Comparative Analysis of Clinical and CT Findings in Patients with SARS-CoV-2 Original Strain, Delta and Omicron Variants.

OBJECTIVES: To compare the clinical characteristics and chest CT findings of patients infected with Omicron and Delta variants and the original strain of COVID-19. METHODS: A total of 503 patients infected with the original strain (245 cases), Delta variant (90 cases), and Omicron variant (168 cases) were retrospectively analyzed. The differences in clinical severity and chest CT findings were analyzed. We also compared the infection severity of patients with different vaccination statuses and quantified pneumonia by a deep-learning approach. RESULTS: The rate of severe disease decreased significantly from the original strain to the Delta variant and Omicron variant (27% vs. 10% vs. 4.8%, p < 0.001). In the Omicron group, 44% (73/168) of CT scans were categorized as abnormal compared with 81% (73/90) in the Delta group and 96% (235/245, p < 0.05) in the original group. Trends of a gradual decrease in total CT score, lesion volume, and lesion CT value of AI evaluation were observed across the groups (p < 0.001 for all). Omicron patients who received the booster vaccine had less clinical severity (p = 0.015) and lower lung involvement rate than those without the booster vaccine (36% vs. 57%, p = 0.009). CONCLUSIONS: Compared with the original strain and Delta variant, the Omicron variant had less clinical severity and less lung injury on CT scans.

Epidemiological and clinical characteristics of respiratory syncytial virus and influenza infections in hospitalized children before and during the COVID-19 pandemic in Central China.

Background: Globally, the epidemiology of non-SARS-CoV-2 respiratory viruses like respiratory syncytial virus (RSV) and influenza virus was remarkably influenced by the implementation of non-pharmacological interventions (NPIs) during the COVID-19 pandemic. Our study explored the epidemiological and clinical characteristics of pediatric patients hospitalized with RSV or influenza infection before and during the pandemic after relaxation of NPIs in central China. Methods: This hospital-based prospective case-series study screened pediatric inpatients (age ≤ 14 years) enrolled with acute respiratory infections (ARI) for RSV or influenza infection from 2018 to 2021. The changes in positivity rates of viral detection, epidemiological, and clinical characteristics were analyzed and compared. Results: Median ages of all eligible ARI patients from 2018-2019 were younger than those from 2020-2021, so were ages of cases infected with RSV or influenza (RSV: 4.2 months vs. 7.2 months; influenza: 27.3 months vs. 37.0 months). Where the positivity rate for influenza was considerably decreased in 2020-2021 (1.4%, 27/1964) as compared with 2018-2019 (2.9%, 94/3275, P < 0.05), it was increased for RSV (11.4% [372/3275] vs. 13.3% [262/1964], P < 0.05) in the same period. The number of severe cases for both RSV and influenza infection were also decreased in 2020-2021 compared with 2018-2019. Conclusions: The implemented NPIs have had varied impacts on common respiratory viruses. A more effective prevention strategy for RSV infections in childhood is needed.

Epidemiological and clinical characteristics of respiratory syncytial virus and influenza infections in hospitalized children before and during the COVID‐19 pandemic in Central China

Background Globally, the epidemiology of non‐SARS‐CoV‐2 respiratory viruses like respiratory syncytial virus (RSV) and influenza virus was remarkably influenced by the implementation of non‐pharmacological interventions (NPIs) during the COVID‐19 pandemic. Our study explored the epidemiological and clinical characteristics of pediatric patients hospitalized with RSV or influenza infection before and during the pandemic after relaxation of NPIs in central China. Methods This hospital‐based prospective case‐series study screened pediatric inpatients (age ≤ 14 years) enrolled with acute respiratory infections (ARI) for RSV or influenza infection from 2018 to 2021. The changes in positivity rates of viral detection, epidemiological, and clinical characteristics were analyzed and compared. Results Median ages of all eligible ARI patients from 2018–2019 were younger than those from 2020–2021, so were ages of cases infected with RSV or influenza (RSV: 4.2 months vs. 7.2 months;influenza: 27.3 months vs. 37.0 months). Where the positivity rate for influenza was considerably decreased in 2020–2021 (1.4%, 27/1964) as compared with 2018–2019 (2.9%, 94/3275, P < 0.05), it was increased for RSV (11.4% [372/3275] vs. 13.3% [262/1964], P < 0.05) in the same period. The number of severe cases for both RSV and influenza infection were also decreased in 2020–2021 compared with 2018–2019. Conclusions The implemented NPIs have had varied impacts on common respiratory viruses. A more effective prevention strategy for RSV infections in childhood is needed.

Seasonal variation and ecological risk assessment of Pharmaceuticals and Personal Care Products (PPCPs) in a typical semi-enclosed bay — The Bohai Bay in northern China

The Bohai Bay as a typical semi-enclosed bay in northern China with poor water exchange capacity and significant coastal urbanization, is greatly influenced by land-based inputs and human activities. As a class of pseudo-persistent organic pollutants, the spatial and temporal distribution of Pharmaceuticals and Personal Care Products (PPCPs) is particularly important to the ecological environment, and it will be imperfect to assess the ecological risk of PPCPs for the lack of systematic investigation of their distribution in different season. 14 typical PPCPs were selected to analyze the spatial and temporal distribution in the Bohai Bay by combining online solid-phase extraction (SPE) and HPLC-MS/MS techniques in this study, and their ecological risks to aquatic organisms were assessed by risk quotients (RQs) and concentration addition (CA) model. It was found that PPCPs widely presented in the Bohai Bay with significant differences of spatial and seasonal distribution. The concentrations of ∑PPCPs were higher in autumn than in summer. The distribution of individual pollutants also showed significant seasonal differences. The high values were mainly distributed in estuaries and near-shore outfalls. Mariculture activities in the northern part of the Bohai Bay made a greater contribution to the input of PPCPs. Caffeine, florfenicol, enrofloxacin and norfloxacin were the main pollutants in the Bohai Bay, with detection frequencies exceeding 80 %. The ecological risk of PPCPs to algae was significantly higher than that to invertebrates and fish. CA model indicated that the potential mixture risk of total PPCPs was not negligible, with 34 % and 88 % of stations having mixture risk in summer and autumn, respectively. The temporary stagnation of productive life caused by Covid-19 weakened the input of PPCPs to the Bohai Bay, reducing the cumulative effects of the pollutants. This study was the first full-coverage investigation of PPCPs in the Bohai Bay for different seasons, providing an important basis for the ecological risk assessment and pollution prevention of PPCPs in the bay.

Fine-Grained Assessment of COVID-19 Severity Based on Clinico-Radiological Data Using Machine Learning.

BACKGROUND: The severe and critical cases of COVID-19 had high mortality rates. Clinical features, laboratory data, and radiological features provided important references for the assessment of COVID-19 severity. The machine learning analysis of clinico-radiological features, especially the quantitative computed tomography (CT) image analysis results, may achieve early, accurate, and fine-grained assessment of COVID-19 severity, which is an urgent clinical need. OBJECTIVE: To evaluate if machine learning algorithms using CT-based clinico-radiological features could achieve the accurate fine-grained assessment of COVID-19 severity. METHODS: The clinico-radiological features were collected from 78 COVID-19 patients with different severities. A neural network was developed to automatically measure the lesion volume from CT images. The severity was clinically diagnosed using two-type (severe and non-severe) and fine-grained four-type (mild, regular, severe, critical) classifications, respectively. To investigate the key features of COVID-19 severity, statistical analyses were performed between patients' clinico-radiological features and severity. Four machine learning algorithms (decision tree, random forest, SVM, and XGBoost) were trained and applied in the assessment of COVID-19 severity using clinico-radiological features. RESULTS: The CT imaging features (CTscore and lesion volume) were significantly related with COVID-19 severity (p < 0.05 in statistical analysis for both in two-type and fine-grained four-type classifications). The CT imaging features significantly improved the accuracy of machine learning algorithms in assessing COVID-19 severity in the fine-grained four-type classification. With CT analysis results added, the four-type classification achieved comparable performance to the two-type one. CONCLUSIONS: CT-based clinico-radiological features can provide an important reference for the accurate fine-grained assessment of illness severity using machine learning to achieve the early triage of COVID-19 patients.

A Smartphone-Based Detection Method of Colloidal Gold Immunochromatographic Strip

The outbreak of the new coronavirus (SARS-CoV-2) infection has become a global public health crisis. Antigen detection strips (colloidal gold) can be widely used in novel coronavirus clinical screening and can even be extended to home self-testing, which provides a practical and effective way for people to obtain health status information away from the crowd. In this paper, a colloidal gold detection system without complex devices is proposed, which is based on smartphone usage along with a mobile-phone software embedded with normalization algorithms and a special designed background paper. The basic principle of the device relies on image processing. First, the data of the green channel of the image captured by a smartphone are selected to be processed. Second, the calibration curves are established using standard black and white card, and the calibration values under different detection environments are obtained by calibration curves. Finally, to verify the validity of the proposed method, various standard solutions with different concentrations are tested. Results show that this method can eliminate the influence of different environments on the test results, the test results in different detection environments have good stability and the variation coefficients are less than 5%. It fully proves that the detection system designed in this paper can detect the result of colloidal gold immunochromatographic strip in time, conveniently and accurately in different environments.

Association between clinical characteristics and CT findings in patients with coronavirus disease-2019.

ABSTRACT: This retrospective study was to investigate the association between clinical characteristics and computerized tomography (CT) findings in patients with coronavirus disease-2019 (COVID-19). The clinical data of COVID-19 patients were retrospectively analyzed. Spearman correlation analysis was used to identify the correlation. Totally 209 consecutive COVID-19 patients were eligible for the study, with the mean age of 47.53 ±â€Š13.52 years. At onset of the disease, the most common symptoms were fever (85.65%) and cough (61.24%). The CT features of COVID-19 included pulmonary, bronchial, and pleural changes, with the significant pulmonary presentation of ground-glass opacification (93.30%), consolidation (48.80%), ground-glass opacification plus a reticular pattern (54.07%), telangiectasia (84.21%), and pulmonary fibrotic streaks (49.76%). Spearman analysis showed that the CT findings had significantly inverse associations with the platelets, lymphocyte counts, and sodium levels, but were positively related to the age, erythrocyte sedimentation rate, D-dimer, lactic dehydrogenase, α-hydroxybutyrate dehydrogenase, and C-reactive protein levels (P < .05). In conclusion, the severity of lung abnormalities on CT in COVID-19 patients is inversely associated with the platelets, lymphocyte count, and sodium levels, whereas positively with the age, erythrocyte sedimentation rate, D-dimer, lactic dehydrogenase, hydroxybutyrate dehydrogenase, and C-reactive protein levels.

Automatic detection of multiple types of pneumonia: Open dataset and a multi-scale attention network.

The quick and precise identification of COVID-19 pneumonia, non-COVID-19 viral pneumonia, bacterial pneumonia, mycoplasma pneumonia, and normal lung on chest CT images play a crucial role in timely quarantine and medical treatment. However, manual identification is subject to potential misinterpretations and time-consumption issues owing the visual similarities of pneumonia lesions. In this study, we propose a novel multi-scale attention network (MSANet) based on a bag of advanced deep learning techniques for the automatic classification of COVID-19 and multiple types of pneumonia. The proposed method can automatically pay attention to discriminative information and multi-scale features of pneumonia lesions for better classification. The experimental results show that the proposed MSANet can achieve an overall precision of 97.31%, recall of 96.18%, F1-score of 96.71%, accuracy of 97.46%, and macro-average area under the receiver operating characteristic curve (AUC) of 0.9981 to distinguish between multiple classes of pneumonia. These promising results indicate that the proposed method can significantly assist physicians and radiologists in medical diagnosis. The dataset is publicly available at https://doi.org/10.17632/rf8x3wp6ss.1.

News coverage of drug development: implications for the conveyance of health information.

BACKGROUND: Technical information regarding health-related advances is sometimes esoteric for the general public. News media, therefore, plays a key role in public health promotion via health information conveyance. In this study, we use China as a sample country and analyze the claims and frames in news coverage of health-related advances, with special focus on news coverage of the development and performance of newly developed or tested drugs. METHODS: A keyword search was performed to retrieve news articles from four representative news agencies in China. In total, 3029 news reports were retrieved, of which 128 were selected for further analysis. RESULTS: Four aspects of news coverage of drug development were identified: (1) the characteristics of new drugs covered, (2) the sources of information, (3) the accuracy of health information in newspapers, and (4) textual features of news coverage. CONCLUSIONS: Our findings reveal that guidelines should be established to facilitate more systematic news reporting on health-related advances. Additionally, literacy among the general public and professionalism in health information conveyance should be promoted to negate the "illusion of knowing" about health-related advances.

The evolution of chest CT findings from admission to follow-up in 30 moderate to severe adult patients with COVID-19 pneumonia.

OBJECTIVE: To analyze the evolution of chest computed tomography (CT) findings from admission to follow-up in moderate to severe patients with coronavirus disease-19 (COVID-19) pneumonia. METHODS: During December 2019-April 2020, the sequential CT images of 30 patients with COVID-19 pneumonia were retrospectively analyzed from admission to follow-up. The qualitative evolution tendency of lung abnormalities and semi-quantitative CT scores were analyzed for temporal change. RESULTS: The mean hospitalized period was 24.5 ± 9.6 days (range 6-49 days). The average time from the first, second, third, fourth and follow-up CT examination to the initial symptom onset were 4.2 ± 3.1 days, 10.7 ± 4.4 days, 17.1 ± 3.9 days, 24.6 ± 7.5 days, and 42.4 ± 15.6 days, respectively. During illness day 0-5, groundglass opacity (GGO) was the main pattern. The following illness day 6-11, the main CT pattern was consolidation and reticular pattern. The consolidation and reticular pattern gradually dissipate during illness day 12-23, and the reticular pattern and light GGO increased. When illness day was ≥ 24 days, the reticular pattern and light GGO gradually decrease until complete dissipation. The highest CT score was at illness day 6-11. Pearson correlation analysis showed that the mean and maximum CT score were not correlated with the length of fever (r = 0.018, p = 0.923 and r = 0.086, p = 0.652) and hospitalization (r = 0.192, p = 0.31 and r = 0.273, p = 0.144). CONCLUSIONS: The dynamic evolution of CT manifestation in moderate to severe COVID-19 pneumonia patients followed a specific pattern over time. During illness day 6-11, the extent of lung abnormalities on chest CT was the most severe. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s42058-021-00058-2.

Automatic distinction between COVID-19 and common pneumonia using multi-scale convolutional neural network on chest CT scans.

The COVID-19 pneumonia is a global threat since it emerged in early December 2019. Driven by the desire to develop a computer-aided system for the rapid diagnosis of COVID-19 to assist radiologists and clinicians to combat with this pandemic, we retrospectively collected 206 patients with positive reverse-transcription polymerase chain reaction (RT-PCR) for COVID-19 and their 416 chest computed tomography (CT) scans with abnormal findings from two hospitals, 412 non-COVID-19 pneumonia and their 412 chest CT scans with clear sign of pneumonia are also retrospectively selected from participating hospitals. Based on these CT scans, we design an artificial intelligence (AI) system that uses a multi-scale convolutional neural network (MSCNN) and evaluate its performance at both slice level and scan level. Experimental results show that the proposed AI has promising diagnostic performance in the detection of COVID-19 and differentiating it from other common pneumonia under limited number of training data, which has great potential to assist radiologists and physicians in performing a quick diagnosis and mitigate the heavy workload of them especially when the health system is overloaded. The data is publicly available for further research at https://data.mendeley.com/datasets/3y55vgckg6/1https://data.mendeley.com/datasets/3y55vgckg6/1.