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1.
Crit Care ; 26(1): 252, 2022 08 22.
Article in English | MEDLINE | ID: covidwho-2038845

ABSTRACT

Pulmonary microbial diversity may be influenced by biotic or abiotic conditions (e.g., disease, smoking, invasive mechanical ventilation (MV), etc.). Specially, invasive MV may trigger structural and physiological changes in both tissue and microbiota of lung, due to gastric and oral microaspiration, altered body posture, high O2 inhalation-induced O2 toxicity in hypoxemic patients, impaired airway clearance and ventilator-induced lung injury (VILI), which in turn reduce the diversity of the pulmonary microbiota and may ultimately lead to poor prognosis. Furthermore, changes in (local) O2 concentration can reduce the diversity of the pulmonary microbiota by affecting the local immune microenvironment of lung. In conclusion, systematic literature studies have found that invasive MV reduces pulmonary microbiota diversity, and future rational regulation of pulmonary microbiota diversity by existing or novel clinical tools (e.g., lung probiotics, drugs) may improve the prognosis of invasive MV treatment and lead to more effective treatment of lung diseases with precision.


Subject(s)
Lung , Microbiota , Respiration, Artificial , Humans , Lung/microbiology , Respiration, Artificial/adverse effects , Ventilator-Induced Lung Injury/epidemiology
2.
Front Med (Lausanne) ; 9: 900958, 2022.
Article in English | MEDLINE | ID: covidwho-1979045

ABSTRACT

Objective: In order to facilitate education for clinical users, performance aspects of the high-flow nasal cannula (HFNC) devices were evaluated in the present study. A multidimensional HFNC clinical evaluation system was established accordingly. Materials and Methods: Clinical staff from Chinese hospitals were invited to participate in an online questionnaire survey. The questionnaire was mainly about the accuracy of temperature, flow rate, and oxygen concentration of HFNC, as well as its humidification capacity. We also investigated how the clinical staff of different professions made decisions on HFNC evaluation indicators. Based on the results of the questionnaire survey of clinicians with rich experience in using HFNC, the relative weights of temperature accuracy, flow velocity accuracy, oxygen concentration accuracy, and humidification ability of HFNC equipment were calculated by the AHP to establish a clinical evaluation system. Four kinds of common HFNC devices were tested and evaluated, and the clinical performance of the four kinds of HFNC devices was evaluated by the new scoring system. Results: A total of 356 clinicians participated in and completed the questionnaire survey. To ensure the reliability of the HFNC evaluation system, we only adopted the questionnaire results of clinicians with rich experience in using HFNCs. Data from 247 questionnaires (80 doctors, 105 nurses, and 62 respiratory therapists [RTs]) were analyzed. A total of 174 participants used HFNC more than once a week; 88.71% of RTs used HFNC ≥ 1 score daily, 62.86% of nurses used HFNC ≥ 1 score daily, and 66.25% of doctors used HFNC ≥ 1 daily. There was no significant difference in the frequency of use between doctors and nurses. Finally, the relative weights of temperature accuracy (0.088), humidification capacity (0.206), flow velocity accuracy (0.311), and oxygen concentration accuracy (0.395) in the HFNC clinical evaluation system were obtained. The relative weights of clinicians with different occupations and the frequency of HFNC use were obtained. After testing four kinds of HFNC devices through the evaluation system, it was found that the four kinds of HFNC devices have different advantages in different clinical performances, and AiRVO2 has excellent performance with regard to temperature accuracy and humidification ability. HF-75A and NeoHiF-i7 are good at ensuring the stability of oxygen concentration and the accuracy of the flow velocity of the transported gas, while OH-80S is relatively stable in all aspects. Conclusion: The clinical evaluation system of HFNC is based on the weight of the experience of clinical personnel with different medical backgrounds. Although the existing practitioners have different educational backgrounds (academic qualifications, majors), our evaluation system can enhance clinical staff's awareness of HFNC and further optimize the clinical use of HFNC.

3.
Front Immunol ; 12: 753940, 2021.
Article in English | MEDLINE | ID: covidwho-1463477

ABSTRACT

Lung macrophages play important roles in the maintenance of homeostasis, pathogen clearance and immune regulation. The different types of pulmonary macrophages and their roles in lung diseases have attracted attention in recent years. Alveolar macrophages (AMs), including tissue-resident alveolar macrophages (TR-AMs) and monocyte-derived alveolar macrophages (Mo-AMs), as well as interstitial macrophages (IMs) are the major macrophage populations in the lung and have unique characteristics in both steady-state conditions and disease states. The different characteristics of these three types of macrophages determine the different roles they play in the development of disease. Therefore, it is important to fully understand the similarities and differences among these three types of macrophages for the study of lung diseases. In this review, we will discuss the physiological characteristics and unique functions of these three types of macrophages in acute and chronic lung diseases. We will also discuss possible methods to target macrophages in lung diseases.


Subject(s)
COVID-19/immunology , Lung Diseases/immunology , Lung/immunology , Macrophages/immunology , SARS-CoV-2/physiology , Animals , Homeostasis , Humans , Inflammation
4.
Front Med (Lausanne) ; 8: 663608, 2021.
Article in English | MEDLINE | ID: covidwho-1337647

ABSTRACT

Ventilators in the intensive care units (ICU) are life-support devices that help physicians to gain additional time to cure the patients. The aim of the study was to establish a scoring system to evaluate the ventilator performance in the context of COVID-19. The scoring system was established by weighting the ventilator performance on five different aspects: the stability of pressurization, response to leaks alteration, performance of reaction, volume delivery, and accuracy in oxygen delivery. The weighting factors were determined with analytic hierarchy process (AHP). Survey was sent out to 66 clinical and mechanical experts. The scoring system was built based on 54 valid replies. A total of 12 commercially available ICU ventilators providing non-invasive ventilation were evaluated using the novel scoring system. A total of eight ICU ventilators with non-invasive ventilation mode and four dedicated non-invasive ventilators were tested according to the scoring system. Four COVID-19 phenotypes were simulated using the ASL5000 lung simulator, namely (1) increased airway resistance (IR) (10 cm H2O/L/s), (2) low compliance (LC) (compliance of 20 ml/cmH2O), (3) low compliance plus increased respiratory effort (LCIE) (respiratory rate of 40 and inspiratory effort of 10 cmH2O), (4) high compliance (HC) (compliance of 50 ml/cmH2O). All of the ventilators were set to three combinations of pressure support and positive end-expiratory pressure levels. The data were collected at baseline and at three customized leak levels. Significant inaccuracies and variations in performance between different non-invasive ventilators were observed, especially in the aspect of leaks alteration, oxygen and volume delivery. Some ventilators have stable performance in different simulated phenotypes whereas the others have over 10% scoring differences. It is feasible to use the proposed scoring system to evaluate the ventilator performance. In the COVID-19 pandemic, clinicians should be aware of possible strengths and weaknesses of ventilators.

5.
Aerobiologia (Bologna) ; 37(3): 575-583, 2021.
Article in English | MEDLINE | ID: covidwho-1220488

ABSTRACT

To clarify the characteristics and distribution of hospital environmental microbiome associated with confirmed COVID-19 patients. Environmental samples with varying degrees of contamination which were associated with confirmed COVID-19 patients were collected, including 13 aerosol samples collected near eight patients in different wards, five swabs from one patient's skin and his personal belongings, and two swabs from the surface of positive pressure respiratory protective hood and the face shield from a physician who had close contact with one patient. Metagenomic next-generation sequencing (mNGS) was used to analyze the composition of the microbiome. One of the aerosol samples (near patient 4) was detected positive for COVID-19, and others were all negative. The environmental samples collected in different wards possessed protean compositions and community structures, the dominant genera including Pseudomonas, Corynebacterium, Neisseria, Staphylococcus, Acinetobacter, and Cutibacterium. Top 10 of genera accounted for more than 76.72%. Genera abundance and proportion of human microbes and pathogens radiated outward from the patient, while the percentage of environmental microbes increased. The abundance of the pathogenic microorganism of medical supplies is significantly higher than other surface samples. The microbial compositions of the aerosol collected samples nearby the patients were mostly similar to those from the surfaces of the patient's skin and personal belongings, but the abundance varied greatly. The positive rate of COVID-19 RNA detected from aerosol around patients in general wards was quite low. The ward environment was predominantly inhabited by species closely related to admitted patients. The spread of hospital microorganisms via aerosol was influenced by the patients' activity. Supplementary Information: The online version contains supplementary material available at 10.1007/s10453-021-09708-5.

6.
J Inflamm Res ; 14: 1207-1216, 2021.
Article in English | MEDLINE | ID: covidwho-1175488

ABSTRACT

BACKGROUND: Disease severity in COVID-19 ranges from asymptomatic infection to severe disease and death, especially in older subjects. The risk for severe infection and death has been reported to be 2X in those between 30 and 40 years, 3X in those between 40 and 50 years, and 4X in those between 50 and 65 years, compared to the reference group of 18-29 years. OBJECTIVE: To investigate the early changes in host immune responses that are altered with age and the difference in the early host inflammatory response that dictates a symptomatic versus asymptomatic course of COVID-19. PATIENTS AND METHODS: COVID-19 subjects were identified by screening at the airport upon arrival from a foreign destination to China. Patients were either asymptomatic or had a mild disease when the first oro-pharyngeal (OP) swab samples were collected. Patients were quarantined and blood and throat swabs were collected during the course of the disease, allowing identification of the earliest host response to COVID-19. These patients were followed until their OP sample turned COVID-19 negative. RESULTS: Data were obtained from 126 PCR-confirmed COVID-19 patients. The blood samples were obtained within 48 days of qPCR confirmation of viral infection. Older subjects (>30 years) had significantly elevated levels of anti-inflammatory cytokine IL-10, a significant decrease in the percentage of CD8+ T cells, and expansion in NKT cell fraction. This was associated with significantly elevated viral load and a delayed humoral response in older subjects. Compared to symptomatic subjects, asymptomatic patients had an early increase in pro-inflammatory cytokine IL-2, while a decrease in both T regulatory cells and anti-inflammatory cytokine IL-10. Further, asymptomatic disease was associated with early humoral response and faster viral clearance. CONCLUSION: Early inflammatory response potentially plays a critical role for host-defense in COVID-19. The impaired early inflammatory response was associated with older age while a robust early inflammation was associated with asymptomatic disease.

8.
J Med Internet Res ; 22(11): e23128, 2020 11 11.
Article in English | MEDLINE | ID: covidwho-976118

ABSTRACT

BACKGROUND: Patients with COVID-19 in the intensive care unit (ICU) have a high mortality rate, and methods to assess patients' prognosis early and administer precise treatment are of great significance. OBJECTIVE: The aim of this study was to use machine learning to construct a model for the analysis of risk factors and prediction of mortality among ICU patients with COVID-19. METHODS: In this study, 123 patients with COVID-19 in the ICU of Vulcan Hill Hospital were retrospectively selected from the database, and the data were randomly divided into a training data set (n=98) and test data set (n=25) with a 4:1 ratio. Significance tests, correlation analysis, and factor analysis were used to screen 100 potential risk factors individually. Conventional logistic regression methods and four machine learning algorithms were used to construct the risk prediction model for the prognosis of patients with COVID-19 in the ICU. The performance of these machine learning models was measured by the area under the receiver operating characteristic curve (AUC). Interpretation and evaluation of the risk prediction model were performed using calibration curves, SHapley Additive exPlanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), etc, to ensure its stability and reliability. The outcome was based on the ICU deaths recorded from the database. RESULTS: Layer-by-layer screening of 100 potential risk factors finally revealed 8 important risk factors that were included in the risk prediction model: lymphocyte percentage, prothrombin time, lactate dehydrogenase, total bilirubin, eosinophil percentage, creatinine, neutrophil percentage, and albumin level. Finally, an eXtreme Gradient Boosting (XGBoost) model established with the 8 important risk factors showed the best recognition ability in the training set of 5-fold cross validation (AUC=0.86) and the verification queue (AUC=0.92). The calibration curve showed that the risk predicted by the model was in good agreement with the actual risk. In addition, using the SHAP and LIME algorithms, feature interpretation and sample prediction interpretation algorithms of the XGBoost black box model were implemented. Additionally, the model was translated into a web-based risk calculator that is freely available for public usage. CONCLUSIONS: The 8-factor XGBoost model predicts risk of death in ICU patients with COVID-19 well; it initially demonstrates stability and can be used effectively to predict COVID-19 prognosis in ICU patients.


Subject(s)
COVID-19/epidemiology , Machine Learning/standards , Algorithms , Female , Humans , Intensive Care Units , Male , Prognosis , Reproducibility of Results , Retrospective Studies , Risk Factors
9.
EClinicalMedicine ; 26: 100529, 2020 Sep.
Article in English | MEDLINE | ID: covidwho-773737
10.
Ann Transl Med ; 8(14): 881, 2020 Jul.
Article in English | MEDLINE | ID: covidwho-721677

ABSTRACT

BACKGROUND: Coronavirus disease 2019 (COVID-19) has become a worldwide pandemic, affecting countries across the globe. With no current vaccine, treatment is still a critical intervention for minimizing morbidity and preventing disease-specific mortality. This study aimed to assess the clinical outcomes of critically ill COVID-19 patients using Tocilizumab treatment to provide recommendations for the treatment of COVID-19 patients with severe disease. METHODS: This was a retrospective analysis of medical records of six critically ill patients admitted to the Third People's Hospital of Shenzhen, China, from January 11 to February 26, 2020. Patient-related outcomes, including demographic, clinical, and laboratory characteristics before and after the initiation of Tocilizumab, were descriptively analyzed. Four to eight milligrams (mg)/kilogram (kg) of Tocilizumab was prescribed, with Chinese treatment guidelines. RESULTS: By the end of the last follow-up, Patient 1 and Patient 2 developed complications and died after using Tocilizumab for three to four days. Patient 4 died of multiple organ failure caused by cerebral infarction after using Tocilizumab for 39 days. Patient 3 and Patient 6 were discharged after 29 days and 33 days on Tocilizumab, respectively. Clinical symptoms, including fever, heart rate, and oxygen levels, improved after Tocilizumab use. Two patients appeared transient abnormal of liver or renal function indicator, and they can gradually recover. All elevated serum levels of inflammatory factors gradually decreased, except in Patient 2. Patient 3 and Patient 6's inflammatory lesions also significantly improved after initiating Tocilizumab. CONCLUSIONS: Anti-inflammatory treatment with Tocilizumab was found to improve inflammatory responses in critically ill COVID-19 patients. Although some side reactions will occur, patients can gradually recover without affecting the efficacy of the therapy. However, the proper timing to start patients on Tocilizumab patients should be explored. Further prospective, randomized controlled clinical trials are called for.

11.
Stem Cell Res Ther ; 11(1): 305, 2020 07 22.
Article in English | MEDLINE | ID: covidwho-662506

ABSTRACT

Acute respiratory distress syndrome (ARDS) develops rapidly and has a high mortality rate. Survivors usually have low quality of life. Current clinical management strategies are respiratory support and restricted fluid input, and there is no suggested pharmacological treatment. Mesenchymal stromal cells (MSCs) have been reported to be promising treatments for lung diseases. MSCs have been shown to have a number of protective effects in some animal models of ARDS by releasing soluble, biologically active factors. In this review, we will focus on clinical progress in the use of MSCs as a cell therapy for ARDS, which may have clinical implications during the coronavirus disease 2019 (COVID-19) pandemic.


Subject(s)
Cell- and Tissue-Based Therapy/methods , Coronavirus Infections/therapy , Mesenchymal Stem Cell Transplantation/methods , Pneumonia, Viral/therapy , Respiratory Distress Syndrome/therapy , Betacoronavirus , COVID-19 , Cytokine Release Syndrome/therapy , Humans , Mesenchymal Stem Cells , Pandemics , SARS-CoV-2
13.
J Allergy Clin Immunol Pract ; 8(8): 2585-2591.e1, 2020 09.
Article in English | MEDLINE | ID: covidwho-609222

ABSTRACT

BACKGROUND: The clinical management of coronavirus disease 2019 (COVID-19) is dependent on understanding the underlying factors that contribute to the disease severity. In the absence of effective antiviral therapies, other host immunomodulatory therapies such as targeting inflammatory response are currently being used without clear evidence of their effectiveness. Because inflammation is an essential component of host antiviral mechanisms, therapies targeting inflammation may adversely affect viral clearance and disease outcome. OBJECTIVE: To understand whether the persistent presence of the virus is a key determinant in the disease severity during COVID-19 and to determine whether the viral reactivation in some patients is associated with infectious viral particles. METHODS: The data for patients were available including the onset of the disease, duration of viral persistence, measurements of inflammatory markers such as IL-6 and C-reactive protein, chest imaging, disease symptoms, and their durations among others. Follow-up tests were performed to determine whether the viral negative status persists after their recovery. RESULTS: Our data show that patients with persistent viral presence (>16 days) have more severe disease outcomes including extensive lung involvement and requirement of respiratory support. Two patients who died of COVID-19 were virus-positive at the time of their death. Four patients demonstrated virus-positive status on the follow-up tests, and these patient samples were sent to viral culture facility where virus culture could not be established. CONCLUSIONS: These data suggest that viral persistence is the key determining factor of the disease severity. Therapies that may impair the viral clearance may impair the host recovery from COVID-19.


Subject(s)
Coronavirus Infections/physiopathology , Inflammation/physiopathology , Pneumonia, Viral/physiopathology , Adolescent , Adult , Aged , Betacoronavirus , C-Reactive Protein/immunology , COVID-19 , Child , Child, Preschool , Comorbidity , Coronavirus Infections/diagnostic imaging , Coronavirus Infections/epidemiology , Coronavirus Infections/immunology , Female , Glucocorticoids/therapeutic use , Humans , Infant , Inflammation/epidemiology , Inflammation/immunology , Inflammation Mediators/immunology , Interleukin-6/immunology , Male , Middle Aged , Pandemics , Pneumonia, Viral/diagnostic imaging , Pneumonia, Viral/epidemiology , Pneumonia, Viral/immunology , Real-Time Polymerase Chain Reaction , Respiration, Artificial , SARS-CoV-2 , Severity of Illness Index , Young Adult
14.
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