Inflammatory and Fibrinolytic System in Acute Respiratory Distress Syndrome

Gouda, Mahesh Manjunath; Shaikh, Sadiya Bi; Bhandary, Yashodhar P.

doi:10.1007/s00408-018-0150-6

Cited by 56 publications

(51 citation statements)

References 84 publications

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“…As previously mentioned, the physio pathological event leading to intensive care unit (ICU) and to death is an ARDS, a dramatic event whose treatment is usually only supportive, requiring mechanical ventilation. Regardless of the etiology (Aisiku et al, 2016), a hyper-activation of immune innate system is thought to have a paramount role in this condition: inflammatory cytokines and chemokines, such as tumor necrosis factor (TNF) a, interleukin (IL) 1b, IL-6, IL-8, IL-17 and IL-18, as well as several growth factors, are overexpressed in ARDS, triggering apoptotic cascade and epithelialmesenchymal transition (Gouda et al, 2018). Moreover, their high serum and bronchoalveolar lavage level seem related to a poorer prognosis (Butt et al, 2016).…”

Section: Resultsmentioning

confidence: 99%

Can atmospheric pollution be considered a co-factor in extremely high level of SARS-CoV-2 lethality in Northern Italy?

Conticini

Frediani

Caro

2020

Environmental Pollution

811

727

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a b s t r a c tThis paper investigates the correlation between the high level of Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) lethality and the atmospheric pollution in Northern Italy. Indeed, Lombardy and Emilia Romagna are Italian regions with both the highest level of virus lethality in the world and one of Europe's most polluted area. Based on this correlation, this paper analyzes the possible link between pollution and the development of acute respiratory distress syndrome and eventually death. We provide evidence that people living in an area with high levels of pollutant are more prone to develop chronic respiratory conditions and suitable to any infective agent. Moreover, a prolonged exposure to air pollution leads to a chronic inflammatory stimulus, even in young and healthy subjects. We conclude that the high level of pollution in Northern Italy should be considered an additional co-factor of the high level of lethality recorded in that area.

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Section: Resultsmentioning

confidence: 99%

Can atmospheric pollution be considered a co-factor in extremely high level of SARS-CoV-2 lethality in Northern Italy?

Conticini

Frediani

Caro

2020

Environmental Pollution

811

727

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“…10 Postoperative hypoxaemia is also considered to be the cause of myocardial ischaemia, arrhythmia, and neurological dysfunction. 11 Clinical research on hypoxaemia in patients with femoral neck fractures has rarely been performed because hypoxaemia is generally transient and has multiple underlying causes. The prevention and treatment of postoperative hypoxaemia is an important factor for a successful operation.…”

Section: Discussionmentioning

confidence: 99%

Risk factors for and predictive nomogram of postoperative hypoxaemia in elderly patients with femoral neck fractures

et al. 2020

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Objective To investigate the related risk factors and predictive nomogram of postoperative hypoxaemia in elderly patients with femoral neck fractures. Methods This study included patients aged ≥65 years who underwent surgical treatment of acute femoral neck fractures. Univariate and multivariate logistic analyses were performed to determine the incidence of and risk factors for postoperative hypoxaemia. A predictive nomogram was constructed based on the multivariable model. Using the bootstrap method, discrimination was determined by the C-index and calibration plot. Results The logistic regression analysis showed that the anaesthesia type, surgical procedure, American Society of Anesthesiologists (ASA) classification, preoperative hypoxaemia occurrence, and age were independent predictors of development of postoperative hypoxaemia. The predictive formula for hypoxaemia was established as follows: hypoxaemia=−0.8668×spinal anaesthesia (whether)+0.1162×nerve anaesthesia (whether)+1.9555×plate/screw fixation (whether)+1.4950×hip replacement (whether)+0.4883×ASA classification+1.7153×preoperative oxygenation index+0.1608×age. With the bootstrap method, the prediction curve fit well with the ideal curve, suggesting that the prediction curve constructed in this study has good predictive ability. Conclusions Anaesthesia type, surgical procedure, ASA classification, preoperative hypoxaemia occurrence, and age were risk factors for postoperative hypoxaemia in elderly patients with femoral neck fractures. The predictive nomogram was designed for preoperative assessment of the risk of postoperative hypoxaemia by calculating the risk score.

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“…Given that EMT has been linked to ARDs 72 , we speculated that the evidence connecting EMT and SARS2 acquired through our analysis might be reflected in the relatively strong intersection between ARDs markers in SARS2 HCTs compared to other viral HCTs. To test this hypothesis we carried out a PubMed search to identify a set of 88 expression biomarkers of ARDs or its associated pathology, acute lung injury (ALI).…”

Section: Hypothesis Generation Use Case 3: Association Of An Epithelimentioning

confidence: 99%

“…Epithelial to mesenchymal transition (EMT) is the process by which epithelial cells lose their polarity and adhesive properties and acquire the migratory and invasive characteristics of mesenchymal stem cells 69 . EMT is known to contribute to pulmonary fibrosis 70 , acute interstitial pneumonia 71 and acute respiratory distress syndrome (ARDs) 72 , all of which have been reported in connection with SARS2 infection in COVID-19 [73][74][75] . We were interested to note therefore that significant HCT intersections for three well characterized EMT-promoting transcription factors were specific to SARS2 infection (q-values: SNAI2/Slug 76 , 2e-2; EPAS1/HIF2α 77 , 9e-9; LEF1 78 , 1e-3; Fig To investigate the connection between SARS2 infection and EMT implied by these HCT intersections, we then computed intersections between the individual viral HCTs and a list of 335 genes manually curated from the research literature as EMT markers 80 (figshare File F1, section 11).…”

Section: Hypothesis Generation Use Case 3: Association Of An Epithelimentioning

confidence: 99%

Consensus transcriptional regulatory networks of coronavirus-infected human cells

Ochsner

Pillich

McKenna

2020

Preprint

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15Identifying transcriptional responses that are most consistently associated with 16 experimental coronavirus (CoV) infection can help illuminate human cellular signaling 17 pathways impacted by CoV infection. Here, we distilled over 3,000,000 data points from 18 publically archived CoV infection transcriptomic datasets into consensus regulatory 19 signatures, or consensomes, that rank genes based on their transcriptional 20 responsiveness to infection of human cells by MERS, SARS-CoV-1 and SARS-CoV-2 21 subtypes. We computed overlap between genes with elevated rankings in the CoV 22 consensomes against those from transcriptomic and ChIP-Seq consensomes for nearly 23 880 cellular signaling pathway nodes. Validating the CoV infection consensomes, we 24 identified robust overlap between their highly ranked genes and high confidence targets 25 of signaling pathway nodes with known roles in CoV infection. We then developed a 26 series of use cases that illustrate the utility of the CoV consensomes for hypothesis 27 generation around mechanistic aspects of the cellular response to CoV infection. We 28 make the CoV infection consensomes and their universe of underlying data points freely 29 accessible through the Signaling Pathways Project web knowledgebase. 30 31 65 points in a series of use cases illuminates previously uncharacterized intersections 66 between CoV infection and human cellular signaling pathways. The CoV infection 67 consensome and its underlying datasets provide researchers with a unique and freely 68 accessible framework within which to generate and pressure test hypotheses around 69 human cellular signaling pathways impacted by CoV infection.70 71 72 73 5 Results 74Generation of the CoV consensomes 75 We first set out to generate a set of consensomes ranking human genes based on the 76 frequency of their significant differential expression in response to infection by MERS, 77 SARS1 and SARS2 CoVs. To do this we searched the Gene Expression Omnibus 78 (GEO) and ArrayExpress databases to identify datasets involving infection of human 79 cells by these species. From this initial collection of datasets, we next carried out a 80 three step quality control check as previously described (Ochsner et al., 2019), yielding 81 a total of 3,041,047 million data points in 111 experiments from 25 independent CoV 82 infection transcriptomic datasets (Supplementary information, Section 1). Using these 83 curated datasets, we next generated consensomes for each CoV species, as well as 84 one ranking genes across all CoV infection experiments (ALL CoV). As a reference 85 consensome for a virus whose transcriptional impact on human cells has been studied 86 in depth, we also generated a consensome for human influenza A virus (IAV) infection. 87The Supplementary information files contain the full human ALL CoV (Section 2), MERS 88 (Section 3), SARS1 (Section 4), SARS2 (Section 5) and IAV (Section 6) infection 89 transcriptomic consensomes. To assist researchers in inferring transcriptional regulation 90 of sig...

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Inflammatory and Fibrinolytic System in Acute Respiratory Distress Syndrome

Cited by 56 publications

References 84 publications

Can atmospheric pollution be considered a co-factor in extremely high level of SARS-CoV-2 lethality in Northern Italy?

Can atmospheric pollution be considered a co-factor in extremely high level of SARS-CoV-2 lethality in Northern Italy?

Risk factors for and predictive nomogram of postoperative hypoxaemia in elderly patients with femoral neck fractures

Consensus transcriptional regulatory networks of coronavirus-infected human cells

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