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- Carraminana, Albert, et al.
(författare)
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Rationale and Study Design for an Individualized Perioperative Open Lung Ventilatory Strategy in Patients on One-Lung Ventilation (iPROVE-OLV)
- 2019
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Ingår i: Journal of Cardiothoracic and Vascular Anesthesia. - : W B SAUNDERS CO-ELSEVIER INC. - 1053-0770 .- 1532-8422. ; 33:9, s. 2492-2502
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Tidskriftsartikel (refereegranskat)abstract
- Objective: The aim of this clinical trial is to examine whether it is possible to reduce postoperative complications using an individualized perioperative ventilatory strategy versus using a standard lung-protective ventilation strategy in patients scheduled for thoracic surgery requiring one-lung ventilation. Design: International, multicenter, prospective, randomized controlled clinical trial. Setting: A network of university hospitals. Participants: The study comprises 1,380 patients scheduled for thoracic surgery. Interventions: The individualized group will receive intraoperative recruitment maneuvers followed by individualized positive end-expiratory pressure (open lung approach) during the intraoperative period plus postoperative ventilatory support with high-flow nasal cannula, whereas the control group will be managed with conventional lung-protective ventilation. Measurements and Main Results: Individual and total number of postoperative complications, including atelectasis, pneumothorax, pleural effusion, pneumonia, acute lung injury; unplanned readmission and reintubation; length of stay and death in the critical care unit and in the hospital will be analyzed for both groups. The authors hypothesize that the intraoperative application of an open lung approach followed by an individual indication of high-flow nasal cannula in the postoperative period will reduce pulmonary complications and length of hospital stay in high-risk surgical patients. (C) 2019 Published by Elsevier Inc.
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| 2. |
- Serra-Musach, Jordi, et al.
(författare)
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Cancer network activity associated with therapeutic response and synergism
- 2016
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Ingår i: Genome Medicine. - : BIOMED CENTRAL LTD. - 1756-994X. ; 8:88
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Tidskriftsartikel (refereegranskat)abstract
- Background: Cancer patients often show no or only modest benefit from a given therapy. This major problem in oncology is generally attributed to the lack of specific predictive biomarkers, yet a global measure of cancer cell activity may support a comprehensive mechanistic understanding of therapy efficacy. We reasoned that network analysis of omic data could help to achieve this goal. Methods: A measure of "cancer network activity" (CNA) was implemented based on a previously defined network feature of communicability. The network nodes and edges corresponded to human proteins and experimentally identified interactions, respectively. The edges were weighted proportionally to the expression of the genes encoding for the corresponding proteins and relative to the number of direct interactors. The gene expression data corresponded to the basal conditions of 595 human cancer cell lines. Therapeutic responses corresponded to the impairment of cell viability measured by the half maximal inhibitory concentration (IC50) of 130 drugs approved or under clinical development. Gene ontology, signaling pathway, and transcription factor-binding annotations were taken from public repositories. Predicted synergies were assessed by determining the viability of four breast cancer cell lines and by applying two different analytical methods. Results: The effects of drug classes were associated with CNAs formed by different cell lines. CNAs also differentiate target families and effector pathways. Proteins that occupy a central position in the network largely contribute to CNA. Known key cancer-associated biological processes, signaling pathways, and master regulators also contribute to CNA. Moreover, the major cancer drivers frequently mediate CNA and therapeutic differences. Cell-based assays centered on these differences and using uncorrelated drug effects reveals novel synergistic combinations for the treatment of breast cancer dependent on PI3K-mTOR signaling. Conclusions: Cancer therapeutic responses can be predicted on the basis of a systems-level analysis of molecular interactions and gene expression. Fundamental cancer processes, pathways, and drivers contribute to this feature, which can also be exploited to predict precise synergistic drug combinations.
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