Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 3 de 3
Indian J Med Microbiol ; 38(1): 87-93, 2020.
Article in English | MEDLINE | ID: covidwho-688925


Objective: This study aims to provide scientific basis for rapid screening and early diagnosis of the coronavirus disease 2019 (COVID-19) through analysing the clinical characteristics and early imaging/laboratory findings of the inpatients. Methods: Three hundred and three patients with laboratory-confirmed COVID-19 from the East Hospital of People's Hospital of Wuhan University (Wuhan, China) were selected and divided into four groups: youth (20-40 years, n = 64), middle-aged (41-60 years, n = 89), older (61-80 years, n = 118) and elderly (81-100 years, n = 32). The clinical characteristics and imaging/laboratory findings including chest computed tomography (CT), initial blood count, C-reactive protein [CRP]), procalcitonin (PCT) and serum total IgE were captured and analysed. Results: (1) The first symptoms of all age groups were primarily fever (76%), followed by cough (12%) and dyspnoea (5%). Beside fever, the most common initial symptom of elderly patients was fatigue (13%). (2) Fever was the most common clinical manifestation (80%), with moderate fever being the most common (40%), followed by low fever in patients above 40 years old and high fever in those under 40 years (35%). Cough was the second most common clinical manifestation and was most common (80%) in the middle-aged. Diarrhoea was more common in the middle-aged (21%) and the older (19%). Muscle ache was more common in the middle-aged (15%). Chest pain was more common in the youth (13%), and 13% of the youth had no symptoms. (3) The proportion of patients with comorbidities increased with age. (4) Seventy-one per cent of the patients had positive reverse transcription-polymerase chain reaction results and 29% had positive chest CT scans before admission to the hospital. (5) Lesions in all lobes of the lung were observed as the main chest CT findings (76%). (6) Decrease in lymphocytes and increase in monocytes were common in the patients over 40 years old but rare in the youth. Eosinophils (50%), red blood cells (39%) and haemoglobin (40%) decreased in all age groups. (7) The proportion of patients with CRP and PCT elevation increased with age. (8) Thirty-nine per cent of the patients had elevated IgE, with the highest proportion in the old (49%). Conclusion: The clinical characteristics and imaging/laboratory findings of COVID-19 patients vary in different age groups. Personalised criteria should be formulated according to different age groups in the early screening and diagnosis stage.

Betacoronavirus/growth & development , Clinical Laboratory Techniques/methods , Coronavirus Infections/diagnosis , Coronavirus Infections/pathology , Diagnostic Tests, Routine/methods , Pneumonia, Viral/diagnosis , Pneumonia, Viral/pathology , Tomography, X-Ray Computed/methods , Adult , Age Factors , Aged , Aged, 80 and over , China , Coronavirus Infections/diagnostic imaging , Early Diagnosis , Female , Hospitals, University , Humans , Male , Mass Screening/methods , Middle Aged , Pandemics , Pneumonia, Viral/diagnostic imaging , Young Adult
Chemosphere ; 261: 127571, 2020 Jul 08.
Article in English | MEDLINE | ID: covidwho-635404


The aim of this study was to establish a method for predicting heavy metal concentrations in PM1 (aerosol particles with an aerodynamic diameter ≤ 1.0 µm) based on back propagation artificial neural network (BP-ANN) and support vector machine (SVM) methods. The annual average PM1 concentration was 26.31 µg/m3 (range: 7.00-73.40 µg/m3). The concentrations of most metals were higher in winter and lower in autumn and summer. Mn and Ni had the highest noncarcinogenic risk, and Cr the highest carcinogenic risk. The hazard index was below safe limit, and the integrated carcinogenic risk was less than precautionary value. There were no obvious differences in the simulation performances of BP-ANN and SVM models. However, in both models many elements had better simulation effects when input variables were atmospheric pollutants (SO2, NO2, CO, O3 and PM2.5) rather than PM1 and meteorological factors (temperature, relative humidity, atmospheric pressure and wind speed). Models performed better for Pb, Tl and Zn, as evidenced by training R and test R values consistently >0.85, whereas their performances for Ti and V were relatively poor. Predicted results by the fully trained models showed atmospheric heavy metal pollution was heavier in December and January and lighter in August and July of 2019. For the period covering the COVID-19 outbreak in China, from January to March 2020, most of the predicted element concentrations were lower than in 2018 and 2019, and the concentrations of nearly all metals were lowest during the nationwide implementation of countermeasures taken against the pandemic.