Evolution of Communities in the Medical Sciences: Evidence from the Medical Words Network

Shirazi, Amir Hossein Masnadi; Modiri, Arash Badie; Heydari, Sara; Rohn, Jennifer L.; Jafari, G. R.; Mani, Ali R.

doi:10.1371/journal.pone.0167546

Cited by 5 publications

(3 citation statements)

References 10 publications

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“…It is now also being applied to organ system analysis at a functional level. A network approach also has non-biological medical applications, including use in the prediction of evolution of research communities (Yu et al, 2012;Shirazi et al, 2016) and health informatics.…”

Section: Introductionmentioning

confidence: 99%

Organ System Network Disruption Is Associated With Poor Prognosis in Patients With Chronic Liver Failure

2020

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Background: A healthy individual has a high degree of functional connectivity between organ systems, which can be represented graphically in a network map. Disruption of this system connectivity is associated with mortality in life-threatening acute illnesses, demonstrated by a network approach. However, this approach has not been applied to chronic multisystem diseases and may be more reliable than conventional individual organ prognostic scoring methods. Cirrhosis is a chronic disease of the liver with multisystem involvement. Development of an efficient model for prediction of mortality in cirrhosis requires a profound understanding of the pathophysiologic processes that lead to poor prognosis. In the present study, we use a network approach to evaluate the differences in organ system connectivity between survivors and non-survivors in a group of well-characterized patients with cirrhosis. Methods: 201 patients with cirrhosis originally referred to the Clinic five at the University Hospital of Padova, with 13 clinical variables available representing hepatic, metabolic, haematopoietic, immune, neural and renal organ systems were retrospectively enrolled and followed up for 3, 6, and 12 months. Software was designed to compute the correlation network maps of organ system interaction in survivors and non-survivors using analysis indices: A. Bonferroni corrected Pearson's correlation coefficient and B. Mutual Information. Network structure was quantitatively evaluated using the measures of edges, average degree of connectivity and closeness, and qualitatively using clinical significance. Pair-matching was also implemented as a further step after initial general analysis to control for sample size and Model for End-Stage Liver Disease (MELD-Na) score between the groups. Results: There was a higher number of significant correlations in survivors, as indicated by both the analysis indices of Bonferroni corrected Pearson's correlation coefficient and the Mutual Information analysis. The number of edges, average degree of connectivity and closeness were significantly higher in survivors compared to non-survivors group. Pair-matching for MELD-Na was also associated with increased connectivity in survivors compared to non-survivors over 3 and 6 months follow up. Conclusion: This study demonstrates the application of a network approach in evaluating functional connectivity of organ systems in liver cirrhosis, demonstrating a

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Section: Introductionmentioning

confidence: 99%

Organ System Network Disruption Is Associated With Poor Prognosis in Patients With Chronic Liver Failure

2020

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“…However, our study introduces a new phenomenon which is suitable for analysis using techniques that are being developed in the emerging field of Network Physiology (Bashan et al 2012, Ivanov et al 2016, Kanter et al 2015. In recent past decades hepatology has benefited enormously from collaboration with other scientific communities (Shirazi et al 2016). A multidisciplinary approach is more likely to introduce alternative models and uncover the mechanism of experimental therapies such as fractal-like ventilation in patients with liver failure.…”

Section: Discussionmentioning

confidence: 99%

The effect of fractal-like mechanical ventilation on vital signs in a rat model of acute-on-chronic liver failure

et al. 2018

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The network of interactions between different organs is impaired in liver cirrhosis. Liver cirrhosis is associated with multi-system involvement which eventually leads to multiple organ failure. This process is accelerated by a precipitating factor such as bacterial infection which leads to respiratory distress, circulatory shock, neural dysfunction and very high mortality. Cirrhotic patients often have blunted respiratory sinus arrhythmia and impaired cardio-respiratory variability. Fractal-like mechanical ventilation is reported to enhance respiratory sinus arrhythmia and attenuate respiratory distress in experimental models. In present study we hypothesised that fractal-like mechanical ventilation may improve the outcome of cirrhotic rats with multiple organ failure. Cirrhosis was induced by chronic biliary obstruction in rats. Acute multiple organ failure was induced by intraperitoneal injection of bacterial endotoxin in cirrhotic rats. The effect of conventional mechanical ventilation (with constant tidal volume and respiratory rate) or fractal-like ventilation (with the same average but variable tidal volume and respiratory rate) were assessed on vital signs, oxygen saturation and plasma alanine aminotransferase in anaesthetised cirrhotic rats. We demonstrated that fractal-like mechanical ventilation was accompanied by improved oxygen saturation, reduced heart rate and decreased liver injury following injection of bacterial endotoxin. Moreover, variable mechanical ventilation in cirrhotic rats reduced mortality and prevented a fall in short-term heart rate variability following endotoxin challenge in comparison with rats with constant mechanical ventilation. We suggest further investigations on beneficial effects of fractal-like ventilation strategy in critically ill patients with liver failure requiring organ support and mechanical ventilation.

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“…For a complex physiological network containing a high number of nodes, understanding the cluster of tightly interconnected nodes could provide insight into the core structures within the network. The k-clique percolation method is a cluster detection technique that is robust to the overlap of shared characteristics between clusters within a network [29,30]. The technique defines all the cliques (a subgraph of a network where all member nodes are adjacent to each other) within a network that shares k-1 (at least one) nodes [31].…”

Section: Detection Of Local Clusters Within the Networkmentioning

confidence: 99%

Application of physiological network mapping in the prediction of survival in critically ill patients with acute liver failure

Oyelade,

Moore,

Mani

2024

Preprint

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Reduced functional connectivity of physiological systems is associated with poor prognosis is critically ill patients. However, physiological network analysis is not commonly used in clinical practice and awaits quantitative evidence. Acute liver failure (ALF) is associated with multiorgan failure and mortality. Prognostication in ALF is highly important for clinical management but is currently dependent on models that do not consider the interaction between organ systems. This study is aimed to examine the impact of physiological network analysis, in prognostication of patients with ALF. Data from 640 adult patients admitted to the ICU for paracetamol-induced ALF were extracted from the MIMIC-III database. Parenclitic network analysis was performed on the routine biomarkers and network clusters were identified using the k-clique percolation method. Network analysis showed that the liver function biomarkers were more clustered in survivors than in non-survivors. Arterial pH was also found to cluster with serum creatinine and bicarbonate in survivors compared with non-survivors, where it clustered with respiratory nodes indicating physiologically distinctive compensatory mechanism. Deviation along the pH-bicarbonate and pH-creatinine axes could significantly predict mortality independent of current prognostic indicators. These results demonstrate that network analysis can provide pathophysiologic insight and predict survival in critically ill patients with ALF.

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Evolution of Communities in the Medical Sciences: Evidence from the Medical Words Network

Cited by 5 publications

References 10 publications

Organ System Network Disruption Is Associated With Poor Prognosis in Patients With Chronic Liver Failure

Organ System Network Disruption Is Associated With Poor Prognosis in Patients With Chronic Liver Failure

The effect of fractal-like mechanical ventilation on vital signs in a rat model of acute-on-chronic liver failure

Application of physiological network mapping in the prediction of survival in critically ill patients with acute liver failure

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