Using Topological Data Analysis for diagnosis pulmonary embolism

Pulmonary Embolism (PE) is a common and potentially lethal condition. Most patients die within the first few hours from the event. Despite diagnostic advances, delays and underdiagnosis in PE are common.To increase the diagnostic performance in PE, current diagnostic work-up of patients with suspected acute pulmonary embolism usually starts with the assessment of clinical pretest probability using plasma d-Dimer measurement and clinical prediction rules. The most validated and widely used clinical decision rules are the Wells and Geneva Revised scores. We aimed to develop a new clinical prediction rule (CPR) for PE based on topological data analysis and artificial neural network. Filter or wrapper methods for features reduction cannot be applied to our dataset: the application of these algorithms can only be performed on datasets without missing data. Instead, we applied Topological data analysis (TDA) to overcome the hurdle of processing datasets with null values missing data. A topological network was developed using the Iris software (Ayasdi, Inc., Palo Alto). The PE patient topology identified two ares in the pathological group and hence two distinct clusters of PE patient populations. Additionally, the topological netowrk detected several sub-groups among healthy patients that likely are affected with non-PE diseases. TDA was further utilized to identify key features which are best associated as diagnostic factors for PE and used this information to define the input space for a back-propagation artificial neural network (BP-ANN). It is shown that the area under curve (AUC) of BP-ANN is greater than the AUCs of the scores (Wells and revised Geneva) used among physicians. The results demonstrate topological data analysis and the BP-ANN, when used in combination, can produce better predictive models than Wells or revised Geneva scores system for the analyzed cohortComment: 18 pages, 5 figures, 6 tables. arXiv admin note: text overlap with arXiv:cs/0308031 by other authors without attributio

HO-1 up-regulation: a key point in high-risk neuroblastoma resistance to bortezomib.

AbstractHigh-risk neuroblastoma (NB) is characterized by the development of chemoresistance, and bortezomib (BTZ), a selective inhibitor of proteasome, has been proposed in order to overcome drug resistance. Considering the involvement of the nuclear factor-erythroid-derived 2-like 2 (Nrf2) and heme oxygenase-1 (HO-1) in the antioxidant and detoxifying ability of cancer cells, in this study we have investigated their role in differently aggressive NB cell lines treated with BTZ, focusing on the modulation of HO-1 to improve sensitivity to therapy. We have shown that MYCN amplified HTLA-230 cells were slightly sensitive to BTZ treatment, due to the activation of Nrf2 that led to an impressive up-regulation of HO-1. BTZ-treated HTLA-230 cells down-regulated p53 and up-regulated p21, favoring cell survival. The inhibition of HO-1 activity obtained by Zinc (II) protoprophyrin IX (ZnPPIX) was able to significantly increase the pro-apoptotic effect of BTZ in a p53- and p21-independent way. However, MYCN non-amplified SH-SY5Y cells showed a greater sensitivity to BTZ in relation to their inability to up-regulate HO-1. Therefore, we have shown that HO-1 inhibition improves the sensitivity of aggressive NB to proteasome inhibition-based therapy, suggesting that HO-1 up-regulation can be used as a marker of chemoresistance in NB. These results open up a new scenario in developing a combined therapy to overcome chemoresistance in high-risk neuroblastoma

Risk prediction of clinical adverse outcomes with machine learning in a cohort of critically ill patients with atrial fibrillation

Critically ill patients affected by atrial fibrillation are at high risk of adverse events: however, the actual risk stratification models for haemorrhagic and thrombotic events are not validated in a critical care setting. With this paper we aimed to identify, adopting topological data analysis, the risk factors for therapeutic failure (in-hospital death or intensive care unit transfer), the in-hospital occurrence of stroke/TIA and major bleeding in a cohort of critically ill patients with pre-existing atrial fibrillation admitted to a stepdown unit; to engineer newer prediction models based on machine learning in the same cohort. We selected all medical patients admitted for critical illness and a history of pre-existing atrial fibrillation in the timeframe 01/01/2002-03/08/2007. All data regarding patients' medical history, comorbidities, drugs adopted, vital parameters and outcomes (therapeutic failure, stroke/TIA and major bleeding) were acquired from electronic medical records. Risk factors for each outcome were analyzed adopting topological data analysis. Machine learning was used to generate three different predictive models. We were able to identify specific risk factors and to engineer dedicated clinical prediction models for therapeutic failure (AUC: 0.974, 95%CI: 0.934-0.975), stroke/TIA (AUC: 0.931, 95%CI: 0.896-0.940; Brier score: 0.13) and major bleeding (AUC: 0.930:0.911-0.939; Brier score: 0.09) in critically-ill patients, which were able to predict accurately their respective clinical outcomes. Topological data analysis and machine learning techniques represent a concrete viewpoint for the physician to predict the risk at the patients' level, aiding the selection of the best therapeutic strategy in critically ill patients affected by pre-existing atrial fibrillation

PKCδ Sensitizes Neuroblastoma Cells to L-Buthionine-Sulfoximine and Etoposide Inducing Reactive Oxygen Species Overproduction and DNA Damage

Neuroblastoma is a type of pediatric cancer. The sensitivity of neuroblastoma (NB) cancer cells to chemotherapy and radiation is inhibited by the presence of antioxidants, such as glutathione (GSH), which is crucial in counteracting the endogenous production of reactive oxygen species (ROS). We have previously demonstrated that cells depleted of GSH undergo apoptosis via oxidative stress and Protein kinase C (PKC) δ activation. In the present study, we transfected PKCδ in NB cells resistant to oxidative death induced by L-buthionine-S,R-sulfoximine (BSO), a GSH-depleting agent. Cell responses, in terms of ROS production, apoptosis and DNA damage were evaluated. Moreover, PKCδ activation was monitored by analyzing the phosphorylation status of threonine 505 residue, carrying out PKC activity assay and investigating the subcellular localization of the kinase. The cell responses obtained in BSO-resistant cells were also compared with those obtained in BSO-sensitive cells subjected to the same experimental protocol. Our results demonstrate, for the first time, that PKCδ induces DNA oxidation and ROS overproduction leading to apoptosis of BSO-resistant NB cells and potentiates the cytotoxic effects induced by BSO in sensitive cells. Moreover, PKCδ overexpression enhances the sensitivity of NB cells to etoposide, a well-characterised drug, commonly used in neuroblastoma therapy. Altogether our data provide evidence of a pro-oxidant role of PKCδ that might be exploited to design new therapeutic strategies aimed at selective killing of cancer cells and overcoming drug resistance. However, it becomes evident that a more detailed understanding of ROS-mediated signaling in cancer cells is necessary for the development of redox-modulated therapeutic approaches

Neural hypernetwork approach for pulmonary embolism diagnosis

Background Hypernetworks are based on topological simplicial complexes and generalize the concept of two-body relation to many-body relation. Furthermore, Hypernetworks provide a significant generalization of network theory, enabling the integration of relational structure, logic and analytic dynamics. A pulmonary embolism is a blockage of the main artery of the lung or one of its branches, frequently fatal. Results Our study uses data on 28 diagnostic features of 1427 people considered to be at risk of pulmonary embolism enrolled in the Department of Internal and Subintensive Medicine of an Italian National Hospital “Ospedali Riuniti di Ancona”. Patients arrived in the department after a first screening executed by the emergency room. The resulting neural hypernetwork correctly recognized 94 % of those developing pulmonary embolism. This is better than previous results obtained with other methods (statistical selection of features, partial least squares regression, topological data analysis in a metric space). Conclusion In this work we successfully derived a new integrative approach for the analysis of partial and incomplete datasets that is based on Q-analysis with machine learning. The new approach, called Neural Hypernetwork, has been applied to a case study of pulmonary embolism diagnosis. The novelty of this method is that it does not use clinical parameters extracted by imaging analysis

Hormesis and Oxidative Distress: Pathophysiology of Reactive Oxygen Species and the Open Question of Antioxidant Modulation and Supplementation

Alterations of redox homeostasis leads to a condition of resilience known as hormesis that is due to the activation of redox-sensitive pathways stimulating cell proliferation, growth, differentiation, and angiogenesis. Instead, supraphysiological production of reactive oxygen species (ROS) exceeds antioxidant defence and leads to oxidative distress. This condition induces damage to biomolecules and is responsible or co-responsible for the onset of several chronic pathologies. Thus, a dietary antioxidant supplementation has been proposed in order to prevent aging, cardiovascular and degenerative diseases as well as carcinogenesis. However, this approach has failed to demonstrate efficacy, often leading to harmful side effects, in particular in patients affected by cancer. In this latter case, an approach based on endogenous antioxidant depletion, leading to ROS overproduction, has shown an interesting potential for enhancing susceptibility of patients to anticancer therapies. Therefore, a deep investigation of molecular pathways involved in redox balance is crucial in order to identify new molecular targets useful for the development of more effective therapeutic approaches. The review herein provides an overview of the pathophysiological role of ROS and focuses the attention on positive and negative aspects of antioxidant modulation with the intent to find new insights for a successful clinical application