DXd-induced ILD, the patient was diagnosed with T-DXd-induced ILD, following which, prednisolone was started, leading to improvement in the symptoms and fading of shadows. Even in patients suspected of COVID-19 pneumonia, physicians should consider the possibility of DILD, particularly in patients undergoing cancer treatment.

Point Prevalence Survey of Antimicrobial Use and Resistance during the COVID-19 Era among Hospitals in Saudi Arabia and the Implications

The inappropriate prescribing of antimicrobials increases antimicrobial resistance (AMR), which poses an appreciable threat to public health, increasing morbidity and mortality. Inappropriate antimicrobial prescribing includes their prescribing in patients hospitalized with COVID-19, despite limited evidence of bacterial infections or coinfections. Knowledge of current antimicrobial utilization in Saudi Arabia is currently limited. Consequently, the objective of this study was to document current antimicrobial prescribing patterns among Saudi hospitals during the COVID-19 pandemic. This study included patients with or without COVID-19 who were admitted to five hospitals in Makkah, Saudi Arabia. Data were gathered using the Global PPS methodology and analyzed using descriptive statistics. Out of 897 hospitalized patients, 518 were treated with antibiotics (57.7%), with an average of 1.9 antibiotics per patient. There were 174 culture reports collected, representing 36.5% of all cases. The most common indication for antibiotics use was community-acquired infections, accounting for 61.4% of all cases. 'Watch' antibiotics were the most commonly prescribed antibiotics, with the cephalosporins and carbapenems representing 38.7% of all antibiotics prescribed, followed by the penicillins (23.2%). Notably, Piperacillin/Tazobactam and Azithromycin were prescribed at relatively higher rates for COVID-19 patients. These findings highlight the need for continuous efforts to optimize the rational use of antibiotics through instigating appropriate antimicrobial stewardship programs in hospitals and, as a result, reduce AMR in the country.

Effectiveness of the forced usage of alternative digital platforms during the COVID-19 pandemic in project communication management

The COVID-19 pandemic social distancing forced a shift from Face-to-Face (F2F) to virtual work sessions, applying innovative digital tools. These tools have previously been neglected, mainly due to a conservative approach or prioritization. Consequently, their effectiveness was never tested in depth. While applying these innovative digital tools during the COVID-19 pandemic was clearly preferable to shutting down organizational activity, managers and workers recognized the advantages of these alternatives and tended to apply them in the post-COVID-19 period. However, in this post-pandemic period, which is free from social distancing limitations, a relatively full space of choices was introduced again, which raised the question whether these alternatives should be kept. Therefore, this study examines whether digital communication tools can adequately substitute F2F sessions in project management. We conducted an experiment with participants ( n = 269 ) , asking them to perform project-oriented tasks on four platforms: as individuals, in an F2F group meeting, on Zoom, or using WhatsApp. The results indicate that while an F2F meeting is more effective than individual work, Zoom and WhatsApp are not. These findings appear surprising and may contradict the concept of group empowerment. The use of digital sharing platforms did not affect the tasks' performances nor create synergy. This raises the issue of whether these digital means are here to stay, should be discarded, or must be upgraded.

Investigation of the Radiographic Imaging Volume and Occupational Dose of Radiologic Technologists before and during the COVID-19 Pandemic

ABSTRACT

This study aimed to assess occupational radiation doses for radiologic technologists (RTs) in Saudi Arabia shortly before and during the COVID-19 pandemic, considering changes in imaging volume during that time. This retrospective study included the imaging volume data and the RTs' occupational dose records from a central hospital for 2019 and 2020. The occupational dose-in terms of annual and quarterly mean effective doses (AMEDs and QMEDs)-was estimated for 115 RTs using thermoluminescent dosimeter records. There was a 22% increase in the AMED in 2020 compared with 2019, though the overall imaging volume decreased by 9% in 2020. The percentage changes in AMEDs between 2019 and 2020 for general radiography (GR), computed tomography (CT), interventional radiology (IR), nuclear medicine (NM), and mammography (MG) were 45%, 56%, 9%, 18% and -2%, respectively. The highest contribution to AMEDs in 2020 for modalities was due to GR and CT procedures, accounting for 0.50 mSv and 0.58 mSv, respectively. The percentage change in imaging volumes between 2019 and 2020 depicted a slight decrease in Q2 (-1%) and a substantial decrease in Q1 (-10%), Q3 (-12%), and Q4 (-11%) for 2020. The overall percentage changes in imaging volumes in 2020 for GR (conventional and mobile), CT, IR, NM, and MG were -7% (-19% and 48%), -11%, 13%, -26%, and -46%, respectively. Investigating the changes in 2020 by comparing Q1 of 2020 (before the pandemic restrictions) with Q2 (during the pandemic restrictions and changes in workflow) revealed that the QMED during Q2 increased by 5% with a 17.4% decrease in the imaging volume. However, CT procedures were increased by 11.1% during the pandemic restrictions in Q2 of 2020, with an increase in the corresponding QMED of 66%. Moreover, mobile GR procedures increased by 21% in Q2 of 2020 compared to Q1. This study indicated the impact of the COVID-19 pandemic on imaging volume and occupational dose. Overall, the study observed a decrease in the imaging volume and an increase in RTs' effective doses by 2020. However, there was an increase in mobile GR and CT examinations during the COVID-19 pandemic restrictions in 2020. This study suggested that the increased mobile GR and CT examinations contributed to greater effective doses for RTs in 2020.

EVALUATION OF PATIENTS WITH COVID-19 IN THE EARLY HYPOXEMIC STAGE AND PATIENTS WITH VIRAL RESPIRATORY TRACT INFECTION IN TERMS OF PULMONARY HYPERTENSION

Arterial hypoxemia occurs in many COVID-19 patients. Hypoxemia is one of the causes of pulmonary hypertension (PH). Main pulmonary artery dilatation and the main pulmonary artery diameter (mPAD) to ascending aorta diameter (AAD) ratio of ≥1 are significant findings regarding PH. In this study, COVID-19 patients and non-COVID-19 patients with viral respiratory tract infection were evaluated retrospectively in terms of PH. A total of 124 patients (71 male and 53 female), age range 18-85 years, were included in the study as case group and control group. Thoracic computed tomography (CT) images, blood and biochemical parameters, and demographic information were compared between the case group and control group. The normality of numerical variables was examined with Kolmogorov-Smirnov test and homogeneity of the variances with Levene's test. This is the first study researching the effect of early hypoxemic stage COVID-19 infection on development of PH. As a result, it was specified that COVID-19 infection had no effects on mPAD, whereas it had a positive effect on AAD and thus led to a decrease in the mPAD/AAD ratio. Through these values, which could be easily calculated from thoracic CT images, the changes caused by COVID-19 infection on vessel diameters were put forward.

Analysis of COVID-19 Resulting Cough Using Formants and Automatic Speech Recognition System

As part of our contributions to researches on the ongoing COVID-19 pandemic worldwide, we have studied the cough changes to the infected people based on the Hidden Markov Model (HMM) speech recognition classification, formants frequency and pitch analysis. In this paper, An HMM-based cough recognition system was implemented with 5 HMM states, 8 Gaussian Mixture Distributions (GMMs) and 13 dimensions of the basic Mel-Frequency Cepstral Coefficients (MFCC) with 39 dimensions of the overall feature vector. A comparison between formants frequency and pitch extracted values is realized based on the cough of COVID-19 infected people and healthy ones to confirm our cough recognition system results. The experimental results present that the difference between the recognition rates of infected and non-infected people is 6.7%. Whereas, the formant analysis variation based on the cough of infected and non-infected people is clearly observed with F1, F3, and F4 and lower for F0 and F2.

Smart stimuli-responsive drug delivery systems in spotlight of COVID-19

The world has been dealing with a novel severe acute respiratory syndrome (SARS-CoV-2) since the end of 2019, which threatens the lives of many people worldwide. COVID-19 causes respiratory infection with different symptoms, from sneezing and coughing to pneumonia and sometimes gastric symptoms. Researchers worldwide are actively developing novel drug delivery systems (DDSs), such as stimuli-responsive DDSs. The ability of these carriers to respond to external/internal and even multiple stimuli is essential in creating "smart" DDS that can effectively control dosage, sustained release, individual variations, and targeted delivery. To conduct a comprehensive literature survey for this article, the terms "Stimuli-responsive", "COVID-19″ and "Drug delivery" were searched on databases/search engines like "Google Scholar", "NCBI", "PubMed", and "Science Direct". Many different types of DDSs have been proposed, including those responsive to various exogenous (light, heat, ultrasound and magnetic field) or endogenous (microenvironmental changes in pH, ROS and enzymes) stimuli. Despite significant progress in DDS research, several challenging issues must be addressed to fill the gaps in the literature. Therefore, this study reviews the drug release mechanisms and applications of endogenous/exogenous stimuli-responsive DDSs while also exploring their potential with respect to COVID-19.

COVID-19-associated lung weakness (CALW): Systematic review and meta-analysis

OBJECTIVES

To assess mortality and different clinical factors derived from the development of atraumatic pneumothorax (PNX) and/or pneumomediastinum (PNMD) in critically ill patients as a consequence of COVID-19-associated lung weakness (CALW).

DESIGN

Systematic review with meta-analysis.

SETTING

Intensive Care Unit (ICU).

PARTICIPANTS

Original research evaluating patients, with or without the need for protective invasive mechanical ventilation (IMV), with a diagnosis of COVID-19, who developed atraumatic PNX or PNMD on admission or during hospital stay.

INTERVENTIONS

Data of interest were obtained from each article and analyzed and assessed by the Newcastle-Ottawa Scale. The risk of the variables of interest was assessed with data derived from studies including patients who developed atraumatic PNX or PNMD.

MAIN VARIABLES OF INTEREST

Mortality, mean ICU stay and mean PaO2/FiO2 at diagnosis.

RESULTS

Information was collected from 12 longitudinal studies. Data from a total of 4901 patients were included in the meta-analysis. A total of 1629 patients had an episode of atraumatic PNX and 253 patients had an episode of atraumatic PNMD. Despite the finding of significantly strong associations, the great heterogeneity between studies implies that the interpretation of results should be made with caution.

CONCLUSIONS

Mortality among COVID-19 patients was higher in those who developed atraumatic PNX and/or PNMD compared to those who did not. The mean PaO2/FiO2 index was lower in patients who developed atraumatic PNX and/or PNMD. We propose grouping these cases under the term COVID-19-associated lung weakness (CALW).

The binding mechanism of failed, in processing and succeed inhibitors target SARS-CoV-2 main protease

Since the outbreak of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), several variants have caused a persistent pandemic. Consequently, it is crucial to develop new potential anti-SARS-CoV-2 drugs with specificity. To minimize potential failures and preserve valuable clinical resources for the development of other useful drugs, researchers must enhance their understanding of the interactions between drugs and SARS-CoV-2. While numerous crystal structures of the SARS-CoV-2 main protease (SCM) and its inhibitors have been reported, they provide only static snapshots and fail to capture the dynamic nature of SCM/inhibitor interactions. Herein, we conducted molecular dynamics simulations for five SCM complexes: ritonavir (SCM/RTV), lopinavir (SCM/LPV), the identified inhibitor N3 (SCM/N3), the approved inhibitor ensitrelvir (SCM/ESV), and the approved drug nirmatrelvir (SCM/NMV). Additionally, we explored the potential for covalent bond formation in the N3 and NMV inhibitors through QM/MM calculations using Umbrella sampling. The results show that the binding site is highly flexible to fit those five different inhibitors and each compound has its unique binding mode at the same binding site. Moreover, the binding affinities of positive and negative inhibitors to SCM exhibit significant differences. By gaining insights into the dynamics, we can potentially elucidate why lopinavir/ritonavir, initially considered promising, failed to effectively treat COVID-19. Furthermore, understanding the mechanistic aspects of N3 and NMV inhibition on SCM not only contributes to rational drug discovery against COVID-19 but also aids future studies on the catalytic mechanisms of main proteases in other novel coronaviruses.Communicated by Ramaswamy H. Sarma.

Successful treatment of a case of COVID-19 pneumonia following kidney transplantation using paxlovid and tocilizumab

BACKGROUND

Since its initial detection in 2019, coronavirus disease 2019 (COVID-19) pneumonia has rapidly spread throughout the world in a global pandemic. However, reports of COVID-19 pneumonia among patients following kidney transplantation have been limited and no uniform treatment guidelines for these patients have yet to be established.

CASE SUMMARY

Here, we report the case of a 39-year-old patient recovering from kidney transplantation who contracted perioperative COVID-19 pneumonia that was successfully controlled with oral paxlovid and a single intravenous drip infusion of tocilizumab following the discontinuation of immunosuppressive drugs.

CONCLUSION

Given the rapid spread of severe acute respiratory syndrome coronavirus 2 infections, clinicians should be aware of the potential for more cases of COVID-19 among patients following kidney transplantation and be familiar with appropriate treatment options and likely clinical outcomes.

An interpreter ranking index-based MCDM technique for COVID-19 treatments under a bipolar fuzzy environment

The entire world is currently fighting the severe and dangerous pandemic COVID-19, which is causing bodily suffering and mental distress due to the rapidly increasing number of infected patients and deaths worldwide. Many COVID-19 treatments are going on in India, and some treatments are under development for these patients. But, treatment selection for the COVID-19 patients is challenging in the present situation. Through the multi-criteria decision-making technique, they can select the COVID-19 treatments easily. Therefore, we have developed an MCDM technique to select COVID-19 treatments in India. This paper invented the value and ambiguity of bipolar fuzzy (BF) numbers. Additionally, some fundamental theorems and properties of BF-numbers are studied. A novel positive and negative interpreter ranking index of BF numbers has been introduced. In the present day, most human decision-making relies heavily on bipolar fuzzy information. Hence, we developed an MCDM technique with bipolar fuzzy details. A comprehensive range of human decisions for selecting COVID-19 treatments is based on positive and negative double-sided or bipolar judgemental thinking. To select COVID-19 treatments in India, we have applied the proposed MCDM technique with BTrF information. Moreover, to demonstrate the applicability of our proposed MCDM method, we have considered a numerical example with BF data. Finally, we give the comparison study to show the effectiveness of our proposed MCDM method with other existing decision-making methods.

Exploring the knowledge, practices & determinants of antibiotic self-medication among bangladeshi university students in the era of COVID-19: A cross-sectional study

Self-medication with antibiotics is a growing public health concern. Antibiotics are easily accessible on requested from pharmacies throughout the majority of developing countries. The present study aimed to determine the prevalence of self-medication with antibiotics among university students in Bangladesh, as well as to evaluate their knowledge and practices related to antibiotics and its resistance. A structured questionnaire was administered to 1000 students over a month in January 2022 at three universities of Bangladesh. The results showed that 61.0% of the participants had self-medicated with antibiotics in the last six months. In regards to the participants' level of knowledge and practice, a significant proportion, 60.0% exhibited a substandard understanding of antibiotic resistance and appropriate antibiotic usage. Male students (61.7%) were found to self-medicate more often than female students (38.3%). The highest prevalence of self-medication was observed in the age group of 22-25 years (32.2%). The most common reasons for self-medication were previous experience with the illness (40.9%) and the belief that the illness was not serious (36.2%). The most common illnesses for which self-medication was practiced were fever (40.9%) and cough and cold (29.3%). During multivariate logistic regression analyses age, gender, maintaining diet chart, and habit of exercising regularly were found to be associated with the increased risk of self-medication with antibiotics. The study highlighted the critical need for targeted interventions to promote responsible antibiotic use, enhance knowledge about antibiotic resistance, and discourage self-medication among university students in Bangladesh. Addressing these factors would enable the government to mitigate risks associated with self-medication, prevent antibiotic resistance, and alleviate the burden on health and the economy at large.

Fully feature fusion based neural network for COVID-19 lesion segmentation in CT images

Coronavirus Disease 2019 (COVID-19) spreads around the world, seriously affecting people's health. Computed tomography (CT) images contain rich semantic information as an auxiliary diagnosis method. However, the automatic segmentation of COVID-19 lesions in CT images faces several challenges, including inconsistency in size and shape of the lesion, the high variability of the lesion, and the low contrast of pixel values between the lesion and normal tissue surrounding the lesion. Therefore, this paper proposes a Fully Feature Fusion Based Neural Network for COVID-19 Lesion Segmentation in CT Images (F3-Net). F3-Net uses an encoder-decoder architecture. In F3-Net, the Multiple Scale Module (MSM) can sense features of different scales, and Dense Path Module (DPM) is used to eliminate the semantic gap between features. The Attention Fusion Module (AFM) is the attention module, which can better fuse the multiple features. Furthermore, we proposed an improved loss function L o s s C o v i d - B C E that pays more attention to the lesions based on the prior knowledge of the distribution of COVID-19 lesions in the lungs. Finally, we verified the superior performance of F3-Net on a COVID-19 segmentation dataset, experiments demonstrate that the proposed model can segment COVID-19 lesions more accurately in CT images than benchmarks of state of the art.