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1.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-315181

ABSTRACT

Background: The density of snails among schistosomiasis hosts has been kept at a low level and even disappeared in many places in Wuhan. However, from the beginning of the epidemic to the lifting of the seal in Wuhan, which the work of snail detection and extermination has been at a standstill. In order to analyze the potential harm of Coronavirus disease 2019 (COVID-19) on urban schistosomiasis transmission, we investigated the density of snails in the Jiangan and Hongshan districts of Wuhan, which evaluated the possibility of schistosomiasis outbreak in Wuhan city. Methods The density and infection status of snails were monitored by GPS satellite, which the risk value was calculated by adjusting Kaiser model. SigmaPlot was used to draw a three-dimensional risk matrix. Results (i)The living snail frame occurrence rate was 1.48% and the average living snail density was 0.054/0.11 m 2 in 2020. Compared with that in 2019, the area of existing snails Tianxingzhou increased greatly. The area of historical snails was 24187 m 2 has increased which the average density of living snails was 0.019/0.11 m 2 . No infectious snails were found in the survey area. (ii) Experts have high enthusiasm (E = 100%). The authority of experts on the indicators of possibility, harmfulness and uncontrollability is 0.842, 0.870 and 0.866 respectively, all greater than 0.7, indicating that expert evaluation is authoritative. After adjusting the Kaiser model, the top three risk values were the north bank of Tianxingzhou, Tianxingzhou as a whole, and Hongshan as a whole. The existing snail sites in the north bank of Tianxingzhou had the highest risk value and ranked the second Pak sha Chau. The highest risk value was found in the historical snail village of Yangsiji village. The risk events on the north bank of Tianxingzhou are located in the orange zone, which belongs to the high-risk area. The whole Hongshan District, the existing snail Tianxingzhou and the tail of Tianxingzhou are located in the yellow zone, belonging to the moderate risk area. Other risk events are located in the blue or green zones and are in the low risk or negligible sub-zone. (iii)The three dimensional risk matrix shows that the potential risk level of the existing snail spot and the possibility of risk occurrence of Tianxingzhou is high. The existing snail points on the Pak sha Chau, indicating the severity of the risk event;Historical snails, indicating the unpredictability of risk events once they occur. The emergency monitoring points show that once the risk event occurs, the level of uncontrollability rises instantly. The whole Hongshan district indicates the severity of the occurrence of the risk event. Conclusion Under the influence of Covid-19 epidemic, the risk of schistosomiasis infection was high and the historical snail snail appeared again in Wuhan. Therefore, the prevention and control work of schistosomiasis infection should be strengthened in Wuhan.

2.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-315178

ABSTRACT

Background: Among patients with confirmed severe/critical type COVID-19, we found that although the seurm creatinine (Cr) value is in normal range, patients might have occured early renal damage. For severe/critical type COVID-19 patients, whether some chest CT features can be used to predict the early renal damage or clinical prognosis. Methods: : 162 patients with severe/critical type COVID-19 were reviewed retrospectively in 13 medical centers from China. According to the level of eGFR, 162 patients were divided into three groups, group A (eGFR < 60 ml/min/1.73m 2 ), group B (60 ml/min/1.73m 2 ≤ eGFR < 90 ml/min/1.73m 2 group) and group C (eGFR ≥ 90 ml/min/1.73m 2 ). All patients’ baseline clinical characteristics, laboratory data, CT features and clinical outcomes were collected and compared. The eGFR and CT features was assessed using univariate and multivariate Cox regression. Results: : Baseline clinical characteristics showed that there were significant differences in age, hypertension, cough and fatigue among groups A, B and C. Laboratory data analysis revealed significant differences between the three groups of leukocyte count, platelet count, C-reactive protein, aspartate aminotransferase, creatine kinase. Chest CT features analysis indicated that crazy-paving pattern has significant statistical difference in groups A and B compared with group C. The eGFR of patients with crazy-paving pattern was significant lower than those without crazy-paving pattern (76.73 ± 30.50 vs. 101.69 ± 18.24 ml/min/1.73m 2 , p < 0.001), and eGFR (OR = 0.962, 95% CI = 0.940-0.985) was the independent risk factor of crazy-paving pattern. The eGFR (HR = 0.549, 95% CI = 0.331-0.909, p = 0.020) and crazy-paving pattern (HR = 2.996, 95% CI = 1.010-8.714, p = 0.048) were independent risk factors of mortality. Conclusions: : In patients with severe/critical type COVID-19, the presence of crazy-paving pattern on chest CT are more likely occured the decline of eGFR and poor clinical prognosis. The crazy-paving pattern appeared could be used as an early warning indicator of renal damage and to guide clinicians to use drugs reasonably.

3.
Acta Trop ; 226: 106224, 2022 Feb.
Article in English | MEDLINE | ID: covidwho-1487561

ABSTRACT

BACKGROUND: Snails that host the parasitic worm Schistosoma were once controlled or eliminated in Wuhan, China. However, safety measures associated with the outbreak of novel coronavirus disease 2019 (COVID-19) halted snail detection and extermination efforts. The impact of the COVID-19 pandemic on urban schistosomiasis transmission remains unclear. This study aimed to investigate snail density and the associated risk of a schistosomiasis outbreak in Wuhan. METHODS: The density and infection status of snails were monitored by global positioning system satellites, and outbreak risk was calculated by adjusting the Kaiser model. SigmaPlot was used to create a three-dimensional risk matrix. RESULTS: The living snail frame occurrence rate was 1.48%, and the average living snail density was 0.054/0.11 m2 in 2020, indicating an increase relative to the respective 2019 values (0.019/0.11 m2). No infectious snails were observed in the survey area. The possibility, harmfulness, and uncontrollability indicator values were 0.842, 0.870, and 0.866, respectively. The areas at greatest risk were the northern bank of Tianxingzhou and the Tianxingzhou and Hongshan districts overall. The existing snail sites in the northern bank of Tianxingzhou exhibited the highest risk scores, followed by those in Pak Sha Chau, with the highest risk score found in Yangsiji Village. The events likely to occur in Hongshan District were also likely to have high severity. CONCLUSIONS: During the COVID-19 outbreak, the risk of schistosomiasis increased due to snail colonies returning to their sites of origin in Wuhan, suggesting a need for strengthened infection control and prevention measures.


Subject(s)
COVID-19 , Animals , China/epidemiology , Communicable Disease Control , Humans , Pandemics , SARS-CoV-2 , Schistosoma
5.
NPJ Digit Med ; 4(1): 75, 2021 Apr 22.
Article in English | MEDLINE | ID: covidwho-1199320

ABSTRACT

The COVID-19 pandemic overwhelms the medical resources in the stressed intensive care unit (ICU) capacity and the shortage of mechanical ventilation (MV). We performed CT-based analysis combined with electronic health records and clinical laboratory results on Cohort 1 (n = 1662 from 17 hospitals) with prognostic estimation for the rapid stratification of PCR confirmed COVID-19 patients. These models, validated on Cohort 2 (n = 700) and Cohort 3 (n = 662) constructed from nine external hospitals, achieved satisfying performance for predicting ICU, MV, and death of COVID-19 patients (AUROC 0.916, 0.919, and 0.853), even on events happened two days later after admission (AUROC 0.919, 0.943, and 0.856). Both clinical and image features showed complementary roles in prediction and provided accurate estimates to the time of progression (p < 0.001). Our findings are valuable for optimizing the use of medical resources in the COVID-19 pandemic. The models are available here: https://github.com/terryli710/COVID_19_Rapid_Triage_Risk_Predictor .

6.
Nat Biomed Eng ; 5(6): 509-521, 2021 06.
Article in English | MEDLINE | ID: covidwho-1189229

ABSTRACT

Common lung diseases are first diagnosed using chest X-rays. Here, we show that a fully automated deep-learning pipeline for the standardization of chest X-ray images, for the visualization of lesions and for disease diagnosis can identify viral pneumonia caused by coronavirus disease 2019 (COVID-19) and assess its severity, and can also discriminate between viral pneumonia caused by COVID-19 and other types of pneumonia. The deep-learning system was developed using a heterogeneous multicentre dataset of 145,202 images, and tested retrospectively and prospectively with thousands of additional images across four patient cohorts and multiple countries. The system generalized across settings, discriminating between viral pneumonia, other types of pneumonia and the absence of disease with areas under the receiver operating characteristic curve (AUCs) of 0.94-0.98; between severe and non-severe COVID-19 with an AUC of 0.87; and between COVID-19 pneumonia and other viral or non-viral pneumonia with AUCs of 0.87-0.97. In an independent set of 440 chest X-rays, the system performed comparably to senior radiologists and improved the performance of junior radiologists. Automated deep-learning systems for the assessment of pneumonia could facilitate early intervention and provide support for clinical decision-making.


Subject(s)
COVID-19/diagnostic imaging , Databases, Factual , Deep Learning , SARS-CoV-2 , Tomography, X-Ray Computed , Diagnosis, Differential , Female , Humans , Male , Severity of Illness Index
7.
Chin J Acad Radiol ; : 1-10, 2020 Mar 18.
Article in English | MEDLINE | ID: covidwho-47416

ABSTRACT

COVID-19 has become a public health emergency due to its rapid transmission. The appearance of pneumonia is one of the major clues for the diagnosis, progress and therapeutic evaluation. More and more literatures about imaging manifestations and related research have been reported. In order to know about the progress and prospective on imaging of COVID-19, this review focus on interpreting the CT findings, stating the potential pathological basis, proposing the challenge of patients with underlying diseases, differentiating with other diseases and suggesting the future research and clinical directions, which would be helpful for the radiologists in the clinical practice and research.

8.
Journal of Medical Postgraduates ; 33(3):225-229, 2020.
Article | WHO COVID | ID: covidwho-26722

ABSTRACT

In December 2019, an outbreak of pneumonia associated with a novel coronavirus (SARS-CoV-2)emerged in Wuhan and spread rapidly throughout China and beyond. As the first-line imaging modality, thin-section chest CT is easy to perform, fast, available. Combined with epidemiological history and clinical manifestations, positive CT findings can highly suggest the early diagnosis of Coronavirus Disease 2019 (COVID-19) with high sensitivity, so that timely isolation and intervention can be implemented for suspected and confirmed patients. CT can also help assess the disease severity, and surveil disease course, so as to guide clinical decision and provide prognostic information. This paper outlines the CT imaging features of COVID-19 and highlights the value of chest CT in its diagnosis and treatment with the reference to the official documents and latest researches.

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