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1.
Science of The Total Environment ; : 159682, 2022.
Article in English | ScienceDirect | ID: covidwho-2082446

ABSTRACT

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.

2.
Int J Environ Res Public Health ; 19(17)2022 Aug 26.
Article in English | MEDLINE | ID: covidwho-2006013

ABSTRACT

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.


Subject(s)
COVID-19 , Algorithms , COVID-19/diagnostic imaging , Humans , Machine Learning , Neural Networks, Computer , Tomography, X-Ray Computed/methods
3.
Photonics ; 8(12):576, 2021.
Article in English | MDPI | ID: covidwho-1572591

ABSTRACT

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.

4.
Medicine (Baltimore) ; 100(44): e27435, 2021 Nov 05.
Article in English | MEDLINE | ID: covidwho-1570139

ABSTRACT

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.


Subject(s)
COVID-19 , Tomography, X-Ray Computed , Adult , Age Factors , Blood Sedimentation , C-Reactive Protein/analysis , COVID-19/diagnosis , Fibrin Fibrinogen Degradation Products , Humans , Hydroxybutyrate Dehydrogenase , L-Lactate Dehydrogenase , Lung , Lymphocyte Count , Middle Aged , Platelet Count , Retrospective Studies , Sodium/blood
5.
Biomed Signal Process Control ; 73: 103415, 2022 Mar.
Article in English | MEDLINE | ID: covidwho-1559225

ABSTRACT

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.

6.
BMC Public Health ; 21(1): 1799, 2021 10 07.
Article in English | MEDLINE | ID: covidwho-1465316

ABSTRACT

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.


Subject(s)
Mass Media , Pharmaceutical Preparations , China , Health Promotion , Humans , Public Health
7.
Chin J Acad Radiol ; 4(1): 71-77, 2021.
Article in English | MEDLINE | ID: covidwho-1130990

ABSTRACT

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.

8.
Chaos Solitons Fractals ; 140: 110153, 2020 Nov.
Article in English | MEDLINE | ID: covidwho-671961

ABSTRACT

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.

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