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Predicted targets of hsa-miR-375 and cancer pathways. Gene enrichment analysis of the predicted targets by GeneCoDis3 software (http://genecodis.cnb.csic.es/). RASD1 was assigned to the PI3K-pathway (http://www.pantherdb.org/pathway/). The numbers on the x-axis indicate how many of the 15 â overlappingâ genes in Additional file 3: Figure S3 are found in a specific pathway. (PDF 20.2 kb)

Target prediction for hsa-miR-375. The Venn diagram shows the number of predicted targets from TargetScan (blue) http://www.targetscan.org/and PITA (red) http://genie.weizmann.ac.il/pubs/mir07/mir07_data.html; 15 genes were suggested by both algorithms. (PDF 44.5 kb)

Comparison of subgroups according to receptor status. This table shows that in ER-Îą+ patients, the levels of hsa-miR-375 are higher than in TNBC and ERÎąâ patients, with fold changes of 1.87 and 4.17. The p value was estimated with the non-parametric Mann-Whitney U test. None of the fold changes is significant. The group of her2-negative patients has slightly higher levels of hsa-miR-375 than the her2-positive patient group, again the difference is not significant. (PDF 124 kb)

Study flow-chart. This additional file shows the various steps of the study. (PDF 42.8 kb)

In Part I of this series of papers, a general approach for ordering fuzzy sets with respect to fuzzy orderings was presented. Part I also highlighted three limitations of this approach. The present paper addresses these lim- itations and proposes solutions for overcoming them. We rst consider a fuzzi cation of the ordering relation, then ways to compare fuzzy sets with di erent heights, and nally we introduce how to re ne the ordering relation by lexicographic hybridization with a di erent ordering method

The aim of this paper is to present a general framework for comparing fuzzy sets with respect to a general class of fuzzy orderings. This approach includes known techniques based on generalizing the crisp linear ordering of real numbers by means of the extension principle, however, in its general form, it is applicable to any fuzzy subsets of any kind of universe for which a fuzzy ordering is known|no matter whether linear or partial

DestD&al003; International audience; Probability intervals are imprecise probability assignments over elementary events. They constitute a very convenient tool to model uncertain information : two common cases are confidence intervals on parameters of multinomial distributions built from sample data and expert opinions provided in terms of such intervals. In this paper, we study how probability intervals can be transformed into other uncertainty models such as possibility distributions, Ferson's p-boxes, random sets and Neumaier's clouds.

Background: Pulmonary hypertension (PH) poses a significant health threat with high morbidity and mortality, necessitating improved diagnostic tools for enhanced management. Current biomarkers for PH lack functionality and comprehensive diagnostic and prognostic capabilities. Therefore, there is a critical need to develop biomarkers that address these gaps in PH diagnostics and prognosis., Methods: To address this need, we employed a comprehensive metabolomics analysis in 233 blood based samples coupled with machine learning analysis. For functional insights, human pulmonary arteries (PA) of idiopathic pulmonary arterial hypertension (PAH) lungs were investigated and the effect of extrinsic FFAs on human PA endothelial and smooth muscle cells was tested in vitro ., Results: PA of idiopathic PAH lungs showed lipid accumulation and altered expression of lipid homeostasis-related genes. In PA smooth muscle cells, extrinsic FFAs caused excessive proliferation and endothelial barrier dysfunction in PA endothelial cells, both hallmarks of PAH.In the training cohort of 74 PH patients, 30 disease controls without PH, and 65 healthy controls, diagnostic and prognostic markers were identified and subsequently validated in an independent cohort. Exploratory analysis showed a highly impacted metabolome in PH patients and machine learning confirmed a high diagnostic potential. Fully explainable specific free fatty acid (FFA)/lipid-ratios were derived, providing exceptional diagnostic accuracy with an area under the curve (AUC) of 0.89 in the training and 0.90 in the validation cohort, outperforming machine learning results. These ratios were also prognostic and complemented established clinical prognostic PAH scores (FPHR4p and COMPERA2.0), significantly increasing their hazard ratios (HR) from 2.5 and 3.4 to 4.2 and 6.1, respectively., Conclusion: In conclusion, our research confirms the significance of lipidomic alterations in PH, introducing innovative diagnostic and prognostic biomarkers. These findings may have the potential to reshape PH management strategies., Competing Interests: Competing interests Several authors (NB, CM, AO, BMN, HO) are inventors of the patent “Biomarker for the diagnosis of pulmonary hypertension (PH)” WO2017153472A1 (priority date 09.03.2016, granted in US, KR, JP, pending in CA, EP, AU) being jointly held by CBmed Gmbh, Joanneum Research Forschungsgesellschaft mbH, Medical University Graz and Ludwig Boltzmann Gesellschaft GmbH. The authors received no personal financial gain from the patent. During work on this publication NB was partially employed at CBmed GmbH. TP is chief scientific officer (CSO) of CBmed GmbH. EZ and CM were employed at Joanneum Research Forschungsgesellschaft mbH. The employing companies provided support in the form of salaries, materials and reagents but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. VF received honoraria for lectures, presentations, speakers bureaus, manuscript writing, or educational events from Janssen, Chiesi, BMS, and Boehringer Ingelheim and support for attending meetings, and/or travel from Janssen, MSD, and Boehringer Ingelheim outside the submitted work. CN received support for attending meetings, and/or travel from Boehringer Ingelheim and Inventiva pharma outside the submitted work. BAM reports personal fees from Actelion Pharmaceuticals, Tenax and Regeneron, grants from Deerfield Company, NIH (5R01HL139613-03, R01HL163960, R01HL153502, R01HL155096-01), Boston Biomedical Innovation Center (BBIC), Brigham IGNITE award, Cardiovascular Medical research Education Foundation outside the submitted work. BAM reports patent PCT/US2019/059890 (pending), PCT/US2020/066886 (pending) and #9,605,047 (granted) not licensed and outside the submitted work. SU received grants from the Swiss National Science Foundation, Zürich and Swiss Lung League, EMDO-Foundation, Orpha-Swiss, Janssen and MSD all unrelated to the present work. SU received consultancy fees and travel support from Orpha-Swiss, Janssen, MSD and Novartis unrelated to the present work. TJL reports grants for his institution from Acceleron Pharma, Gossamer Bio, Janssen-Cilag, and United Therapeutics; personal fees and non-financial support from Acceleron Pharma, AstraZeneca, Boehringer Ingelheim, Gossamer Bio, Ferrer, Janssen-Cilag, MSD, Orphacare, and Pfizer outside the submitted work. KH is a consultant at Medtronic Österreich GmbH outside the submitted work. TP reports grants from AstraZeneca, Novo Nordisk, Sanofi paid to the Medical University of Graz outside the submitted work. TP reports personal fees and nonfinancial support from Novo Nordisk and Roche Diagnostics outside the submitted work. HO reports grants from Bayer, Unither, Actelion, Roche, Boehringer Ingelheim, and Pfizer. HO reports personal fees and non-financial support from Medupdate and Mondial, AOP, Astra Zeneca, Bayer, Boehringer Ingelheim, Chiesi, Ferrer, Menarini, MSD, and GSK, Iqvia, Janssen, Novartis, and Pfizer outside the submitted work. AO received honoraria for presentations and support for attending meetings, and/or travel from MSD outside the submitted work. No conflict of interest, financial or otherwise, are declared by the authors HL and UB.