Effectiveness of COVID-19 diagnosis and management tools: A review

Total severity score and age predict long-term hospitalization in COVID-19 pneumonia

Background

Severe COVID-19 pneumonia implies increased oxygen demands and length of hospitalization (LOS). We aimed to assess a possible correlation between LOS and COVID-19 patients' clinical laboratory data of admission, including the total severity score (TSS) from chest computed tomography (CT).

Methods

Data were assessed retrospectively at the General Hospital "Agios Pavlos" in Greece. Clinical laboratory data, TSS, and LOS were recorded.

Results

A total of 317 patients, 136 women and 181 men, with a mean age of 66.58 ± 16.02 years were studied. Significant comorbidities were hypertension (56.5%), dyslipidemia (33.8%), type 2 diabetes mellitus (22.7%), coronary heart disease (12.9%), underlying pulmonary disease (10.1%), and malignancy (4.4%). Inpatient time was related to age (p < 0.001), TSS (p < 0.001), time from symptom onset to hospitalization (p = 0.006), inhaled oxygen fraction (p < 0.001), fibrinogen (p = 0.024), d-dimers (p < 0.001), and C-reactive protein (p = 0.025), as well as a history of hypertension (p < 0.001) and type 2 diabetes mellitus (p < 0.008). The multivariate analysis showed a significant association of the LOS with age (p < 0.001) and TSS (p < 0.001) independent of the above-mentioned factors.

Conclusion

Early identification of disease severity using the TSS and patients' age could be useful for inpatient resource allocation and for maintaining vigilance for those requiring long-term hospitalizations.

Model exploration for discovering COVID-19 targeted traditional Chinese medicine

In terms of treatment, a particularly targeted drug is needed to combat the COVID-19 pandemic. Although there are currently no specific drugs for COVID-19, traditional Chinese medicine(TCM) is clearly effective. It is recommended that through data analysis and mining of TCM cases (expert experience) and population evidence (RCT and cohort studies), core prescriptions for various efficacy can be obtained. Starting from a multidimensional model of regulating immunity, improving inflammation, and protecting multiple organs, this paper constructs a multidimensional model of targeted drug discovery, integrating molecular, cellular, and animal efficacy evaluation. Through functional activity testing, biophysical detection of compound binding to target proteins, multidimensional pharmacodynamic evaluation systems of cells (Vero E6, Vero, Vero81, Huh7, and caca2) and animals (mice infected with the new coronavirus, rhesus macaques, and hamsters), the effectiveness of effective preparations was evaluated, and various efficacy effects including lung moisturizing, dehumidification and detoxification were obtained. Using modern technology, it is now possible to understand how the immune system is controlled, how inflammation is reduced, and how various organs are protected. Complete early drug characterization and finally obtain effective targeted TCM. This article provides a demonstration resource for the development of new drugs specifically for TCM.

Surveillance of Side Effects after Two Doses of COVID-19 Vaccines among Patients with Comorbid Conditions: A Sub-Cohort Analysis from Saudi Arabia

Background: Individuals with underlying chronic illnesses have demonstrated considerable hesitancy towards COVID-19 vaccines. These concerns are primarily attributed to their concerns over the safety profile. Real-world data on the safety profile among COVID-19 vaccinees with comorbid conditions are scarce. This study aimed to ascertain the side-effects profile after two doses of COVID-19 vaccines among chronic-disease patients. Methodology: A cross-sectional questionnaire-based study was conducted among faculty members with comorbid conditions at a public educational institute in Saudi Arabia. A 20-item questionnaire recorded the demographics and side effects after the two doses of COVID-19 vaccines. The frequency of side effects was recorded following each dose of vaccine, and the association of the side-effects score with the demographics was ascertained through appropriate statistics. Results: A total of 204 patients with at least one comorbid condition were included in this study. A total of 24 side effects were reported after the first dose and 22 after second dose of the COVID-19 vaccine. The incidence of at least one side effect was 88.7% and 95.1% after the first and second doses of the vaccine, respectively. The frequent side effects after the first dose were pain at the injection site (63.2%), fatigue (58.8%), fever (47.5%), muscle and joint pain (38.7%), and headache (36.3%). However, pain at the injection site (71.1%), muscle and joint pain (62.7%), headache (49.5%), fever (45.6%), and stress (33.3%) were frequent after the second dose. The average side-effects score was 4.41 ± 4.18 (median: 3, IQR: 1, 6) and 4.79 ± 3.54 (median 4, IQR: 2, 6) after the first and second dose, respectively. Female gender, diabetes mellitus, hypertension, hyperlipidemia, comorbidity > 2, family history of COVID-19, and the AstraZeneca vaccine were significantly associated with higher side-effect scores. Only 35.8% of study participants were satisfied with the safety of COVID-19 vaccines. Conclusions: Our analysis showed a high proportion of transient and short-lived side effects of Pfizer and AstraZeneca vaccines among individuals with chronic illnesses. However, the side-effects profile was comparable with the safety reports of phase 3 clinical trials of these vaccines. The frequency of side effects was found to be associated with certain demographics, necessitating the need for further investigations to establish a causal relationship. The current study’s findings will help instill confidence in the COVID-19 vaccines among people living with chronic conditions, overcome vaccine hesitancy, and increase vaccine coverage in this population.

Comparison of Chest CT and RT-PCR Assay for Indication of Disease Course of Coronavirus Disease 2019 (COVID-19) Pneumonia

BACKGROUND

COVID-19 patients' courses vary in length, indicating a variable prognosis. The disease duration revealed by different examination methods may differ.

OBJECTIVE

The study aims to compare the differences in the disease course of patients with COVID-19 by chest computed tomography (CT) and reverse-transcription polymerase chain reaction (RT-PCR) assay and explore the factors that affect the course of the illness.

METHODS

106 patients confirmed with COVID-19 were enrolled and divided into two groups (age <60 years and age ≥60 years). The clinical characteristics of the two groups were analyzed. The intervals from symptoms onset to initial positive time point (ISIP), symptoms onset to the initial negative time point (ISIN), and initial positive to initial negative time point (IIPN) indicated by chest CT and RTPCR assay were analyzed. Multiple regression analysis was performed to assess the correlations between independent factors and the intervals.

RESULTS

Chest CT showed an earlier positive time point, a later negative time point, and a longer disease duration than the RT-PCR assay (P<.001, respectively). Older patients over 60 years old showed a later negative time point and a longer disease duration by chest CT than younger patients (P<.01 vs. P<.05, respectively). The CT score and clinical grades of older patients were greater than those of younger patients (P<.001, respectively). Age and clinical grades were significantly correlated with the disease course shown by chest CT (P<.05, respectively), and CT score was positively correlated with the illness course shown by chest CT and RT-PCR assay (P<.01, respectively).

CONCLUSION

The disease course revealed by chest CT and RT-PCR assay was asynchronous. Chest CT showed a 17-day longer period compared to the RT-PCR assay. Older patients had a longer duration than younger ones. A prolonged course is predicted by increasing age, CT score, and clinical grades.

Discovery of the key active compounds in Citri Reticulatae Pericarpium (Citrus reticulata "Chachi") and their therapeutic potential for the treatment of COVID-19 based on comparative metabolomics and network pharmacology

Edible herbal medicines contain macro- and micronutrients and active metabolites that can take part in biochemical processes to help achieve or maintain a state of well-being. Citri Reticulatae Pericarpium (CRP) is an edible and medicinal herb used as a component of the traditional Chinese medicine (TCM) approach to treating COVID-19 in China. However, the material basis and related mechanistic research regarding this herb for the treatment of COVID-19 are still unclear. First, a wide-targeted UPLC-ESI-MS/MS-based comparative metabolomics analysis was conducted to screen for the active metabolites of CRP. Second, network pharmacology was used to uncover the initial linkages among these metabolites, their possible targets, and COVID-19. Each metabolite was then further studied via molecular docking with the identified potential SARS-CoV-2 targets 3CL hydrolase, host cell target angiotensin-converting enzyme II, spike protein, and RNA-dependent RNA polymerase. Finally, the most potential small molecule compound was verified by in vitro and in vivo experiments, and the mechanism of its treatment of COVID-19 was further explored. In total, 399 metabolites were identified and nine upregulated differential metabolites were screened out as potential key active metabolites, among which isorhamnetin have anti-inflammatory activity in vitro validation assays. In addition, the molecular docking results also showed that isorhamnetin had a good binding ability with the key targets of COVID-19. Furthermore, in vivo results showed that isorhamnetin could significantly reduced the lung pathological injury and inflammatory injury by regulating ATK1, EGFR, MAPK8, and MAPK14 to involve in TNF signaling pathway, PI3K-Akt signalling pathway, and T cell receptor signaling pathway. Our results indicated that isorhamnetin, as screened from CRP, may have great potential for use in the treatment of patients with COVID-19. This study has also demonstrated that comparative metabolomics combined with network pharmacology strategy could be used as an effective approach for discovering potential compounds in herbal medicines that are effective against COVID-19.

Association between the Angle of the Left Subclavian Artery and Procedural Time for Percutaneous Coronary Intervention in Patients with Acute Coronary Syndrome

Background

The effect of left subclavian artery tortuosity during percutaneous coronary intervention (PCI) in patients with acute coronary syndrome (ACS) remains unclear.

Methods

Of 245 ACS patients (from November 2019 and May 2021), 79 who underwent PCI via a left radial approach (LRA) were included. We measured the angle of the left subclavian artery in the coronal view on CT imaging as an indicator of the tortuosity and investigated the association between that angle and the clinical variables and procedural time.

Results

Patients with a left subclavian artery angle of a median of <70 degrees (severe tortuosity) were older (75.4 ± 11.7 vs. 62.9 ± 12.3 years, P < 0.001) and had a higher prevalence of female sex (42.1% vs. 14.6%, P=0.007), hypertension (94.7% vs. 75.6%, P=0.02), and subclavian artery calcification (73.7% vs. 34.2%, P < 0.001) than those with that ≥70 degrees. The left subclavian artery angle correlated negatively with the sheath cannulation to the first balloon time (ρ = -0.51, P < 0.001) and total procedural time (ρ = -0.32, P=0.004). A multiple linear regression analysis revealed that the natural log transformation of the sheath insertion to first balloon time was associated with a subclavian artery angle of <70 degrees (β = 0.45, P < 0.001).

Conclusion

Our study showed that lower left subclavian artery angles as a marker of the tortuosity via the LRA were strongly associated with a longer sheath insertion to balloon time and subsequent entire procedure time during the PCI.

Identification of hospitalized mortality of patients with COVID-19 by machine learning models based on blood inflammatory cytokines

Coronavirus disease 2019 (COVID-19) spread worldwide and presented a significant threat to people's health. Inappropriate disease assessment and treatment strategies bring a heavy burden on healthcare systems. Our study aimed to construct predictive models to assess patients with COVID-19 who may have poor prognoses early and accurately. This research performed a retrospective analysis on two cohorts of patients with COVID-19. Data from the Barcelona cohort were used as the training set, and data from the Rotterdam cohort were used as the validation set. Cox regression, logistic regression, and different machine learning methods including random forest (RF), support vector machine (SVM), and decision tree (DT) were performed to construct COVID-19 death prognostic models. Based on multiple clinical characteristics and blood inflammatory cytokines during the first day of hospitalization for the 138 patients with COVID-19, we constructed various models to predict the in-hospital mortality of patients with COVID-19. All the models showed outstanding performance in identifying high-risk patients with COVID-19. The accuracy of the logistic regression, RF, and DT models is 86.96, 80.43, and 85.51%, respectively. Advanced age and the abnormal expression of some inflammatory cytokines including IFN-α, IL-8, and IL-6 have been proven to be closely associated with the prognosis of patients with COVID-19. The models we developed can assist doctors in developing appropriate COVID-19 treatment strategies, including allocating limited medical resources more rationally and early intervention in high-risk groups.

Pregnancy and COVID-19

Evidence indicates that SARS-CoV-2 infection increases the likelihood of adverse pregnancy outcomes. Modifications in the circulatory, pulmonary, hormonal, and immunological pathways induced by pregnancy render pregnant women as a high-risk group. A growing body of research shows that SARS-CoV-2 infection during pregnancy is connected to a number of maternal complications, including pneumonia and intensive care unit (ICU) hospitalization. Miscarriages, stillbirth, preterm labor, as well as pre-eclampsia and intrauterine growth restriction are also among the most often documented fetal implications, particularly among expecting women who have significant COVID-19 symptoms, often affecting the timing and route of delivery. Thus, prevention of infection and pharmacological treatment options should aim to minimize the aforementioned risks and ameliorate maternal, obstetric and fetal/neonatal outcomes.

The relationship between cumulative dose of immunosuppressive agents and COVID-19-associated mucormycosis: A multicenter cross-sectional study

Background and Aims

Immunosuppressive therapy has a key role in developing coronavirus disease-2019 (COVID-19)-associated mucormycosis. In this study, we investigated the effect of the type and cumulative dose of immunosuppressive agents on COVID-19-associated mucormycosis.

Methods

We designed a descriptive cross-sectional study involving three COVID-19 hospitals in Iran. Clinical and demographic data were gathered from the medical records and checked by two independent researchers to minimize errors in data collection.

Results

Seventy-three patients were included in the study. The mean age of cases was 57.41 (SD = 12.64) and 43.8% were female. Among patients, 20.5% were admitted to the intensive care unit (ICU) during COVID-19. Furthermore, 17 patients (23.29%) had a history of diabetes mellitus. Sixty-nine patients (94.52%) had a history of receiving corticosteroids (dexamethasone) during treatment of COVID-19, and of those, five patients (6.85%) received Tocilizumab beside. The mean cumulative dose of corticosteroids prescribed was 185.22 mg (SD = 114.738). The average cumulative dosage of tocilizumab was 720 mg (SD = 178.89). All of the included patients received amphotericin B for mucormycosis treatment, and 42 survived (57.53%). Also, there was a significant relationship between hospitalization in ICU for COVID-19 and the mucormycosis outcome (p = 0.007). However, there weren't any significant associations between cumulative doses of immunosuppressive drugs and mucormycosis outcome (p = 0.52).

Conclusion

The prevalence of COVID-19-associated mucormycosis is increasing and should be considered in the treatment protocols of COVID-19. Controlling risk factors such as diabetes, malignancy and the administration of immunosuppressive agents based on recommended dosage in validated guidelines are ways to prevent mucormycosis.

A comparison of conventional and advanced electroanalytical methods to detect SARS-CoV-2 virus: A concise review

Respiratory viruses are a serious threat to human wellbeing that can cause pandemic disease. As a result, it is critical to identify virus in a timely, sensitive, and precise manner. The present novel coronavirus-2019 (COVID-19) disease outbreak has increased these concerns. The research of developing various methods for COVID-19 virus identification is one of the most rapidly growing research areas. This review article compares and addresses recent improvements in conventional and advanced electroanalytical approaches for detecting COVID-19 virus. The popular conventional methods such as polymerase chain reaction (PCR), loop mediated isothermal amplification (LAMP), serology test, and computed tomography (CT) scan with artificial intelligence require specialized equipment, hours of processing, and specially trained staff. Many researchers, on the other hand, focused on the invention and expansion of electrochemical and/or bio sensors to detect SARS-CoV-2, demonstrating that they could show a significant role in COVID-19 disease control. We attempted to meticulously summarize recent advancements, compare conventional and electroanalytical approaches, and ultimately discuss future prospective in the field. We hope that this review will be helpful to researchers who are interested in this interdisciplinary field and desire to develop more innovative virus detection methods.

AI-Supported Comprehensive Detection and Quantification of Biomarkers of Subclinical Widespread Diseases at Chest CT for Preventive Medicine

Automated image analysis plays an increasing role in radiology in detecting and quantifying image features outside of the perception of human eyes. Common AI-based approaches address a single medical problem, although patients often present with multiple interacting, frequently subclinical medical conditions. A holistic imaging diagnostics tool based on artificial intelligence (AI) has the potential of providing an overview of multi-system comorbidities within a single workflow. An interdisciplinary, multicentric team of medical experts and computer scientists designed a pipeline, comprising AI-based tools for the automated detection, quantification and characterization of the most common pulmonary, metabolic, cardiovascular and musculoskeletal comorbidities in chest computed tomography (CT). To provide a comprehensive evaluation of each patient, a multidimensional workflow was established with algorithms operating synchronously on a decentralized Joined Imaging Platform (JIP). The results of each patient are transferred to a dedicated database and summarized as a structured report with reference to available reference values and annotated sample images of detected pathologies. Hence, this tool allows for the comprehensive, large-scale analysis of imaging-biomarkers of comorbidities in chest CT, first in science and then in clinical routine. Moreover, this tool accommodates the quantitative analysis and classification of each pathology, providing integral diagnostic and prognostic value, and subsequently leading to improved preventive patient care and further possibilities for future studies.

Do COVID-19 CT features vary between patients from within and outside mainland China? Findings from a meta-analysis

BACKGROUND

Chest computerized tomography (CT) plays an important role in detecting patients with suspected coronavirus disease 2019 (COVID-19), however, there are no systematic summaries on whether the chest CT findings of patients within mainland China are applicable to those found in patients outside.

METHODS

Relevant studies were retrieved comprehensively by searching PubMed, Embase, and Cochrane Library databases before 15 April 2022. Quality assessment of diagnostic accuracy studies (QUADAS) was used to evaluate the quality of the included studies, which were divided into two groups according to whether they were in mainland China or outside. Data on diagnostic performance, unilateral or bilateral lung involvement, and typical chest CT imaging appearances were extracted, and then, meta-analyses were performed with R software to compare the CT features of COVID-19 pneumonia between patients from within and outside mainland China.

RESULTS

Of the 8,258 studies screened, 19 studies with 3,400 patients in mainland China and 14 studies with 554 outside mainland China were included. Overall, the risk of quality assessment and publication bias was low. The diagnostic value of chest CT is similar between patients from within and outside mainland China (93, 91%). The pooled incidence of unilateral lung involvement (15, 7%), the crazy-paving sign (31, 21%), mixed ground-glass opacities (GGO) and consolidations (51, 35%), air bronchogram (44, 25%), vascular engorgement (59, 33%), bronchial wall thickening (19, 12%), and septal thickening (39, 26%) in patients from mainland China were significantly higher than those from outside; however, the incidence rates of bilateral lung involvement (75, 84%), GGO (78, 87%), consolidations (45, 58%), nodules (12, 17%), and pleural effusion (9, 15%) were significantly lower.

CONCLUSION

Considering that the chest CT features of patients in mainland China may not reflect those of the patients abroad, radiologists and clinicians should be familiar with various CT presentations suggestive of COVID-19 in different regions.

Mathematical Models Supporting Control of COVID-19

Mathematical models have played an important role in the management of the coronavirus disease 2019 (COVID-19) pandemic. The aim of this review is to describe the use of COVID-19 mathematical models, their classification, and the advantages and disadvantages of different types of models. We conducted subject heading searches of PubMed and China National Knowledge Infrastructure with the terms "COVID-19," "Mathematical Statistical Model," "Model," "Modeling," "Agent-based Model," and "Ordinary Differential Equation Model" and classified and analyzed the scientific literature retrieved in the search. We categorized the models as data-driven or mechanism-driven. Data-driven models are mainly used for predicting epidemics, and have the advantage of rapid assessment of disease instances. However, their ability to determine transmission mechanisms is limited. Mechanism-driven models include ordinary differential equation (ODE) and agent-based models. ODE models are used to estimate transmissibility and evaluate impact of interventions. Although ODE models are good at determining pathogen transmission characteristics, they are less suitable for simulation of early epidemic stages and rely heavily on availability of first-hand field data. Agent-based models consider influences of individual differences, but they require large amounts of data and can take a long time to develop fully. Many COVID-19 mathematical modeling studies have been conducted, and these have been used for predicting trends, evaluating interventions, and calculating pathogen transmissibility. Successful infectious disease modeling requires comprehensive considerations of data, applications, and purposes.

Mortality predictors in patients with COVID-19 pneumonia: a machine learning approach using eXtreme Gradient Boosting model

Recently, global health has seen an increase in demand for assistance as a result of the COVID-19 pandemic. This has prompted many researchers to conduct different studies looking for variables that are associated with increased clinical risk, and find effective and safe treatments. Many of these studies have been limited by presenting small samples and a large data set. Using machine learning (ML) techniques we can detect parameters that help us to improve clinical diagnosis, since they are a system for the detection, prediction and treatment of complex data. ML techniques can be valuable for the study of COVID-19, especially because they can uncover complex patterns in large data sets. This retrospective study of 150 hospitalized adult COVID-19 patients, of which we established two groups, those who died were called Case group (n = 53) while the survivors were Control group (n = 98). For analysis, a supervised learning algorithm eXtreme Gradient Boosting (XGBoost) has been used due to its good response compared to other methods because it is highly efficient, flexible and portable. In this study, the response to different treatments has been evaluated and has made it possible to accurately predict which patients have higher mortality using artificial intelligence, obtaining better results compared to other ML methods.

Computed Tomographic Imaging Features of COVID-19 Pneumonia Caused by the Delta (B.1.617.2) and Omicron (B.1.1.529) Variant in a German Nested Cohort Pilot Study Group

Background: The aim of this study was to evaluate CT (computed tomography) imaging differences for the Delta and the Omicron variant in COVID-19 infection. Methods: The study population was derived from a retrospective study cohort investigating chest CT imaging patterns in vaccinated and nonvaccinated COVID-19 patients. CT imaging patterns of COVID-19 infection were evaluated by qualitative and semiquantitative scoring systems, as well as imaging pattern analysis. Results: A total of 60 patients (70.00% male, 62.53 ± 17.3 years, Delta: 43 patients, Omicron: 17 patients) were included. Qualitative scoring systems showed a significant correlation with virus variants; “typical appearance” and “very high” degrees of suspicion were detected more often in patients with Delta (RSNA: p = 0.003; CO-RADS: p = 0.002; COV-RADS: p = 0.001). Semiquantitative assessment of lung changes revealed a significant association with virus variants in univariate (Delta: 6.3 ± 3.5; Omicron: 3.12 ± 3.2; p = 0.002) and multivariate analysis. The vacuolar sign was significantly associated with the Delta variant (OR: 14.74, 95% CI: [2.32; 2094.7], p = 0.017). Conclusion: The Delta variant had significantly more extensive lung involvement and showed changes classified as “typical” more often than the Omicron variant, while the Omicron variant was more likely associated with CT findings such as “absence of pulmonary changes”. A significant correlation between the Delta variant and the vacuolar sign was observed.

Development and validation of chest CT-based imaging biomarkers for early stage COVID-19 screening

Coronavirus Disease 2019 (COVID-19) is currently a global pandemic, and early screening is one of the key factors for COVID-19 control and treatment. Here, we developed and validated chest CT-based imaging biomarkers for COVID-19 patient screening from two independent hospitals with 419 patients. We identified the vasculature-like signals from CT images and found that, compared to healthy and community acquired pneumonia (CAP) patients, COVID-19 patients display a significantly higher abundance of these signals. Furthermore, unsupervised feature learning led to the discovery of clinical-relevant imaging biomarkers from the vasculature-like signals for accurate and sensitive COVID-19 screening that have been double-blindly validated in an independent hospital (sensitivity: 0.941, specificity: 0.920, AUC: 0.971, accuracy 0.931, F1 score: 0.929). Our findings could open a new avenue to assist screening of COVID-19 patients.

Evaluation of a novel SARS-CoV-2 rapid antigenic test diagnostic value in respiratory samples; is the reported test accuracy similar to values in the real-world? A cross-sectional study

Background and Aims

Although reverse transcription-polymerase chain reaction (RT-PCR) assay was introduced as the gold standard to detect SARS-CoV-2, the method was known to be time-consuming besides the requirement for an equipped laboratory. This survey aims to investigate a novel SARS-CoV-2 antigen test as a diagnostic tool in COVID-19 patients to overcome these limitations in addition to evaluating COVID-19 population characteristics.

Methods

A retrospective cross-sectional study was carried out during the first semester of 2021, and about 1070 nasopharyngeal samples were collected to compare the E-Health Barakat Company SARS-CoV-2 antigen rapid test results with RT-PCR reports as the reference method.

Results

Totally 537 participants were included in this study for employing RT-PCR and the antigen test sequentially. The novel antigen rapid test sensitivity is considered 21.09% in the real world, though 81% in the manufacturer's instruction has been mentioned. Moreover, the most revealed manifestations were found respiratory symptoms and fatigue sensations.

Conclusion

This study is the first one on evaluating the SARS-CoV-2 antigen test in our country. Although the novel antigen assay was found quick and easy to perform, the test performance was very disappointing. The extensive false-negative results made it an inappropriate candidate for mass screening.

Chest CT Severity Score and Systemic Inflammatory Biomarkers as Predictors of the Need for Invasive Mechanical Ventilation and of COVID-19 Patients' Mortality

Background: Numerous tools, including inflammatory biomarkers and lung injury severity scores, have been evaluated as predictors of disease progression and the requirement for intensive therapy in COVID-19 patients. This study aims to verify the predictive role of inflammatory biomarkers [monocyte to lymphocyte ratio (MLR), neutrophil to lymphocyte ratio (NLR), systemic inflammatory index (SII), Systemic Inflammation Response Index (SIRI), Aggregate Index of Systemic Inflammation (AISI), and interleukin-6 (IL-6)] and the total system score (TSS) in the need for invasive mechanical ventilation (IMV) and mortality in COVID-19 patients. Methods: The present study was designed as an observational, analytical, retrospective cohort study and included all patients over 18 years of age with a diagnosis of COVID-19 pneumonia, confirmed through real time-polymerase chain reaction (RT-PCR) and radiological chest CT findings admitted to County Emergency Clinical Hospital of Targu-Mureș, Romania, and Modular Intensive Care Unit of UMFST “George Emil Palade” of Targu Mures, Romania between January 2021 and December 2021. Results: Non-Survivors patients were associated with higher age (p = 0.01), higher incidence of cardiac disease [atrial fibrillation (AF) p = 0.0008; chronic heart failure (CHF) p = 0.01], chronic kidney disease (CKD; p = 0.02), unvaccinated status (p = 0.001), and higher pulmonary parenchyma involvement (p < 0.0001). Multivariate analysis showed a high baseline value for MLR, NLR, SII, SIRI, AISI, IL-6, and TSS independent predictor of adverse outcomes for all recruited patients. Moreover, the presence of AF, CHF, CKD, and dyslipidemia were independent predictors of mortality. Furthermore, AF and dyslipidemia were independent predictors of IMV need. Conclusions: According to our findings, higher MLR, NLR, SII, SIRI, AISI, IL-6, and TSS values at admission strongly predict IMV requirement and mortality. Moreover, patients above 70 with AF, dyslipidemia, and unvaccinated status highly predicted IMV need and fatality. Likewise, CHF and CKD were independent predictors of increased mortality.

Automated deep learning-based segmentation of COVID-19 lesions from chest computed tomography images

Purpose

The novel coronavirus COVID-19, which spread globally in late December 2019, is a global health crisis. Chest computed tomography (CT) has played a pivotal role in providing useful information for clinicians to detect COVID-19. However, segmenting COVID-19-infected regions from chest CT results is challenging. Therefore, it is desirable to develop an efficient tool for automated segmentation of COVID-19 lesions using chest CT. Hence, we aimed to propose 2D deep-learning algorithms to automatically segment COVID-19-infected regions from chest CT slices and evaluate their performance.

Material and methods

Herein, 3 known deep learning networks: U-Net, U-Net++, and Res-Unet, were trained from scratch for automated segmenting of COVID-19 lesions using chest CT images. The dataset consists of 20 labelled COVID-19 chest CT volumes. A total of 2112 images were used. The dataset was split into 80% for training and validation and 20% for testing the proposed models. Segmentation performance was assessed using Dice similarity coefficient, average symmetric surface distance (ASSD), mean absolute error (MAE), sensitivity, specificity, and precision.

Results

All proposed models achieved good performance for COVID-19 lesion segmentation. Compared with Res-Unet, the U-Net and U-Net++ models provided better results, with a mean Dice value of 85.0%. Compared with all models, U-Net gained the highest segmentation performance, with 86.0% sensitivity and 2.22 mm ASSD. The U-Net model obtained 1%, 2%, and 0.66 mm improvement over the Res-Unet model in the Dice, sensitivity, and ASSD, respectively. Compared with Res-Unet, U-Net++ achieved 1%, 2%, 0.1 mm, and 0.23 mm improvement in the Dice, sensitivity, ASSD, and MAE, respectively.

Conclusions

Our data indicated that the proposed models achieve an average Dice value greater than 84.0%. Two-dimensional deep learning models were able to accurately segment COVID-19 lesions from chest CT images, assisting the radiologists in faster screening and quantification of the lesion regions for further treatment. Nevertheless, further studies will be required to evaluate the clinical performance and robustness of the proposed models for COVID-19 semantic segmentation.

Dynamic change of COVID-19 lung infection evaluated using co-registration of serial chest CT images

Purpose

To evaluate the volumetric change of COVID-19 lesions in the lung of patients receiving serial CT imaging for monitoring the evolution of the disease and the response to treatment.

Materials and methods

A total of 48 patients, 28 males and 20 females, who were confirmed to have COVID-19 infection and received chest CT examination, were identified. The age range was 21-93 years old, with a mean of 54 ± 18 years. Of them, 33 patients received the first follow-up (F/U) scan, 29 patients received the second F/U scan, and 11 patients received the third F/U scan. The lesion region of interest (ROI) was manually outlined. A two-step registration method, first using the Affine alignment, followed by the non-rigid Demons algorithm, was developed to match the lung areas on the baseline and F/U images. The baseline lesion ROI was mapped to the F/U images using the obtained geometric transformation matrix, and the radiologist outlined the lesion ROI on F/U CT again.

Results

The median (interquartile range) lesion volume (cm³) was 30.9 (83.1) at baseline CT exam, 18.3 (43.9) at first F/U, 7.6 (18.9) at second F/U, and 0.6 (19.1) at third F/U, which showed a significant trend of decrease with time. The two-step registration could significantly decrease the mean squared error (MSE) between baseline and F/U images with p < 0.001. The method could match the lung areas and the large vessels inside the lung. When using the mapped baseline ROIs as references, the second-look ROI drawing showed a significantly increased volume, p < 0.05, presumably due to the consideration of all the infected areas at baseline.

Conclusion

The results suggest that the registration method can be applied to assist in the evaluation of longitudinal changes of COVID-19 lesions on chest CT.

Comparative study of the usefulness of risk score assessment in the early stages of COVID ‐19 affected pregnancies: Omicron variant versus previous variants

Aim

To evaluate the utility of the risk score in assessing the current status and prognosis of COVID‐19 in pregnancy.

Methods

Seventy‐seven cases affected before the Omicron variant epidemic and 50 pregnant cases affected by the Omicron variant were included. The risk score consists of maternal background, current condition, and examination findings. We determined the risk score in the early stages of disease onset.

Results

There were no significant differences in the maternal or gestational ages between the groups. The risk score was significantly lower in the After‐Group patients (those affected during the Omicron epoch), while 14.3% of the Before‐Group patients (those affected during the pre‐Delta and Delta epochs), experienced a worsening of disease after the visit to the center, whereas none of the After‐Group patients did. The Before Group's frequency of risk score items was higher among the two groups for “fever for ≥48 h,” “mild pneumonia image,” and “blood tests,” whereas “disease onset 14 days after the second vaccination” was increased in After Group. The blood test parameters for platelet count, C‐reactive protein, and D‐dimer levels were not significantly different between the groups.

Conclusions

The risk score system appeared superior in detecting deteriorating cases. There were no cases of post‐illness deterioration in the After‐Group, suggesting that cases of the Omicron variant in pregnancy may have had a less severe course compared to that of previous variants. However, there was no significant difference between the groups in terms of a specific blood test evaluation, suggesting the need for a combined evaluation of cases affected during pregnancy.

Correlation of Lung Damage on CT Scan with Laboratory Inflammatory Markers in COVID-19 Patients: A Single-Center Study from Romania

(1) Background: This study aims to evaluate the association of CRP, NLR, IL-6, and Procalcitonin with lung damage observed on CT scans; (2) Methods: A cross-sectional study was performed among 106 COVID-19 patients hospitalized in Timisoara Municipal Emergency Hospital. Chest CT and laboratory analysis were performed in all patients. The rank Spearmen correlation was used to assess the association between inflammatory markers and lung involvement. In addition, ROC curve analysis was used to determine the accuracy of inflammatory markers in the diagnosis of severe lung damage; (3) Results: CRP, NLR, and IL-6 were significantly positively correlated with lung damage. All inflammatory markers had good accuracy for diagnosis of severe lung involvement. Moreover, IL-6 has the highest AUC- ROC curve; (4) Conclusions: The inflammatory markers are associated with lung damage and can be used to evaluate COVID-19 severity.

Detection of stage of lung changes in COVID-19 disease based on CT images: a radiomics approach

The aim of this study is to classify patients suspected from COVID-19 to five stages as normal, early, progressive, peak, and absorption stages using radiomics approach based on lung computed tomography images. Lung CT scans of 683 people were evaluated. A set of statistical texture features was extracted from each CT image. The people were classified using the random forest algorithm as an ensemble method based on the decision trees outputs to five stages of COVID-19 disease. Proposed method attains the highest result with an accuracy of 93.55% (96.25% in normal, 74.39% in early, 100% in progressive, 82.19% in peak, and 96% in absorption stage) compared to the other three common classifiers. Radiomics method can be used for the classification of the stage of COVID-19 disease with good accuracy to help decide the length of time required to hospitalize patients, determine the type of treatment process required for patients in each category, and reduce the cost of care and treatment for hospitalized individuals.

Clinical and Genetic Characteristics of Coronaviruses with Particular Emphasis on SARS-CoV-2 Virus

The rapidly spreading Coronavirus Disease 2019 (COVID-19) pandemic has led to a global health crisis and has left a deep mark on society, culture, and the global economy. Despite considerable efforts made to contain the disease, SARS-CoV-2 still poses a threat on a global scale. The current epidemiological situation caused an urgent need to understand the basic mechanisms of the virus transmission and COVID-19 severe course. This review summarizes current knowledge on clinical courses, diagnostics, treatment, and prevention of COVID-19. Moreover, we have included the latest research results on the genetic characterization of SARS-CoV-2 and genetic determinants of susceptibility and severity to infection.

Prevalence and Predictors of Persistent Symptoms After Clearance of SARS-CoV-2 Infection: A Multicenter Study from Egypt

BACKGROUND AND AIM

Little is known about the persistence of symptoms after clearance of SARS-CoV-2 infection. Our study aimed to assess persistent symptoms in COVID-19 patients after clearance of SARS-CoV-2 infection.

METHODOLOGY

A multi-center survey was conducted on first wave COVID-19 patients with confirmed SARS-CoV-2 infection. Sociodemographic and clinical characteristics, including presenting symptoms and persistent symptoms after viral clearance and possible factors contributing to persistence of such symptoms, were collected using an online multicomponent questionnaire. Descriptive and inferential statistical analysis was performed to detect the most persisting symptoms and factors contributing to their persistence.

RESULTS

Overall, 538 patients were enrolled. Mean age was 41.17 (±SD 14.84), 54.1% were males, and 18.6% were smokers. Hypertension and diabetes were the most reported co-morbidities. Mild symptoms were reported in 61.3% of patients, 51.3% were admitted to hospital and 6.5% were admitted to the intensive care unit. Our study identified 49 types of persisting symptoms. Fatigue (59.1%), sense of fever (46.5%), anorexia (24.3%) and diarrhea (24.3%) were the most commonly reported persisting symptoms followed by loss of taste and smell (22.3%), headache (21.4%), cough (20.8) and dyspnea (21%). The use of hydroxychloroquine, azithromycin and multivitamins were significantly associated with persistence of symptoms (OR = 8.03, 8.89 and 10.12, respectively).

CONCLUSION

Our study revealed that in COVID-19 recovered patients, many patients reported persistence of at least one symptom, particularly fatigue and sense of fever. Follow-up of patients after discharge from hospital is recommended until complete resolution of symptoms.

Impact of COVID-19 In-hospital Mortality in Chagas Disease Patients

The COVID-19 virus infection caused by the new SARS-CoV-2 was first identified in Rio de Janeiro (RJ), Brazil, in March 2020. Until the end of 2021, 504,399 COVID-19 cases were confirmed in RJ, and the total death toll reached 68,347. The Evandro Chagas National Institute of Infectious Diseases from Oswaldo Cruz Foundation (INI-Fiocruz) is a referral center for treatment and research of several infectious diseases, including COVID-19 and Chagas disease (CD). The present study aimed to evaluate the impact of COVID-19 on in-hospital mortality of patients with CD during the COVID-19 pandemic period. This observational, retrospective, longitudinal study evaluated all patients with CD hospitalized at INI-Fiocruz from May 1, 2020, to November 30, 2021. One hundred ten hospitalizations from 81 patients with CD (58% women; 68 ± 11 years) were evaluated. Death was the study's main outcome, which occurred in 20 cases. The mixed-effects logistic regression was performed with the following variables to test whether patients admitted to the hospital with a COVID-19 diagnosis would be more likely to die than those admitted with other diagnoses: admission diagnosis, sex, age, COVID-19 vaccination status, CD clinical classification, and the number of comorbidities. Results from multiple logistic regression analysis showed a higher risk of in-hospital mortality in patients diagnosed with COVID-19 (OR 6.37; 95% CI 1.78–22.86) compared to other causes of admissions. In conclusion, COVID-19 infection had a significant impact on the mortality risk of INI-Fiocruz CD patients, accounting for one-third of deaths overall. COVID-19 presented the highest percentage of death significantly higher than those admitted due to other causes during the COVID-19 pandemic.

An Intelligent ECG-Based Tool for Diagnosing COVID-19 via Ensemble Deep Learning Techniques

Diagnosing COVID-19 accurately and rapidly is vital to control its quick spread, lessen lockdown restrictions, and decrease the workload on healthcare structures. The present tools to detect COVID-19 experience numerous shortcomings. Therefore, novel diagnostic tools are to be examined to enhance diagnostic accuracy and avoid the limitations of these tools. Earlier studies indicated multiple structures of cardiovascular alterations in COVID-19 cases which motivated the realization of using ECG data as a tool for diagnosing the novel coronavirus. This study introduced a novel automated diagnostic tool based on ECG data to diagnose COVID-19. The introduced tool utilizes ten deep learning (DL) models of various architectures. It obtains significant features from the last fully connected layer of each DL model and then combines them. Afterward, the tool presents a hybrid feature selection based on the chi-square test and sequential search to select significant features. Finally, it employs several machine learning classifiers to perform two classification levels. A binary level to differentiate between normal and COVID-19 cases, and a multiclass to discriminate COVID-19 cases from normal and other cardiac complications. The proposed tool reached an accuracy of 98.2% and 91.6% for binary and multiclass levels, respectively. This performance indicates that the ECG could be used as an alternative means of diagnosis of COVID-19.

Quantitative inspiratory-expiratory chest CT findings in COVID-19 survivors at the 6-month follow-up

We evaluated pulmonary sequelae in COVID-19 survivors by quantitative inspiratory-expiratory chest CT (QCT) and explored abnormal pulmonary diffusion risk factors at the 6-month follow-up. This retrospective study enrolled 205 COVID-19 survivors with baseline CT data and QCT scans at 6-month follow-up. Patients without follow-up pulmonary function tests were excluded. All subjects were divided into group 1 (carbon monoxide diffusion capacity [DL_CO] < 80% predicted, n = 88) and group 2 (DL_CO ≥ 80% predicted, n = 117). Clinical characteristics and lung radiological changes were recorded. Semiquantitative total CT score (0-25) was calculated by adding five lobes scores (0-5) according to the range of lesion involvement (0: no involvement; 1: < 5%; 2: 5-25%; 3: 26-50%; 4: 51-75%; 5: > 75%). Data was analyzed by two-sample t-test, Spearman test, etc. 29% survivors showed air trapping by follow-up QCT. Semiquantitative CT score and QCT parameter of air trapping in group 1 were significantly greater than group 2 (p < 0.001). Decreased DL_CO was negatively correlated with the follow-up CT score for ground-glass opacity (r = - 0.246, p = 0.003), reticulation (r = - 0.206, p = 0.002), air trapping (r = - 0.220, p = 0.002) and relative lung volume changes (r = - 0.265, p = 0.001). COVID-19 survivors with lung diffusion deficits at 6-month follow-up tended to develop air trapping, possibly due to small-airway impairment.

Surfactant protein D: A useful biomarker for distinguishing COVID‐19 pneumonia from COVID‐19 pneumonia‐like diseases

Introduction

Computed tomography is useful for the diagnosis of coronavirus disease (COVID‐19) pneumonia. However, many types of interstitial lung diseases and even bacterial pneumonia can show abnormal chest shadows that are indistinguishable from those observed in COVID‐19 pneumonia. Thus, it is necessary to identify useful biomarkers that can efficiently distinguish COVID‐19 pneumonia from COVID‐19 pneumonia‐like diseases. Herein, we investigated the usefulness of serum Krebs von den Lungen 6 (KL‐6) and surfactant protein D (SP‐D) for identifying patients with COVID‐19 pneumonia among patients with abnormal chest shadows consistent with COVID‐19 pneumonia.

Method

This was a retrospective cohort study of consecutive patients who underwent evaluation of serum KL‐6 and SP‐D at a single center from February 2019 to December 2020. A total of 54 patients with COVID‐19 pneumonia and 65 patients with COVID‐19 pneumonia‐like diseases were enrolled in this study from the source population. Serum KL‐6 and SP‐D levels in both groups were analyzed.

Result

The serum levels of KL‐6 and SP‐D in patients with COVID‐19 pneumonia were significantly lower than those in patients with COVID‐19 pneumonia‐like disease (median [interquartile range]: 208.5 [157.5–368.5] U/ml vs. 430 [284.5–768.5] U/ml, p < 0.0001 and 24.7 [8.6–51.0] ng/ml vs. 141 [63.7–243.5] ng/ml, p < 0.0001, respectively). According to receiver operating characteristic (ROC) analysis, the areas under the ROC curves (95% confidence intervals) of serum KL‐6 and SP‐D levels for distinguishing COVID‐19 pneumonia from COVID‐19 pneumonia‐like diseases were 0.761 (0.675–0.847) and 0.874 (0.812–0.936), respectively. The area under the ROC curve of serum SP‐D was significantly larger than that of serum KL‐6 (p = 0.0213), suggesting that serum SP‐D can more efficiently distinguish COVID‐19 pneumonia from COVID‐19 pneumonia‐like diseases.

Conclusion

Serum SP‐D is a promising biomarker for distinguishing COVID‐19 pneumonia from COVID‐19 pneumonia‐like diseases. Serum SP‐D can be useful for the management of patients with abnormal chest shadow mimicking COVID‐19 pneumonia.

Circadian rhythm disorder and anxiety as mental health complications in post-COVID-19

In 2020, the world gained dramatic experience of the development of the 2019 coronavirus disease pandemic (COVID-19) caused by severe acute respiratory syndrome 2 (SARS-CoV-2). Recent researches notice an increasing prevalence of anxiety and circadian rhythm disorders during COVID-19 pandemic. The aim of the study was describing clinical features of circadian rhythm disorders and the level of anxiety in persons who have had COVID-19. We have conducted a cohort retrospective study that included 278 patients who were divided into 2 study groups according to medical history: group 1 includes patients with a history of COVID-19; group 2 consists of patients who did not have clinically confirmed COVID-19 and are therefore considered not to have had this disease. To objectify circadian rhythm disorders, they were verified in accordance with the criteria of the International Classification of Sleep Disorders-3. The level of anxiety was assessed by the State-Trait Anxiety Inventory. The most common circadian rhythm disorders were sleep phase shifts. We found that COVID-19 in the anamnesis caused a greater predisposition of patients to the development of circadian rhythm disorders, in particular delayed sleep phase disorder. In addition, it was found that after COVID-19 patients have increased levels of both trait and state anxiety. In our study, it was the first time that relationships between post-COVID-19 anxiety and circadian rhythm disorders had been indicated. Circadian rhythm disorders are associated with increased trait and state anxiety, which may indicate additional ways to correct post-COVID mental disorders and their comorbidity with sleep disorders.

Características clínicas y resultado materno perinatal en mujeres con diagnóstico confirmado por COVID-19 en un hospital de Perú. Estudio de cohorte retrospectivo

Objetivos:

describir las características clínicas, sociodemográficas y la frecuencia de complicaciones maternas y perinatales en mujeres gestantes con diagnóstico confirmado de COVID-19 atendidas en un hospital de alta complejidad en Perú.

Materiales y métodos:

estudio de cohorte retrospectivo descriptivo. Se incluyeron mujeres con 20 semanas o más de gestación con diagnóstico de infección por COVID-19 atendidas en un hospital de referencia nacional del Seguro Social del Perú entre marzo y diciembre del 2020; se excluyeron mujeres cuya gestación no culminó en la institución participante. Las variables medidas fueron: características sociodemográficas y obstétricas de la gestante, nexo epidemiológico, severidad de la infección por COVID-19, síntomas y datos de laboratorio, morbi-mortalidad materna, presencia de anticuerpos en el recién nacido, peso, adaptación y mortalidad perinatal. El análisis fue descriptivo. El protocolo fue aprobado por el comité de ética en investigación del Instituto de Evaluación de Tecnologías en Salud e Investigación de EsSalud.

Resultados:

los criterios de inclusión y exclusión fueron cumplidos por 322 mujeres. La población de estudio se caracterizó por ser predominantemente mujeres menores de 35 años, con educación superior. El 95% de las gestantes presentó síntomas leves o imperceptibles. Los síntomas predominantes fueron fiebre (85%), tos (52%) y cefalea (18%); se documentó leucocitosis (31%), linfopenia (24%) y trombocitopenia (5%). Se registraron 2 muertes maternas (0,6%) y 22 (7,2%) defunciones perinatales. El 0,9% de los neonatos exhibieron una prueba reactiva positiva para COVID-19.

Conclusiones:

durante la gestación, la infección producida por el SARS-CoV-2 suele ser asintomática o leve. En las gestantes con infección moderada y severa se presentaron más frecuentemente complicaciones maternas y perinatales. Se requieren más estudios que analicen el impacto materno fetal de la infección por COVID-19 durante la gestación en la región.

Sustainable materials and COVID-19 detection biosensor: A brief review

COVID-19 is the current global problem. Billions of infected cases due to the pandemic cause an emergency requirement to contain the pandemic. A basic concept to manage the outbreak is an early diagnosis and prompt treatment. To diagnose COVID-19, the new biosensors become new interventions that are hopeful to help effective diagnosis. In clinical material science, the issues on materials of COVID-19 detection biosensor is very interesting. In this brief review, the authors summarize and discuss on sustainable materials and COVID-19 detection biosensor. The paper, cellulose and graphene - based materials are specifically focused and biosensors for RNA sensing, antigenic determination and immune response detection are covered in this short article.

Truncating fined-tuned vision-based models to lightweight deployable diagnostic tools for SARS-CoV-2 infected chest X-rays and CT-scans

In such a brief period, the recent coronavirus (COVID-19) already infected large populations worldwide. Diagnosing an infected individual requires a Real-Time Polymerase Chain Reaction (RT-PCR) test, which can become expensive and limited in most developing countries, making them rely on alternatives like Chest X-Rays (CXR) or Computerized Tomography (CT) scans. However, results from these imaging approaches radiated confusion for medical experts due to their similarities with other diseases like pneumonia. Other solutions based on Deep Convolutional Neural Network (DCNN) recently improved and automated the diagnosis of COVID-19 from CXRs and CT scans. However, upon examination, most proposed studies focused primarily on accuracy rather than deployment and reproduction, which may cause them to become difficult to reproduce and implement in locations with inadequate computing resources. Therefore, instead of focusing only on accuracy, this work investigated the effects of parameter reduction through a proposed truncation method and analyzed its effects. Various DCNNs had their architectures truncated, which retained only their initial core block, reducing their parameter sizes to <1 M. Once trained and validated, findings have shown that a DCNN with robust layer aggregations like the InceptionResNetV2 had less vulnerability to the adverse effects of the proposed truncation. The results also showed that from its full-length size of 55 M with 98.67% accuracy, the proposed truncation reduced its parameters to only 441 K and still attained an accuracy of 97.41%, outperforming other studies based on its size to performance ratio.

A simple risk score for mortality including the PCR Ct value upon admission in patients hospitalized due to COVID-19

Purpose

To develop a simple score for the outcomes from COVID-19 that integrates information obtained at the time of admission including the Ct value (cycle threshold) for SARS-CoV-2.

Methods

Patients with COVID-19 hospitalized from February 1st to May 31st 2021 in RoMed hospitals, Germany, were included. Clinical and laboratory parameters upon admission were recorded and patients followed until discharge or death. Logistic regression analysis was used to determine predictors of outcomes. Regression coefficients were used to develop a risk score for death.

Results

Of 289 patients (46% female, median age 66 years), 29% underwent high-flow nasal oxygen (HFNO) therapy, 28% were admitted to the Intensive Care Unit (ICU, 51% put on invasive ventilation, IV), and 15% died. Age > 70 years, oxygen saturation ≤ 90%, oxygen supply upon admission, eGFR ≤ 60 ml/min and Ct value ≤ 26 were significant (p < 0.05 each) predictors for death, to which 2, 2, 1, 1 and 2 score points, respectively, could be attributed. Sum scores of ≥ 4 or ≥ 5 points were associated with a sensitivity of 95.0% or 82.5%, and a specificity of 72.5% or 81.7% regarding death. The high predictive value of the score was confirmed using data obtained between December 15th 2020 and January 31st 2021 (n = 215).

Conclusion

In COVID-19 patients, a simple scoring system based on data available shortly after hospital admission including the Ct value had a high predictive value for death. The score may also be useful to estimate the likelihood for required interventions at an early stage.

Supplementary Information

The online version contains supplementary material available at 10.1007/s15010-022-01783-1.

Pixel-based analysis of pulmonary changes on CT lung images due to COVID-19 pneumonia

Objectives: Computed tomography (CT) plays a complementary role in the diagnosis of the pneumonia-burden of COVID-19 disease. However, the low contrast of areas of inflammation on CT images, areas of infection are difficult to identify. The purpose of this study is to develop a post-image-processing method for quantitative analysis of COVID-19 pneumonia-related changes in CT attenuation values using a pixel-based analysis rather than more commonly used clustered focal pneumonia volumes. The COVID-19 pneumonia burden is determined by experienced radiologists in the clinic. Previous AI software was developed for the measurement of COVID-19 lesions based on the extraction of local pneumonia features. In this respect, changes in the pixel levels beyond the clusters may be overlooked by deep learning algorithms. The proposed technique focuses on the quantitative measurement of COVID-19 related pneumonia over the entire lung in pixel-by-pixel fashion rather than only clustered focal pneumonia volumes.

Material and Methods: Fifty COVID-19 and 50 age-matched negative control patients were analyzed using the proposed technique and commercially available artificial intelligence (AI) software. The %pneumonia was calculated using the relative volume of parenchymal pixels within an empirically defined CT density range, excluding pulmonary airways, vessels, and fissures. One-way ANOVA analysis was used to investigate the statistical difference between lobar and whole lung %pneumonia in the negative control and COVID-19 cohorts.

Results: The threshold of high-and-low CT attenuation values related to pneumonia caused by COVID-19 were found to be between ₋642.4 HU and 143 HU. The %pneumonia of the whole lung, left upper, and lower lobes were 8.1 ± 4.4%, 6.1 ± 4.5, and 11.3 ± 7.3% for the COVID-19 cohort, respectively, and statistically different (P < 0.01). Additionally, the pixel-based methods correlate well with existing AI methods and are approximately four times more sensitive to pneumonia particularly at the upper lobes compared with commercial software in COVID-19 patients (P < 0.01).

Conclusion: Pixel-by-pixel analysis can accurately assess pneumonia in COVID-19 patients with CT. Pixel-based techniques produce more sensitive results than AI techniques. Using the proposed novel technique, %pneumonia could be quantitatively calculated not only in the clusters but also in the whole lung with an improved sensitivity by a factor of four compared to AI-based analysis. More significantly, pixel-by-pixel analysis was more sensitive to the upper lobe pneumonia, while AI-based analysis overlooked the upper lung pneumonia region. In the future, this technique can be used to investigate the efficiency of vaccines and drugs and post COVID-19 effects.

Severe Thrombocytopenia as a Manifestation of COVID-19 Infection

Clinical manifestations of COVID-19 infection can range from an asymptomatic clinical form to acute respiratory distress depending on the virus gateway, viral load, host immunity, and existing comorbidities. Some patients with COVID-19 infection can present hematological changes depending on the patient’s immune response and the severity of the infection. We present two different manifestations of thrombotic disorders related to COVID-19: one severe form of immune thrombocytopenia in a young woman with no comorbidities and a severe form of thrombocytopenia along with disseminated intravascular coagulation and acute urinary obstructive disease. Interestingly, both patients presented no signs of COVID-19 pneumonia. Failure to diagnose thrombocytopenia rapidly may lead to severe complications. Management with immunosuppressive corticosteroids in high doses should carefully balance the risk of bleeding versus deterioration due to infection.

The application research of AI image recognition and processing technology in the early diagnosis of the COVID-19

Background

This study intends to establish a combined prediction model that integrates the clinical symptoms,the lung lesion volume, and the radiomics features of patients with COVID-19, resulting in a new model to predict the severity of COVID-19.

Methods

The clinical data of 386 patients with COVID-19 at several hospitals, as well as images of certain patients during their hospitalization, were collected retrospectively to create a database of patients with COVID-19 pneumonia. The contour of lungs and lesion locations may be retrieved from CT scans using a CT-image-based quantitative discrimination and trend analysis method for COVID-19 and the Mask R-CNN deep neural network model to create 3D data of lung lesions. The quantitative COVID-19 factors were then determined, on which the diagnosis of the development of the patients' symptoms could be established. Then, using an artificial neural network, a prediction model of the severity of COVID-19 was constructed by combining characteristic imaging features on CT slices with clinical factors. ANN neural network was used for training, and tenfold cross-validation was used to verify the prediction model. The diagnostic performance of this model is verified by the receiver operating characteristic (ROC) curve.

Results

CT radiomics features extraction and analysis based on a deep neural network can detect COVID-19 patients with an 86% sensitivity and an 85% specificity. According to the ROC curve, the constructed severity prediction model indicates that the AUC of patients with severe COVID-19 is 0.761, with sensitivity and specificity of 79.1% and 73.1%, respectively.

Conclusions

The combined prediction model for severe COVID-19 pneumonia, which is based on deep learning and integrates clinical aspects, pulmonary lesion volume, and radiomics features of patients, has a remarkable differential ability for predicting the course of disease in COVID-19 patients. This may assist in the early prevention of severe COVID-19 symptoms.

Evaluation of chest CT scan finding in the patients with acute respiratory symptoms following positive results of RT-PCR-COVID19

BACKGROUND

Reverse transcription-polymerase chain reaction (RT-PCR) is a standard technique for diagnosing coronavirus disease 2019 (COVID-19). The parameters for the diagnosis of COVID-19 included the history of exposure to positive COVID-19 patients, clinical signs and symptoms related to the disease, inflammation factors in the blood test or positive antigen-antibody test, and chest computed tomography (CT) findings. The current study evaluated the chest CT scan findings in patients with respiratory problems following positive RT-PCR of COVID 19.

MATERIALS AND METHODS

This cross-sectional study was performed on 120 patients referred to Ali Ibn-Abi Talib Hospital in Rafsanjan, Kerman Province, Iran, with respiratory symptoms between Dec-2019 to Dec-2020. Two radiologists reviewed the chest CT scans of these patients using the checklist that included parameters such as the types of involvement (consolidation/grand-glass/crazy paving, etc.) and the patterns of involvement (central/peripheral), and the pleural findings.

RESULTS

The CT scan was conducted in 107 patients with a typical condition and 11 patients with an atypical form of the disease. The frequency of the typical CT image of COVID-19 in the male group was significantly higher than that in the female group (P=0.004). The frequency of reverse halo sign, septal thickening, cardiomegaly, and crazy paving was significantly higher in males than in females (P≤0.05). Also, there was a significant difference between age groups based on the number of involved lobes (P=0.04).

CONCLUSION

Chest CT scan is an important diagnostic method for COVID 19 with high sensitivity. The parameters in the CT scan are beneficial for the diagnosis of COVID 19. In addition, some characters in CT scans in the male gender are more specific.

Discovering temporal scientometric knowledge in COVID-19 scholarly production

The mapping and analysis of scientific knowledge makes it possible to identify the dynamics and/or growth of a particular field of research or to support strategic decisions related to different research entities, based on bibliometric and/or scientometric indicators. However, with the exponential growth of scientific production, a systematic and data-oriented approach to the analysis of this large set of productions becomes increasingly essential. Thus, in this work, a data-oriented methodology was proposed, combining Data Analysis, Machine Learning and Complex Network Analysis techniques, and Data Version Control (DVC) tool, for the extraction of implicit knowledge in scientific production bases. In addition, the approach was validated through a case study in a COVID-19 manuscripts dataset, which had 199,895 articles published on arXiv, bioRxiv, medRxiv, PubMed and Scopus databases. The results suggest the feasibility of the proposed methodology, indicating the most active countries and the most explored themes in each period of the pandemic. Therefore, this study has the potential to instrument and expand strategic decisions by the scientific community, aiming at extracting knowledge that supports the fight against the COVID-19 pandemic.

Isolation and Identification of a Rare Spike Gene Double-Deletion SARS-CoV-2 Variant From the Patient With High Cycle Threshold Value

Coronavirus disease 2019 (COVID-19) is an emerging life-threatening pulmonary disease caused by infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which originated in Wuhan, Hubei Province, China, in December 2019. COVID-19 develops after close contact via inhalation of respiratory droplets containing SARS-CoV-2 during talking, coughing, or sneezing by asymptomatic, presymptomatic, and symptomatic carriers. This virus evolved over time, and numerous genetic variants have been reported to have increased disease severity, mortality, and transmissibility. Variants have also developed resistance to antivirals and vaccination and can escape the immune response of humans. Reverse transcription polymerase chain reaction (RT-PCR) is the method of choice among diagnostic techniques, including nucleic acid amplification tests (NAATs), serological tests, and diagnostic imaging, such as computed tomography (CT). The limitation of RT-PCR is that it cannot distinguish fragmented RNA genomes from live transmissible viruses. Thus, SARS-CoV-2 isolation by using cell culture has been developed and makes important contributions in the field of diagnosis, development of antivirals, vaccines, and SARS-CoV-2 virology research. In this research, two SARS-CoV-2 strains were isolated from four RT-PCR-positive nasopharyngeal swabs using VERO E6 cell culture. One isolate was cultured successfully with a blind passage on day 3 post inoculation from a swab with a Ct > 35, while the cells did not develop cytopathic effects without a blind passage until day 14 post inoculation. Our results indicated that infectious SARS-CoV-2 virus particles existed, even with a Ct > 35. Cultivable viruses could provide additional consideration for releasing the patient from quarantine. The results of the whole genome sequencing and bioinformatic analysis suggested that these two isolates contain a spike 68-76del+spike 675-679del double-deletion variation. The double deletion was confirmed by amplification of the regions spanning the spike gene deletion using Sanger sequencing. Phylogenetic analysis revealed that this double-deletion variant was rare (one per million in public databases, including GenBank and GISAID). The impact of this double deletion in the spike gene on the SARS-CoV-2 virus itself as well as on cultured cells and/or humans remains to be further elucidated.

The Predictive Role of NLR, d-NLR, MLR, and SIRI in COVID-19 Mortality

(1) Background: Since its discovery, COVID-19 has caused more than 256 million cases, with a cumulative death toll of more than 5.1 million, worldwide. Early identification of patients at high risk of mortality is of great importance in saving the lives of COVID-19 patients. The study aims to assess the utility of various inflammatory markers in predicting mortality among hospitalized patients with COVID-19. (2) Methods: A retrospective observational study was conducted among 108 patients with laboratory-confirmed COVID-19 hospitalized between 1 May 2021 and 31 October 2021 at Municipal Emergency Clinical Hospital of Timisoara, Romania. Blood cell counts at admission were used to obtain NLR, dNLR, MLR, PLR, SII, and SIRI. The association of inflammatory index and mortality was assessed via Kaplan-Maier curves univariate Cox regression and binominal logistic regression. (3) Results: The median age was 63.31 ± 14.83, the rate of in-hospital death being 15.7%. The optimal cutoff for NLR, dNLR, MLR, and SIRI was 9.1, 9.6, 0.69, and 2.2. AUC for PLR and SII had no statistically significant discriminatory value. The binary logistic regression identified elevated NLR (aOR = 4.14), dNLR (aOR = 14.09), and MLR (aOR = 3.29), as independent factors for poor clinical outcome of COVID-19. (4) Conclusions: NLR, dNLR, MLR have significant predictive value in COVID-19 mortality.

A novel explainable COVID-19 diagnosis method by integration of feature selection with random forest

Several Artificial Intelligence-based models have been developed for COVID-19 disease diagnosis. In spite of the promise of artificial intelligence, there are very few models which bridge the gap between traditional human-centered diagnosis and the potential future of machine-centered disease diagnosis. Under the concept of human-computer interaction design, this study proposes a new explainable artificial intelligence method that exploits graph analysis for feature visualization and optimization for the purpose of COVID-19 diagnosis from blood test samples. In this developed model, an explainable decision forest classifier is employed to COVID-19 classification based on routinely available patient blood test data. The approach enables the clinician to use the decision tree and feature visualization to guide the explainability and interpretability of the prediction model. By utilizing this novel feature selection phase, the proposed diagnosis model will not only improve diagnosis accuracy but decrease the execution time as well.

Recent advances in methods for the diagnosis of Corona Virus Disease 2019

Since the beginning of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic, it has been clear that effective methods for the diagnosis of Corona Virus Disease 2019 (COVID-19) are the key tools to control its epidemic. The current gold standard for diagnosing COVID-19 is the real-time quantitative reverse transcription-polymerase chain reaction (qRT-PCR), which is a sensitive and specific method to detect SARS-CoV-2. Other RNA-based methods include RNA sequencing (RNA-seq), droplet digital reverse transcription-polymerase chain reaction (ddRT-PCR), reverse transcription loop-mediated isothermal amplification (RT-LAMP), and clustered regularly interspaced short palindromic repeats (CRISPR). The serological testing of antibodies (IgM and IgG), nanoparticle-based lateral-flow assay, and enzyme-linked immunosorbent assay (ELISA) can be used to enhance the detection sensitivity and accuracy. Because antibodies are usually detected a week after the onset of symptoms, these tests are used to assess the overall infection rate in the community. Sine the fact that healthcare varies from country to country across the world, different types of diagnosing COVID-19 imaging technologies including chest computed tomography (CT), chest radiography, and lung ultrasound are used in different degrees. Besides, the pooling test is an important public health tool to reduce cost and increase testing capacity in low-risk area, while artificial intelligence (AI) may aid to increase the diagnostic efficiency of imaging-based methods. Finally, depending on the type of samples and stages of the disease, a combination of information on patient demographics and histories, clinical symptoms, results of molecular and serological diagnostic tests, and imaging information is highly recommended to achieve adequate diagnosis of patients with COVID-19.

Automatic COVID-19 detection mechanisms and approaches from medical images: a systematic review

Since early 2020, Coronavirus Disease 2019 (COVID-19) has spread widely around the world. COVID-19 infects the lungs, leading to breathing difficulties. Early detection of COVID-19 is important for the prevention and treatment of pandemic. Numerous sources of medical images (e.g., Chest X-Rays (CXR), Computed Tomography (CT), and Magnetic Resonance Imaging (MRI)) are regarded as a desirable technique for diagnosing COVID-19 cases. Medical images of coronavirus patients show that the lungs are filled with sticky mucus that prevents them from inhaling. Today, Artificial Intelligence (AI) based algorithms have made a significant shift in the computer aided diagnosis due to their effective feature extraction capabilities. In this survey, a complete and systematic review of the application of Machine Learning (ML) methods for the detection of COVID-19 is presented, focused on works that used medical images. We aimed to evaluate various ML-based techniques in detecting COVID-19 using medical imaging. A total of 26 papers were extracted from ACM, ScienceDirect, Springerlink, Tech Science Press, and IEEExplore. Five different ML categories to review these mechanisms are considered, which are supervised learning-based, deep learning-based, active learning-based, transfer learning-based, and evolutionary learning-based mechanisms. A number of articles are investigated in each group. Also, some directions for further research are discussed to improve the detection of COVID-19 using ML techniques in the future. In most articles, deep learning is used as the ML method. Also, most of the researchers used CXR images to diagnose COVID-19. Most articles reported accuracy of the models to evaluate model performance. The accuracy of the studied models ranged from 0.84 to 0.99. The studies demonstrated the current status of AI techniques in using AI potentials in the fight against COVID-19.

Medical Arrangement Strategies for Infertility Female Patients during COVID-19 Mini-Outbreak

Over the past two years, COVID-19 pandemic is an unprecedented health emergency. All countries have taken their<br />own measures to mitigate the spread of the virus in the first and subsequent mini-outbreaks of infection. In view of the<br />current situation of small outbreaks of COVID-19, guidelines on epidemic prevention should be developed specifically<br />for reproductive medical centers. It is necessary to establish a dynamic patient assessment and management system<br />to identify patients who need priority fertility treatment during epidemic control. Female Patients were assigned<br />as grade A and required hospitalization in the inpatient ward after egg retrieval. Patients who underwent controlled<br />ovarian stimulation were classified as grade B, and they can choose to be hospitalizedat home according to their own<br />convenience. Patients undergoing frozen embryo transfer (FET) cycle or planned downregulation with gonadotropinreleasing<br />hormone agonists were defined as grade C, who could continue the assisted reproductive technology (ART)<br />treatment cycle with negative COVID-19 nucleic acid test and there was no fever or respiratory symptoms. This brief<br />comment summarizes the working procedure of the reproductive medical center in the first hospital of Lanzhou University<br />in China to minimize the probability of hospital infection and ensure the safe conduct of assisted reproductive<br />technology therapy.

Analysis and classification of radiological results and epidemiology of patients with COVID-19 pneumonia

The coronavirus disease-2019 (COVID-19) pneumonia which is caused by the severe acute respiratory syndrome coronavirus-2 (SARS CoV-2) virus is the current urgent issue world over. According to the Health Ministry of Turkey, the first COVID-19 patient was diagnosed on March 11, 2020. Since then, approximately 5.5 million patients have been confirmed to be positive SARS CoV-2 virus. In this retrospective study, we aimed at analyzing the epidemiological and radiological findings of COVID-19 cases at the Hospital of Grand National Assembly of Turkey from April 1, 2020 to December 31, 2020.A total of 130 patients (84 male, 25-87 years) were diagnosed as positive with High Resolution Computed Tomography (HRCT) scans and 71 of them confirmed with positive Real Time Polymerase Chain Reaction using the patients' nasopharyngeal and throat samples.HRCT scans were classified into 4 stages. Stage I (39.2%) patients' group; the HRCT results were found to be mosaic perfusion, whereas Stage II (39.2%) were found to be Ground Glass Opacity. Also, consolidation was detected in Stage III (20%). Finally, Stage IV, considered the most severe lung findings, and named as a crazy paving pattern was determined in 2 patients (1.53%). Furthermore, 20% of patients were found to be positive using IgG antibody against to SARS CoV-2 virus.Our findings showed that HRCT could be most prominent technique compared to real time polymerase chain reaction for the diagnosis of COVID-19 pneumonia. The novel classification of HRCT findings will be helpful to early diagnosis of the disease, time saving and eventually cost-effective.

The Impact of False Positive COVID-19 Result

The novel Coronavirus disease 2019 (COVID-19) pandemic has resulted in many adverse outcomes and challenges, and a false-positive result is one of them. Despite that this issue has a substantial impact, there is a scarcity in the literature of its prevalence or impact, and more knowledge is needed. This case report will present the case of a 54-years-old female who was misdiagnosed as COVID-19. The misleading COVID-19 diagnosis can result in significant consequences such as delaying surgeries, unnecessary quarantine and treatments, transplant lists omission, and unnecessary sick leaves. Moreover, as seen in our case, it delayed the other investigations and admitted a healthy patient to a COVID-19 isolation ward. Therefore, physicians should consider the possibility of false-positive results and utilize other investigation tools to further diagnose suspicious cases.

Saudi Radiology Personnel's Perceptions of Artificial Intelligence Implementation: A Cross-Sectional Study

PURPOSE

Artificial intelligence (AI) in radiology has been a subject of heated debate. The external perception is that algorithms and machines cannot offer better diagnosis than radiologists. Reluctance to implement AI maybe due to the opacity in how AI applications work and the challenging and lengthy validation process. In this study, Saudi radiology personnel's familiarity with AI applications and its usefulness in clinical practice were investigated.

METHODS

A cross-sectional study was conducted in Saudi Arabia among radiology personnel from March to April 2021. Radiology personnel nationwide were surveyed electronically using Google form. The questionnaire included 12-questions related to AI usefulness in clinical practice and participants' knowledge about AI and their acceptance level to learn and implement this technology into clinical practice. Participants' trust level was also measured; Kruskal-Wallis test was used to examine differences between groups.

RESULTS

A total of 224 respondents from various radiology-related occupations participated in the survey. The lowest trust level in AI applications was shown by radiologists (p = 0.033). Eighty-two percent of participants (n = 184) had never used AI in their departments. Most respondents (n = 160, 71.4%) reported lack of formal education regarding AI-based applications. Most participants (n = 214, 95.5%) showed strong interest in AI education and are willing to incorporate it into the clinical practice of radiology. Almost half of radiography students (22/46, 47.8%) believe that their job might be at risk due to AI application (p = 0.038).

CONCLUSION

Radiology personnel's knowledge of AI has a significant impact on their willingness to learn, use and adapt this technology in clinical practice. Participants demonstrated a positive attitude towards AI, showed a reasonable understanding and are highly motivated to learn and incorporate it into clinical practice. Some participants felt that their jobs were threatened by AI adaptation, but this belief might change with good training and education programmes.