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1.
Wiley Interdiscip Rev Nanomed Nanobiotechnol ; 14(2): e1763, 2022 03.
Article in English | MEDLINE | ID: covidwho-2173486

ABSTRACT

Pneumonia is a common but serious infectious disease, and is the sixth leading cause for death. The foreign pathogens such as viruses, fungi, and bacteria establish an inflammation response after interaction with lung, leading to the filling of bronchioles and alveoli with fluids. Although the pharmacotherapies have shown their great effectiveness to combat pathogens, advanced methods are under developing to treat complicated cases such as virus-infection and lung inflammation or acute lung injury (ALI). The inflammation modulation nanoparticles (NPs) can effectively suppress immune cells and inhibit inflammatory molecules in the lung site, and thereby alleviate pneumonia and ALI. In this review, the pathological inflammatory microenvironments in pneumonia, which are instructive for the design of biomaterials therapy, are summarized. The focus is then paid to the inflammation-modulating NPs that modulate the inflammatory cells, cytokines and chemokines, and microenvironments of pneumonia for better therapeutic effects. This article is categorized under: Therapeutic Approaches and Drug Discovery > Nanomedicine for Respiratory Disease.


Subject(s)
Acute Lung Injury , Nanoparticles , Pneumonia , Acute Lung Injury/drug therapy , Acute Lung Injury/pathology , Humans , Inflammation/drug therapy , Lung , Nanoparticles/therapeutic use , Pneumonia/drug therapy , Pneumonia/pathology
4.
Infect Control Hosp Epidemiol ; 43(6): 687-713, 2022 06.
Article in English | MEDLINE | ID: covidwho-2185241

ABSTRACT

The purpose of this document is to highlight practical recommendations to assist acute care hospitals to prioritize and implement strategies to prevent ventilator-associated pneumonia (VAP), ventilator-associated events (VAE), and non-ventilator hospital-acquired pneumonia (NV-HAP) in adults, children, and neonates. This document updates the Strategies to Prevent Ventilator-Associated Pneumonia in Acute Care Hospitals published in 2014. This expert guidance document is sponsored by the Society for Healthcare Epidemiology (SHEA), and is the product of a collaborative effort led by SHEA, the Infectious Diseases Society of America, the American Hospital Association, the Association for Professionals in Infection Control and Epidemiology, and The Joint Commission, with major contributions from representatives of a number of organizations and societies with content expertise.


Subject(s)
Cross Infection , Healthcare-Associated Pneumonia , Pneumonia, Ventilator-Associated , Pneumonia , Adult , Child , Cross Infection/prevention & control , Healthcare-Associated Pneumonia/epidemiology , Healthcare-Associated Pneumonia/prevention & control , Hospitals , Humans , Infant, Newborn , Infection Control , Pneumonia, Ventilator-Associated/prevention & control , Ventilators, Mechanical/adverse effects
5.
Lancet Glob Health ; 10(12): e1709-e1710, 2022 Dec.
Article in English | MEDLINE | ID: covidwho-2184808
6.
arxiv; 2023.
Preprint in English | PREPRINT-ARXIV | ID: ppzbmed-2301.10687v1

ABSTRACT

Deep learning technologies have already demonstrated a high potential to build diagnosis support systems from medical imaging data, such as Chest X-Ray images. However, the shortage of labeled data in the medical field represents one key obstacle to narrow down the performance gap with respect to applications in other image domains. In this work, we investigate the benefits of a curricular Self-Supervised Learning (SSL) pretraining scheme with respect to fully-supervised training regimes for pneumonia recognition on Chest X-Ray images of Covid-19 patients. We show that curricular SSL pretraining, which leverages unlabeled data, outperforms models trained from scratch, or pretrained on ImageNet, indicating the potential of performance gains by SSL pretraining on massive unlabeled datasets. Finally, we demonstrate that top-performing SSLpretrained models show a higher degree of attention in the lung regions, embodying models that may be more robust to possible external confounding factors in the training datasets, identified by previous works.


Subject(s)
Pneumonia , Learning Disabilities , COVID-19
7.
biorxiv; 2023.
Preprint in English | bioRxiv | ID: ppzbmed-10.1101.2023.01.23.524390

ABSTRACT

Pulmonary inflammation compromises lung barrier function and underlies many lung diseases including acute lung injury and acute respiratory distress syndrome (ARDS). However, mechanisms by which lung cells respond to the damage caused by the inflammatory insults are not completely understood. Here we show that Fzd6-deficiency in Foxj1+ ciliated cells reduces pulmonary permeability, lipid peroxidation, and alveolar cell death accompanied with an increase in alveolar number in lungs insulted by LPS or a mouse coronavirus. Single-cell RNA sequencing of lung cells indicates that the lack of Fzd6, which is expressed in Foxj1+ cells, increases expression of the aldo-keto reductase Akr1b8 in Foxj1+ cells. Intratracheal administration of the Akr1b8 protein phenocopies Fzd6-deficient lung phenotypes. In addition, ferroptosis inhibitors also phenocopy Fzd6-deficient lung phenotypes and exert no further effects in Fzd6-deficient lungs. These results reveal an important mechanism for protection of alveolar cells from ferroptotic death during pulmonary inflammation by Foxj1+ ciliated cells via paracrine action of Akr1b8.


Subject(s)
Pneumonia , Respiratory Distress Syndrome , Lung Diseases , Adenocarcinoma, Bronchiolo-Alveolar
8.
medrxiv; 2023.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2023.01.22.23284880

ABSTRACT

With the growing amount of COVID-19 cases, especially in developing countries with limited medical resources, it is essential to accurately diagnose COVID-19 with high specificity. Due to characteristic ground-glass opacities (GGOs), present in both COVID-19 and other acute lung diseases, misdiagnosis occurs often: 26.6% of the time in manual interpretations of CT scans. Current deep-learning models can identify COVID-19 but cannot distinguish it from other common lung diseases like bacterial pneumonia. COVision is a multi-classification convolutional neural network (CNN) that can differentiate COVID-19 from other common lung diseases, with a low false-positivity rate. This CNN achieved an accuracy of 95.8%, AUROC of 0.970, and specificity of 98%. We found a statistical significance that our CNN performs better than three independent radiologists with at least 10 years of experience. especially in differentiating COVID-19 from pneumonia. After training our CNN with 105,000 CT slices, we analyzed the activation maps of our CNN and found that lesions in COVID-19 presented peripherally, closer to the pleura, whereas pneumonia lesions presented centrally. Finally, using federated averaging, we ensemble our CNN with a pretrained clinical factors neural network (CFNN) to create a comprehensive diagnostic tool.


Subject(s)
Pneumonia , COVID-19 , Pneumonia, Bacterial , Lung Diseases
9.
researchsquare; 2023.
Preprint in English | PREPRINT-RESEARCHSQUARE | ID: ppzbmed-10.21203.rs.3.rs-2507826.v1

ABSTRACT

Introduction COVID-19 is generally milder in children than in adults, however severe infection has been described in some patients. Few data are available on use of Remdesivir (RDV) in children, as most clinical trials focused on adult patients. We report a multicenter study to investigate the safety of RDV in children affected by COVID-19.Methods We collected the clinical data of children with COVID-19 treated with RDV between March 2020 and February 2022 in 10 Italian hospitals. Clinical data were compared according to the duration of RDV therapy. Linear and logistic regression models were used to determine the association of significant variables from the bivariate analysis to the duration of RDV therapy.Results A total of 50 patients were included, with a median age of 12.8 years. Many patients had at least one comorbidity (78%), mostly obesity. Symptoms were fever (88%), cough (74%) and dyspnea (68%). Most patients were diagnosed with pneumonia of either viral and/or bacterial etiology. Blood test showed leukopenia in 66% and increased C-reactive protein (CRP) levels in 63% of cases. Thirty-six patients received RDV for 5 days, nine patients up to 10 days. Most children who received RDV longer were admitted to the PICU (67%). Treatment with RDV was well tolerated with rare side effects (Table 1): bradycardia was recorded in 6% of cases, solved in less than 24 hours after discontinuation. A mild elevation of transaminases was observed in 26% of cases, however for the 8%, it was still detected before the RDV administration. Therefore, in these cases, we could not establish if it was caused by COVID-19, RDV o both. Patients who received RDV for more than 5 days waited longer for its administration after pneumonia diagnosis. The presence of comorbidities and the duration of O2 administration significantly correlated with the duration of RDV therapy at the linear regression analysis.Conclusion Our experience indicates that RDV against SARS-CoV-2 is safe and well-tolerated in pediatric populations at high risk of developing severe COVID-19. Our data suggest that delaying RDV therapy after diagnosis of pneumonia may be associated with a longer duration of antiviral therapy, especially in patients with comorbidities.


Subject(s)
Pneumonia , Cough , Bradycardia , COVID-19 , Dyspnea , Fever , Obesity , Leukopenia
10.
medrxiv; 2023.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2023.01.17.23284647

ABSTRACT

The rapid spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) led to a global overextension of healthcare. Both Chest X-rays (CXR) and blood test have been demonstrated to have predictive value on Coronavirus Disease 2019 (COVID-19) diagnosis on different prevalence scenarios. With the objective of improving and accelerating the diagnosis of COVID-19, a multi modal prediction algorithm (MultiCOVID) based on CXR and blood test was developed, to discriminate between COVID-19, Heart Failure (HF) and Non-Covid Pneumonia (NCP) and healthy (Control) patients. This retrospective single-center study includes CXR and blood test obtained between January 2017 and May 2020. Multi modal prediction models were generated using opensource DL algorithms. Performance of the MultiCOVID algorithm was compared with interpretations from five experienced thoracic radiologists on 300 random test images using the McNemar-Bowker test. A total of 8578 samples from 6123 patients (mean age 66 +/- 18 years of standard deviation, 3523 men) were evaluated across datasets. For the entire test set, the overall accuracy of MultiCOVID was 84%, with a mean AUC of 0.92 (0.89-0.94). For 300 random test images, overall accuracy of MultiCOVID was significantly higher (69.6%) compared with individual radiologists (range, 43.7%-58.7%) and the consensus of all five radiologists (59.3%, P<.001). Overall, we have developed a multimodal deep learning algorithm, MultiCOVID, that discriminates among COVID-19, heart failure, non-covid pneumonia and healthy patients using both CXR and blood test with a significantly better performance than experienced thoracic radiologists.


Subject(s)
Pneumonia , Heart Failure , Coronavirus Infections , COVID-19 , Learning Disabilities
11.
researchsquare; 2023.
Preprint in English | PREPRINT-RESEARCHSQUARE | ID: ppzbmed-10.21203.rs.3.rs-2472834.v1

ABSTRACT

Objective: Determine whether the tomographic characteristics of patients with COVID-19 pneumonia at the hospital admission and the initial tomographic severity score (TSS) as well as some laboratory tests or clinical characteristics predict mortality. Methods: Retrospective analytical study that included patients with a clinical diagnosis of SARSCoV2 virus infection, performed by reverse transcriptase polymerase chain reaction (RT-PCR), serologic reactive test (IgM/IgG) and/or thoracic computed tomography (CT). Patients were divided into two groups: recovered and deceased. Two radiologists (blind evaluators) described the tomographic findings. TSS, clinical and laboratory parameters in relation to mortality were analyzed. Mortality predictions were made by binary logistic regression. Results: Hypertension was the most frequent associated disease, the most common clinical presentation included cough, discomfort, fever, and dyspnea. The ground glass opacity pattern was the most frequent, followed by consolidation and distortion of the architecture; however, they were not associated with higher mortality. The pattern of pleural effusion and bronchial dilation showed a significant difference from mortality (p <0.05). The binary logistic regression model showed that a moderate and high TSS (≥ 8), as well as a higher degree of lymphopenia, history of asthma and age were associated with an increased risk of death (p< 0.05). Conclusions: TSS is useful in the initial and comprehensive diagnostic evaluation of COVID-19 pneumonia, in conjunction with markers such as lymphopenia that can predict a poor short-term outcome. A high TSS score is a predictor of mortality.


Subject(s)
Pneumonia , Tumor Virus Infections , Pleural Effusion , COVID-19 , Hypertension , Asthma , Lymphopenia , Dyspnea , Fever
12.
researchsquare; 2023.
Preprint in English | PREPRINT-RESEARCHSQUARE | ID: ppzbmed-10.21203.rs.3.rs-2466037.v1

ABSTRACT

To precisely determine the severity of COVID-19-related pneumonia, computed tomography (CT) is an imaging modality beneficial for patient monitoring and therapy planning. Thus, we aimed to develop a deep learning-based image segmentation model to automatically assess lung lesions related to COVID-19 infection and calculate the total severity score (TSS). The entire dataset consists of 100 COVID-19 patients acquired from Chulabhorn Hospital, divided into 25 cases without lung lesions and 75 cases with lung lesions categorized severity by radiologists regarding TSS. The model combines a 3D-UNet with pre-trained DenseNet and ResNet models for lung lobe segmentation and calculation of the percentage of lung involvement related to COVID-19 infection as well as TSS measured by the Dice similarity coefficient (DSC). Our final model, consisting of 3D-UNet integrated with DenseNet169, achieved segmentation of lung lobes and lesions with Dice similarity coefficients of 0.929 and 0.842, respectively. The calculated TSSs are similar to those evaluated by radiologists, with an R2 of 0.833. The correlation between the ground-truth TSS and model prediction was greater than that of the radiologist, which was 0.993 and 0.836, respectively.


Subject(s)
Pneumonia , COVID-19 , Lung Diseases
13.
medrxiv; 2023.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2023.01.09.23284361

ABSTRACT

Objective: This study aimed to investigate the relationship between personality characteristics and psychological health of hospitals frontline medical staff and provide a basis and reference for targeted psychological health education for frontline medical staff and for the staff of related departments to formulate relevant policies. Methods:The self-evaluation scale of symptoms (SCL-90) was used to investigate the mental health status of 150 first-line medical staff in Zhejiang Province in response to the new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pneumonia. Results: The average scores of SCL-90 and somatization, obsessive-compulsive, depression, anxiety, hostility, terror, and psychotic factors were significantly higher than those of the normal sample in the first-aid medical staff of Aihu Hubei. The degree of influence on the mental health status of the frontline medical staff in service in Hubei is as follows, from high to low: the degree of suspicion that they may have been infected when new coronavirus pneumonia-related symptoms occur, the degree of fear of being infected and thus bring the infection to their families, and whether they have received a medical check-up recently, as well as a high level of education (both P<0.05). C onclu sion: The psychological health level of the frontline medical staff is lower than the national norm. In the context of the increasing number of confirmed cases and the new type of coronavirus pneumonia in the absence of any specific curative treatments, the frontline medical staff is under great psychological pressure. It is necessary to institute targeted mental health promotion to relieve the psychological pressure endured by the frontline medical staff, promote their physical and mental health, and better respond to the pandemic in China.


Subject(s)
Pneumonia , Obsessive-Compulsive Disorder , Infections , Coronavirus Infections , Psychotic Disorders , COVID-19 , Severe Acute Respiratory Syndrome , Anxiety Disorders
14.
researchsquare; 2023.
Preprint in English | PREPRINT-RESEARCHSQUARE | ID: ppzbmed-10.21203.rs.3.rs-2458402.v1

ABSTRACT

Background: The nerves in the legs and feet are most frequently damaged by diabetic neuropathy. The COVID-19 infection is associated with a high risk of neuropathy symptoms. Case Presentation: On July 12, 2022, a 58-year-old black female retiree with significant symptoms of numbness and muscle weakness in the hands and legs was brought into the emergency room. 17 years prior, she received a type 2 diabetes mellitus diagnosis. Metformin 1.5 g twice a day and glibenclamide 10 mg twice a day were part of her therapy regimen. When she was admitted to the emergency room, she described a one-day history of shortness of breath, frequent urination, excessive thirst, hyperglycemia, excessive appetite, fever, headache, and dehydration. A chest X-ray showed bilateral diffuse, patchy airspace opacities that could be caused by multifocal pneumonia or viral pneumonia. She started receiving 1000 mL of fluid resuscitation (0.9% normal saline) as soon as she was moved to the critical care unit, along with a drip-in insulin infusion. Conclusion: Diabetes, infections like COVID-19, poor vitamin levels, and other factors can all contribute to diabetic neuropathies. According to the Centers for Disease Control and Prevention, patients with type 2 diabetes mellitus are much more likely to experience severe morbidity and death from coronavirus disease-19. Symptoms of diabetic neuropathy continued for months after a COVID-19 infection test resulted in a positive result.


Subject(s)
Pneumonia , Diabetes Mellitus, Type 2 , Hypesthesia , COVID-19 , Pneumonia, Viral , Headache , Death , Nervous System Diseases , Dyspnea , Fever , Hyperglycemia , Muscle Weakness , Dehydration , Diabetes Mellitus , Diabetic Neuropathies
15.
authorea preprints; 2023.
Preprint in English | PREPRINT-AUTHOREA PREPRINTS | ID: ppzbmed-10.22541.au.167271402.28637149.v1

ABSTRACT

Bilateral spontaneous pneumothorax can occur as a late complication in patients with COVID-19 even without any history of mechanical ventilation. Here, we are presenting a patient with mild COVID-19 pneumonia with a left massive pneumothorax in the third week of the hospitalization, and the addition of right pneumothorax as well.


Subject(s)
Pneumonia , COVID-19
16.
biorxiv; 2023.
Preprint in English | bioRxiv | ID: ppzbmed-10.1101.2022.12.30.522299

ABSTRACT

Pneumonia is an acute respiratory disease of varying aetiology, which drew much attention during the COVID-19 pandemic. Among many thoroughly studied aspects of pneumonia, lipid metabolism has been addressed insufficiently. Here, we report on abnormal lipid metabolism of both COVID-19- and non-COVID-19-associated pneumonias in human lungs. Morphometric analysis revealed extracellular and intracellular lipid depositions, most notably within vessels adjacent to inflamed regions, where they apparently interfere with the blood flow. Lipids were visualized on Sudan III- and Oil Red O-stained cryosections and on OsO4-contrasted semi-thin and ultrathin sections. Chromato-mass spectrometry revealed that unsaturated fatty acid content was elevated at inflammation sites compared with the intact sites of the same lung. The genes involved in lipid metabolism were downregulated in pneumonia, as shown by qPCR and in silico RNAseq analysis. Thus, pneumonias are associated with marked lipid abnormalities, and therefore lipid metabolism can be considered a target for new therapeutic strategies.


Subject(s)
Pneumonia , COVID-19 , Respiratory Tract Diseases , Addison Disease , Inflammation
17.
researchsquare; 2023.
Preprint in English | PREPRINT-RESEARCHSQUARE | ID: ppzbmed-10.21203.rs.3.rs-2436406.v1

ABSTRACT

Purpose In past influenza pandemics and the current COVID-19 pandemic, bacterial endotracheal superinfections are a well-known risk factor for higher morbidity and mortality. The goal of this study was to investigate the influence of a structured, objective, microbiological monitoring on the prognosis of COVID-19 patients with mechanical ventilation. Methods A structured microbiological monitoring (at intubation, then every 3 days) included collection of endotracheal material. Data analysis focused on the spectrum of bacterial pathogens, mortality, as well as ICU-, hospital-, and mechanical ventilation duration. Results 29% of the patients showed bacterial coinfection at the time of intubation or within 48h, 56% developed ventilator-associated pneumonia (VAP). Even though patients with VAP had significantly longer ICU-, hospital and mechanical ventilation duration, there was no significant difference in mortality between patients with ventilator-associated pneumonia and patients without bacterial infection. Conclusion Bacterial coinfections and ventilator-associated pneumonia are common complications in influenza and COVID-19 patients. In contrast to already published studies, in our study implementing a structured microbiological monitoring, COVID-19 patients with ventilator-associated pneumonia did not show higher mortality. Thus, a standardized, objective, microbiological screening can help detect coinfections and ventilator-associated infections, refining the anti-infective therapy and influencing the patient outcome positively.


Subject(s)
Pneumonia , COVID-19 , Bacterial Infections , Pneumonia, Ventilator-Associated
18.
researchsquare; 2022.
Preprint in English | PREPRINT-RESEARCHSQUARE | ID: ppzbmed-10.21203.rs.3.rs-2428958.v1

ABSTRACT

Introduction: Healthcare-associated infections (HAI) and bacterial antimicrobial resistance posed a therapeutic risk during the coronavirus disease 2019 (COVID-19) pandemic. The aim of this study was to analyze the HAIs in COVID-19 patients in the Intensive Care Unit (ICU) and non-ICU at the University Hospital in Krakow (UHK) with an emphasis on the susceptibility of the most frequently isolated pathogens and the prevalence of extensively drug resistant (XDR) microorganisms. Methods: This laboratory-based study was carried out at the University Hospital in Krakow in the ICU and non-ICUs dedicated to COVID-19 patients between May 2021 and January 2022. All isolates of Klebsiella pneumoniae were analyzed using PFGE protocol. Results: 288 independent HAI cases were identified, with the predominance of urinary tract infections (UTI), especially in the non-ICU setting. The most common ICU syndrome was pneumonia (PNA). The prevalence of XDR organisms was 29.1% in the ICU and 26.4% in non-ICUs among all isolates. The incidence of carbapenem-resistant Enterobacteriaceae infection was 24.8 cases per 10,000 hospitalizations and the carbapenem-resistant A. baumannii infection incidence was 208.8 cases per 10,000 hospitalizations. The prevalence of XDR strains was highest in Acinetobacter spp, in PNA cases. The PFGE typing demonstrated that almost all XDR strains varied widely from each other. Conclusions: In this study, there was a high incidence of HAI in COVID-19 patients. Similarly, the prevalence of XDR microorganisms, especially XDR-A.baumannii, was also high. PFGE did not confirm the horizontal spread of any organism strains.


Subject(s)
Pneumonia , Infections , Schistosomiasis mansoni , COVID-19 , Lymphohistiocytosis, Hemophagocytic , Klebsiella Infections
19.
arxiv; 2022.
Preprint in English | PREPRINT-ARXIV | ID: ppzbmed-2212.13929v1

ABSTRACT

Computer tomography (CT) have been routinely used for the diagnosis of lung diseases and recently, during the pandemic, for detecting the infectivity and severity of COVID-19 disease. One of the major concerns in using ma-chine learning (ML) approaches for automatic processing of CT scan images in clinical setting is that these methods are trained on limited and biased sub-sets of publicly available COVID-19 data. This has raised concerns regarding the generalizability of these models on external datasets, not seen by the model during training. To address some of these issues, in this work CT scan images from confirmed COVID-19 data obtained from one of the largest public repositories, COVIDx CT 2A were used for training and internal vali-dation of machine learning models. For the external validation we generated Indian-COVID-19 CT dataset, an open-source repository containing 3D CT volumes and 12096 chest CT images from 288 COVID-19 patients from In-dia. Comparative performance evaluation of four state-of-the-art machine learning models, viz., a lightweight convolutional neural network (CNN), and three other CNN based deep learning (DL) models such as VGG-16, ResNet-50 and Inception-v3 in classifying CT images into three classes, viz., normal, non-covid pneumonia, and COVID-19 is carried out on these two datasets. Our analysis showed that the performance of all the models is comparable on the hold-out COVIDx CT 2A test set with 90% - 99% accuracies (96% for CNN), while on the external Indian-COVID-19 CT dataset a drop in the performance is observed for all the models (8% - 19%). The traditional ma-chine learning model, CNN performed the best on the external dataset (accu-racy 88%) in comparison to the deep learning models, indicating that a light-weight CNN is better generalizable on unseen data. The data and code are made available at https://github.com/aleesuss/c19.


Subject(s)
Pneumonia , COVID-19 , Learning Disabilities , Lung Diseases
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