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1.
International journal of biological sciences ; 18(7):3066-3081, 2022.
Article in English | EuropePMC | ID: covidwho-1823884

ABSTRACT

During the development of COVID-19 caused by SARS-CoV-2 infection from mild disease to severe disease, it can trigger a series of complications and stimulate a strong cellular and humoral immune response. However, the precise identification of blood immune cell response dynamics and the relevance to disease progression in COVID-19 patients remains unclear. We propose for the first time to use changes in cell numbers to establish new subgroups, which were divided into four groups: first from high to low cell number (H_L_Group), first from low to high (L_H_Group), continuously high (H_Group), and continuously low (L_Group). It was found that in the course of disease development. In the T cell subgroup, the immune response is mainly concentrated in the H_L_Group cell type, and the complications are mainly in the L_H_Group cell type. In the NK cell subgroup, the moderate patients are mainly related to cellular immunity, and the severe patients are mainly caused by the disease, while severe patients are mainly related to complications caused by diseases. Our study provides a dynamic response of immune cells in human blood during SARS-CoV-2 infection and the first subgroup analysis using dynamic changes in cell numbers, providing a new reference for clinical treatment of COVID-19.

2.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-324487

ABSTRACT

Background: The World Health Organization characterized the 2019 novel coronavirus disease (COVID-19) as a pandemic on March 11. Many clinical trials on COVID-19 have been registered, and we aim to review the characteristics of the trials and provide guidance for future trials to avoid duplicated effort. Methods All the studies on COVID-19 registered before Mar 3, 2020 on eight registry platforms worldwide were searched and the data of design, participants, interventions, and outcomes were extracted and analyzed. The most promising trials were screened based on study design, rationale, and resource availability. Results 393 studies registered were identified until Mar 3 2020 and 380 (96.7%) studies were from mainland China, while 3 in Japan, 3 in France, 2 in the US, and 3 were international collaborative studies. 363 studies (92.4%) recruited participants from hospitals and 266 studies (67.7%) aimed at therapeutic effect, others were for prevention, diagnosis, prognosis, etc. 202 studies (51.4%) were randomized controlled trials (RCTs). The average sample size was 1061 and ranged from 8 to 150,000 per study. 177 out of 266 therapeutic studies (66.5% ) tested Western medicines including antiviral drugs (17.7%), stem cell and cord blood therapy (10.2%), chloroquine and derivatives (8.3%), 16 (6.0%) on Chinese medicines, and 73 (27.4%) on integrated therapy of Western and Chinese medicines. 14 Chinese medicines had its clear rationale for evaluation of therapeutic effects. 31 studies among 266 therapeutic studies (11.7%) used mortality as primary outcome, while the most designed secondary outcomes were symptoms and signs (47.0%). 106 studies (27.0%) were funded by the government, and 268 (68.2%) demonstrated ethical approval. 45.5% studies (179 out of 266) had not started recruiting till Mar 3. Eight RCTs were evaluated as the most promising trials. Conclusions Majority of the studies focused on assessing therapeutics for COVID-19 but inappropriate outcome setting, delayed recruitment and insufficient numbers of new cases in China implied many studies may fail to complete. Strategies and protocols of the studies with robust and rapid data sharing from international collaboration are warranted for emergency public health events, helping to accelerate priority setting for timely evidence-based decision-making.

3.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-323775

ABSTRACT

Coronavirus disease 2019 (COVID-19) is a highly contagious virus spreading all around the world. Deep learning has been adopted as an effective technique to aid COVID-19 detection and segmentation from computed tomography (CT) images. The major challenge lies in the inadequate public COVID-19 datasets. Recently, transfer learning has become a widely used technique that leverages the knowledge gained while solving one problem and applying it to a different but related problem. However, it remains unclear whether various non-COVID19 lung lesions could contribute to segmenting COVID-19 infection areas and how to better conduct this transfer procedure. This paper provides a way to understand the transferability of non-COVID19 lung lesions. Based on a publicly available COVID-19 CT dataset and three public non-COVID19 datasets, we evaluate four transfer learning methods using 3D U-Net as a standard encoder-decoder method. The results reveal the benefits of transferring knowledge from non-COVID19 lung lesions, and learning from multiple lung lesion datasets can extract more general features, leading to accurate and robust pre-trained models. We further show the capability of the encoder to learn feature representations of lung lesions, which improves segmentation accuracy and facilitates training convergence. In addition, our proposed Hybrid-encoder learning method incorporates transferred lung lesion features from non-COVID19 datasets effectively and achieves significant improvement. These findings promote new insights into transfer learning for COVID-19 CT image segmentation, which can also be further generalized to other medical tasks.

4.
J Med Virol ; 94(5): 2237-2249, 2022 May.
Article in English | MEDLINE | ID: covidwho-1664417

ABSTRACT

As the coronavirus disease 2019 (COVID-19) pandemic is still ongoing and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants are circulating worldwide, an increasing number of breakthrough infections are being detected despite the good efficacy of COVID-19 vaccines. Data on 88 COVID-19 breakthrough cases (breakthrough infections group) and 41 unvaccinated cases (unvaccinated group) from June 1 to August 22, 2021, were extracted from a cloud database established at Beijing Ditan Hospital to evaluate the clinical, immunological, and genomic characteristics of COVID-19 breakthrough infections. Among these 129 COVID-19 cases, 33 whole genomes were successfully sequenced, of which 23 were Delta variants, including 15 from the breakthrough infections group. Asymptomatic and mild cases predominated in both groups, but two patients developed severe disease in the unvaccinated group. The median time of viral shedding in the breakthrough infections group was significantly lower than that in the unvaccinated group (p = 0.003). In the breakthrough infections group, the IgG titers showed a significantly increasing trend (p = 0.007), and the CD4 + T lymphocyte count was significantly elevated (p = 0.018). For people infected with the Delta variant in the two groups, no significant difference was observed in either the quantitative reverse-transcription polymerase chain reaction results or viral shedding time. In conclusion, among vaccinated patients, the cases of COVID-19 vaccine breakthrough infections were mainly asymptomatic and mild, IgG titers were significantly increased and rose rapidly, and the viral shedding time was shorter.


Subject(s)
COVID-19 , Beijing/epidemiology , COVID-19/epidemiology , COVID-19/prevention & control , COVID-19 Vaccines , Genomics , Humans , SARS-CoV-2/genetics
6.
Open forum infectious diseases ; 8(Suppl 1):S361-S361, 2021.
Article in English | EuropePMC | ID: covidwho-1564485

ABSTRACT

Background BRII-196 and BRII-198 are human monoclonal antibodies (mAb) with an extended half-life targeting distinct epitopes of the spike protein on SARS-CoV-2. Mutations in these epitope regions are continuously emerging, potentially conferring resistance to COVID-19 therapeutics in development. Individual phase I studies showed that BRII-196 or BRII-198 alone were safe and well tolerated in healthy subjects. The BRII-196 and BRII-198 cocktail is currently under evaluation in Phase 2/3 studies for the treatment of COVID-19. Methods Preclinical study: BRII-196 and BRII-198 were evaluated in the microneutralization assay using pseudo-viruses encoding mutations identified in the spike protein of a panel of SARS-CoV-2 variants of concerns, including strains originating in UK, SA, BR, CA, and India. The fold-change in neutralization IC50 titers relative to wild-type virus was calculated. Phase 1 study: healthy adults received sequential IV BRII-196 and BRII-198 (n=9) or placebo (n=3);and were followed for 180 days. Two dose levels (750mg/750mg and 1500mg/1500mg) were evaluated for safety, pharmacokinetics and immunogenicity. Interim analysis results are presented. Results Preclinical: BRII-196 and BRII-198 exhibited neutralizing activity against pseudo-virus variants that contained spike mutations of a panel of variants including B.1.1.7 (UK), B.1.351(SA), P.1(BR), B.1.427/429 (CA), B.1.526 (NY), and B.1.617 (IN), comparable to that against wild-type virus. Phase I study: BRII-196 plus BRII-198 was well tolerated with no dose-limiting adverse events (AEs), deaths, serious adverse events, or infusion reactions. The majority of AEs were isolated asymptomatic grade 1-2 laboratory abnormalities. (Table 1). Each mAb displayed pharmacokinetic characteristics expected of extended half-life YTE-antibodies. Conclusion The BRII-196 and BRII-198 cocktail was well-tolerated, and maintains neutralization against currently reported circulating variants of concern. These preclinical and clinical results support further development of BRII-196 and BRII-198 as a therapeutic or prophylactic option for SARS-CoV-2. Disclosures David A. Margolis, MD MPH, Brii Biosciences (Employee) Yao Zhang, MD, Brii Biosciences (Employee) Yun Ji, PhD, Brii Biosciences (Employee, Shareholder)

7.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-296279

ABSTRACT

This paper proposes a semi-automatic system based on quantitative characterization of the specific image patterns in lung ultrasound (LUS) images, in order to assess the lung conditions of patients with COVID-19 pneumonia, as well as to differentiate between the severe / and no-severe cases. Specifically, four parameters are extracted from each LUS image, namely the thickness (TPL) and roughness (RPL) of the pleural line, and the accumulated with (AWBL) and acoustic coefficient (ACBL) of B lines. 27 patients are enrolled in this study, which are grouped into 13 moderate patients, 7 severe patients and 7 critical patients. Furthermore, the severe and critical patients are regarded as the severe cases, and the moderate patients are regarded as the non-severe cases. Biomarkers among different groups are compared. Each single biomarker and a classifier with all the biomarkers as input are utilized for the binary diagnosis of severe case and non-severe case, respectively. The classifier achieves the best classification performance among all the compared methods (area under the receiver operating characteristics curve = 0.93, sensitivity = 0.93, specificity = 0.85). The proposed image analysis system could be potentially applied to the grading and prognosis evaluation of patients with COVID-19 pneumonia.

8.
Front Psychiatry ; 12: 686177, 2021.
Article in English | MEDLINE | ID: covidwho-1450841

ABSTRACT

Background: Since the Coronavirus disease 2019 (COVID-19) pandemic emerged, Internet usage has increased among adolescents. Due to this trend, the prevalence of Internet addiction disorder (IAD) may have increased within this group. This study examined the prevalence of IAD and its correlates among clinically stable adolescents with psychiatric disorders in China during the COVID-19 outbreak. Method: A multi-center, cross-sectional study was carried out between April 29 and June 9, 2020 in three major tertiary mental health centers in China. IAD and depressive symptoms were assessed using the Internet Addiction Test (IAT) and the 9-item Patient Health Questionnaire (PHQ-9), respectively. Results: A total of 1,454 adolescent psychiatric patients were included in final analyses. The prevalence of IAD was 31.2% (95% CI: 28.8-33.6%) during the COVID-19 pandemic. A multiple logistic regression analysis revealed that poor relationships with parents (P < 0.001, OR = 2.34, 95%CI: 1.49-3.68) and elevated total PHQ-9 scores (P < 0.001, OR = 1.19, 95%CI: 1.16-1.21) were significantly associated with higher risk for IAD while longer daily physical exercise durations (P = 0.04, OR = 0.67, 95%CI: 0.46-0.98) and rural residence (P = 0.003, OR = 0.62, 95%CI: 0.46-0.85) were significant correlates of lower risk for IAD. Conclusions: IAD was common among adolescent patients with clinically stable psychiatric disorders during the COVID-19 pandemic; regular physical exercise, healthy relationships with parents and fewer symptoms of depression were associated with lower risk within this population.

9.
BMC Infect Dis ; 21(1): 1012, 2021 Sep 27.
Article in English | MEDLINE | ID: covidwho-1440914

ABSTRACT

BACKGROUND: The receptor of severe respiratory syndrome coronavirus 2 (SARS-CoV-2), angiotensin-converting enzyme 2, is more abundant in kidney than in lung tissue, suggesting that kidney might be another important target organ for SARS-CoV-2. However, our understanding of kidney injury caused by Coronavirus Disease 2019 (COVID-19) is limited. This study aimed to explore the association between kidney injury and disease progression in patients with COVID-19. METHODS: A retrospective cohort study was designed by including 2630 patients with confirmed COVID-19 from Huoshenshan Hospital (Wuhan, China) from 1 February to 13 April 2020. Kidney function indexes and other clinical information were extracted from the electronic medical record system. Associations between kidney function indexes and disease progression were analyzed using Cox proportional-hazards regression and generalized linear mixed model. RESULTS: We found that estimated glomerular filtration rate (eGFR) and creatinine clearance (Ccr) decreased in 22.0% and 24.0% of patients with COVID-19, respectively. Proteinuria was detected in 15.0% patients and hematuria was detected in 8.1% of patients. Hematuria (HR 2.38, 95% CI 1.50-3.78), proteinuria (HR 2.16, 95% CI 1.33-3.51), elevated baseline serum creatinine (HR 2.84, 95% CI 1.92-4.21) and blood urea nitrogen (HR 3.54, 95% CI 2.36-5.31), and decrease baseline eGFR (HR 1.58, 95% CI 1.07-2.34) were found to be independent risk factors for disease progression after adjusted confounders. Generalized linear mixed model analysis showed that the dynamic trajectories of uric acid was significantly related to disease progression. CONCLUSION: There was a high proportion of early kidney function injury in COVID-19 patients on admission. Early kidney injury could help clinicians to identify patients with poor prognosis at an early stage.


Subject(s)
Acute Kidney Injury , COVID-19 , Cohort Studies , Disease Progression , Humans , Kidney , Retrospective Studies , Risk Factors , SARS-CoV-2
10.
Diabetes Res Clin Pract ; 180: 109041, 2021 Oct.
Article in English | MEDLINE | ID: covidwho-1401412

ABSTRACT

AIMS: We aimed to investigate the role of Fasting Plasma Glucose (FPG) and glucose fluctuation in the prognosis of COVID-19 patients stratified by pre-existing diabetes. METHODS: The associations of FPG and glucose fluctuation indexes with prognosis of COVID-19 in 2,642 patients were investigated by multivariate Cox regression analysis. The primary outcome was in-hospital mortality; the secondary outcome was disease progression. The longitudinal changes of FPG over time were analyzed by the latent growth curve model in COVID-19 patients stratified by diabetes and severity of COVID-19. RESULTS: We found FPG as an independent prognostic factor of overall survival after adjustment for age, sex, diabetes and severity of COVID-19 at admission (HR: 1.15, 95% CI: 1.06-1.25, P = 1.02 × 10-3). Multivariate logistic regression analysis indicated that the standard deviation of blood glucose (SDBG) and largest amplitude of glycemic excursions (LAGE) were also independent risk factors of COVID-19 progression (P = 0.03 and 0.04, respectively). The growth trajectory of FPG over the first 3 days of hospitalization was steeper in patients with critical COVID-19 in comparison to moderate patients. CONCLUSIONS: Hyperglycemia and glucose fluctuation were adverse prognostic factors of COVID-19 regardless of pre-existing diabetes. This stresses the importance of glycemic control in addition to other therapeutic management.


Subject(s)
COVID-19 , Diabetes Mellitus , Blood Glucose , Diabetes Mellitus/epidemiology , Fasting , Glucose , Humans , Prognosis , Retrospective Studies , Risk Factors , SARS-CoV-2
11.
IEEE Trans Ultrason Ferroelectr Freq Control ; 69(1): 73-83, 2022 01.
Article in English | MEDLINE | ID: covidwho-1371802

ABSTRACT

Specific patterns of lung ultrasound (LUS) images are used to assess the severity of coronavirus disease 2019 (COVID-19) pneumonia, while such assessment is mainly based on clinicians' qualitative and subjective observations. In this study, we quantitatively analyze the LUS images to assess the severity of COVID-19 pneumonia by characterizing the patterns related to the pleural line (PL) and B-lines (BLs). Twenty-seven patients with COVID-19 pneumonia, including 13 moderate cases, seven severe cases, and seven critical cases, are enrolled. Features related to the PL, including the thickness (TPL) and roughness of the PL (RPL), and the mean (MPLI) and standard deviation (SDPLI) of the PL intensities are extracted from the LUS images. Features related to the BLs, including the number (NBL), accumulated width (AWBL), attenuation coefficient (ACBL), and accumulated intensity (AIBL) of BLs, are also extracted. The correlations of these features with the disease severity are evaluated. The performances of the binary severe/non-severe classification are assessed for each feature and support vector machine (SVM) classifiers with various combinations of features as input. Several features, including the RPL, NBL, AWBL, and AIBL, show significant correlations with disease severity (all ). The classification performance is optimal using the SVM classifier using all the features as input (area under the receiver operating characteristic (ROC) curve = 0.96, sensitivity = 0.93, and specificity = 1). These findings demonstrate that the proposed method may be a promising tool for automatic grading diagnosis and follow-up of patients with COVID-19 pneumonia.


Subject(s)
COVID-19 , Pneumonia , Humans , Lung/diagnostic imaging , Pneumonia/diagnostic imaging , SARS-CoV-2 , Ultrasonography
12.
Chinese Journal of Nosocomiology ; 30(24):3681-3685, 2020.
Article in English | GIM | ID: covidwho-1318612

ABSTRACT

OBJECTIVE: To analyze the use of antibiotics in patients with coronavirus disease 2019(COVID-19) in Shanghai and to provide evidence for the treatment of COVID-19 and the management of antibacterial drugs. METHODS: The clinical data of 616 patients with COVID-19 in Shanghai Public Health Clinical Center from 20 th, Jan. 2020 to 30 th Apr., 2020 were collected retrospectively, including demographic data, time of admission, time of discharge, and use of antibacterial drugs. All patients were followed up until they were discharged. The frequency of antibacterial drug usage, AUD and the situation of antibacterial using were analyzed. RESULTS: Among 616 patients, 137 were mild, 382 were common, 79 were severe and 18 were critical severe. There were 343 males with an average age of 41.1 years and a median length of stay of 16 days, 273 female cases with an average age of 42.8 years and a median length of stay of 14 days. A total of 165 patients(26.8%) received antibiotics therapy. The usage rates of antibiotics in the mild, common, severe and critical severe subgroups were 4.3%, 21.7%, 73.4% and 100.0%, respectively, which was closely related to clinical classification. The overall usage rates of antibacterial drugs in hospitalized patients gradually decreased with the increase of months. In common patients, the usage rates of antibacterial drugs in March and April were significantly lower than that in January and February. The AUD in all patients was 25.3. As the clinical classification worsened, the AUD in each subgroup gradually increased(0.9, 11.9, 46.2, and 143.8). In total, mild and common patients, the AUD showed a downward trend in January, February, March and April. The total frequency of antibacterial drugs was 286 times, and the top 5 most frequently used drugs were fluoroquinolones, beta-lactamase/beta-lactamase inhibitors, carbapenems, cephalosporins, and linezolid. In severe patients, the antibacterial drugs were mainly restricted use grade antibiotics, and in critical severe patients were mainly special use grade antibiotics. CONCLUSION: In the treatment of COVID-19 patients, the usage rates of antibacterial drugs and AUD were related to clinical classification. As our knowledge and understanding of COVID-19 deepen, our usage rates and strategies of antibacterial drugs are being adjusted, in order to avoid inappropriate use of antibacterial drugs as much as possible.

13.
Cell Discov ; 7(1): 49, 2021 Jul 06.
Article in English | MEDLINE | ID: covidwho-1298837

ABSTRACT

SARS-CoV-2 infection causes a wide spectrum of clinical manifestations in humans, and olfactory dysfunction is one of the most predictive and common symptoms in COVID-19 patients. However, the underlying mechanism by which SARS-CoV-2 infection leads to olfactory disorders remains elusive. Herein, we demonstrate that intranasal inoculation with SARS-CoV-2 induces robust viral replication in the olfactory epithelium (OE), not the olfactory bulb (OB), resulting in transient olfactory dysfunction in humanized ACE2 (hACE2) mice. The sustentacular cells and Bowman's gland cells in the OE were identified as the major target cells of SARS-CoV-2 before invasion into olfactory sensory neurons (OSNs). Remarkably, SARS-CoV-2 infection triggers massive cell death and immune cell infiltration and directly impairs the uniformity of the OE structure. Combined transcriptomic and quantitative proteomic analyses revealed the induction of antiviral and inflammatory responses, as well as the downregulation of olfactory receptor (OR) genes in the OE from the infected animals. Overall, our mouse model recapitulates olfactory dysfunction in COVID-19 patients and provides critical clues for understanding the physiological basis for extrapulmonary manifestations of COVID-19.

14.
Emerging Markets Finance and Trade ; : 1-13, 2021.
Article in English | Taylor & Francis | ID: covidwho-1272890
15.
Front Neurol ; 12: 682729, 2021.
Article in English | MEDLINE | ID: covidwho-1268266

ABSTRACT

Few studies have focused on immune status and disease activity in MS patients during the coronavirus disease 2019 (COVID-19) pandemic. The aim of this study is to investigate immune status, COVID-19 infection, and attacks in MS patients during the pandemic. An online questionnaire about COVID-19 infection, MS attack, and MS treatment during the pandemic was administered to all 525 MS patients registered in our hospital database from January 1, 2011, to June 1, 2020. Only 384 responded, of which 361 patients could be included in the final analysis. During the pandemic, 42.1% of the 361 patients and 65.0% of the 234 patients on immunotherapies were exposed to teriflunomide. Compared to patients who didn't receive treatment, patients exposed to DMTs had significantly lower levels of neutrophils (P < 0.01) and immunoglobulin G (P < 0.01), and patients exposed to immunosuppressants had significantly lower levels of immunoglobulin G (P < 0.05). Over 80% of our patients followed effective protective measures and none of the 361 MS patients in our cohort contracted COVID-19. Patients whose treatment was disrupted had a significantly higher annualized relapse rate (ARR) during than before the pandemic (P < 0.01), while the ARR of patients with continuous treatment or without treatment remained unchanged. During the pandemic, the risk of MS attack due to treatment disruption possibly outweighs the risk of COVID-19 infection under preventive measures, and MS treatment maintenance might be necessary.

16.
Front Neurol ; 12: 657037, 2021.
Article in English | MEDLINE | ID: covidwho-1172972

ABSTRACT

There is an increasing need for better understanding of the impact of coronavirus disease 2019 (COVID-19) on patients with neuromyelitis optica spectrum disorder (NMOSD). A few pilot studies have investigated COVID-19 infections in NMOSD, but few studies have addressed disease activity and immune status of these patients during the pandemic. We carried out a cross-sectional study to examine immune status, relapses, and COVID-19 infections in a cohort of NMOSD patients using an electronic patient registry (MSNMOBase) for multiple sclerosis and related disorders. An online questionnaire was administered to all NMOSD patients in the registry from January 1, 2011, to June 1, 2020. Clinical demographic characteristics, immune status, relapses, treatments, COVID-19 infections, and preventive measures were evaluated. Of the 752 registered patients, 535 (71.1%) with qualified data were included. A total of 486 used preventive therapies during the pandemic, including mycophenolate mofetil (71.2%), azathioprine (13.3%), and other immunosuppressants (6.4%). Neither median immune cell counts nor immunoglobulin levels (p > 0.05) were significantly different between patients with or without immunosuppression. During the pandemic, no patients were diagnosed with COVID-19, and the majority (>95%) took one or more effective protective measures (e.g., wearing a mask and social distancing). However, a significantly higher annualized relapse rate (ARR) was observed in the 33 patients with treatment interruptions due to the pandemic compared to before it (p < 0.05), whereas ARR changes were not found in patients with continuous treatments or those without treatments (p > 0.05). Interruption frequency was significantly higher in patients with relapses compared to those without (34.9 vs. 15.7%, p < 0.01). For stable NMOSD patients during the pandemic, the risk of relapse due to treatment interruption may be higher than the risk of COVID-19 infection when protective measures are used, and continuous relapse-prevention treatments may be necessary.

17.
J Microbiol Immunol Infect ; 54(5): 808-815, 2021 Oct.
Article in English | MEDLINE | ID: covidwho-1164098

ABSTRACT

BACKGROUND: In COVID-19 patients, information regarding superinfection, antimicrobial assessment, and the value of metagenomic sequencing (MS) could help develop antimicrobial stewardship. METHOD: This retrospective study analyzed 323 laboratory-confirmed COVID-19 patients for co-infection rate and antimicrobial usage in the Shanghai Public Health Clinical Center (SPHCC) from January 23rd to March 14th 2020. The microbiota composition was also investigated in patients with critically severe COVID-19. RESULTS: The total population co-infection rate was 17/323 (5.3%) and 0/229 (0), 4/78 (5.1%), and 13/16 (81.3%) for the mild, severe, and critically severe subgroups, respectively. Proven fungal infection was significantly associated with a higher mortality rate (p = 0.029). In critically severe patients, the rate of antimicrobials and carbapenem usage were 16/16 (100%) and 13/16 (81.3%), respectively, in which the preemptive and empiric antimicrobial days accounted for 51.6% and 30.1%, respectively. Targeted therapy only accounted for 18.3%. MS was implemented to detect non-COVID-19 virus co-existence and the semi-quantitative surveillance of bacteremia, with clear clinical benefit seen in cases with MS-based precision antimicrobial management. Airway microbiome analysis suggested that the microbiota compositions in critically severe COVID-19 patients were likely due to intubation and mechanical ventilation. CONCLUSIONS: In the SPHCC cohort, we observed a non-negligible rate of super-infection, especially for the critically ill COVID-19 patients. Fungal co-infection requires intensive attention due to the high risk of mortality, and the clinical benefit of MS in guiding antimicrobial management warrants further investigation.


Subject(s)
Anti-Bacterial Agents/therapeutic use , COVID-19 , Metagenomics , Microbiota/physiology , Respiratory System/microbiology , Superinfection/drug therapy , Adult , Aged , Aged, 80 and over , Antimicrobial Stewardship , China , Cohort Studies , Coinfection/drug therapy , Critical Illness , Female , High-Throughput Nucleotide Sequencing , Humans , Male , Microbiota/genetics , Middle Aged , Mycoses/drug therapy , Retrospective Studies , SARS-CoV-2
18.
BMC Pregnancy Childbirth ; 21(1): 259, 2021 Mar 26.
Article in English | MEDLINE | ID: covidwho-1153993

ABSTRACT

BACKGROUND: Computed tomography (CT) is the preferred imaging technique for the evaluation of COVID-19 pneumonia. However, it is not suitable as a monitoring tool for pregnant women because of the risk of ionizing radiation damage to the fetus as well as the possible infection of others. In this study, we explored the value of bedside lung ultrasound (LUS) as an alternative to CT for the detection and monitoring of lung involvement in pregnant women with COVID-19. METHODS: Clinical and LUS data of 39 pregnant women with COVID-19 were retrospectively reviewed. All LUS and CT images were analyzed to summarize the findings and calculate LUS scores and CT scores for each patient. LUS findings were compared with CT, and correlation between LUS scores and CT scores was evaluated. RESULTS: Among the 39 pregnant women, there were 6 mild-type cases, 29 common-type cases, 4 severe-type cases, and no critical-type cases. The most common LUS findings of COVID-19 pneumonia in pregnant women were various grades of multiple B-lines (84.6%), thickened and irregular pleural lines (71.8%), pleural effusion (61.5%) and small multifocal consolidation limited to the subpleural space (35.9%). The mean LUS score at admission was 0 points in mild-type cases, 10.6 points in common-type cases and 15.3 points in severe-type cases (P < 0.01). The correlation between LUS scores and CT was 0.793. All patients were clinically cured and each underwent an average of three LUS follow-ups during hospitalization. The mean LUS score at discharge was 5.6 points lower than that at admission. The consistency of LUS and chest CT during follow-up was 0.652. CONCLUSIONS: Quantitative LUS scoring can effectively instead of CT for detecting and monitoring of COVID-19 pneumonia in pregnant women and protect fetuses from the risk of ionizing radiation.


Subject(s)
COVID-19/diagnostic imaging , Lung/diagnostic imaging , Pregnancy Complications, Infectious/diagnostic imaging , Ultrasonography/methods , Adult , Female , Hospitalization , Humans , Pregnancy , Retrospective Studies , SARS-CoV-2 , Severity of Illness Index , Tomography, X-Ray Computed , Young Adult
19.
Comput Methods Programs Biomed ; 202: 106004, 2021 Apr.
Article in English | MEDLINE | ID: covidwho-1118366

ABSTRACT

BACKGROUND AND OBJECTIVE: Coronavirus disease 2019 (COVID-19) is a highly contagious virus spreading all around the world. Deep learning has been adopted as an effective technique to aid COVID-19 detection and segmentation from computed tomography (CT) images. The major challenge lies in the inadequate public COVID-19 datasets. Recently, transfer learning has become a widely used technique that leverages the knowledge gained while solving one problem and applying it to a different but related problem. However, it remains unclear whether various non-COVID19 lung lesions could contribute to segmenting COVID-19 infection areas and how to better conduct this transfer procedure. This paper provides a way to understand the transferability of non-COVID19 lung lesions and a better strategy to train a robust deep learning model for COVID-19 infection segmentation. METHODS: Based on a publicly available COVID-19 CT dataset and three public non-COVID19 datasets, we evaluate four transfer learning methods using 3D U-Net as a standard encoder-decoder method. i) We introduce the multi-task learning method to get a multi-lesion pre-trained model for COVID-19 infection. ii) We propose and compare four transfer learning strategies with various performance gains and training time costs. Our proposed Hybrid-encoder Learning strategy introduces a Dedicated-encoder and an Adapted-encoder to extract COVID-19 infection features and general lung lesion features, respectively. An attention-based Selective Fusion unit is designed for dynamic feature selection and aggregation. RESULTS: Experiments show that trained with limited data, proposed Hybrid-encoder strategy based on multi-lesion pre-trained model achieves a mean DSC, NSD, Sensitivity, F1-score, Accuracy and MCC of 0.704, 0.735, 0.682, 0.707, 0.994 and 0.716, respectively, with better genetalization and lower over-fitting risks for segmenting COVID-19 infection. CONCLUSIONS: The results reveal the benefits of transferring knowledge from non-COVID19 lung lesions, and learning from multiple lung lesion datasets can extract more general features, leading to accurate and robust pre-trained models. We further show the capability of the encoder to learn feature representations of lung lesions, which improves segmentation accuracy and facilitates training convergence. In addition, our proposed Hybrid-encoder learning method incorporates transferred lung lesion features from non-COVID19 datasets effectively and achieves significant improvement. These findings promote new insights into transfer learning for COVID-19 CT image segmentation, which can also be further generalized to other medical tasks.


Subject(s)
COVID-19 , Image Processing, Computer-Assisted , Lung/diagnostic imaging , Lung/physiopathology , Tomography, X-Ray Computed , Algorithms , Databases, Factual , Humans , SARS-CoV-2
20.
Aging (Albany NY) ; 13(3): 3176-3189, 2021 02 09.
Article in English | MEDLINE | ID: covidwho-1076957

ABSTRACT

To establish an effective nomogram for predicting in-hospital mortality of COVID-19, a retrospective cohort study was conducted in two hospitals in Wuhan, China, with a total of 4,086 hospitalized COVID-19 cases. All patients have reached therapeutic endpoint (death or discharge). First, a total of 3,022 COVID-19 cases in Wuhan Huoshenshan hospital were divided chronologically into two sets, one (1,780 cases, including 47 died) for nomogram modeling and the other (1,242 cases, including 22 died) for internal validation. We then enrolled 1,064 COVID-19 cases (29 died) in Wuhan Taikang-Tongji hospital for external validation. Independent factors included age (HR for per year increment: 1.05), severity at admission (HR for per rank increment: 2.91), dyspnea (HR: 2.18), cardiovascular disease (HR: 3.25), and levels of lactate dehydrogenase (HR: 4.53), total bilirubin (HR: 2.56), blood glucose (HR: 2.56), and urea (HR: 2.14), which were finally selected into the nomogram. The C-index for the internal resampling (0.97, 95% CI: 0.95-0.98), the internal validation (0.96, 95% CI: 0.94-0.98), and the external validation (0.92, 95% CI: 0.86-0.98) demonstrated the fair discrimination ability. The calibration plots showed optimal agreement between nomogram prediction and actual observation. We established and validated a novel prognostic nomogram that could predict in-hospital mortality of COVID-19 patients.


Subject(s)
COVID-19 , Hospital Mortality , Nomograms , Age Factors , Aged , Blood Chemical Analysis/methods , Blood Chemical Analysis/statistics & numerical data , COVID-19/blood , COVID-19/diagnosis , COVID-19/mortality , COVID-19/physiopathology , Cardiovascular Diseases/epidemiology , China/epidemiology , Female , Hospitalization/statistics & numerical data , Humans , Male , Middle Aged , Prognosis , Retrospective Studies , Risk Assessment/methods , Risk Factors , SARS-CoV-2/isolation & purification , Severity of Illness Index , Survival Analysis , Symptom Assessment/methods , Symptom Assessment/statistics & numerical data
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