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1.
Zhonghua Yu Fang Yi Xue Za Zhi ; 55(12): 1496-1499, 2021 Dec 06.
Article in Chinese | MEDLINE | ID: covidwho-1600035

ABSTRACT

A questionnaire was used to investigate the emergency training needs of novel coronavirus pneumonia of disease prevention and control institutions in provinces, deputy provincial level regions and cities specifically designated in the state plan, and the effect evaluation of emergency training activities conducted by Chinese Center for Disease Control and Prevention (China CDC). The results showed that 67.4% of 47 disease prevention and control institutions (31/46) believed that the emergency training at the initial stage of the epidemic should be conducted as soon as possible, and the form of network training should be given priority. The training should focus on the urgently needed technologies such as epidemiological investigation, formulation and response of prevention and control strategies, laboratory testing, etc. The teaching materials should highlight pertinence and practicability and be presented in the form of electronic video. The average satisfaction score of the video training conducted by China CDC was (8.81±1.125) and the score of audio-video courseware was (8.97±0.893). The needs analysis and evaluation of novel coronavirus pneumonia prevention and control in disease prevention and control institutions could provide reference for the follow-up training and improve the emergency training management.


Subject(s)
COVID-19 , Pneumonia , China/epidemiology , Humans , Pneumonia/prevention & control , SARS-CoV-2 , Surveys and Questionnaires
2.
J Healthc Eng ; 2021: 3514821, 2021.
Article in English | MEDLINE | ID: covidwho-1595649

ABSTRACT

The World Health Organization (WHO) recognized COVID-19 as the cause of a global pandemic in 2019. COVID-19 is caused by SARS-CoV-2, which was identified in China in late December 2019 and is indeed referred to as the severe acute respiratory syndrome coronavirus-2. The whole globe was hit within several months. As millions of individuals around the world are infected with COVID-19, it has become a global health concern. The disease is usually contagious, and those who are infected can quickly pass it on to others with whom they come into contact. As a result, monitoring is an effective way to stop the virus from spreading further. Another disease caused by a virus similar to COVID-19 is pneumonia. The severity of pneumonia can range from minor to life-threatening. This is particularly hazardous for children, people over 65 years of age, and those with health problems or immune systems that are affected. In this paper, we have classified COVID-19 and pneumonia using deep transfer learning. Because there has been extensive research on this subject, the developed method concentrates on boosting precision and employs a transfer learning technique as well as a model that is custom-made. Different pretrained deep convolutional neural network (CNN) models were used to extract deep features. The classification accuracy was used to measure performance to a great extent. According to the findings of this study, deep transfer learning can detect COVID-19 and pneumonia from CXR images. Pretrained customized models such as MobileNetV2 had a 98% accuracy, InceptionV3 had a 96.92% accuracy, EffNet threshold had a 94.95% accuracy, and VGG19 had a 92.82% accuracy. MobileNetV2 has the best accuracy of all of these models.


Subject(s)
COVID-19 , Deep Learning , Pneumonia , Child , Humans , Pandemics , Pneumonia/diagnosis , SARS-CoV-2
3.
Medicine (Baltimore) ; 100(52): e28470, 2021 Dec 30.
Article in English | MEDLINE | ID: covidwho-1592821

ABSTRACT

INTRODUCTION: The outbreak of novel coronavirus (severe acute respiratory syndrome coronavirus 2), which causes the coronavirus disease 2019 (COVID-19), is the most important current health problem. The number of patients is increasing worldwide. Pneumonia is the most life-threatening complication of the disease. Prolonged viral shedding in hematological patients with COVID-19 has been demonstrated; however, data on COVID-19 patients receiving anti-CD20 monoclonal antibody therapy are limited. Accordingly, focusing on humoral immunity, herein, we present 4 COVID-19 patients who were on anti-CD20 monoclonal antibody treatment and had prolonged pneumonia. PATIENT CONCERNS: Two of 4 patients were on rituximab and the other 2 were on obinutuzumab therapy. DIAGNOSIS: The polymerase chain reaction test results for severe acute respiratory syndrome coronavirus 2 were positive for all 4 patients and their COVID pneumonia lasted for >50 days. INTERVENTIONS: Although all patients were treated with an adequate amount of convalescent plasma, prolonged polymerase chain reaction positivity and prolonged pneumonia were possibly due to the lack of ability of the immune system to initiate its antibody response. OUTCOMES: Despite the administration of standard therapies, recurrent pneumonia observed in the present case series of non-neutropenic patients, in whom primary malignancies were under control. CONCLUSIONS: It is suggested that further investigations should be performed to understand the underlying pathophysiology.


Subject(s)
Antibodies, Monoclonal, Humanized/therapeutic use , Antibodies, Monoclonal/therapeutic use , Antineoplastic Agents, Immunological/therapeutic use , COVID-19/drug therapy , Pneumonia/epidemiology , Rituximab/therapeutic use , Adult , Aged , COVID-19/diagnosis , COVID-19/therapy , COVID-19 Nucleic Acid Testing , Female , Humans , Immunization, Passive , Middle Aged , Polymerase Chain Reaction , Recurrence , SARS-CoV-2 , Treatment Outcome
4.
PLoS One ; 16(11): e0259910, 2021.
Article in English | MEDLINE | ID: covidwho-1581787

ABSTRACT

BACKGROUND: Clinical observations have shown that there is a relationship between coronavirus disease 2019 (COVID-19) and atypical lymphocytes in the peripheral blood; however, knowledge about the time course of the changes in atypical lymphocytes and the association with the clinical course of COVID-19 is limited. OBJECTIVE: Our purposes were to investigate the dynamics of atypical lymphocytes in COVID-19 patients and to estimate their clinical significance for diagnosis and monitoring disease course. MATERIALS AND METHODS: We retrospectively identified 98 inpatients in a general ward at Kashiwa Municipal Hospital from May 1st, 2020, to October 31st, 2020. We extracted data on patient demographics, symptoms, comorbidities, blood test results, radiographic findings, treatment after admission and clinical course. We compared clinical findings between patients with and without atypical lymphocytes, investigated the behavior of atypical lymphocytes throughout the clinical course of COVID-19, and determined the relationships among the development of pneumonia, the use of supplemental oxygen and the presence of atypical lymphocytes. RESULTS: Patients with atypical lymphocytes had a significantly higher prevalence of pneumonia (80.4% vs. 42.6%, p < 0.0001) and the use of supplemental oxygen (25.5% vs. 4.3%, p = 0.0042). The median time to the appearance of atypical lymphocytes after disease onset was eight days, and atypical lymphocytes were observed in 16/98 (16.3%) patients at the first visit. Atypical lymphocytes appeared after the confirmation of lung infiltrates in 31/41 (75.6%) patients. Of the 13 oxygen-treated patients with atypical lymphocytes, approximately two-thirds had a stable or improved clinical course after the appearance of atypical lymphocytes. CONCLUSION: Atypical lymphocytes frequently appeared in the peripheral blood of COVID-19 patients one week after disease onset. Patients with atypical lymphocytes were more likely to have pneumonia and to need supplemental oxygen; however, two-thirds of them showed clinical improvement after the appearance of atypical lymphocytes.


Subject(s)
COVID-19/diagnosis , Leukocyte Disorders/diagnosis , Pneumonia/diagnosis , Respiratory Tract Infections/diagnosis , Adult , COVID-19/complications , COVID-19/epidemiology , COVID-19/virology , Female , Hospitalization , Humans , Intensive Care Units , Leukocyte Disorders/complications , Leukocyte Disorders/epidemiology , Leukocyte Disorders/virology , Leukocytes, Mononuclear/pathology , Lymphocytes/pathology , Male , Middle Aged , Oxygen/blood , Pneumonia/blood , Pneumonia/epidemiology , Pneumonia/virology , Respiratory Tract Infections/complications , Respiratory Tract Infections/epidemiology , Respiratory Tract Infections/virology , SARS-CoV-2/pathogenicity
5.
Front Immunol ; 12: 784028, 2021.
Article in English | MEDLINE | ID: covidwho-1581324

ABSTRACT

Background: Extracellular vesicles (EVs) are mediators of cell-to-cell communication in inflammatory lung diseases. They function as carriers for miRNAs which regulate mRNA transcripts and signaling pathways after uptake into recipient cells. We investigated whether miRNAs associated with circulating EVs regulate immunologic processes in COVID-19. Methods: We prospectively studied 20 symptomatic patients with COVID-19 pneumonia, 20 mechanically ventilated patients with severe COVID-19 (severe acute respiratory corona virus-2 syndrome, ARDS) and 20 healthy controls. EVs were isolated by precipitation, total RNA was extracted, profiled by small RNA sequencing and evaluated by differential gene expression analysis (DGE). Differentially regulated miRNAs between groups were bioinformatically analyzed, mRNA target transcripts identified and signaling networks constructed, thereby comparing COVID-19 pneumonia to the healthy state and pneumonia to severe COVID-19 ARDS. Results: DGE revealed 43 significantly and differentially expressed miRNAs (25 downregulated) in COVID-19 pneumonia when compared to controls, and 20 miRNAs (15 downregulated) in COVID-19 ARDS patients in comparison to those with COVID-19 pneumonia. Network analysis for comparison of COVID-19 pneumonia to healthy controls showed upregulated miR-3168 (log2FC=2.28, padjusted<0.001), among others, targeting interleukin-6 (IL6) (25.1, 15.2 - 88.2 pg/ml in COVID-19 pneumonia) and OR52N2, an olfactory smell receptor in the nasal epithelium. In contrast, miR-3168 was significantly downregulated in COVID-19 ARDS (log2FC=-2.13, padjusted=0.003) and targeted interleukin-8 (CXCL8) in a completely activated network. Toll-like receptor 4 (TLR4) was inhibited in COVID-19 pneumonia by miR-146a-5p and upregulated in ARDS by let-7e-5p. Conclusion: EV-derived miRNAs might have important regulative functions in the pathophysiology of COVID-19: CXCL8 regulates neutrophil recruitment into the lung causing epithelial damage whereas activated TLR4, to which SARS-CoV-2 spike protein binds strongly, increases cell surface ACE2 expression and destroys type II alveolar cells that secrete pulmonary surfactants; both resulting in pulmonary-capillary leakage and ARDS. These miRNAs may serve as biomarkers or as possible therapeutic targets.


Subject(s)
Biomarkers/blood , COVID-19/immunology , Extracellular Vesicles/immunology , MicroRNAs/immunology , Aged , Aged, 80 and over , COVID-19/pathology , Disease Progression , Female , Humans , Male , Middle Aged , Pneumonia/immunology , Pneumonia/pathology , SARS-CoV-2 , Signal Transduction/immunology
6.
Tuberk Toraks ; 69(4): 492-498, 2021 Dec.
Article in English | MEDLINE | ID: covidwho-1580009

ABSTRACT

Introduction: To date, there is limited data on the long-term changes in the lungs of patients recovering from coronavirus (COVID-19) pneumonia. In order to evaluate pulmonary sequelae, it was planned to investigate fibrotic changes observed as sequelae in lung tissue in 3-6-month control thorax computerized tomography (CT) scans of moderate-to-severe COVID-19 pneumonia survivors. Materials and Methods: A total of 84 patients (mean age: 67.3 years ±15) with moderate-to-severe pneumonia on chest tomography at the time of diagnosis were included in the study, of which 51 (61%) were males and 33 (39%) were females. Initial and follow-up CT scans averaged 8.3 days ± 2.2 and 112.1 days ± 14.6 after symptom onset, respectively. Participants were recorded in two groups as those with and without fibrotic-like changes such as traction bronchiectasis, fibrotic - parenchymal bands, honeycomb appearance according to 3-6 months follow-up CT scans. Differences between the groups were evaluated with a two-sampled t-test. Logistic regression analyzes were performed to determine independent predictive factors of fibrotic-like sequelae changes. Result: On follow-up CTs, fibrotic-like changes were observed in 29 (35%) of the 84 participants (Group 1), while the remaining 55 (65%) showed complete radiological recovery (Group 2). With logistic regression analysis, hospital stay of 22 days or longer (OR: 4.9; 95% CI: 20, 32; p< 0.05) and a CT score of 15 or more at diagnosis (OR: 2.2; 95% CI: 13.5, 18; p< 0.05) were found to be an independent predictor for sequelae fibrotic changes in lung tissue. Conclusions: More than one-third of patients who survived COVID-19 pneumonia had fibrotic-like sequelae changes in the lung parenchyma. These changes were found to be associated with the presence of severe pneumonia at the time of diagnosis and longer hospital stay.


Subject(s)
COVID-19 , Pneumonia , Aged , Female , Follow-Up Studies , Humans , Lung/diagnostic imaging , Male , Pneumonia/diagnostic imaging , Retrospective Studies , SARS-CoV-2 , Thorax , Tomography, X-Ray Computed
7.
Med Clin (Barc) ; 157(3): 99-105, 2021 08 13.
Article in English, Spanish | MEDLINE | ID: covidwho-1575444

ABSTRACT

OBJECTIVES: Compare the accuracy of PSI, CURB-65, MuLBSTA and COVID-GRAM prognostic scores to predict mortality, the need for invasive mechanical ventilation in patients with pneumonia caused by SARS-CoV-2 and assess the coexistence of bacterial respiratory tract infection during admission. METHODS: Retrospective observational study that included hospitalized adults with pneumonia caused by SARS-CoV-2 from 15/03 to 15/05/2020. We excluded immunocompromised patients, nursing home residents and those admitted in the previous 14 days for another reasons. Analysis of ROC curves was performed, calculating the area under the curve for the different scales, as well as sensitivity, specificity and predictive values. RESULTS: A total of 208 patients were enrolled, aged 63±17 years, 57,7% were men; 38 patients were admitted to ICU (23,5%), of these patients 33 required invasive mechanical ventilation (86,8%), with an overall mortality of 12,5%. Area under the ROC curves for mortality of the scores were: PSI 0,82 (95% CI: 0,73-0,91), CURB-65 0,82 (0,73-0,91), MuLBSTA 0,72 (0,62-0,81) and COVID-GRAM 0,86 (0,70-1). Area under the curve for needing invasive mechanical ventilation was: PSI 0,73 (95% CI: 0,64-0,82), CURB-65 0,66 (0,55-0,77), MuLBSTA 0,78 (0,69-0,86) and COVID-GRAM 0,76 (0,67-0,85), respectively. Patients with bacterial co-infections of the respiratory tract were 20 (9,6%), the most frequent strains being Pseudomonas aeruginosa and Klebsiella pneumoniae. CONCLUSIONS: In our study, the COVID-GRAM score was the most accurate to identify patients with higher mortality with pneumonia caused by SARS-CoV-2; however, none of these scores accurately predicts the need for invasive mechanical ventilation with ICU admission. The 10% of patients admitted presented bacterial respiratory co-infection.


Subject(s)
COVID-19 , Pneumonia , Aged , COVID-19/pathology , Female , Hospitalization , Humans , Male , Middle Aged , Pneumonia/pathology , Respiration, Artificial , Retrospective Studies , Severity of Illness Index
8.
PLoS One ; 16(3): e0247839, 2021.
Article in English | MEDLINE | ID: covidwho-1574949

ABSTRACT

As SARS-CoV-2 has spread quickly throughout the world, the scientific community has spent major efforts on better understanding the characteristics of the virus and possible means to prevent, diagnose, and treat COVID-19. A valid approach presented in the literature is to develop an image-based method to support COVID-19 diagnosis using convolutional neural networks (CNN). Because the availability of radiological data is rather limited due to the novelty of COVID-19, several methodologies consider reduced datasets, which may be inadequate, biasing the model. Here, we performed an analysis combining six different databases using chest X-ray images from open datasets to distinguish images of infected patients while differentiating COVID-19 and pneumonia from 'no-findings' images. In addition, the performance of models created from fewer databases, which may imperceptibly overestimate their results, is discussed. Two CNN-based architectures were created to process images of different sizes (512 × 512, 768 × 768, 1024 × 1024, and 1536 × 1536). Our best model achieved a balanced accuracy (BA) of 87.7% in predicting one of the three classes ('no-findings', 'COVID-19', and 'pneumonia') and a specific balanced precision of 97.0% for 'COVID-19' class. We also provided binary classification with a precision of 91.0% for detection of sick patients (i.e., with COVID-19 or pneumonia) and 98.4% for COVID-19 detection (i.e., differentiating from 'no-findings' or 'pneumonia'). Indeed, despite we achieved an unrealistic 97.2% BA performance for one specific case, the proposed methodology of using multiple databases achieved better and less inflated results than from models with specific image datasets for training. Thus, this framework is promising for a low-cost, fast, and noninvasive means to support the diagnosis of COVID-19.


Subject(s)
COVID-19/diagnostic imaging , Databases, Factual , Neural Networks, Computer , Pneumonia/diagnostic imaging , Algorithms , Bias , Deep Learning , Humans , Image Interpretation, Computer-Assisted , Radiography, Thoracic
9.
J Med Internet Res ; 23(2): e23390, 2021 02 22.
Article in English | MEDLINE | ID: covidwho-1574113

ABSTRACT

BACKGROUND: The initial symptoms of patients with COVID-19 are very much like those of patients with community-acquired pneumonia (CAP); it is difficult to distinguish COVID-19 from CAP with clinical symptoms and imaging examination. OBJECTIVE: The objective of our study was to construct an effective model for the early identification of COVID-19 that would also distinguish it from CAP. METHODS: The clinical laboratory indicators (CLIs) of 61 COVID-19 patients and 60 CAP patients were analyzed retrospectively. Random combinations of various CLIs (ie, CLI combinations) were utilized to establish COVID-19 versus CAP classifiers with machine learning algorithms, including random forest classifier (RFC), logistic regression classifier, and gradient boosting classifier (GBC). The performance of the classifiers was assessed by calculating the area under the receiver operating characteristic curve (AUROC) and recall rate in COVID-19 prediction using the test data set. RESULTS: The classifiers that were constructed with three algorithms from 43 CLI combinations showed high performance (recall rate >0.9 and AUROC >0.85) in COVID-19 prediction for the test data set. Among the high-performance classifiers, several CLIs showed a high usage rate; these included procalcitonin (PCT), mean corpuscular hemoglobin concentration (MCHC), uric acid, albumin, albumin to globulin ratio (AGR), neutrophil count, red blood cell (RBC) count, monocyte count, basophil count, and white blood cell (WBC) count. They also had high feature importance except for basophil count. The feature combination (FC) of PCT, AGR, uric acid, WBC count, neutrophil count, basophil count, RBC count, and MCHC was the representative one among the nine FCs used to construct the classifiers with an AUROC equal to 1.0 when using the RFC or GBC algorithms. Replacing any CLI in these FCs would lead to a significant reduction in the performance of the classifiers that were built with them. CONCLUSIONS: The classifiers constructed with only a few specific CLIs could efficiently distinguish COVID-19 from CAP, which could help clinicians perform early isolation and centralized management of COVID-19 patients.


Subject(s)
COVID-19/diagnosis , Community-Acquired Infections/diagnosis , Machine Learning , Pneumonia/diagnosis , SARS-CoV-2/pathogenicity , Area Under Curve , COVID-19/blood , COVID-19/virology , Community-Acquired Infections/blood , Female , Humans , Laboratories , Leukocyte Count , Logistic Models , Male , Middle Aged , Pneumonia/blood , Procalcitonin/blood , ROC Curve , Retrospective Studies
10.
J Med Microbiol ; 70(12)2021 Dec.
Article in English | MEDLINE | ID: covidwho-1570171

ABSTRACT

Introduction. During the early days of coronavirus disease 2019 (COVID-19) in Singapore, Tan Tock Seng Hospital implemented an enhanced pneumonia surveillance (EPS) programme enrolling all patients who were admitted from the Emergency Department (ED) with a diagnosis of pneumonia but not meeting the prevalent COVID-19 suspect case definition.Hypothesis/Gap Statement. There is a paucity of data supporting the implementation of such a programme.Aims. To compare and contrast our hospital-resource utilization of an EPS programme for COVID-19 infection detection with a suitable comparison group.Methodology. We enrolled all patients admitted under the EPS programme from TTSH's ED from 7 February 2020 (date of EPS implementation) to 20 March 2020 (date of study ethics application) inclusive. We designated a comparison cohort over a similar duration the preceding year. Relevant demographic and clinical data were extracted from the electronic medical records.Results. There was a 3.2 times higher incidence of patients with an admitting diagnosis of pneumonia from the ED in the EPS cohort compared to the comparison cohort (P<0.001). However, there was no significant difference in the median length of stay of 7 days (P=0.160). Within the EPS cohort, stroke and fluid overload occur more frequently as alternative primary diagnoses.Conclusions. Our study successfully evaluated our hospital-resource utilization demanded by our EPS programme in relation to an appropriate comparison group. This helps to inform strategic use of hospital resources to meet the needs of both COVID-19 related services and essential 'peace-time' healthcare services concurrently.


Subject(s)
COVID-19 , Epidemiological Monitoring , Health Resources/organization & administration , Pneumonia , Emergency Service, Hospital , Hospitalization , Hospitals , Humans , Pandemics , Pneumonia/diagnosis , Pneumonia/epidemiology , Retrospective Studies , Singapore
11.
Turk J Med Sci ; 51(5): 2274-2284, 2021 10 21.
Article in English | MEDLINE | ID: covidwho-1566690

ABSTRACT

Background/aim: COVID-19 patients have a wide spectrum of disease severity. Several biomarkers were evaluated as predictors for progression towards severe disease. IL-21 is a member of common γ-chain cytokine family and creates some specific effects during programming and maintenance of antiviral immunity. We aimed to assess IL-21 as a biomarker for diagnosis and outcome prediction in patients hospitalized with COVID-19. Materials and methods: Patients with a preliminary diagnosis of COVID-19 and pneumonia other than COVID-19 admitted to a tertiary care hospital were included consecutively in this comparative study. Results: The study population consisted of 51 patients with COVID-19 and 11 patients with non-COVID-19 pneumonia. Serum IL-21 concentration was markedly higher, and serum CRP concentration was significantly lower in COVID-19 patients compared to non-COVID-19 pneumonia patients. Within COVID-19 patients, 10 patients showed radiological and clinical progression. Patients with clinical worsening had lower lymphocyte count and haemoglobin. In addition to that, deteriorating patients had higher urea, LDH levels, and elevated concentration of both IL-6 and IL-21. The cut-off value of 106 ng/L for IL-21 has 80.0% sensitivity, %60.9 specificity for discriminating patients with clinical worsening. Multivariable analysis performed to define risk factors for disease progression identified IL-6 and IL-21 as independent predictors. Odds ratio for serum IL-6 concentrations ≥ 3.2 pg/mL was 8.07 (95% CI: 1.37-47.50, p = 0.04) and odds ratio for serum IL-21 concentrations ≥ 106 ng/L was 6.24 (95% CI: 1.04 ­ 37.3, p = 0.02). Conclusion: We identified specific differences in serum IL-21 between COVID-19 and non-COVID-19 pneumonia patients. Serum IL-21 measurement has promising predictive value for disease progression in COVID-19 patients. High serum IL-6 and IL-21 levels obtained upon admission are independent risk factors for clinical worsening.


Subject(s)
COVID-19/diagnosis , Interleukins/blood , Adult , Aged , Biomarkers/blood , COVID-19/blood , Diagnosis, Differential , Female , Humans , Male , Middle Aged , Pneumonia/blood , Pneumonia/diagnosis , Prognosis
12.
Stud Health Technol Inform ; 285: 112-117, 2021 Oct 27.
Article in English | MEDLINE | ID: covidwho-1566635

ABSTRACT

Today pneumonia is one of the main problems of all countries around the world. This disease can lead to early disability, serious complications, and severe cases of high probabilities of lethal outcomes. A big part of cases of pneumonia are complications of COVID-19 disease. This type of pneumonia differs from ordinary pneumonia in symptoms, clinical course, and severity of complications. For optimal treatment of disease, humans need to study specific features of providing 19 pneumonia in comparison with well-studied ordinary pneumonia. In this article, the authors propose a new approach to identifying these specific features. This method is based on creating dynamic disease models for COVID and non-COVID pneumonia based on Bayesian Network design and Hidden Markov Model architecture and their comparison. We build models using real hospital data. We created a model for automatically identifying the type of pneumonia (COVID-19 or ordinary pneumonia) without special COVID tests. And we created dynamic models for simulation future development of both types of pneumonia. All created models showed high quality. Therefore, they can be used as part of decision support systems for medical specialists who work with pneumonia patients.


Subject(s)
COVID-19 , Pneumonia , Bayes Theorem , COVID-19/diagnosis , Forecasting , Humans , Pneumonia/diagnosis
15.
Cells ; 10(12)2021 11 25.
Article in English | MEDLINE | ID: covidwho-1542428

ABSTRACT

Acute respiratory distress syndrome (ARDS) is a serious lung condition characterized by severe hypoxemia leading to limitations of oxygen needed for lung function. In this study, we investigated the effect of anandamide (AEA), an endogenous cannabinoid, on Staphylococcal enterotoxin B (SEB)-mediated ARDS in female mice. Single-cell RNA sequencing data showed that the lung epithelial cells from AEA-treated mice showed increased levels of antimicrobial peptides (AMPs) and tight junction proteins. MiSeq sequencing data on 16S RNA and LEfSe analysis demonstrated that SEB caused significant alterations in the microbiota, with increases in pathogenic bacteria in both the lungs and the gut, while treatment with AEA reversed this effect and induced beneficial bacteria. AEA treatment suppressed inflammation both in the lungs as well as gut-associated mesenteric lymph nodes (MLNs). AEA triggered several bacterial species that produced increased levels of short-chain fatty acids (SCFAs), including butyrate. Furthermore, administration of butyrate alone could attenuate SEB-mediated ARDS. Taken together, our data indicate that AEA treatment attenuates SEB-mediated ARDS by suppressing inflammation and preventing dysbiosis, both in the lungs and the gut, through the induction of AMPs, tight junction proteins, and SCFAs that stabilize the gut-lung microbial axis driving immune homeostasis.


Subject(s)
Arachidonic Acids/therapeutic use , Endocannabinoids/therapeutic use , Gastrointestinal Microbiome , Gastrointestinal Tract/pathology , Lung/pathology , Polyunsaturated Alkamides/therapeutic use , Respiratory Distress Syndrome/drug therapy , Respiratory Distress Syndrome/microbiology , Animals , Arachidonic Acids/pharmacology , Butyrates/metabolism , Cecum/pathology , Cell Separation , Colon/drug effects , Colon/pathology , Discriminant Analysis , Dysbiosis/complications , Dysbiosis/microbiology , Endocannabinoids/pharmacology , Enterotoxins , Female , Gastrointestinal Tract/drug effects , Lymph Nodes/drug effects , Lymph Nodes/pathology , Lymphocyte Activation/drug effects , Mice, Inbred C57BL , Pneumonia/drug therapy , Pneumonia/microbiology , Polyunsaturated Alkamides/pharmacology , Respiratory Distress Syndrome/complications , T-Lymphocytes/drug effects
17.
Curr Oncol Rep ; 23(11): 134, 2021 10 22.
Article in English | MEDLINE | ID: covidwho-1530397

ABSTRACT

PURPOSE OF REVIEW: Since the past year, the fast spread of coronavirus disease 2019 (COVID-19) has represented a global health threat, especially for cancer patients, that has required an urgent reorganization of clinical activities. Here, we will critically revise the profound impact that the pandemic has generated in lung cancer patients, as well the most significant challenges that oncologists have to face to maintain the highest possible standards in the management of lung cancer patients in the pandemic era. RECENT FINDINGS: Evidences suggested a higher susceptibility and mortality of lung cancer patients due to COVID-19. The hard management of this patient population has been also due to the potential cross interference of anti-tumor drugs on SARS-Cov-2 infection and to the differential diagnosis between COVID-19 pneumonitis and drug-related pneumonitis. COVID-19 pandemic has generated a profound reshaping of oncological activities and the development of recommendations by the oncology scientific community to prioritize anti-tumor treatments for lung cancer patients.


Subject(s)
COVID-19/complications , COVID-19/mortality , Lung Neoplasms/complications , Lung Neoplasms/mortality , Pneumonia/diagnosis , Antineoplastic Agents/pharmacology , COVID-19 Vaccines , Comorbidity , Diagnosis, Differential , Humans , Medical Oncology/methods , Pandemics , Risk Factors , SARS-CoV-2 , Tomography, X-Ray Computed
19.
Am J Respir Crit Care Med ; 204(9): 1011-1013, 2021 11 01.
Article in English | MEDLINE | ID: covidwho-1526563
20.
PLoS One ; 16(11): e0259732, 2021.
Article in English | MEDLINE | ID: covidwho-1518359

ABSTRACT

Mesenchymal stem cell derived extracellular vesicles (MSC-EVs) are bioactive particles that evoke beneficial responses in recipient cells. We identified a role for MSC-EV in immune modulation and cellular salvage in a model of SARS-CoV-2 induced acute lung injury (ALI) using pulmonary epithelial cells and exposure to cytokines or the SARS-CoV-2 receptor binding domain (RBD). Whereas RBD or cytokine exposure caused a pro-inflammatory cellular environment and injurious signaling, impairing alveolar-capillary barrier function, and inducing cell death, MSC-EVs reduced inflammation and reestablished target cell health. Importantly, MSC-EV treatment increased active ACE2 surface protein compared to RBD injury, identifying a previously unknown role for MSC-EV treatment in COVID-19 signaling and pathogenesis. The beneficial effect of MSC-EV treatment was confirmed in an LPS-induced rat model of ALI wherein MSC-EVs reduced pro-inflammatory cytokine secretion and respiratory dysfunction associated with disease. MSC-EV administration was dose-responsive, demonstrating a large effective dose range for clinical translation. These data provide direct evidence of an MSC-EV-mediated improvement in ALI and contribute new insights into the therapeutic potential of MSC-EVs in COVID-19 or similar pathologies of respiratory distress.


Subject(s)
Acute Lung Injury/complications , Acute Lung Injury/virology , COVID-19/pathology , Extracellular Vesicles/metabolism , Mesenchymal Stem Cells/metabolism , Pneumonia/complications , Pneumonia/virology , Angiotensin-Converting Enzyme 2/metabolism , Animals , Disease Models, Animal , Extracellular Vesicles/ultrastructure , Humans , Immunomodulation , Male , Models, Biological , Pneumonia/pathology , Rats, Sprague-Dawley , SARS-CoV-2/physiology , Signal Transduction , THP-1 Cells
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