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1.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-318010

ABSTRACT

A novel and highly pathogenic coronavirus (2019-nCoV)-induced pneumonia spread worldwide in a short time. However, studies on the effects of 2019-nCoV on the male reproductive system are limited. The aim of this study is to describe the clinical characteristics of the male reproductive system of COVID-19 patients and to explore the presence of 2019-nCoV in semen. Retrospective, single-center case series of 112 male patients with confirmed COVID-19 who were admitted to Renmin Hospital of Wuhan University from January 2 to March 7, 2020. Demographic data, symptoms and signs related to the male reproductive system, throat swabs and semen samples were collected and analyzed. 2019-nCoV RNA measured in throat swab and semen samples. The organ distribution of ACE2 mRNA and protein in human tissue on The Human Protein Atlas portal and investigated immunohistochemistry (IHC) images of the testis. The HPA dataset revealed relatively high levels of ACE2 protein and RNA expression in the testis. A total of 3 severe COVID-19 patients (2.7%) presented with orchidoptosis, while no patients experienced other symptoms or signs related to the male reproductive system. The analysis of 2019-nCoV RNA in semen included 17 patients with fertility needs. Among these patients, 9 (52.9%) remained positive for 2019-nCoV according to throat swab analysis, and 8 (47.1%) became negative. In the semen 2019-nCoV analysis, all 17 patients were negative for the N gene and ORF1ab gene. In view of the potential impairment, long-term follow-up for male COVID-19 patients with fertility needs is of great significance.

4.
Front Physiol ; 12: 630038, 2021.
Article in English | MEDLINE | ID: covidwho-1259363

ABSTRACT

BACKGROUND: Previous studies suggest that coronavirus disease 2019 (COVID-19) is a systemic infection involving multiple systems, and may cause autonomic dysfunction. OBJECTIVE: To assess autonomic function and relate the findings to the severity and outcomes in COVID-19 patients. METHODS: We included consecutive patients with COVID-19 admitted to the 21st COVID-19 Department of the east campus of Renmin Hospital of Wuhan University from February 6 to March 7, 2020. Clinical data were collected. Heart rate variability (HRV), N-terminal pro-B-type natriuretic peptide (NT-proBNP), D-dimer, and lymphocytes and subsets counts were analysed at two time points: nucleic-acid test positive and negative. Psychological symptoms were assessed after discharge. RESULTS: All patients were divided into a mild group (13) and a severe group (21). The latter was further divided into two categories according to the trend of HRV. Severe patients had a significantly lower standard deviation of the RR intervals (SDNN) (P < 0.001), standard deviation of the averages of NN intervals (SDANN) (P < 0.001), and a higher ratio of low- to high-frequency power (LF/HF) (P = 0.016). Linear correlations were shown among SDNN, SDANN, LF/HF, and laboratory indices (P < 0.05). Immune function, D-dimer, and NT-proBNP showed a consistent trend with HRV in severe patients (P < 0.05), and severe patients without improved HRV parameters needed a longer time to clear the virus and recover (P < 0.05). CONCLUSION: HRV was associated with the severity of COVID-19. The changing trend of HRV was related to the prognosis, indicating that HRV measurements can be used as a non-invasive predictor for clinical outcome.

5.
Sci Rep ; 10(1): 19196, 2020 11 05.
Article in English | MEDLINE | ID: covidwho-912912

ABSTRACT

Computed tomography (CT) is the preferred imaging method for diagnosing 2019 novel coronavirus (COVID19) pneumonia. We aimed to construct a system based on deep learning for detecting COVID-19 pneumonia on high resolution CT. For model development and validation, 46,096 anonymous images from 106 admitted patients, including 51 patients of laboratory confirmed COVID-19 pneumonia and 55 control patients of other diseases in Renmin Hospital of Wuhan University were retrospectively collected. Twenty-seven prospective consecutive patients in Renmin Hospital of Wuhan University were collected to evaluate the efficiency of radiologists against 2019-CoV pneumonia with that of the model. An external test was conducted in Qianjiang Central Hospital to estimate the system's robustness. The model achieved a per-patient accuracy of 95.24% and a per-image accuracy of 98.85% in internal retrospective dataset. For 27 internal prospective patients, the system achieved a comparable performance to that of expert radiologist. In external dataset, it achieved an accuracy of 96%. With the assistance of the model, the reading time of radiologists was greatly decreased by 65%. The deep learning model showed a comparable performance with expert radiologist, and greatly improved the efficiency of radiologists in clinical practice.


Subject(s)
Coronavirus Infections/complications , Deep Learning , Image Processing, Computer-Assisted/methods , Pneumonia, Viral/complications , Pneumonia/complications , Pneumonia/diagnostic imaging , Signal-To-Noise Ratio , Tomography, X-Ray Computed , Adult , COVID-19 , Female , Humans , Male , Middle Aged , Pandemics , Retrospective Studies
6.
Small ; 16(32): e2002169, 2020 08.
Article in English | MEDLINE | ID: covidwho-612774

ABSTRACT

The ongoing global novel coronavirus pneumonia COVID-19 outbreak has engendered numerous cases of infection and death. COVID-19 diagnosis relies upon nucleic acid detection; however, currently recommended methods exhibit high false-negative rates and are unable to identify other respiratory virus infections, thereby resulting in patient misdiagnosis and impeding epidemic containment. Combining the advantages of targeted amplification and long-read, real-time nanopore sequencing, herein, nanopore targeted sequencing (NTS) is developed to detect SARS-CoV-2 and other respiratory viruses simultaneously within 6-10 h, with a limit of detection of ten standard plasmid copies per reaction. Compared with its specificity for five common respiratory viruses, the specificity of NTS for SARS-CoV-2 reaches 100%. Parallel testing with approved real-time reverse transcription-polymerase chain reaction kits for SARS-CoV-2 and NTS using 61 nucleic acid samples from suspected COVID-19 cases show that NTS identifies more infected patients (22/61) as positive, while also effectively monitoring for mutated nucleic acid sequences, categorizing types of SARS-CoV-2, and detecting other respiratory viruses in the test sample. NTS is thus suitable for COVID-19 diagnosis; moreover, this platform can be further extended for diagnosing other viruses and pathogens.


Subject(s)
Betacoronavirus/genetics , Betacoronavirus/isolation & purification , Coronavirus Infections/diagnosis , Coronavirus Infections/virology , Nanopores , Nucleic Acid Amplification Techniques/methods , Pneumonia, Viral/diagnosis , Pneumonia, Viral/virology , Betacoronavirus/classification , COVID-19 , Coronavirus Infections/epidemiology , DNA, Viral/genetics , DNA, Viral/isolation & purification , Genes, Viral , Humans , Limit of Detection , Mutation , Nanotechnology , Nucleic Acid Amplification Techniques/statistics & numerical data , Pandemics , Pneumonia, Viral/epidemiology , RNA, Viral/genetics , RNA, Viral/isolation & purification , Real-Time Polymerase Chain Reaction , Respiratory Tract Infections/diagnosis , Respiratory Tract Infections/virology , Reverse Transcriptase Polymerase Chain Reaction , SARS-CoV-2 , Sensitivity and Specificity
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